Advertisement

Unbiased High-Throughput Drug Combination Pilot Screening Identifies Synergistic Drug Combinations Effective against Patient-Derived and Drug-Resistant Melanoma Cell Lines

  • David A. Close
    Affiliations
    Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA
    Search for articles by this author
  • John M. Kirkwood
    Affiliations
    Departments of Medicine, Dermatology, and Translational Science, and Melanoma and Skin Cancer Program, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
    Search for articles by this author
  • Ronald J. Fecek
    Affiliations
    Department of Microbiology, Lake Erie College of Osteopathic Medicine at Seton Hill, Greensburg, PA, USA
    Search for articles by this author
  • Walter J. Storkus
    Affiliations
    Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    Departments of Dermatology, Immunology, Bioengineering, and Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
    Search for articles by this author
  • Paul A. Johnston
    Correspondence
    Corresponding Author: Paul A. Johnston, PhD, Associate Professor, Department of Pharmaceutical Sciences, University of Pittsburgh School of Pharmacy, Room 4101 Pittsburgh Technology Center, 700 Technology Drive, Pittsburgh, PA 15219, USA. Email: paj18@pitt.edu
    Affiliations
    Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA

    Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
    Search for articles by this author

      Abstract

      We describe the development, optimization, and validation of 384-well growth inhibition assays for six patient-derived melanoma cell lines (PDMCLs), three wild type (WT) for BRAF and three with V600E-BRAF mutations. We conducted a pilot drug combination (DC) high-throughput screening (HTS) of 45 pairwise 4×4 DC matrices prepared from 10 drugs in the PDMCL assays: two B-Raf inhibitors (BRAFi), a MEK inhibitor (MEKi), and a methylation agent approved for melanoma; cytotoxic topoisomerase II and DNA methyltransferase chemotherapies; and drugs targeting the base excision DNA repair enzyme APE1 (apurinic/apyrimidinic endonuclease-1/redox effector factor-1), SRC family tyrosine kinases, the heat shock protein 90 (HSP90) molecular chaperone, and histone deacetylases.
      Pairwise DCs between dasatinib and three drugs approved for melanoma therapy—dabrafenib, vemurafenib, or trametinib—were flagged as synergistic in PDMCLs. Exposure to fixed DC ratios of the SRC inhibitor dasatinib with the BRAFis or MEKis interacted synergistically to increase PDMCL sensitivity to growth inhibition and enhance cytotoxicity independently of PDMCL BRAF status. These DCs synergistically inhibited the growth of mouse melanoma cell lines that either were dabrafenib-sensitive or had acquired resistance to dabrafenib with cross resistance to vemurafenib, trametinib, and dasatinib. Dasatinib DCs with dabrafenib, vemurafenib, or trametinib activated apoptosis and increased cell death in melanoma cells independently of their BRAF status or their drug resistance phenotypes. These preclinical in vitro studies provide a data-driven rationale for the further investigation of DCs between dasatinib and BRAFis or MEKis as candidates for melanoma combination therapies with the potential to improve outcomes and/or prevent or delay the emergence of disease resistance.

      Keywords

      Introduction

      Melanoma represents <5% of skin cancers but accounts for 80% of skin cancer–related deaths.
      • Bertolotto C.
      Melanoma: From Melanocyte to Genetic Alterations and Clinical Options.
      ,
      • Gorantla V.
      • Kirkwood J.M.
      State of Melanoma: An Historic Overview of a Field in Transition.
      Melanoma incidence is rising at ~3% per annum and represents the sixth most common cancer, fifth and seventh in men and women, respectively.
      • Gorantla V.
      • Kirkwood J.M.
      State of Melanoma: An Historic Overview of a Field in Transition.
      ,
      • Vennepureddy A.
      • Thumallapally N.
      • Motilal Nehru V.
      • et al.
      Novel Drugs and Combination Therapies for the Treatment of Metastatic Melanoma.
      In the United States in 2020, it is projected that there will be 100,350 new melanoma patients, and 6850 will die of the disease. UV exposure is the predominant risk factor for cutaneous melanoma.
      • Bertolotto C.
      Melanoma: From Melanocyte to Genetic Alterations and Clinical Options.
      ,
      • Gorantla V.
      • Kirkwood J.M.
      State of Melanoma: An Historic Overview of a Field in Transition.
      Somatic mutation rates of cutaneous melanoma are significantly higher than in other cancers, and the four histologic subtypes (uveal, cutaneous, mucosal, and acral) exhibit considerable heterogeneity in both the number and patterns of driver mutations involved.
      • Bertolotto C.
      Melanoma: From Melanocyte to Genetic Alterations and Clinical Options.
      ,
      • Gorantla V.
      • Kirkwood J.M.
      State of Melanoma: An Historic Overview of a Field in Transition.
      ,
      • Davar D.
      • Lin Y.
      • Kirkwood J.M.
      Unfolding the Mutational Landscape of Human Melanoma.
      The Cancer Genome Atlas Network
      Genomic Classification of Cutaneous Melanoma.
      • Zhang T.
      • Dutton-Regester K.
      • Brown K.M.
      • et al.
      The Genomic Landscape of Cutaneous Melanoma.
      Thirteen core genes are frequently mutated in melanomas: BRAF, NRAS, TP53, NF1, CDKN2A, ARID2, PTEN, PPP6C, RAC1, IDH1, DDX3X, MAP2K1, and RB1.
      The Cancer Genome Atlas Network
      Genomic Classification of Cutaneous Melanoma.
      ,
      • Zhang T.
      • Dutton-Regester K.
      • Brown K.M.
      • et al.
      The Genomic Landscape of Cutaneous Melanoma.
      The largest genomic subtype exhibits BRAF hotspot mutations; the second is characterized by RAS hotspot mutations; the third exhibits somatic mutations in NF1, a GTPase-activating protein known to downregulate RAS activity; and the fourth is a heterogeneous subgroup lacking hotspot BRAF, N/H/KRAS, or NF1 mutations termed the Triple-WT subtype that features KIT mutations, focal amplifications, and complex structural rearrangements.
      The Cancer Genome Atlas Network
      Genomic Classification of Cutaneous Melanoma.
      ,
      • Zhang T.
      • Dutton-Regester K.
      • Brown K.M.
      • et al.
      The Genomic Landscape of Cutaneous Melanoma.
      Patients with localized stage I melanomas are largely curable by surgical excision with 5-year survival rates of >98%.
      • Bertolotto C.
      Melanoma: From Melanocyte to Genetic Alterations and Clinical Options.
      ,
      • Gorantla V.
      • Kirkwood J.M.
      State of Melanoma: An Historic Overview of a Field in Transition.
      ,
      • Tang T.
      • Eldabaje R.
      • Yang L.
      Current Status of Biological Therapies for the Treatment of Metastatic Melanoma.
      After melanoma progresses to regional or distal sites, 5-year survival declines to 62.4% and 17.9%, respectively.
      • Bertolotto C.
      Melanoma: From Melanocyte to Genetic Alterations and Clinical Options.
      ,
      • Gorantla V.
      • Kirkwood J.M.
      State of Melanoma: An Historic Overview of a Field in Transition.
      ,
      • Tang T.
      • Eldabaje R.
      • Yang L.
      Current Status of Biological Therapies for the Treatment of Metastatic Melanoma.
      Patients with nonresectable stage III or IV melanoma have a median survival of 6–10 months despite existing systemic therapies.
      • Bertolotto C.
      Melanoma: From Melanocyte to Genetic Alterations and Clinical Options.
      ,
      • Gorantla V.
      • Kirkwood J.M.
      State of Melanoma: An Historic Overview of a Field in Transition.
      Dacarbazine and recombinant human interleukin-2 are approved by the US Food and Drug Administration (FDA) for metastatic melanoma.
      • Bertolotto C.
      Melanoma: From Melanocyte to Genetic Alterations and Clinical Options.
      ,
      • Gorantla V.
      • Kirkwood J.M.
      State of Melanoma: An Historic Overview of a Field in Transition.
      ,
      • Tang T.
      • Eldabaje R.
      • Yang L.
      Current Status of Biological Therapies for the Treatment of Metastatic Melanoma.
      • Johnson D.
      • Sosman J.A.
      Therapeutic Advances and Treatment Options in Metastatic Melanoma.
      • Michielin O.
      • Hoeller C.
      Gaining Momentum: New Options and Opportunities for the Treatment of Advanced Melanoma.
      These agents yielded objective responses (ORs) in ≤20% of patients, however, with no discernable overall benefit to either progression-free survival (PFS) or overall survival (OS).
      • Bertolotto C.
      Melanoma: From Melanocyte to Genetic Alterations and Clinical Options.
      ,
      • Gorantla V.
      • Kirkwood J.M.
      State of Melanoma: An Historic Overview of a Field in Transition.
      ,
      • Tang T.
      • Eldabaje R.
      • Yang L.
      Current Status of Biological Therapies for the Treatment of Metastatic Melanoma.
      • Johnson D.
      • Sosman J.A.
      Therapeutic Advances and Treatment Options in Metastatic Melanoma.
      • Michielin O.
      • Hoeller C.
      Gaining Momentum: New Options and Opportunities for the Treatment of Advanced Melanoma.
      Several molecularly targeted drugs and immunotherapies have recently been approved for advanced melanoma treatment: drugs that inhibit B-Raf or MEK, and monoclonal antibody (MAbs) immunotherapies that block immune checkpoints (IC-MAbs).
      • Vennepureddy A.
      • Thumallapally N.
      • Motilal Nehru V.
      • et al.
      Novel Drugs and Combination Therapies for the Treatment of Metastatic Melanoma.
      ,
      • Tang T.
      • Eldabaje R.
      • Yang L.
      Current Status of Biological Therapies for the Treatment of Metastatic Melanoma.
      • Johnson D.
      • Sosman J.A.
      Therapeutic Advances and Treatment Options in Metastatic Melanoma.
      • Michielin O.
      • Hoeller C.
      Gaining Momentum: New Options and Opportunities for the Treatment of Advanced Melanoma.
      • Merlino G.H.M.
      • Fisher D.E.
      • Bastian B.C.
      • et al.
      The State of Melanoma: Challenges and Opportunities.
      Monotherapy with first-generation inhibitors of B-Raf (BRAFi, vemurafenib, or dabrafenib) or MEK (MEKi, trametinib) significantly improved patient OR rates and prolonged median PFS and OS.
      • Vennepureddy A.
      • Thumallapally N.
      • Motilal Nehru V.
      • et al.
      Novel Drugs and Combination Therapies for the Treatment of Metastatic Melanoma.
      ,
      • Tang T.
      • Eldabaje R.
      • Yang L.
      Current Status of Biological Therapies for the Treatment of Metastatic Melanoma.
      • Johnson D.
      • Sosman J.A.
      Therapeutic Advances and Treatment Options in Metastatic Melanoma.
      • Michielin O.
      • Hoeller C.
      Gaining Momentum: New Options and Opportunities for the Treatment of Advanced Melanoma.
      • Merlino G.H.M.
      • Fisher D.E.
      • Bastian B.C.
      • et al.
      The State of Melanoma: Challenges and Opportunities.
      BRAFi and MEKi monotherapies were well tolerated with low-grade adverse events (AEs) that can be managed.
      • Vennepureddy A.
      • Thumallapally N.
      • Motilal Nehru V.
      • et al.
      Novel Drugs and Combination Therapies for the Treatment of Metastatic Melanoma.
      ,
      • Johnson D.
      • Sosman J.A.
      Therapeutic Advances and Treatment Options in Metastatic Melanoma.
      ,
      • Banzi M.
      • De Blasio S.
      • Lallas A.
      • et al.
      Dabrafenib: A New Opportunity for the Treatment of BRAF V600-Positive Melanoma.
      • Bollag G.
      • Tsai J.
      • Zhang J.
      • et al.
      Vemurafenib: The First Drug Approved for BRAF-Mutant Cancer.
      • Swaika A.
      • Crozier J.A.
      • Joseph R.W.
      Vemurafenib: An Evidence-Based Review of Its Clinical Utility in the Treatment of Metastatic Melanoma.
      Although BRAFis and MEKis induce rapid disease stabilization with ~50% and 22% ORs, respectively, PFS is limited to 5–7 months because drug resistance emerges and the disease progresses.
      • Lo J.
      • Fisher D.E.
      The Melanoma Revolution: From UV Carcinogensis to a New Era of Therapeutics.
      • Grazia G.
      • Penna I.
      • Perotti V.
      • et al.
      Towards Combinatorial Targeted Therapy in Melanoma: From Pre-Clinical Evidence to Clinical Application (Review).
      • Tentori L.
      • Lacal P.M.
      • Graziani G.
      Challenging Resistance Mechanisms to Therapies for Metastatic Melanoma.
      Second-generation BRAFis like encorafenib (BRAFTOVI) and MEKis cobimetinib (Cotellic) and binimetinib (MEKTOVI) exhibit improved efficacy, and the ecorafenib plus binimetinib drug combination (DC) produced median PFS of 14.9 months and OS of 33.6 months for metastatic melanoma.
      • Dummer R.
      • Ramelyte E.
      • Schindler S.
      • et al.
      MEK Inhibition and Immune Responses in Advanced Melanoma.
      • Dummer R.
      • Mangana J.
      • Frauchiger A.L.
      • et al.
      How I Treat Metastatic Melanoma.
      • Koelblinger P.
      • Thuerigen O.
      • Dummer R.
      Development of Encorafenib for BRAF-Mutated Advanced Melanoma.
      BRAFis plus MEKis DCs are the standard of care treatment for locally advanced or metastatic V600E-mutated BRAF melanoma. IC-MAbs such as ipilimumab [anti-cytotoxic T-lymphocyte antigen-4 (CTLA4)] or pembrolizumab and nivolumab [anti-programmed cell death-1 protein (PD1)] have lower OR rates, but more durable responses that prolong both median PFS and OS.
      • Vennepureddy A.
      • Thumallapally N.
      • Motilal Nehru V.
      • et al.
      Novel Drugs and Combination Therapies for the Treatment of Metastatic Melanoma.
      ,
      • Tang T.
      • Eldabaje R.
      • Yang L.
      Current Status of Biological Therapies for the Treatment of Metastatic Melanoma.
      • Johnson D.
      • Sosman J.A.
      Therapeutic Advances and Treatment Options in Metastatic Melanoma.
      • Michielin O.
      • Hoeller C.
      Gaining Momentum: New Options and Opportunities for the Treatment of Advanced Melanoma.
      • Merlino G.H.M.
      • Fisher D.E.
      • Bastian B.C.
      • et al.
      The State of Melanoma: Challenges and Opportunities.
      The clinical benefits of ipilimumab are balanced by low response rates (~10%), slow onset of action, and significant (~25%) autoimmune toxicity.
      • Merlino G.H.M.
      • Fisher D.E.
      • Bastian B.C.
      • et al.
      The State of Melanoma: Challenges and Opportunities.
      ,
      • Boutros C.
      • Tarhini A.
      • Routier E.
      • et al.
      Safety Profiles of Anti-CTLA-4 and Anti-PD-1 Antibodies Alone and in Combination.
      Pembrolizumab and nivolumab achieve higher clinical response rates (~20% and 30–40%, respectively) with lower levels of autoimmune toxicity (<10%).
      • Merlino G.H.M.
      • Fisher D.E.
      • Bastian B.C.
      • et al.
      The State of Melanoma: Challenges and Opportunities.
      ,
      • Boutros C.
      • Tarhini A.
      • Routier E.
      • et al.
      Safety Profiles of Anti-CTLA-4 and Anti-PD-1 Antibodies Alone and in Combination.
      Although small-molecule drugs and IC-MAbs have revolutionized melanoma therapy and improved tumor response rates, patient responses are rarely durable, and no cure exists for advanced metastatic melanoma. There is a need to identify new drugs and/or treatment options for the clinical management of melanoma.
      Intrinsic and acquired drug resistance are major obstacles to achieving long-term clinical benefit for advanced-stage melanoma.
      • Grazia G.
      • Penna I.
      • Perotti V.
      • et al.
      Towards Combinatorial Targeted Therapy in Melanoma: From Pre-Clinical Evidence to Clinical Application (Review).
      ,
      • Tentori L.
      • Lacal P.M.
      • Graziani G.
      Challenging Resistance Mechanisms to Therapies for Metastatic Melanoma.
      ,
      • Das Thakur M.
      • Stuart D.D.
      Molecular Pathways: Response and Resistance to BRAF and MEK Inhibitors in BRAF(V600E) Tumors.
      • Holohan C.
      • Van Schaeybroeck S.
      • Longley D.B.
      • et al.
      Cancer Drug Resistance: An Evolving Paradigm.
      • Lovly C.
      • Shaw A.T.
      Molecular Pathways: Resistance to Kinase Inhibitors and Implications for Therapeutic Strategies.
      • Rizos H.
      • Menzies A.M.
      • Pupo G.M.
      • et al.
      BRAF Inhibitor Resistance Mechanisms in Metastatic Melanoma: Spectrum and Clinical Impact.
      • Welsh S.
      • Rizos H.
      • Scolyer R.A.
      • et al.
      Resistance to Combination BRAF and MEK Inhibition in Metastatic Melanoma: Where to Next?.
      5–20% of melanoma patients don’t respond to BRAFis due to innate resistance mechanisms.
      • Vennepureddy A.
      • Thumallapally N.
      • Motilal Nehru V.
      • et al.
      Novel Drugs and Combination Therapies for the Treatment of Metastatic Melanoma.
      ,
      • Tentori L.
      • Lacal P.M.
      • Graziani G.
      Challenging Resistance Mechanisms to Therapies for Metastatic Melanoma.
      ,
      • Das Thakur M.
      • Stuart D.D.
      Molecular Pathways: Response and Resistance to BRAF and MEK Inhibitors in BRAF(V600E) Tumors.
      ,
      • Rizos H.
      • Menzies A.M.
      • Pupo G.M.
      • et al.
      BRAF Inhibitor Resistance Mechanisms in Metastatic Melanoma: Spectrum and Clinical Impact.
      ,
      • Welsh S.
      • Rizos H.
      • Scolyer R.A.
      • et al.
      Resistance to Combination BRAF and MEK Inhibition in Metastatic Melanoma: Where to Next?.
      Acquired resistance to single-agent BRAFis develops within 6–7 months due to mitogen-activated protein kinase (MAPK) signaling reactivation or activation of alternative signaling pathways.
      • Vennepureddy A.
      • Thumallapally N.
      • Motilal Nehru V.
      • et al.
      Novel Drugs and Combination Therapies for the Treatment of Metastatic Melanoma.
      ,
      • Grazia G.
      • Penna I.
      • Perotti V.
      • et al.
      Towards Combinatorial Targeted Therapy in Melanoma: From Pre-Clinical Evidence to Clinical Application (Review).
      ,
      • Tentori L.
      • Lacal P.M.
      • Graziani G.
      Challenging Resistance Mechanisms to Therapies for Metastatic Melanoma.
      ,
      • Rizos H.
      • Menzies A.M.
      • Pupo G.M.
      • et al.
      BRAF Inhibitor Resistance Mechanisms in Metastatic Melanoma: Spectrum and Clinical Impact.
      • Welsh S.
      • Rizos H.
      • Scolyer R.A.
      • et al.
      Resistance to Combination BRAF and MEK Inhibition in Metastatic Melanoma: Where to Next?.
      • Kwon C.H.
      • Wheeldon I.
      • Kachouie N.N.
      • et al.
      Drug-Eluting Microarrays for Cell-Based Screening of Chemical-Induced Apoptosis.
      Only ~10% of melanoma patients respond to ipilimumab, and most responses are short-lived, suggesting the emergence of resistance.
      • Tentori L.
      • Lacal P.M.
      • Graziani G.
      Challenging Resistance Mechanisms to Therapies for Metastatic Melanoma.
      Although pembrolizumab and nivolumab achieve higher ORs, PD1-resistant melanomas exhibit markedly lower T-cell infiltration and lower tumor inflammation.
      • Merlino G.H.M.
      • Fisher D.E.
      • Bastian B.C.
      • et al.
      The State of Melanoma: Challenges and Opportunities.
      Single-agent cancer therapy rarely provides long-term cures due to the genetic complexity and heterogeneity of cancers and their propensity for drug resistance.
      • Holohan C.
      • Van Schaeybroeck S.
      • Longley D.B.
      • et al.
      Cancer Drug Resistance: An Evolving Paradigm.
      ,
      • Al-Lazikani B.
      • Banerji U.
      • Workman P.
      Combinatorial Drug Therapy for Cancer in the Post-Genomic Era.
      • Dancey J.
      • Chen H.X.
      Strategies for Optimizing Combinations of Molecularly Targeted Anticancer Agents.
      • Keith C.
      • Borisy A.A.
      • Stockwell B.R.
      Multicomponent Therapeutics for Networked Systems.
      • Kummar S.
      • Chen H.X.
      • Wright J.
      • et al.
      Utilizing Targeted Cancer Therapeutic Agents in Combination: Novel Approaches and Urgent Requirements.
      • Ocaña A.
      • Pandiella A.
      Personalized Therapies in the Cancer “Omics” Era.
      • Rodon J.
      • Perez J.
      • Kurzrock R.
      Combining Targeted Therapies: Practical Issues to Consider at the Bench and Bedside.
      Animal and clinical studies demonstrate that DCs are more effective than single-agent therapies.
      • Holohan C.
      • Van Schaeybroeck S.
      • Longley D.B.
      • et al.
      Cancer Drug Resistance: An Evolving Paradigm.
      ,
      • Al-Lazikani B.
      • Banerji U.
      • Workman P.
      Combinatorial Drug Therapy for Cancer in the Post-Genomic Era.
      • Dancey J.
      • Chen H.X.
      Strategies for Optimizing Combinations of Molecularly Targeted Anticancer Agents.
      • Keith C.
      • Borisy A.A.
      • Stockwell B.R.
      Multicomponent Therapeutics for Networked Systems.
      • Kummar S.
      • Chen H.X.
      • Wright J.
      • et al.
      Utilizing Targeted Cancer Therapeutic Agents in Combination: Novel Approaches and Urgent Requirements.
      • Ocaña A.
      • Pandiella A.
      Personalized Therapies in the Cancer “Omics” Era.
      • Rodon J.
      • Perez J.
      • Kurzrock R.
      Combining Targeted Therapies: Practical Issues to Consider at the Bench and Bedside.
      The most effective cytotoxic DCs increase tumor cell killing either additively or synergistically by combining agents with different molecular mechanisms and non-overlapping toxicities.
      • Holohan C.
      • Van Schaeybroeck S.
      • Longley D.B.
      • et al.
      Cancer Drug Resistance: An Evolving Paradigm.
      ,
      • Al-Lazikani B.
      • Banerji U.
      • Workman P.
      Combinatorial Drug Therapy for Cancer in the Post-Genomic Era.
      • Dancey J.
      • Chen H.X.
      Strategies for Optimizing Combinations of Molecularly Targeted Anticancer Agents.
      • Keith C.
      • Borisy A.A.
      • Stockwell B.R.
      Multicomponent Therapeutics for Networked Systems.
      • Kummar S.
      • Chen H.X.
      • Wright J.
      • et al.
      Utilizing Targeted Cancer Therapeutic Agents in Combination: Novel Approaches and Urgent Requirements.
      • Ocaña A.
      • Pandiella A.
      Personalized Therapies in the Cancer “Omics” Era.
      • Rodon J.
      • Perez J.
      • Kurzrock R.
      Combining Targeted Therapies: Practical Issues to Consider at the Bench and Bedside.
      DCs are being implemented clinically to improve efficacy in melanoma and to prevent or delay drug resistance.
      • Michielin O.
      • Hoeller C.
      Gaining Momentum: New Options and Opportunities for the Treatment of Advanced Melanoma.
      ,
      • Grazia G.
      • Penna I.
      • Perotti V.
      • et al.
      Towards Combinatorial Targeted Therapy in Melanoma: From Pre-Clinical Evidence to Clinical Application (Review).
      ,
      • Tentori L.
      • Lacal P.M.
      • Graziani G.
      Challenging Resistance Mechanisms to Therapies for Metastatic Melanoma.
      ,
      • Welsh S.
      • Rizos H.
      • Scolyer R.A.
      • et al.
      Resistance to Combination BRAF and MEK Inhibition in Metastatic Melanoma: Where to Next?.
      ,
      • McArthur G.
      Combination Therapies to Inhibit the RAF/MEK/ERK Pathway in Melanoma: We Are Not Done Yet.
      • Verstovsek S.
      • Kantarjian H.
      • Mesa R.A.
      • et al.
      Safety and Efficacy of INCB018424, a JAK1 and JAK2 Inhibitor, in Myelofibrosis.
      • Voskoboynik M.
      • Arkenau H.T.
      Combination Therapies for the Treatment of Advanced Melanoma: A Review of Current Evidence.
      Even though BRAFi plus MEKi DCs improved PFS and OS rates compared to individual drug treatment, acquired drug resistance is a major limitation to good clinical outcomes.
      • Vennepureddy A.
      • Thumallapally N.
      • Motilal Nehru V.
      • et al.
      Novel Drugs and Combination Therapies for the Treatment of Metastatic Melanoma.
      ,
      • McArthur G.
      Combination Therapies to Inhibit the RAF/MEK/ERK Pathway in Melanoma: We Are Not Done Yet.
      ,
      • Voskoboynik M.
      • Arkenau H.T.
      Combination Therapies for the Treatment of Advanced Melanoma: A Review of Current Evidence.
      ,
      • Kwong L.
      • Davies M.A.
      Targeted Therapy for Melanoma: Rational Combinatorial Approaches.
      Immuno-oncology DC strategies are also emerging, either anti-CTLA4 plus anti-PD1 approaches, or combinations of IC-MAbs with small-molecule drugs.
      • Vennepureddy A.
      • Thumallapally N.
      • Motilal Nehru V.
      • et al.
      Novel Drugs and Combination Therapies for the Treatment of Metastatic Melanoma.
      ,
      • Johnson D.
      • Sosman J.A.
      Therapeutic Advances and Treatment Options in Metastatic Melanoma.
      ,
      • Tentori L.
      • Lacal P.M.
      • Graziani G.
      Challenging Resistance Mechanisms to Therapies for Metastatic Melanoma.
      ,
      • Welsh S.
      • Rizos H.
      • Scolyer R.A.
      • et al.
      Resistance to Combination BRAF and MEK Inhibition in Metastatic Melanoma: Where to Next?.
      ,
      • Voskoboynik M.
      • Arkenau H.T.
      Combination Therapies for the Treatment of Advanced Melanoma: A Review of Current Evidence.
      It is difficult, however, to predict which DCs will effectively provide long-term benefits to patients beyond single-agent therapy. Systematic high-throughput screening (HTS) of DCs against tumor cell line panels provides an unbiased data-driven strategy to identify DCs that might have potential to be developed into effective therapeutic regimens.
      • Al-Lazikani B.
      • Banerji U.
      • Workman P.
      Combinatorial Drug Therapy for Cancer in the Post-Genomic Era.
      • Dancey J.
      • Chen H.X.
      Strategies for Optimizing Combinations of Molecularly Targeted Anticancer Agents.
      • Keith C.
      • Borisy A.A.
      • Stockwell B.R.
      Multicomponent Therapeutics for Networked Systems.
      • Kummar S.
      • Chen H.X.
      • Wright J.
      • et al.
      Utilizing Targeted Cancer Therapeutic Agents in Combination: Novel Approaches and Urgent Requirements.
      • Ocaña A.
      • Pandiella A.
      Personalized Therapies in the Cancer “Omics” Era.
      • Rodon J.
      • Perez J.
      • Kurzrock R.
      Combining Targeted Therapies: Practical Issues to Consider at the Bench and Bedside.
      ,
      • Axelrod M.
      • Gordon V.L.
      • Conaway M.
      • et al.
      Combinatorial Drug Screening Identifies Compensatory Pathway Interactions and Adaptive Resistance Mechanisms.
      • Chan G.
      • Wilson S.
      • Schmidt S.
      • et al.
      Unlocking the Potential of High-Throughput Drug Combination Assays Using Acoustic Dispensing.
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      • Feng Z.
      • Kochanek S.
      • Close D.
      • et al.
      Design and Activity of AP Endonuclease-1 Inhibitors.
      • Greco W.R.
      • Faessel H.
      • Levasseur L.
      The Search for Cytotoxic Synergy between Anticancer Agents: A Case of Dorothy and the Ruby Slippers?.
      • Holbeck S.L.
      • Camalier R.
      • Crowell J.A.
      • et al.
      The National Cancer Institute ALMANAC: A Comprehensive Screening Resource for the Detection of Anticancer Drug Pairs with Enhanced Therapeutic Activity.
      • Kochanek S.
      • Close D.A.
      • Wang A.X.
      • et al.
      Confirmation of Selected Synergistic Cancer Drug Combinations Identified in an HTS Campaign and Exploration of Drug Efflux Transporter Contributions to the Mode of Synergy.
      • Korfi K.
      • Smith M.
      • Swan J.
      • et al.
      BIM Mediates Synergistic Killing of B-Cell Acute Lymphoblastic Leukemia Cells by BCL-2 and MEK Inhibitors.
      • Mathews Griner L.
      • Guha R.
      • Shinn P.
      • et al.
      High-Throughput Combinatorial Screening Identifies Drugs That Cooperate with Ibrutinib to Kill Activated B-Cell-Like Diffuse Large B-Cell Lymphoma Cells.
      • O’Neil J.
      • Benita Y.
      • Feldman I.
      • et al.
      An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies.
      • Peifer M.
      • Weiss J.
      • Sos M.L.
      • et al.
      Analysis of Compound Synergy in High-Throughput Cellular Screens by Population-Based Lifetime Modeling.
      We implemented a DC HTS campaign in the National Cancer Institute’s (NCI) NCI-60 tumor cell line panel to screen pairwise DC matrices (DCMs) prepared from 100 FDA-approved cancer drugs that generated 3.04 million data points to populate the ALMANAC (A Large Matrix of Anti-Neoplastic Agent Combinations) database for the NCI.
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      ,
      • Feng Z.
      • Kochanek S.
      • Close D.
      • et al.
      Design and Activity of AP Endonuclease-1 Inhibitors.
      ,
      • Holbeck S.L.
      • Camalier R.
      • Crowell J.A.
      • et al.
      The National Cancer Institute ALMANAC: A Comprehensive Screening Resource for the Detection of Anticancer Drug Pairs with Enhanced Therapeutic Activity.
      ,
      • Kochanek S.
      • Close D.A.
      • Wang A.X.
      • et al.
      Confirmation of Selected Synergistic Cancer Drug Combinations Identified in an HTS Campaign and Exploration of Drug Efflux Transporter Contributions to the Mode of Synergy.
      Selected synergistic DCs from the HTS were confirmed in vitro, potential synergy mechanisms of action (MOAs) were explored, and several DCs exhibited greater than single-agent efficacy in mouse xenograft human cancer models.
      • Holbeck S.L.
      • Camalier R.
      • Crowell J.A.
      • et al.
      The National Cancer Institute ALMANAC: A Comprehensive Screening Resource for the Detection of Anticancer Drug Pairs with Enhanced Therapeutic Activity.
      Phase I clinical trial enrollments were opened for the bortezomib plus clofarabine DC in patients with advanced refractory myelodysplastic syndromes and lymphomas (NCT02211755), and for the nilotinib plus paclitaxel DC in adults with refractory solid tumors (NCT02379416). Four DCs flagged in the NCI-60 DC HTS were confirmed to interact synergistically in vitro in several tumor cell lines, and it was shown that interactions between adenosine triphosphate (ATP)-binding cassette (ABC) drug efflux inhibitors and their substrates likely contributed to the observed synergy.
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      ,
      • Kochanek S.
      • Close D.A.
      • Wang A.X.
      • et al.
      Confirmation of Selected Synergistic Cancer Drug Combinations Identified in an HTS Campaign and Exploration of Drug Efflux Transporter Contributions to the Mode of Synergy.
      Tumor resistance to anticancer drugs that generate DNA adducts is often associated with enhanced apurinic/apyrimidinic endonuclease-1/redox effector factor-1 (APE1) expression, a critical component of base excision repair (BER).
      • Feng Z.
      • Kochanek S.
      • Close D.
      • et al.
      Design and Activity of AP Endonuclease-1 Inhibitors.
      The APE1 inhibitor AJAY4 has a median 50% growth inhibition (GI50) of 4.19 µM across the NCI-60 cell line panel, and a DCM with the BRAFi vemurafenib was flagged for synergistic growth inhibition in a DC HTS in the SK-MEL5 melanoma cell line that expresses V600E-mutated B-Raf, but not in SK-MEL2 cells expressing wild-type (WT) B-Raf.
      • Feng Z.
      • Kochanek S.
      • Close D.
      • et al.
      Design and Activity of AP Endonuclease-1 Inhibitors.
      This article describes the development and implementation of an unbiased pairwise DC pilot HTS of 10 selected anticancer agents in patient-derived melanoma cell lines (PDMCLs) equally represented by WT and V600E mutant BRAF genotypes. We wished to determine whether the vemurafenib plus AJAY4 DC would behave synergistically in PDMCLs, if synergy depended on the BRAF status, and if other novel synergistic DC interactions would be identified. Novel synergistic DCs that are effective in PDMCLs and/or overcome BRAFi or MEKi resistance have the potential to be developed into effective melanoma therapeutic regimens.

      Materials and Methods

      Reagents

      DMSO (99.9% high-performance liquid chromatography grade) was obtained from Alfa Aesar (Ward Hill, MA). Dulbecco’s Mg2+-free and Ca2+-free phosphate-buffered saline (PBS) was purchased from Gibco (Grand Island, NY). Roswell Park Memorial Institute Medium (RPMI 1640) and Dulbecco’s modified Eagle’s medium (DMEM) were purchased from Corning (Manassas, VA). Fetal bovine serum (FBS), L-glutamine (L-glut), HEPES, minimal essential amino acids [minimal essential medium (MEM)], and penicillin and streptomycin (P/S) were purchased from Thermo Fisher Scientific (Waltham, MA). FDA-approved anticancer compounds were obtained from commercial sources and provided by the NCI Developmental Therapeutics Program (DTP), as previously reported.
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      The APE1 inhibitor AJAY4 was provided by Dr. Barry Gold,
      • Feng Z.
      • Kochanek S.
      • Close D.
      • et al.
      Design and Activity of AP Endonuclease-1 Inhibitors.
      and STA-9090 (Ganetispib) was obtained from Dr. Walter Storkus. The homogeneous cellular ATP detection reagent Cell Titer Glo (CTG) and the homogeneous cellular Caspase-Glo 3/7 apoptosis detection reagents (CG-3/7) were purchased from Promega (Madison, WI).

      Cancer Drug Mechanisms of Action

      Four of the cancer drugs have been approved for the treatment of melanoma: dabrafenib (Tafinlar) and vemurafenib (Zelboraf) are B-Raf kinase inhibitors, trametinib (Mekinist) is a MEK1/2 kinase inhibitor, and temozolomide (Temodar) is a derivative of the methylation agent dacarbazine. AJAY4 is an inhibitor of the base excision DNA repair enzyme APE1.
      • Feng Z.
      • Kochanek S.
      • Close D.
      • et al.
      Design and Activity of AP Endonuclease-1 Inhibitors.
      Dasatinib (BMS-354825, Sprycel) is a multi-BCR/Abl and SRC family tyrosine kinase (SFK) inhibitor approved for patients with chronic myelogenous leukemia and acute lymphoblastic leukemia who are Philadelphia chromosome positive. STA9090 (Ganetespib) is a heat shock protein 90 (HSP90) inhibitor that is currently in clinical trials, either alone or in combination with other drugs, for a variety of cancers. Romidepsin (Istodax) is a histone deacetylase inhibitor approved for the treatment of peripheral and cutaneous T-cell lymphomas. The anthracycline antibiotic doxorubicin (Adriamycin) is a DNA intercalator and topoisomerase II inhibitor approved to treat non-Hodgkin’s lymphoma, multiple myeloma, acute leukemias, Kaposi sarcoma, Ewing sarcoma, Wilms tumor, and cancers of the breast, adrenal cortex, endometrium, lung, ovary, and other sites. Decitabine (Dacogen) is a cytidine analog that inhibits DNA methyltransferase, resulting in hypomethylated DNA that is used for the treatment of myelodysplastic syndromes and for acute myeloid leukemia.

      Patient-Derived Melanoma Cell Lines

      Ten PDMCLs were established and provided by investigators in the Melanoma and Skin Cancer SPORE (Specialized Program of Research Excellence) at the University of Pittsburgh Medical Center’s Hillman Cancer Center (Suppl. Table 1). PDMCLs were established between 2007 and 2013 from four female and two male melanoma patients with tumors isolated from different anatomical sites. The cell lines were initiated after manual and enzymatic digestion of resected melanoma tumor samples from patients who had been subjected to a variety of different treatment regimens: individual or combination therapy with high-dose interferon-α, interleukin-2, melphalan perfusion, ipilimumab, or vemurafenib. Five of the PDMCLs are WT for BRAF, and five bear the V600E-BRAF mutation. After receipt of frozen cell pellets, the 10 PDMCLs were thawed and placed into tissue culture at 37 °C, 5% CO2, and 95% humidity, and expanded through several passages before centrifugation and resuspension in 90% FBS plus 10% DMSO and then freezing in liquid nitrogen. The PDMCLs were maintained in RPMI 1640 medium supplemented with 10% heat-inactivated FBS, 1% L-glut, 1% MEM, 1% HEPES, and 100 U/mL P/S. For quality control purposes, we tracked cell doubling times during the cell expansion and HTS process. PDMCLs were kept in culture for no more than 20 passages.

      Murine Melanoma Cell Lines

      Two murine melanoma cell lines, BP-WT and BP-R20, were provided by Dr. Ron Fecek (University of Pittsburgh School of Medicine, Pittsburgh, PA). The BP-WT cell line was obtained from Dr. Jen Wargo at MD Anderson Cancer Center (Houston, TX) and was developed from BRAFV600E mice obtained from the Bosenberg Lab at Yale University.
      • Cooper Z.
      • Juneja V.R.
      • Sage P.T.
      • et al.
      Response to BRAF Inhibition in Melanoma Is Enhanced When Combined with Immune Checkpoint Blockade.
      The dabrafenib-resistant BP-R20 melanoma population was isolated by Dr. Fecek from BP-WT cells continuously passaged in culture medium containing 20 µM dabrafenib.

      Chelvanambi M., Fecek R. J., Taylor J. L.; et al. Manuscript in preparation, 2020.

      The BP-WT and BP-R20 cell lines were maintained in DMEM supplemented with 10% FBS, 1% L-glut, and 1× P/S. BP-R20 dabrafenib resistance was preserved by supplementing the media with 20 µM Dab at every other passage.

      Determination of Individual-Drug GI50s in Patient-Derived Melanoma Cell Lines and the BP-WT and BP-R20 Murine Melanoma Cell Lines

      The homogeneous 384-well PDMCL growth inhibition assays using the CTG cellular ATP detection reagent were adapted from previously described tumor cell line growth inhibition (GI) assays.
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      ,
      • Kochanek S.
      • Close D.A.
      • Wang A.X.
      • et al.
      Confirmation of Selected Synergistic Cancer Drug Combinations Identified in an HTS Campaign and Exploration of Drug Efflux Transporter Contributions to the Mode of Synergy.
      Briefly, PDMCLs and murine melanoma cell lines (WT and BP-R20) were harvested by trypsinization and centrifugation, and viable trypan blue–excluding cells were counted using a hemocytometer. 45 µL of cells at the appropriate cell density were seeded into the wells of white, opaque, clear-bottomed, time 0 (T0) and time 72 h (T72), 384-well barcoded assay plates (cat. no. 781098; Greiner BioOne, Monroe, NC) using a Matrix multichannel pipettor (Thermo Fisher Scientific) or a Microflo (BioTek, Winooski, VT) bulk reagent dispenser. T0 and T72 assay plates were then incubated at 37 °C in 5% CO2 and 95% humidity for 24 h. After 24 h, 5 µL of test drugs was transferred into the test wells of the assay plate (0.2% DMSO final) using the 384-well transfer head on a Janus MDT Mini (Perkin Elmer, Waltham, MA) robotic liquid-handling platform; plates were centrifuged at 100×g for 1 min and returned to an incubator at 37 °C in 5% CO2 and 95% humidity for 72 h. Also, on day 2, the T0 control cell-seeding plates were removed from the incubator, and 25 µL of the CTG detection reagent was added to the wells using a Matrix multichannel pipettor, and after a 15 min incubation at room temperature, the relative luminescence signals (RLUs) of the T0 control plates were captured on the SpectraMax M5e (Molecular Devices, Sunnyvale, CA) microtiter plate reader. After 72 h, assay plates were removed from the incubator, 25 µL of CTG was added to the wells using a Microflo bulk reagent dispenser, and after 15 min the RLUs were read on the SpectraMax M5e plate reader. To analyze the growth inhibition data, we used a concentration–response template to process the raw RLU data to % growth (PG); generate HTS assay performance statistics, S:B ratios, and Z-factor coefficients; and fit the data to curves and derive the GI50 values. PG was calculated using the standard NCI-60 protocol, and as described in Ref.
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      .
      PG=[(TiTz)/(CTz)]×100forconcentrationsforwhichTiTz
      PG=[(TiTz)/Tz]×100forconcentrationsforwhichTi<Tz
      where Ti is the compound well test value at 72 h, Tz is the average of the test values from the T0 control plate at time zero (n = 384), and C is the average of the test values (n = 64) in the T72 DMSO (0.2%) control wells. GraphPad Prism 5 software (GraphPad, San Diego, CA) was used to plot and fit data to curves using the sigmoidal concentration–response variable slope equation: Y = Bottom + [Top – Bottom] / [1 + 10^(LogEC50 – X) × HillSlope], where Bottom is the Y value at the bottom plateau, Top is the Y value at the top plateau, Log EC50 is the X value when the response is halfway between the bottom and top, and HillSlope describes the steepness of the curve. The growth inhibition 50 (GI50) value represents the concentration at which cell growth was inhibited by 50%, the total growth inhibition (TGI) value represents the concentration at which cell growth was fully inhibited, and the lethal concentration 50 (LC50) value represents the concentration at which 50% of the cells were killed.

      Preparation of Drug Combination Matrix (DCM) Master and Replica Daughter Plates

      For the pilot DC HTS, a total of 45 pairwise 4×4 DCMs were generated from the 10 test compounds, and these were arrayed onto 3×384-well master plates. In addition to the nine DC wells, each 4×4 DCM contained a DMSO control and three single-drug control wells for each of the two drugs at concentrations tested within the matrix. Source A (40 µL) and Source B (20 µL) 10× master plates were arrayed manually using a matrix pipettor (Suppl. Fig. 1). The 384-well transfer head of the Janus MDT Mini platform was used to transfer 20 µL from Plate A into Plate B and mix the DC master plates. 20 µL of DMSO was added to single-drug wells, and 40 µL to DMSO control wells. The 384-well transfer head of the Janus MDT Mini was used to transfer 2 µL from the DC master plates into barcoded replica daughter plates, which were then centrifuged at 50×g for 5 min, sealed with aluminum foil, and stored at –20 °C until use.

      Patient-Derived Melanoma Cell Line Drug Combination Matrix Screening

      On day 1 of the assay, PDMCLs were harvested, counted, and seeded in 45 µL of complete growth medium at the appropriate cell density into T0 and T72 384-well assay plates using the Multiflo bulk dispenser. On day 2, the T0 control cell-seeding plates were removed from the incubator, 25 µL of the CTG was added to the wells, and the RLU signals were acquired as described above. Also on day 2, DCM replica daughter plates prepared as described above were thawed at 37 °C and diluted in 98 µL of serum-free RPMI 1640 medium using a Microfil dispenser to an intermediate drug concentration (2% DMSO), and then 5 µL was transferred into the test wells of the T72 assay plates using the 384-well transfer head on a Janus MDT Mini robotic liquid-handling platform, and the plates were then returned to an incubator at 37 °C in 5% CO2 and 95% humidity for 72 h. On day 5, the compound-treated T72 assay plates were removed from the incubator, 25 µL of CTG was added to the wells using a Multiflo bulk reagent dispenser, the RLU signals were captured on the SpectraMax M5e, and PG was calculated using the standard NCI-60 protocol as described above.

      Drug Interaction Index Score Analysis of HTS Data

      To take advantage of our DC HTS strategy in which individual drugs are tested in multiple replicates as well as in combination, we developed a drug interaction index score (DIIS) to classify the interaction status of two drugs,
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      as shown in Equation (1):
      DIIS=(FAB(μA+μB-μAxμB))/2((N1-1)δA2+(N2-1)δB2)/(N1+N22)
      (1)


      where F’AB is the experimentally observed % growth inhibition (%GI) for each drug combination, µA and µB are the sample means of measured replicates of %GI or CLF at X and Y concentrations of drug A or drug B alone, δA and δB are the sample standard deviations of measured replicates of drug A or drug B alone, and N1 and N2 are the number of sample replicates of drug A or drug B alone. The drug interaction score analysis was implemented in MATLAB (Natick, MA). The drug interaction score calculation is a modification of the Bliss independence model.
      • Bliss C.
      The Toxicity of Poisons Applied Jointly.
      The numerator represents the experimentally observed %GI for each DC in the DCM minus the %GI for a Bliss additive effect calculated from the sample means of all measured replicates of the individual concentrations of drug A and drug B alone. It represents the difference between the experimentally observed %GI of a DC and the calculated Bliss additive effect of replicate means of the two drugs individually. The denominator of Equation (1) takes advantage of the individual drug concentration replicates and uses the sample standard deviations of measured replicates of drug A and drug B alone to calculate a value for the variability associated with the individual drug measurements, which when divided into the difference between the DC %GI and the calculated Bliss independence additivity for the individual drugs can be used to classify the interactions between the two drugs. A DIIS >3 indicates synergy, a DIIS <–3 indicates antagonism, and for –3 < DIIS < 3, the interaction is additive.

      Confirmation of Drug Combinations Scored as Synergistic in the Pilot DC HTS

      We arrayed 10×10 DCMs onto 384-well master plates. Each DCM included 9×9 DC wells (81 total) together with nine wells (18 total) containing each of the corresponding individual drug concentrations, and one DMSO control well. Two 10×10 DCMs were arrayed in columns 3 to 22 of the 384-well plates, together with DMSO (0.2%) controls in columns 1, 2, 23, and 24 (n = 64). We used the Chou–Talalay median-effect model to calculate a combination index (CI) score.
      • Chou T.
      Drug Combination Studies and Their Synergy Quantification Using the Chou-Talalay Method.
      CI = (D1 / DX1) + (D2 / DX2), where D1 and D2 denote, respectively, the concentrations of compound 1 and compound 2 required to reach an effect of X% as individual drug treatments; and DX1 and DX2 are, respectively, the concentrations needed in combination to inhibit X%. DCs with CI > 1 exhibit antagonistic interactions, DCs with CI = 1 exhibit additivity, and DCs with CI < 1 exhibit synergistic interactions. The COMPUSYN freeware program (Combosyn, http://www.combosyn.com) was used to calculate CI values and evaluate DC synergy, as described previously.
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      ,
      • Kochanek S.
      • Close D.A.
      • Wang A.X.
      • et al.
      Confirmation of Selected Synergistic Cancer Drug Combinations Identified in an HTS Campaign and Exploration of Drug Efflux Transporter Contributions to the Mode of Synergy.

      Determination of Apoptosis in Patient-Derived and Murine Melanoma Cell Lines

      To measure compound-induced activation of apoptosis in PDMCLs and the BP-WT and BP-R20 murine melanoma cell lines, we used the homogeneous Caspase-Glo 3/7 reagent from Promega. The CG-3/7 reagent provides a pro-luminescent substrate (Z-DEVD-aminoluciferin), which is cleaved by caspase 3/7, to release aminoluciferin, a substrate of luciferase used to produce light. The CG-3/7 reagent has been optimized for cell lysis and luciferase output to measure caspase 3/7 activity.
      • O’Brien M.
      • Daily W.J.
      • Hesselberth P.E.
      • et al.
      Homogeneous, Bioluminescent Protease Assays: Caspase-3 as a Model.
      ,
      • Riss T.
      • Moravec R.A.
      Use of Multiple Assay Endpoints to Investigate the Effects of Incubation Time, Dose of Toxin, and Plating Density in Cell-Based Cytotoxicity Assays.
      Briefly, PDMCLs and murine melanoma cell lines were harvested by trypsinization and centrifugation, and viable trypan blue–excluding cells were counted using a hemocytometer. 45 µL of cells at the appropriate cell density was seeded into the wells of white, opaque, 384-well barcoded assay plates using a Matrix multichannel pipettor. Assay plates were then incubated at 37 °C in 5% CO2 and 95% humidity for 24 h. After 24 h, 5 µL of test drugs was transferred into the test wells of the assay plate (0.2% DMSO final) using the 384-well transfer head on a Janus MDT Mini robotic liquid-handling platform; plates were centrifuged at 100×g for 1 min and returned to an incubator at 37 °C in 5% CO2 and 95% humidity for 24 h. After 24 h, assay plates were removed from the incubator, 25 µL of CG-3/7 was added to the wells using a Microflo bulk reagent dispenser, and after 1 h the RLUs were read on the SpectraMax M5e. GraphPad Prism 5 software was used to plot and fit data to curves using the sigmoidal concentration–response variable slope equation described above.

      Results

      PDMCL Growth Inhibition Assay Development, Validation, and Individual Drug GI50 Determinations

      To determine whether PDMCLs, whether WT for BRAF or with V600E-BRAF mutations, would be compatible with 384-well growth conditions, we conducted cell-seeding density and time course experiments in 10 PDMCLs to select 384-well cell-seeding densities that allowed for active proliferation throughout a 96 h culture period with acceptable HTS assay performance statistics (Fig. 1A and 1B, and Suppl. Table 1). Figure 1A illustrates the linearity (r2 = 0.99) of CTG RLU signals with respect to the number of viable cells seeded per well in 384-well assay plates for six PDMCLs, and Figure 1B illustrates their exponential proliferation (r2 > 0.93) throughout time in 384-well culture. To determine and compare the performance and assay quality control statistics of the PDMCL growth assays, cells were seeded into two 384-well assay plates, and the CTG signals of control wells (n = 32) were captured at T0, 24 h post seeding, and after an additional 72 h in culture (T72), and were used to calculate signal-to-background ratios (S:B T72:T0) and Z’-factor coefficients (Suppl. Table 1). When seeded at 1000 cells per well, the PDMCLs exhibited relatively slow proliferation rates with doubling times in the 38 h to 68 h range (Fig. 1B and Suppl. Table 1). Four of the PDMCLs, two WT for BRAF (TPF-11-743 and TFP-12-352) and two with V600E-BRAF mutations (TPF-12-524 and TPF-12-338), failed to reproducibly exhibit robust growth in the 384-well plate format (Suppl. Table 1). Even though TPF-11-743 and TFP-12-352 exhibited doubling times of 45 h and 50.7 h, respectively, both these WT cell lines failed to produce robust and reproducible S:B ratios and Z’-factor coefficients that would be compatible with HTS (Suppl. Table 1). Although longer doubling times correspond to lower assay signal windows (S:B ratios), on average five of the six PDMCLs produced Z’-factor coefficients >0.5, indicating they were excellent assays, and the other yielded Z’-factor coefficients of 0.52 and 0.27 in independent experiments. To illustrate that these 384-well growth conditions were suitable for GI50 determinations, we exposed the six PDMCLs to the indicated concentrations of the BRAFi vemurafenib and the APE1 inhibitor AJAY4 (Fig. 1C and 1D, and Table 1). Two of the V600E-BRAF PDMCLs (TPF-12-207 and TPF-12-542) were substantially (3–5-fold) more sensitive to growth inhibition by vemurafenib than three other (two WT and one V600E mutant) cell lines, with one WT PDMCL (1167.1) exhibiting intermediate sensitivity between these two groups (Fig. 1C and Table 1). In addition to substantially lower average GI50s, vemurafenib produced more pronounced cytotoxicity in the two sensitive V600E-BRAF PDMCLs (Fig. 1C and Table 1). In marked contrast, growth inhibition curves for the APE1 inhibitor AJAY4 were superimposable in all six PDMCLs with GI50s ~1.25 µM and complete cytotoxicity at concentrations ≥2 µM (Fig. 1C and Table 1).
      Figure 1
      Figure 1Formatting patient-derived melanoma cell line (PDMCL) growth inhibition assays into 384-well Cell Titer Glo (CTG) format and drug growth inhibition 50 (GI50) determinations.
      (A) Linearity of PDMCL CTG signal versus viable cell number. Linear regression analysis of CTG relative light units (RLUs) produced by seeding the indicated numbers of viable PDMCL cells into the wells of 384-well assay plates. The mean CTG RLUs ± SD (n = 32) of 24 replicate wells at each seeding density are presented together with the corresponding linear regression analysis of the data, r2 ≥ 0.99 for all six cell lines. Representative experimental data from one of at least two independent experiments are shown.
      (B) Exponential PDMCL cell growth and doubling times. Exponential cell growth analysis of the CTG RLUs produced after PDMCLs were seeded into the wells of 384-well assay plates at 1000 cells per well and cultured in an incubator for the indicated times. The mean CTG RLUs ± SD (n = 24) of 24 replicate wells at each time point are presented together with the corresponding exponential cell growth analysis of the data, r2 ≥ 0.93 for all six cell lines. The doubling times of 1167.1 WT BRAF, TPF-12-545 WT BRAF, TPF-13-370 WT BRAF, TPF-12-510 V600E-BRAF, TPF-12-542 V600E-BRAF, and TPF-13-207 V600E-BRAF were 44.8 h, 57.5 h, 55.4 h, 37.5 h, 57.4 h, and 42.2 h, respectively. Representative data from one experiment are shown.
      (C) Vemurafenib PDMCL growth inhibition curves. The six PDMCLs were seeded into T0 and T72 384-well assay plates and cultured in an incubator for 24 h. After 24 h, the CTG RLUs’ signal was captured for the T0 control wells, and the indicated concentrations of vemurafenib were transferred into the test wells of the T72 assay plates that were then returned to the incubator for 72 h before the CTG RLUs were captured.
      (D) AJAY4 PDMCL growth inhibition curves. The six PDMCLs were seeded into T0 and T72 384-well assay plates and cultured in an incubator for 24 h. After 24 h, the CTG RLUs’ signal was captured for the T0 control wells, and the indicated concentrations of AJAY4 were transferred into the test wells of the T72 assay plates that were then returned to the incubator for 72 h before the CTG RLUs were captured.
      PDMCL cell lines: 1167.1 WT BRAF (), TPF-12-545 WT BRAF (), TPF-13-370 WT BRAF (), TPF-12-510 V600E-BRAF (), TPF-12-542 V600E-BRAF (), and TPF-13-207 V600E-BRAF ().
      The % growth (PG) was calculated using the T0 (n = 32) and T72 (n = 32) DMSO control wells and the standard NCI 60 protocol as described in the Materials and Methods section. The GI50 value represents the concentration at which cell growth was inhibited by 50%, the TGI value represents the concentration at which cell growth was fully inhibited, and the LC50 value represents the concentration at which 50% of the cells were killed. The mean PG ± SD (n = 3) data from triplicate wells for each drug concentration are presented. Representative growth inhibition data from three independent experiments are shown.
      Table 1Test Compound Growth Inhibition 50 (GI50) Values in Patient-Derived and Mouse Melanoma Cell Lines.
      Cell Line IDPatient-Derived Melanoma Cell Lines: Wild-Type BRAFPatient-Derived Melanoma Cell Lines: V600E-Mutated BRAFMouse Melanoma Cell Lines
      1167.1TPF-12-352TPF-13-370TPF-12-510TPF-12-542TPF-13-207BP-WTBP-R20
      CompoundGI50 µMSDGI50 µMSDGI50 µMSDGI50 µMSDGI50 µMSDGI50 µMSDGI50 µMSDGI50 µMSD
      Decitabine687905>1000>1000583909>1000>1000
      Temozolomide479>500>50046411.5>500491>500>500
      Dasatinib<0.0050.0260.0390.1261.470.3810.6230.078<0.0050.2690.082
      Romidepsin0.0010.0000.0010.0000.0010.0010.0020.0000.0020.0000.0010.0000.0050.0010.0030.000
      Doxorubicin0.0050.0030.1770.2290.1230.1560.0130.0130.2060.2120.0140.015<0.005<0.005
      Dabrafenib0.0670.0430.1300.1300.003>3.30.0210.0110.0120.0090.0060.001>10
      Vemurafenib0.4710.0700.9680.4530.8180.3160.9960.4450.2230.0590.1450.0430.0760.00827.9199.342
      Trametinib0.0830.0570.0330.0290.1150.084>50.0090.0050.0060.0030.0010.0000.0980.024
      Ganetespib0.0220.0070.0230.0070.0220.0010.0170.0040.6200.5040.0260.0040.0150.0000.0120.001
      AJAY41.130.3351.370.4751.000.4450.9200.0611.790.2541.550.0440.9230.1931.2520.490
      SD = Standard deviation of the mean.
      GI50 values are given in micromolars (µM) from three independent experiments. When GI50 values are reported for decitabine and temozolomide, they represent data from one of three independent experiments. In the other two experiments, the GI50s were greater than the maximum concentration tested: >1000 µM and >500 µM for decitabine and temozolomide, respectively.
      To further validate the PDMCL 384-well growth inhibition assays and to generate data for the selection of the three individual drug concentrations in the HTS DC matrices, we conducted GI50 determinations throughout the six PDMCLs for all 10 selected anticancer drugs (Table 1 and Suppl. Table 2). The reported mechanisms of action attributed to the drugs selected for the pilot PDMCL DC HTS are presented in Supplemental Table 2. Neither decitabine nor temozolomide effectively inhibited PDMCL growth in the concentration ranges tested (Table 1). STA-9090, doxorubicin, and romidepsin exhibited mid- to low-nanomolar GI50s, while AJAY4 exhibited GI50s of ~1.25 µM among all six PDMCLs (Fig. 1C and Table 1). STA-9090, doxorubicin, romidepsin, and AJAY4 (Fig. 1D) inhibited the growth of all six PDMCLs irrespective of their BRAF genotypes (Table 1). As described above, vemurafenib produced GI50s in the 0.145 to 1 µM range against the six PDMCLs, with two of the V600E-BRAF PDMCLs (TPF-12-207 and TPF-12-542) showing 3–5-fold more sensitivity to growth inhibition by vemurafenib (Table 1). Dabrafenib, trametinib, and dasatinib each inhibited five of six PDMCLs in the mid- to low-nanomolar GI50 range (Table 1). Dabrafenib produced GI50s in the 0.012 to 0.13 µM range, with one V600E-BRAF PDMCL (TPF-12-501) demonstrating resistance to dabrafenib at concentrations ≤3.3 µM (Table 1). Two V600E-BRAF PDMCLs (TPF-12-207 and TPF-12-542) exhibited 3–5-fold more sensitivity to growth inhibition by dabrafenib than PDMCLs WT for BRAF (Table 1). Trametinib produced GI50s in the 0.006 to 0.115 µM range, with one V600E-BRAF PDMCL (TPF-12-501) demonstrating resistance to trametinib at concentrations ≤5 µM (Table 1). Again, two V600E-BRAF PDMCLs (TPF-12-207 and TPF-12-542) exhibited 3–5-fold more sensitivity to growth inhibition by trametinib than PDMCLs WT for BRAF (Table 1). Dasatinib produced GI50s ≤623 nM in five out of six PDMCLs and a GI50 of 1.5 µM in one V600E-BRAF PDMCL (TPF-12-542; Table 1). PDMCLs WT for BRAF were markedly more sensitive to dasatinib (greater than threefold) than V600E-BRAF PDMCLs.

      Drug Combination Matrix Pilot HTS in Patient-Derived Melanoma Cell Lines

      When the concentration of a single agent achieves a profound inhibition of growth by itself, the remaining response window is too small to detect any synergistic growth inhibition effects of a second agent.
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      Consistent with previous DC HTS campaigns
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      ,
      • Feng Z.
      • Kochanek S.
      • Close D.
      • et al.
      Design and Activity of AP Endonuclease-1 Inhibitors.
      ,
      • Kochanek S.
      • Close D.A.
      • Wang A.X.
      • et al.
      Confirmation of Selected Synergistic Cancer Drug Combinations Identified in an HTS Campaign and Exploration of Drug Efflux Transporter Contributions to the Mode of Synergy.
      and based on the individual GI50 determination data (Fig. 1C and 1D, and Table 1), we selected top concentrations of the 10 drugs for the DCMs that individually achieved ≤30% GI in the majority of PDMCLs. The middle and low drug concentrations represent 10-fold serial dilutions of the top concentration in the DCM, ensuring that even the most sensitive lines had at least two concentrations in which growth was not highly inhibited. Whenever possible, concentrations were also selected to be close to or within the range of the human peak plasma concentration (Cmax) at the approved clinical concentration (Suppl. Table 2).
      • Liston D.
      • Davis M.
      Clinically Relevant Concentrations of Anticancer Drugs: A Guide for Nonclinical Studies.
      Each individual drug was tested in pairwise DCs with the nine other test drugs. In addition to nine DC wells, each 4×4 DCM contained a DMSO control and three single-drug control wells for each of the two drugs at concentrations tested within the DCM (Suppl. Fig. 1A). Supplemental Figure 1B shows how individual drug matrices were arrayed on A and B source plates that were then combined to create a total of 45 pairwise 4×4 DCMs arrayed in 3×384-well DCM master plates.
      Replica daughter plates prepared from the DCM master plates were used to conduct two independent DC pilot screens in four of the six PDMCLs. The 24×384-well PDMCL assay plates of the two independent DC pilot screens passed our quality control criteria with S:B ratios greater than or equal to twofold, and Z’-factor coefficients ≥0.25; 19 of 24 assay plates produced Z’-factor coefficients ≥0.5, and the others were ≥0.47. Figure 2 shows representative scatterplots of the PG inhibition data (Fig. 2A and 2C) relative to DMSO controls, and their corresponding DIISs (Fig. 2B and 2D), from the first iteration of the DC pilot screen in the 1167.1 WT BRAF PDMCL (Fig. 2A and 2B) and the TFP-13-207 V600E-BRAF PDMCL (Fig. 2C and 2D). The majority of individual drug-treated wells exhibited CTG signals consistent with DMSO controls, indicating that they did not inhibit the growth of the PDMCLs (Fig. 2A and 2C). In contrast, many DC wells produced lower CTG signals consistent with substantial inhibition of PDMCL growth. Figure 2B and 2D depict the corresponding DIISs from the DC HTS; a DIIS >3 indicates synergy, a DIIS <–3 indicates antagonism, and for –3 < DIIS < 3, the interaction is considered additive. More DIISs ≥3 were observed in DCMs containing dasatinib in combination with either of the BRAFis dabrafenib or vemurafenib, or with the MEKi trametinib. Supplemental Figure 2 shows representative DCM % growth inhibition data and corresponding DIISs for the apparent synergistic inhibition of growth by the dasatinib plus vemurafenib DCM in the TFP-13-207 V600E-BRAF PDMCL.
      Figure 2
      Figure 2Unbiased drug combination (DC) pilot high-throughput screening (HTS) in patient-derived melanoma cell line (PDMCL) growth inhibition assays. Each individual drug was tested in pairwise DCs with the nine other test drugs. In addition to nine DC wells, each 4×4 drug combination matrix (DCM) contained a DMSO control and three control wells at the individual drug concentrations in the matrix. A total of 45 pairwise 4×4 DCMs were generated from the 10 test compounds, and these were arrayed onto 3×384-well master plates.
      (A) Scatterplot of DC pilot HTS percentage of DMSO control growth data in the 1167.1 PDMCL. Data on the percentage of DMSO control growth are presented from the first iteration of the DC pilot screen conducted in the 1167.1 PDMCL, which is WT for BRAF; DMSO control wells are represented by () red dots, individual drug treatment control wells are represented by black dots (), and DCM wells that received DCs are represented by green dots ().
      (B) Scatterplot of DC pilot HTS drug interaction index scores (DIISs) in the 1167.1 PDMCL. The corresponding DIISs for the DCM wells that received DCs in the DC pilot screen conducted in the 1167.1 PDMCL, which is WT for BRAF, are represented as black dots (). DIISs were calculated using Equation (1), as described in the Materials and Methods section and in Ref.
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      .
      (C) Scatterplot of DC pilot HTS percentage of DMSO control growth data in the TFP-13-207 PDMCL. Data on the percentage of DMSO control growth are presented from the first iteration of the DC pilot screen conducted in the TFP-13-207 PDMCL, which is V600E for BRAF; DMSO control wells are represented by () red dots, individual drug treatment control wells are represented by black dots (), and DCM wells that received DCs are represented by green dots ().
      (D) Scatterplot of DC pilot HTS DIISs in the TFP-13-207 PDMCL. The corresponding DIISs for the DCM wells that received DCs in the DC pilot screen conducted in the TFP-13-207 PDMCL, which is V600E for BRAF, are represented as black dots (). DIISs were calculated using Equation (1), as described in the Materials and Methods section and in Ref.
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      .

      Confirmation of Synergistic Drug Interactions Flagged in the Drug Combination Matrix Pilot HTS in Patient-Derived Melanoma Cell Lines

      To choose DCs flagged as synergistic in the PDMCL DC HTS for confirmation, we selected DCMs in which synergy had been indicated in multiple DC wells of the DCM and against more than one PDMCL. Only DCMs that contained dasatinib in combination with the BRAFis dabrafenib or vemurafenib, or with the MEKi trametinib, met these criteria. To confirm flagged synergies, we generated an expanded 10×10 DCM to test against the PDMCLs.
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      ,
      • Feng Z.
      • Kochanek S.
      • Close D.
      • et al.
      Design and Activity of AP Endonuclease-1 Inhibitors.
      ,
      • Kochanek S.
      • Close D.A.
      • Wang A.X.
      • et al.
      Confirmation of Selected Synergistic Cancer Drug Combinations Identified in an HTS Campaign and Exploration of Drug Efflux Transporter Contributions to the Mode of Synergy.
      Figure 3 shows the GI50 determinations and Chou–Talalay median-effects model CI analysis for the DCs of dasatinib plus dabrafenib, vemurafenib, or trametinib in two PDMCLs: the 1167.1 WT BRAF PDMCL and the TFP-13-207 V600E-BRAF PDMCL. In concentration–response curves, we present the GI50 curves for individual drugs together with the concentration response of one of the drugs at a fixed concentration of the other drug. In the CI plots, selected fixed DC ratios extracted from diagonal wells in the 10×10 DCMs are presented. In both PDMCLs, exposure to the DC of dasatinib with dabrafenib, vemurafenib, or trametinib dramatically left-shifted the concentration–response curves, consistent with substantially enhanced sensitivity relative to treatments with the individual drugs, and produced more cytotoxicity (Fig. 3A, 3C, 3E, 3G, 3I, and 3K). Furthermore, multiple fixed dasatinib DC ratios with dabrafenib, vemurafenib, or trametinib produced CI scores <1 in both PDMCLs (Fig. 3B, 3D, 3F, 3H, 3J, and 3L), confirming that these DCs interacted synergistically to inhibit PDMCL growth independently of their BRAF status.
      Figure 3
      Figure 3Confirmation of synergistic drug interactions identified in the drug combination (DC) pilot high-throughput screening (HTS) conducted in patient-derived melanoma cell line (PDMCL) growth inhibition assays.
      Drug combination matrices (DCMs) that contained dasatinib (Das) in combination with the BRAFis dabrafenib (Dab) or vemurafenib (Vem), or with the MEKi trametinib (Tra), produced drug interaction index scores (DIISs) in which synergy was indicated in multiple DC wells of a DCM and against more than one PDMCL. Expanded 10×10 DCMs were generated to confirm synergies in the PDMCLs,
      • Close D.A.
      • Wang A.X.
      • Kochanek S.J.
      • et al.
      Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
      ,
      • Feng Z.
      • Kochanek S.
      • Close D.
      • et al.
      Design and Activity of AP Endonuclease-1 Inhibitors.
      ,
      • Kochanek S.
      • Close D.A.
      • Wang A.X.
      • et al.
      Confirmation of Selected Synergistic Cancer Drug Combinations Identified in an HTS Campaign and Exploration of Drug Efflux Transporter Contributions to the Mode of Synergy.
      and we present individual and DC growth inhibition 50 (GI50) determinations and Chou–Talalay median-effects model combination index (CI) analysis for fixed DC ratios in the 1167.1 WT BRAF PDMCL and TFP-13-207 V600E-BRAF PDMCL.
      Growth inhibition curves for individual drugs and for Das DCs with Vem, Dab, or Tra in the WT BRAF 1167.1 PDMCL: (A) Das + Vem, (E) Das + Dab, and (I) Das + Tra; and V600E-BRAF TFP-13-207 PDMCL: (C) Das + Vem, (G) Das + Dab, and (K) Das + Tra. PDMCLs were seeded into T0 and T72 384-well assay plates and cultured in an incubator for 24 h. After 24 h, the CTG RLUs’ signal was captured for the T0 control wells, and the indicated concentrations of individual drugs or fixed-ratio DCs were transferred into the test wells of the T72 assay plates that were then returned to the incubator for 72 h before the CTG RLUs were captured. The % growth (PG) was calculated using the standard NCI 60 protocol, as described in the Materials and Methods section. The mean PG ± SD (n = 3) data from triplicate wells for each drug concentration are presented. Das alone is represented by blue dots (); Vem, Dab, or Tra alone, by red dots (); and Das + a fixed concentration of Vem, Dab, or Tra, by purple dots (). Representative growth inhibition data from three independent experiments are shown.
      CI analysis for Das fixed-ratio DCs with Vem, Dab, or Tra in the WT BRAF 1167.1 PDMCL: (B) Das:Vem 0.04:1 () and 0.01:1 (), (F) Das:Dab 3.3:1 () and 10:1 (), and (J) Das:Tra 19.8:1 () and 59.1:1 (); and in the V600E-BRAF TFP-13-207 PDMCL: (D) Das:Vem 0.04:1 () and 0.01:1 (), (H) Das:Dab 3.3:1 () and 10:1 (), and (L) Das:Tra 2.2:1 () and 6.6:1 (). The growth inhibition and fraction of PDMCL cells affected in the more extensive 10×10 DCMs were analyzed in the Chou–Talalay median-effects model using the COMPUSYN software to calculate CI scores and fit curves for selected fixed ratios of Das in combination with Vem, Dab, or Tra. CI scores <1.0 indicate that two drugs interact synergistically. Representative CI data from one of two independent experiments are shown.

      Evaluation of Synergistic Drug Interactions in Murine Melanoma Cell Line Models Sensitive or Resistant to Dabrafenib

      Drug resistance, both intrinsic and acquired, remains a major barrier to the attainment of long-term clinical benefit in advanced melanoma.
      • Grazia G.
      • Penna I.
      • Perotti V.
      • et al.
      Towards Combinatorial Targeted Therapy in Melanoma: From Pre-Clinical Evidence to Clinical Application (Review).
      ,
      • Tentori L.
      • Lacal P.M.
      • Graziani G.
      Challenging Resistance Mechanisms to Therapies for Metastatic Melanoma.
      ,
      • Das Thakur M.
      • Stuart D.D.
      Molecular Pathways: Response and Resistance to BRAF and MEK Inhibitors in BRAF(V600E) Tumors.
      • Holohan C.
      • Van Schaeybroeck S.
      • Longley D.B.
      • et al.
      Cancer Drug Resistance: An Evolving Paradigm.
      • Lovly C.
      • Shaw A.T.
      Molecular Pathways: Resistance to Kinase Inhibitors and Implications for Therapeutic Strategies.
      • Rizos H.
      • Menzies A.M.
      • Pupo G.M.
      • et al.
      BRAF Inhibitor Resistance Mechanisms in Metastatic Melanoma: Spectrum and Clinical Impact.
      • Welsh S.
      • Rizos H.
      • Scolyer R.A.
      • et al.
      Resistance to Combination BRAF and MEK Inhibition in Metastatic Melanoma: Where to Next?.
      We wanted to explore whether the DCs between dasatanib and BRAFis or MEKis might also behave synergistically in drug-resistant melanoma cell lines. The dabrafenib-resistant BP-R20 murine melanoma cell population was isolated from BP-WT cells continuously passaged in culture medium containing 20 µM dabrafenib. BP-R20 cells were completely resistant to growth inhibition by dabrafenib (≤10 µM), while BP-WT cells were exquisitely sensitive with a GI50 ~6 nM (Fig. 4A and Table 1). Compared to BP-WT cells, BP-R20 cells also exhibited differential cross resistance to vemurafenib, trametinib, and dasatinib (Fig. 4B, 4C, and 4D, and Table 1). In contrast, the GI50s for doxorubicin, STA-9090, romidepsin, and AJAY4 (Fig. 4E) were essentially identical in both mouse cell lines (Fig. 4E and Table 1). Fixed DC ratios of dasatinib plus dabrafenib, vemurafenib, or trametinib produced CI scores <1 in BP-WT and BP-R20 cell lines (Fig. 4F, 4G, and 4H), confirming that they interacted synergistically to inhibit the growth of both dabrafenib-sensitive and -resistant mouse melanoma cell lines.
      Figure 4
      Figure 4Individual drug growth inhibition 50 (GI50) determinations and combination index (CI) analysis in dabrafenib-sensitive (BP-WT) and -resistant (BP-R20) mouse melanoma cell lines. The dabrafenib-resistant BP-R20 murine melanoma cell population was isolated from BP-WT cells continuously passaged in culture medium containing 20 µM dabrafenib.
      (A) Dabrafenib (Dab), (B) vemurafenib (Vem), (C) trametinib (Tra), (D) dasatinib (Das), and (E) AJAY4 GI50 curves in BP-WT and BP-R20 cell lines. BP-WT and BP-R20 murine melanoma cell lines were seeded into T0 and T72 384-well assay plates and cultured in an incubator for 24 h. After 24 h, the CTG RLUs’ signal was captured for the T0 control wells, and the indicated concentrations of individual drugs were transferred into the test wells of the T72 assay plates that were then returned to the incubator for 72 h before the CTG RLUs were captured. The % growth (PG) was calculated using the standard NCI-60 protocol as described in the Materials and Methods section. The mean PG ± SD (n = 3) data from triplicate wells for each drug concentration are presented: BP-WT cells are represented by blue dots (); and BR-R20 cells, by red dots (). Representative growth inhibition data from three independent experiments are shown.
      CI analysis of Das fixed DC ratios with Dab, Vem, or Tra in BP-WT and BP-R20 cell lines: (F) Das:Dab 0.04:1, (G) Das:Vem 0.11:1, and (H) Das:Tra 0.72:1. The growth inhibition and fraction of BR-WT or BP-R20 cells affected by dasatinib fixed-ratio combinations with dabrafenib, vemurafenib, or trametinib were analyzed in the Chou–Talalay median-effects model using the COMPUSYN software to calculate CI scores and fit curves: BP-WT cells () and BR-R20 cells (). CI scores <1.0 indicate that two drugs interact synergistically. Representative CI data from one of two independent experiments are shown.
      To further investigate the synergy between dasatinib DCs with BRAFis or MEKis, we examined their ability to activate apoptosis in four melanoma cell line models: the 1167.1 WT BRAF PDMCL, the TFP-13-207 V600E-BRAF PDMCL, and the BP-WT and dabrafenib-resistant BP-R20 murine melanoma cell lines (Fig. 5). We used the Caspase-Glo 3/7 reagent to measure the activation of effector caspases-3 and -7 in melanoma cell lines exposed to the individual drugs or dasatinib DCs with dabrafenib, vemurafenib, or trametinib for 24 h (Fig. 5). In comparison to the individual drugs, exposure to fixed-ratio DCs of dasatinib with dabrafenib, vemurafenib, or trametinib increased the levels of apoptosis activation and/or increased the sensitivity (left-shifted the concentration responses) of apoptosis induction (Fig. 5). These data indicate that dasatinib DCs with dabrafenib, vemurafenib, or trametinib activated apoptosis and increased cell death in melanoma cells independently of their BRAF status or their drug resistance phenotypes.
      Figure 5
      Figure 5Activation of apoptosis in patient-derived melanoma cell lines (PDMCLs) and dabrafenib-sensitive and -resistant mouse melanoma cell lines by individual drugs and drug combinations (DCs).
      PDMCLs and murine melanoma cell lines were seeded into the wells of assay plates and incubated at 37 °C in 5% CO2 and 95% humidity. After 24 h, the individual test drugs or fixed DC ratios were transferred into the test wells of the assay plate and returned to the incubator. After 24 h, Caspase-Glo 3/7 reagent was added to the wells, and the RLUs were captured.
      Apoptosis activation curves for individual drugs and fixed DC ratios for dasatinib (Das) with Dabrafenib (Dab), Vemurafenib (Vem), or Trametinib (Tra) were, in the 1167.1 WT BRAF PDMCL: (A) Das, Vem, and 0.11:1 ratio, (E) Das, Dab, and 0.12:1 ratio, and (I) Das, Tra, and 2.2:1 ratio; in the V600E-BRAF TFP-13-207 PDMCL: (B) Das, Vem, and 0.11:1 ratio, (F) Das, Dab, and 0.12:1 ratio, and (J) Das, Tra, and 2.2:1 ratio; in the BP-WT dabrafenib-sensitive cell line: (C) Das, Vem, and 0.11:1 ratio, (G) Das, Dab, and 0.12:1 ratio, and (K) Das, Tra, and 2.2:1 ratio; and, in the BP-R20 dabrafenib-resistant cell line: (D) Das, Vem, and 0.11:1 ratio, (H) Das, Dab, and 0.12:1 ratio, and (L) Das, Tra, and 2.2:1 ratio. GraphPad Prism 5 software was used to plot and fit data to curves using the sigmoidal concentration–response variable slope equation as described in the Materials and Methods section. The mean RLUs ± SD (n = 3) data from triplicate wells for each drug concentration are presented; Das alone is represented by black dots (); Vem, Dab, or Tra alone, by blue dots (); and the Das + fixed DC ratio of Das plus Vem, Dab, or Tra, by red dots (). Representative data from one of three independent experiments are shown.

      Discussion

      We describe the development, optimization, and validation of 384-well growth inhibition assays for six PDMCLs, three WT for BRAF and three with V600E-BRAF mutations (Fig. 1, Table 1, and Suppl. Table 1). PDMCL growth inhibition assays were used to conduct a pilot DC HTS of 45 pairwise 4×4 drug DC matrices prepared from 10 drugs: two BRAFis, a MEKi, and a methylation agent approved for melanoma; cytotoxic topoisomerase II and DNA methyltransferase chemotherapies; and drugs targeting the base excision DNA repair enzyme APE1, Src family tyrosine kinases, the HSP90 chaperone, and HDACs (Suppl. Table 2). Pairwise DCs between the SFK inhibitor dasatinib and three approved melanoma drugs—dabrafenib, vemurafenib, or trametinib—were flagged synergistic in WT and V600E-BRAF PDMCLs (Fig. 2). Exposure to fixed DC ratios of dasatinib plus any of the BRAFis or MEKis interacted synergistically to increase sensitivity to growth inhibition and enhance cytotoxicity independently of PDMCL BRAF status (Fig. 3). Fixed DC ratios of dasatinib plus the BRAFis or MEKis synergistically inhibited the growth of mouse melanoma cell lines that were either sensitive to dabrafenib or had acquired resistance to dabrafenib with cross resistance to vemurafenib, trametinib, and datastinib (Fig. 4). Dasatinib DCs with dabrafenib, vemurafenib, or trametinib activated apoptosis and increased cell death in melanoma cells independently of their BRAF status or their drug resistance phenotypes (Fig. 5). These preclinical in vitro studies provide a data-driven rationale to explore DCs between dasatinib and the BRAFis or MEKis as candidates for melanoma combination therapies to improve outcomes and/or to prevent or delay the emergence of resistance.
      Dasatinib is a small-molecule, broad-spectrum, ATP-competitive tyrosine kinase inhibitor (TKI) that targets BCR-Abl/SRC but also inhibits other SFK members and growth factor receptor tyrosine kinases (RTKs), including c-KIT, c-FMS, PDGFRα and β, discoidin domain receptor-1, and Ephrin receptors.
      • Aleshin A.
      • Finn R.S.
      SRC: A Century of Science Brought to the Clinic.
      ,
      • Montero J.
      • Seoane S.
      • Ocaña A.
      • et al.
      Inhibition of SRC Family Kinases and Receptor Tyrosine Kinases by Dasatinib: Possible Combinations in Solid Tumors.
      Dasatinib has demonstrated robust antiproliferative and antitumor activity against numerous hematologic and solid tumor cell lines in vitro, and exhibited in vivo activity in tumor xenograft models.
      • Aleshin A.
      • Finn R.S.
      SRC: A Century of Science Brought to the Clinic.
      ,
      • Montero J.
      • Seoane S.
      • Ocaña A.
      • et al.
      Inhibition of SRC Family Kinases and Receptor Tyrosine Kinases by Dasatinib: Possible Combinations in Solid Tumors.
      In addition to promoting cell cycle arrest, growth inhibition, and the induction of apoptosis, dasatinib inhibited angiogenesis, cellular adhesion, migration, and invasion, and reduced osteoclast-mediated bone resorption and metastasis.
      • Aleshin A.
      • Finn R.S.
      SRC: A Century of Science Brought to the Clinic.
      ,
      • Montero J.
      • Seoane S.
      • Ocaña A.
      • et al.
      Inhibition of SRC Family Kinases and Receptor Tyrosine Kinases by Dasatinib: Possible Combinations in Solid Tumors.
      The SRC family of protein tyrosine kinases (c-SRC, LYN, FYN, LCK, HCK, FGR, BLK, YRK, and YES) are nonreceptor tyrosine kinases that regulate the signal transduction of diverse receptors and intracellular pathways that modulate key cellular processes: growth, proliferation, differentiation, cell shape, adhesion, migration, invasion, angiogenesis, and cell survival.
      • Aleshin A.
      • Finn R.S.
      SRC: A Century of Science Brought to the Clinic.
      ,
      • Parsons S.
      • Parsons J.T.
      Src Family Kinases, Key Regulators of Signal Transduction.
      SFKs have been identified as cellular oncogenes implicated in the development and progression of many cancers, including colon, lung, pancreatic, breast, prostate, and melanomas.
      • Aleshin A.
      • Finn R.S.
      SRC: A Century of Science Brought to the Clinic.
      ,
      • Parsons S.
      • Parsons J.T.
      Src Family Kinases, Key Regulators of Signal Transduction.
      • Eustace A.J.
      • Kennedy S.
      • Larkin A.-M.
      • et al.
      Predictive Biomarkers for Dasatinib Treatment in Melanoma.
      • Homsi J.
      • Cubitt C.L.
      • Zhang S.
      • et al.
      Src Activation in Melanoma and Src Inhibitors as Therapeutic Agents in Melanoma.
      Dasatinib is approved for acute lymphoblastic leukemia (ALL) and chronic myelogenous leukemia (CML) that are Philadelphia chromosome positive. Clinical evidence suggests that TKI drugs are associated with adverse effects, including cardiotoxicity, cardiac dysfunction, and cardiomyocyte damage, that may result in cardiovascular complications.
      • Chaar M.
      • Kamta J.
      • Ait-Oudhia S.
      Mechanisms, Monitoring, and Management of Tyrosine Kinase Inhibitors–Associated Cardiovascular Toxicities.
      Pooled safety outcomes from dasatinib clinical trials have produced mixed conclusions about the significance of cardiotoxicity. Single-arm dasatinib trials revealed a statistically significant QTc prolongation of 3–6 ms in the electroencephalograms (ECGs) of patients receiving dasatinib. The clinical significance of the QTc prolongation is unresolved, however, since only 1% of patients exceeded a clinically significant threshold of 500 ms, and only 2.9% experienced an elevation beyond a significant threshold of 60 ms from baseline.
      • Chaar M.
      • Kamta J.
      • Ait-Oudhia S.
      Mechanisms, Monitoring, and Management of Tyrosine Kinase Inhibitors–Associated Cardiovascular Toxicities.
      Although 4% of patients treated with dasatinib experienced congestive heart failure or ventricular dysfunction, >50% of patients had a prior history of cardiovascular disease. The incidence of cardiovascular ischemia in patients receiving dasatinib was 2–4%; however, most patients had a history of or risk factors for atherosclerosis. When adjusted for age and sex, dasatinib-treated patients did not have a significantly higher risk of cardiovascular ischemia compared to control populations. Superficial edema (11%), fluid retention (27%), and pleural effusion (15%) were observed in the DASISION trial, and 36 months into the follow-up, pulmonary arterial hypertension (PAH) was identified in 3% of patients that received dasatinib.
      • Chaar M.
      • Kamta J.
      • Ait-Oudhia S.
      Mechanisms, Monitoring, and Management of Tyrosine Kinase Inhibitors–Associated Cardiovascular Toxicities.
      Dasatinib product labeling currently includes warnings for fluid retention, cardiac ischemia, PAH, and QT prolongation. A search of the clinical trials database (clinicaltrials.gov) returned 294 studies for dasatinib individually (189) or in combination with other agents (105), in a variety of leukemias and solid tumor conditions.
      • Montero J.
      • Seoane S.
      • Ocaña A.
      • et al.
      Inhibition of SRC Family Kinases and Receptor Tyrosine Kinases by Dasatinib: Possible Combinations in Solid Tumors.
      Around 50% of the clinical trials investigating dasatinib administration either alone or in combination have been completed; ~25% were suspended, withdrawn, or terminated; and ~25% remain active, with 13% still in the recruiting phase. Six dasatinib studies were listed for melanoma: four for metastatic, recurrent, stage IIIA, or stage IIIB melanoma (NCT00700882, NCT01092728, NCT00436605, and NCT01916135, respectively); one in combination with dacarbazine (NCT00597038); and one with dendritic cell vaccines (NCT01876212). It is unclear whether dasatinib either alone or in combination will be approved for other indications such as melanoma, but it would seem prudent to exercise caution if using dasatinib combinations with cardiotoxic therapies or in patients with a history of cardiovascular disease, and to monitor cardiac function during therapy.
      Several preclinical studies support the use of SRC inhibitors as therapeutic agents for melanoma.
      • Kwong L.
      • Davies M.A.
      Targeted Therapy for Melanoma: Rational Combinatorial Approaches.
      ,
      • Aleshin A.
      • Finn R.S.
      SRC: A Century of Science Brought to the Clinic.
      ,
      • Montero J.
      • Seoane S.
      • Ocaña A.
      • et al.
      Inhibition of SRC Family Kinases and Receptor Tyrosine Kinases by Dasatinib: Possible Combinations in Solid Tumors.
      ,
      • Eustace A.J.
      • Kennedy S.
      • Larkin A.-M.
      • et al.
      Predictive Biomarkers for Dasatinib Treatment in Melanoma.
      ,
      • Homsi J.
      • Cubitt C.L.
      • Zhang S.
      • et al.
      Src Activation in Melanoma and Src Inhibitors as Therapeutic Agents in Melanoma.
      ,
      • Vergani E.
      • Vallacchi V.
      • Frigerio S.
      • et al.
      Identification of MET and SRC Activation in Melanoma Cell Lines Showing Primary Resistance to PLX4032.
      ,
      • Girotti M.R.
      • Pedersen M.
      • Sanchez-Laorden B.
      • et al.
      Inhibiting EGF Receptor or SRC Family Kinase Signaling Overcomes BRAF Inhibitor Resistance in Melanoma.
      Melanoma cell lines and tumor samples express SFK members, and c-SRC expression is elevated compared to normal melanocytes.
      • Eustace A.J.
      • Kennedy S.
      • Larkin A.-M.
      • et al.
      Predictive Biomarkers for Dasatinib Treatment in Melanoma.
      ,
      • Homsi J.
      • Cubitt C.L.
      • Zhang S.
      • et al.
      Src Activation in Melanoma and Src Inhibitors as Therapeutic Agents in Melanoma.
      In 124 melanoma patient samples analyzed by immunohistochemistry, 77% of tumors were positive for c-SRC expression;
      • Eustace A.J.
      • Kennedy S.
      • Larkin A.-M.
      • et al.
      Predictive Biomarkers for Dasatinib Treatment in Melanoma.
      and in 35 samples from patients with primary cutaneous (13), mucosal (13), or metastatic (9) melanomas, 48% of biopsy samples were positive for activated SRC (SRC-pY416).
      • Homsi J.
      • Cubitt C.L.
      • Zhang S.
      • et al.
      Src Activation in Melanoma and Src Inhibitors as Therapeutic Agents in Melanoma.
      In this study, dasatinib produced GI50s ≤623 nM in five of six PDMCLs tested and a GI50 of 1.5 µM in the TPF-12-542 V600E-BRAF PDMCL (Table 1). PDMCLs WT for BRAF had greater than threefold sensitivity to dasatinib exposure compared to V600E-BRAF PDMCLs, and the GI50s for four of the six PDMCLs tested were at or lower than the clinically attainable 264 nM Cmax of dasatinib.
      • Liston D.
      • Davis M.
      Clinically Relevant Concentrations of Anticancer Drugs: A Guide for Nonclinical Studies.
      The dasatinib GI50 data in PDMCLs are in alignment with previously published studies in melanoma cell lines.
      • Eustace A.J.
      • Kennedy S.
      • Larkin A.-M.
      • et al.
      Predictive Biomarkers for Dasatinib Treatment in Melanoma.
      ,
      • Homsi J.
      • Cubitt C.L.
      • Zhang S.
      • et al.
      Src Activation in Melanoma and Src Inhibitors as Therapeutic Agents in Melanoma.
      In eight melanoma cell lines, three sensitive cell lines displayed >30% growth inhibition after exposure to 155 nM dasatinib for 5 days, while five resistant cell lines exhibited <25% growth inhibition after 5-day exposure to 310 nM dasatinib.
      • Eustace A.J.
      • Kennedy S.
      • Larkin A.-M.
      • et al.
      Predictive Biomarkers for Dasatinib Treatment in Melanoma.
      In another study, SFK inhibitors dasatinib and bosutinib inhibited the growth of three melanoma cell lines with IC50s in the 3–10 µM and 1–6 µM ranges, respectively.
      • Homsi J.
      • Cubitt C.L.
      • Zhang S.
      • et al.
      Src Activation in Melanoma and Src Inhibitors as Therapeutic Agents in Melanoma.
      In PDMCLs, vemurafenib produced GI50s in the 0.145 to 1 µM range, with two of the V600E-BRAF PDMCLs (TPF-12-207 and TPF-12-542) showing 3–5-fold more sensitivity to growth inhibition by vemurafenib (Table 1). PDMCL vemurafenib GI50s are substantially lower than the clinically relevant 127 µM Cmax of vemurafenib.
      • Liston D.
      • Davis M.
      Clinically Relevant Concentrations of Anticancer Drugs: A Guide for Nonclinical Studies.
      The two sensitive V600E-BRAF PDMCL vemurafenib GI50s are consistent with a study conducted in seven WT or 20 V600E mutant BRAF melanoma cell lines, in which vemurafenib sensitivity depended on V600E-BRAF but was independent of other gene alterations common in melanomas.
      • Vergani E.
      • Vallacchi V.
      • Frigerio S.
      • et al.
      Identification of MET and SRC Activation in Melanoma Cell Lines Showing Primary Resistance to PLX4032.
      In that study, 18 of the 20 V600E-BRAF cell lines exhibited vemurafenib IC50s ranging between 0.01 and 1 µM, with two resistant V600E-BRAF cell lines producing IC50s ~10 µM.
      • Vergani E.
      • Vallacchi V.
      • Frigerio S.
      • et al.
      Identification of MET and SRC Activation in Melanoma Cell Lines Showing Primary Resistance to PLX4032.
      Dabrafenib produced GI50s in the 0.012 to 0.13 µM range, with one V600E-BRAF PDMCL (TPF-12-510) demonstrating resistance to dabrafenib at concentrations ≤3.3 µM (Table 1). The same two V600E-BRAF PDMCLs (TPF-12-207 and TPF-12-542) exhibited 3–5-fold more sensitivity to growth inhibition by dabrafenib than PDMCLs WT for BRAF (Table 1). Dabrafenib GI50s in PDMCLs are much lower than the clinically achievable 4.86 µM Cmax of dabrafenib.
      • Liston D.
      • Davis M.
      Clinically Relevant Concentrations of Anticancer Drugs: A Guide for Nonclinical Studies.
      Trametinib produced GI50s in the 0.006 to 0.115 µM range in PDMCLs, with one V600E-BRAF PDMCL (TPF-12-510) demonstrating resistance to trametinib at concentrations ≤5 µM (Table 1). Again, two V600E-BRAF PDMCLs (TPF-12-207 and TPF-12-542) exhibited 3–5-fold more sensitivity to growth inhibition by trametinib than PDMCLs WT for BRAF (Table 1), and only these PDMCL GI50s were lower than the clinically relevant 0.021 µM Cmax of trametinib.
      • Liston D.
      • Davis M.
      Clinically Relevant Concentrations of Anticancer Drugs: A Guide for Nonclinical Studies.
      Dasatinib DCs with vemurafenib, dabrafenib, or trametinib synergistically increased PDMCL sensitivity to growth inhibition and enhanced cell death by activating apoptosis independently of BRAF status (Figs. 3 and 5). It has been shown before that dasatinib DCs with cisplatin, but not with either temozolomide or paclitaxel, synergistically reduced cell viability in melanoma cell lines.
      • Homsi J.
      • Cubitt C.L.
      • Zhang S.
      • et al.
      Src Activation in Melanoma and Src Inhibitors as Therapeutic Agents in Melanoma.
      In the BP-WT mouse melanoma cell line, the average GI50s for dabrafenib, vemurafenib, trametinib, and dasatinib were 0.006 µM, 0.076 µM, 0.001 µM, and <0.005 µM, respectively (Fig. 4 and Table 1). In the BP-R20 dabrafenib-resistant mouse melanoma cell line, the average GI50s for dabrafenib, vemurafenib, trametinib, and dasatinib were >10 µM, 27.9 µM, 0.098 µM, and 0.269 µM, respectively (Fig. 4 and Table 1). The fold shifts in GI50s between dabrafenib-sensitive and -resistant murine cell lines for dabrafenib, vemurafenib, trametinib, and dasatinib were >1666-fold, 367-fold, 98-fold, and >54-fold, respectively (Fig. 4 and Table 1). Murine BP-R20 melanoma cells that had acquired resistance to dabrafenib also exhibited cross resistance to vemurafenib, trametinib, and datastinib (Fig. 4 and Table 1). In contrast, the GI50s for doxorubicin, STA-9090, romidepsin, and AJAY4 were essentially identical in the dabrafenib-sensitive and -resistant mouse melanoma cell lines (Table 1). Dasatinib DCs with the BRAFis or MEKis synergistically inhibited the growth of mouse melanoma cell lines and increased cell death by activating apoptosis independently of their drug resistance phenotypes (Figs. 4 and 5, and Table 1). Our studies agree with two previous reports that have also implicated activation of SRC/SFK signaling in the intrinsic and acquired resistance of melanoma cell lines to vemurafenib.
      • Vergani E.
      • Vallacchi V.
      • Frigerio S.
      • et al.
      Identification of MET and SRC Activation in Melanoma Cell Lines Showing Primary Resistance to PLX4032.
      ,
      • Girotti M.R.
      • Pedersen M.
      • Sanchez-Laorden B.
      • et al.
      Inhibiting EGF Receptor or SRC Family Kinase Signaling Overcomes BRAF Inhibitor Resistance in Melanoma.
      In two V600E-BRAF mutant melanoma cell lines that were intrinsically resistant to vemurafenib (IC50s ~10 µM), phospho-tyrosine analysis revealed activation of the MET proto-oncogene receptor–tyrosine kinase axis in one cell line and the SRC–FAK axis in another.
      • Vergani E.
      • Vallacchi V.
      • Frigerio S.
      • et al.
      Identification of MET and SRC Activation in Melanoma Cell Lines Showing Primary Resistance to PLX4032.
      DCs of vemurafenib with MET inhibitors (SU11274) or small interfering RNAs (siRNAs) reducing MET expression inhibited cell growth, migration, and invasion in the melanoma cell line with MET amplification.
      • Vergani E.
      • Vallacchi V.
      • Frigerio S.
      • et al.
      Identification of MET and SRC Activation in Melanoma Cell Lines Showing Primary Resistance to PLX4032.
      Dasatinib exhibited weak antiproliferative effects in vemurafenib-resistant melanoma cells with SRC activation, but DCs with vemurafenib displayed significant antiproliferative and cytotoxic effects, reduced cell migration and invasion, decreased matrix metalloproteinase-2 (MMP2) production, and reduced β1-integrin expression.
      • Vergani E.
      • Vallacchi V.
      • Frigerio S.
      • et al.
      Identification of MET and SRC Activation in Melanoma Cell Lines Showing Primary Resistance to PLX4032.
      Two vemurafenib-resistant V600E-BRAF melanoma cell lines with acquired resistance were generated after culturing cells in increasing concentrations of vemurafenib, and a third resistant cell line was established from mouse xenograft models that grew through vemurafenib treatment.
      • Girotti M.R.
      • Pedersen M.
      • Sanchez-Laorden B.
      • et al.
      Inhibiting EGF Receptor or SRC Family Kinase Signaling Overcomes BRAF Inhibitor Resistance in Melanoma.
      Phospho-array analysis of RTKs that were hyperactivated in vemurafenib-resistant cell lines identified increased phosphorylation of the epidermal growth factor receptor (EGFR) and downstream pathway components.
      • Girotti M.R.
      • Pedersen M.
      • Sanchez-Laorden B.
      • et al.
      Inhibiting EGF Receptor or SRC Family Kinase Signaling Overcomes BRAF Inhibitor Resistance in Melanoma.
      The EGFR inhibitor and TKI gefitinib cooperated with BRAFis to block the growth of vemurafenib-resistant cells in vitro and in vivo.
      • Girotti M.R.
      • Pedersen M.
      • Sanchez-Laorden B.
      • et al.
      Inhibiting EGF Receptor or SRC Family Kinase Signaling Overcomes BRAF Inhibitor Resistance in Melanoma.
      Elevated phosphorylation of the SFKs LYN, YES, and FYN was observed in vemurafenib-resistant melanoma cell lines that were sensitive to growth inhibition by dasatinib both in vitro and in mouse xenograft tumors developed from these cell lines in vivo.
      • Girotti M.R.
      • Pedersen M.
      • Sanchez-Laorden B.
      • et al.
      Inhibiting EGF Receptor or SRC Family Kinase Signaling Overcomes BRAF Inhibitor Resistance in Melanoma.
      The EGFR–SRC–STAT3 pathway was activated in vemurafenib-resistant cell lines, driving proliferation and stimulating invasion and metastasis.
      • Girotti M.R.
      • Pedersen M.
      • Sanchez-Laorden B.
      • et al.
      Inhibiting EGF Receptor or SRC Family Kinase Signaling Overcomes BRAF Inhibitor Resistance in Melanoma.
      Inhibition of either EGFR or SFK signaling overcame BRAFi resistance in melanoma cells in vitro and in vivo.
      • Girotti M.R.
      • Pedersen M.
      • Sanchez-Laorden B.
      • et al.
      Inhibiting EGF Receptor or SRC Family Kinase Signaling Overcomes BRAF Inhibitor Resistance in Melanoma.
      Several RTKs have been implicated in melanoma growth and metastasis, including c-KIT, PDGFβR, and Eph receptor families, prompting the investigation of the broad-spectrum BCR-Abl/SRC inhibitor dasatinib that also inhibits SFK members, c-KIT, c-FMS, PDGFRα and β, discoidin domain receptor 1, and Ephrin receptors in the melanoma clinical context.
      • Aleshin A.
      • Finn R.S.
      SRC: A Century of Science Brought to the Clinic.
      ,
      • Montero J.
      • Seoane S.
      • Ocaña A.
      • et al.
      Inhibition of SRC Family Kinases and Receptor Tyrosine Kinases by Dasatinib: Possible Combinations in Solid Tumors.
      ,
      • Eustace A.J.
      • Kennedy S.
      • Larkin A.-M.
      • et al.
      Predictive Biomarkers for Dasatinib Treatment in Melanoma.
      ,
      • Algazi A.P.
      • Weber J.S.
      • Andrews S.C.
      • et al.
      Phase I Clinical Trial of the Src Inhibitor Dasatinib with Dacarbazine in Metastatic Melanoma.
      • Kalinsky K.
      • Lee S.
      • Rubin K.M.
      • et al.
      A Phase 2 Trial of Dasatinib in Patients with Locally Advanced or Stage IV Mucosal, Acral, or Vulvovaginal Melanoma: A Trial of the ECOG-ACRIN Cancer Research Group (E2607).
      • Kluger H.
      • Dudek A.Z.
      • McCann C.
      • et al.
      A Phase 2 Trial of Dasatinib in Advanced Melanoma.
      In a Phase II study of 36 evaluable patients with stage III–IV chemotherapy and naïve unresectable melanomas, initially administered 100 mg of dasatinib twice daily, it was concluded that dasatinib had minimal activity in unresectable melanoma patients.
      • Kluger H.
      • Dudek A.Z.
      • McCann C.
      • et al.
      A Phase 2 Trial of Dasatinib in Advanced Melanoma.
      Two patients exhibited partial responses (response rate: 5%), three had minor responses, the median PFS was 8 weeks, and the 6-month PFS rate was 13%.
      • Kluger H.
      • Dudek A.Z.
      • McCann C.
      • et al.
      A Phase 2 Trial of Dasatinib in Advanced Melanoma.
      One patient with a c-KIT mutation had a partial response, while another showed disease progression. Dasatinib was poorly tolerated, requiring dose reduction to 70 mg or interruption due to adverse events (AEs), with the most common grade 3–4 AEs being fatigue, dyspnea, pleural effusion, nausea, and anorexia.
      • Kluger H.
      • Dudek A.Z.
      • McCann C.
      • et al.
      A Phase 2 Trial of Dasatinib in Advanced Melanoma.
      A Phase I clinical trial of dasatinib in combination with dacarbazine was conducted in 51 patients with stage III–IV unresectable melanomas.
      • Algazi A.P.
      • Weber J.S.
      • Andrews S.C.
      • et al.
      Phase I Clinical Trial of the Src Inhibitor Dasatinib with Dacarbazine in Metastatic Melanoma.
      Dose expansion cohorts at three levels were accrued, and dose-limiting toxicities were observed at dasatinib 70 mg PO b.i.d. (by mouth, twice daily) and dacarbazine 1000 mg m–2, with the most common grade 3–4 AEs (50% of patients) being neutropenia, anemia, and thrombocytopenia.
      • Algazi A.P.
      • Weber J.S.
      • Andrews S.C.
      • et al.
      Phase I Clinical Trial of the Src Inhibitor Dasatinib with Dacarbazine in Metastatic Melanoma.
      In 29 patients receiving dasatinib 70 mg PO b.i.d., the OR rate was 13.8%, the 6-month PFS was 20.7%, and the 12-month OS was 34.5%, and a Phase II dose of dasatinib 70 mg PO b.i.d. and dacarbazine 800 mg m–2 was recommended.
      • Algazi A.P.
      • Weber J.S.
      • Andrews S.C.
      • et al.
      Phase I Clinical Trial of the Src Inhibitor Dasatinib with Dacarbazine in Metastatic Melanoma.
      A Phase II trial of dasatinib was conducted in 57 patients with locally advanced or Stage IV mucosal, acral, or vulvovaginal melanoma.
      • Kalinsky K.
      • Lee S.
      • Rubin K.M.
      • et al.
      A Phase 2 Trial of Dasatinib in Patients with Locally Advanced or Stage IV Mucosal, Acral, or Vulvovaginal Melanoma: A Trial of the ECOG-ACRIN Cancer Research Group (E2607).
      Patients received 70 mg of oral dasatinib twice daily, and the worst degree of toxicity observed was grade 3 (44% of patients), with fatigue, dyspnea, nausea, anemia, and pleural effusion being the most prevalent AEs in patients.
      • Kalinsky K.
      • Lee S.
      • Rubin K.M.
      • et al.
      A Phase 2 Trial of Dasatinib in Patients with Locally Advanced or Stage IV Mucosal, Acral, or Vulvovaginal Melanoma: A Trial of the ECOG-ACRIN Cancer Research Group (E2607).
      5.9% of evaluable patients (51) achieved a partial response, all were KIT–, and in a second stage conducted in KIT+ patients, 18.2% of evaluable patients (22) achieved a partial response.
      • Kalinsky K.
      • Lee S.
      • Rubin K.M.
      • et al.
      A Phase 2 Trial of Dasatinib in Patients with Locally Advanced or Stage IV Mucosal, Acral, or Vulvovaginal Melanoma: A Trial of the ECOG-ACRIN Cancer Research Group (E2607).
      The median PFS was 2.1 months, and median OS was 7.5 months; both were independent of KIT status or subtype.
      • Kalinsky K.
      • Lee S.
      • Rubin K.M.
      • et al.
      A Phase 2 Trial of Dasatinib in Patients with Locally Advanced or Stage IV Mucosal, Acral, or Vulvovaginal Melanoma: A Trial of the ECOG-ACRIN Cancer Research Group (E2607).
      Due to the low accrual rate and modest clinical activity of dasatinib observed in unresectable KIT+ melanoma, it was recommended that imatinib remain the KIT inhibitor of choice for this patient population.
      • Kalinsky K.
      • Lee S.
      • Rubin K.M.
      • et al.
      A Phase 2 Trial of Dasatinib in Patients with Locally Advanced or Stage IV Mucosal, Acral, or Vulvovaginal Melanoma: A Trial of the ECOG-ACRIN Cancer Research Group (E2607).
      It was concluded that it will be critical to identify predictive mutational profiles and/or biomarkers for the future development of dasatinib in melanoma, either alone or in combination.
      • Aleshin A.
      • Finn R.S.
      SRC: A Century of Science Brought to the Clinic.
      ,
      • Montero J.
      • Seoane S.
      • Ocaña A.
      • et al.
      Inhibition of SRC Family Kinases and Receptor Tyrosine Kinases by Dasatinib: Possible Combinations in Solid Tumors.
      ,
      • Eustace A.J.
      • Kennedy S.
      • Larkin A.-M.
      • et al.
      Predictive Biomarkers for Dasatinib Treatment in Melanoma.
      ,
      • Algazi A.P.
      • Weber J.S.
      • Andrews S.C.
      • et al.
      Phase I Clinical Trial of the Src Inhibitor Dasatinib with Dacarbazine in Metastatic Melanoma.
      • Kalinsky K.
      • Lee S.
      • Rubin K.M.
      • et al.
      A Phase 2 Trial of Dasatinib in Patients with Locally Advanced or Stage IV Mucosal, Acral, or Vulvovaginal Melanoma: A Trial of the ECOG-ACRIN Cancer Research Group (E2607).
      • Kluger H.
      • Dudek A.Z.
      • McCann C.
      • et al.
      A Phase 2 Trial of Dasatinib in Advanced Melanoma.
      In that respect, elevated expression of Annexin-A1 (ANXA1), Caveolin-1 (CAV1), and Ephrin-A2 (EphA2) proteins correlated with dasatinib sensitivity in melanoma cell lines, and 81%, 44%, and 74% of 124 melanoma patient tumor samples analyzed by immunohistochemistry were positive for ANXA1, CAV1, and EphA2 expression, respectively.
      • Eustace A.J.
      • Kennedy S.
      • Larkin A.-M.
      • et al.
      Predictive Biomarkers for Dasatinib Treatment in Melanoma.
      In a DC HTS campaign conducted in 10 NCI 60 cell lines, we identified and confirmed that DCs between the BRAFi vemurafenib and APE1 inhibitor AJAY4 synergistically inhibited the growth of the V600E-BRAF SK-MEL5 melanoma cell line, but not that of the WT BRAF SK-MEL2 cell line.
      • Feng Z.
      • Kochanek S.
      • Close D.
      • et al.
      Design and Activity of AP Endonuclease-1 Inhibitors.
      The synergy between vemurafenib and AJAY4 was confirmed in the V600E-BRAF MALME3M and SK-MEL28 melanoma cell lines, but was not observed in MCF7 or MDA-MB-468 breast cancer cell lines that are WT for BRAF.
      • Feng Z.
      • Kochanek S.
      • Close D.
      • et al.
      Design and Activity of AP Endonuclease-1 Inhibitors.
      In this study, however, the vemurafenib plus AJAY4 DC did not synergistically inhibit the growth of either WT or V600E-BRAF PDMCLs, which may reflect differences in the mutational profiles and/or signaling pathway alterations between PDMCLs and the SK-MEL5 cell line. SK-MEL5 cells were isolated more than 44 years ago,
      • Carey T.E.
      • Takahashi T.
      • Resnick L.A.
      • et al.
      Cell Surface Antigens of Human Malignant Melanoma: Mixed Hemadsorption Assays for Humoral Immunity to Cultured Autologous Melanoma Cells.
      and genomic comparisons between established cell lines and primary tumors have shown that while many (51%) genetic alterations may be shared at similar mutation frequencies, subsets of mutations are unique to patient tumors or cell lines, and those that are unique to cell lines favor immortalization and continuous maintenance in tissue culture.
      • Li H.
      • Wawrose J.S.
      • Gooding W.E.
      • et al.
      Genomic Analysis of Head and Neck Squamous Cell Carcinoma Cell Lines and Human Tumors: A Rational Approach to Preclinical Model Selection.
      The in vitro studies presented here provide a data-driven rationale to explore DCs between dasatinib and BRAFis or MEKis as candidates for melanoma combination therapies with the potential to improve outcomes and/or to prevent or delay the emergence of resistance. Effective DC strategies are also a focus of immuno-oncology research, either in combinations of immunotherapies or with small-molecule chemotherapies and/or targeted drugs.
      • Vennepureddy A.
      • Thumallapally N.
      • Motilal Nehru V.
      • et al.
      Novel Drugs and Combination Therapies for the Treatment of Metastatic Melanoma.
      ,
      • Johnson D.
      • Sosman J.A.
      Therapeutic Advances and Treatment Options in Metastatic Melanoma.
      ,
      • Tentori L.
      • Lacal P.M.
      • Graziani G.
      Challenging Resistance Mechanisms to Therapies for Metastatic Melanoma.
      ,
      • Welsh S.
      • Rizos H.
      • Scolyer R.A.
      • et al.
      Resistance to Combination BRAF and MEK Inhibition in Metastatic Melanoma: Where to Next?.
      ,
      • Voskoboynik M.
      • Arkenau H.T.
      Combination Therapies for the Treatment of Advanced Melanoma: A Review of Current Evidence.
      Several studies suggest that immune responses against mutant forms of BRAF can be harnessed in melanoma to boost antitumor immune responses.
      • Ilieva K.
      • Correa I.
      • Josephs D.H.
      • et al.
      Effects of BRAF Mutations and BRAF Inhibition on Immune Responses to Melanoma.
      Conversely, increased expression of mediators [interleukin-6 (IL6), IL10, and vascular endothelial growth factor (VEGF)] by V600E-BRAF melanoma cells could promote the recruitment of immunosuppressive myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs).
      • Ilieva K.
      • Correa I.
      • Josephs D.H.
      • et al.
      Effects of BRAF Mutations and BRAF Inhibition on Immune Responses to Melanoma.
      Clinically relevant concentrations of BRAFis do not affect the viability or function of lymphocytes, and BRAFi treatment of melanoma cell lines or primary tumor samples produced enhanced expression of melanocyte differentiation antigens (gp100 and MART1) important for immune recognition.
      • Ilieva K.
      • Correa I.
      • Josephs D.H.
      • et al.
      Effects of BRAF Mutations and BRAF Inhibition on Immune Responses to Melanoma.
      There is clinical evidence that melanoma treatment with BRAFis reverses some tumor-associated immunosuppressive signals but that the stimulation of immune responses subsides with the emergence of resistance and disease progression.
      • Ilieva K.
      • Correa I.
      • Josephs D.H.
      • et al.
      Effects of BRAF Mutations and BRAF Inhibition on Immune Responses to Melanoma.
      BRAFis and MEKis may condition the tumor microenvironment to favor immune activation by promoting antigen expression, antigen recognition, and T-cell infiltration.
      • Ilieva K.
      • Correa I.
      • Josephs D.H.
      • et al.
      Effects of BRAF Mutations and BRAF Inhibition on Immune Responses to Melanoma.
      Although it has been reported that dasatinib blocks T-cell activation by inhibiting LCK-mediated T-cell receptor signaling, administration of dasatinib in vivo can enhance T-effector-cell activation, expansion, and function.
      • Lowe D.
      • Bose A.
      • Taylor J.L.
      • et al.
      Dasatinib Promotes the Expansion of a Therapeutically Superior T-Cell Repertoire in Response to Dendritic Cell Vaccination against Melanoma.
      In an M05 (B16.OVA) mouse melanoma model, 7-day administration of dasatinib combined with a dendritic cell–based vaccine against an OVA peptide epitope more potently inhibited tumor growth and extended OS compared to the individual treatment groups.
      • Lowe D.
      • Bose A.
      • Taylor J.L.
      • et al.
      Dasatinib Promotes the Expansion of a Therapeutically Superior T-Cell Repertoire in Response to Dendritic Cell Vaccination against Melanoma.
      The combination of dasatinib plus vaccine treatment reduced levels of MDSCs and Tregs in the melanoma tumor microenvironment, and increased type 1 T-cell CXCR3 ligand-recruiting chemokines in the stroma, which correlated with recruitment and activation of type 1 CXCR3+CD8+ tumor-infiltrating lymphocytes and CD11c+ dendritic cells.
      • Lowe D.
      • Bose A.
      • Taylor J.L.
      • et al.
      Dasatinib Promotes the Expansion of a Therapeutically Superior T-Cell Repertoire in Response to Dendritic Cell Vaccination against Melanoma.
      The combination of dasatinib plus vaccine treatment promoted a broader therapeutic CD8+ T-cell repertoire in both the draining lymph nodes and tumor.
      • Lowe D.
      • Bose A.
      • Taylor J.L.
      • et al.
      Dasatinib Promotes the Expansion of a Therapeutically Superior T-Cell Repertoire in Response to Dendritic Cell Vaccination against Melanoma.
      In addition to their superior direct antitumor effects, DCs between dasatinib and BRAFis or MEKis might therefore also be beneficial in combination with immunotherapies. Some of the critical safety and efficacy factors that would need to be established for immunotherapies in combination with dasatinib plus BRAFis or MEKis will be optimal dosing, timing, and sequencing regimens. A critical next step for the studies reported herein will be to investigate whether dasatinib DCs with dabrafenib, vemurafenib, or trametinib exhibit enhanced in vivo outcomes in mouse melanoma cell line or PDMCL xenograft models and/or melanoma models of BRAFi resistance. For future DC HTS campaigns, it would be important to characterize the mutational profiles of the PDMCLs more extensively beyond their BRAF status to facilitate the investigation of the mechanism of action of synergistic drug interactions. Similarly, increasing the number of approved and/or investigational cancer drugs screened would improve the probability of finding novel DCs with potential to be developed into effective melanoma therapeutic regimens.
      Declaration of Conflicting Interests
      The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

      Acknowledgments

      The authors would like to thank Cindy Sander, who provided the patient-derived melanoma cell lines, and Dr. Ron Fecek for providing the two murine melanoma cell lines, BP-WT and BP-R20.
      Supplemental material is available online with this article.

      Abbreviations

      ABC:
      ATP-binding cassette
      AE:
      adverse event
      ALMANAC:
      A Large Matrix of Anti-Neoplastic Agent Combinations
      ATP:
      adenosine triphosphate
      BRAFi:
      B-Raf inhibitor
      CI:
      combination index
      CTG:
      Cell Titer Glo
      DC:
      drug combination
      DCM:
      drug combination matrix
      DIIS:
      drug interaction index score
      DMEM:
      Dulbecco’s modified Eagle’s medium
      EGF:
      epidermal growth factor
      EGFR:
      epidermal growth factor receptor
      FBS:
      fetal bovine serum
      FDA:
      US Federal Drug Administration
      GI50:
      50% growth inhibitory concentration
      HTS:
      high-throughput screening
      IC-MAbs:
      monoclonal antibody immunotherapies that block immune checkpoints
      LC50:
      lethal concentration 50
      MAb:
      monoclonal antibody
      MEKi:
      MEK inhibitor
      MOA:
      mechanism of action
      NCI:
      National Cancer Institute
      NCI 60:
      National Cancer Institute panel of 60 tumor cell lines
      OR:
      objective response
      OS:
      overall survival
      PBS:
      Dulbecco’s Mg2+- and Ca2+-free phosphate-buffered saline
      PDMCL:
      patient-derived melanoma cell line
      PFS:
      progression-free survival
      P/S:
      penicillin and streptomycin
      RLU:
      relative light unit
      TGI:
      total growth inhibition
      TKI:
      tyrosine kinase inhibitor
      V600E-BRAF:
      BRAFV600E mutation
      WT:
      wild type

      Funding

      The authors received the following financial support for the research, authorship, and/or publication of this article: These studies were funded in part by a Developmental Research Project (PI: Paul Johnston) award from the SPORE (Specialized Program of Research Excellence) in Skin Cancer (P50 CA121973; PI: John Kirkwood) and an award (R01 CA214018; PI: Walter J. Storkus) at the University of Pittsburgh Medical Center and Hillman Cancer Center.

      Supplemental Material

      References

        • Bertolotto C.
        Melanoma: From Melanocyte to Genetic Alterations and Clinical Options.
        Scientifica. 2013; 2013 (doi:1155/2013/635203.): 635203
        • Gorantla V.
        • Kirkwood J.M.
        State of Melanoma: An Historic Overview of a Field in Transition.
        Hematol. Oncol. Clin. North. Am. 2014; 28: 415-435
        • Vennepureddy A.
        • Thumallapally N.
        • Motilal Nehru V.
        • et al.
        Novel Drugs and Combination Therapies for the Treatment of Metastatic Melanoma.
        J. Clin. Med. Res. 2016; 8: 63-75
        • Davar D.
        • Lin Y.
        • Kirkwood J.M.
        Unfolding the Mutational Landscape of Human Melanoma.
        J. Invest. Dermatol. 2015; 135: 659-662
        • The Cancer Genome Atlas Network
        Genomic Classification of Cutaneous Melanoma.
        Cell. 2015; 161: 1681-1696
        • Zhang T.
        • Dutton-Regester K.
        • Brown K.M.
        • et al.
        The Genomic Landscape of Cutaneous Melanoma.
        Pigment Cell Melanoma Res. 2016; 29: 266-283
        • Tang T.
        • Eldabaje R.
        • Yang L.
        Current Status of Biological Therapies for the Treatment of Metastatic Melanoma.
        Anticancer Res. 2016; 36: 3229-3241
        • Johnson D.
        • Sosman J.A.
        Therapeutic Advances and Treatment Options in Metastatic Melanoma.
        JAMA Oncol. 2015; 1: 380-386
        • Michielin O.
        • Hoeller C.
        Gaining Momentum: New Options and Opportunities for the Treatment of Advanced Melanoma.
        Cancer Treat Rev. 2015; 41: 660-670
        • Merlino G.H.M.
        • Fisher D.E.
        • Bastian B.C.
        • et al.
        The State of Melanoma: Challenges and Opportunities.
        Pigment Cell Melanoma Res. 2016; 29: 404-416
        • Banzi M.
        • De Blasio S.
        • Lallas A.
        • et al.
        Dabrafenib: A New Opportunity for the Treatment of BRAF V600-Positive Melanoma.
        Onco. Targets Ther. 2016; 9: 2725-2733
        • Bollag G.
        • Tsai J.
        • Zhang J.
        • et al.
        Vemurafenib: The First Drug Approved for BRAF-Mutant Cancer.
        Nat. Rev. Drug Discov. 2012; 11: 873-886
        • Swaika A.
        • Crozier J.A.
        • Joseph R.W.
        Vemurafenib: An Evidence-Based Review of Its Clinical Utility in the Treatment of Metastatic Melanoma.
        Drug Des. Devel. Ther. 2014; 16: 775-787
        • Lo J.
        • Fisher D.E.
        The Melanoma Revolution: From UV Carcinogensis to a New Era of Therapeutics.
        Science. 2014; 346: 945-949
        • Grazia G.
        • Penna I.
        • Perotti V.
        • et al.
        Towards Combinatorial Targeted Therapy in Melanoma: From Pre-Clinical Evidence to Clinical Application (Review).
        Int. J. Oncol. 2014; 45: 929-949
        • Tentori L.
        • Lacal P.M.
        • Graziani G.
        Challenging Resistance Mechanisms to Therapies for Metastatic Melanoma.
        Trends Pharmacol. Sci. 2013; 34: 656-666
        • Dummer R.
        • Ramelyte E.
        • Schindler S.
        • et al.
        MEK Inhibition and Immune Responses in Advanced Melanoma.
        Oncoimmunology. 2017; 6 (doi:10.1080/2162402X.2017.1335843.): e1335843
        • Dummer R.
        • Mangana J.
        • Frauchiger A.L.
        • et al.
        How I Treat Metastatic Melanoma.
        ESMO Open. 2019; 4 (doi:10.1136/esmoopen-2019-000509.): e000509
        • Koelblinger P.
        • Thuerigen O.
        • Dummer R.
        Development of Encorafenib for BRAF-Mutated Advanced Melanoma.
        Curr. Opin. Oncol. 2018; 30: 125-133
        • Boutros C.
        • Tarhini A.
        • Routier E.
        • et al.
        Safety Profiles of Anti-CTLA-4 and Anti-PD-1 Antibodies Alone and in Combination.
        Nat. Rev. Clin. Oncol. 2016; 13: 473-486
        • Das Thakur M.
        • Stuart D.D.
        Molecular Pathways: Response and Resistance to BRAF and MEK Inhibitors in BRAF(V600E) Tumors.
        Clin. Cancer Res. 2014; 20: 1074-1080
        • Holohan C.
        • Van Schaeybroeck S.
        • Longley D.B.
        • et al.
        Cancer Drug Resistance: An Evolving Paradigm.
        Nat. Rev. Cancer. 2013; 13: 714-726
        • Lovly C.
        • Shaw A.T.
        Molecular Pathways: Resistance to Kinase Inhibitors and Implications for Therapeutic Strategies.
        Clin. Cancer Res. 2014; 20: 2249-2256
        • Rizos H.
        • Menzies A.M.
        • Pupo G.M.
        • et al.
        BRAF Inhibitor Resistance Mechanisms in Metastatic Melanoma: Spectrum and Clinical Impact.
        Clin. Cancer Res. 2014; 20: 1965-1977
        • Welsh S.
        • Rizos H.
        • Scolyer R.A.
        • et al.
        Resistance to Combination BRAF and MEK Inhibition in Metastatic Melanoma: Where to Next?.
        Eur. J. Cancer. 2016; 62: 76-85
        • Kwon C.H.
        • Wheeldon I.
        • Kachouie N.N.
        • et al.
        Drug-Eluting Microarrays for Cell-Based Screening of Chemical-Induced Apoptosis.
        Anal. Chem. 2011; 83: 4118-4125
        • Al-Lazikani B.
        • Banerji U.
        • Workman P.
        Combinatorial Drug Therapy for Cancer in the Post-Genomic Era.
        Nat. Biotechnol. 2012; 30: 679-692
        • Dancey J.
        • Chen H.X.
        Strategies for Optimizing Combinations of Molecularly Targeted Anticancer Agents.
        Nat. Rev. Drug Discov. 2006; 5: 649-659
        • Keith C.
        • Borisy A.A.
        • Stockwell B.R.
        Multicomponent Therapeutics for Networked Systems.
        Nat. Rev. Drug Discov. 2005; 4: 71-78
        • Kummar S.
        • Chen H.X.
        • Wright J.
        • et al.
        Utilizing Targeted Cancer Therapeutic Agents in Combination: Novel Approaches and Urgent Requirements.
        Nat. Rev. Drug Discov. 2010; 9: 843-856
        • Ocaña A.
        • Pandiella A.
        Personalized Therapies in the Cancer “Omics” Era.
        Mol. Cancer. 2010; 9: 202-214
        • Rodon J.
        • Perez J.
        • Kurzrock R.
        Combining Targeted Therapies: Practical Issues to Consider at the Bench and Bedside.
        The Oncologist. 2010; 15: 37-50
        • McArthur G.
        Combination Therapies to Inhibit the RAF/MEK/ERK Pathway in Melanoma: We Are Not Done Yet.
        Front. Oncol. 2015; 5: 161-166
        • Verstovsek S.
        • Kantarjian H.
        • Mesa R.A.
        • et al.
        Safety and Efficacy of INCB018424, a JAK1 and JAK2 Inhibitor, in Myelofibrosis.
        New Engl. J. Med. 2010; 363: 1117-1127
        • Voskoboynik M.
        • Arkenau H.T.
        Combination Therapies for the Treatment of Advanced Melanoma: A Review of Current Evidence.
        Biochem. Res. Int. 2014; 2014: 307059
        • Kwong L.
        • Davies M.A.
        Targeted Therapy for Melanoma: Rational Combinatorial Approaches.
        Oncogene. 2014; 33: 1-9
        • Axelrod M.
        • Gordon V.L.
        • Conaway M.
        • et al.
        Combinatorial Drug Screening Identifies Compensatory Pathway Interactions and Adaptive Resistance Mechanisms.
        Oncotarget. 2013; 4: 622-635
        • Chan G.
        • Wilson S.
        • Schmidt S.
        • et al.
        Unlocking the Potential of High-Throughput Drug Combination Assays Using Acoustic Dispensing.
        J. Lab. Autom. 2015; 21: 125-132
        • Close D.A.
        • Wang A.X.
        • Kochanek S.J.
        • et al.
        Implementation of the NCI-60 Human Tumor Cell Line Panel to Screen 2260 Cancer Drug Combinations to Generate >3 Million Data Points Used to Populate A Large Matrix of Anti-Neoplastic Agent Combinations (ALMANAC) Database.
        SLAS Discov. 2018; 24: 242-263
        • Feng Z.
        • Kochanek S.
        • Close D.
        • et al.
        Design and Activity of AP Endonuclease-1 Inhibitors.
        J. Chem. Biol. 2015; 8: 79-93
        • Greco W.R.
        • Faessel H.
        • Levasseur L.
        The Search for Cytotoxic Synergy between Anticancer Agents: A Case of Dorothy and the Ruby Slippers?.
        J. Natl. Cancer Inst. 1996; 88: 699-700
        • Holbeck S.L.
        • Camalier R.
        • Crowell J.A.
        • et al.
        The National Cancer Institute ALMANAC: A Comprehensive Screening Resource for the Detection of Anticancer Drug Pairs with Enhanced Therapeutic Activity.
        Cancer Res. 2017; 77: 3564-3576
        • Kochanek S.
        • Close D.A.
        • Wang A.X.
        • et al.
        Confirmation of Selected Synergistic Cancer Drug Combinations Identified in an HTS Campaign and Exploration of Drug Efflux Transporter Contributions to the Mode of Synergy.
        SLAS Discov. 2019; 24: 653-668
        • Korfi K.
        • Smith M.
        • Swan J.
        • et al.
        BIM Mediates Synergistic Killing of B-Cell Acute Lymphoblastic Leukemia Cells by BCL-2 and MEK Inhibitors.
        Cell Death Dis. 2016; 7: e2177
        • Mathews Griner L.
        • Guha R.
        • Shinn P.
        • et al.
        High-Throughput Combinatorial Screening Identifies Drugs That Cooperate with Ibrutinib to Kill Activated B-Cell-Like Diffuse Large B-Cell Lymphoma Cells.
        Proc. Natl. Acad. Sci. USA. 2014; 111: 2349-2354
        • O’Neil J.
        • Benita Y.
        • Feldman I.
        • et al.
        An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies.
        Mol. Cancer Ther. 2016; 15: 1152-1162
        • Peifer M.
        • Weiss J.
        • Sos M.L.
        • et al.
        Analysis of Compound Synergy in High-Throughput Cellular Screens by Population-Based Lifetime Modeling.
        PLoS One. 2010; 5: e8919
        • Cooper Z.
        • Juneja V.R.
        • Sage P.T.
        • et al.
        Response to BRAF Inhibition in Melanoma Is Enhanced When Combined with Immune Checkpoint Blockade.
        Cancer Immunol. Res. 2014; 2: 643-654
      1. Chelvanambi M., Fecek R. J., Taylor J. L.; et al. Manuscript in preparation, 2020.

        • Bliss C.
        The Toxicity of Poisons Applied Jointly.
        Ann. Appl. Biol. 1939; 26: 585-615
        • Chou T.
        Drug Combination Studies and Their Synergy Quantification Using the Chou-Talalay Method.
        Cancer Res. 2010; 70: 440-446
        • O’Brien M.
        • Daily W.J.
        • Hesselberth P.E.
        • et al.
        Homogeneous, Bioluminescent Protease Assays: Caspase-3 as a Model.
        J. Biomol. Screen. 2005; 10: 137-148
        • Riss T.
        • Moravec R.A.
        Use of Multiple Assay Endpoints to Investigate the Effects of Incubation Time, Dose of Toxin, and Plating Density in Cell-Based Cytotoxicity Assays.
        Assay Drug Dev. Technol. 2004; 2: 51-62
        • Liston D.
        • Davis M.
        Clinically Relevant Concentrations of Anticancer Drugs: A Guide for Nonclinical Studies.
        Clin. Cancer Res. 2017; 23: 3489-3498
        • Aleshin A.
        • Finn R.S.
        SRC: A Century of Science Brought to the Clinic.
        Neoplasia. 2010; 12: 599-607
        • Montero J.
        • Seoane S.
        • Ocaña A.
        • et al.
        Inhibition of SRC Family Kinases and Receptor Tyrosine Kinases by Dasatinib: Possible Combinations in Solid Tumors.
        Clin. Cancer Res. 2011; 17: 5546-5552
        • Parsons S.
        • Parsons J.T.
        Src Family Kinases, Key Regulators of Signal Transduction.
        Oncogene. 2004; 23: 7906-7909
        • Eustace A.J.
        • Kennedy S.
        • Larkin A.-M.
        • et al.
        Predictive Biomarkers for Dasatinib Treatment in Melanoma.
        Oncoscience. 2014; 1: 158-166
        • Homsi J.
        • Cubitt C.L.
        • Zhang S.
        • et al.
        Src Activation in Melanoma and Src Inhibitors as Therapeutic Agents in Melanoma.
        Melanoma Res. 2009; 19: 167-175
        • Chaar M.
        • Kamta J.
        • Ait-Oudhia S.
        Mechanisms, Monitoring, and Management of Tyrosine Kinase Inhibitors–Associated Cardiovascular Toxicities.
        OncoTargets Ther. 2018; 11: 6227-6237
        • Vergani E.
        • Vallacchi V.
        • Frigerio S.
        • et al.
        Identification of MET and SRC Activation in Melanoma Cell Lines Showing Primary Resistance to PLX4032.
        Neoplasia. 2011; 13: 1132-1142
        • Girotti M.R.
        • Pedersen M.
        • Sanchez-Laorden B.
        • et al.
        Inhibiting EGF Receptor or SRC Family Kinase Signaling Overcomes BRAF Inhibitor Resistance in Melanoma.
        Cancer Discov. 2013; 3: 158-167
        • Algazi A.P.
        • Weber J.S.
        • Andrews S.C.
        • et al.
        Phase I Clinical Trial of the Src Inhibitor Dasatinib with Dacarbazine in Metastatic Melanoma.
        Br. J. Cancer. 2012; 106: 85-91
        • Kalinsky K.
        • Lee S.
        • Rubin K.M.
        • et al.
        A Phase 2 Trial of Dasatinib in Patients with Locally Advanced or Stage IV Mucosal, Acral, or Vulvovaginal Melanoma: A Trial of the ECOG-ACRIN Cancer Research Group (E2607).
        Cancer. 2017; 123: 2688-2697
        • Kluger H.
        • Dudek A.Z.
        • McCann C.
        • et al.
        A Phase 2 Trial of Dasatinib in Advanced Melanoma.
        Cancer. 2011; 117: 2202-2208
        • Carey T.E.
        • Takahashi T.
        • Resnick L.A.
        • et al.
        Cell Surface Antigens of Human Malignant Melanoma: Mixed Hemadsorption Assays for Humoral Immunity to Cultured Autologous Melanoma Cells.
        Proc. Natl. Acad. Sci. USA. 1976; 73: 3278-3282
        • Li H.
        • Wawrose J.S.
        • Gooding W.E.
        • et al.
        Genomic Analysis of Head and Neck Squamous Cell Carcinoma Cell Lines and Human Tumors: A Rational Approach to Preclinical Model Selection.
        Mol. Cancer Res. 2014; 12: 571-582
        • Ilieva K.
        • Correa I.
        • Josephs D.H.
        • et al.
        Effects of BRAF Mutations and BRAF Inhibition on Immune Responses to Melanoma.
        Mol. Cancer Ther. 2014; 13: 2769-2783
        • Lowe D.
        • Bose A.
        • Taylor J.L.
        • et al.
        Dasatinib Promotes the Expansion of a Therapeutically Superior T-Cell Repertoire in Response to Dendritic Cell Vaccination against Melanoma.
        Oncoimmunology. 2014; 3 (doi:10.4161/onci.27589.): e27589