Algorithm for Codevelopment of New Drug-Predictive Biomarker Combinations: Accounting for Inter- and Intrapatient Tumor Heterogeneity
2012; Elsevier BV; Volume: 13; Issue: 5 Linguagem: Inglês
10.1016/j.cllc.2012.05.004
ISSN1938-0690
AutoresDavid R. Gandara, Tianhong Li, Primo N. Lara, Philip C. Mack, Karen Kelly, Suzanne Miyamoto, Neal Goodwin, Laurel Beckett, Mary W. Redman,
Tópico(s)Colorectal Cancer Treatments and Studies
ResumoPersonalized cancer therapy, based on molecular profiling of each patient's cancer, is increasingly viewed as likely to increase the overall effectiveness of cancer treatment and to do so in both a clinically meaningful and cost-effective manner by sparing patients who are unlikely to benefit from the costs and adverse effects of ineffective therapies. 1 Gandara D.R. Lara Jr, P.N. Mack P. et al. Individualizing therapy for non-small-cell lung cancer: a paradigm shift from empiric to integrated decision-making. Clin Lung Cancer. 2009; 10: 148-150 Abstract Full Text PDF PubMed Scopus (25) Google Scholar , 2 Tursz T. Andre F. Lazar V. et al. Implications of personalized medicine: perspective from a cancer center. Nat Rev Clin Oncol. 2011; 8: 177-183 Crossref PubMed Scopus (50) Google Scholar , 3 Roychowdhury S. Iyer M.K. Robinson D.R. et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci Transl Med. 2011; 3: 111-121 Crossref Scopus (490) Google Scholar Thus, in the emerging era of new anticancer agents directed against molecular targets present in only a small subset of patients within a general population, such as non–small-cell lung cancer (NSCLC), it is increasingly important to consider simultaneous and early codevelopment of an associated predictive biomarker. To emphasize this point, one need only recall the poor track record of phase III randomized controlled clinical trials (RCT) of chemotherapy with or without a so-called targeted agent in advanced NSCLC (Table 1), only 2 trials met the criterion gold standard of success, improved survival, regardless of the drug class or molecular target. Importantly, none of these trials incorporated prospective evaluation of a potential predictive biomarker for the new agent being studied, with the sole exception of basic epidermal growth factor receptor (EGFR) protein expression in FLEX. 4 Pirker R. Pereira J.R. Szczesna A. et al. FLEX Study TeamCetuximab plus chemotherapy in patients with advanced non-small-cell lung cancer (FLEX): an open-label randomised phase III trial. Lancet. 2009; 373: 1525-1531 Abstract Full Text Full Text PDF PubMed Scopus (1249) Google Scholar In retrospect, it now seems overly naive to have thought that these targeted therapies would lead to clinical benefit in the overall population of patients with NSCLC in view of what we now know about the tremendous degree of interpatient tumor heterogeneity that exists within the umbrella diagnosis of NSCLC, arguably a collection of biologically and molecularly distinct malignancies. This point is emphasized by recent literature of a variety of molecularly defined and drug treatment–specific patient subsets of adenocarcinoma, such as those that harbor activating mutations of EGFR or anaplastic lymphoma kinase (ALK) fusion proteins. 5 Lynch T.J. Bell D.W. Sordella R. et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small cell lung cancer to gefitinib. N Engl J Med. 2004; 350: 2129-2139 Crossref PubMed Scopus (9864) Google Scholar , 6 Mok T.Z. Wu Y.-L. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med. 2009; 361: 947-957 Crossref PubMed Scopus (6917) Google Scholar , 7 Soda M. Choi Y.L. Enomoto M. et al. Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature. 2007; 448: 561-566 Crossref PubMed Scopus (4205) Google Scholar , 8 Shaw A.T. Yeap B.Y. Soloman B.J. et al. Effect of crizotinib on overall survival in patients with advanced non-small-cell lung cancer harbouring ALK gene rearrangement: a retrospective analysis. Lancet Oncol. 2011; 12: 1004-1012 Abstract Full Text Full Text PDF PubMed Scopus (772) Google Scholar , 9 Engelman J.A. Zejnullahu K. Mitsudomi T. et al. MET amplification leads to gefitinib resistance in lung cancer by activating ERBB3 signaling. Science. 2007; 316: 1039-1043 Crossref PubMed Scopus (3829) Google Scholar Further, genome-wide profiling of individual patient tumors now offers the possibility of identifying complex mechanisms of tumor proliferation and/or drug resistance, which are actionable by currently available or developing targeted therapies.x 2 Tursz T. Andre F. Lazar V. et al. Implications of personalized medicine: perspective from a cancer center. Nat Rev Clin Oncol. 2011; 8: 177-183 Crossref PubMed Scopus (50) Google Scholar , 10 Shedden K. Taylor J.M. et al. Director's Challenge Consortium for the Molecular Classification of Lung AdenocarcinomaGene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med. 2008; 14: 822-827 Crossref PubMed Scopus (836) Google Scholar , 11 Subramanian A. Tamayo P. Mootha V.K. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005; 102: 15545-15550 Crossref PubMed Scopus (24472) Google Scholar , 12 Lee W. Jiang Z. Liu J. et al. The mutation spectrum revealed by paired genome sequences from a lung cancer patient. Nature. 2010; 465: 473-477 Crossref PubMed Scopus (413) Google Scholar Recent data from the Lung Cancer Mutation Consortium, for example, demonstrated “actionable” abnormalities in 54% of lung adenocarcinomas. 13 Johnson B. Kris M.G. Kwiatkowski D. et al. Clinical characteristics of planned 1000 patients with adenocarcinoma of lung (ACL) undergoing genomic characterization in the US Lung Cancer Mutation Consortium (LCMC). in: Thor J. Oncology, Proc 14th World Conf on Lung Cancer. 6. 2011: S344 Google Scholar It appears that clinical trial designs enriched for the target population by a predictive biomarker or statistically powered to prospectively evaluate a biomarker as a coprimary or secondary endpoint will most likely demonstrate patient subgroups that benefit. A classic example in support of this concept is provided by the successful biomarker-driven pivotal trial that evaluated chemotherapy with or without trastuzumab in breast cancer, in which mathematical modeling clearly demonstrated that an unselected RCT design would have failed to identify the benefit of this important anticancer agent. 14 Pegram M.D. Pietras R. Bajamonde A. et al. Targeted therapy: wave of the future. J Clin Oncol. 2005; 23: 1776-1781 Crossref PubMed Scopus (43) Google Scholar Table 1Classic Randomized Controlled Trial Design (Unselected): Recent Phase III Trials of Chemotherapy With and Without a Targeted Agent a In combination with platinum-based chemotherapy vs. chemotherapy. in First-Line Advanced Stage NSCLC Target Agent Survival Benefit MMP Prinomastat, others No EGFR TKI Gefitinib or erlotinib No Farnesyl Transferase (RAS) Lonafarnib No PKCα ISIS 3521 No RXR Bexarotene No VEGFR (TKI) Sorafenib No VEGF (MoAb) Bevacizumab Yes EGFR (MoAb) Panitumumab No TLR9 Agonist PF-351 No EGFR (MoAb) Cetuximab Yes b EGFR immunohistochemistry positive. IGR-1-R Figitumumab No VDA ASA-404 No Abbreviations: EGFR = epidermal growth factor receptor; IGF-1-R = insulin growth factor-1 receptor; MoAb = monoclonal antibody; MMP = matrix metalloproteinase; NSCLC = non–small-cell lung cancer; PKCα = protein kinase C; RXR = retinoid X receptor; TKI = tyrosine kinase inhibitor; TLR9 = toll-like receptor 9; VDA = vascular disrupting agent; VEGFR = vascular endothelial growth factor receptor. a In combination with platinum-based chemotherapy vs. chemotherapy. b EGFR immunohistochemistry positive. Open table in a new tab Abbreviations: EGFR = epidermal growth factor receptor; IGF-1-R = insulin growth factor-1 receptor; MoAb = monoclonal antibody; MMP = matrix metalloproteinase; NSCLC = non–small-cell lung cancer; PKCα = protein kinase C; RXR = retinoid X receptor; TKI = tyrosine kinase inhibitor; TLR9 = toll-like receptor 9; VDA = vascular disrupting agent; VEGFR = vascular endothelial growth factor receptor. CorrectionClinical Lung CancerVol. 14Issue 1PreviewIn the article by Gandara et al., entitled “Algorithm for Codevelopment of New Drug-Predictive Biomarker Combinations: Accounting for Inter- and Intrapatient Tumor Heterogeneity”, volume 13, number 5 ( http://dx.doi.org/10/1016/j.cllc.2012.05.004 ), the summary statement was erroneously omitted. The summary statement is as follows: Full-Text PDF
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