The Shifting Paradigm in Clinical Trial Design for Anti-cancer Drugs

Article

Applied Clinical Trials

How molecular profiling is impacting today's cancer medicine.

With the advent of highly cost- and time-efficient genomic analysis technologies, cancer medicine is progressively shifting from empirical testing of “one-size-fits-all” therapies to an unprecedented realm in which the oncologist is regarded as a  “clinical cancer biologist” (as envisioned by Dr. George Sledge in his 2011 ASCO presidential address).1 In this new reality, cancer is increasingly understood as a complex constellation of diseases characterized by highly diverse alterations in specific genes and proteins that are responsible for spectacular responses in some patients, but, unfortunately, are associated with a lack of drug activity in the majority of unselected patients. For that reason alone, it has become more common for clinical trials to seek patients based on their tumors’ molecular signatures (as illustrated in the accompanying figure). As a result, molecular profiling is viewed as a critical element in the design and conduct of oncology clinical trials, as it allows investigators to match the biomarkers within an individual’s tumor with agents that specifically target those biomarkers. In the context of daily clinical practice, increasing numbers of oncologists are using molecular profiling to obtain insights into the dominant mechanism of their patients’ cancers to find appropriate anticancer therapies, either through clinical trials or through off-label use of a growing number of the FDA-approved targeted drugs.

Suppressing the dominant oncogenic mechanism. Genetic characterization of large series of tissue type-specific tumors consistently reveals a high level of diversity of oncogenic mechanisms, so that an individual tumor has a unique combination of genetic lesions that probably have not been seen in other tumors of the same kind.2-5 To add another layer of complexity, there is a growing appreciation of the existence of multiple genetically distinct clones within the same tumor,6 which can compete for fitness and survival under selective pressures of anti-cancer therapies. Despite this branched genetic phylogeny, the critical “founding” oncogenic lesions are shared between the tumor clones.7 In this context, molecular profiling can be used to uncover the dominant oncogenic mechanism in a tumor, and to select therapies that target the oncogenic driver.8 While this approach may seem new, it dates back to 1959, when Nowell and Hungerford described the famous Philadelphia chromosome translocation t(8,21) activating the ABL kinase and the malignant transformation in chronic myeloid leukemia (CML).9 Four decades later, a compound known as ST1571, later to be renamed imatinib (Gleevec), was used for the first time in humans to suppress the culprit tyrosine kinase and to reverse the cancer process clinically and biologically.10

The amazing success of applying a biologically relevant drug with laser precision to an oncogene-addicted cancer did not stand alone for too long:  targeting HER2-positive carcinomas with an antibody, trastuzumab, or a small molecule inhibitor, lapatinib, produced remarkable gains in cure rates and longevity in breast and esophagogastric cancers.11-15  Pfizer also used this approach in developing crizotinib, an ALK inhibitor for the treatment of non-small-cell lung cancer (NSCLC); this is the first example of a “big pharma” company addressing a specific population of patients deemed more likely to benefit from a targeted agent. By contrast, previous approaches to targeted therapy involved administering targeted drugs to all comers even though only a minority of patients was found to benefit. In Pfizer’s case, targeting a small subset of patients (~5%) produced a spectacular response.16 It made perfect biological and ethical sense for Pfizer to seek and treat those individuals with ALK-mutated cancers and not to subject the remaining 95% of patients to therapies that would most likely be futile.

Other examples abound. The tumor suppressor genes BRCA1, BRCA2 and PALB2 (partner and localizer of BRCA2), which are implicated in hereditary breast and ovarian cancers, also confer the extreme sensitivity to platinum and mitomycin C that has been observed in patients with pancreatic cancer.17 Efforts to suppress the dominant oncogenic mechanism have also engendered the use of PARP inhibitors to target BRCAdeficiency in advanced breast and ovarian cancers,18-22 based on the fact that deficiency of the BRCA1/2 proteins makes cancer cells totally dependent on the alternative DNA repair mechanism mediated by PARP, while reducing these cancers’ ability to repair DNA damage, making them more sensitive to chemotherapy. More recently, researchers at Memorial Sloan-Kettering Cancer Center have reported that inactivation of the RAD52 protein is synthetically lethal in cells that are deficient in BRCA1, BRCA2 and PALB2 .23

Similar discoveries have led to the use of erlotinib and gefitinib to target EGFR mutations in lung cancer.24-27 Recent trials of BRAF inhibitors in melanoma revealed that despite initial response, there was rapid evolution of resistance via the mitogen-activated protein kinase pathway, receptor tyrosine kinases and N-RAS.28, 29 These discoveries prompted various groups of investigators to design trials that would nullify these potential escape mechanisms from BRAF blockade through inhibition of MEK, IGF-1R/PI3K, EGFR and SRC family kinases.30-33 Similarly, neuroendocrine carcinomas and breast cancers carrying PI3K-mTOR pathway mutations have been shown to be sensitized to the inhibitors of the pathway.34 More recently, researchers at the Whitehead Institute described the identification of additional alterations in the mTOR complex 1 (mTORC1) signaling pathway that may sensitize tumors to rapamycin analogs.35

Exploiting synthetic lethality to block signaling proteins. The concept of synthetic lethality refers to the process by which tumor cells are rendered non-viable by the combined presence of two mutations or alleles, neither of which is sufficient to trigger cell death by itself.36 In pharmacogenetic terms, this concept would apply to a drug that does not affect wild-type cells but kills cells carrying a specific genetic alteration.37 By extension, systematic synthetic lethality screening approaches have been successfully used to identify new synergistic targets to improve the activity of established drugs.38 This phenomenon is consistent with the view of signaling as a network of proteins characterized by dense connectivity, lack of hierarchy, compensatory activity of feedback signaling loops, and protective overlapping functions among the network components.39 According to the network theory, cancer cells inhibited at a single node will engage rescue signaling inputs from the web of interactions centered on the target of inhibition. Our group has tested this concept by using small interference RNA (siRNA) screening to probe the EGFR-centric network for potential sensitizers. Promising synergies between EGFR antagonists (e.g., erlotinib, cetuximab) and drugs targeting protein kinase C, Aurora kinase, or the transcriptional regulator STAT3 are now being tested in ongoing clinical trials.38 The synthetic lethality approach may be particularly valuable for the treatment of cancers with “undruggable” oncogenic drivers such as KRAS, MYC, or b-catenin.

Exploring biomarkers of chemotherapy resistance and susceptibility. This approach was first used in the Bisgrove study, a phase 2 trial that identified FDA-approved agents that can be directed toward molecular targets in refractory tumors for which there are no standard-of-care treatment options.40 In this study, tissue samples from 86 patients with refractory solid tumors underwent various molecular profiling techniques, which were considered beneficial for any patient with a progression-free survival (PFS) ratio (PFS on molecular profiling-selected therapy/time to progression on prior therapy) of ≥1.3. A molecular target was identified in 84 of the 86 patients (98%), and 66 of those patients were started on a molecular profiling-identified regimen. The study demonstrated that 18 of the 66 patients, or 27% (95% confidence interval, 17-38), had longer PFS in comparison to the prior line of therapy by a factor of ≥1.3, thus exceeding the pre-planned threshold of 15% to reach this goal. The results conclusively demonstrated that molecular profiling of tumors using the Bisgrove study platform improved the efficacy of biomarker-selected chemotherapy agents.

Future directions. Leading academic cancer centers around the world are developing programs that incorporate advances in molecular profiling of tumors to advance personalized cancer medicine initiatives. At M.D. Anderson Cancer Center, where researchers recently matched targeted therapies with molecular aberrations in patients’ tumors,  therapies impinging on the relevant oncogenic mechanisms were associated with a higher overall response rate, longer time to treatment failure, and longer survival, compared to treatment without matching.41 To build on that initial success, the proposed NCI-MATCH (Molecular Analysis for Therapy Choice) study will use rapid next-generation sequencing (NGS) to screen patients with solid tumors or lymphoma for molecular features that may predict response to a targeted agent with a specific mechanism of action, and will serve as an umbrella for multiple, single-arm phase 2 trials.42 As molecular profiling gains greater currency in oncology clinical trials and cancer clinics, studies such as these will lead to dramatic shifts in the general design of clinical trials and drug utilization: from organ-of-origin or tissue type-specific trials and drug licensing to molecularly defined application of antineoplastic agents across the old histological boundaries. We foresee that ultimately, as a criterion for treatment selection, the molecular mechanisms in a tumor will prevail over the appearance of a cancer under the microscope.

Figure caption: The shifting paradigm in clinical trial design for anticancer drugs

“One-drug-for-all” trials (A) treat a large number of patients with a drug that ideally should benefit the majority of participants. If there are only a few true beneficiaries, and the majority of patients derive no benefit from the drug, the trial will likely fail to detect the positive effect in the drug-matched minority. By contrast, mechanism-matched trials (B) screen large number of patients to identify rare individuals in whom genetic and expression analyses match the drugs with the molecular mechanisms of patients’ tumors. This approach, potentially costly upfront, identifies likely beneficiaries and detects the treatment effect more readily by not treating predicted failures.

Igor Astsaturov, MD, PhD is Assistant Professor, Fox Chase Cancer Center, Philadelphia, PA

References

  1. P. Goldberg, "Prepare for 'tsunami' of genomic information, sledge urges in ASCO presidential address,” Cancer Lett, 37 (23) 1-4 (2011).
  2. A.V. Biankin et al, “Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes,” Nature, 491 (7424) 399-405 (2012).
  3. C. Kandoth et al, “Integrated genomic characterization of endometrial carcinoma,” Nature, 497 (7447) 67-73 (2013).
  4. Cancer Genome Atlas Network, “Comprehensive molecular portraits of human breast tumours,” Nature, 490 (7418) 61-70 (2012).
  5. Cancer Genome Atlas Network, “Comprehensive molecular characterization of human colon and rectal cancer,” Nature, 487 (7407) 330-337 (2012).
  6. X Xu et al, “Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor,” Cell, 148 (5) 886-895 (2012).
  7. M. Gerlinger et al, “Intratumor heterogeneity and branched evolution revealed by multiregion sequencing,” N Engl J Med, 366 (10) 883-892 (2012).
  8. J. Luo et al, “Principles of cancer therapy: oncogene and non-oncogene addiction,” Cell, 136 (5) 823-837 (2009).
  9. P. Nowell and D.A. Hungerford, “A minute chromosome in chronic granulocytic leukemia,” Science, 132 (3438) 1497 (1960).
  10. B.J. Druker et al, “Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia,” N Engl J Med, 344 (14) 1031-1037 (2001).
  11. D.J. Slamon et al, “Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2,” N Engl J Med 344 (11) 783-792 (2001).
  12. M.J. Piccart-Gebhart et al, “Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer,” N Engl J Med, 353 (16) 1659-1672 (2005).
  13. E.H. Romond et al, “Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer,” N Engl J Med,  353 (16) 1673-1684 (2005).
  14. C.E. Geyer et al, “Lapatinib plus capecitabine for HER2-positive advanced breast cancer,” N Engl J Med,  355 (26) 2733-2743 (2006).
  15. Y.J. Bang et al, “Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial,”  Lancet, 376 (9742) 687-697 (2010).
  16. D.W. Kim et al, “Results of a global phase II study with crizotinib in advanced ALK-positive non-small cell lung cancer (NSCLC),” J Clin Oncol, 30 (suppl) abstr 7533 (2012).
  17. C. Bowman-Colin et al, “Palb2 synergizes with Trp53 to suppress mammary tumor formation in a model of inherited breast cancer,” Proc Natl Acad Sci USA, 110 (21) 8632-8637 (2013).
  18. M.W. Audeh et al, “Oral poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1 or BRCA2 mutations and recurrent ovarian cancer: a proof-of-concept trial,” Lancet, 376 (9737) 245-251 (2010).
  19. A. Tutt et al, “Oral poly(ADP-ribose) polymerase inhibitor olaparib in patients with BRCA1 or BRCA2 mutations and advanced breast cancer: a proof-of-concept trial,” Lancet, 376 (9737) 235-244 (2010).
  20. M.S. Goldberg et al, “Nanoparticle-mediated delivery of siRNA targeting Parp1 extends survival of mice bearing tumors derived from Brca1-deficient ovarian cancer cells,” Proc Natl Acad Sci USA, 108 (2) 745-50 (2011).
  21. U. Kortmann et al, “Tumor growth inhibition by olaparib in BRCA2 germline-mutated patient-derived ovarian cancer tissue xenografts,” Clin Cancer Res, 17 (4) 783-791 (2011).
  22. K. Do and A.P. Chen, “Molecular pathways: targeting PARP in cancer treatment,” Clin Cancer Res, 19 (5) 977-984 (2013).
  23. B.H. Lok et al, “RAD52 inactivation is synthetically lethal with deficiencies in BRCA1 and PALB2 in addition to BRCA2 through RAD51-mediated homologous recombination,” Oncogene, 32 (30) 3552-3558 (2013).
  24. F.A. Shepherd et al, “Erlotinib in previously treated non-small-cell lung cancer,” N Engl J Med 353 (2) 123-132
  25. M.S. Tsao et al, “Erlotinib in lung cancer - molecular and clinical predictors of outcome,” N Engl J Med, 353 (2) 133-144 (2005)
  26. T.S. Mok et al, “Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma,” N Engl J Med, 361 (10) 947-957 (2009).
  27. M. Maemondo et al, “Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR,” N Engl J Med,  362 (25) 2380-2388 (2010).
  28. C.M. Johannessen et al, “COT drives resistance to RAF inhibition through MAP kinase pathway reactivation,” Nature, 468 (7326) 968-972 (2010).
  29. R. Nazarian et al, “Melanomas acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregulation,” Nature, 468 (7326) 973-977 (2010).
  30. J. Villanueva et al, “Acquired resistance to BRAF inhibitors mediated by a RAF kinase switch in melanoma can be overcome by cotargeting MEK and IGF-1R/PI3K,” Cancer Cell, 18 (6) 683-695 (2010).
  31. T. R. Wilson et al, “Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors,” Nature, 487 (7408) 505-509 (2012).
  32. M.R. Girotti et al, “Inhibiting EGF receptor or SRC family kinase signaling overcomes BRAF inhibitor resistance in melanoma,” Cancer Discov, 3 (2) 158-167 (2013).
  33. K. Trunzer et al, “Pharmacodynamic effects and mechanisms of resistance to vemurafenib in patients with metastatic melanoma,” J Clin Oncol, 31 (14) 1767-1774 (2013).
  34. J. Baselga et al, “Phase II randomized study of neoadjuvant everolimus plus letrozole compared with placebo plus letrozole in patients with estrogen receptor-positive breast cancer,” J Clin Oncol, 27 (16) 2630-2637 (2009).
  35. L. Bar-Peled et al, “A tumor suppressor complex with GAP activity for the Rag GTPases that signal amino acid sufficiency to mTORC1,” Science, 340 (6136) 1100-1106 (2013).
  36. L.H. Hartwell et al, “Integrating genetic approaches into the discovery of anticancer drugs,” Science, 278 (5340) 1064-1068 (1997).
  37. A. Reddy and W. G. Kaelin, Jr., “Using cancer genetics to guide the selection of anticancer drug targets,” Curr Opin Pharmacol, 2 (4) 366-373 (2002).
  38. I. Astsaturov et al, “Synthetic lethal screen of an EGFR-centered network to improve targeted therapies,” Sci Signal, 3 (140) ra67 (2010).
  39. A. Friedman and N. Perrimon, “Genetic screening for signal transduction in the era of network biology,” Cell, 128 (2) 225-231 (2007).
  40. D.D. von Hoff et al, “Pilot study using molecular profiling of patients' tumors to find potential targets and select treatments for their refractory cancers,” J Clin Oncol, 28 (33) 4877-4883 (2010).
  41. A. M. Tsimberidou et al, “Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative,” Clin Cancer Res, 18 (22) 6373-6383 (2012).
  42. Frederick National Laboratory for Cancer Research, “Request for information (RFI): Efficient implementation of a sequencing network for the proposed NCI-MATCH clinical trial,” (2013), http://www.fdbdo.com/rfi13-151/.
Related Content
© 2024 MJH Life Sciences

All rights reserved.