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Predictive biomarkers are taking a lead role in the clinical development of targeted anticancer therapies, as they are utilised to identify and develop companion diagnostics.

A biomarker is defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”(1). Over the past decade, biomarkers have helped bring about an era of personalised healthcare for cancer patients. Utilisation of appropriate predictive biomarkers based on strong scientific rationales has improved clinical trial success and accelerated the regulatory approval of anticancer agents by identifying a subset of patients most likely to respond to treatment. In oncology clinical research, biomarkers can be divided into different categories based on their functions and clinical applications, including pharmacokinetic, pharmacodynamic, prognostic, predictive, pharmacogenomic and outcome biomarkers. This article focuses on predictive biomarkers and their critical roles in the clinical development of targeted anticancer therapies.

Predictive Biomarker

A predictive biomarker can predict tumour response and clinical benefi t or adverse reaction to a specific treatment. A well-known example of a predictive biomarker is HER2 amplifi cation/overexpression status in breast cancer that predicts response to anti-HER2 agents such as trastuzumab. A predictive biomarker must meet the criteria of analytical validity, clinical validity and clinical utility. Analytical validity ensures that an analytic method is available to measure biomarker alterations accurately in cancer patients. Clinical validity requires that the biomarker can predict response to treatment in the clinical setting. Clinical utility evaluates the likelihood that using the pretreatment biomarker results to help make treatment decisions will lead to an improved outcome; for example predictive versus prognostic, cost versus benefi ts and so on. Table 1 summarises notable predictive biomarkers that have been validated in clinical studies and successfully applied to determine patient eligibility for the respective therapies.

Predictive biomarkers are sometimes confused with prognostic biomarkers, which are predictive of patient outcomes independent of treatment. One reason for this confusion is that some biomarkers are capable of both functions. For example, the positive status of oestrogen receptor and/or the progesterone receptor predicts a better survival outcome for breast cancer patients, as well as a positive response to anti-endocrine therapy in the adjuvant and metastatic setting (2).

Integrated Testing of Predictive Biomarkers and Anticancer Agents in Clinical Trials

Putative predictive biomarkers for a targeted anticancer agent are typically identified and characterised in preclinical studies. Once a method is developed and validated for clinical sample analysis, candidate biomarkers can be tested in early phases of clinical studies and confirmed in appropriately designed Phase 2 or Phase 3 clinical trials. It is a growing trend to evaluate predictive biomarkers early in Phase 1 clinical trials, especially in enlarged expansion cohorts at the maximum tolerated dose (MTD) or at the recommended Phase 2 dose (RP2D).

The goal of Phase 1 studies incorporating predictive biomarkers is to identify early signals that link putative biomarkers to objective responses and clinical benefits. In a recent Phase 1 trial, the investigators evaluated relationship between PIK3CA mutations and several PI3K pathway inhibitors in 140 patients who were prospectively screened (3). In the 23 patients positive for the PIK3CA mutations, treatment with regimens that contained one of the PI3K/ mTOR/AKT inhibitors achieved objective response in seven (30 per cent) patients. In contrast, in 70 patients who were negative for the PIK3CA mutations, only seven responses (10 per cent) were observed. Thus, the authors concluded that PIK3CA mutations were associated with enhanced sensitivity to treatment that included the PI3K pathway inhibitors. These results should be confirmed in appropriately designed clinical trials in large patient population.

In Phase 1 biomarker studies, patients may be enriched based on putative biomarkers, but exclusive selection of patients based on these biomarkers is not recommended unless there is overwhelming supportive preclinical evidence or the biomarkers have been validated previously in the same class of drug. For example, in a first-in-human Phase 1 study on a PIK3Ca specific  inhibitor BYL719, the investigators, based on convincing preclinical studies, selected only patients whose tumours harbour mutations or amplification of the PIK3CA gene (4). More recently, Juric (2012 AACR annual meeting) showed that four partial responses were achieved among 35 evaluable patients (5). This result was much better then those reported for other Phase 1 studies on PIK3Cα inhibitors which did not utilise patient selection (6-8).




To clinically validate a predictive biomarker, it must be tested in appropriately designed and statistically powered Phase 2 or 3 clinical studies. The gold standard and most commonly used clinical designs are randomised Phase 2 or 3 trials on prospectively screened biomarker-positive patients. A notable example is the pivotal Phase 3 trial (BRIM3) on vemurafenib in BRAF V600E-positive melanoma (9). In this study, 675 patients with previously untreated, metastatic melanoma with the BRAF V600E mutation were randomly assigned to receive either vemurafenib or dacarbazine, the standard treatment. The interim result at six months was overwhelmingly positive, with an overall survival of 84 per cent in the vemurafenib group and 64 per cent in the dacarbazine group. Response rates were 48 per cent for vemurafenib and five per cent for dacarbazine. Based on these results, and a Phase 2 study (BRIM2) on pretreated melanoma, vemurafenib was approved for the treatment of BRAF V600E mutation-positive, inoperable or metastatic melanoma, together with a companion diagnostic test, the Cobas 4800 BRAF V600 Mutation Test (10).

While prospective trials are preferred for biomarker validation, retrospective biomarker evaluation may be conducted on randomised clinical trials due to concerns surrounding cost, time, assay readiness or sample logistics. In this case, it is recommended that the biomarkers and methods of analysis are prespecified in the trial design. Retrospective analysis of archived tissues from well controlled clinical trials is also an important approach to biomarker discovery. A successful example is the identification of KRAS mutation as a biomarker that predicts resistance to cetuximab and panitumumab in colorectal cancer patients (11).

For low prevalence biomarkers, randomised clinical trials may not be practical. In these cases, single-arm clinical studies may be conducted for biomarkers of high confidence, as exemplified by the accelerated development of crizotinib for anaplastic lymphoma kinase (ALK)-positive, non-small-cell lung cancer (NSCLC) patients. The ALK-positive tumours harbour an EML4 (echinoderm microtubuleassociated protein-like 4) and ALK fusion gene that has been identified as an oncogenic driver in approximately 5 per cent of NSCLC cases (12). During Phase 1 dose escalation, two patients with ALK+ NSCLC responded dramatically to crizotinib. Based on this finding, a decision was made to select for ALK+ NSCLC patients in the MTD expansion cohort and objective response was achieved in 61 per cent of 119 patients (13). A follow-up Phase 2, single-arm study on 136 ALK+ NSCLC patients resulted in an objective response in 50 per cent of patients (14). Based on these two single-arm studies, the FDA approved crizotinib with a companion diagnostic test (the Vysis ALK Break Apart FISH Probe Kit) in 2011 for ALK+ NSCLC patients, while requesting a post-market randomised study to confirm its survival benefits over the standard of care (15). As such, a new paradigm is emerging in oncology drug development, where accelerated approval based on large randomised or single-arm Phase 2 studies can be granted for the treatment of a biomarker-defined subset of patients, with final approval contingent upon definitive benefits to be generated from timely executed post-market clinical studies.

Adaptive design was recently utilised to test multiple biomarkers for several targeted therapeutics in Phase 2 clinical trials, including the Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trials and the Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis (I-SPY) trials (16-20). In this type of design, an ongoing trial can be modified based on the response of tumours harbouring various biomarkers, so that the most predictive biomarkers can be identified and matched for respective treatments. Compared to conventional clinical trials, adaptive design can minimise the number of biomarker-negative patients who are treated but are unlikely to benefit from experimental agents.

As most malignancies are the consequences of multiple molecular alterations, rather than a single gene mutation, it is becoming apparent that combinations of targeted therapies are required to effectively treat most cancers. A case in point is the ongoing clinical trials to combine agents that inhibit the PI3K/AKT/mTOR and the RAS/RAF/MEK pathways, respectively. In these clinical studies, multiple predictive biomarkers are needed to identify responsive tumours for each of the targeted therapies. This is also true when multiple molecular targets are inhibited by a single agent, such as non-specific kinase inhibitors. Novel clinical trial strategies including the adaptive designs are needed to facilitate identifying and validating multiple predictive biomarkers in these scenarios.

Conclusion

Predictive biomarkers have become an integral part and played a key role in the development of personalised cancer therapies such as vemurafenib and crizotinib. In clinical trials, biomarkers are simultaneously evaluated with experimental drugs to identify responsive patient population and to develop companion diagnostics. There is a growing trend to evaluate predictive biomarkers in enriched patient populations in early phases of clinical trial. Novel biomarker-driven, adaptive clinical trial designs can facilitate rapid evaluation of drugs, help validate multiple predictive biomarkers, minimise exposure of patients to ineffective therapies, and potentially allow accelerated drug approval in molecularly defined populations

References
  1. Atkinson AJ, Colburn WA, DeGruttola VG et al, Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework, Clin Pharmacol Ther 69(3): pp89-95, 2001
  2. Early Breast Cancer Trialists’ Collaborative Group (EBCTCG). Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials, Lancet 365: pp1,687-1,717, 2005
  3. Janku F, Wheler JJ, Westin SN et al, PI3K/AKT/mTOR inhibitors in patients with breast and gynecological malignancies harboring PIK3CA mutations, J ClinOncol 30: pp777-782, 2012
  4. Juric D, Rodon J and Gonzalez-Angulo MA. BYL719, a next generation PI3K alpha specifi c inhibitor: Preliminary safety, PK, and effi cacy results from the fi rst-in-human study, Cancer Res 72(8 Supplement): CT-01, 2012
  5. Juric D, Presentation at AACR annual meeting at Chicago, 31 March-4 April, 2012
  6. Munster PN, van der Noll R and Voest EE, Phase I fi rst-in-human study of the PI3 kinase inhibitor GSK2126458 (GSK458) in patients with advanced solid tumors (study P3K112826) J Clin Oncol 29: (suppl; abstr 3018), 2011
  7. Wagner AJ, Bendell JC and Dolly S, A fi rst-in-human phase I study to evaluate GDC-0980, an oral PI3K/mTOR inhibitor, administered QD in patients with advanced solid tumors, J Clin Oncol 29: (suppl; abstr 3020), 2011
  8. Burris H, Rodon J, Sharma S and Herbst RS, First-in-man Phase I study of the oral dual PI3K and mTORC1/2 inhibitor BEZ235 in patients with advanced solid tumors, J Clin Oncol 28:15s, (suppl; abstr 3005), 2010
  9. Chapman PB, Hauschild A and Robert C, Improved survival with vemurafenib in melanoma with BRAF V600E mutations, N Engl J Med 30364(26): pp2,507-2,516, June 2011 (published online ahead of print 5 June, 2011)
  10. Ribas A, Kim KB and Schuchter LM, BRIM-2: An open-label, multicenter phase II study of vemurafenib in previously treated patients with BRAF V600E mutation-positive metastatic melanoma, J Clin Oncol 29: (suppl; abstr 8509) 2011
  11. Amado RG, Wolf M, Peeters M et al, Wild-Type KRAS is Required for Panitumumab Efficacy in Patients With Metastatic Colorectal Cancer, J Clin Onco 26 (10): pp1,626-1,634, 2008
  12. Soda M, Choi YL, Enomoto M et al, Identifi cation of the transforming EML4-ALK fusion gene in non-small-cell lung cancer, Nature 448: pp561-566, 2007
  13. Kwak EL, Bang YJ and Camidge DR, Anaplastic Lymphoma Kinase Inhibition in Non-Small-Cell Lung Cancer, N Engl J Med 363: pp1,693-1,703, 2010
  14. Crinò L, Kim D-W, Riely G et al, Initial phase II results with crizotinib in advanced ALK-positive non-small cell lung cancer (NSCLC): PROFILE 1005, J Clin Oncol 29 (suppl) [Abstract 7514] 2011
  15. Pfizer Inc, Xalkori Prescribing Information, www.accessdata.fda. gov/drugsatfda_docs/label/2011/202570s000lbl.pdf. Accessed 7 October, 2011
  16. Zhou X, Liu S, Kim ES and Lee JJ, Bayesian adaptive design for targeted therapy development in lung cancer – a step toward personalized medicine, Clin Trials, 2008
  17. Kim ES, Herbst RS, Wistuba II et al ,The BATTLE Trial: ersonalizing Therapy for Lung Cancer, Cancer Discovery 1(1): pp44-53, June 2011
  18. Esserman LJ, Berry DA and Cheang MC, Chemotherapy response and recurrence-free survival in neoadjuvant breast cancer depend on biomarker profi les: results from the I-SPY 1 TRIAL (CALGB 150007/150012; ACRIN 6657), Breast Cancer Res Treat, 132(3):pp1,049-1,062, April 2012
  19. Hylton NM, Blume JD, Bernreuter WK and Pisano ED, Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy results from ACRIN 6657/I-SPY TRIAL, Radiology 263(3): pp663-72, 2012
  20. Esserman LJ, Berry DA, Demichele A et al, Pathologic Complete Response Predicts Recurrence-Free Survival More Effectively by Cancer Subset: Results From the I-SPY 1 TRIAL- -CALGB 150007/150012, ACRIN 6657, J Clin Oncol 39: pp2,779, 2011

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Jason Yang serves as the Oncology Therapeutic Area Lead for Covance Biomarker Center of Excellence and Translational Medicine Hub. He leads scientific activities in support of global oncology biomarker and in vivo pharmacology operations at Covance. Jason has more than 19 years of biomedical research experience, including more than 14 years of pharmaceutical R&D experience at Pfizer, Tularik/Amgen. He received his Medical Degree from Hubei Medical College, his PhD from the University of Texas, Southwestern Medical Center at Dallas, and postdoctoral training at Harvard University.

Martine Poelman is an Executive Medical Director at Covance and is the European Lead Physician for Oncology Early Development and Clinical Trials in haematological malignancies. She was trained as a paediatrician and haemato-oncologist in Belgium, Netherlands, and the US. She has more than 20 years of clinical drug development experience.

Rachel Reams is the Director of the Biomarker Center of Excellence, Covance Laboratories, located in Greenfield, Ind. Rachel earned her DVM, and her PhD in Pathology, from Purdue University School of Veterinary Medicine, and is a Diplomate of the American College of Veterinary Pathologists. She has more than 25 years of pathology and biomarker experience gained in leadership roles in industry, academia and government.

Thomas Turi is the Vice President of Science and Technology for Discovery and Translational Services. He joined Covance in 2008 to establish the Biomarker Center of Excellence after a 15-year career at Pfizer. He has served on the Boards of Life Sciences Foundation and Caprion Proteomics. He received dual bachelor’s degrees in Biochemistry and Chemistry from the University of Illinois at Urbana-Champaign, a doctorate in Molecular Genetics from the University of Cincinnati College of Medicine and postdoctoral training at Yale University School of Medicine.

Nasser Azli serves as the Global Therapeutic Area Head for Oncology and the Translational Oncology Hub Coordinator. He is a medical oncologist with more than 20 years of pharmaceutical industry experience. He leads global clinical development of several early and late-stage compounds and contributed to the development and registration of several anti-cancer drugs. He is experienced in translational and experimental medicine and familiar with the In-Licensing process and due-diligence. He received his medical degree in clinical oncology at the University of Paris VI. Email: nasser.azli@covance.com

 

 

 

 

 

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Jason Yang
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Martine Poelman
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Rachel Reams
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Thomas Turi
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