Response Phenotype As a Predictive Biomarker to Guide Treatment With Targeted Therapies
Authors: Geoffrey R. Oxnard, Lawrence H. Schwartz
Journal: Journal of Clinical Oncology (2013)

Tumor response is one of the oldest biomarkers in solid tumor oncology, first assessed by manual palpation and simple radiographs, and then in recent decades by computed tomography, magnetic resonance imaging, or more advanced imaging. Clinicians have recognized intuitively that patients with responding tumors do better than those with no response, and those with tumor growth do worse. In this way, clinicians use tumor response to help guide the decision about which patients might benefit from additional treatment despite toxicity, which patients might do better with dose adjustment or delay, and which patients instead require a change in therapy. What magnitude of response is most closely associated with benefit from a specific therapeutic regimen? This is not well understood, in part because of a competing definition of response, termed objective response, which was developed for a very different purpose. Objective response is also an old biomarker, first codified in 1981 to establish a unified system for calculating an objective response rate for a trial. Despite changes in tumor measurement techniques, the definition of objective response has remained mathematically consistent across multiple guidelines, facilitating comparison of modern and historical trial results. But whereas objective response is widely accepted as a tool for trial analysis, it was never intended for clinical use, as clearly stated in the WHO, RECIST version 1.0, and RECIST version 1.1 guidelines. Although objective response per RECIST (defined as a 30% or greater decrease in summed tumor diameters) does have some value as a prognostic marker, some investigators have recently studied whether alternate definitions of response can be derived on the basis of biology rather than historical precedent. These studies aim to identify a specific response phenotype, rather than a tumor genotype, that offers insight into disease biology. Studying patients with advanced colorectal cancer treated with cetuximab, Belgian investigators have shown that a 10% diameter decrease optimally predicts for improved survival in patients with KRAS wild-type cancers. Investigating patients with advanced renal cell carcinoma treated with first-line vascular endothelial growth factor inhibitors, Krajewski et al found that a 10% diameter decrease optimally differentiated patients with a poor prognosis from those with a good prognosis. Studying patients with early-stage esophageal cancer treated with induction chemotherapy, German investigators determined that a 35% reduction in [F]fluorodeoxyglucose uptake on positron emission tomography optimally predicts for improved survival and improved chance of pathologic response. In a study of patients with early-stage non–small-cell lung cancer (NSCLC) treated with a neoadjuvant epidermal growth factor receptor (EGFR) kinase inhibitor, Zhao et al found that 25% volumetric response on computed tomography was best at identifying those cancers harboring EGFR mutations, a population known to have dramatically better outcomes when treated with EGFR kinase inhibitors. In the article that accompanies this editorial, Piessevaux et al add to this literature with a comprehensive biomarker analysis of response magnitude in 1,289 patients with advanced colorectal cancer receiving first-line combination chemotherapy. As their data set, the authors studied tumor measurements from two prospective trials of chemotherapy with or without cetuximab (Cetuximab Combined With Irinotecan in First-Line Therapy for Metastatic Colorectal Cancer [CRYSTAL] and Oxaliplatin and Cetuximab in First-Line Treatment of Metastatic Colorectal Cancer [OPUS]). These two industry-sponsored trials accrued between 2004 and 2006, and both had imaging submitted centrally for tumor measurements. Given the well-described challenges of measurement variability and their potential impact on the utility of tumor measurements, the use of prospectively acquired measurements is a major strength of this research. Using this rich data set, the authors first identified on multivariate analysis that a greater magnitude of tumor response at 8 weeks was associated with improved progression-free survival (PFS) and overall survival, with hazard ratios (HRs) in the range of 0.749 to 0.923 per 10% diameter decrease. As might be anticipated, and as previously shown for patients enrolled onto phase I studies, magnitude of tumor shrinkage is a prognostic marker. The novelty of the analysis by Piessevaux et al lies in their attempt to use early tumor shrinkage to identify a subset of patients who gain maximal benefit from a targeted therapy (cetuximab) when added to chemotherapy in a randomized trial. This is in contrast to the previously described analyses, all of which studied single-arm trials. Here the authors use a Subpopulation Treatment Effect Pattern Plot analysis, which facilitates comparison of the HR between the two arms across a spectrum of values of a biomarker. Looking first at KRAS-mutant cancers (see Fig 3 of the article by Piessevaux et al), it is clear that the lack of added benefit from cetuximab extends across the JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 31 NUMBER 30 OCTOBER 2