Pilot studies for personalized cancer medicine: focusing on the patient for treatment selection

Abstract

Advances in diagnostics and targeted therapies during the past decade have changed how oncology is viewed. "Stratified medicine" has emerged from the accumulated evidence garnered from matching targeted therapies with tumor molecular aberrations. Concomitantly, current knowledge derived from large-scale, massively parallel sequencing technologies and global research initiatives such as the international 1000 Genomes Project, the Cancer Genome Atlas, the International Cancer Genome Consortium, and publicly available catalogs such as the Catalogue of Somatic Mutations in Cancer and Genomics of Drug Sensitivity in Cancer have illuminated the utility of understanding the molecular basis of cancer through genome analysis. In addition, multiple collaborative efforts are widening the possibility of universally personalizing cancer care. Although several key challenges of personalized cancer medicine (PCM) need to be addressed, some pilot studies are transforming the way we analyze tumor tissue molecular aberrations, design clinical trials, and measure treatment efficacy. Taken together, these pilot studies are paving the way for clinical trials that are designed to empirically test the concept of PCM. In this paper, we describe lessons learned from the first pilot initiatives of PCM and how this knowledge is being used to design novel clinical trials.

Keywords: Genomics; Personalized medicine; Phase I clinical trials; Pilot studies.

Figure 1.

Personalized cancer medicine, its comprehensive nature, and its challenges presage its reality for cancer patient treatment. The figure depicts four foundations on which personalized cancer medicine relies. (A): Patients will have their biological samples analyzed to reveal the unique fingerprint of their genomic alterations. These biological samples include tumor tissue and/or surrogate tumor tissue markers (e.g., CTCs and ctDNA). Biomarker identification will uncover novel targets that could have a role in cancer diagnosis, prognosis, selection of an appropriate treatment plan, and monitoring of therapeutic response and resistance. (B): Genomic analysis has increasing importance in tailoring targeted therapy to individual patients. Apart from challenges inherent in sequencing approaches (e.g., different massively parallel sequencing platforms, huge amount of data generated, potentially actionable genomic alterations, costs of novel agents), targeting genomic alterations has the potential to improve treatment outcomes and to optimize the costs of new drug discovery in select patients while sparing others from unnecessary treatments and costs. (C): Matching targeted therapies with genomic molecular alterations requires a portfolio of drugs, including approved drugs, those under development, and those used as compassionate agents. (D): Data generated from single-center or multicenter clinical trials in oncology, particularly early clinical trials and related translational research, will likely guide further clinical drug development. New technologies for individualizing measurements of treatment efficacy are expected to help better predict responses to targeted therapies. PFS1/PFS2 indicates the ratio based on PFS endpoints using individual patients as their own controls [24]. Abbreviations: CTCs, circulating tumor cells; ctDNA, circulating tumor DNA; FDA, U.S. Food and Drug Administration; FFPE, formalin fixed and paraffin embedded; PFS, progression-free survival.