Precision Oncology Medicine: The Clinical Relevance of Patient Specific Biomarkers Used to Optimize Cancer Treatment

Precision medicine in oncology is the result of an increasing awareness of patient specific clinical features coupled with the development of genomic-based diagnostics and targeted therapeutics. Companion diagnostics designed for specific drug-target pairs were the first to widely utilize clinically applicable tumor biomarkers (e.g. HER2, EGFR), directing treatment for patients whose tumors exhibit a mutation susceptible to a FDA approved targeted therapy (e.g. trastuzumab, erlotinib). Clinically relevant germline mutations in drug metabolizing enzymes and transporters (e.g. TPMT, DPYD) have been shown to impact drug response, providing rationale for individualized dosing to optimize treatment. The use of multigene expression-based assays to analyze an array of prognostic biomarkers have been shown to help direct treatment decisions, especially in breast cancer (e.g. Oncotype DX). More recently, the use of Next-Generation Sequencing to detect many potential “actionable” cancer molecular alterations is further shifting the one gene-one drug paradigm towards a more comprehensive, multi-gene approach. Currently, many clinical trials (e.g. NCI-MATCH, NCI-MPACT) are assessing novel diagnostic tools with a combination of different targeted therapeutics, while also examining tumor biomarkers that were previously unexplored in a variety of cancer histologies. Results from ongoing trials like the NCI-MATCH will help determine the clinical utility and future development of the precision-medicine approach.

Precision medicine in cancer care relies on the use of genomic technologies at the point-of-care to inform clinical treatment decisions. This allows for more accurate and efficient prediction of individualized therapies that is most suited for specific patients. Advancement in the field is the result of recent development of biological databases, increased affordability and reliability of powerful methods to characterize patient tumors (such as genomics, proteomics, metabolomics, improved cellular assays and platforms), and computational tools for analyzing large omics data sets. This revolution has given rise to cancer landscape studies identifying key oncogenic drivers, inter- and intratumoral genetic heterogeneities and therapies to specifically target these alterations that confer clinical benefit. Ultimately, the goal is to build the evidence base in cancer genomics needed to guide clinical practice.

The concept of targeted therapy focuses on finding relevant unique molecular abnormalities associated with specific cancers. These cancer biomarkers, which include both germ-line and somatic mutations, may influence disease outcome and/or response to therapy and can be classified as prognostic (associated with disease outcome) or predictive (associated with drug response). Selection of a particular anticancer therapy is based on the presence of the actionable target and interfering with its function in driving cancer cell growth or progression. Information on key genomic changes, including mutations, somatic copy number alterations, and polymorphisms affecting drug metabolism, has already helped shape the development and use of some of the newest targeted cancer treatments, underscoring the importance of cancer genomics in advancing personalized medicine.

Precision oncology medicine is centered on the concept of predicting which patients are more likely to respond to specific cancer therapies and to determine optimum individualized therapies. In addition to patient prognosis and tumor response, tumor biomarkers are also associated with a drug’s metabolism, response, and toxicity. Clinically relevant germline mutations that have been shown to impact drug response include thiopurine-S-methyl transferase (TPMT), uridine-diphosphate glucuronosyltransferase 1A1 (UGT1A1), and cytochrome P450 2D6 (CYP2D6). These examples will be briefly discussed in the next section along with mention of actionable prescribing decisions from either the FDA-approved drug labels or the Clinical Pharmacogenomics Implementation Consortium (CPIC) published genotype-based drug guidelines to assist in optimizing drug therapy.

Improvements in molecular profiling techniques have given rise to the identification of gene signatures used to define cancer subtypes to help guide treatment decisions, making the transition from a single gene assay to a multigene panel inevitable. The development of multigene expression-based assays (e.g., Oncotype DX, MammaPrint, Mammostrat, and Prosigna) has resulted in a paradigm shift in the management and treatment of breast cancer, particularly in the setting of early stage breast cancer. Oncotype DX, a 21-gene expression (including HER2 amplification) RT-PCR assay, is used to estimate a woman’s risk of recurrence of early-stage, hormone-receptor-positive breast cancer. The assay generates a score ranking a patient’s 10-year risk of recurrence, ranging from low (<18), intermediate (18–30), and high (>30) risk category patients, to also determine whether the addition of chemotherapy is beneficial after breast cancer surgery.

The rapid development of molecularly targeted cancer therapeutics has expanded the utility of multigene sequencing panels for detecting tumor-specific mutations. The development of next generation sequencing (NGS) and associated target sequence enrichment technologies are robust platforms that can detect these “actionable” cancer molecular alterations in a large number of genes in a single multiplexed assay. As a result of these large-scale technologies, precision medicine has shifted from a one gene-one drug paradigm to a multigene-many drugs model.

Optimal trial design for genomics-based clinical studies remains critical. "Basket" (or bucket) trials are genotype-focused evaluating a single drug on a specific mutation or mutations across various cancer types. Within such a histology-agnostic trial, patients with the different types of cancer can be grouped into separate study arms (or baskets), allowing separate analysis of patient responses with each type of cancer as well as to assess the impact of the drug on the entire group of the patients as a whole. A basket trial design is especially advantageous when the mutation or cancer type is rare as it provides an important opportunity to test therapies for rare cancers (possessing the eligible molecular abnormality), which are severely underrepresented in clinical trials. "Umbrella" trials are designed to test the impact of different drugs targeting different mutations either in a single cancer subtype or in a variety of tumor subtypes. After analysis of the molecular profile of each patient’s tumor, a molecularly-guided algorithm is formulated to determine an individualized treatment plan. "Hybrid" trials represent a mix of "umbrella" and "basket" trial components, incorporating either multiple "umbrella" subtrials (same histology, different molecular aberrations), or multiple "basket" subtrials (same molecular aberrations, different histologies) into one protocol.