Major technologic advances in genomics have made possible the identification of critical genetic alterations in cancer, which has led to a rapid paradigm shift in what is now considered personalized cancer treatment. This exponential growth in the understanding of cancer genomics is reshaping the drug development process, from drug discovery to novel designs of clinical trials. It is also exposing the critical importance of rigorous validation of molecular diagnostics platforms, such as the use of whole-exome sequencing and patient-derived xenografts, which may eventually be utilized to guide treatment decisions and may ultimately enable the true practice of personalized oncology. This review describes the achievements in therapeutic and molecular diagnostics, details evolving molecular platforms, and highlights the challenges for the translation of these developments to daily clinical practice.
Impact of Precision Medicine on the Developmental Therapeutics Paradigm
The rapid advances in the genomic profiling of malignancies and in molecular diagnostics technologies have had a profound impact on the general infrastructure and strategy of developmental therapeutics programs. This impact can be seen from the computational modeling of compounds to the matching of specific targets identified through sequencing, and from the planning of preclinical experiments to the novel design of clinical trials. Some of the challenges of large, lengthy, phase III clinical trials that enroll thousands of patients to document a benefit in a small fraction have been resolved in part by pathway-driven clinical trials. In fact, a meta-analysis of 570 phase II oncology trials showed that the use of biomarker-based precision medicine strategies resulted in improved outcomes.
A multivariable analysis of 641 single-agent treatment arms demonstrated that personalized therapy compared with nonpersonalized treatment was associated with a higher response rate (31% vs 10.5%), prolonged PFS (5.9 vs 2.7 months), and longer OS (13.7 vs 8.9 months). Another meta-analysis of clinical trials of US Food and Drug Administration–approved anticancer drugs between 1998 and 2013 (57 randomized and 55 nonrandomized trials; total of 38,104 patients) also supported the benefit of a personalized strategy. Specifically looking only at the experimental arms of 112 registration trials, personalized therapy led to a higher response rate (48% vs 23%), longer PFS (8.3 vs 5.5 months), and longer OS (19.3 vs 13.5 months). Nevertheless, the first prospective randomized trial that compared conventional treatment of advanced solid tumors with treatment with targeted agents selected based on the genomic profile of tumors showed disappointing results.
The SHIVA trial randomized 195 patients with metastatic solid tumors (26 histologic types) and molecular alterations affecting three signaling pathways (estrogen/progesterone/androgen receptor, PI3K/AKT/mammalian target of rapamycin [mTOR], and RAF/MEK) to either matched approved targeted agents (including off-label indications of agents such as sorafenib, erlotinib, imatinib, vemurafenib, abiraterone, and letrozole) or physician’s choice. Molecular analyses included assessment of mutations by NGS; gene copy number alterations; and expression of estrogen, progesterone, and androgen receptors by immunohistochemistry. This study failed to meet its primary endpoint, with a median PFS of 2.3 months in the experimental group vs 2 months in the control group. Numerous limitations might have contributed to these results, including potential lack of adequate matching of patients with multiple molecular alterations treated with single or inadequate targeted therapy, restricted number of signaling pathways, available approved drugs, and the challenge of prioritization of therapies with monotherapy. For instance, 55 patients (28%) assigned to one of the monotherapy groups had two or more potentially targetable molecular alterations. Despite the overall results of the SHIVA trial, this important proof-of-concept study highlights the critical need for further validation and refinement of the personalized treatment strategy, with close integration between clinical trial design and molecular diagnostics. Innovative clinical designs (ie, basket and umbrella trials) and major initiatives, such as the National Cancer Institute–sponsored Molecular Analysis for Therapy Choice (MATCH) and the American Society of Clinical Oncology Targeted Agent and Profiling Utilization Registry (TAPUR) trials, hold promise to further advance the field.
Basket trials are based on the premise that a molecular marker can predict tumor response to a targeted therapy independent of tumor histologic origin. Two basket trials published in 2015 exemplify this strategy. Hyman et al conducted a phase II study that included patients with BRAF V600–mutated nonmelanoma cancers who were treated with the BRAF inhibitor vemurafenib. The most frequent tumor responses were observed in patients with NSCLC (8 of 20 had partial responses) and those with Erdheim-Chester disease or Langerhans cell histiocytosis. Only one partial response was documented among the 27 patients with colorectal cancer treated with vemurafenib and cetuximab, although approximately half of these patients had a reduction in tumor size that did not meet the threshold for partial response. Few responses were also observed in the subgroups of patients with anaplastic thyroid cancer, cholangiocarcinoma, salivary duct cancer, soft-tissue sarcoma, and ovarian cancer.
A second and larger phase II trial included 647 patients with intrathoracic malignancies (NSCLC, small-cell lung cancer, and thymic malignancies). After tumor genomic profiling, patients received standard-of-care treatment or one of the following five treatments based on the genomic aberration identified: erlotinib (EGFR mutations), selumetinib (KRAS, NRAS, HRAS, or BRAF mutations), MK-2206 (PIK3CA, AKT, or PTEN mutations), lapatinib (ERBB2 mutations or amplifications), or sunitinib (KIT or PDGFRA mutations or amplifications). Nearly half of the total number of patients had a genomic abnormality identified in one of the genes tested. However, only 45 patients were ultimately enrolled in one of the treatment arms (212 patients were considered screen failures: 68% related to previous use of erlotinib or early closure of the selumetinib arm). The study was not able to complete accrual to 13 of the 15 treatment arms because of several limitations, including the rare incidence of some genetic abnormalities in these thoracic malignancies (ie, ERBB2, PIK3CA, PTEN, AKT, KIT, PDGFRA), restricted number of genes analyzed and the number of histologies included in the design, variability of genomic tests performed, and delay in obtaining results.
These basket trials epitomize the clinical potential and challenge of departing from the paradigm of histology-based treatments to therapies tailored to specific genomic alterations across multiple histologies. The first study that focused on one genetic lesion (BRAF V600 mutation) demonstrated the feasibility of this approach and provided hypothesis-generating data of tumor responses in rare tumor subtypes that could potentially inform future trials. On the other hand, the second trial clearly shows the logistical difficulties of testing several actionable targets in a relatively small number of histologies in two cancer centers.
Umbrella trials represent another novel design in which patients with a single tumor type are screened for several aberrations at study entry and subsequently assigned to subprotocols based on the matched molecular abnormality identified. This prescreening strategy allows for a lower screening-to-accrual ratio and may avoid early closure of studies involving patients whose tumors harbor low-frequency aberrations. This innovative strategy is being tested in the phase II/III LUNG-MAP (Biomarker-Driven Master Protocol for Second-Line Therapy of Squamous Cell Lung Cancer) study. The tumors of patients who have advanced squamous cell lung cancer with one previous treatment are screened for genetic alterations in more than 200 genes using targeted sequencing. As a result of this analysis, patients are then recommended for one of five subtrials within the umbrella framework. Of the five clinical trials, four are enrichment trials, with eligibility limited to those patients whose tumors harbor a specified genomic alteration in a gene that is directly targeted by a therapy in the clinical trial.[75,76]
The adaptive randomization design of phase II trials provides another strategy for accelerating the development of new cancer drugs matched to specific tumor aberrations. In these studies, regimens with higher Bayesian prediction probability of being more effective than standard therapy will be allowed to graduate from the trial with their corresponding biomarker signature. Regimens will be dropped if they show low probability of efficacy with any signature, resulting in smaller sample sizes compared with conventional clinical trials. The I-SPY 2 neoadjuvant breast cancer trial exemplifies this strategy. This phase II screening trial used adaptive randomization within biomarker subtypes to evaluate a series of novel agents/combinations when added to standard neoadjuvant therapy for women with high-risk stage II/III breast cancer. The primary endpoint was pathologic complete response (pCR) at surgery. Veliparib plus carboplatin met the 85% predictive probability criterion in the hormone receptor–negative cohort of only 72 patients, and there were 62 concurrently randomized controls. In triple-negative breast cancer, the pCR was 52% in the veliparib/carboplatin arm compared with 24% in the standard chemotherapy arm, with a 92% probability of success in a phase III trial. Confirmatory phase III data are pending. These innovative clinical trial design strategies are aligned with advances in genomic profiling of tumors that hold the promise of transforming the developmental therapeutics landscape.
Critical Alignment of Tumor Biomarker Assay Development
A tumor biomarker assay is any test that can measure or detect a tumor-related phenomenon. Its ultimate value to patients lies in its clinical utility. The optimal assay requires robust analytic validity and needs to impart information predictive of benefit from a given therapy. Its clinical utility also depends on easy access, low risk, and low cost of the test (Table). Adequate clinical validity assessment—the process of predictive and/or prognostic information analysis of the assay—can be performed through retrospective observational studies or prospective clinical trials. Methodologic constraints secondary to inadequate tumor samples, lack of standardized prospective tumor tissue and biological specimen collection, and lack of adjunct clinical data are some of the obstacles that have hampered the development of companion diagnostic assays in the past several years.
Integration of Precision Medicine With Immuno-Oncology
Immunotherapy with checkpoint inhibitors has made a significant impact on the treatment of melanoma and lung cancer. For instance, nivolumab has shown clinically meaningful activity in two phase III trials that included patients with NSCLC.[82,83] Nivolumab is a fully human immunoglobulin G4 programmed death 1 (PD-1) immune checkpoint inhibitor antibody that selectively blocks the interaction of the PD-1 receptor with its two known programmed death ligands, PD-L1 and PD-L2, disrupting the negative signal that regulates T-cell activation and proliferation. In these trials patients received palliative treatment with nivolumab or docetaxel after platinum chemotherapy failure.[82,83] Nivolumab improved OS in both small-cell lung cancer patients (hazard ratio [HR], 0.73) and NSCLC patients (HR, 0.59). Intriguingly, tumor PD-L1 expression was not predictive of any efficacy endpoint in the small-cell lung cancer trial. By contrast, regression modeling showed significant interaction between nivolumab treatment and all efficacy endpoints for patients with NSCLC expressing PD-L1.
PD-L1 expression was also assessed in the phase III trial that compared nivolumab with dacarbazine in patients with metastatic melanoma without BRAF mutation. Nivolumab improved outcomes in both PD-L1–positive and PD-L1–negative patients compared with dacarbazine, although higher tumor response rates were observed among those with positive PD-L1 status (52% vs 33%). Improved outcomes in renal cell carcinoma were also observed regardless of PD-L1 status in a large phase III study. Accumulating evidence indicates that a subset of patients sustain prolonged responses and experience clinical benefit from immune checkpoint inhibitors across other tumor types, such as bladder cancer and ovarian carcinoma.[87,88] These results have fostered the search for predictive biomarkers of response to checkpoint inhibitors.
It is likely that future biomarkers will have to integrate both tumor and immune cell profiling in order to address the need for adequate patient selection. In support of this hypothesis, Le et al recently reported an improved benefit from pembrolizumab therapy in patients with metastatic solid tumors and increased genomic instability secondary to mismatch repair deficiency. The underlying mechanistic hypothesis involved potential increased stimulation of the immune system against epitopes generated in the setting of increased genomic instability. This study showed that mismatch repair status predicted clinical benefit of immune checkpoint blockade with pembrolizumab. Furthermore, Nagalla et al analyzed breast cancer tissue samples by hierarchical clustering of gene expression profiles, which revealed a large cluster of distant metastasis-free survival–associated genes with known immunologic functions. Those genes were further subdivided into three distinct immune metagenes that likely reflected (1) B cells and/or plasma cells, (2) T cells and natural killer cells, and (3) monocytes and/or dendritic cells. Another area of intense investigation relates to the possible correlation between tumor neoantigen burden and response to immunotherapy. The ability to identify new epitopes linked to genetic aberrations is enabling researchers to explore neoantigen-specific T-cell therapy, with encouraging preliminary study results.[92,93] Two important studies have also recently shown a correlation between mutational burden in melanoma and NSCLC and improved outcomes from treatment with checkpoint inhibitors (ie, anti–CTLA-4 and anti–PD-1), paving the way for personalized immuno-oncology.[94,95]
Personalized therapy has been the cornerstone of cancer medicine in recent years, with numerous examples of unprecedented improvements in diagnostics and therapeutics. The widespread adoption of clinical NGS for solid tumor sequencing continues to generate a plethora of data regarding tumor biology, which subsequently allows for the potential development of more effective targeted therapies. Clearly, we are entering the age of true personalized medicine. However, if this review is to serve as a snapshot of the current state of precision medicine, a key take-away point is the challenge and potential of integrating the various topics discussed into coherent and synchronized clinical applications. Integration is a multifaceted issue that involves meticulous clinical validation along with continued advancements in informatics and computational technologies. The latter of these two topics is particularly interesting to consider. Medicine for the most part does not readily identify itself with computer science or artificial intelligence (predictive analytics). However, going forward, if oncologists want to optimize treatment for a patient based on integrating disparate sets of data from genomics, proteomics, metabolomics, and pharmacogenomics, they may need to incorporate artificial intelligence systems in their daily practice. Physicians will also have to guide the development of personalized medicine from its current state, in which most of the fields exist in individual silos, to clinical research settings designed to validate the new technologies, and incorporating the perspective of application beyond cancer therapeutics, with expansion to cancer prevention and early diagnosis.
The 21st century of cancer medicine is clearly a time of optimism, but oncologists cannot avoid facing the challenges ahead, including the issues of tumor assay predictive validation, the resolution of tumor biologic heterogeneity, clinical trial design improvement, and regulatory points of contention (Figure 2). Personalized cancer therapies also carry the potential to forge and transform global scientific collaborations through sharing of therapeutic and diagnostic strategies, data, and standards of care that ultimately could expedite drug development efforts, facilitate progress in health education and health information technology, and inform public health policies.
Financial Disclosure: The authors have no significant financial interest in or other relationship with the manufacturer of any product or provider of any service mentioned in this article.
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