Companion diagnostic

About: Companion diagnostic is a research topic. Over the lifetime, 427 publications have been published within this topic receiving 7475 citations.

The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors

Abstract: The development of immune checkpoint inhibitors has changed the treatment paradigm for advanced cancers across many tumor types. Despite encouraging and sometimes durable responses in a subset of patients, most patients do not respond. Tumors have adopted the PD-1/PD-L1 axis for immune escape to facilitate tumor growth, which can be leveraged as a potential target for immune checkpoint inhibitors. On this basis, PD-L1 protein expression on tumor or immune cells emerged as the first potential predictive biomarker for sensitivity to immune checkpoint blockade. The goal of our study was to evaluate PD-L1 as a predictive biomarker based on all US Food and Drug Administration (FDA) drug approvals of immune checkpoint inhibitors. We evaluated the primary studies associated with 45 FDA drug approvals from 2011 until April 2019. In total, there were approvals across 15 tumor types. Across all approvals, PD-L1 was predictive in only 28.9% of cases, and was either not predictive (53.3%) or not tested (17.8%) in the remaining cases. There were 9 FDA approvals linked to a specific PD-L1 threshold and companion diagnostic: bladder cancer (N = 3), non-small cell lung cancer (N = 3), triple-negative breast cancer (N = 1), cervical cancer (N = 1), and gastric/gastroesophageal junction cancer (N = 1) with 8 of 9 (88.9%) with immune checkpoint inhibitor monotherapy. The PD-L1 thresholds were variable both within and across tumor types using several different assays, including approvals at the following PD-L1 thresholds: 1, 5, and 50%. PD-L1 expression was also measured in a variable fashion either on tumor cells, tumor-infiltrating immune cells, or both. In conclusion, our findings indicate that PD-L1 expression as a predictive biomarker has limitations and that the decision to pursue testing must be carefully implemented for clinical decision-making.

PD-L1 as a biomarker of response to immune-checkpoint inhibitors

Abstract: Immune-checkpoint inhibitors targeting PD-1 or PD-L1 have already substantially improved the outcomes of patients with many types of cancer, although only 20-40% of patients derive benefit from these new therapies. PD-L1, quantified using immunohistochemistry assays, is currently the most widely validated, used and accepted biomarker to guide the selection of patients to receive anti-PD-1 or anti-PD-L1 antibodies. However, many challenges remain in the clinical use of these assays, including the necessity of using different companion diagnostic assays for specific agents, high levels of inter-assay variability in terms of both performance and cut-off points, and a lack of prospective comparisons of how PD-L1+ disease diagnosed using each assay relates to clinical outcomes. In this Review, we describe the current role of PD-L1 immunohistochemistry assays used to inform the selection of patients to receive anti-PD-1 or anti-PD-L1 antibodies, we discuss the various technical and clinical challenges associated with these assays, including regulatory issues, and we provide some perspective on how to optimize PD-L1 as a selection biomarker for the future treatment of patients with solid tumours.

Predictive biomarkers: a paradigm shift towards personalized cancer medicine.

Abstract: Advances in our understanding of the intricate molecular mechanisms for transformation of a normal cell to a cancer cell, and the aberrant control of complementary pathways, have presented a much more complex set of challenges for the diagnostic and therapeutic disciplines than originally appreciated. The oncology field has entered an era of personalized medicine where treatment selection for each cancer patient is becoming individualized or customized. This advance reflects the molecular and genetic composition of the tumors and progress in biomarker technology, which allow us to align the most appropriate treatment according to the patient's disease. There is a worldwide acceptance that advances in our ability to identify predictive biomarkers and provide them as companion diagnostics for stratifying and subgrouping patients represents the next leap forward in improving the quality of clinical care in oncology. As such, we are progressing from a population-based empirical 'one drug fits all' treatment model, to a focused personalized approach where rational companion diagnostic tests support the drug's clinical utility by identifying the most responsive patient subgroup.