A biomarker consists of two components – one or more "features" and an algorithm that uses those features to provide a diagnostic or prognostic classification. Advanced computational methods including artificial intelligence and machine learning, provide unique opportunities to integrate features from diverse datasets, provided we have the right data on which to train our models, the foresight to avoid classification pitfalls, and a commitment to reproducible research.

 T cell–inflamed gene expression profile (GEP) and tumor mutational burden (TMB) are clinically validated biomarkers that independently predict pembrolizumab response. KEYNOTE-495/KeyImPaCT is a group-sequential, adaptively randomized, multisite, open-label, Phase II study investigating first-line three pembrolizumab-based combinations in patients with advanced NSCLC. Recent study results demonstrate the feasibility and clinical usefulness of prospective GEP and TMB assessment to study the clinical activity of three pembrolizumab-based combination therapies in 1L patients with advanced NSCLC. Although sample sizes were small, the GEPhigh TMBhigh subgroup demonstrated the best response among the biomarker subgroups for all three combination therapies.

The pace of new data acquisition from the diverse assays to measure biomarkers in oncology is accelerating. These diverse assays and the complementary nature of the data generated make possible the realization of Precision Medicine. These range from new imaging molecular probes to measure key molecular targets; to digital pathology; to many analytes from liquid biopsies; to data analysis from AI, machine learning and validation from large, annotated and standardized databases.

Gritstone has developed a personalized, comprehensive, and sensitive circulating tumor DNA (ctDNA) monitoring assay to understand the efficacy of GRANITE – Gritstone’s personalized neoantigen directed immunotherapy program – in late-stage cancer patients who lack pre-existing immune infiltration. Updated results from Phase 1/2 study will focus on longitudinal ctDNA monitoring and other comprehensive translational genomics analyses to understand neoantigen dynamics, tumor burden and in some patient cases therapy induced acquired resistance.

Blood biomarkers are necessary for real-time monitoring of aggressive tumors. Over the years, circulating cell-free nucleic acids have shown the utility in early diagnosis, prognosis, treatment decisions. Recently, our group has utilized HTG EdgeSeq microRNA Whole Transcriptome Assay and Next-Generation Sequencing, to profile plasma and tissue samples and to quantify >2,000 microRNAs. Specific cell-free microRNA signatures were found in plasma and paired tissue samples from patients with aggressive tumor.

NCI-MATCH multi-arm Phase II clinical trial tested tumor tissue from 5,954 patients with advanced refractory cancer to assign treatment based on the molecular profile. Molecular profiling from tissue was successful for 93% of patients. For 267 patients who did not have adequate tissue for profiling, plasma cfDNA profiling results provided insight into the potential utility of blood-based testing in a broad spectrum of histologies when tissue is not obtainable.

Cell-free DNA (cfDNA) consists of small nucleic acid fragments entering the bloodstream during apoptosis or necrosis. cfDNA fragmentation patterns detected by low-coverage whole-genome sequencing can be used to detect the presence of circulating tumor DNA (ctDNA) in a background of cfDNA mostly derived from hematologic cells. This presentation will describe the development of a blood-based, whole-genome, next-generation sequencing (NGS) test to detect early stages of cancer.

There is at present substantial interest in the application of liquid biopsy for multi-cancer early detection. While such an approach has merit, detecting any one of a large number of cancers by means of liquid biopsy represents a substantial challenge as it requires sufficient sensitivity at very high specificity. Alternatives to make liquid biopsy impactful for cancer screening and early detection will be presented.

We have optimized a process to identify and quantify aberrant tumor-specific DNA junctions in the plasma of patients with advanced cancer and developed personalized assays for MRD, relapse detection and therapy monitoring. We first survey the whole genome of the patient’s tumor for chromosomal rearrangements using either MPseq, FFPEseq or WGS. MPseq differs from standard sequencing approaches by tiling the whole genome with large DNA fragments that are more likely to span chromosomal rearrangements, thus improving the detection of such. We identified that multiple, unique inter- and intra- chromosomal rearrangements were present in more than 98% of specimens we tested. In our data to date, we demonstrate that some of these rearrangements can be detected in the plasma of patients with advanced cancers. It is our central hypothesis that cell-free chromosomal rearrangements can improve upon standard imaging-based disease monitoring approaches. This will be demonstrated using case studies from several cancers such as serous endometrial and breast cancer.

Although Precision Medicine therapies have demonstrated benefit to cancer patients, if biomarker testing is not performed, they may receive suboptimal care. Four key barriers to the use of biomarker diagnostics have been identified, and examples of strategies to overcome these barriers will be discussed with respect to liquid biopsy tests. Intrinsic characteristics of liquid biopsy tests make them well-suited to overcome some barriers to use in today’s healthcare system but also at a disadvantage to other barriers.

This talk will describe the FNIH Biomarkers Consortium ctDNA quality control materials (QCM) public-private partnership (PPP), which was designed to develop recognized QCMs for widespread use in ctDNA testing and highlight FNIH partnership design and development. The project is a member of the International Liquid Biopsy Standardization Alliance (ILSA) and the development and objectives of this new, recognized Collaborative Community will also be discussed.

As the potential of liquid biopsies for prognostic, predictive or early cancer detection applications grows, so does the demand for technical advances that enable the ever-increasing range of applications. We present new technologies allowing highly parallel, PCR-free elimination of wild type alleles, boosting the ability of all downstream detection methods for detecting point mutations in liquid biopsies and clinical samples. New developments boosting the detection of methylated or un-methylated sequences will also be described.

The world has faced an unfamiliar territory during the SARS-CoV-2 (COVID-19) pandemic. This unprecedented situation has only served to emphasize the need for methods that rapidly collect low-volume, high-quality samples remotely. These ‘micro-sampling devices’ are integral in the execution of well-designed and essential decentralized trials, and with combined efforts, we can continue to move towards this new and promising patient-centric clinical trial model. Objectives: (1) introducing and discussing micro-sampling devices that have shown promise in patient centric study models; (2) sharing results and data from recent Novartis trials that have utilized these micro-sampling devices and platforms with success; (3) discussing various methods with which micro-sampling can be utilized and deployed; (4) examining potential avenues to attain implementation of micro-sampling in decentralized trials.