Microsatellite Instability (MSI), the spontaneous loss or gain of nucleotides from repetitive DNA tracts, is a molecular phenotype in oncology that is associated with genomic hypermutability and mutations in DNA repair enzymes. Although originally described in colorectal tumors, MSI has lately been regarded with newfound importance for cancers in general, owing to the recent discoveries that MSI-affected tumors are susceptible to immune checkpoint inhibitor immunotherapies, and that MSI is a generalized tumor phenotype found across virtually all cancer types. Despite its emerging importance as a powerful pan-cancer therapeutic marker, existing diagnostics for MSI are limited in throughput, sensitivity, specificity, quantitation, prognostic capabilities, and generality across tumor types, among other important features. There is consequently a need for clinical diagnostics with improved performance and enhanced diagnostic capabilities. We have recently developed a new experimental paradigm (smMIP capture) that can overcome these limitations. In our approach, each copy of a target sequence that is present in a sample is molecularly tagged during the first cycle of a multiplex capture reaction with a unique random sequence. After amplification, target amplicons and their corresponding molecular tags are subjected to massively parallel sequencing. During analysis, the molecular tags are used to associate sequence reads sharing a common origin. Through oversampling, reads bearing the same molecular tag error-correct one another to yield an independent haploid consensus for each progenitor molecule, effectively bypassing length-altering slippage mutations which occur at repetitive microsatellite tracts (?stutter? artifact) and enabling sensitive quantitation of even low prevalence microsatellite mutations. Among other benefits, the approach is scalable to large numbers of genomic targets and is sensitive to at least 1 variant in a background of 10,000 unmutated templates. Here we propose the advanced development and validation of smMIP capture as a clinical diagnostic for MSI. In our first Aim, we will develop a multiplexed panel to broadly target ~1,000 highly informative microsatellite loci, as well as protocols and analytic methods for its use and interpretation. In our second Aim, we will define clinical performance characteristics of the assay, and will validate it against existing, clinical standard-of-care MSI diagnostics. In our third Aim, we will apply the smMIP panel prospectively in a clinical trial cohort, and assess its potential impact on clinical care and correlation with patient outcomes compared to standard testing. This work will provide information and deliverables with direct, transformative benefit cancer to patients, by expanding the scope of MSI testing across tumor types, and by providing better stratification for immunotherapy treatment, prognostic estimation, and quantitative data on mutational burden that is biologically-meaningful for understanding patient outcomes and identifying MSI subtypes.

Public Health Relevance

New forms of cancer treatment are extremely effective against tumors affected by genomic instability, but our ability to practicably identify and classify tumors with this feature in clinical practice is limiting. This proposal outlines the advanced development and validation of ultrasensitive, cost effective, and highly accurate methods for detecting, classifying, and analyzing genomic instability in tumor specimens. This technology will be suitable for use as a clinical diagnostic, and will provide enhanced capabilities including improved prognostic approximation and generality across tumor types, while offering improved performance characteristics over existing standards of care.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants Phase II (R33)
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Special Emphasis Panel (ZCA1)
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Divi, Rao L
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University of Washington
Schools of Medicine
United States
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