Abstract 2267: A novel method for classification of microsatellite instability (MSI) using the Oncomine Tumor Mutation Load assay
Authors: Aleksandr Pankov, Sameh El-Difrawy, Warren Tom, Jeffrey Conroy, Sean Glenn, Sarabjot Pabla, Carl Morrison, Fiona Hyland, Simon Cawley
Journal: Cancer Research (2018)

Abstract Cancer-associated changes in short tandem repeats (STRs) throughout the genome have been shown to be predictive of response to immunotherapy treatment. Traditional assays efficiently rely on a small number of genomic loci to assign an MSI status, but are only effective in a small number of cancer types. Genome-wide profiling of aberrations in STRs has the potential to reveal a comprehensive signature of MSI and could allow for the association of response with samples that are labeled as microsatellite stable (MSS) by traditional assays.To facilitate the discovery of informative repeat sites across the genome, we have extended the Oncomine Tumor Mutation Load assay* to allow for the detection of variants in STRs. Our method addresses challenges of semiconductor sequencing of long STR elements including indel and partial incorporation signal errors. We developed an algorithm to avoid and correct these errors in flowspace, rather than base space, to ensure accurate signal assessment. Our pipeline is able to determine the optimal anchor locations used to identify the exact position of a repeat sequence within a read and then quantify the size, sequence, and number of reads associated with each allele. An additional complication of accurately quantifying the length of an STR is the increased error rates of the polymerase during DNA amplification. By evaluating the similarity of every loci between tumor and normal pairs of an individual using a metric derived from information theory, we are able to account for both the sequencing and PCR associated noise to robustly identify the genome-wide changes. Furthermore by evaluating a set of 24 MSI-High and 24 MSS samples along with their paired normal samples, we created a novel method to identify a highly informative subset of STR loci. Creating a classifier using only these highly informative loci, we are able to recapitulate the traditional MSI assay with 95% accuracy on these initial samples. This general methodology can be further extended to a model where MSI status is predicted using only the tumor sample, without the need for a control from the same individual. Due to the flexibility of this method, we are able to examine an increased number of potentially informative loci and use the total mutation load along with mutation calls in the MMR genes to provide comprehensive, multi-dimensional genomic perspective of samples. While this novel method has been developed and tested on the Oncomine Tumor Mutation Load assay, we have created a novel general approach that can be used on the output of any semiconductor sequencing assay. The extended information derived from individual mutations and the total mutation load, along with STR aberration information allows for a comprehensive evaluation of every sample and promotes the discovery of novel genomic components important for understanding the mechanisms of immune-oncology. *For research use only Citation Format: Aleksandr Pankov, Sameh El-Difrawy, Warren Tom, Jeffrey Conroy, Sean Glenn, Sarabjot Pabla, Carl Morrison, Fiona Hyland, Simon Cawley. A novel method for classification of microsatellite instability (MSI) using the Oncomine Tumor Mutation Load assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2267.