LAUSR

We propose a method to leverage spatial transcriptomics and bioinformatic analysis to identify sub-clonal loss of heterozygosity that could otherwise be missed by standard bulk sequencing in glioblastoma tumors. Glioblastomas are highly invasive and aggressive brain tumors with a low five-year survival rate of less than 10%. Current treatment strategies are not sufficient for long term disease outcome, and innovative approaches to analyze glioblastoma are imperative for future improved options. Many factors contribute to difficulty of treatment, though one critical consideration is the diffuse nature and known intratumoral heterogeneity of this cancer. It is important to determine LOH heterogeneity, as this genomic alteration is associated with copy number variation and specific classifications of gliomas. Loss of heterozygosity is an irreversible deletion of DNA that is a common alteration in cancer. Application of spatial transcriptomics can increase our understanding of genomic complexity across a tissue, and unsupervised graph clustering can computationally separate spatial regions with similar gene expression patterns. Our method investigates spatial clusters individually at known heterozygous SNP sites to identify evidence for LOH with Bayesian inference and a hidden Markov model. Our analysis centers on the balance of reference (A) and non-reference (B) spatial transcriptomics read counts at SNPs of interest. B allele frequency is the number of reads aligned to the alternative allele divided by the total number of reads counted at the SNP. At a normal two-copy state at a heterozygous site, B allele frequency is ideally 50 percent. LOH can be identified as this value deviates toward 0 or 100, and copy number alterations impact this frequency. Our method utilizes Bayes factor values, or likelihood ratios between two models, using allele counts to provide support for either LOH or a heterozygous event. For our analysis, spatial transcriptomics BAMs are processed with a graph clustering algorithm and split into cluster-specific BAMs. We calculate allele count coverage for each cluster at pre-selected heterozygous SNP positions identified through germline exome sequencing. Bayes factor values are then calculated at each SNP, resulting in evidence for LOH or a heterozygous event. Chromosomal regions are segmented by a hidden Markov approach. Cumulative metrics for each segment are evaluated to determine a “state” label of heterozygous, LOH, or undefined. We tested our method on three WHO grade 4 IDH wild-type EGFRvIII positive fresh frozen glioblastoma tissue samples. Sub-clonal heterogeneity was identified in two samples and confirmed by bulk exome sequencing, bulk RNA sequencing, and analysis of initial clinical reports. Citation Format: Michelle G. Webb, Frances Chow, Carmel McCullough, Bohan Zhang, Kyle Hurth, John Carpten, Gabriel Zada, David W. Craig. Identification of sub-clonal loss of heterozygosity (LOH) in glioblastomas analyzed with spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3124.