Scientists at the Translational Genomics Research Institute (TGen), an affiliate of City of Hope, report the development of LumosVar, a computer program that can help identify cancer-causing mutations from patient tumor samples. The study (“Leveraging Spatial Variation in Tumor Purity for Improved Somatic Variant Calling of Archival Tumor Only Samples”) appears in Frontiers in Oncology.

Archival tumor samples represent a rich resource of annotated specimens for translational genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available in the retrospective setting, making it difficult to distinguish between true somatic variants and individual-specific germline variants. Archival sections often contain adjacent normal tissue, but this tissue can include infiltrating tumor cells. As existing comparative somatic variant callers are designed to exclude variants present in the normal sample, a novel approach is required to leverage adjacent normal tissue with infiltrating tumor cells for somatic variant calling,” the investigators wrote.

“Here we present LumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient, built upon our previous single sample tumor only variant caller LumosVar 1.0. The approach assumes that the allelic fraction of somatic variants and germline variants follow different patterns as tumor content and copy number state change. LumosVar 2.0 estimates allele specific copy number and tumor sample fractions from the data, and uses a model to determine expected allelic fractions for somatic and germline variants and to classify variants accordingly. To evaluate the utility of LumosVar 2.0 to jointly call somatic variants with tumor and adjacent normal samples, we used a glioblastoma dataset with matched high and low tumor content and germline whole exome sequencing data (for true somatic variants) available for each patient. Both sensitivity and positive predictive value were improved when analyzing the high tumor and low tumor samples jointly compared to analyzing the samples individually or in silico pooling of the two samples.

“Finally, we applied this approach to a set of breast and prostate archival tumor samples for which tumor blocks containing adjacent normal tissue were available for sequencing. Joint analysis using LumosVar 2.0 detected several variants, including known cancer hotspot mutations that were not detected by standard somatic variant calling tools using the adjacent tissue as presumed normal reference. Together, these results demonstrate the utility of leveraging paired tissue samples to improve somatic variant calling when a constitutional sample is not available.

“In the case of archived samples from patients for which treatment outcome results are known, these represent a treasure trove of information that could accelerate research by investigators and physicians in predicting responses of future patients to particular treatments.”

“There are many open questions in precision oncology that can only be answered by collecting large amounts of patient genomic data linked to treatment response and clinical outcomes,” said Rebecca Halperin, PhD, research assistant professor in TGen’s quantitative medicine and systems biology program. “The approach we outline in this study should enable researchers to use archival samples more effectively. Accurately calling, or identifying, somatic variants—those DNA changes specific to a patient’s cancer—are the first step in any analysis.”

However, archived tumor samples are frequently not accompanied by the patients’ normal—or germline—genetic information, making it difficult to distinguish the patient’s normal DNA variants to their mutated and cancerous DNA changes.

LumosVar is a precise enough tool that it not only can detect the cancerous DNA from a patient sample, but it also can differentiate the adjacent normal DNA that may surround the tumor in the sample, according to Halperin, the study’s lead author. Comparing the patient’s normal DNA from a suspected cancer-causing mutation is critical to eliminating false positives, she added.

A high level of accuracy is needed for physicians to use this information in precision medicine, determining what treatment each individual patient should receive.

“The sequencing of DNA from tissue adjacent to the tumor could help identify somatic, or cancer-causing, mutations when another source of normal tissue is not available,” said Sara Byron, PhD, research assistant professor in TGen’s integrated cancer genomics division, and also the study’s senior author.

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