Howe, Michael

Abstract Motivation Efforts to address health disparities are often limited by the lack of robust computational tools for inferring genetic ancestry by calculating an individual’s genetic similarity to continental groups. We have already shown that a preferred alternative to self-described race is using ancestry-informative markers (AIMs) that can be classified into ancestral components and used to estimate their similarity to those of known populations to identify continental groups. However, real-world genomic data can present challenges, including limited availability of germline DNA, a small number of AIMs for each sample, and the use of different variant calling software, limiting the application of existing solutions. Results Here, we describe a novel supervised machine-learning tool AncestryGeni, which infers genetic ancestry for samples with even a hundred markers and is applicable to any genomic data, including whole exome sequencing (WES) and RNA sequencing (RNA-Seq) data. Applying AncestryGeni to a real-world genomic dataset obtained from the Multiple Myeloma Research Foundation (MMRF) CoMMpass study, we show that it is more accurate than the commonly used FastNGSadmix when using nonstandard genomic material. We also demonstrate that when using AncestryGeni, the tumor-derived sequence obtained from WES and RNA-Seq can be a robust data source to accurately estimate an individual’s genetic similarity to a continental group. Availability and implementation AncestryGeni pipeline is available at https://github.com/eelhaik/AncestryGeni/tree/main.

Abstract Extramedullary disease (EMD) is associated with an inferior prognosis and lower response rates to conventional multiple myeloma (MM)–directed therapies compared to MM without EMD. A deeper understanding of the molecular landscape and underlying drivers of EMD is essential to identify potential targets for novel therapeutic strategies. To address this, we performed whole-exome sequencing on EMD tumor tissue from 18 unique patients and bone marrow aspirates (BMAs) from 20 patients at the time of EMD development. Notably, paired EMD and BMA samples were collected from 6 patients at the point of EMD diagnosis, allowing for direct comparison of molecular profiles. Our analysis revealed a near-universal presence of mutations within the MAPK pathway in EMD samples (94%), compared to BMAs (60%; odds ratio, 10.7; P = .02; q < 0.1). Additionally, mutations in established driver genes (NRAS, KRAS, and BRAF) were common and frequently clonal, suggesting their central role in EMD pathogenesis. We also identified alterations in genes associated with cell adhesion and migration (ROBO1, ROBO2, and FAT1) and the SWI/SNF complex and epigenetic regulators (ARID1A, KMT2C, KMT2D, and EP300), although these were predominantly subclonal. Furthermore, we frequently detected biallelic alterations in the tumor suppressor genes MAX (22%), a binding partner for MYC, and CDKN2C (17%). Genomic complexity was significantly higher in EMD samples than BMAs, as evidenced by increased tumor mutational burden and the enrichment of 1q gain/amplifications. These findings underscore the distinct molecular profile of EMD compared to BMA and highlight the genomically complex and heterogeneous nature of extramedullary disease in MM.