Saad Usmani | PubMed Health

PubMed2025

Mutagenic Impact and Evolutionary Influence of Chemoradiotherapy in Hematologic Malignancies.

UNLABELLED: Ionizing radiotherapy (RT) is a widely used treatment strategy for malignancies. In solid tumors, RT-induced double-strand breaks lead to the accumulation of insertion-deletions (indels; ID), and their repair by nonhomologous end joining has been linked to the ID8 mutational signature in surviving cells. However, the extent of RT-induced mutagenesis in hematologic malignancies and its impact on their mutational profiles and interplay with commonly used chemotherapies has not yet been explored. In this study, we interrogated 580 whole-genome sequence (WGS) samples from patients with large B-cell lymphoma, multiple myeloma, and myeloid neoplasms and identified ID8 only in relapsed disease. Yet ID8 was detected after exposure to both RT and mutagenic chemotherapy (i.e., platinum and melphalan). Using WGS of single-cell colonies derived from treated lymphoma cells, we revealed a dose-response relationship between RT and platinum and ID8. Finally, using ID8 as a genomic barcode, we demonstrate that a single RT-surviving cell may seed distant relapse. SIGNIFICANCE: RT and the ID8 indel signature are related, but their genomic impact on hematologic malignancies is unclear. Leveraging WGS, we linked ID8 to both RT and mutagenic chemotherapy and validated that platinum can induce ID8. We used ID8 as a genomic barcode to reveal that RT-resistant cells may seed systemic relapse.

Humans↗

PubMed2025

AncestryGeni: a novel genetic ancestry classification pipeline for small and noisy sequence data.

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.

Humans↗