Background: Oncoprotein genes are over-represented in statically defined, low mutation-frequency fractions of cancer genome atlas (TCGA) datasets, consistent with a higher driver mutation density.
Materials And Methods: We developed a "continuously variable fraction" (CVF) approach to defining high and low mutation-frequency groups.
Results And Conclusion: Using the CVF approach, an oncoprotein set was shown to be associated with a TCGA, low mutation-frequency group in nine distinct cancer types, versus six, for statically defined sets; and a tumor-suppressor set was over-represented in the low mutation-frequency group in seven cancer types, notably including BRCA. The CVF approach identified single-mutation driver candidates, such as BRAF V600E in the thyroid cancer dataset. The CVF approach allowed investigation of cytoskeletal protein-related coding regions (CPCRs), leading to the conclusion that mutation of CPCRs occurs at a statistically significant, higher density in low mutation-frequency groups. Supporting online material for this article can be found at www.universityseminarassociates.com/Supporting_online_material_for_scholarly_pubs.php.
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Cancer Sci
January 2025
Department of colorectal surgery, The First Affiliated Hospital of Ningbo University, Ningbo, China.
This study analyzed targeted sequencing data from 6530 tissue samples from patients with metastatic Chinese colorectal cancer (CRC) to identify low mutation frequency and subgroup-specific driver genes, using three algorithms for overall CRC as well as across different clinicopathological subgroups. We analyzed 425 cancer-related genes, identifying 101 potential driver genes, including 36 novel to CRC. Notably, some genes demonstrated subgroup specificity; for instance, ERBB4 was found as a male-specific driver gene and mutations of ERBB4 only influenced the prognosis of male patients with CRC.
View Article and Find Full Text PDFBMC Biol
January 2025
Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518120, China.
Background: Plant mitochondrial genomes (mitogenomes) exhibit extensive structural variation yet extremely low nucleotide mutation rates, phenomena that remain only partially understood. The genus Gossypium, a globally important source of cotton, offers a wealth of long-read sequencing resources to explore mitogenome and plastome variation and dynamics accompanying the evolutionary divergence of its approximately 50 diploid and allopolyploid species.
Results: Here, we assembled 19 mitogenomes from Gossypium species, representing all genome groups (diploids A through G, K, and the allopolyploids AD) based on a uniformly applied strategy.
Genetics
January 2025
Max Planck Research Group Behavioural Genomics, Max Planck Institute for Evolutionary Biology, August-Thienemann-Straße 2, 24306 Plön, Germany.
Multiple methods of demography inference are based on the ancestral recombination graph. This powerful approach uses observed mutations to model local genealogies changing along chromosomes by historical recombination events. However, inference of underlying genealogies is difficult in regions with high recombination rate relative to mutation rate due to the lack of mutations representing genealogies.
View Article and Find Full Text PDFClin Cancer Res
December 2024
Vall d'Hebron Institute of Oncology, Barcelona, Spain.
Purpose: The randomized GeparOla trial reported comparable pathological complete response (pCR) rates with neoadjuvant containing olaparib vs. carboplatin treatment. Here, we evaluate the association between functional homologous repair deficiency (HRD) by RAD51 foci and pCR, and the potential of improving patient selection by combining RAD51 and stromal tumor infiltrating lymphocytes (sTILs).
View Article and Find Full Text PDFNat Commun
January 2025
Department of Physics and Astronomy, University of California, Riverside, Riverside, CA, USA.
New and more transmissible variants of SARS-CoV-2 have arisen multiple times over the course of the pandemic. Rapidly identifying mutations that affect transmission could improve our understanding of viral biology and highlight new variants that warrant further study. Here we develop a generic, analytical epidemiological model to infer the transmission effects of mutations from genomic surveillance data.
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