Mutation of the human genome results in three classes of genomic variation: single nucleotide variants; short insertions or deletions; and large structural variants (SVs). Some mutations occur during normal processes, such as meiotic recombination or B cell development, and others result from DNA replication or aberrant repair of breaks in sequence-specific contexts. Regardless of mechanism, mutations are subject to selection, and some hotspots can manifest in disease. Here, we discuss genomic regions prone to mutation, mechanisms contributing to mutation susceptibility, and the processes leading to their accumulation in normal and somatic genomes. With further, more accurate human genome sequencing, additional mutation hotspots, mechanistic details of their formation, and the relevance of hotspots to evolution and disease are likely to be discovered. HighlightsGenetic mutations are influenced by sequence context, structure, and genomic features.Mechanisms responsible for many mutational hotspots have been identified.Hotspots are largely related to loci prone to mutation during replication or DNA repair.Selection leads to recurrent mutations in somatic tissues that can be exploited for therapeutic purposes.
Tumors display widespread transcriptome alterations, but the full repertoire of isoform-level alternative splicing in cancer is unknown. We developed a long-read (LR) RNA sequencing and analytical platform that identifies and annotates full-length isoforms and infers tumor-specific splicing events. Application of this platform to breast cancer samples identifies thousands of previously unannotated isoforms; ~30% affect protein coding exons and are predicted to alter protein localization and function. We performed extensive cross-validation with -omics datasets to support transcription and translation of novel isoforms. We identified 3059 breast tumor–specific splicing events, including 35 that are significantly associated with patient survival. Of these, 21 are absent from GENCODE and 10 are enriched in specific breast cancer subtypes. Together, our results demonstrate the complexity, cancer subtype specificity, and clinical relevance of previously unidentified isoforms and splicing events in breast cancer that are only annotatable by LR-seq and provide a rich resource of immuno-oncology therapeutic targets.
Because of the similar phenotypes they generate and their proximate reported locations on Chromosome 7, we tested the recessive retarded hair growth (rhg) and frizzy (fr) mouse mutations for allelism, but found instead that these defects complement. To discover the molecular basis of rhg, we analyzed a large intraspecific backcross panel that segregated for rhg and restricted this locus to a 0.9 Mb region that includes fewer than ten genes, only five of which have been reported to be expressed in skin. Complementation testing between rhg and a recessive null allele of fibroblast growth factor receptor 2 eliminated Fgfr2 as the possible basis of the retarded hair growth phenotype, but DNA sequencing of another of these candidates, ornithine aminotransferase (Oat), revealed a G to C transversion specifically associated with the rhg allele that would result in a glycine to alanine substitution at residue 353 of the gene product. To test whether this missense mutation might cause the mutant phenotype, we crossed rhg/rhg mice with mice that carried a recessive, perinatal-lethal, null mutation in Oat (designated OatΔ herein). Hybrid offspring that inherited both rhg and OatΔ displayed markedly delayed postnatal growth and hair development, indicating that these two mutations are allelic, and suggesting strongly that the G to C mutation in Oat is responsible for the retarded hair growth phenotype. Comparisons among +/+, rhg/+, rhg/rhg and rhg/OatΔ mice showed plasma ornithine levels and ornithine aminotransferase activities (in liver lysates) consistent with this assignment. Because histology of 7- and 12-month-old rhg/rhg and rhg/OatΔ retinas revealed chorioretinal degeneration similar to that described previously for OatΔ/OatΔ mice, we suggest that the rhg mutant may offer an ideal model for gyrate atrophy of the choroid and retina (GACR) in humans, which is also caused by the substitution of glycine 353 in some families.
Gyrate atrophy of the choroid and retina (GACR) is a hereditary form of progressive blindness caused by homozygosity for lossof-function mutations in the ornithine aminotransferase gene (Oat). The high levels of circulating ornithine that lead to ophthalmic symptoms in young adults are also displayed by 2 ornithine aminotransferase (OAT)-deficient mouse models of GACR. Here, we have developed an inexpensive and quantitative bacteria-based test for detecting hyperornithinemia in blood or urine samples from these mutant mice, a test that we suggest could be used to facilitate the identification and treatment of OAT-deficient humans before the onset of visual impairment.Keywords argE mutant E coli, gyrate atrophy of the choroid and retina, ornithine biosensor, metabolic screening, animal models of human disease
SummaryTumors display widespread transcriptome alterations, but the full repertoire of isoform-level alternative splicing in cancer is not known. We developed a long-read RNA sequencing and analytical platform that identifies and annotates full-length isoforms, and infers tumor-specific splicing events. Application of this platform to breast cancer samples vastly expands the known isoform landscape of breast cancer, identifying thousands of previously unannotated isoforms of which ~30% impact protein coding exons and are predicted to alter protein localization and function, including of the breast cancer-associated genes ESR1 and ERBB2. We performed extensive cross-validation with -omics data sets to support transcription and translation of novel isoforms. We identified 3,059 breast tumor-specific splicing events, including 35 that are significantly associated with patient survival. Together, our results demonstrate the complexity, cancer subtype-specificity, and clinical relevance of novel isoforms in breast cancer that are only annotatable by LR-seq, and provide a rich resource of immuno-oncology therapeutic targets.
4525 Background: RNA sequencing has shown promise in defining the biology of individual renal cell carcinoma (RCC) tumors. However, these biomarkers have not yet been translated to the clinic for prospectively assigning optimal treatments to patients. Challenges for RNA-seq biomarker development include translating classifiers across different assays/platforms, normalization of data collected from single patients in the clinic and establishing robust thresholds for assigning prediction groups. We have designed the prospective phase II OPTIC RCC clinical trial (clinicaltrials.gov NCT05361720) to test the utility of an RNA-seq based biomarker in predicting treatment based on biologic drivers relevant to angiogenesis (anti-angiogenesis; TKI) and immune microenvironment (immunotherapy; IO). Here, we will describe the development and optimization of a machine learning model for assigning individual patients to biologically driven clusters in real time to facilitate RNA-seq based biomarker trials. Methods: We have utilized RNA-seq data from the IMmotion 151 trial (ref) to develop a machine learning model. Clusters were grouped into three based on their association to tumor biology and treatment: (1) Cluster 1+2 (angiogenic signature → TKI+IO therapy), (2) Cluster 4+5 (immune/proliferative signature → dual IO therapy), (3) Cluster 3+6 (neither signature → exclude). Random forest classifier was used to train a multi-class model to predict the three groups. The model is evaluated using bootstrapped cross-validation. Results: Our machine learning classifier was built using 188 genes and has a cross-validation accuracy of 85% and sensitivity of >90% in predicting patients into one of the three biological clusters from our training data. Predictions of the classifier are significantly associated with progression-free survival across different treatments within each of the predicted groups. We also observed significant odds ratios when comparing responders (CR/PR/SD) to non-responders (PD) across the treatment groups. The model was then validated in two independent test sets treated with Angio and IO inhibitors: (1) 61 patient renal cell carcinoma cohort (2) 12 patient clear cell renal cell carcinoma cohort. We observed a significant enrichment of responders to Angio+IO treatment for the predicted Cluster_Angio patients compared to IO treatment (p=0.05), highlighting the importance of matching patients to their optimal treatment. Conclusions: We have developed an accurate machine learning model to assign individual patients to RNA-seq clusters in real time. This classifier will facilitate the prospective OPTIC RCC trial. If successful, our biomarker strategy will serve as a proof of concept for selecting optimal treatments for RCC patients.
Immunotherapy has emerged as a promising new therapy for kidney cancer with durable clinical responses in a subset of the patients. However, discovery of biomarkers that predict patient response to immunotherapy has thus far been unsuccessful. Diverse sets of biomarkers have been proposed, (e.g., PD-L1 immunohistochemistry, tumor mutation burden, gene expression signatures), but have failed to validate in clinical studies. There is an urgent need to identify predictive biomarkers for selecting kidney cancer patients most likely to respond to immunotherapy. Spatial biology technologies provide molecular data at single cell resolution along with spatial coordinates in patient’s tumor slides. We hypothesized that the presence of immune cell types and their spatial 2-D patterns in a histology image could potentially predict response to immunotherapy. For this project we are using Tissue Microarray (TMAs). We are using two cohorts of data: (Dataset-1) 70 clear cell renal cell carcinoma (CCRCC) patients treated with checkpoint inhibitors, and (Dataset-2) 180 patients, early stage papillary RCC (PRCC) cohort. Dataset-1 was assayed using CosMx (Nanostring technologies) for a RNA panel of 980 immune-related genes. Dataset-2 was assayed with PhenoCycler (Akoya Biosciences) for a 31 marker protein panel with immune-related genes. We have developed methodology and novel algorithms to process both the datasets and to identify spatial phenotypes. Our pipelines include QC, normalization, dimensionality reduction and clustering. For annotation of cell types, we used as reference single cell datasets from RCC to identify tumor-type specific cell types. These cell types were mapped back to their spatial coordinates and visualized. We verified the annotations based on visual checks of their cellular morphology and organization. Next, we identified cellular neighborhoods around each cell and identified spatial clusters. We also performed a network-based analysis to identify additional spatial patterns. We performed correlation analysis with clinical phenotypes and identified spatial clusters of immune cell subtypes located in proximity of cancer cells to be associated with response to therapy. Citation Format: Kathryn Beckermann, Scott Haake, Alex Nesta, Michael Caponegro, N R Nirmala, Anupama Reddy. Developing spatial molecular correlates of response to immunotherapy in kidney cancer [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 6622.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.