Browsing by Subject "Cancer genomics"
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Item A breast cancer classification and immune landscape analysis based on cancer stem-cell-related risk panel(Springer Nature, 2023-12-08) Hu, Haihong; Zou, Mingxiang; Hu, Hongjuan; Hu, Zecheng; Jiang, Lingxiang; Escobar, David; Zhu, Hongxia; Zhan, Wendi; Yan, Ting; Zhang, Taolan; Radiation Oncology, School of MedicineThis study sought to identify molecular subtypes of breast cancer (BC) and develop a breast cancer stem cells (BCSCs)-related gene risk score for predicting prognosis and assessing the potential for immunotherapy. Unsupervised clustering based on prognostic BCSC genes was used to determine BC molecular subtypes. Core genes of BC subtypes identified by non-negative matrix factorization algorithm (NMF) were screened using weighted gene co-expression network analysis (WGCNA). A risk model based on prognostic BCSC genes was constructed using machine learning as well as LASSO regression and multivariate Cox regression. The tumor microenvironment and immune infiltration were analyzed using ESTIMATE and CIBERSORT, respectively. A CD79A+CD24-PANCK+-BCSC subpopulation was identified and its spatial relationship with microenvironmental immune response state was evaluated by multiplexed quantitative immunofluorescence (QIF) and TissueFAXS Cytometry. We identified two distinct molecular subtypes, with Cluster 1 displaying better prognosis and enhanced immune response. The constructed risk model involving ten BCSC genes could effectively stratify patients into subgroups with different survival, immune cell abundance, and response to immunotherapy. In subsequent QIF validation involving 267 patients, we demonstrated the existence of CD79A+CD24-PANCK+-BCSC in BC tissues and revealed that this BCSC subtype located close to exhausted CD8+FOXP3+ T cells. Furthermore, both the densities of CD79A+CD24-PANCK+-BCSCs and CD8+FOXP3+T cells were positively correlated with poor survival. These findings highlight the importance of BCSCs in prognosis and reshaping the immune microenvironment, which may provide an option to improve outcomes for patients.Item Bottom-up, integrated -omics analysis identifies broadly dosage-sensitive genes in breast cancer samples from TCGA(PLOS, 2019-01-17) Kechavarzi, Bobak D.; Wu, Huanmei; Doman, Thompson N.; Biohealth Informatics, School of Informatics and ComputingThe massive genomic data from The Cancer Genome Atlas (TCGA), including proteomics data from Clinical Proteomic Tumor Analysis Consortium (CPTAC), provides a unique opportunity to study cancer systematically. While most observations are made from a single type of genomics data, we apply big data analytics and systems biology approaches by simultaneously analyzing DNA amplification, mRNA and protein abundance. Using multiple genomic profiles, we have discovered widespread dosage compensation for the extensive aneuploidy observed in TCGA breast cancer samples. We do identify 11 genes that show strong correlation across all features (DNA/mRNA/protein) analogous to that of the well-known oncogene HER2 (ERBB2). These genes are generally less well-characterized regarding their role in cancer and we advocate their further study. We also discover that shRNA knockdown of these genes has an impact on cancer cell growth, suggesting a vulnerability that could be used for cancer therapy. Our study shows the advantages of systematic big data methodologies and also provides future research directions.Item CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer(Springer Nature, 2021-02-25) Craven, Kelly E.; Gökmen‑Polar, Yesim; Badve, Sunil S.; Pathology and Laboratory Medicine, School of MedicineStudies have shown that the presence of tumor infiltrating lymphocytes (TILs) in Triple Negative Breast Cancer (TNBC) is associated with better prognosis. However, the molecular mechanisms underlying these immune cell differences are not well delineated. In this study, analysis of hematoxylin and eosin images from The Cancer Genome Atlas (TCGA) breast cancer cohort failed to show a prognostic benefit of TILs in TNBC, whereas CIBERSORT analysis, which quantifies the proportion of each immune cell type, demonstrated improved overall survival in TCGA TNBC samples with increased CD8 T cells or CD8 plus CD4 memory activated T cells and in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) TNBC samples with increased gamma delta T cells. Twenty-five genes showed mutational frequency differences between the TCGA high and low T cell groups, and many play important roles in inflammation or immune evasion (ATG2B, HIST1H2BC, PKD1, PIKFYVE, TLR3, NOTCH3, GOLGB1, CREBBP). Identification of these mutations suggests novel mechanisms by which the cancer cells attract immune cells and by which they evade or dampen the immune system during the cancer immunoediting process. This study suggests that integration of mutations with CIBERSORT analysis could provide better prediction of outcomes and novel therapeutic targets in TNBC cases.Item Co-expression based cancer staging and application(Nature Publishing group, 2020-06-30) Yu, Xiangchun; Cao, Sha; Zhou, Yi; Yu, Zhezhou; Xu, Ying; Biochemistry and Molecular Biology, School of MedicineA novel method is developed for predicting the stage of a cancer tissue based on the consistency level between the co-expression patterns in the given sample and samples in a specific stage. The basis for the prediction method is that cancer samples of the same stage share common functionalities as reflected by the co-expression patterns, which are distinct from samples in the other stages. Test results reveal that our prediction results are as good or potentially better than manually annotated stages by cancer pathologists. This new co-expression-based capability enables us to study how functionalities of cancer samples change as they evolve from early to the advanced stage. New and exciting results are discovered through such functional analyses, which offer new insights about what functions tend to be lost at what stage compared to the control tissues and similarly what new functions emerge as a cancer advances. To the best of our knowledge, this new capability represents the first computational method for accurately staging a cancer sample.Item ColoType: a forty gene signature for consensus molecular subtyping of colorectal cancer tumors using whole-genome assay or targeted RNA-sequencing(Nature Publishing group, 2020-07-21) Buechler, Steven A.; Stephens, Melissa T.; Hummon, Amanda B.; Ludwig, Katelyn; Cannon, Emily; Carter, Tonia C.; Resnick, Jeffrey; Gökmen-Polar, Yesim; Badve, Sunil S.; Pathology and Laboratory Medicine, School of MedicineColorectal cancer (CRC) tumors can be partitioned into four biologically distinct consensus molecular subtypes (CMS1-4) using gene expression. Evidence is accumulating that tumors in different subtypes are likely to respond differently to treatments. However, to date, there is no clinical diagnostic test for CMS subtyping. In this study, we used novel methodology in a multi-cohort training domain (n = 1,214) to develop the ColoType scores and classifier to predict CMS1-4 based on expression of 40 genes. In three validation cohorts (n = 1,744, in total) representing three distinct gene-expression measurement technologies, ColoType predicted gold-standard CMS subtypes with accuracies 0.90, 0.91, 0.88, respectively. To accommodate for potential intratumoral heterogeneity and tumors of mixed subtypes, ColoType was designed to report continuous scores measuring the prevalence of each of CMS1–4 in a tumor, in addition to specifying the most prevalent subtype. For analysis of clinical specimens, ColoType was also implemented with targeted RNA-sequencing (Illumina AmpliSeq). In a series of formalin-fixed, paraffin-embedded CRC samples (n = 49), ColoType by targeted RNA-sequencing agreed with subtypes predicted by two independent methods with accuracies 0.92, 0.82, respectively. With further validation, ColoType by targeted RNA-sequencing, may enable clinical application of CMS subtyping with widely-available and cost-effective technology.Item Genetic subtypes of smoldering multiple myeloma are associated with distinct pathogenic phenotypes and clinical outcomes(Springer, 2022-06-15) Bustoros, Mark; Anand, Shankara; Sklavenitis-Pistofidis, Romanos; Redd, Robert; Boyle, Eileen M.; Zhitomirsky, Benny; Dunford, Andrew J.; Tai, Yu-Tzu; Chavda, Selina J.; Boehner, Cody; Neuse, Carl Jannes; Rahmat, Mahshid; Dutta, Ankit; Casneuf, Tineke; Verona, Raluca; Kastritis, Efstathis; Trippa, Lorenzo; Stewart, Chip; Walker, Brian A.; Davies, Faith E.; Dimopoulos, Meletios-Athanasios; Bergsagel, P. Leif; Yong, Kwee; Morgan, Gareth J.; Aguet, François; Getz, Gad; Ghobrial, Irene M.; Medicine, School of MedicineSmoldering multiple myeloma (SMM) is a precursor condition of multiple myeloma (MM) with significant heterogeneity in disease progression. Existing clinical models of progression risk do not fully capture this heterogeneity. Here we integrate 42 genetic alterations from 214 SMM patients using unsupervised binary matrix factorization (BMF) clustering and identify six distinct genetic subtypes. These subtypes are differentially associated with established MM-related RNA signatures, oncogenic and immune transcriptional profiles, and evolving clinical biomarkers. Three genetic subtypes are associated with increased risk of progression to active MM in both the primary and validation cohorts, indicating they can be used to better predict high and low-risk patients within the currently used clinical risk stratification models.Item Identification of germline cancer predisposition variants during clinical ctDNA testing(Springer Nature, 2021-07) Stout, Leigh Anne; Kassem, Nawal; Hunter, Cynthia; Philips, Santosh; Radovich, Milan; Schneider, Bryan P.; Medical and Molecular Genetics, School of MedicineNext-generation sequencing of circulating tumor DNA (ctDNA) is a non-invasive method to guide therapy selection for cancer patients. ctDNA variant allele frequency (VAF) is commonly reported and may aid in discerning whether a variant is germline or somatic. We report on the fidelity of VAF in ctDNA as a predictor for germline variant carriage. Two patient cohorts were studied. Cohort 1 included patients with known germline variants. Cohort 2 included patients with any variant detected by the ctDNA assay with VAF of 40–60%. In cohort 1, 36 of 91 (40%) known germline variants were identified through ctDNA analysis with a VAF of 39–87.6%. In cohort 2, 111 of 160 (69%) variants identified by ctDNA analysis with a VAF between 40 and 60% were found to be germline. Therefore, variants with a VAF between 40 and 60% should induce suspicion for germline status but should not be used as a replacement for germline testing.Item The International Conference on Intelligent Biology and Medicine (ICIBM) 2019: bioinformatics methods and applications for human diseases(BMC, 2019-12-20) Zhao, Zhongming; Dai, Yulin; Zhang, Chi; Mathé, Ewy; Wei, Lai; Wang, Kai; Medical and Molecular Genetics, School of MedicineBetween June 9–11, 2019, the International Conference on Intelligent Biology and Medicine (ICIBM 2019) was held in Columbus, Ohio, USA. The conference included 12 scientific sessions, five tutorials or workshops, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics in bioinformatics, medical informatics, systems biology and intelligent computing. Here, we describe 13 high quality research articles selected for publishing in BMC Bioinformatics.Item Mutational landscape of RNA-binding proteins in human cancers(Taylor & Francis, 2018-01-02) Neelamraju, Yaseswini; Gonzalez-Perez, Abel; Bhat-Nakshatri, Poornima; Nakshatri, Harikrishna; Janga, Sarath Chandra; BioHealth Informatics, School of Informatics and ComputingRNA Binding Proteins (RBPs) are a class of post-transcriptional regulatory molecules which are increasingly documented to be dysfunctional in cancer genomes. However, our current understanding of these alterations is limited. Here, we delineate the mutational landscape of ∼1300 RBPs in ∼6000 cancer genomes. Our analysis revealed that RBPs have an average of ∼3 mutations per Mb across 26 cancer types. We identified 281 RBPs to be enriched for mutations (GEMs) in at least one cancer type. GEM RBPs were found to undergo frequent frameshift and inframe deletions as well as missense, nonsense and silent mutations when compared to those that are not enriched for mutations. Functional analysis of these RBPs revealed the enrichment of pathways associated with apoptosis, splicing and translation. Using the OncodriveFM framework, we also identified more than 200 candidate driver RBPs that were found to accumulate functionally impactful mutations in at least one cancer. Expression levels of 15% of these driver RBPs exhibited significant difference, when transcriptome groups with and without deleterious mutations were compared. Functional interaction network of the driver RBPs revealed the enrichment of spliceosomal machinery, suggesting a plausible mechanism for tumorogenesis while network analysis of the protein interactions between RBPs unambiguously revealed the higher degree, betweenness and closeness centrality for driver RBPs compared to non-drivers. Analysis to reveal cancer-specific Ribonucleoprotein (RNP) mutational hotspots showed extensive rewiring even among common drivers between cancer types. Knockdown experiments on pan-cancer drivers such as SF3B1 and PRPF8 in breast cancer cell lines, revealed cancer subtype specific functions like selective stem cell features, indicating a plausible means for RBPs to mediate cancer-specific phenotypes. Hence, this study would form a foundation to uncover the contribution of the mutational spectrum of RBPs in dysregulating the post-transcriptional regulatory networks in different cancer types.Item MutSignatures: an R package for extraction and analysis of cancer mutational signatures(Nature Publishing Group, 2020-10-26) Fantini, Damiano; Vidimar, Vania; Yu, Yanni; Condello, Salvatore; Meeks, Joshua J.; Obstetrics and Gynecology, School of MedicineCancer cells accumulate somatic mutations as result of DNA damage, inaccurate repair and other mechanisms. Different genetic instability processes result in characteristic non-random patterns of DNA mutations, also known as mutational signatures. We developed mutSignatures, an integrated R-based computational framework aimed at deciphering DNA mutational signatures. Our software provides advanced functions for importing DNA variants, computing mutation types, and extracting mutational signatures via non-negative matrix factorization. Specifically, mutSignatures accepts multiple types of input data, is compatible with non-human genomes, and supports the analysis of non-standard mutation types, such as tetra-nucleotide mutation types. We applied mutSignatures to analyze somatic mutations found in smoking-related cancer datasets. We characterized mutational signatures that were consistent with those reported before in independent investigations. Our work demonstrates that selected mutational signatures correlated with specific clinical and molecular features across different cancer types, and revealed complementarity of specific mutational patterns that has not previously been identified. In conclusion, we propose mutSignatures as a powerful open-source tool for detecting the molecular determinants of cancer and gathering insights into cancer biology and treatment.