Cancer Genomic Data Research Articles

Unveiling Tim-3 immune checkpoint expression in hepatocellular carcinoma through abdominal contrast-enhanced CT habitat radiomics

IntroductionImmune checkpoint inhibitors (ICIs) are important systemic therapeutic agents for hepatocellular carcinoma (HCC), among which T-cell immunoglobulin and mucin-domain containing protein 3 (Tim-3) is considered an emerging target for ICI therapy. This study aims to evaluate the prognostic value of Tim-3 expression and develop a predictive model for Tim-3 infiltration in HCC.MethodsWe collected data from 424 HCC patients in The Cancer Genome Atlas (TCGA) and data from 102 pathologically confirmed HCC patients from our center for prognostic analysis. Multivariate Cox regression analyses were performed on both datasets to determine the prognostic significance of Tim-3 expression. In radiomics analysis, we used the K-means algorithm to cluster regions of interest in arterial phase enhancement and venous phase enhancement images from patients at our center. Radiomic features were extracted from three subregions as well as the entire tumor using pyradiomics. Five machine learning methods were employed to construct Habitat models based on habitat features and Rad models based on traditional radiomic features. The predictive performance of the models was compared using ROC curves, DCA curves, and calibration curves.ResultsMultivariate Cox analyses from both our center and the TCGA database indicated that high Tim-3 expression is an independent risk factor for poor prognosis in HCC patients. Higher levels of Tim-3 expression were significantly associated with worse prognosis. Among the ten models evaluated, the Habitat model constructed using the LightGBM algorithm showed the best performance in predicting Tim-3 expression status (training set vs. test set AUC 0.866 vs. 0.824).DiscussionThis study confirmed the importance of Tim-3 as a prognostic marker in HCC. The habitat radiomics model we developed effectively predicted intratumoral Tim-3 infiltration, providing valuable insights for the evaluation of ICI therapy in HCC patients.

Aneuploidy as a driver of human cancer.

Aneuploidy, an abnormal chromosome composition, is a major contributor to cancer development and progression and an important determinant of cancer therapeutic responses and clinical outcomes. Despite being recognized as a hallmark of human cancer, the exact role of aneuploidy as a 'driver' of cancer is still largely unknown. Identifying the specific genetic elements that underlie the recurrence of common aneuploidies remains a major challenge of cancer genetics. In this Review, we discuss recurrent aneuploidies and their function as drivers of tumor development. We then delve into the context-dependent identification and functional characterization of the driver genes underlying driver aneuploidies and examine emerging strategies to uncover these driver genes using cancer genomics data and cancer models. Lastly, we explore opportunities for targeting driver aneuploidies in cancer by leveraging the functional consequences of these common genetic alterations.

A heterogeneous graph transformer framework for accurate cancer driver gene prediction and downstream analysis

Accurately predicting cancer driver genes remains a formidable challenge amidst the burgeoning volume and intricacy of cancer genomic data. In this investigation, we propose HGTDG, an innovative heterogeneous graph transformer framework tailored for precisely predicting cancer driver genes and exploring downstream tasks. A heterogeneous graph construction module is central to the framework, which assembles a gene-protein heterogeneous network leveraging the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interactions sourced from the STRING (search tool for recurring instances of neighboring genes) database. Moreover, our framework introduces a pioneering heterogeneous graph transformer module, harnessing multi-head attention mechanisms for nuanced node embedding. This transformative module proficiently captures distinct representations for both nodes and edges, thereby enriching the model's predictive capacity. Subsequently, the generated node embeddings are seamlessly integrated into a classification module, facilitating the discrimination between driver and non-driver genes. Our experimental findings evince the superiority of HGTDG over existing methodologies, as evidenced by the enhanced performance metrics, including the area under the receiver operating characteristic curves (AUROC) and the area under the precision-recall curves (AUPRC). Furthermore, the downstream analysis utilizing the newly identified cancer driver genes underscores the efficacy and versatility of our proposed framework.

Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.

Variable selection methods have been extensively developed for and applied to cancer genomics data to identify important omics features associated with complex disease traits, including cancer outcomes. However, the reliability and reproducibility of the findings are in question if valid inferential procedures are not available to quantify the uncertainty of the findings. In this article, we provide a gentle but systematic review of high-dimensional frequentist and Bayesian inferential tools under sparse models which can yield uncertainty quantification measures, including confidence (or Bayesian credible) intervals, p values and false discovery rates (FDR). Connections in high-dimensional inferences between the two realms have been fully exploited under the "unpenalized loss function + penalty term" formulation for regularization methods and the "likelihood function × shrinkage prior" framework for regularized Bayesian analysis. In particular, we advocate for robust Bayesian variable selection in cancer genomics studies due to its ability to accommodate disease heterogeneity in the form of heavy-tailed errors and structured sparsity while providing valid statistical inference. The numerical results show that robust Bayesian analysis incorporating exact sparsity has yielded not only superior estimation and identification results but also valid Bayesian credible intervals under nominal coverage probabilities compared with alternative methods, especially in the presence of heavy-tailed model errors and outliers.

POU4F3 Is a Sensitive and Specific Marker of Merkel Cell Carcinoma

Although of therapeutic importance, a single sensitive and specific immunostain to distinguish Merkel cell carcinoma (MCC) from mimics is not currently available. In addition, single tumor cells are difficult to detect in sentinel lymph node biopsy. Leveraging publicly available data sets of 9264 solid tumors and >600,000 single-cell transcriptomes, we identified POU4F3 to be a specific marker of MCC. Analyses of Pan-Cancer RNA bulk sequencing data of 24 tumor types from Tumor Cancer Genomic Atlas data sets as well as non-Tumor Cancer Genomic Atlas small cell lung carcinoma and MCC data sets confirmed POU4F3 specificity for MCC. Single-cell RNA-sequencing analyses also confirmed the lack of POU4F3 expression in lung small cell carcinoma as well as a variety of normal tissues. Nuclear POU4F3 immunohistochemical expression was noted in 98.7% of 153 MCCs and in only 1.7% of mimics (3 of 180 cases, including 95 small cell carcinomas, of which 55 were from lungs and the remainder from other sites). Three POU4F3-positive non-MCC cases were from lungs (2 cases) and vagina (1 case). All 153 tested MCC cases were negative for ASCL1, a key transcriptional regulator highly expressed in small cell lung carcinoma. NeuroD1 was seen in a subset of MCC cases (20.9%, 32/153). POU4F3 immunostain was performed on 29 sentinel lymph nodes, and strong POU4F3 nuclear expression facilitated the ease of metastasis detection, even single tumor cells. Our study built on prior works shows that POU4F3 is a sensitive and specific clinical marker of MCC.

Design and implementation of an asynchronous online course-based undergraduate research experience (CURE) in computational genomics.

As genomics technologies advance, there is a growing demand for computational biologists trained for genomics analysis but instructors face significant hurdles in providing formal training in computer programming, statistics, and genomics to biology students. Fully online learners represent a significant and growing community that can contribute to meet this need, but they are frequently excluded from valuable research opportunities which mostly do not offer the flexibility they need. To address these opportunity gaps, we developed an asynchronous course-based undergraduate research experience (CURE) for computational genomics specifically for fully online biology students. We generated custom learning materials and leveraged remotely accessible computational tools to address 2 novel research questions over 2 iterations of the genomics CURE, one testing bioinformatics approaches and one mining cancer genomics data. Here, we present how the instructional team distributed analysis needed to address these questions between students over a 7.5-week CURE and provided concurrent training in biology and statistics, computer programming, and professional development. Scores from identical learning assessments administered before and after completion of each CURE showed significant learning gains across biology and coding course objectives. Open-response progress reports were submitted weekly and identified self-reported adaptive coping strategies for challenges encountered throughout the course. Progress reports identified problems that could be resolved through collaboration with instructors and peers via messaging platforms and virtual meetings. We implemented asynchronous communication using the Slack messaging platform and an asynchronous journal club where students discussed relevant publications using the Perusall social annotation platform. The online genomics CURE resulted in unanticipated positive outcomes, including students voluntarily discussing plans to continue research after the course. These outcomes underscore the effectiveness of this genomics CURE for scientific training, recruitment and student-mentor relationships, and student successes. Asynchronous genomics CUREs can contribute to a more skilled, diverse, and inclusive workforce for the advancement of biomedical science.

Leveraging hybrid 1D-CNN and RNN approach for classification of brain cancer gene expression

Leveraging deep learning (DL) approaches in genomics data has led to significant advances in cancer prediction. The continuous availability of gene expression datasets over the preceding years has made them one of the most accessible sources of genome-wide data, advancing cancer bioinformatics research and advanced prediction of cancer genomic data. To contribute to this topic, the proposed work is based on DL prediction in both convolutional neural network (CNN) and recurrent neural network (RNN) for five classes in brain cancer using gene expression data obtained from Curated Microarray Database (CuMiDa). This database is used for cancer classification and is publicly accessible on the official CuMiDa website. This paper implemented DL approaches using a One Dimensional-Convolutional Neural Network (1D-CNN) followed by an RNN classifier with and without Bayesian hyperparameter optimization (BO). The accuracy of this hybrid model combination of (BO + 1D-CNN + RNN) produced the highest classification accuracy of 100% instead of the 95% for the ML model in prior work and 90% for the (1D-CNN + RNN) algorithm considered in the paper. Therefore, the classification of brain cancer gene expression according to the hybrid model (BO + 1D-CNN + RNN) provides more accurate and useful assessments for patients with different types of brain cancers. Thus, gene expression data are used to create a DL classification-based- hybrid model that will hold senior promise in the treatment of brain cancer.

The Psychoneurologic Symptom Cluster and Its Association With Breast Cancer Genomic Instability.

To phenotype the psychoneurologic (PN) symptom cluster in individuals with metastatic breast cancer and associate those phenotypes with individual characteristics and cancer genomic variables from circulating tumor DNA. This study included 201 individuals with metastatic breast cancer recruited in western Pennsylvania. A descriptive, cross-sectional design was used. Symptom data were collected via the MD Anderson Symptom Inventory, and cancer genomic data were collected via ultra-low-pass whole-genome sequencing of circulating tumor DNA from participant blood. Three distinct PN symptom phenotypes were described in a population with metastatic breast cancer: mild symptoms, moderate symptoms, and severe mood-related symptoms. Breast cancer TP53 deletion was significantly associated with membership in a moderate to severe symptoms phenotype (p = 0.013). Specific cancer genomic changes associated with increased genomic instability may be predictive of PN symptoms. This finding may enable proactive treatment or reveal new therapeutic targets for symptom management.

Modeling metastatic progression from cross-sectional cancer genomics data.

Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation. We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations. All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN.

Exploring the role of UMP as a molecular marker in the tumor microenvironment of lung adenocarcinoma.

e20064 Background: Currently, there is a relatively extensive research emphasis on tumor microenvironment based on single-cell transcriptomics. However, the investigation of the metabolic aspects of tumor microenvironment is relatively rare due to technological limitations. To address this gap, our study employes scFEA and MEBOCOST, which are metabolic level prediction tools based on single-cell transcriptomics, to explore the metabolic landscape of tumor microenvironment in lung adenocarcinoma(LUAD). Methods: This study utilized four single-cell sequencing (scRNA-seq) datasets, two targeted metabolomics datasets, and the TCGA-LUAD RNA sequencing dataset obtained from the Cancer Genomics Data Portal. To perform dimensionality reduction, the Seurat package was employed. Cell annotation was carried out using scType, followed by manual correction using the FindAllMarkers function. Tumor cell clusters were identified by utilizing tumor marker genes and the R package inferCNV. Single-cell metabolic levels were inferred using scFEA. The TCGA-LUAD datasets underwent analysis to estimate and analyze immune levels using estimate and cibersort algorithms. Additionally, scFEA was used for metabolic level analysis, and survival analysis was conducted. UMP differential expression analysis was performed on the targeted metabolomics datasets. Results: Across all four single-cell sequence datasets, metabolites such as (GlcNAc)4 (Man)3 (Asn)1[G00015], B-Alanine[C00099], and UMP[C00105] exhibited significantly higher levels in immune cells compared to tumor cells (P < 0.05). These findings were further validated using the TCGA-LUAD datasets. Cox regression analysis was performed using immune scores and tumor purity scores and based on the median value of risk scores, high and low-risk groups were distinguished. it was observed that UMP levels were higher in the low-risk group. Moreover, higher UMP levels were associated with elevated immune levels and decreased tumor purity. Analysis of targeted metabolomics datasets from various pan-cancer cell lines revealed that UMP expression level in tumor cells was lower than that in B and T cells. Furthermore, in different subtypes of LUAD, including AAH, AIS, MIA, and IAC, UMP expression in tissues gradually decreased as the severity of the subtype increased (P < 0.05). Conclusions: The study revealed that UMP metabolites are expressed at higher levels in immune cells compared to tumor cells within LUAD. Moreover, UMP expression levels showed significant variations among different disease severities of LUAD. These findings indicate that UMP has the potential to serve as a molecular marker for distinguishing between different disease severities. Further research is needed to investigate the underlying mechanisms and validate the diagnostic usefulness of UMP in LUAD.

Plasma ctDNA enhances the tissue-based detection of oncodriver mutations in colorectal cancer

PurposeThe advent of circulating tumor DNA (ctDNA) technology has provided a convenient and noninvasive means to continuously monitor cancer genomic data, facilitating personalized cancer treatment. This study aimed to evaluate the supplementary benefits of plasma ctDNA alongside traditional tissue-based next-generation sequencing (NGS) in identifying targetable mutations and tumor mutational burden (TMB) in colorectal cancers (CRC).MethodsOur study involved 76 CRC patients, collecting both tissue and plasma samples for NGS. We assessed the concordance of gene mutational status between ctDNA and tissue, focusing on actionable genes such as KRAS, NRAS, PIK3CA, BRAF, and ERBB2. Logistic regression analysis was used to explore variables associated with discordance and positive mutation rates.ResultsIn total, 26 cancer-related genes were identified. The most common variants in tumor tissues and plasma samples were in APC (57.9% vs 19.7%), TP53 (55.3% vs 22.4%) and KRAS (47.4% vs 43.4%). Tissue and ctDNA showed an overall concordance of 73.53% in detecting actionable gene mutations. Notably, plasma ctDNA improved detection for certain genes and gene pools. Variables significantly associated with discordance included gender and peritoneal metastases. TMB analysis revealed a higher detection rate in tissues compared to plasma, but combining both increased detection.ConclusionsOur study highlights the importance of analyzing both tissue and plasma for detecting actionable mutations in CRC, with plasma ctDNA offering added value. Discordance is associated with gender and peritoneal metastases, and TMB analysis can benefit from a combination of tissue and plasma data. This approach provides valuable insights for personalized CRC treatment.

NestedBD: Bayesian inference of phylogenetic trees from single-cell copy number profiles under a birth-death model.

Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via "bulk sequencing," the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution. We introduce a new birth-death evolutionary model of CNAs and a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data. We evaluated NestedBD's performance using simulated data sets, benchmarking its accuracy against traditional phylogenetic tools as well as state-of-the-art methods. The results show that NestedBD infers more accurate topologies and branch lengths, and that the birth-death model can improve the accuracy of copy number estimation. And when applied to biological data sets, NestedBD infers plausible evolutionary histories of two colorectal cancer samples. NestedBD is available at https://github.com/Androstane/NestedBD .

Abstract 7406: Visualization and analysis of cancer genomics data using UCSC Xena

Abstract UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. We support Mac, Windows, and Linux. Questions Xena can help you answer:* Is overexpression of this gene associated with lower/higher survival?* What genes are differentially expressed between these two groups of samples?* What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, GSEA, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. New features include: * Genome-wide differential gene expression analysis * GSEA analysis * Select samples directly from the screen for filtering and creating subgroups * Loading of Microsoft Excel files Our beta prototype site for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site. If you use us please cite our publication in Nature Biotechnology: https://www.nature.com/articles/s41587-020-0546-8 Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7406.

Abstract 921: The value of cancer genomic data sharing

Abstract Our understanding of and ability to combat cancer relies on the generation, analysis, and sharing of data. The Epidemiology and Genomics Research Program in the National Cancer Institute’s Division of Cancer Control and Population Sciences (DCCPS) has supported generation of large genomic data sets and advocated for and/or required broad sharing of this data. One of DCCPS’s scientific priorities is focused on data strategies namely, “the use, enhancement, expansion, and accessibility of new and existing DCCPS-supported data resources to accelerate cancer control research” (https://cancercontrol.cancer.gov/overview-highlights/2022/future_directions.html).EGRP funded the Genetic Associations and Mechanisms in Oncology (GAME-ON) Initiative, a multidisciplinary collaboration of researchers focused on understanding the genetic architecture, biology, and clinical and epidemiologic utility of common cancer risk variants. This program focused on 5 common cancer types - breast, colorectal, lung, ovarian, and prostate - and required research that incorporated multiple research domains and sharing of any data generated. A major accomplishment of this program was development of the OncoArray genotyping chip, which used information from genetic epidemiology studies, biological/functional analyses, and clinical and epidemiologic insights to create a comprehensive, high-density genotyping array for pan-cancer studies that has led to the discovery of hundreds of new cancer predisposition variants. This chip was used in a massive, coordinated genotyping effort that culminated in the generation of 7 datasets comprising genotyping data for nearly 500,000 individuals. All the data were deposited in dbGaP between 2014 and 2021 and are available (via controlled access) to any investigators. To understand the secondary use of these data, we determined the number of times the data had been downloaded, who downloaded the data, and types of research for which it was used. We also identified publications using OncoArray data and characterized the publications by research type, cancer type, and other details. Through this analysis, we explore how well the GAME-ON initiative and the OncoArray projects promote the DCCPS scientific priority of optimizing secondary research by making the data findable, accessible, interoperable, and reusable (FAIR). Citation Format: Stefanie Nelson, Charlisse Caga Anan, Elizabeth Gillanders. The value of cancer genomic data sharing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 921.

Proteomic Analysis Highlights the Impact of the Sphingolipid Metabolizing Enzyme β-Galactosylceramidase on Mitochondrial Plasticity in Human Melanoma.

Mitochondrial plasticity, marked by a dynamism between glycolysis and oxidative phosphorylation due to adaptation to genetic and microenvironmental alterations, represents a characteristic feature of melanoma progression. Sphingolipids play a significant role in various aspects of cancer cell biology, including metabolic reprogramming. Previous observations have shown that the lysosomal sphingolipid-metabolizing enzyme β-galactosylceramidase (GALC) exerts pro-oncogenic functions in melanoma. Here, mining the cBioPortal for a Cancer Genomics data base identified the top 200 nuclear-encoded genes whose expression is negatively correlated with GALC expression in human melanoma. Their categorization indicated a significant enrichment in Gene Ontology terms and KEGG pathways related to mitochondrial proteins and function. In parallel, proteomic analysis by LC-MS/MS of two GALC overexpressing human melanoma cell lines identified 98 downregulated proteins when compared to control mock cells. Such downregulation was confirmed at a transcriptional level by a Gene Set Enrichment Analysis of the genome-wide expression profiling data obtained from the same cells. Among the GALC downregulated proteins, we identified a cluster of 42 proteins significantly associated with GO and KEGG categorizations related to mitochondrion and energetic metabolism. Overall, our data indicate that changes in GALC expression may exert a significant impact on mitochondrial plasticity in human melanoma cells.

Potts Hamiltonian Models and Molecular Dynamics Free Energy Simulations for Predicting the Impact of Mutations on Protein Kinase Stability.

Single-point mutations in kinase proteins can affect their stability and fitness, and computational analysis of these effects can provide insights into the relationships among protein sequence, structure, and function for this enzyme family. To assess the impact of mutations on protein stability, we used a sequence-based Potts Hamiltonian model trained on a kinase family multiple-sequence alignment (MSA) to calculate the statistical energy (fitness) effects of mutations and compared these against relative folding free energies (ΔΔGs) calculated from all-atom molecular dynamics free energy perturbation (FEP) simulations in explicit solvent. The fitness effects of mutations in the Potts model (ΔEs) showed good agreement with experimental thermostability data (Pearson r = 0.68), similar to the correlation we observed with ΔΔGs predicted from structure-based relative FEP simulations. Recognizing the possible advantages of using Potts models to rapidly estimate protein stability effects of kinase mutations seen in cancer genomics data, we used the Potts statistical energy model to estimate the stability effects of 65 conservative and nonconservative mutations across three distinct kinases (Wee1, Abl1, and Cdc7) with somatic mutations reported in the Genomic Data Commons (GDC) database. The ΔEs of these mutations calculated from the Potts model are consistent with the corresponding ΔΔGs from FEP simulations (Pearson ratio of 0.72). The agreement between these methods suggests that the Potts model may be used as a sequence-based tool for high-throughput screening of mutational effects as part of a computational pipeline for predicting the stability effects of mutations. We also demonstrate how the scalability of the fitness-based Potts model calculations permits analyses that are not easily accessed using FEP simulations. To this end, we employed site-saturation mutagenesis in the Potts model in order to investigate the relative stability effects of mutations seen in different cancer evolutionary scenarios. We used this approach to analyze the effects of drug pressure in Abl kinase by contrasting the relative fitness penalties of somatic mutations seen in miscellaneous cancer types with those calculated for mutations associated with cancer drug resistance. We observed that, in contrast to somatic mutations of Abl seen in various tumors that appear to have evolved neutrally, cancer mutations that evolved under drug pressure in Abl-targeted therapies tend to preserve enzyme stability.

Privacy-Preserving Identification of Cancer Subtype-Specific Driver Genes Based on Multigenomics Data with Privatedriver.

Identifying cancer subtype-specific driver genes from a large number of irrelevant passengers is crucial for targeted therapy in cancer treatment. Recently, the rapid accumulation of large-scale cancer genomics data from multiple institutions has presented remarkable opportunities for identification of cancer subtype-specific driver genes. However, the insufficient subtype samples, privacy issues, and heterogenous of aberration events pose great challenges in precisely identifying cancer subtype-specific driver genes. To address this, we introduce privatedriver, the first model for identifying subtype-specific driver genes that integrates genomics data from multiple institutions in a data privacy-preserving collaboration manner. The process of identifying subtype-specific cancer driver genes using privatedriver involves the following two steps: genomics data integration and collaborative training. In the integration process, the aberration events from multiple genomics data sources are combined for each institution using the forward and backward propagation method of NetICS. In the collaborative training process, each institution utilizes the federated learning framework to upload encrypted model parameters instead of raw data of all institutions to train a global model by using the non-negative matrix factorization algorithm. We applied privatedriver on head and neck squamous cell and colon cancer from The Cancer Genome Atlas website and evaluated it with two benchmarks using macro-Fscore. The comparison analysis demonstrates that privatedriver achieves comparable results to centralized learning models and outperforms most other nonprivacy preserving models, all while ensuring the confidentiality of patient information. We also demonstrate that, for varying predicted driver gene distributions in subtype, our model fully considers the heterogeneity of subtype and identifies subtype-specific driver genes corresponding to the given prognosis and therapeutic effect. The success of privatedriver reveals the feasibility and effectiveness of identifying cancer subtype-specific driver genes in a data protection manner, providing new insights for future privacy-preserving driver gene identification studies.

TOSCCA: a framework for interpretation and testing of sparse canonical correlations.

In clinical and biomedical research, multiple high-dimensional datasets are nowadays routinely collected from omics and imaging devices. Multivariate methods, such as Canonical Correlation Analysis (CCA), integrate two (or more) datasets to discover and understand underlying biological mechanisms. For an explorative method like CCA, interpretation is key. We present a sparse CCA method based on soft-thresholding that produces near-orthogonal components, allows for browsing over various sparsity levels, and permutation-based hypothesis testing. Our soft-thresholding approach avoids tuning of a penalty parameter. Such tuning is computationally burdensome and may render unintelligible results. In addition, unlike alternative approaches, our method is less dependent on the initialization. We examined the performance of our approach with simulations and illustrated its use on real cancer genomics data from drug sensitivity screens. Moreover, we compared its performance to Penalized Matrix Analysis (PMA), which is a popular alternative of sparse CCA with a focus on yielding interpretable results. Compared to PMA, our method offers improved interpretability of the results, while not compromising, or even improving, signal discovery. The software and simulation framework are available at https://github.com/nuria-sv/toscca.

CRUX, a platform for visualising, exploringand analysing cancer genome cohort data.

To better understand how tumours develop, identify prognostic biomarkersand find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and tocreate visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (i) it has an easy-to-use graphical interface; (ii) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (iii) it allows analyses to be run locally to address data privacy concerns (though an online version is also available)and (iv) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUX's capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).

Arsenic is a potent co-mutagen of ultraviolet light

Arsenic enhances the carcinogenicity of ultraviolet radiation (UVR). However, the mechanisms of arsenic-driven oncogenesis are not well understood. Here, we utilize experimental systems to investigate the carcinogenic and mutagenic properties of co-exposure to arsenic and UVR. In vitro and in vivo exposures indicate that, by itself, arsenic is not mutagenic. However, in combination with UVR, arsenic exposure has a synergistic effect leading to an accelerated mouse skin carcinogenesis and to more than 2-fold enrichment of UVR mutational burden. Notably, mutational signature ID13, previously found only in UVR-associated human skin cancers, is observed exclusively in mouse skin tumors and cell lines jointly exposed to arsenic and UVR. This signature was not observed in any model system exposed purely to arsenic or purely to UVR, making ID13, to the best of our knowledge, the first co-exposure signature to be reported using controlled experimental conditions. Analysis of existing skin cancer genomics data reveals that only a subset of cancers harbor ID13 and these exhibit an elevated UVR mutagenesis. Our results report a unique mutational signature caused by a co-exposure to two environmental carcinogens and provide comprehensive evidence that arsenic is a potent co-mutagen and co-carcinogen of UVR.