Deep multi-omics integration by learning correlation-maximizing representation identifies prognostically stratified cancer subtypes.

Motivation: Molecular subtyping by integrative modeling of multi-omics and clinical data can help the identification of robust and clinically actionable disease subgroups; an essential step in developing precision medicine approaches. Results: We developed a novel outcome-guided molecular subgrouping framework, called Deep Multi-Omics Integrative Subtyping by Maximizing Correlation (DeepMOIS-MC), for integrative learning from multi-omics data by maximizing correlation between all input -omics views. DeepMOIS-MC consists of two parts: clustering and classification. In the clustering part, the preprocessed high-dimensional multi-omics views are input into two-layer fully connected neural networks. The outputs of individual networks are subjected to Generalized Canonical Correlation Analysis loss to learn the shared representation. Next, the learned representation is filtered by a regression model to select features that are related to a covariate clinical variable, for example, a survival/outcome. The filtered features are used for clustering to determine the optimal cluster assignments. In the classification stage, the original feature matrix of one of the -omics view is scaled and discretized based on equal frequency binning, and then subjected to feature selection using RandomForest. Using these selected features, classification models (for example, XGBoost model) are built to predict the molecular subgroups that were identified at clustering stage. We applied DeepMOIS-MC on lung and liver cancers, using TCGA datasets. In comparative analysis, we found that DeepMOIS-MC outperformed traditional approaches in patient stratification. Finally, we validated the robustness and generalizability of the classification models on independent datasets. We anticipate that the DeepMOIS-MC can be adopted to many multi-omics integrative analyses tasks. Availability and implementation: Source codes for PyTorch implementation of DGCCA and other DeepMOIS-MC modules are available at GitHub (https://github.com/duttaprat/DeepMOIS-MC). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Real-world Application of Racial and Ethnic Imputation and Cohort Balancing Techniques to Deliver Equitable Clinical Trial Recruitment.

Enhancing diversity and inclusion in clinical trial recruitment, especially for historically marginalized populations including Black, Indigenous, and People of Color individuals, is essential. This practice ensures that generalizable trial results are achieved to deliver safe, effective, and equitable health and healthcare. However, recruitment is limited by two inextricably linked barriers - the inability to recruit and retain enough trial participants, and the lack of diversity amongst trial populations whereby racial and ethnic groups are underrepresented when compared to national composition. To overcome these barriers, this study describes and evaluates a framework that combines 1) probabilistic and machine learning models to accurately impute missing race and ethnicity fields in real-world data including medical and pharmacy claims for the identification of eligible trial participants, 2) randomized controlled trial experimentation to deliver an optimal patient outreach strategy, and 3) stratified sampling techniques to effectively balance cohorts to continuously improve engagement and recruitment metrics.

DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome.

MOTIVATION: Deciphering the language of non-coding DNA is one of the fundamental problems in genome research. Gene regulatory code is highly complex due to the existence of polysemy and distant semantic relationship, which previous informatics methods often fail to capture especially in data-scarce scenarios. RESULTS: To address this challenge, we developed a novel pre-trained bidirectional encoder representation, named DNABERT, to capture global and transferrable understanding of genomic DNA sequences based on up and downstream nucleotide contexts. We compared DNABERT to the most widely used programs for genome-wide regulatory elements prediction and demonstrate its ease of use, accuracy and efficiency. We show that the single pre-trained transformers model can simultaneously achieve state-of-the-art performance on prediction of promoters, splice sites and transcription factor binding sites, after easy fine-tuning using small task-specific labeled data. Further, DNABERT enables direct visualization of nucleotide-level importance and semantic relationship within input sequences for better interpretability and accurate identification of conserved sequence motifs and functional genetic variant candidates. Finally, we demonstrate that pre-trained DNABERT with human genome can even be readily applied to other organisms with exceptional performance. We anticipate that the pre-trained DNABERT model can be fined tuned to many other sequence analyses tasks. AVAILABILITY AND IMPLEMENTATION: The source code, pretrained and finetuned model for DNABERT are available at GitHub (https://github.com/jerryji1993/DNABERT). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Frizzled-7 Identifies Platinum-Tolerant Ovarian Cancer Cells Susceptible to Ferroptosis.

Defining traits of platinum-tolerant cancer cells could expose new treatment vulnerabilities. Here, new markers associated with platinum-tolerant cells and tumors were identified using in vitro and in vivo ovarian cancer models treated repetitively with carboplatin and validated in human specimens. Platinum-tolerant cells and tumors were enriched in ALDH+ cells, formed more spheroids, and expressed increased levels of stemness-related transcription factors compared with parental cells. Additionally, platinum-tolerant cells and tumors exhibited expression of the Wnt receptor Frizzled-7 (FZD7). Knockdown of FZD7 improved sensitivity to platinum, decreased spheroid formation, and delayed tumor initiation. The molecular signature distinguishing FZD7+ from FZD7- cells included epithelial-to-mesenchymal (EMT), stemness, and oxidative phosphorylation-enriched gene sets. Overexpression of FZD7 activated the oncogenic factor Tp63, driving upregulation of glutathione metabolism pathways, including glutathione peroxidase 4 (GPX4), which protected cells from chemotherapy-induced oxidative stress. FZD7+ platinum-tolerant ovarian cancer cells were more sensitive and underwent ferroptosis after treatment with GPX4 inhibitors. FZD7, Tp63, and glutathione metabolism gene sets were strongly correlated in the ovarian cancer Tumor Cancer Genome Atlas (TCGA) database and in residual human ovarian cancer specimens after chemotherapy. These results support the existence of a platinum-tolerant cell population with partial cancer stem cell features, characterized by FZD7 expression and dependent on the FZD7-ß-catenin-Tp63-GPX4 pathway for survival. The findings reveal a novel therapeutic vulnerability of platinum-tolerant cancer cells and provide new insight into a potential "persister cancer cell" phenotype. SIGNIFICANCE: Frizzled-7 marks platinum-tolerant cancer cells harboring stemness features and altered glutathione metabolism that depend on GPX4 for survival and are highly susceptible to ferroptosis.

FTO-Dependent N 6-Methyladenosine Modifications Inhibit Ovarian Cancer Stem Cell Self-Renewal by Blocking cAMP Signaling.

N 6-Methyladenosine (m6A) is the most abundant modification of mammalian mRNAs. RNA methylation fine tunes RNA stability and translation, altering cell fate. The fat mass- and obesity-associated protein (FTO) is an m6A demethylase with oncogenic properties in leukemia. Here, we show that FTO expression is suppressed in ovarian tumors and cancer stem cells (CSC). FTO inhibited the self-renewal of ovarian CSC and suppressed tumorigenesis in vivo, both of which required FTO demethylase activity. Integrative RNA sequencing and m6A mapping analysis revealed significant transcriptomic changes associated with FTO overexpression and m6A loss involving stem cell signaling, RNA transcription, and mRNA splicing pathways. By reducing m6A levels at the 3'UTR and the mRNA stability of two phosphodiesterase genes (PDE1C and PDE4B), FTO augmented second messenger 3', 5'-cyclic adenosine monophosphate (cAMP) signaling and suppressed stemness features of ovarian cancer cells. Our results reveal a previously unappreciated tumor suppressor function of FTO in ovarian CSC mediated through inhibition of cAMP signaling. SIGNIFICANCE: A new tumor suppressor function of the RNA demethylase FTO implicates m6A RNA modifications in the regulation of cyclic AMP signaling involved in stemness and tumor initiation.

In silico analysis of alternative splicing on drug-target gene interactions.

Identifying and evaluating the right target are the most important factors in early drug discovery phase. Most studies focus on one protein ignoring the multiple splice-variant or protein-isoforms, which might contribute to unexpected therapeutic activity or adverse side effects. Here, we present computational analysis of cancer drug-target interactions affected by alternative splicing. By integrating information from publicly available databases, we curated 883 FDA approved or investigational stage small molecule cancer drugs that target 1,434 different genes, with an average of 5.22 protein isoforms per gene. Of these, 618 genes have ≥5 annotated protein-isoforms. By analyzing the interactions with binding pocket information, we found that 76% of drugs either miss a potential target isoform or target other isoforms with varied expression in multiple normal tissues. We present sequence and structure level alignments at isoform-level and make this information publicly available for all the curated drugs. Structure-level analysis showed ligand binding pocket architectures differences in size, shape and electrostatic parameters between isoforms. Our results emphasize how potentially important isoform-level interactions could be missed by solely focusing on the canonical isoform, and suggest that on- and off-target effects at isoform-level should be investigated to enhance the productivity of drug-discovery research.

Platform-Independent Classification System to Predict Molecular Subtypes of High-Grade Serous Ovarian Carcinoma.

PURPOSE: Molecular cancer subtyping is an important tool in predicting prognosis and developing novel precision medicine approaches. We developed a novel platform-independent gene expression-based classification system for molecular subtyping of patients with high-grade serous ovarian carcinoma (HGSOC). METHODS: Unprocessed exon array (569 tumor and nine normal) and RNA sequencing (RNA-seq; 376 tumor) HGSOC data sets, with clinical annotations, were downloaded from the Genomic Data Commons portal. Sample clustering was performed by non-negative matrix factorization by using isoform-level expression estimates. The association between the subtypes and overall survival was evaluated by Cox proportional hazards regression model after adjusting for the covariates. A novel classification system was developed for HGSOC molecular subtyping. Robustness and generalizability of the gene signatures were validated using independent microarray and RNA-seq data sets. RESULTS: Sample clustering recaptured the four known The Cancer Genome Atlas molecular subtypes but switched the subtype for 22% of the cases, which resulted in significant (P = .006) survival differences among the refined subgroups. After adjusting for covariate effects, the mesenchymal subgroup was found to be at an increased hazard for death compared with the immunoreactive subgroup. Both gene- and isoform-level signatures achieved more than 92% prediction accuracy when tested on independent samples profiled on the exon array platform. When the classifier was applied to RNA-seq data, the subtyping calls agreed with the predictions made from exon array data for 95% of the 279 samples profiled by both platforms. CONCLUSION: Isoform-level expression analysis successfully stratifies patients with HGSOC into groups with differing prognosis and has led to the development of robust, platform-independent gene signatures for HGSOC molecular subtyping. The association of the refined The Cancer Genome Atlas HGSOC subtypes with overall survival, independent of covariates, enhances the clinical annotation of the HGSOC cohort.

IDH3α regulates one-carbon metabolism in glioblastoma.

Mutation or transcriptional up-regulation of isocitrate dehydrogenases 1 and 2 (IDH1 and IDH2) promotes cancer progression through metabolic reprogramming and epigenetic deregulation of gene expression. Here, we demonstrate that IDH3α, a subunit of the IDH3 heterotetramer, is elevated in glioblastoma (GBM) patient samples compared to normal brain tissue and promotes GBM progression in orthotopic glioma mouse models. IDH3α loss of function reduces tricarboxylic acid (TCA) cycle turnover and inhibits oxidative phosphorylation. In addition to its impact on mitochondrial energy metabolism, IDH3α binds to cytosolic serine hydroxymethyltransferase (cSHMT). This interaction enhances nucleotide availability during DNA replication, while the absence of IDH3α promotes methionine cycle activity, S-adenosyl methionine generation, and DNA methylation. Thus, the regulation of one-carbon metabolism via an IDH3α-cSHMT signaling axis represents a novel mechanism of metabolic adaptation in GBM.