Microbial gene expression analysis of healthy and cancerous esophagus uncovers bacterial biomarkers of clinical outcomes.

Local microbiome shifts are implicated in the development and progression of gastrointestinal cancers, and in particular, esophageal carcinoma (ESCA), which is among the most aggressive malignancies. Short-read RNA sequencing (RNAseq) is currently the leading technology to study gene expression changes in cancer. However, using RNAseq to study microbial gene expression is challenging. Here, we establish a new tool to efficiently detect viral and bacterial expression in human tissues through RNAseq. This approach employs a neural network to predict reads of likely microbial origin, which are targeted for assembly into longer contigs, improving identification of microbial species and genes. This approach is applied to perform a systematic comparison of bacterial expression in ESCA and healthy esophagi. We uncover bacterial genera that are over or underabundant in ESCA vs healthy esophagi both before and after correction for possible covariates, including patient metadata. However, we find that bacterial taxonomies are not significantly associated with clinical outcomes. Strikingly, in contrast, dozens of microbial proteins were significantly associated with poor patient outcomes and in particular, proteins that perform mitochondrial functions and iron-sulfur coordination. We further demonstrate associations between these microbial proteins and dysregulated host pathways in ESCA patients. Overall, these results suggest possible influences of bacteria on the development of ESCA and uncover new prognostic biomarkers based on microbial genes. In addition, this study provides a framework for the analysis of other human malignancies whose development may be driven by pathogens.

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.

Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

Multi-omics and machine learning reveal context-specific gene regulatory activities of PML::RARA in acute promyelocytic leukemia.

The PML::RARA fusion protein is the hallmark driver of Acute Promyelocytic Leukemia (APL) and disrupts retinoic acid signaling, leading to wide-scale gene expression changes and uncontrolled proliferation of myeloid precursor cells. While known to be recruited to binding sites across the genome, its impact on gene regulation and expression is under-explored. Using integrated multi-omics datasets, we characterize the influence of PML::RARA binding on gene expression and regulation in an inducible PML::RARA cell line model and APL patient ex vivo samples. We find that genes whose regulatory elements recruit PML::RARA are not uniformly transcriptionally repressed, as commonly suggested, but also may be upregulated or remain unchanged. We develop a computational machine learning implementation called Regulatory Element Behavior Extraction Learning to deconvolute the complex, local transcription factor binding site environment at PML::RARA bound positions to reveal distinct signatures that modulate how PML::RARA directs the transcriptional response.

A deep learning approach reveals unexplored landscape of viral expression in cancer.

About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.

A modeling framework for embedding-based predictions for compound-viral protein activity.

MOTIVATION: A global effort is underway to identify compounds for the treatment of COVID-19. Since de novo compound design is an extremely long, time-consuming and expensive process, efforts are underway to discover existing compounds that can be repurposed for COVID-19 and new viral diseases.We propose a machine learning representation framework that uses deep learning induced vector embeddings of compounds and viral proteins as features to predict compound-viral protein activity. The prediction model in-turn uses a consensus framework to rank approved compounds against viral proteins of interest. RESULTS: Our consensus framework achieves a high mean Pearson correlation of 0.916, mean R2 of 0.840 and a low mean squared error of 0.313 for the task of compound-viral protein activity prediction on an independent test set. As a use case, we identify a ranked list of 47 compounds common to three main proteins of SARS-COV-2 virus (PL-PRO, 3CL-PRO and Spike protein) as potential targets including 21 antivirals, 15 anticancer, 5 antibiotics and 6 other investigational human compounds. We perform additional molecular docking simulations to demonstrate that majority of these compounds have low binding energies and thus high binding affinity with the potential to be effective against the SARS-COV-2 virus. AVAILABILITY AND IMPLEMENTATION: All the source code and data is available at: https://github.com/raghvendra5688/Drug-Repurposing and https://dx.doi.org/10.17632/8rrwnbcgmx.3. We also implemented a web-server at: https://machinelearning-protein.qcri.org/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.