Transfer RNAs (tRNAs) are central players in protein synthesis, which in Eukarya need to be delivered from the nucleus to the cytoplasm by specific transport receptors, most of which belong to the evolutionarily conserved beta-importin family. Based on the available literature, we identified two candidates, Xpo-t and Xpo-5 for tRNA export in Trypanosoma brucei. However, down-regulation of expression of these genes did not disrupt the export of tRNAs to the cytoplasm. In search of alternative pathways, we tested the mRNA export complex Mex67-Mtr2, for a role in tRNA nuclear export, as described previously in yeast. Down-regulation of either exporter affected the subcellular distribution of tRNAs. However, contrary to yeast, TbMex67 and TbMtr2 accumulated different subsets of tRNAs in the nucleus. While TbMtr2 perturbed the export of all the tRNAs tested, silencing of TbMex67, led to the nuclear accumulation of tRNAs that are typically modified with queuosine. In turn, inhibition of tRNA nuclear export also affected the levels of queuosine modification in tRNAs. Taken together, the results presented demonstrate the dynamic nature of tRNA trafficking in T. brucei and its potential impact not only on the availability of tRNAs for protein synthesis but also on their modification status.

Alterations in immune-related pathways are common hallmarks of cancer. A comprehensive understanding of how cancer mutations rewire immune signaling networks and functional output across cancer types is instrumental to realize the full potential of immunotherapy. Here, we systematically interrogated somatic mutations involved in immune signaling that alter immune responses in patients with cancer. To do so, we developed a Network-based Integrative model to Prioritize Potential immune respondER genes (NIPPER). Identified mutations were enriched in essential protein domains and genes identified by NIPPER were associated with responsiveness to multiple immunotherapy modalities. These genes were used to devise an interactome network propagation framework integrated with drug-associated gene signatures to identify potential immunomodulatory drug candidates. Together, our systems-level analysis results help interpret the heterogeneous immune responses among patients and serve as a resource for future functional studies and targeted therapeutics. Significance: This study demonstrates that integration of multi-omics data can help identify critical molecular determinants for effective targeted therapeutics.

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the interaction profile mediated by a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath (edgetic Mutation-mediated Pathway perturbations), a network-based computational method to identify candidate ‘edgetic’ mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect.

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Effective and precise classification of breast cancer patients for their disease risks is critical to improve early diagnosis and patient survival. In the recent past, a significant amount of multi-omics data derived from cancer patients has emerged. However, a robust framework for integrating multi-omics data to subgroup cancer patients and predict survival prognosis is still lacking. In addition, effective therapeutic targets for treating breast cancer patients with poor prognoses are in dire need. To begin to resolve this difficulty, we developed and optimized a sophisticated deep learning-based model in breast cancer that can accurately stratify patients based on their prognosis. We built a survival-associated predictive framework integrating transcription profile, miRNA expression, somatic mutations, copy number variation, DNA methylation and protein expression. This framework achieved promising performance in distinguishing high-risk breast cancer patients from those with good prognoses. Furthermore, we constructed multiple fully connected neural networks that are trained on prioritized multi-omics signatures or even only potential single-omics signatures, based on our customized scoring system. Together, the landmark multi-omics signatures we identified may serve as potential therapeutic targets in breast cancer.

Clinical evidence suggests that right-sided colorectal cancer (CRC) tends to have a worse prognosis compared to left-sided CRC. The distinct functional mechanisms underlying this “sidedness” remain cryptic and poorly understood. Thus, molecular classification of CRC is of paramount in targeting specific subtypes and personalizing treatment for patients. To identify targetable molecular features to improve treatment regimens, we performed multi-faceted network analyses of hundreds of CRC patients. In our network model, epigenetic features, differential expression of transcript abundance, and mutational frequency were used to identify widespread gene dysregulation and perturbed pathways to reveal disrupted networks distinct to right or left sided colorectal cancer. We discovered histone related genes and distinct epigenetic signals are at play in right-sided CRC. Additionally, our studies reveal “at risk” patient characteristics suggestive of survival differences, to improve diagnostic predictors based on CRC sidedness. Overall, our integrative approach serves to link molecular profiles distinct between left and right sided colorectal cancer, while improving upon previous models of CRC and paving the way towards identifying more effective treatment alternatives from a previously overlooked, readily characterized subtype of colorectal cancer. Citation Format: Brandon Burgman, Xingxin Pan, S. Stephen Yi. Tumor sideomics: spatial characteristics and functional deregulation in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB177.

<p>Table S4. Gene mutations that perturb binding of HLAs.</p>