Despite tremendous advances in targeted therapies against lung adenocarcinoma, the majority of patients do not benefit from personalized treatments. A deeper understanding of potential therapeutic targets is crucial to increase the survival of patients. One promising target, ADAR, is amplified in 13% of lung adenocarcinomas and in-vitro studies have demonstrated the potential of its therapeutic inhibition to inhibit tumor growth. ADAR edits millions of adenosines to inosines within the transcriptome, and while previous studies of ADAR in cancer have solely focused on protein-coding edits, > 99% of edits occur in non-protein coding regions. Here, we develop a pipeline to discover the regulatory potential of RNA editing sites across the entire transcriptome and apply it to lung adenocarcinoma tumors from The Cancer Genome Atlas. This method predicts that 1413 genes contain regulatory edits, predominantly in non-coding regions. Genes with the largest numbers of regulatory edits are enriched in both apoptotic and innate immune pathways, providing a link between these known functions of ADAR and its role in cancer. We further show that despite a positive association between ADAR RNA expression and apoptotic and immune pathways, ADAR copy number is negatively associated with apoptosis and several immune cell types' signatures.

Motivation: Technologies that generate high-throughput omics data are flourishing, creating enormous, publicly available repositories of multi-omics data. As many data repositories continue to grow, there is an urgent need for computational methods that can leverage these data to create comprehensive clusters of patients with a given disease. Results: Our proposed approach creates a patient-to-patient similarity graph for each data type as an intermediate representation of each omics data type and merges the graphs through subspace analysis on a Grassmann manifold. We hypothesize that this approach generates more informative clusters by preserving the complementary information from each level of omics data. We applied our approach to The Cancer Genome Atlas (TCGA) breast cancer dataset and show that by integrating gene expression, microRNA and DNA methylation data, our proposed method can produce clinically useful subtypes of breast cancer. We then investigate the molecular characteristics underlying these subtypes. We discover a highly expressed cluster of genes on chromosome 19p13 that strongly correlates with survival in TCGA breast cancer patients and validate these results in three additional breast cancer datasets. We also compare our approach with previous integrative clustering approaches and obtain comparable or superior results.

Scatterplot matrices or SPLOMs provide a feasible method of visualizing and representing multi‐dimensional data especially for a small number of dimensions. For very high dimensional data, we introduce a novel technique to summarize a SPLOM, as a clustered matrix of glyphs, or a Glyph SPLOM. Each glyph visually encodes a general measure of dependency strength, distance correlation, and a logical dependency class based on the occupancy of the scatterplot quadrants. We present the Glyph SPLOM as a general alternative to the traditional correlation based heatmap and the scatterplot matrix in two examples: demography data from the World Health Organization (WHO), and gene expression data from developmental biology. By using both, dependency class and strength, the Glyph SPLOM illustrates high dimensional data in more detail than a heatmap but with more summarization than a SPLOM. More importantly, the summarization capabilities of Glyph SPLOM allow for the assertion of “necessity” causal relationships in the data and the reconstruction of interaction networks in various dynamic systems.

Our results suggest that mRNA and protein data possess independent biological and clinical importance, which can be leveraged to create higher-powered expression biomarkers.

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High-dimensional, localized ribonucleic acid (RNA) sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffer from high noise and dropout events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single-cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage of ST data. In this study, we present a novel two-factor smoothing technique, spatial and pattern combined smoothing (SPCS), that employs the k-nearest neighbor (kNN) technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC) and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy and biological interpretability than the ones smoothed by preexisting one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).

Background: Biomarkers predictive of response to chemotherapy are critically needed for the precise selection of treatment protocols in lung adenocarcinoma (LUAD). Aquaporins (AQPs), transmembrane water channels, are emerging targets in cancer. Recently discovered, non-ubiquitous family member aquaporin 11 (AQP11), a tissue-specific endoplasmic reticulum (ER) resident, was identified as a cellular pro-survival factor implicated in the maintenance of ER homeostasis. AQP11 is mapped to 11q13-q14 amplicon harboring oncogenic drivers and associated with poor prognosis in cancer patients. We recently showed that high AQP11 expression is an in-vitro therapeutic biomarker of cisplatin therapy, which directly interferes with AQP11 functional structure. In addition, silencing of AQP11 expression in human lung cancer cell lines significantly increased response to cisplatin treatment. We hypothesized that LUAD tumors expressing high levels of AQP11 depend on AQP11-mediated cytoprotection and would be more responsive to platinum-based chemotherapy targeting AQP11. Method: We downloaded and curated matched mRNA expression, survival, and drug response data from The Cancer Genome Atlas (TCGA) LUAD dataset (N=369). Results: Analysis of PRECOG and TCGA databases showed that high AQP11 mRNA expression is negatively prognostic in patients with LUAD. TCGA LUAD cases were categorized by AQP11 mRNA levels into high and low expression (high = AQP11 mRNA expression mean + 1 standard deviation). Patients with high tumor AQP11 mRNA expression (10.3%) had significantly worse overall survival (OS) compared to patients with low AQP11 expression (p=0.0015). An analysis of patients treated with platinum-based chemotherapy (N=74) showed that patients with high AQP11 mRNA expression (7%) had significantly higher OS then patients with LUAD expressing low levels of AQP11 (p=0.0263). Conclusions: This study identifies AQP11 as a new biomarker of OS and chemotherapy in LUAD patients. It is conceivable that lung tumors expressing high levels of AQP11 are dependent on its function and elevated AQP11 expression renders tumor resistance to microenvironment and therapy-induced stress and associates with lower OS of patients. At the same time, as cisplatin efficiently targets AQP11 functional multimeric structure, these AQP11-depending tumors are more prone to platinum based chemotherapy. This study provides a rationale for combination anti-AQP11 and chemotherapy in LUAD tumors with high AQP11 expression. Citation Format: Michael Sharpnack, David P. Carbone, Mikhail M. Dikov, Elena E. Tchekneva. Aquaporin 11 as a new predictive biomarker of overall survival and platinum-based chemotherapy response in lung adenocarcinoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2620.

Motivation Tumor specific antigen (TSA) identification in human cancer predicts response to immunotherapy and provides targets for cancer vaccine and adoptive T-cell therapies with curative potential, and TSAs that are highly expressed at the RNA level are more likely to be presented on MHC-I. Direct measurements of the RNA expression of peptides would allow for generalized prediction of TSAs. Methods HLA-I genotypes were predicted with seq2HLA. RNAseq fastq files were translated into all possible peptides of length 8-11, and peptides with high and low expressions in the tumor and control samples, respectively, were tested for their MHC-I binding potential with netMHCpan-4.0. Results A novel pipeline for TSA prediction from RNAseq was used to predict all possible unique peptides size 8-11 on previously published murine and human lung and lymphoma tumors and validated on matched tumor and control lung adenocarcinoma (LUAD) samples. We show that neoantigens predicted by exomeSeq are typically poorly expressed at the RNA level, and a fraction are expressed in matched normal samples. TSAs presented in the proteomics data have higher RNA abundance and lower MHC-I binding percentile, and these attributes are used to discover high confidence TSAs within the validation cohort. Finally, a subset of these high confidence TSAs is expressed in a majority of LUAD tumors and represent attractive vaccine targets. Availability and implementation TSAFinder is open-source software written in python and R. It is licensed under CC-BY-NC-SA and can be downloaded at https://github.com/RNAseqTSA.