Assessing equivalent and inverse change in genes between diverse experiments.

Background: It is important to identify when two exposures impact a molecular marker (e.g., a gene's expression) in similar ways, for example, to learn that a new drug has a similar effect to an existing drug. Currently, statistically robust approaches for making comparisons of equivalence of effect sizes obtained from two independently run treatment vs. control comparisons have not been developed. Results: Here, we propose two approaches for evaluating the question of equivalence between effect sizes of two independent studies: a bootstrap test of the Equivalent Change Index (ECI), which we previously developed, and performing Two One-Sided t-Tests (TOST) on the difference in log-fold changes directly. The ECI of a gene is computed by taking the ratio of the effect size estimates obtained from the two different studies, weighted by the maximum of the two p-values and giving it a sign indicating if the effects are in the same or opposite directions, whereas TOST is a test of whether the difference in log-fold changes lies outside a region of equivalence. We used a series of simulation studies to compare the two tests on the basis of sensitivity, specificity, balanced accuracy, and F1-score. We found that TOST is not efficient for identifying equivalently changed gene expression values (F1-score = 0) because it is too conservative, while the ECI bootstrap test shows good performance (F1-score = 0.95). Furthermore, applying the ECI bootstrap test and TOST to publicly available microarray expression data from pancreatic cancer showed that, while TOST was not able to identify any equivalently or inversely changed genes, the ECI bootstrap test identified genes associated with pancreatic cancer. Additionally, when investigating publicly available RNAseq data of smoking vs. vaping, no equivalently changed genes were identified by TOST, but ECI bootstrap test identified genes associated with smoking. Conclusion: A bootstrap test of the ECI is a promising new statistical approach for determining if two diverse studies show similarity in the differential expression of genes and can help to identify genes which are similarly influenced by a specific treatment or exposure. The R package for the ECI bootstrap test is available at https://github.com/Hecate08/ECIbootstrap.

Evolutionary Analysis of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Reveals Genomic Divergence with Implications for Universal Vaccine Efficacy.

Coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is one of the pressing contemporary public health challenges. Investigations into the genomic structure of SARS-CoV-2 may inform ongoing vaccine development efforts and/or provide insights into vaccine efficacy to fight against COVID-19. Evolutionary analysis of 540 genomes spanning 20 different countries/territories was conducted and revealed an increase in the genomic divergence across successive generations. The ancestor of the phylogeny was found to be the isolate from the 2019/2020 Wuhan outbreak. Its transmission was outlined across 20 countries/territories as per genomic similarity. Our results demonstrate faster evolving variations in the genomic structure of SARS-CoV-2 when compared to the isolates from early stages of the pandemic. Genomic alterations were predominantly located and mapped onto the reported vaccine candidates of structural genes, which are the main targets for vaccine candidates. S protein showed 34, N protein 25, E protein 2, and M protein 3 amino acid variations in 246 genomes among 540. Among identified mutations, 23 in S protein, 1 in E, 2 from M, and 7 from N protein were mapped with the reported vaccine candidates explaining the possible implications on universal vaccines. Hence, potential target regions for vaccines would be ideally chosen from the structural regions of the genome that lack high variation. The increasing variations in the genome of SARS-CoV-2 together with our observations in structural genes have important implications for the efficacy of a successful universal vaccine against SARS-CoV-2.

Improving survival prediction using a novel feature selection and feature reduction framework based on the integration of clinical and molecular data.

The accurate prediction of a cancer patient's risk of progression or death can guide clinicians in the selection of treatment and help patients in planning personal affairs. Predictive models based on patient-level data represent a tool for determining risk. Ideally, predictive models will use multiple sources of data (e.g., clinical, demographic, molecular, etc.). However, there are many challenges associated with data integration, such as overfitting and redundant features. In this paper we aim to address those challenges through the development of a novel feature selection and feature reduction framework that can handle correlated data. Our method begins by computing a survival distance score for gene expression, which in combination with a score for clinical independence, results in the selection of highly predictive genes that are non-redundant with clinical features. The survival distance score is a measure of variation of gene expression over time, weighted by the variance of the gene expression over all patients. Selected genes, in combination with clinical data, are used to build a predictive model for survival. We benchmark our approach against commonly used methods, namely lasso- as well as ridge-penalized Cox proportional hazards models, using three publicly available cancer data sets: kidney cancer (521 samples), lung cancer (454 samples) and bladder cancer (335 samples). Across all data sets, our approach built on the training set outperformed the clinical data alone in the test set in terms of predictive power with a c.Index of 0.773 vs 0.755 for kidney cancer, 0.695 vs 0.664 for lung cancer and 0.648 vs 0.636 for bladder cancer. Further, we were able to show increased predictive performance of our method compared to lasso-penalized models fit to both gene expression and clinical data, which had a c.Index of 0.767, 0.677, and 0.645, as well as increased or comparable predictive power compared to ridge models, which had a c.Index of 0.773, 0.668 and 0.650 for the kidney, lung, and bladder cancer data sets, respectively. Therefore, our score for clinical independence improves prognostic performance as compared to modeling approaches that do not consider combining non-redundant data. Future work will concentrate on optimizing the survival distance score in order to achieve improved results for all types of cancer.

Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Health Records.

Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the data contains unstructured text, presenting an obstacle to automated extraction. Natural language processing (NLP) can structure and learn from text, but NLP algorithms were not designed for the unique characteristics of EHR. Here, we propose Relevant Word Order Vectorization (RWOV) to aid with structuring. RWOV is based on finding the positional relationship between the most relevant words to predicting the class of a text. This facilitates machine learning algorithms to use the interaction of not just keywords but positional dependencies (e.g. a relevant word occurs 5 relevant words before some term of interest). As a proof-of-concept, we attempted to classify the hormone receptor status of breast cancer patients treated at the University of Kansas Medical Center, comparing RWOV to other methods using the F1 score and AUC. RWOV performed as well as, or better than other methods in all but one case. For F1 score, RWOV had a clear edge on most tasks. AUC tended to be closer, but for HER2, RWOV was significantly better for most comparisons. These results suggest RWOV should be further developed for EHR-related NLP.

Olaparib-induced Adaptive Response Is Disrupted by FOXM1 Targeting that Enhances Sensitivity to PARP Inhibition.

FOXM1 transcription factor network is activated in over 84% of cases in high-grade serous ovarian cancer (HGSOC), and FOXM1 upregulates the expression of genes involved in the homologous recombination (HR) DNA damage and repair (DDR) pathway. However, the role of FOXM1 in PARP inhibitor response has not yet been studied. This study demonstrates that PARP inhibitor (PARPi), olaparib, induces the expression and nuclear localization of FOXM1. On the basis of ChIP-qPCR, olaparib enhances the binding of FOXM1 to genes involved in HR repair. FOXM1 knockdown by RNAi or inhibition by thiostrepton decreases FOXM1 expression, decreases the expression of HR repair genes, such as BRCA1 and RAD51, and enhances sensitivity to olaparib. Comet and PARP trapping assays revealed increases in DNA damage and PARP trapping in FOXM1-inhibited cells treated with olaparib. Finally, thiostrepton decreases the expression of BRCA1 in rucaparib-resistant cells and enhances sensitivity to rucaparib. Collectively, these results identify that FOXM1 plays an important role in the adaptive response induced by olaparib and FOXM1 inhibition by thiostrepton induces "BRCAness" and enhances sensitivity to PARP inhibitors.Implications: FOXM1 inhibition represents an effective strategy to overcome resistance to PARPi, and targeting FOXM1-mediated adaptive pathways may produce better therapeutic effects for PARP inhibitors. Mol Cancer Res; 16(6); 961-73. ©2018 AACR.

Genetic Evidence for Early Peritoneal Spreading in Pelvic High-Grade Serous Cancer.

BACKGROUND: Most pelvic high-grade serous (HGS) carcinomas have been proposed to arise from tubal primaries that progress rapidly to advanced disease. However, the temporal sequence of ovarian and peritoneal metastases is not well characterized. METHODS: To establish the sequence of metastases, phylogenetic relationships among the ovarian and peritoneal carcinomas were determined from single-nucleotide variations (SNVs) in nine tumor regions from each patient with pelvic HGS carcinomas. Somatic SNVs from each tumor sample were used to reconstruct phylogenies of samples from each patient. Variant allele frequencies were used to reconstruct subclone phylogenies in each tumor sample. RESULTS: We show that pelvic HGS carcinomas are highly heterogeneous, only sharing less than 4% of somatic SNVs among all nine carcinoma implants in one patient. TP53 mutations are found in all nine carcinoma implants in each patient. The phylogenetic analyses reveal that peritoneal metastases arose from early branching events that preceded branching events for ovarian carcinomas in some patients. Finally, subclone phylogenies indicate the presence of multiple subclones at each tumor implant and early tumor clones in peritoneal implants. CONCLUSION: The genetic evidence that peritoneal implants arose before or concurrently with ovarian implants is consistent with the emerging concept of the extra-ovarian origin of pelvic HGS cancer. Our results challenge the concept of stepwise spatial progression from the fallopian primary to ovarian carcinomas to peritoneal dissemination and suggest an alternative progression model where peritoneal spreading of early clones occurs before or in parallel with ovarian metastases.

VaDiR: an integrated approach to Variant Detection in RNA.

Background: Advances in next-generation DNA sequencing technologies are now enabling detailed characterization of sequence variations in cancer genomes. With whole-genome sequencing, variations in coding and non-coding sequences can be discovered. But the cost associated with it is currently limiting its general use in research. Whole-exome sequencing is used to characterize sequence variations in coding regions, but the cost associated with capture reagents and biases in capture rate limit its full use in research. Additional limitations include uncertainty in assigning the functional significance of the mutations when these mutations are observed in the non-coding region or in genes that are not expressed in cancer tissue. Results: We investigated the feasibility of uncovering mutations from expressed genes using RNA sequencing datasets with a method called Variant Detection in RNA(VaDiR) that integrates 3 variant callers, namely: SNPiR, RVBoost, and MuTect2. The combination of all 3 methods, which we called Tier 1 variants, produced the highest precision with true positive mutations from RNA-seq that could be validated at the DNA level. We also found that the integration of Tier 1 variants with those called by MuTect2 and SNPiR produced the highest recall with acceptable precision. Finally, we observed a higher rate of mutation discovery in genes that are expressed at higher levels. Conclusions: Our method, VaDiR, provides a possibility of uncovering mutations from RNA sequencing datasets that could be useful in further functional analysis. In addition, our approach allows orthogonal validation of DNA-based mutation discovery by providing complementary sequence variation analysis from paired RNA/DNA sequencing datasets.

VCP inhibitors induce endoplasmic reticulum stress, cause cell cycle arrest, trigger caspase-mediated cell death and synergistically kill ovarian cancer cells in combination with Salubrinal.

Valosin-containing protein (VCP) or p97, a member of AAA-ATPase protein family, has been associated with various cellular functions including endoplasmic reticulum-associated degradation (ERAD), Golgi membrane reassembly, autophagy, DNA repair, and cell division. Recent studies identified VCP and ubiquitin proteasome system (UPS) as synthetic lethal targets in ovarian cancer. Here, we describe the preclinical activity of VCP inhibitors in ovarian cancer. Results from our studies suggest that quinazoline-based VCP inhibitors initiate G1 cell cycle arrest, attenuate cap-dependent translation and induce programmed cell death via the intrinsic and the extrinsic modes of apoptosis. Mechanistic studies point to the unresolved unfolded protein response (UPR) as a mechanism by which VCP inhibitors contribute to cytotoxicity. These results support an emerging concept that UPR and endoplasmic reticulum (ER) stress pathways may be targeted in ovarian cancer as a source of vulnerability. Since prolonged ER stress may result in CHOP-mediated cell death, we tested the hypothesis that VCP inhibitors act synergistically with compounds that enhance CHOP expression. Here, we show that VCP inhibitors act synergistically with Salubrinal, an inhibitor of eIF2α dephosphorylation, by enhancing CHOP expression in ovarian cancer cell lines. Our results provide a proof-of-concept that VCP inhibitors can be used as a single agent and can be synergized with compounds that enhance CHOP expression to induce cell death in ovarian cancer cells.