Exploration of regression models for cancer noncoding mutation recurrence

An important impact of the genome technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Integrated with current well-studied massive knowledge and findings about the role of protein-coding mutations in cancer, demystifying the functional role of human "junk" DNA (non-protein-coding DNA) mutations for cancer development and progression is one of the most popular and promising approaches these days to improve our understanding of the complicated cellular mechanisms in cancer. In light of one recent finding that non-protein-coding driver mutations tend to be highly recurrent, in this thesis we explore three different kinds of regression models for predicting non-protein-coding mutation recurrence: generalized linear models, conventional machine learning approaches and deep neural network learning models. We compare the regression model results and find the most accurate model for non-protein-coding mutation recurrence prediction so that we can prioritize somatic mutations based on their predicted recurrence and provide insights for further biological validation and eventually improved cancer therapy.Keywords: Recurrence, Regression, Cancer, Noncoding Mutatio

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies

Whole-Genome and Transcriptome Sequencing Identified NOTCH2 and HES1 as Potential Markers of Response to Imatinib in Desmoid Tumor (Aggressive Fibromatosis): A Phase II Trial Study

Purpose: Desmoid tumor, also known as aggressive fibromatosis, is well-characterized by abnormal Wnt/β-catenin signaling. Various therapeutic options, including imatinib, are available to treat desmoid tumor. However, the molecular mechanism of why imatinib works remains unclear. Here, we describe potential roles of NOTCH2 and HES1 in clinical response to imatinib at genome and transcriptome levels. Materials and methods: We identified somatic mutations in coding and noncoding regions via whole-genome sequencing. To validate the genetic interaction with expression level in desmoid-tumor condition, we utilized large-scale whole-genome sequencing and transcriptome datasets from the Pan-Cancer Analysis of Whole Genomes project. RNA-sequencing was performed using prospective and retrospective cohort samples to evaluate the expressional relevance with clinical response. Results: Among 20 patients, four (20%) had a partial response and 14 (66.7%) had stable disease, 11 of which continued for ≥ 1 year. With gene-wise functional analyses, we detected a significant correlation between recurrent NOTCH2 noncoding mutations and clinical response to imatinib. Based on Pan-Cancer Analysis of Whole Genomes data analyses, NOTCH2 mutations affect expression levels particularly in the presence of CTNNB1 missense mutations. By analyzing RNA-sequencing with additional desmoid tumor samples, we found that NOTCH2 expression was significantly correlated with HES1 expression. Interestingly, NOTCH2 had no statistical power to discriminate between responders and non-responders. Instead, HES1 was differentially expressed with statistical significance between responders and non-responders. Conclusion: Imatinib was effective and well tolerated for advanced desmoid tumor treatment. Our results show that HES1, regulated by NOTCH2, as an indicator of sensitivity to imatinib, and an important therapeutic consideration for desmoid tumor.ope

Deep mutation modelling in cancer driver mutation and cancer driver gene detection

Cancer is a leading cause of death worldwide. Unlike its name would suggest, cancer is not a single disease. It is a group of diseases that arises from the expansion of a somatic cell clone. This expansion is thought to be a result of mutations that confer a selective advantage to the cell clone. These mutations that are advantageous to cells that result in their proliferation and escape of normal cell constraints are called driver mutations. The genes that contain driver mutations are known as driver genes. Studying these mutations and genes is important for understanding how cancer forms and evolves. Various methods have been developed that can discover these mutations and genes. This thesis focuses on a method called Deep Mutation Modelling, a deep learning based approach to predicting the probability of mutations. Deep Mutation Modelling’s output probabilities offer the possibility of creating sample and cancer type specific probability scores for mutations that reflect the pathogenicity of the mutations. Most methods in the past have made scores that are the same for all cancer types. Deep Mutation Modelling offers the opportunity to make a more personalised score. The main objectives of this thesis were to examine the Deep Mutation Modelling output as it was unknown what kind of features it has, see how the output compares against other scoring methods and how the probabilities work in mutation hotspots. Lastly, could the probabilities be used in a common driver gene discovery method. Overall, the goal was to see if Deep Mutation Modelling works and if it is competitive with other known methods. The findings indicate that Deep Mutation Modelling works in predicting driver mutations, but that it does not have sufficient power to do this reliably and requires further improvements

ANLN, TLE2 and MIR31HG transcripts in muscle invasive bladder cancer: a functional and clinical analysis based on molecular subtypes

ANLN and TLE2 are associated with cancer patient survival and progression. The impact of their gene expression on survival of patients with MIBC treated with RC and subtype association has not yet been investigated. This study provides in silico and in vitro evidence supporting the prognostic potential of ANLN and TLE2 for patients with MIBC. In the Mannheim cohort, tumors with high ANLN expression were associated with lower OS and DSS, while high TLE2 expression was associated with a favorable OS. The TCGA cohort confirmed that high ANLN and low TLE2 expression was associated with shorter OS and DFS. In both cohorts, multivariable analyses showed ANLN and TLE2 expression as independent outcome predictors. Furthermore, ANLN was more highly expressed in cell lines and patients with the basal subtype, while TLE2 expression was higher in cell lines and patients with the luminal subtype. ANLN and TLE2 are promising biomarkers for individualized BLCA therapy including cancer subclassification and informed MIBC prognosis. These results indicate that developing ANLN and TLE2 as new biomarkers will help to further optimize personalized therapy for these patients. Growing evidence supports the pivotal role of lncRNAs in the regulation of cancer development and progression. Their expression patterns and biological function in MIBC remain elusive. In this study, a decreased expression of lncRNA MIR31HG was found in BLCA cells and tissues, except in the basal subtype. In vitro experiments revealed that knockdown of MIR31HG inhibits cell proliferation, colony formation and migration in BLCA. Survival analysis showed that high expression of MIR31HG was associated with poor OS and DFS in patients with MIBC of basal subtype. Two splice variants of MIR31HG lacking exon 1 (MIR31HGΔE1) and exon 3 (MIR31HGΔE3) were identified to have specific expression patterns in different subtypes of both MIBC cohorts. MIR31HGΔE3 was highly expressed in tumors with basal subtype. After knockdown of splice variants of MIR31HG, cell proliferation and migration assays showed corresponding roles for the full-length transcript. A high expression of MIR31HGΔE1 and MIR31HGΔE3 was associated with worse OS and DFS in the Mannheim cohort. This study demonstrates that MIR31HG and its splice variants could serve as biomarkers for the classification and prognosis prediction of patients with MIBC. Taking together, this thesis provides new insights into studies for the molecular classification and relevance of RNA variants in MIBC

Typing tumors using pathways selected by somatic evolution.

Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient's tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available

The Role of Long Noncoding RNA SChLAP1 in Prostate Cancer

Prostate cancer is the most common malignancy in U.S. men, accounting for nearly 30,000 deaths annually. While the majority of prostate cancers are indolent, a subset of patients has aggressive disease. However, the molecular basis for this clinical heterogeneity remains incompletely understood. Long noncoding RNAs (lncRNAs) are an emerging class of regulatory molecules implicated in a diverse range of human malignancies. Here, SChLAP1 is identified as a novel, highly prognostic lncRNA that is expressed in 15-30% of prostate cancers. Functionally, SChLAP1 coordinates cancer cell invasion in vitro and metastatic spread in vivo. Mechanistically, SChLAP1 interacts with and antagonizes the tumor-suppressive SWI/SNF nucleosome-remodeling complex. While deleterious SWI/SNF mutations occur in 20% of all cancers, they are relatively rare in prostate cancer. Within prostate cancer, SWI/SNF mutations are associated with low SChLAP1 expression, suggesting that high SChLAP1 expression may represent a mutation-independent modality of SWI/SNF inhibition. Employing a previously described antagonistic model between SWI/SNF and Polycomb Repressive Complex 2 (PRC2), SChLAP1 is found to enhance PRC2 function in prostate cancer. Additionally, SChLAP1-expressing cells are more sensitive to pharmacologic EZH2 inhibition. Further characterization of SChLAP1 reveals a 250bp region near the 3’-end that mediates its invasive phenotype and coordinates its interaction with SWI/SNF. Additionally, SChLAP1 interacts with BRG1-containing but not BRM-containing SWI/SNF complexes, and knockdown of BRM in SChLAP1-expressing cells exposes a synthetic lethal vulnerability in prostate cancer. Finally, the largest biomarker discovery project to date in prostate cancer identifies SChLAP1 as one of the best genes for predicting metastatic progression. Characterization of SChLAP1 expression by in situ hybridization shows that SChLAP1 expression is enriched in metastatic samples. Additionally, SChLAP1 can be detected in patient urine samples and may be useful as a non-invasive biomarker. Lastly, targeting SChLAP1 with antisense oligonucleotides (ASO) suggests that directly targeting SChLAP1 may be an effective therapeutic strategy in prostate cancer. Taken together, this work defines an essential role for SChLAP1 in aggressive prostate cancer, uncovers novel aspects of lncRNA biology, and has broad implications for cancer biology.PHDMolecular & Cellular Path PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137075/1/asahu_1.pd