A Distance-Based Test of Association Between Paired Heterogeneous Genomic Data

Due to rapid technological advances, a wide range of different measurements can be obtained from a given biological sample including single nucleotide polymorphisms, copy number variation, gene expression levels, DNA methylation and proteomic profiles. Each of these distinct measurements provides the means to characterize a certain aspect of biological diversity, and a fundamental problem of broad interest concerns the discovery of shared patterns of variation across different data types. Such data types are heterogeneous in the sense that they represent measurements taken at very different scales or described by very different data structures. We propose a distance-based statistical test, the generalized RV (GRV) test, to assess whether there is a common and non-random pattern of variability between paired biological measurements obtained from the same random sample. The measurements enter the test through distance measures which can be chosen to capture particular aspects of the data. An approximate null distribution is proposed to compute p-values in closed-form and without the need to perform costly Monte Carlo permutation procedures. Compared to the classical Mantel test for association between distance matrices, the GRV test has been found to be more powerful in a number of simulation settings. We also report on an application of the GRV test to detect biological pathways in which genetic variability is associated to variation in gene expression levels in ovarian cancer samples, and present results obtained from two independent cohorts

SSA-ME Detection of cancer driver genes using mutual exclusivity by small subnetwork analysis

Because of its clonal evolution a tumor rarely contains multiple genomic alterations in the same pathway as disrupting the pathway by one gene often is sufficient to confer the complete fitness advantage. As a result, many cancer driver genes display mutual exclusivity across tumors. However, searching for mutually exclusive gene sets requires analyzing all possible combinations of genes, leading to a problem which is typically too computationally complex to be solved without a stringent a priori filtering, restricting the mutations included in the analysis. To overcome this problem, we present SSA-ME, a network-based method to detect cancer driver genes based on independently scoring small subnetworks for mutual exclusivity using a reinforced learning approach. Because of the algorithmic efficiency, no stringent upfront filtering is required. Analysis of TCGA cancer datasets illustrates the added value of SSA-ME: well-known recurrently mutated but also rarely mutated drivers are prioritized. We show that using mutual exclusivity to detect cancer driver genes is complementary to state-of-the art approaches. This framework, in which a large number of small subnetworks are being analyzed in order to solve a computationally complex problem (SSA), can be generically applied to any problem in which local neighborhoods in a network hold useful information

Development of a simple artificial intelligence method to accurately subtype breast cancers based on gene expression barcodes

>Magister Scientiae - MScINTRODUCTION: Breast cancer is a highly heterogeneous disease. The complexity of achieving an accurate diagnosis and an effective treatment regimen lies within this heterogeneity. Subtypes of the disease are not simply molecular, i.e. hormone receptor over-expression or absence, but the tumour itself is heterogeneous in terms of tissue of origin, metastases, and histopathological variability. Accurate tumour classification vastly improves treatment decisions, patient outcomes and 5-year survival rates. Gene expression studies aided by transcriptomic technologies such as microarrays and next-generation sequencing (e.g. RNA-Sequencing) have aided oncology researcher and clinician understanding of the complex molecular portraits of malignant breast tumours. Mechanisms governing cancers, which include tumorigenesis, gene fusions, gene over-expression and suppression, cellular process and pathway involvementinvolvement, have been elucidated through comprehensive analyses of the cancer transcriptome. Over the past 20 years, gene expression signatures, discovered with both microarray and RNA-Seq have reached clinical and commercial application through the development of tests such as Mammaprint®, OncotypeDX®, and FoundationOne® CDx, all which focus on chemotherapy sensitivity, prediction of cancer recurrence, and tumour mutational level. The Gene Expression Barcode (GExB) algorithm was developed to allow for easy interpretation and integration of microarray data through data normalization with frozen RMA (fRMA) preprocessing and conversion of relative gene expression to a sequence of 1's and 0's. Unfortunately, the algorithm has not yet been developed for RNA-Seq data. However, implementation of the GExB with feature-selection would contribute to a machine-learning based robust breast cancer and subtype classifier. METHODOLOGY: For microarray data, we applied the GExB algorithm to generate barcodes for normal breast and breast tumour samples. A two-class classifier for malignancy was developed through feature-selection on barcoded samples by selecting for genes with 85% stable absence or presence within a tissue type, and differentially stable between tissues. A multi-class feature-selection method was employed to identify genes with variable expression in one subtype, but 80% stable absence or presence in all other subtypes, i.e. 80% in n-1 subtypes. For RNA-Seq data, a barcoding method needed to be developed which could mimic the GExB algorithm for microarray data. A z-score-to-barcode method was implemented and differential gene expression analysis with selection of the top 100 genes as informative features for classification purposes. The accuracy and discriminatory capability of both microarray-based gene signatures and the RNA-Seq-based gene signatures was assessed through unsupervised and supervised machine-learning algorithms, i.e., K-means and Hierarchical clustering, as well as binary and multi-class Support Vector Machine (SVM) implementations. RESULTS: The GExB-FS method for microarray data yielded an 85-probe and 346-probe informative set for two-class and multi-class classifiers, respectively. The two-class classifier predicted samples as either normal or malignant with 100% accuracy and the multi-class classifier predicted molecular subtype with 96.5% accuracy with SVM. Combining RNA-Seq DE analysis for feature-selection with the z-score-to-barcode method, resulted in a two-class classifier for malignancy, and a multi-class classifier for normal-from-healthy, normal-adjacent-tumour (from cancer patients), and breast tumour samples with 100% accuracy. Most notably, a normal-adjacent-tumour gene expression signature emerged, which differentiated it from normal breast tissues in healthy individuals. CONCLUSION: A potentially novel method for microarray and RNA-Seq data transformation, feature selection and classifier development was established. The universal application of the microarray signatures and validity of the z-score-to-barcode method was proven with 95% accurate classification of RNA-Seq barcoded samples with a microarray discovered gene expression signature. The results from this comprehensive study into the discovery of robust gene expression signatures holds immense potential for further R&F towards implementation at the clinical endpoint, and translation to simpler and cost-effective laboratory methods such as qtPCR-based tests

Developing statistical and bioinformatic analysis of genomic data from tumours

Previous prognostic signatures for melanoma based on tumour transcriptomic data were developed predominantly on cohorts of AJCC (American Joint Committee on Cancer) stages III and IV melanoma. Since 92% of melanoma patients are diagnosed at AJCC stages I and II, there is an urgent need for better prognostic biomarkers to allow patient stratification for receiving early adjuvant therapies. This study uses genome-wide tumour gene expression levels and clinico-histopathological characteristics of patients from the Leeds Melanoma Cohort (LMC). Several unsupervised and supervised classification approaches were applied to the transcriptomic data, to identify biological classes of melanoma, and to develop prognostic classification models respectively. Unsupervised clustering identified six biologically distinct primary melanoma classes (LMC classes). Unlike previous molecular classes of melanoma, the LMC classes were prognostic in both the whole LMC dataset and in stage I tumours. The prognostic value of the LMC classes was replicated in an independent dataset, but insufficient data were available to replicate in an AJCC stage I subset. Supervised classification using the Random Forest (RF) approach provided improved performances when adjustments were made to deal with class imbalance, while this did not improve performance of the Support Vector Machine (SVM). However, RF and SVM had similar results overall, with RF only marginally better. Combining clinical and transcriptomic information in the RF further improved the performance of the prediction model in comparison to using clinical information alone. Finally, the agnostically derived LMC classes and the supervised RF model showed convergence in their association with outcome in some groups of patients, but not in others. In conclusion, this study reports six molecular classes of primary melanoma with prognostic value in stage I disease and overall, and a prognostic classification model that predicts outcome in primary melanoma

A gradient tree boosting and network propagation derived pan-cancer survival network of the tumor microenvironment

Predicting cancer survival from molecular data is an important aspect of biomedical research because it allows quantifying patient risks and thus individualizing therapy. We introduce XGBoost tree ensemble learning to predict survival from transcriptome data of 8,024 patients from 25 different cancer types and show highly competitive performance with state-of-the-art methods. To further improve plausibility of the machine learning approach we conducted two additional steps. In the first step, we applied pan-cancer training and showed that it substantially improves prognosis compared with cancer subtype-specific training. In the second step, we applied network propagation and inferred a pan-cancer survival network consisting of 103 genes. This network highlights cross-cohort features and is predictive for the tumor microenvironment and immune status of the patients. Our work demonstrates that pan-cancer learning combined with network propagation generalizes over multiple cancer types and identifies biologically plausible features that can serve as biomarkers for monitoring cancer survival

Netboost: boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington’s disease

State-of-the art selection methods fail to identify weak but cumulative effects of features found in many high-dimensional omics datasets. Nevertheless, these features play an important role in certain diseases. We present Netboost, a three-step dimension reduction technique. First, a boosting-based filter is combined with the topological overlap measure to identify the essential edges of the network. Second, sparse hierarchical clustering is applied on the selected edges to identify modules and finally module information is aggregated by the first principal components. We demonstrate the application of the newly developed Netboost in combination with CoxBoost for survival prediction of DNA methylation and gene expression data from 180 acute myeloid leukemia (AML) patients and show, based on cross-validated prediction error curve estimates, its prediction superiority over variable selection on the full dataset as well as over an alternative clustering approach. The identified signature related to chromatin modifying enzymes was replicated in an independent dataset, the phase II AMLSG 12-09 study. In a second application we combine Netboost with Random Forest classification and improve the disease classification error in RNA-sequencing data of Huntington's disease mice. Netboost is a freely available Bioconductor R package for dimension reduction and hypothesis generation in high-dimensional omics applications

Survival of pancreatic cancer cells lacking KRAS function

Activating mutations in the proto-oncogene KRAS are a hallmark of pancreatic ductal adenocarcinoma (PDAC), an aggressive malignancy with few effective therapeutic options. Despite efforts to develop KRAS-targeted drugs, the absolute dependence of PDAC cells on KRAS remains incompletely understood. Here we model complete KRAS inhibition using CRISPR/Cas-mediated genome editing and demonstrate that KRAS is dispensable in a subset of human and mouse PDAC cells. Remarkably, nearly all KRAS deficient cells exhibit phosphoinositide 3-kinase (PI3K)-dependent mitogen-activated protein kinase (MAPK) signaling and induced sensitivity to PI3K inhibitors. Furthermore, comparison of gene expression profiles of PDAC cells retaining or lacking KRAS reveal a role of KRAS in the suppression of metastasis-related genes. Collectively, these data underscore the potential for PDAC resistance to even the very best KRAS inhibitors and provide insights into mechanisms of response and resistance to KRAS inhibition

Time to Recurrence and Survival in Serous Ovarian Tumors Predicted from Integrated Genomic Profiles

Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ∼100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS). prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72).We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients

Topological Tumor Graphs: A Graph-Based Spatial Model to Infer Stromal Recruitment for Immunosuppression in Melanoma Histology.

Despite the advent of immunotherapy, metastatic melanoma represents an aggressive tumor type with a poor survival outcome. The successful application of immunotherapy requires in-depth understanding of the biological basis and immunosuppressive mechanisms within the tumor microenvironment. In this study, we conducted spatially explicit analyses of the stromal-immune interface across 400 melanoma hematoxylin and eosin (H&E) specimens from The Cancer Genome Atlas. A computational pathology pipeline (CRImage) was used to classify cells in the H&E specimen into stromal, immune, or cancer cells. The estimated proportions of these cell types were validated by independent measures of tumor purity, pathologists' estimate of lymphocyte density, imputed immune cell subtypes, and pathway analyses. Spatial interactions between these cell types were computed using a graph-based algorithm (topological tumor graphs, TTG). This approach identified two stromal features, namely stromal clustering and stromal barrier, which represented the melanoma stromal microenvironment. Tumors with increased stromal clustering and barrier were associated with reduced intratumoral lymphocyte distribution and poor overall survival independent of existing prognostic factors. To explore the genomic basis of these TTG-derived stromal phenotypes, we used a deep learning approach integrating genomic (copy number) and transcriptomic data, thereby inferring a compressed representation of copy number-driven alterations in gene expression. This integrative analysis revealed that tumors with high stromal clustering and barrier had reduced expression of pathways involved in naïve CD4 signaling, MAPK, and PI3K signaling. Taken together, our findings support the immunosuppressive role of stromal cells and T-cell exclusion within the vicinity of melanoma cells. SIGNIFICANCE: Computational histology-based stromal phenotypes within the tumor microenvironment are significantly associated with prognosis and immune exclusion in melanoma