Developing genomic models for cancer prevention and treatment stratification

Malignant tumors remain one of the leading causes of mortality with over 8.2 million deaths worldwide in 2012. Over the last two decades, high-throughput profiling of the human transcriptome has become an essential tool to investigate molecular processes involved in carcinogenesis. In this thesis I explore how gene expression profiling (GEP) can be used in multiple aspects of cancer research, including prevention, patient stratification and subtype discovery. The first part details how GEP could be used to supplement or even replace the current gold standard assay for testing the carcinogenic potential of chemicals. This toxicogenomic approach coupled with a Random Forest algorithm allowed me to build models capable of predicting carcinogenicity with an area under the curve of up to 86.8% and provided valuable insights into the underlying mechanisms that may contribute to cancer development. The second part describes how GEP could be used to stratify heterogeneous populations of lymphoma patients into therapeutically relevant disease sub-classes, with a particular focus on diffuse large B-cell lymphoma (DLBCL). Here, I successfully translated established biomarkers from the Affymetrix platform to the clinically relevant Nanostring nCounter© assay. This translation allowed us to profile custom sets of transcripts from formalin-fixed samples, transforming these biomarkers into clinically relevant diagnostic tools. Finally, I describe my effort to discover tumor samples dependent on altered metabolism driven by oxidative phosphorylation (OxPhos) across multiple tissue types. This work was motivated by previous studies that identified a therapeutically relevant OxPhos sub-type in DLBCL, and by the hypothesis that this stratification might be applicable to other solid tumor types. To that end, I carried out a transcriptomics-based pan-cancer analysis, derived a generalized PanOxPhos gene signature, and identified mTOR as a potential regulator in primary tumor samples. High throughput GEP coupled with statistical machine learning methods represent an important toolbox in modern cancer research. It provides a cost effective and promising new approach for predicting cancer risk associated to chemical exposure, it can reduce the cost of the ever increasing drug development process by identifying therapeutically actionable disease subtypes, and it can increase patients’ survival by matching them with the most effective drugs.2016-12-01T00:00:00

GCOD - GeneChip Oncology Database

<p>Abstract</p> <p>Background</p> <p>DNA microarrays have become a nearly ubiquitous tool for the study of human disease, and nowhere is this more true than in cancer. With hundreds of studies and thousands of expression profiles representing the majority of human cancers completed and in public databases, the challenge has been effectively accessing and using this wealth of data.</p> <p>Description</p> <p>To address this issue we have collected published human cancer gene expression datasets generated on the Affymetrix GeneChip platform, and carefully annotated those studies with a focus on providing accurate sample annotation. To facilitate comparison between datasets, we implemented a consistent data normalization and transformation protocol and then applied stringent quality control procedures to flag low-quality assays.</p> <p>Conclusion</p> <p>The resulting resource, the GeneChip Oncology Database, is available through a publicly accessible website that provides several query options and analytical tools through an intuitive interface.</p

iBBiG: iterative binary bi-clustering of gene sets

Motivation: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods. Results: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set—phenotype association that predicted tumor metastases within tumor subtypes

Spartalizumab or placebo in combination with dabrafenib and trametinib in patients with BRAF\textit{BRAF}V600-mutant melanoma: exploratory biomarker analyses from a randomized phase 3 trial (COMBI-i)

BackgroundThe randomized phase 3 COMBI-i trial did not meet its primary endpoint of improved progression-free survival (PFS) with spartalizumab plus dabrafenib and trametinib (sparta-DabTram) vs placebo plus dabrafenib and trametinib (placebo-DabTram) in the overall population of patients with unresectable/metastatic $\textit{BRAF}$V600-mutant melanoma. This prespecified exploratory biomarker analysis was performed to identify subgroups that may derive greater treatment benefit from sparta-DabTram.MethodsIn COMBI-i (ClinicalTrials.gov, NCT02967692), 532 patients received spartalizumab 400 mg intravenously every 4 weeks plus dabrafenib 150 mg orally two times daily and trametinib 2 mg orally one time daily or placebo-DabTram. Baseline/on-treatment pharmacodynamic markers were assessed via flow cytometry-based immunophenotyping and plasma cytokine profiling. Baseline programmed death ligand 1 (PD-L1) status and T-cell phenotype were assessed via immunohistochemistry; $\textit{BRAF}$V600 mutation type, tumor mutational burden (TMB), and circulating tumor DNA (ctDNA) via DNA sequencing; gene expression signatures via RNA sequencing; and CD4$^{+}$/CD8$^{+}$ T-cell ratio via immunophenotyping.ResultsExtensive biomarker analyses were possible in approximately 64% to 90% of the intention-to-treat population, depending on sample availability and assay. Subgroups based on PD-L1 status/TMB or T-cell inflammation did not show significant differences in PFS benefit with sparta-DabTram vs placebo-DabTram, although T-cell inflammation was prognostic across treatment arms. Subgroups defined by $\textit{BRAF}$V600K mutation (HR 0.45 (95% CI 0.21 to 0.99)), detectable ctDNA shedding (HR 0.75 (95% CI 0.58 to 0.96)), or CD4$^{+}$/CD8$^{+}$ ratio above median (HR 0.58 (95% CI 0.40 to 0.84)) derived greater PFS benefit with sparta-DabTram vs placebo-DabTram. In a multivariate analysis, ctDNA emerged as strongly prognostic (p=0.007), while its predictive trend did not reach significance; in contrast, CD4$^{+}$/CD8$^{+}$ ratio was strongly predictive (interaction p=0.0131).ConclusionsThese results support the feasibility of large-scale comprehensive biomarker analyses in the context of a global phase 3 study. T-cell inflammation was prognostic but not predictive of sparta-DabTram benefit, as patients with high T-cell inflammation already benefit from targeted therapy alone. Baseline ctDNA shedding also emerged as a strong independent prognostic variable, with predictive trends consistent with established measures of disease burden such as lactate dehydrogenase levels. CD4$^{+}$/CD8$^{+}$ T-cell ratio was significantly predictive of PFS benefit with sparta-DabTram but requires further validation as a biomarker in melanoma. Taken together with previous observations, further study of checkpoint inhibitor plus targeted therapy combination in patients with higher disease burden may be warranted

Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients

Background: While immune checkpoint blockade has greatly improved clinical outcomes in diseases such as melanoma, there remains a need for predictive biomarkers to determine who will likely benefit most from which therapy. To date, most biomarkers of response have been identified in the tumors themselves. Biomarkers that could be assessed from peripheral blood would be even more desirable, because of ease of access and reproducibility of sampling. Methods: We used mass cytometry (CyTOF) to comprehensively profile peripheral blood of melanoma patients, in order to find predictive biomarkers of response to anti-CTLA-4 or anti-PD-1 therapy. Using a panel of ~ 40 surface and intracellular markers, we performed in-depth phenotypic and functional immune profiling to identify potential predictive biomarker candidates. Results: Immune profiling of baseline peripheral blood samples using CyTOF revealed that anti-CTLA-4 and anti-PD-1 therapies have distinct sets of candidate biomarkers. The distribution of CD4+ and CD8+ memory/non-memory cells and other memory subsets was different between responders and non-responders to anti-CTLA-4 therapy. In anti-PD-1 (but not anti-CTLA-4) treated patients, we discovered differences in CD69 and MIP-1β expressing NK cells between responders and non-responders. Finally, multivariate analysis was used to develop a model for the prediction of response. Conclusions: Our results indicate that anti-CTLA-4 and anti-PD-1 have distinct predictive biomarker candidates. CD4+ and CD8+ memory T cell subsets play an important role in response to anti-CTLA-4, and are potential biomarker candidates. For anti-PD-1 therapy, NK cell subsets (but not memory T cell subsets) correlated with clinical response to therapy. These functionally active NK cell subsets likely play a critical role in the anti-tumor response triggered by anti-PD-1. Electronic supplementary material The online version of this article (10.1186/s40425-018-0328-8) contains supplementary material, which is available to authorized users

In silico modeling for uncertain biochemical data

Analyzing and modeling data is a well established research area and a vast variety of different methods have been developed over the last decades. Most of these methods assume fixed positions of data points; only recently uncertainty in data has caught attention as potentially useful source of information. In order to provide a deeper insight into this subject, this thesis concerns itself with the following essential question: Can information on uncertainty of feature values be exploited to improve in silico modeling? For this reason a state-of-art random forest algorithm is developed using Matlab R. In addition, three techniques of handling uncertain numeric features are presented and incorporated in different modified versions of random forests. To test the hypothesis six realworld data sets were provided by AstraZeneca. The data describe biochemical features of chemical compounds, including the results of an Ames test; a widely used technique to determine the mutagenicity of chemical substances. Each of the datasets contains a single uncertain numeric feature, represented as an expected value and an error estimate. Themodified algorithms are then applied on the six data sets in order to obtain classifiers, able to predict the outcome of an Ames test. The hypothesis is tested using a paired t-test and the results reveal that information on uncertainty can indeed improve the performance of in silico models

Genomic Models of Short-Term Exposure Accurately Predict Long-Term Chemical Carcinogenicity and Identify Putative Mechanisms of Action

<div><p>Background</p><p>Despite an overall decrease in incidence of and mortality from cancer, about 40% of Americans will be diagnosed with the disease in their lifetime, and around 20% will die of it. Current approaches to test carcinogenic chemicals adopt the 2-year rodent bioassay, which is costly and time-consuming. As a result, fewer than 2% of the chemicals on the market have actually been tested. However, evidence accumulated to date suggests that gene expression profiles from model organisms exposed to chemical compounds reflect underlying mechanisms of action, and that these toxicogenomic models could be used in the prediction of chemical carcinogenicity.</p><p>Results</p><p>In this study, we used a rat-based microarray dataset from the NTP DrugMatrix Database to test the ability of toxicogenomics to model carcinogenicity. We analyzed 1,221 gene-expression profiles obtained from rats treated with 127 well-characterized compounds, including genotoxic and non-genotoxic carcinogens. We built a classifier that predicts a chemical's carcinogenic potential with an AUC of 0.78, and validated it on an independent dataset from the Japanese Toxicogenomics Project consisting of 2,065 profiles from 72 compounds. Finally, we identified differentially expressed genes associated with chemical carcinogenesis, and developed novel data-driven approaches for the molecular characterization of the response to chemical stressors.</p><p>Conclusion</p><p>Here, we validate a toxicogenomic approach to predict carcinogenicity and provide strong evidence that, with a larger set of compounds, we should be able to improve the sensitivity and specificity of the predictions. We found that the prediction of carcinogenicity is tissue-dependent and that the results also confirm and expand upon previous studies implicating DNA damage, the peroxisome proliferator-activated receptor, the aryl hydrocarbon receptor, and regenerative pathology in the response to carcinogen exposure.</p></div