Consensus clustering applied to multi-omics disease subtyping

Background: Facing the diversity of omics data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. Results: Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omics data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering. ClustOmics computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganize data into consensus clusters. Conclusion: We applied ClustOmics to multi-omics disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high-quality consensus clusters, both from a computational and a biological point of view. The comparison to a state-of-the-art consensus-based integration tool, COCA, further corroborated this statement. However, the main interest of ClustOmics is not to compete with other tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance. Availability: The ClustOmics source code, released under MIT license, and the results obtained on TCGA cancer data are available on GitHub: https://github.com/galadrielbriere/ClustOmics

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1 %) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones, We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.Peer reviewe

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes.

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data

Intratumoral B and T cell receptors: reconstruction and analysis

When cells divide, mistakes happen. However, an intricate surveillance system has evolved to detect and eliminate anomalous cells before they become detrimental to the host organism. In cancer, abnormal cells manage to escape the immune system and grow uncontrollably. In this sense, cancer can be considered as an oversight of the immune system, as immune escape is a defining feature of clinically detectable cancers. The role of the immune system in fighting cancer is becoming increasingly indisputable as our understanding of its underlying mechanisms expand owing to technological advances in genomics, cancer biology, and computational sciences. In particular, significant research effort is undertaken in the field of cancer immunotherapy, where the immune system is stimulated to recognize and attack cancerous cells. In this thesis, I investigate certain aspects of the immune system in the context of cancer by computationally reconstructing and analyzing intratumoral B and T cell receptors. Applying a novel immune cell receptor profiling protocol to original single-cell RNA sequencing (scRNA-seq) data obtained from melanoma patients, I present a complete computational reconstruction of intratumoral immune receptors in this cancer type. The scRNA-seq results are consistent with the presence of an ongoing intratumoral immune response, likely involving tertiary lymphoid structures and the cooperation between B and T cells. Additionally, using a dataset of paired tumor biopsies collected pre- and post-treatment, I show that B cell infiltration increases after immunotherapy in pancreatic and colorectal cancer. This thesis includes the sequences of the most clonally expanded intratumoral antibodies expressed in these biopsies which I computationally reconstructed from bulk RNA sequencing reads. Furthermore, by combining scRNA-seq and immune cell receptor profiling of samples collected from a novel mouse model, I present a comprehensive statistical analysis of gene expression in clonal tumor-reactive T cells. I also show the distribution of tumor-reactive clones across the tumor and spleen. This study forms a first proof-of-principle effort for the in-depth assessment of tumor-reactive T and B cell clones, in-vivo, and paves the way for further, more extensive experiments

Multilevel mixed-type data analysis for validating partitions of scrapie isolates

The dissertation arises from a joint study with the Department of Food Safety and Veterinary Public Health of the Istituto Superiore di Sanità. The aim is to investigate and validate the existence of distinct strains of the scrapie disease taking into account the availability of a priori benchmark partition formulated by researchers. Scrapie of small ruminants is caused by prions, which are unconventional infectious agents of proteinaceous nature a ecting humans and animals. Due to the absence of nucleic acids, which precludes direct analysis of strain variation by molecular methods, the presence of di erent sheep scrapie strains is usually investigated by bioassay in laboratory rodents. Data are collected by an experimental study on scrapie conducted at the Istituto Superiore di Sanità by experimental transmission of scrapie isolates to bank voles. We aim to discuss the validation of a given partition in a statistical classification framework using a multi-step procedure. Firstly, we use unsupervised classification to see how alternative clustering results match researchers’ understanding of the heterogeneity of the isolates. We discuss whether and how clustering results can be eventually exploited to extend the preliminary partition elicited by researchers. Then we motivate the subsequent partition validation based on the predictive performance of several supervised classifiers. Our data-driven approach contains two main methodological original contributions. We advocate the use of partition validation measures to investigate a given benchmark partition: firstly we discuss the issue of how the data can be used to evaluate a preliminary benchmark partition and eventually modify it with statistical results to find a conclusive partition that could be used as a “gold standard” in future studies. Moreover, collected data have a multilevel structure and for each lower-level unit, mixed-type data are available. Each step in the procedure is then adapted to deal with multilevel mixed-type data. We extend distance-based clustering algorithms to deal with multilevel mixed-type data. Whereas in supervised classification we propose a two-step approach to classify the higher-level units starting from the lower-level observations. In this framework, we also need to define an ad-hoc cross validation algorithm

Graphlet-adjacencies provide complementary views on the functional organisation of the cell and cancer mechanisms

Recent biotechnological advances have led to a wealth of biological network data. Topo- logical analysis of these networks (i.e., the analysis of their structure) has led to break- throughs in biology and medicine. The state-of-the-art topological node and network descriptors are based on graphlets, induced connected subgraphs of different shapes (e.g., paths, triangles). However, current graphlet-based methods ignore neighbourhood infor- mation (i.e., what nodes are connected). Therefore, to capture topology and connectivity information simultaneously, I introduce graphlet adjacency, which considers two nodes adjacent based on their frequency of co-occurrence on a given graphlet. I use graphlet adjacency to generalise spectral methods and apply these on molecular networks. I show that, depending on the chosen graphlet, graphlet spectral clustering uncovers clusters en- riched in different biological functions, and graphlet diffusion of gene mutation scores predicts different sets of cancer driver genes. This demonstrates that graphlet adjacency captures topology-function and topology-disease relationships in molecular networks. To further detail these relationships, I take a pathway-focused approach. To enable this investigation, I introduce graphlet eigencentrality to compute the importance of a gene in a pathway either from the local pathway perspective or from the global network perspective. I show that pathways are best described by the graphlet adjacencies that capture the importance of their functionally critical genes. I also show that cancer driver genes characteristically perform hub roles between pathways. Given the latter finding, I hypothesise that cancer pathways should be identified by changes in their pathway-pathway relationships. Within this context, I propose pathway- driven non-negative matrix tri-factorisation (PNMTF), which fuses molecular network data and pathway annotations to learn an embedding space that captures the organisation of a network as a composition of subnetworks. In this space, I measure the functional importance of a pathway or gene in the cell and its functional disruption in cancer. I apply this method to predict genes and the pathways involved in four major cancers. By using graphlet-adjacency, I can exploit the tendency of cancer-related genes to perform hub roles to improve the prediction accuracy

Molecular serum portraits - A step towards personalized medicine

Antibody microarray technology has the potential for playing a large roll in identifying serological biomarker panels for personalized medicine. The aim of this thesis, based on four original papers, was to investigate if information in the serum proteome could be extracted and used for diagnostic, classificational, prognostic or treatment predictive purposes in a range of diseases. In two studies (paper I and paper IV), the diagnosis and prognosis of breast cancer was addressed, also being the main focus of this thesis. In paper I, we identified a biomarker panel capable of stratifying serum samples from metastatic breast cancer patients from those of healthy controls. In paper IV, another panel, pre-validated in the same study, was deciphered that could be used to identify patients destined for metastatic disease in a group of newly diagnosed breast cancer patients. Paper II and III targeted immunotherapy of glioblastoma multiforme and the diagnosis and sub- stratification of two autoimmune diseases (SLE and SSc), respectively. Also in these cases, multiple biomarker panels were identified, each capable of separating predefined cohorts of patients with relevance for applications within personalized medicine. In conclusion, this thesis introduces the concept of personalized medicine; details the antibody microarray technology in general and the platform used for the experiments in paper I to IV; and describes the subsequent microarray data analysis