The Genomic and Immune Landscapes of Lethal Metastatic Breast Cancer.

The detailed molecular characterization of lethal cancers is a prerequisite to understanding resistance to therapy and escape from cancer immunoediting. We performed extensive multi-platform profiling of multi-regional metastases in autopsies from 10 patients with therapy-resistant breast cancer. The integrated genomic and immune landscapes show that metastases propagate and evolve as communities of clones, reveal their predicted neo-antigen landscapes, and show that they can accumulate HLA loss of heterozygosity (LOH). The data further identify variable tumor microenvironments and reveal, through analyses of T cell receptor repertoires, that adaptive immune responses appear to co-evolve with the metastatic genomes. These findings reveal in fine detail the landscapes of lethal metastatic breast cancer.CRUK

Spatially resolved clonal copy number alterations in benign and malignant tissue

Publisher Copyright: © 2022, The Author(s).Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer1. Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics2 to infer spatial copy number variations in >120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy.Peer reviewe

Integrative computational approaches for studying stem cell differentiation and complex diseases

The biological functions of the molecular components (genes, proteins, miRNAs, siRNAs,..etc) of biological cells and mutations/perturbations thereof are tightly connected with cellular malfunctions and disease pathways. Moreover, these molecular elements interact with each other forming a complex interwoven regulatory machinery that governs, on one hand, regular cellular pathways, and on the other hand, their dysregulation or malfunction in pathological processes. Therefore, revealing these critical molecular interactions in complex living systems is being considered as one of the major goals of current systems biology. In this dissertation, we introduce practical computational approaches implemented as freely available software tools to integrate heterogeneous sources of large-scale genomic data and unravel the combinatorial regulatory interactions between different molecular elements. First, we present an automated GRN pipeline that constructs the genomic regulatory machinery of a cell from expression, sequencing, and annotation datasets through three modules implemented as separated software components (plugins) and hosted by our software framework Mebitoo that aims at automation of bioinformatics workflows. Then, we extended this pipeline to a general integrative network-based approach that involves also post-transcriptional interactions and reports the computational analysis of gene and miRNA transcriptomes, DNA methylome, and somatic mutations. This workflow enables users to identify putative disease drivers and novel targets for therapeutic treatment. Regarding the incorporation of somatic mutations with other genomic data sets, a stand-alone pipeline named “SnvDMiR” was implemented to explore possible genomic proximity relationships between somatic variants and both differentially methylated CpG sites as well as differentially expressed miRNAs. Along the same lines, but targeting the effects of genomic mutations, we developed an NGS pipeline and applied it to two groups of bacterial isolates (nasal and invasive) to investigate the phylogenetic positions of the recently emerged t504 clone (Spa-type t504) in the Saarland province of Germany and to better understand the infectivity mechanism of the invasive group. Motivated by all of this, we developed TFmiR as a freely available web server for deep and integrative downstream analysis of combinatorial regulatory interactions between TFs/genes and miRNAs that are involved in the pathogenesis of human diseases. In the frame of this thesis, we employed these approaches to investigate the molecular mechanisms of cellular differentiation (namely hematopoiesis) as an example for biological processes and human breast cancer and diabetes as examples for complex diseases. In summary, the work presented in this thesis has led to the development of interesting computational approaches that have been made available as non-commercial software toolkits. The provided topological and functional analyses of our approaches as validated on cellular differentiation and complex diseases promotes them as reliable systems biology tools for researchers across the life science communities.Die Funktionsweise verschiedener molekularer Elemente (Gene, Proteine, Mutationen, miRNAs, siRNAs,... etc.) ist mit den darunterliegenden zellulären Fehlfunktionen als auch mit Krankheits-assoziierten zellulären Signalwegen verknüpft. Darüber hinaus interagieren diese molekularen Elemente auch miteinander und bilden eine komplexe ineinander verwobene regulatorische Maschinerie, die wiederum zelluläre Signalwege oder auch Krankheitsentwicklungen auf zellulärer Ebene beeinflusst. Aufgrund dessen ist heutzutage die Aufklärung dieser molekularen Interaktionen in komplexen lebenden Systemen eines der Hauptziele der Systembiologie. In dieser Dissertation stellen wir rechnerbasierte Ansätze vor welche als Software frei verfügbar sind und die Integration von großen genomischen Datensätzen als auch eine damit verbundene Aufklärung der kombinatorischen Vielfalt dieser regulatorischen Interaktionen zwischen den verschiedenen molekularen Elementen, ermöglichten. Dafür entwickelten wir anfangs eine automatisierte GRN Pipeline, welche die regulatorische Maschinerie einer Zelle auf der Grundlage von Daten zur Genexpression, über Sequenzierung als auch Annotierung von Datensätzen konstruiert. Diese Pipeline wurde in drei separate Module aufgeteilt, die alle als Software plugins verfügbar sind, und in unser Framework Mebitoo, welches bioinformatische Arbeitsabläufe automatisiert, integriert sind. Daraufhin erweiterten wir unser bisheriges Framework um einem allgemeinen und integrativen Netzwerk-basierten Ansatz, welcher post-transkriptionelle Interaktionen berücksichtigt und die rechnerbasierte Analyse von Genen als auch miRNA Transkriptomen, dem DNA Methylom und somatischen Mutationen mit einbezieht. Unser Ziel war es, dabei vermeintliche Verursacher von Krankheitsbildern als auch neue Ziele für die therapeutische Behandlung von Krankheiten zu identifizieren. Für die Integration somatischer Mutationen wurde eine eigenständige Pipeline namens „SnvDMiR“ entwickelt, welche die Analyse von möglichen genomischen Nachbarschaftsbeziehungen zwischen somatischen Mutationen und differentiell methylierten CpG Positionen als auch differentiell exprimierten miRNAs, ermöglicht. Für die Analyse von somatischen Mutationen entwickelten wir zudem eine NGS Pipeline und wendeten diese auf zwei unterschiedliche Gruppen von bakteriellen Isolaten (nasale und invasive) an, um einerseits die phylogenetische Position des kürzlich im Saarland aufgekommenen Klons t504 (Spa-type t504) zu untersuchen, aber auch um den Mechanismus, der zu einer Infektion durch invasive Stämme führt, besser zu verstehen. All dies motivierte uns dazu TFmiR als frei verfügbare Web-Applikation zu entwickeln, welche eine tief gehende integrative Analyse von den kombinatorischen regulatorischen Interaktionen zwischen TFs/Genen und miRNAs ermöglicht, die an der Krankheitsentwicklung im Menschen beteiligt sind. Die entwickelten Methoden wurden auf die zelluläre Differenzierung (Hämatopoese), als Beispiel für einen biologischen Prozess, als auch auf Brustkrebs und Diabetes, als Beispiele für komplexe Krankheiten, angewendet um deren molekulare Mechanismen zu untersuchen. Zusammenfassend hat diese Arbeit zur Entwicklung von interessanten, rechnergestützten Methoden geführt, welche als nicht-kommerzielle Software publiziert wurden. Die Validierung unserer Methoden anhand von topologischen und funktionsbasierten Analysen sowohl in zellulärer Differenzierung als auch komplexen Krankheiten, machen diese zu verlässlichen systembiologischen Werkzeugen für Wissenschaftler aus den unterschiedlichsten Naturwissenschaftsbereichen

Tumor Evolution in Two Patients with Basal-like Breast Cancer: A Retrospective Genomics Study of Multiple Metastases

Metastasis is the main cause of cancer patient deaths and remains a poorly characterized process. It is still unclear when in tumor progression the ability to metastasize arises and whether this ability is inherent to the primary tumor or is acquired well after primary tumor formation. Next-generation sequencing and analytical methods to define clonal heterogeneity provide a means for identifying genetic events and the temporal relationships between these events in the primary and metastatic tumors within an individual

Refphase: Multi-sample phasing reveals haplotype-specific copy number heterogeneity

Most computational methods that infer somatic copy number alterations (SCNAs) from bulk sequencing of DNA analyse tumour samples individually. However, the sequencing of multiple tumour samples from a patient’s disease is an increasingly common practice. We introduce Refphase, an algorithm that leverages this multi-sampling approach to infer haplotype-specific copy numbers through multi-sample phasing. We demonstrate Refphase’s ability to infer haplotype-specific SCNAs and characterise their intra-tumour heterogeneity, to uncover previously undetected allelic imbalance in low purity samples, and to identify parallel evolution in the context of whole genome doubling in a pan-cancer cohort of 336 samples from 99 tumours

Heterogeneous Tumor-Immune Microenvironments among Differentially Growing Metastases in an Ovarian Cancer Patient.

We present an exceptional case of a patient with high-grade serous ovarian cancer, treated with multiple chemotherapy regimens, who exhibited regression of some metastatic lesions with concomitant progression of other lesions during a treatment-free period. Using immunogenomic approaches, we found that progressing metastases were characterized by immune cell exclusion, whereas regressing and stable metastases were infiltrated by CD8+ and CD4+ T cells and exhibited oligoclonal expansion of specific T cell subsets. We also detected CD8+ T cell reactivity against predicted neoepitopes after isolation of cells from a blood sample taken almost 3 years after the tumors were resected. These findings suggest that multiple distinct tumor immune microenvironments co-exist within a single individual and may explain in part the heterogeneous fates of metastatic lesions often observed in the clinic post-therapy. VIDEO ABSTRACT.Cancer Research UK core grant (C14303/A17197), Memorial Sloan Kettering Cancer Cencter core grant (P30 CA008748