Recognition of Multiomics-Based Molecule-Pattern Biomarker for Precise Prediction, Diagnosis, and Prognostic Assessment in Cancer

Cancer is a complex whole-body chronic disease, is involved in multiple causes, multiple processes, and multiple consequences, which are associated with a series of molecular alterations in the different levels of genome, transcriptome, proteome, metabolome, and radiome, with between-molecule mutual interactions. Those molecule-panels are the important resources to recognize the reliable molecular pattern biomarkers for precise prediction, diagnosis, and prognostic assessment in cancer. Pattern recognition is an effective methodology to identify those molecule-panels. The rapid development of computation biology, systems biology, and multiomics is driving the development of pattern recognition to discover reliable molecular pattern biomarkers for cancer treatment. This book chapter addresses the concept of pattern recognition and pattern biomarkers, status of multiomics-based molecular patterns, and future perspective in prediction, diagnosis, and prognostic assessment of a cancer

The role of chromosomal instability and parallel evolution in cancer

Although chromosomal instability (CIN) is recognised as an initiating process in cancer, the extent and relevance of ongoing somatic copy number alterations (SCNAs) that result from it later in tumour development is unclear. In this thesis I describe a comprehensive analysis, including 1421 tumour samples (394 patients; 22 tumour types), to evaluate ongoing CIN and SCNAs in tumour evolution and show that intratumor heterogeneity mediated through chromosomal instability is associated with an increased risk of recurrence or death in non-small cell lung cancer (NSCLC), a finding that supports the potential value of CIN as a prognostic predictor. I also uncover pervasive SCNA intratumour heterogeneity across cancers, with recurrent clonal and subclonal events identified and found to demonstrate enrichment for cancer genes. I develop novel techniques for obtaining a phasing of heterozygous SNPs from multi-region next generation sequencing data and apply them to observe recurrent parallel evolutionary events converging upon disruption to the same genes in distinct subclones within 146 individual tumours. The most prevalent recurrent parallel loss event involved chromosome 14, including HIF1A and HIF1B. In addition, chromosome 5p, including TERT, was recurrently gained and subject to parallel evolution in 7 tumour types. Tumour type-specific constraints to early tumour development were identified in the form of obligatory clonal LOH, including LOH of 3p in clear cell renal cell carcinoma, lung squamous cell carcinoma (LUSC) and triple-negative breast cancer and LOH of 17p in LUSC, colorectal adenocarcinoma, triple negative and HER2+ breast cancer. Wholegenome doubling (WGD) was generally an early event in tumour evolution, associated with an increased acquisition of both clonal and subclonal SCNAs. For instance, CCNE1 amplifications, which occurred exclusively in WGD tumours, were subclonal in 45% of these cases, suggesting this event may be selected following a WGD event. Mathematical modelling of subclonal SCNA evolution demonstrated that models that incorporate ongoing selection with respect to SCNAs significantly outperform evolutionary neutral models, particularly in the context of WGD. This thesis highlights the importance of ongoing CIN and recurrent subclonal chromosomal alterations in tumour evolution, reveals parallel evolution of SCNAs, and sheds light on the dynamics and order of events that influence metastasis

DNA content based flow sorting combined with genomic high-resolution profiling in the context of the development of castration resistance in prostate cancer

Prostate cancer (PCa) is a common malignant disease in men. Due to increased screening efforts, PCa is often diagnosed in early stages and patients are therefore eligible for curative surgical approaches. Yet many patients, mostly due to age or extended disease, are treated primarily using androgen deprivation therapy (ADT). While this approach usually leads to disease stability or even reduction, unfortunately, this effect is usually not sustainable and tumors acquire resistance toward ADT commonly referred to as castration resistance. The resulting tumor progression usually leads to long term disease progression with increased morbidity and eventually the death of the patient. Among other factors, changes in the tumor genome may confer resistance towards ADT, similar to what has previously been demonstrated for therapy resistance in other malignancies. This thesis focuses on different translational approaches aimed at tracking down the phenotypic and genomic changes occurring in PCa upon ADT. In the first paper included in the thesis (“ERG rearrangement and protein expression in the progression to castration-resistant prostate cancer”), we explored one of the prototypic genomic alterations, namely the TMPRSS2-ERG gene fusion, and its role in the development of castration-resistant PCa. We used a tissue microarray (TMA) that comprised clinical samples of PCa prior to and after ADT using classical molecular analysis methods such as immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). Interestingly we were able to identify a PCa subgroup that appeared to be associated with a partial shutdown of the androgen receptor (AR)-driven signaling axis. Further, the notion of tumor evolution and intra-tumor heterogeneity has spawned an increased interest in recent years. The advent of high resolution, high throughput genomic analysis methods have provided the necessary tools to decipher the clonal complexity of tumors. This led to an improved understanding of the clonal evolution of malignant diseases. While most computational approaches focus on the bulk of the tumor, we developed and refined a combined approach relying on prior flow sorting of cytogenetically distinct tumor subpopulations and subsequent molecular and computational analyses to obtain a different perspective on intra-tumor heterogeneity. The resulting findings are summarized in the second paper entitled “Delineation of human prostate cancer evolution identifies chromothripsis as a polyclonal event selecting for FKBP4 driven castration resistance”. To the best of our knowledge, this was the first paper describing tumor populations exhibiting features of chromothripsis, the catastrophic shattering, and the reconnection of chromosomes, that are related but distinct in the different subpopulations. This gains further clinical significance as in the process the tumor acquired a new amplification of a chaperone protein, namely FKBP4, which is involved in AR signaling and highly correlates with patients’ survival. Taken together, these findings, therefore, might open new doors in the development of prognostic and therapeutic tools

Molecular Portraits of Cancer Evolution and Ecology

Research on the molecular lesions that drive cancers holds the translational promise of unmasking distinct disease subtypes in otherwise pathologically identical patients. Yet clinical adoption is hindered by the reproducibility crisis for cancer biomarkers. In this thesis, a novel metric uncovered transcriptional diversity within individual non-small cell lung cancers, driven by chromosomal instability. Existing prognostic biomarkers were confounded by tumour sampling bias, arising from this diversity, in ~50% of patients assessed. An atlas of consistently expressed genes was derived to address this diagnostic challenge, yielding a clonal biomarker robust to sampling bias. This diagnostic based on cancer evolutionary principles maintained prognostic value in a metaanalysis of >900 patients, and over known risk factors in stage I disease, motivating further development as a clinical assay. Next, in situ RNA profiles of immune, fibroblast and endothelial cell subsets were generated from cancerous and adjacent non-malignant lung tissue. The phenotypic adaptation of stromal cells in the tumour microenvironment undermined the performance of existing molecular signatures for cell-type enumeration. Transcriptome-wide analysis delineated ~10% of genes displaying cell-type-specific expression, paving the way for high-fidelity signatures for the accurate digital dissection of tumour ecology. Lastly, the impact of branching, Darwinian evolution on the detection of epistatic interactions was evaluated in a pan-cancer analysis. The clonal status of driver genes was associated with the proportion of significant epistatic findings in 44-78% of the cancer-types assessed. Integrating the clonal architecture of tumours in future analyses could help decipher evolutionary dependencies. This work provides pragmatic solutions for refining molecular portraits of cancer in the light of their evolutionary and ecological features, moving the needle for precision cancer diagnostics