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Anaplastic thyroid carcinoma is arguably the most lethal human malignancy. It often co-occurs with differentiated thyroid cancers, yet the molecular origins of its aggressivity are unknown. We sequenced tumor DNA from 329 regions of thyroid cancer, including 213 from patients with primary anaplastic thyroid carcinomas. We also whole genome sequenced 9 patients using multi-region sequencing of both differentiated and anaplastic thyroid cancer components. Using these data, we demonstrate thatanaplastic thyroid carcinomas have a higher burden of mutations than other thyroid cancers, with distinct mutational signatures and molecular subtypes. Further, different cancer driver genes are mutated in anaplastic and differentiated thyroid carcinomas, even those arising in a single patient. Finally, we unambiguously demonstrate that anaplastic thyroid carcinomas share a genomic origin with co-occurring differentiated carcinomas and emerge from a common malignant field through acquisition of characteristic clonal driver mutations.

Richter transformation (RT) is a paradigmatic evolution of chronic lymphocytic leukemia (CLL) into a very aggressive large B cell lymphoma conferring a dismal prognosis. The mechanisms driving RT remain largely unknown. We characterized the whole genome, epigenome and transcriptome, combined with single-cell DNA/RNA-sequencing analyses and functional experiments, of 19 cases of CLL developing RT. Studying 54 longitudinal samples covering up to 19 years of disease course, we uncovered minute subclones carrying genomic, immunogenetic and transcriptomic features of RT cells already at CLL diagnosis, which were dormant for up to 19 years before transformation. We also identified new driver alterations, discovered a new mutational signature (SBS-RT), recognized an oxidative phosphorylation (OXPHOS)high–B cell receptor (BCR)low-signaling transcriptional axis in RT and showed that OXPHOS inhibition reduces the proliferation of RT cells. These findings demonstrate the early seeding of subclones driving advanced stages of cancer evolution and uncover potential therapeutic targets for RT., The authors thank the Hematopathology Collection registered at the Biobank of Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and the Biobank HUB-ICO-IDIBELL (PT20/00171) for sample procurement, S. Martín, F. Arenas, the Genomics Core Facility of the IDIBAPS, CNAG Sequencing Unit, Mission Bio, Omniscope and Barcelona Supercomputing Center for the technical support and the computer resources at MareNostrum4 (RES activity, BCV-2018-3-0001). This study was supported by the la Caixa Foundation (CLLEvolution-LCF/PR/HR17/52150017, Health Research 2017 Program HR17-00221, to E.C.), the European Research Council under the European Union’s Horizon 2020 Research and Innovation Program (810287, BCLLatlas, to E.C., J.I.M.-S., H.H. and I.G.), the Instituto de Salud Carlos III and the European Regional Development Fund Una Manera de Hacer Europa (PMP15/00007 to E.C. and RTI2018-094584-B-I00 to D.C.), the American Association for Cancer Research (2021 AACR-Amgen Fellowship in Clinical/Translational Cancer Research, 21-40-11-NADE to F.N.), the European Hematology Association (EHA Junior Research Grant 2021, RG-202012-00245 to F.N.), the Lady Tata Memorial Trust (International Award for Research in Leukaemia 2021-2022, LADY_TATA_21_3223 to F.N.), the Generalitat de Catalunya Suport Grups de Recerca AGAUR (2017-SGR-1142 to E.C., 2017-SGR-736 to J.I.M.-S. and 2017-SGR-1009 to D.C.), the Accelerator award CRUK/AIRC/AECC joint funder partnership (AECC_AA17_SUBERO to J.I.M.-S.), the Fundació La Marató de TV3 (201924-30 to J.I.M.-S.), the Centro de Investigación Biomédica en Red Cáncer (CIBERONC; CB16/12/00225, CB16/12/00334, CB16/12/00236), the Ministerio de Ciencia e Innovación (PID2020-117185RB-I00 to X.S.P.), the Fundación Asociación Española Contra el Cáncer (FUNCAR-PRYGN211258SUÁR to X.S.P.), the Associazione Italiana per la Ricerca sul Cancro Foundation (AIRC 5 × 1,000 no. 21198 to G.G.) and the CERCA Programme/Generalitat de Catalunya. H.P.-A. i, Peer Reviewed, "Article signat per 52 autors/es: Ferran Nadeu, Romina Royo, Ramon Massoni-Badosa, Heribert Playa-Albinyana, Beatriz Garcia-Torre, Martí Duran-Ferrer, Kevin J. Dawson, Marta Kulis, Ander Diaz-Navarro, Neus Villamor, Juan L. Melero, Vicente Chapaprieta, Ana Dueso-Barroso, Julio Delgado, Riccardo Moia, Sara Ruiz-Gil, Domenica Marchese, Ariadna Giró, Núria Verdaguer-Dot, Mónica Romo, Guillem Clot, Maria Rozman, Gerard Frigola, Alfredo Rivas-Delgado, Tycho Baumann, Miguel Alcoceba, Marcos González, Fina Climent, Pau Abrisqueta, Josep Castellví, Francesc Bosch, Marta Aymerich, Anna Enjuanes, Sílvia Ruiz-Gaspà, Armando López-Guillermo, Pedro Jares, Sílvia Beà, Salvador Capella-Gutierrez, Josep Ll. Gelpí, Núria López-Bigas, David Torrents, Peter J. Campbell, Ivo Gut, Davide Rossi, Gianluca Gaidano, Xose S. Puente, Pablo M. Garcia-Roves, Dolors Colomer, Holger Heyn, Francesco Maura, José I. Martín-Subero & Elías Campo ", Postprint (published version)

Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity1. The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather ‘plastic’. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour.

Colorectal malignancies are a leading cause of cancer-related death1and have undergone extensive genomic study2,3. However, DNA mutations alone do not fully explain malignant transformation4–7. Here we investigate the co-evolution of the genome and epigenome of colorectal tumours at single-clone resolution using spatial multi-omic profiling of individual glands. We collected 1,370 samples from 30 primary cancers and 8 concomitant adenomas and generated 1,207 chromatin accessibility profiles, 527 whole genomes and 297 whole transcriptomes. We found positive selection for DNA mutations in chromatin modifier genes and recurrent somatic chromatin accessibility alterations, including in regulatory regions of cancer driver genes that were otherwise devoid of genetic mutations. Genome-wide alterations in accessibility for transcription factor binding involved CTCF, downregulation of interferon and increased accessibility for SOX and HOX transcription factor families, suggesting the involvement of developmental genes during tumourigenesis. Somatic chromatin accessibility alterations were heritable and distinguished adenomas from cancers. Mutational signature analysis showed that the epigenome in turn influences the accumulation of DNA mutations. This study provides a map of genetic and epigenetic tumour heterogeneity, with fundamental implications for understanding colorectal cancer biology.

The heterogenous tumour microenvironment (TME) has been well established in the progression of cancer and patient outcome. In particular, the success of anticancer therapy is influenced by various constituents and intercommunicating networks within the TME. Drug development research often focuses on targeting cancer cells alone without considering all the cellular and non-cellular components of the TME, which plays a crucial role in chemoresistance and disease relapse. Thus, there is a necessity to understand the individual features of the TME in order to deliver a more personalised therapy that improves the rate of successful outcomes while reducing the recurrence of the disease. However, the mechanisms of acquired drug resistance and pharmacokinetics within the tumour still remains elusive. Currently, there is a lack of high-throughput methods suitable to study the functional effects of anticancer drugs on tumour samples where the interplay between cancer cells and the TME remains intact. This thesis embodies the work in the development of the ALTEN (Alginate-based Tissue Engineering) platform, a biomimetic hydrogel system for rapid functional testing of anticancer drugs in explanted tumours. The alginate hydrogels provides a 3D scaffold that resembles the native extracellular matrix (ECM) that preserve the original characteristics of the tumour to permit detailed assessment of the TME and the complex molecular interactions between cell species. Here, we use high-resolution single-cell RNA-seq technologies to analyse the molecular effects of anticancer treatments within the tumour to study the mechanisms of acquired drug resistance. The combination of ALTEN and scRNA-seq technology enable high-throughput and high-resolution screening of tumour explants exposed to cytotoxic agents and immunomodulators. The impact of drug therapies within an intact TME could be evaluated to 1) determine treatment efficacy and 2) characterise drug resistant cancer populations. In sum

Epithelial mesenchymal plasticity in cancer generally refers to the ability of a cancer cell to transform into a different cell form, which facilitates the metastatic spread of a cancer. This thesis explores the roles of four cancer-associated genes that affect the transition of the cell state during cancer metastasis, and includes extensive research on two of the four gene targets, namely TRIM28 and TGFBI. The effects of these genes in breast cancer systems indicated great potential for improving therapeutic responses towards cancer drugs, which would alleviate the suffering of breast cancer patients.

Despite improvements in surgical oncology and precision medicine, 5-year survival rates for those with late-stage colorectal cancer (CRC) remain extremely poor and innovative treatment strategies are needed. Although T-cell directed immunotherapies are strikingly effective in many solid cancer types, durable responses are limited to approximately 5% of all CRC patients. Accordingly, there is an urgent need to explore alternate immunotherapeutic strategies which harness other cytotoxic cell types, most notably NK cells. However, the inter-patient variability of NK cell involvement in CRC, and particularly in colorectal liver metastases (CRLMs), is poorly characterised. Moreover, very little is known regarding the influence of tumour heterogeneity on immunotherapy response, mandating the development of novel methodologies which can dissect divergent responses to immuno-, chemo- and targeted therapies at the level of individual tumour cell subpopulations. In this thesis, the scope of tumour and immunological heterogeneity in primary CRC and CRLMs was assessed from transcriptomic, spatial and functional perspectives. A CRC-specific NK cell gene signature which infers the NK cell load of individual tumours from bulk RNAseq data was designed and validated. Differential expression analysis revealed that tumours with high evidence of this NK cell signature were characterised by the upregulation of chemotactic and cytolytic transcriptional programs. Furthermore, amongst patients with primary CRC, those with high NK scores were shown to have better survival outcomes in two independent cohorts. Focussing on metastatic disease, Cell type Identification by Estimating the Relative Subsets of RNA Transcripts (CIBERSORT) analysis revealed significant variance in terms of immune cell composition between CRLMs and adjacent normal (AdjN) liver tissue. Using a novel mIHC panel to quantify the scope of NK cell infiltration and NK cell ligand expression, it was determined that neoadjuvan

Every tumour is unique and characterised by its genetic, epigenetic, phenotypic, and morphological signature. The diversity observed between and within tumours, and over time, is termed tumour heterogeneity. An increased heterogeneity within a tumour correlates with cancer progression, higher resistance rates, and poorer outcome. Heterogeneity between tumours explains aspects of a treatment’s ineffectiveness. Depending on a tumour’s unique signature, common processes like unhindered cell proliferation, invasiveness, or treatment resistance characterise tumour progression. Studying tumour heterogeneity aims to understand cancer causes and evolution, and eventually to improve cancer treatment outcomes. This thesis presents application and development of computational methods to study tumour heterogeneity. Papers I and II concern the in-depth investigation of clinical tissue samples taken from prostate cancer patients. The findings range from spatial expansion of gene expression patterns based on high-resolution data to a gene expression signature of non-responding cancer cells revealed by spatio-temporal analysis. These cells underwent a transition from an epithelial to a mesenchymal phenotype pre-treatment. Papers III and IV present tools to detect fusion transcripts and copy number variations, respectively. Both tools, applicable to high-resolution data, enable the in-depth study of mutations, which are the driving force behind tumour heterogeneity. The results in this thesis demonstrate how the beneficial combination of high-resolution data and computational methods leads to novel insights of tumour heterogeneity., At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript. Paper 4: Manuscript.

Every tumour is unique and characterised by its genetic, epigenetic, phenotypic, and morphological signature. The diversity observed between and within tumours, and over time, is termed tumour heterogeneity. An increased heterogeneity within a tumour correlates with cancer progression, higher resistance rates, and poorer outcome. Heterogeneity between tumours explains aspects of a treatment’s ineffectiveness. Depending on a tumour’s unique signature, common processes like unhindered cell proliferation, invasiveness, or treatment resistance characterise tumour progression. Studying tumour heterogeneity aims to understand cancer causes and evolution, and eventually to improve cancer treatment outcomes. This thesis presents application and development of computational methods to study tumour heterogeneity. Papers I and II concern the in-depth investigation of clinical tissue samples taken from prostate cancer patients. The findings range from spatial expansion of gene expression patterns based on high-resolution data to a gene expression signature of non-responding cancer cells revealed by spatio-temporal analysis. These cells underwent a transition from an epithelial to a mesenchymal phenotype pre-treatment. Papers III and IV present tools to detect fusion transcripts and copy number variations, respectively. Both tools, applicable to high-resolution data, enable the in-depth study of mutations, which are the driving force behind tumour heterogeneity. The results in this thesis demonstrate how the beneficial combination of high-resolution data and computational methods leads to novel insights of tumour heterogeneity., At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript. Paper 4: Manuscript.

Cell migration and proliferation are essential for normal physiological processes, however their misregulation contributes to pathologies including cancer. In this thesis we develop and analyse new mathematical models of cell migration and proliferation, based on new experimental studies that provide visualisation of cell cycle progression, to improve understanding of the migration and proliferation of cells. In particular, we investigate cell migration as a function of cell cycle dynamics, normally-hidden cell synchronisation in cellular assays, whether cell migration and proliferation are mutually exclusive processes, and cellular mechanisms that contribute to heterogeneous cell proliferation.

Every tumour is unique and characterised by its genetic, epigenetic, phenotypic, and morphological signature. The diversity observed between and within tumours, and over time, is termed tumour heterogeneity. An increased heterogeneity within a tumour correlates with cancer progression, higher resistance rates, and poorer outcome. Heterogeneity between tumours explains aspects of a treatment’s ineffectiveness. Depending on a tumour’s unique signature, common processes like unhindered cell proliferation, invasiveness, or treatment resistance characterise tumour progression. Studying tumour heterogeneity aims to understand cancer causes and evolution, and eventually to improve cancer treatment outcomes. This thesis presents application and development of computational methods to study tumour heterogeneity. Papers I and II concern the in-depth investigation of clinical tissue samples taken from prostate cancer patients. The findings range from spatial expansion of gene expression patterns based on high-resolution data to a gene expression signature of non-responding cancer cells revealed by spatio-temporal analysis. These cells underwent a transition from an epithelial to a mesenchymal phenotype pre-treatment. Papers III and IV present tools to detect fusion transcripts and copy number variations, respectively. Both tools, applicable to high-resolution data, enable the in-depth study of mutations, which are the driving force behind tumour heterogeneity. The results in this thesis demonstrate how the beneficial combination of high-resolution data and computational methods leads to novel insights of tumour heterogeneity., At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript. Paper 4: Manuscript.

Circulating tumour cells (CTCs) are shed into the bloodstream from both primary and secondary tumours and provide a non-invasive means to study tumor progression and response to treatment. Assessment of ribonucleic acid (RNA) and monitoring dynamic changes in gene expression profiles of CTCs extends their clinical and prognostic power and establish their role in guiding treatment. Among these methods, droplet digital (RT-ddPCR) technique provides a high sensitivity and detectibility of CTCs. RNA-sequencing (RNAseq) is the most comprehensive method, that would allow the simultaneous measurement of a large number of genes and theoretically the whole transcriptome. Since CTCs are heterogeneous in nature, single cell RNAseq methods are very valuable in assessing population dynamics and functional states of CTCs. While RNA in situ hybridization (RNA-ISH) is used relatively less frequently, it also allows for the assessment of expression of multiple genes within individual CTCs. Epithelial to Mesenchymal Transition (EMT) is a major contributor to metastasis, providing a mechanism to allow cells to become migratory and invasive, and to survive in the bloodstream. Monitoring CTCs undergoing EMT may lead to improvement in their prognostic and predictive power. Here, we review various RNA analysis of CTCs and those that undergo EMT and their application in diagnosis, prognosis and treatment of cancers.

Glioblastoma (GBM) is a treatment-refractory central nervous system (CNS) tumour, and better therapies to treat this aggressive disease are urgently needed. Primary GBM models that represent the true disease state are essential to better understand disease biology and for accurate preclinical therapy assessment. We have previously presented a comprehensive transcriptome characterisation of a panel (n = 12) of primary GBM models (Q-Cell). We have now generated a systematic, quantitative, and deep proteome abundance atlas of the Q-Cell models grown in 3D culture, representing 6167 human proteins. A recent study has highlighted the degree of functional heterogeneity that coexists within individual GBM tumours, describing four cellular states (MES-like, NPC-like, OPC-like and AC-like). We performed comparative proteomic analysis, confirming a good representation of each of the four cell-states across the 13 models examined. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified upregulation of a number of GBM-associated cancer pathway proteins. Bioinformatics analysis, using the OncoKB database, identified a number of functional actionable targets that were either uniquely or ubiquitously expressed across the panel. This study provides an in-depth proteomic analysis of the GBM Q-Cell resource, which should prove a valuable functional dataset for future biological and preclinical investigations.

Intra and inter-tumour heterogeneity poses a challenge for associating molecular and immunohistochemical markers with clinical outcomes. Sequencing technologies has enabled detailed assessment of tumour heterogeneity, facilitating the genomic characterisation of tumours. Whilst such technologies have revealed mutational landscapes and have identified key driver alterations for tumorigenesis, pan-cancer clonal evolution reconstructions are lacking. In order to bridge this gap, I used discrete-time branching models to derive biological insights into tumour progression and reconstructed the clonal evolution in 1,800 patients, successfully linking mutations with growth patterns of disease progression. I first modified a discrete time-branching process to account for individual clonal subpopulations and derived analytical solutions for expectation and variance of both clonal and tumour expansions. Additionally, I derived the expected time for any given clone to successfully expand as \hat{\tau}, and with the use of these analytical solutions, I showed the likely driver and clonal compositions of the tumours and their phylogenies. Secondly, I generated a database of results from four different versions of time-branching process models that covered multiple parameters. Here I identified how an increase in diversity arises by both increased mutation rate and reduced fitness. I further corroborated that total number of drug resistant cells is directly proportional to lineage extinction probability (\delta) and tumour size as shown in previous studies. I also showed that this effect can be extrapolated to other types of functional passenger mutations involved in cancer-specific mortality. Moreover, I showed how commonly used sequencing cut-offs limit the accurate inference of tumour’s average selective advantage and driver mutation rate. Thirdly, I identified that a minimum distance metric can provide accurate fits of simulated cancer cell fractions to real patient tumour dat

The advent of next-generation sequencing (NGS) has allowed researchers to appreciate the enormous heterogeneity that exists between cells within a single tumour. This intratumour heterogeneity leads to diverse phenotypic outcomes, resulting in functionally distinct subpopulations of cancer cells. This functional heterogeneity fuels tumour evolution and therapeutic resistance and is thus a major barrier to producing cures in cancer. Acute myeloid leukaemia (AML) is an aggressive and heterogeneous malignancy with a high relapse rate. The prevailing paradigm to explain relapse in AML posits that genetic heterogeneity leads to pre-existing or acquired mutations that render certain cells refractory to therapy, resulting in the outgrowth of a resistant clone. Large-scale sequencing studies aimed at cataloguing genetic heterogeneity in AML have revealed several important observations. Firstly, AML has one of the lowest mutational burdens of any cancer. Secondly, a significant proportion of clinical relapse events cannot be attributed to an underlying genetic change. These important findings raise the possibility that mutations alone are insufficient to fully explain therapeutic resistance in AML. Indeed, we are now beginning to appreciate that both tumour evolution and clinical relapse can be driven by non-genetic processes. However, characterising the full extent of non-genetic heterogeneity and its relative contribution to both the evolutionary trajectory of the disease and therapeutic resistance requires innovative single cell methodologies. Single-cell RNA sequencing (scRNA-seq) has been instrumental in revealing the phenotypic heterogeneity of rare subpopulations of cells within a complex tumour. However, it is difficult to infer clonal relationships from scRNA-seq alone and this has hampered our ability to understand how individual malignant cells evolve over time. To overcome some of these challenges, we present a lentiviral method of tagging cells with unique herit

Every tumour is unique and characterised by its genetic, epigenetic, phenotypic, and morphological signature. The diversity observed between and within tumours, and over time, is termed tumour heterogeneity. An increased heterogeneity within a tumour correlates with cancer progression, higher resistance rates, and poorer outcome. Heterogeneity between tumours explains aspects of a treatment’s ineffectiveness. Depending on a tumour’s unique signature, common processes like unhindered cell proliferation, invasiveness, or treatment resistance characterise tumour progression. Studying tumour heterogeneity aims to understand cancer causes and evolution, and eventually to improve cancer treatment outcomes. This thesis presents application and development of computational methods to study tumour heterogeneity. Papers I and II concern the in-depth investigation of clinical tissue samples taken from prostate cancer patients. The findings range from spatial expansion of gene expression patterns based on high-resolution data to a gene expression signature of non-responding cancer cells revealed by spatio-temporal analysis. These cells underwent a transition from an epithelial to a mesenchymal phenotype pre-treatment. Papers III and IV present tools to detect fusion transcripts and copy number variations, respectively. Both tools, applicable to high-resolution data, enable the in-depth study of mutations, which are the driving force behind tumour heterogeneity. The results in this thesis demonstrate how the beneficial combination of high-resolution data and computational methods leads to novel insights of tumour heterogeneity., At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript. Paper 4: Manuscript.

Every tumour is unique and characterised by its genetic, epigenetic, phenotypic, and morphological signature. The diversity observed between and within tumours, and over time, is termed tumour heterogeneity. An increased heterogeneity within a tumour correlates with cancer progression, higher resistance rates, and poorer outcome. Heterogeneity between tumours explains aspects of a treatment’s ineffectiveness. Depending on a tumour’s unique signature, common processes like unhindered cell proliferation, invasiveness, or treatment resistance characterise tumour progression. Studying tumour heterogeneity aims to understand cancer causes and evolution, and eventually to improve cancer treatment outcomes. This thesis presents application and development of computational methods to study tumour heterogeneity. Papers I and II concern the in-depth investigation of clinical tissue samples taken from prostate cancer patients. The findings range from spatial expansion of gene expression patterns based on high-resolution data to a gene expression signature of non-responding cancer cells revealed by spatio-temporal analysis. These cells underwent a transition from an epithelial to a mesenchymal phenotype pre-treatment. Papers III and IV present tools to detect fusion transcripts and copy number variations, respectively. Both tools, applicable to high-resolution data, enable the in-depth study of mutations, which are the driving force behind tumour heterogeneity. The results in this thesis demonstrate how the beneficial combination of high-resolution data and computational methods leads to novel insights of tumour heterogeneity., At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript. Paper 4: Manuscript.

Every tumour is unique and characterised by its genetic, epigenetic, phenotypic, and morphological signature. The diversity observed between and within tumours, and over time, is termed tumour heterogeneity. An increased heterogeneity within a tumour correlates with cancer progression, higher resistance rates, and poorer outcome. Heterogeneity between tumours explains aspects of a treatment’s ineffectiveness. Depending on a tumour’s unique signature, common processes like unhindered cell proliferation, invasiveness, or treatment resistance characterise tumour progression. Studying tumour heterogeneity aims to understand cancer causes and evolution, and eventually to improve cancer treatment outcomes. This thesis presents application and development of computational methods to study tumour heterogeneity. Papers I and II concern the in-depth investigation of clinical tissue samples taken from prostate cancer patients. The findings range from spatial expansion of gene expression patterns based on high-resolution data to a gene expression signature of non-responding cancer cells revealed by spatio-temporal analysis. These cells underwent a transition from an epithelial to a mesenchymal phenotype pre-treatment. Papers III and IV present tools to detect fusion transcripts and copy number variations, respectively. Both tools, applicable to high-resolution data, enable the in-depth study of mutations, which are the driving force behind tumour heterogeneity. The results in this thesis demonstrate how the beneficial combination of high-resolution data and computational methods leads to novel insights of tumour heterogeneity., At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 3: Manuscript. Paper 4: Manuscript.

Multiparametric MRI (mpMRI) is an imaging modality that combines anatomical MR imaging with one or more functional MRI sequences. It has become a versatile tool for detecting and characterising prostate cancer (PCa). The traditional role of mpMRI was confined to PCa staging, but due to the advanced imaging techniques, its role has expanded to various stages in clinical practises including tumour detection, disease monitor during active surveillance and sequential imaging for patient follow-up. Meanwhile, with the growing speed of data generation and the increasing volume of imaging data, it is highly demanded to apply computerised methods to process mpMRI data and extract useful information. Hence quantitative analysis for imaging data using radiomics has become an emerging paradigm. The application of radiomics approaches in prostate cancer has not only enabled automatic localisation of the disease but also provided a non-invasive solution to assess tumour biology (e.g. aggressiveness and the presence of hypoxia). This article reviews mpMRI and its expanding role in PCa detection, staging and patient management. Following that, an overview of prostate radiomics will be provided, with a special focus on its current applications as well as its future directions.

Hereditary endocrine neoplasias, including phaeochromocytoma/paraganglioma and medullary thyroid cancer, are caused by autosomal dominant mutations in several familial cancer genes. A common feature of these diseases is the presentation of multiple primary tumours, or multifocal disease representing independent tumour clones that have arisen from the same initiating genetic lesion, but have undergone independent clonal evolution. Such tumours provide an opportunity to discover common cooperative changes required for tumourigenesis, while controlling for the genetic background of the individual. We performed genomic analysis of synchronous and metachronous tumours from five patients bearing germline mutations in the genes SDHB, RET, and MAX. Using whole exome sequencing and high-density single-nucleotide polymorphism arrays, we analysed two to four primary tumours from each patient. We also applied multi-region sampling, to assess intratumoural heterogeneity and clonal evolution, in two cases involving paraganglioma and medullary thyroid cancer, respectively. Heterogeneous patterns of genomic change existed between synchronous or metachronous tumours, with evidence of branching evolution. We observed striking examples of evolutionary convergence involving the same rare somatic copy-number events in synchronous primary phaeochromocytoma/paraganglioma. Convergent events also occurred during clonal evolution of metastatic medullary thyroid cancer. These observations suggest that genetic or epigenetic changes acquired early within precursor cells, or pre-existing within the genetic background of the individual, create contingencies that determine the evolutionary trajectory of the tumour.

Cancers arise from single progenitor cells that acquire mutations, eventually dividing into mixed populations with distinct genotypes. These populations can be estimated by identifying common mutational profiles, using computational techniques applied to sequencing data from tumour tissue samples. Existing methods have largely focused on single nucleotide variants (SNVs), despite growing evidence of the importance of structural variation (SV) as drivers in certain subtypes of cancer. While some approaches use copy-number aberrant SVs, no method has incorporated balanced rearrangements. To address this, I developed a Bayesian inference approach for estimating SV cancer cell fraction called SVclone. I validated SVclone using in silico mixtures of real samples in known proportions and found that clonal deconvolution using SV breakpoints can yield comparable results to SNV-based clustering. I then applied the method to 2,778 whole-genomes across 39 distinct tumour types, uncovering a subclonal copy-number neutral rearrangement phenotype with decreased overall survival. This clinically relevant finding could not have been found using existing methods. To further expand the methodology, and demonstrate its application to low data quality contexts, I developed a novel statistical approach to test for clonal differences in high-variance, formalin-fixed, paraffin-embedded (FFPE) samples. Together with variant curation strategies to minimise FFPE artefact, I applied the approach to longitudinal samples from a cohort of neo-adjuvant treated prostate cancer patients to investigate whether clonal differences can be inferred in highly noisy data. This thesis demonstrates that characterising the evolution of structural variation, particularly balanced rearrangements, results in clinically relevant insights. Identifying the patterns and dynamics of structural variation in the context of tumour evolution will ultimately help improve understanding of common pathways of tumour progres

Significant advances in next-generation sequencing technologies have allowed the identification of genomic alterations in breast cancer. These alterations offer the opportunity to conduct studies with targeted drugs. However, there are still several scientific challenges to be addressed before precision medicine is widely used in the clinic. Nonetheless, different solutions are developed to overcome these obstacles such as the improvement of bioinformatics tools and the use of "liquid biopsy" to assess circulating tumour DNA., SCOPUS: ed.j, info:eu-repo/semantics/published

BackgroundTumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer.ResultsTo simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets.ConclusionsThe ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.

Glioblastoma mutiforme (GBM) is a heterogeneous tumour of the brain with a poor prognosis. Genome-wide profiling has revealed four molecular subtypes, yet there is no significant difference in long-term survival between subtypes. Recurrence and resistance to GBM therapy is believed to be due to an underlying Glioma Stem Cell (GSC) subpopulation. To identify gene expression differences with the ability to predict patient survival, the cancer stem cell subpopulation of Patient Derived Glioma Cells (PDGCs) was isolated based on the expression of the putative stem cell marker CD133. RNA-seq libraries were prepared from six different PDGCs with the identification of 37 differentially expressed genes. Downstream characterisation of the cellular phenotypes exhibited by CD133+ and CD133- cells indicated that CD133 does not enrich for stem and malignant phenotypes. Genes coexpressed with GSCs markers were used to build a gene signature that classified patients based on a CD133 coexpression module signature (CD133-M) or a CD44 coexpression module signature (CD44-M) subtype. CD133-M tumours were enriched for the Proneural GBM subtype and correspondingly CD44-M tumours were enriched for the Mesenchymal subtype. Gene set enrichment identified proliferative pathways as activated in CD133-M and invasion/ migration pathways as enriched in CD44-M. CD133-M patients benefitted most from radiotherapy while CD44-M classified patients responded equally well to temozolomide or radiotherapy. In different PDGCs there was a culture specific equilibrium of distinct PN and MES subpopulations, potentially due to the genetic background of the original patient. The distribution of Proneural and Mesenchymal cells in the same tumour was measured in GBM sections using the expression of Olig2 and CD44 proteins as markers. Heterogeneous expression of these markers in tumours was observed, consistent with the heterogeneity observed in cell cultures. The influence of oxygen tension and chemoradiotherapy

BackgroundTumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer.ResultsTo simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets.ConclusionsThe ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.

As regards their morphology and biology, tumours consist of heterogeneous cell populations. The cancer stem cell (CSC) hypothesis assumes that a tumour is hierarchically organized and not all of the cells are equally capable of generating descendants, similarly to normal tissue. The only cells being able to self-renew and produce a heterogeneous tumour cell population are cancer stem cells. CSCs probably derive from normal stem cells, although progenitor cells may be taken into consideration as the source of cancer stem cells. CSCs reside in the niche defined as the microenvironment formed by stromal cells, vasculature and extracellular matrix. The CSC assays include FACS sorting, xenotransplantation to immunodeficient mice (SCID), incubation with Hoechst 33342 dye, cell culture in non-adherent conditions, cell culture with bromodeoxyuridine. CSCs have certain properties that make them resistant to anticancer therapy, which suggests they may be the target for potential therapeutic strategies.

Despite many new and significant discoveries made in cancer research in the last few decades, only limited improvements in overall responses to treatment have occurred. The frustrating gaps between the scientific evidence and minor advances in clinical outcomes as well as unacceptable treatment failure must be overcome by improving current cancer therapies and developing more effective anti-cancer strategies. The term "tumour heterogeneity" refers to the formation of a tumour containing areas with distinguishable phenotypic features within its microenvironment. The realisation that a tumour tissue is not a cluster of single clonally derived cells but rather comprises a well-organised and tumour-protective environment necessitates a drastic change in the direction of cancer research to breakdown these protective barriers. Over the last century, the field of glycobiology has grown in significance and attracted a number of scientists from a variety of fields including immunology and oncology. Involvement of carbohydrates is critical for pathological aspects of cancer progression and metastasis as well as for normal biological systems such as immune reaction and recognition. Therefore, the studies of lectin-glycan interactions currently are providing much opportunity for unveiling many novel pathological features of cancer. Galectin-1 is a [beta]-galactoside binding protein abundantly secreted by almost all types of malignant tumour cells and its expression plays vital pro-tumourigenic roles within the tumour microenvironment. In particular, galectin-1 suppresses the T cell-mediated anticancer immune responses, inducing apoptosis of the activated effector T cells by binding to their glycosylated surface receptors., Thesis (PhD Doctorate), Doctor of Philosophy (PhD), School of Medical Science, Griffith Health, Full Text

Full Text, Thesis (PhD Doctorate), Doctor of Philosophy (PhD), School of Biomolecular and Physical Sciences, Science, Environment, Engineering and Technology, Breast cancer is a heterogenous disease, composed of tumour cells with differing gene expressions and phenotypes. Tumour heterogeneity has several important consequences for breast cancer including: (i) making classification by morphological and expression analysis more difficult because of the diversity within single tumours with the consequence that the majority of cells of the tumour will dominate this classification whether or not these cells are critical for diagnosis or treatment; (ii) treatments may fail to eradicate tumours simply by failing to eliminate one of the cell subtypes within the tumour; and (iii) differing abilities of the cell subtypes for dissemination and metastasis. Recently, a rare subpopulation of cells within tumours has been described with the ability to initiate and sustain tumour growth, to resist traditional therapies and to allow for secondary tumour dissemination. These cells are termed tumour-initiating cells or cancer stem cells, or alternatively, in the case of breast cancer, breast cancer stem cells. The therapeutic targeting of these cells has the potential to eliminate residual disease and may become an important component of a multi-modality treatment of cancer. Presented here is an investigation into: (i) ways to functionally and phenotypically identify breast cancer stem cells; (ii) the role of breast cancer stem cells in disease from both clinical samples and using xenograft assays; and (iii) the potential to target these cells.

Breast cancer is a heterogenous disease, composed of tumour cells with differing gene expressions and phenotypes. Tumour heterogeneity has several important consequences for breast cancer including: (i) making classification by morphological and expression analysis more difficult because of the diversity within single tumours with the consequence that the majority of cells of the tumour will dominate this classification whether or not these cells are critical for diagnosis or treatment; (ii) treatments may fail to eradicate tumours simply by failing to eliminate one of the cell subtypes within the tumour; and (iii) differing abilities of the cell subtypes for dissemination and metastasis. Recently, a rare subpopulation of cells within tumours has been described with the ability to initiate and sustain tumour growth, to resist traditional therapies and to allow for secondary tumour dissemination. These cells are termed tumour-initiating cells or cancer stem cells, or alternatively, in the case of breast cancer, breast cancer stem cells. The therapeutic targeting of these cells has the potential to eliminate residual disease and may become an important component of a multi-modality treatment of cancer. Presented here is an investigation into: (i) ways to functionally and phenotypically identify breast cancer stem cells; (ii) the role of breast cancer stem cells in disease from both clinical samples and using xenograft assays; and (iii) the potential to target these cells., Thesis (PhD Doctorate), Doctor of Philosophy (PhD), School of Biomolecular and Physical Sciences, Science, Environment, Engineering and Technology, Full Text

Recently, we presented evidence - based on the analysis of benign hyperproliferative lesions of the breast - for the presence of cells that express the stem cell marker cytokeratin (CK) 15 in combination with CK19, a protein widely expressed by mammary epithelial cells. Here we report the finding of a subset of breast carcinomas characterized by expression of CK15. CK15 expressing tumors constituted 5% (6 out of 120; 4 of ductal type and 2 of lobular type) of the high-risk breast carcinomas examined by gel-based proteomics and immunohistochemistry. Five out of the six CK15+ carcinomas were CK15+/CK19-. The remaining tumor was mainly composed of cells expressing both CK15 and CK19 (CK15+/CK19+), but it also contained invasive areas with cells expressing only one of these makers (CK15+/CK19- and CK15-/CK19+ cells). To address the relationship between putative luminal progenitor/amplified CK15+ cells and malignant disease, and to determine whether cells/lesions lose expression of CK15 as a result of tumour initiation and/or progression, we searched among our sample set for carcinomas in which invasive tumor areas co-existed with non-malignant cells and hyperproliferative and known pre-malignant lesions. Only one such tumour was found (T71), a CK15-/CK19+/p53+ carcinoma that contained p53 negative non-malignant epithelial cells exhibiting a variety of, CK15/CK19 cellular phenotypes (CK15+/CK19+; CK15+/CK19-; CK15-/CK19+; CK15-/CK19-), often associated with simple columnar cells. Single layers of epithelial cells exhibiting all four CK15/CK19 phenotypes were observed contiguous to areas of atypical ductal hyperplasia that contained p53 positive cells that lost CK15 expression (CK15-/CK19+) and had a very similar phenotype to those of the neighboring ductal carcinoma in situ (DCIS) and invasive cells. The undifferentiated CK15+/CK19+ cells, which had the phenotype CK15+/CK19+/CK14+/CK8+ and -/ER-/PgR-/AR-/CD44+ (weak)/CK17-/p63-/vimentin+/Ki67-/Bcl-2+ (weak)/GATA-3-/p5

Recently, we presented evidence - based on the analysis of benign hyperproliferative lesions of the breast - for the presence of cells that express the stem cell marker cytokeratin (CK) 15 in combination with CK19, a protein widely expressed by mammary epithelial cells. Here we report the finding of a subset of breast carcinomas characterized by expression of CK15. CK15 expressing tumors constituted 5% (6 out of 120; 4 of ductal type and 2 of lobular type) of the high-risk breast carcinomas examined by gel-based proteomics and immunohistochemistry. Five out of the six CK15+ carcinomas were CK15+/CK19-. The remaining tumor was mainly composed of cells expressing both CK15 and CK19 (CK15+/CK19+), but it also contained invasive areas with cells expressing only one of these makers (CK15+/CK19- and CK15-/CK19+ cells). To address the relationship between putative luminal progenitor/amplified CK15+ cells and malignant disease, and to determine whether cells/lesions lose expression of CK15 as a result of tumour initiation and/or progression, we searched among our sample set for carcinomas in which invasive tumor areas co-existed with non-malignant cells and hyperproliferative and known pre-malignant lesions. Only one such tumour was found (T71), a CK15-/CK19+/p53+ carcinoma that contained p53 negative non-malignant epithelial cells exhibiting a variety of, CK15/CK19 cellular phenotypes (CK15+/CK19+; CK15+/CK19-; CK15-/CK19+; CK15-/CK19-), often associated with simple columnar cells. Single layers of epithelial cells exhibiting all four CK15/CK19 phenotypes were observed contiguous to areas of atypical ductal hyperplasia that contained p53 positive cells that lost CK15 expression (CK15-/CK19+) and had a very similar phenotype to those of the neighboring ductal carcinoma in situ (DCIS) and invasive cells. The undifferentiated CK15+/CK19+ cells, which had the phenotype CK15+/CK19+/CK14+/CK8+ and -/ER-/PgR-/AR-/CD44+ (weak)/CK17-/p63-/vimentin+/Ki67-/Bcl-2+ (weak)/GATA-3-/p5