Andrea Sottoriva

Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics

Glioblastoma (GB) is the most common and aggressive primary brain malignancy, with poor prognosis and a lack of effective therapeutic options. Accumulating evidence suggests that intratumor heterogeneity likely is the key to understanding treatment failure. However, the extent of intratumor heterogeneity as a result of tumor evolution is still poorly understood. To address this, we developed a unique surgical multisampling scheme to collect spatially distinct tumor fragments from 11 GB patients. We present an integrated genomic analysis that uncovers extensive intratumor heterogeneity, with most patients displaying different GB subtypes within the same tumor. Moreover, we reconstructed the phylogeny of the fragments for each patient, identifying copy number alterations in EGFR and CDKN2A/B/p14ARF as early events, and aberrations in PDGFRA and PTEN as later events during cancer progression. We also characterized the clonal organization of each tumor fragment at the single-molecule level, detecting multiple coexisting cell lineages. Our results reveal the genome-wide architecture of intratumor variability in GB across multiple spatial scales and patient-specific patterns of cancer evolution, with consequences for treatment design.

The shaping and functional consequences of the microRNA landscape in breast cancer.

MicroRNAs (miRNAs) show differential expression across breast cancer subtypes, and have both oncogenic and tumour-suppressive roles. Here we report the miRNA expression profiles of 1,302 breast tumours with matching detailed clinical annotation, long-term follow-up and genomic and messenger RNA expression data. This provides a comprehensive overview of the quantity, distribution and variation of the miRNA population and provides information on the extent to which genomic, transcriptional and post-transcriptional events contribute to miRNA expression architecture, suggesting an important role for post-transcriptional regulation. The key clinical parameters and cellular pathways related to the miRNA landscape are characterized, revealing context-dependent interactions, for example with regards to cell adhesion and Wnt signalling. Notably, only prognostic miRNA signatures derived from breast tumours devoid of somatic copy-number aberrations (CNA-devoid) are consistently prognostic across several other subtypes and can be validated in external cohorts. We then use a data-driven approach to seek the effects of miRNAs associated with differential co-expression of mRNAs, and find that miRNAs act as modulators of mRNA–mRNA interactions rather than as on–off molecular switches. We demonstrate such an important modulatory role for miRNAs in the biology of CNA-devoid breast cancers, a common subtype in which the immune response is prominent. These findings represent a new framework for studying the biology of miRNAs in human breast cancer.

Defining Stem Cell Dynamics in Models of Intestinal Tumor Initiation

Limiting Tumor Initiation What is the competitive advantage of cells with frequently occurring mutations during tumor development? Vermeulen et al. (p. 995; see the Perspective by Bozic and Nowak) quantified the advantages of Apc-loss, Kras activation, and P53 mutation during tumor initiation in the mouse intestine. The mutations conferred only a limited clonal advantage. Indeed, many mutated stem cells were stochastically replaced by wild-type stem cells, helping to limit tumor initiation. Common genetic alterations during tumor initiation in the mouse gut reveal clonal advantages. [Also see Perspective by Bozic and Nowak] Cancer is a disease in which cells accumulate genetic aberrations that are believed to confer a clonal advantage over cells in the surrounding tissue. However, the quantitative benefit of frequently occurring mutations during tumor development remains unknown. We quantified the competitive advantage of Apc loss, Kras activation, and P53 mutations in the mouse intestine. Our findings indicate that the fate conferred by these mutations is not deterministic, and many mutated stem cells are replaced by wild-type stem cells after biased, but still stochastic events. Furthermore, P53 mutations display a condition-dependent advantage, and especially in colitis-affected intestines, clones harboring mutations in this gene are favored. Our work confirms the previously theoretical notion that the tissue architecture of the intestine suppresses the accumulation of mutated lineages.

Identification of neutral tumor evolution across cancer types

Despite extraordinary efforts to profile cancer genomes, interpreting the vast amount of genomic data in the light of cancer evolution remains challenging. Here we demonstrate that neutral tumor evolution results in a power-law distribution of the mutant allele frequencies reported by next-generation sequencing of tumor bulk samples. We find that the neutral power law fits with high precision 323 of 904 cancers from 14 types and from different cohorts. In malignancies identified as evolving neutrally, all clonal selection seemingly occurred before the onset of cancer growth and not in later-arising subclones, resulting in numerous passenger mutations that are responsible for intratumoral heterogeneity. Reanalyzing cancer sequencing data within the neutral framework allowed the measurement, in each patient, of both the in vivo mutation rate and the order and timing of mutations. This result provides a new way to interpret existing cancer genomic data and to discriminate between functional and non-functional intratumoral heterogeneity.

Cancer Stem Cell Tumor Model Reveals Invasive Morphology and Increased Phenotypical Heterogeneity

The recently developed concept of cancer stem cells (CSC) sheds new light on various aspects of tumor growth and progression. Here, we present a mathematical model of malignancies to investigate how a hierarchical organized cancer cell population affects the fundamental properties of solid malignancies. We establish that tumors modeled in a CSC context more faithfully resemble human malignancies and show invasive behavior, whereas tumors without a CSC hierarchy do not. These findings are corroborated by in vitro studies. In addition, we provide evidence that the CSC model is accompanied by highly altered evolutionary dynamics compared with the ones predicted to exist in a stochastic, nonhierarchical tumor model. Our main findings indicate that the CSC model allows for significantly higher tumor heterogeneity, which may affect therapy resistance. Moreover, we show that therapy which fails to target the CSC population is not only unsuccessful in curing the patient, but also promotes malignant features in the recurring tumor. These include rapid expansion, increased invasion, and enhanced heterogeneity.

Cancer Evolution and the Limits of Predictability in Precision Cancer Medicine.

The ability to predict the future behavior of an individual cancer is crucial for precision cancer medicine. The discovery of extensive intratumor heterogeneity and ongoing clonal adaptation in human tumors substantiated the notion of cancer as an evolutionary process. Random events are inherent in evolution and tumor spatial structures hinder the efficacy of selection, which is the only deterministic evolutionary force. This review outlines how the interaction of these stochastic and deterministic processes, which have been extensively studied in evolutionary biology, limits cancer predictability and develops evolutionary strategies to improve predictions. Understanding and advancing the cancer predictability horizon is crucial to improve precision medicine outcomes.

Patient-derived organoids model treatment response of metastatic gastrointestinal cancers

Cancer organoids to model therapy response Cancer organoids are miniature, three-dimensional cell culture models that can be made from primary patient tumors and studied in the laboratory. Vlachogiannis et al. asked whether such “tumor-in-a-dish” approaches can be used to predict drug responses in the clinic. They generated a live organoid biobank from patients with metastatic gastrointestinal cancer who had previously been enrolled in phase I or II clinical trials. This allowed the authors to compare organoid drug responses with how the patient actually responded in the clinic. Encouragingly, the organoids had similar molecular profiles to those of the patient tumor, reinforcing their value as a platform for drug screening and development. Science, this issue p. 920 Organoids can recapitulate patient responses in the clinic, with potential for drug screening and personalized medicine. Patient-derived organoids (PDOs) have recently emerged as robust preclinical models; however, their potential to predict clinical outcomes in patients has remained unclear. We report on a living biobank of PDOs from metastatic, heavily pretreated colorectal and gastroesophageal cancer patients recruited in phase 1/2 clinical trials. Phenotypic and genotypic profiling of PDOs showed a high degree of similarity to the original patient tumors. Molecular profiling of tumor organoids was matched to drug-screening results, suggesting that PDOs could complement existing approaches in defining cancer vulnerabilities and improving treatment responses. We compared responses to anticancer agents ex vivo in organoids and PDO-based orthotopic mouse tumor xenograft models with the responses of the patients in clinical trials. Our data suggest that PDOs can recapitulate patient responses in the clinic and could be implemented in personalized medicine programs.

Single-Molecule Genomic Data Delineate Patient-Specific Tumor Profiles and Cancer Stem Cell Organization

Substantial evidence supports the concept that cancers are organized in a cellular hierarchy with cancer stem cells (CSC) at the apex. To date, the primary evidence for CSCs derives from transplantation assays, which have known limitations. In particular, they are unable to report on the fate of cells within the original human tumor. Because of the difficulty in measuring tumor characteristics in patients, cellular organization and other aspects of cancer dynamics have not been quantified directly, although they likely play a fundamental role in tumor progression and therapy response. As such, new approaches to study CSCs in patient-derived tumor specimens are needed. In this study, we exploited ultradeep single-molecule genomic data derived from multiple microdissected colorectal cancer glands per tumor, along with a novel quantitative approach to measure tumor characteristics, define patient-specific tumor profiles, and infer tumor ancestral trees. We show that each cancer is unique in terms of its cellular organization, molecular heterogeneity, time from malignant transformation, and rate of mutation and apoptosis. Importantly, we estimate CSC fractions between 0.5% and 4%, indicative of a hierarchical organization responsible for long-lived CSC lineages, with variable rates of symmetric cell division. We also observed extensive molecular heterogeneity, both between and within individual cancer glands, suggesting a complex hierarchy of mitotic clones. Our framework enables the measurement of clinically relevant patient-specific characteristics in vivo, providing insight into the cellular organization and dynamics of tumor growth, with implications for personalized patient care.

Modeling Evolutionary Dynamics of Epigenetic Mutations in Hierarchically Organized Tumors

The cancer stem cell (CSC) concept is a highly debated topic in cancer research. While experimental evidence in favor of the cancer stem cell theory is apparently abundant, the results are often criticized as being difficult to interpret. An important reason for this is that most experimental data that support this model rely on transplantation studies. In this study we use a novel cellular Potts model to elucidate the dynamics of established malignancies that are driven by a small subset of CSCs. Our results demonstrate that epigenetic mutations that occur during mitosis display highly altered dynamics in CSC-driven malignancies compared to a classical, non-hierarchical model of growth. In particular, the heterogeneity observed in CSC-driven tumors is considerably higher. We speculate that this feature could be used in combination with epigenetic (methylation) sequencing studies of human malignancies to prove or refute the CSC hypothesis in established tumors without the need for transplantation. Moreover our tumor growth simulations indicate that CSC-driven tumors display evolutionary features that can be considered beneficial during tumor progression. Besides an increased heterogeneity they also exhibit properties that allow the escape of clones from local fitness peaks. This leads to more aggressive phenotypes in the long run and makes the neoplasm more adaptable to stringent selective forces such as cancer treatment. Indeed when therapy is applied the clone landscape of the regrown tumor is more aggressive with respect to the primary tumor, whereas the classical model demonstrated similar patterns before and after therapy. Understanding these often counter-intuitive fundamental properties of (non-)hierarchically organized malignancies is a crucial step in validating the CSC concept as well as providing insight into the therapeutical consequences of this model.

Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence

Glioblastoma (GB) is the most common primary malignant brain tumor, and despite the availability of chemotherapy and radiotherapy to combat the disease, overall survival remains low with a high incidence of tumor recurrence. Technological advances are continually improving our understanding of the disease, and in particular, our knowledge of clonal evolution, intratumor heterogeneity, and possible reservoirs of residual disease. These may inform how we approach clinical treatment and recurrence in GB. Mathematical modeling (including neural networks) and strategies such as multiple sampling during tumor resection and genetic analysis of circulating cancer cells, may be of great future benefit to help predict the nature of residual disease and resistance to standard and molecular therapies in GB.