Spiteri, I., Caravagna, G., Cresswell, G.D., Vatsiou, A., Nichol, D., Acar, A., Ermini, L., Chkhaidze, K., Werner, B., Mair, R., et al.
(2019). Evolutionary dynamics of residual disease in human glioblastoma. Annals of oncology,
Vol.30
(3),
pp. 456-463.
Turajlic, S., Sottoriva, A., Graham, T. & Swanton, C.
(2019). Resolving genetic heterogeneity in cancer. Nature reviews genetics,
Vol.20
(7),
pp. 404-416.
Williams, M.J., Sottoriva, A. & Graham, T.A.
(2019). Measuring Clonal Evolution in Cancer with Genomics. Annual review of genomics and human genetics,
Vol.20
(1),
pp. 309-329.
show abstract
Cancers originate from somatic cells in the human body that have accumulated genetic alterations. These mutations modify the phenotype of the cells, allowing them to escape the homeostatic regulation that maintains normal cell number. Viewed through the lens of evolutionary biology, the transformation of normal cells into malignant cells is evolution in action. Evolution continues throughout cancer growth, progression, treatment resistance, and disease relapse, driven by adaptation to changes in the cancer's environment, and intratumor heterogeneity is an inevitable consequence of this evolutionary process. Genomics provides a powerful means to characterize tumor evolution, enabling quantitative measurement of evolving clones across space and time. In this review, we discuss concepts and approaches to quantify and measure this evolutionary process in cancer using genomics. .
Williams, M.J., Werner, B., Barnes, C.P., Graham, T.A. & Sottoriva, A.
(2018). Reply: Is the evolution of tumors Darwinian or non-Darwinian?. National science review,
Vol.5
(1),
pp. 17-19.
Sottoriva, A.
(2018). Divergent adaptation in thyroid cancers. Annals of oncology,
Vol.29
(6),
pp. 1353-1353.
Barry, P., Vatsiou, A., Spiteri, I., Nichol, D., Cresswell, G.D., Acar, A., Trahearn, N., Hrebien, S., Garcia-Murillas, I., Chkhaidze, K., et al.
(2018). The Spatiotemporal Evolution of Lymph Node Spread in Early Breast Cancer. Clinical cancer research,
Vol.24
(19),
pp. 4763-4770.
Cross, W., Kovac, M., Mustonen, V., Temko, D., Davis, H., Baker, A.-., Biswas, S., Arnold, R., Chegwidden, L., Gatenbee, C., et al.
(2018). The evolutionary landscape of colorectal tumorigenesis. Nature ecology & evolution,
Vol.2
(10),
pp. 1661-1672.
Heindl, A., Khan, A.M., Rodrigues, D.N., Eason, K., Sadanandam, A., Orbegoso, C., Punta, M., Sottoriva, A., Lise, S., Banerjee, S., et al.
(2018). Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity. Nature communications,
Vol.9
(1).
Williams, M.J., Werner, B., Heide, T., Barnes, C.P., Graham, T.A. & Sottoriva, A.
(2018). Reply to ‘Revisiting signatures of neutral tumor evolution in the light of complexity of cancer genomic data’. Nature genetics,
Vol.50
(12),
pp. 1628-1630.
Heide, T., Zapata, L., Williams, M.J., Werner, B., Caravagna, G., Barnes, C.P., Graham, T.A. & Sottoriva, A.
(2018). Reply to ‘Neutral tumor evolution?’. Nature genetics,
Vol.50
(12),
pp. 1633-1637.
Graham, T.A. & Sottoriva, A.
(2017). Measuring cancer evolution from the genome. The journal of pathology,
Vol.241
(2),
pp. 183-191.
Sottoriva, A., Barnes, C.P. & Graham, T.A.
(2017). Catch my drift? Making sense of genomic intra-tumour heterogeneity. Biochimica et biophysica acta (bba) - reviews on cancer,
Vol.1867
(2),
pp. 95-100.
Williams, M.J., Werner, B., Barnes, C.P., Graham, T.A. & Sottoriva, A.
(2017). Reply: Uncertainties in tumor allele frequencies limit power to infer evolutionary pressures. Nature genetics,
Vol.49
(9),
pp. 1289-1291.
Maley, C.C., Aktipis, A., Graham, T.A., Sottoriva, A., Boddy, A.M., Janiszewska, M., Silva, A.S., Gerlinger, M., Yuan, Y., Pienta, K.J., et al.
(2017). Classifying the evolutionary and ecological features of neoplasms. Nature reviews cancer,
Vol.17
(10),
pp. 605-619.
Baker, A.-., Huang, W., Wang, X.-., Jansen, M., Ma, X.-., Kim, J., Anderson, C.M., Wu, X., Pan, L., Su, N., et al.
(2017). Robust RNA-based in situ mutation detection delineates colorectal cancer subclonal evolution. Nature communications,
Vol.8
(1).
Werner, B., Scott, J.G., Sottoriva, A., Anderson, A.R., Traulsen, A. & Altrock, P.M.
(2016). The Cancer Stem Cell Fraction in Hierarchically Organized Tumors Can Be Estimated Using Mathematical Modeling and Patient-Specific Treatment Trajectories. Cancer research,
Vol.76
(7),
pp. 1705-1713.
Williams, M.J., Werner, B., Barnes, C.P., Graham, T.A. & Sottoriva, A.
(2016). Identification of neutral tumor evolution across cancer types. Nature genetics,
Vol.48
(3),
pp. 238-244.
Williams, M.J., Werner, B., Graham, T.A. & Sottoriva, A.
(2016). Functional versus non-functional intratumor heterogeneity in cancer. Molecular & cellular oncology,
Vol.3
(4),
pp. e1162897-e1162897.
Eskilsson, E., Rosland, G.V., Talasila, K.M., Knappskog, S., Keunen, O., Sottoriva, A., Foerster, S., Solecki, G., Taxt, T., Jirik, R., et al.
(2016). EGFRvIII mutations can emerge as late and heterogenous events in glioblastoma development and promote angiogenesis through Src activation. Neuro-oncology,
Vol.18
(12),
pp. 1644-1655.
Lipinski, K.A., Barber, L.J., Davies, M.N., Ashenden, M., Sottoriva, A. & Gerlinger, M.
(2016). Cancer Evolution and the Limits of Predictability in Precision Cancer Medicine. Trends in cancer,
Vol.2
(1),
pp. 49-63.
show abstract
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..
Piccirillo, S.G., Spiteri, I., Sottoriva, A., Touloumis, A., Ber, S., Price, S.J., Heywood, R., Francis, N.-., Howarth, K.D., Collins, V.P., et al.
(2015). Contributions to drug resistance in glioblastoma derived from malignant cells in the sub-ependymal zone. Cancer res,
Vol.75
(1),
pp. 194-202.
show abstract
Glioblastoma, the most common and aggressive adult brain tumor, is characterized by extreme phenotypic diversity and treatment failure. Through fluorescence-guided resection, we identified fluorescent tissue in the sub-ependymal zone (SEZ) of patients with glioblastoma. Histologic analysis and genomic characterization revealed that the SEZ harbors malignant cells with tumor-initiating capacity, analogous to cells isolated from the fluorescent tumor mass (T). We observed resistance to supramaximal chemotherapy doses along with differential patterns of drug response between T and SEZ in the same tumor. Our results reveal novel insights into glioblastoma growth dynamics, with implications for understanding and limiting treatment resistance..
Sottoriva, A., Kang, H., Ma, Z., Graham, T.A., Salomon, M.P., Zhao, J., Marjoram, P., Siegmund, K., Press, M.F., Shibata, D., et al.
(2015). A Big Bang model of human colorectal tumor growth. Nat genet,
Vol.47
(3),
pp. 209-216.
show abstract
What happens in early, still undetectable human malignancies is unknown because direct observations are impractical. Here we present and validate a 'Big Bang' model, whereby tumors grow predominantly as a single expansion producing numerous intermixed subclones that are not subject to stringent selection and where both public (clonal) and most detectable private (subclonal) alterations arise early during growth. Genomic profiling of 349 individual glands from 15 colorectal tumors showed an absence of selective sweeps, uniformly high intratumoral heterogeneity (ITH) and subclone mixing in distant regions, as postulated by our model. We also verified the prediction that most detectable ITH originates from early private alterations and not from later clonal expansions, thus exposing the profile of the primordial tumor. Moreover, some tumors appear 'born to be bad', with subclone mixing indicative of early malignant potential. This new model provides a quantitative framework to interpret tumor growth dynamics and the origins of ITH, with important clinical implications. .
Gubernator, M., Slater, S.C., Spencer, H.L., Spiteri, I., Sottoriva, A., Riu, F., Rowlinson, J., Avolio, E., Katare, R., Mangialardi, G., et al.
(2015). Epigenetic profile of human adventitial progenitor cells correlates with therapeutic outcomes in a mouse model of limb ischemia. Arterioscler thromb vasc biol,
Vol.35
(3),
pp. 675-688.
show abstract
OBJECTIVE: We investigated the association between the functional, epigenetic, and expressional profile of human adventitial progenitor cells (APCs) and therapeutic activity in a model of limb ischemia. APPROACH AND RESULTS: Antigenic and functional features were analyzed throughout passaging in 15 saphenous vein (SV)-derived APC lines, of which 10 from SV leftovers of coronary artery bypass graft surgery and 5 from varicose SV removal. Moreover, 5 SV-APC lines were transplanted (8×10(5) cells, IM) in mice with limb ischemia. Blood flow and capillary and arteriole density were correlated with functional characteristics and DNA methylation/expressional markers of transplanted cells. We report successful expansion of tested lines, which reached the therapeutic target of 30 to 50 million cells in ≈10 weeks. Typical antigenic profile, viability, and migratory and proangiogenic activities were conserved through passaging, with low levels of replicative senescence. In vivo, SV-APC transplantation improved blood flow recovery and revascularization of ischemic limbs. Whole genome screening showed an association between DNA methylation at the promoter or gene body level and microvascular density and to a lesser extent with blood flow recovery. Expressional studies highlighted the implication of an angiogenic network centered on the vascular endothelial growth factor receptor as a predictor of microvascular outcomes. FLT-1 gene silencing in SV-APCs remarkably reduced their ability to form tubes in vitro and support tube formation by human umbilical vein endothelial cells, thus confirming the importance of this signaling in SV-APC angiogenic function. CONCLUSIONS: DNA methylation landscape illustrates different therapeutic activities of human APCs. Epigenetic screening may help identify determinants of therapeutic vasculogenesis in ischemic disease..
Kang, H., Salomon, M.P., Sottoriva, A., Zhao, J., Toy, M., Press, M.F., Curtis, C., Marjoram, P., Siegmund, K. & Shibata, D., et al.
(2015). Many private mutations originate from the first few divisions of a human colorectal adenoma. The journal of pathology,
Vol.237
(3),
pp. 355-362.
Sottoriva, A., Spiteri, I., Shibata, D., Curtis, C. & Tavaré, S.
(2013). Single-molecule genomic data delineate patient-specific tumor profiles and cancer stem cell organization. Cancer res,
Vol.73
(1),
pp. 41-49.
show abstract
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..
Sottoriva, A., Spiteri, I., Piccirillo, S.G., Touloumis, A., Collins, V.P., Marioni, J.C., Curtis, C., Watts, C. & Tavaré, S.
(2013). Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc natl acad sci u s a,
Vol.110
(10),
pp. 4009-4014.
show abstract
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..
Dvinge, H., Git, A., Gräf, S., Salmon-Divon, M., Curtis, C., Sottoriva, A., Zhao, Y., Hirst, M., Armisen, J., Miska, E.A., et al.
(2013). The shaping and functional consequences of the microRNA landscape in breast cancer. Nature,
Vol.497
(7449),
pp. 378-382.
show abstract
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..
Vermeulen, L., Morrissey, E., van der Heijden, M., Nicholson, A.M., Sottoriva, A., Buczacki, S., Kemp, R., Tavaré, S. & Winton, D.J.
(2013). Defining stem cell dynamics in models of intestinal tumor initiation. Science,
Vol.342
(6161),
pp. 995-998.
show abstract
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. .
Ageron, M., Aguilar, J.A., Al Samarai, I., Albert, A., Ameli, F., André, M., Anghinolfi, M., Anton, G., Anvar, S., Ardid, M., et al.
(2011). ANTARES: The first undersea neutrino telescope. Nuclear instruments and methods in physics research section a: accelerators, spectrometers, detectors and associated equipment,
Vol.656,
pp. 11-38.
show abstract
The ANTARES Neutrino Telescope was completed in May 2008 and is the first operational Neutrino Telescope in the Mediterranean Sea. The main purpose of the detector is to perform neutrino astronomy and the apparatus also offers facilities for marine and Earth sciences. This paper describes the design, the construction and the installation of the telescope in the deep sea, offshore from Toulon in France. An illustration of the detector performance is given..
Sottoriva, A., Vermeulen, L. & Tavaré, S.
(2011). Modeling Evolutionary Dynamics of Epigenetic Mutations in Hierarchically Organized Tumors. Plos computational biology,
Vol.7.
Sottoriva, A., Verhoeff, J.J., Borovski, T., McWeeney, S.K., Naumov, L., Medema, J.P., Sloot, P.M. & Vermeulen, L.
(2010). Cancer stem cell tumor model reveals invasive morphology and increased phenotypical heterogeneity. Cancer res,
Vol.70
(1),
pp. 46-56.
show abstract
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..
Sottoriva, A., Sloot, P.M., Medema, J.P. & Vermeulen, L.
(2010). Exploring cancer stem cell niche directed tumor growth. Cell cycle,
Vol.9
(8),
pp. 1472-1479.
show abstract
The finding that only a sub-fraction of tumor cells, so called Cancer Stem Cells (CSC), is endowed with the capacity to initiate new tumors has important consequences for fundamental as well as clinical cancer research. Previously we established by computational modeling techniques that CSC driven tumor growth instigates infiltrative behavior, and perhaps most interesting, stimulates tumor cell heterogeneity. An important question that remains is to what extend CSC functions are intrinsically regulated or whether this capacity is orchestrated by the microenvironment, i.e. a putative CSC niche. Here we investigate how extrinsic regulation of CSC properties affects the characteristics of malignancies. We find that highly invasive growth in tumors dependent on a small subset of cells is not restricted to CSC-driven tumors, but is also observed in tumors where the CSC capacity of tumor cells is completely defined by the microenvironment. Importantly, also the high level of heterogeneity that was observed for CSC-driven tumors is preserved and partially even increased in malignancies with a microenvironmentally orchestrated CSC population. This indicates that invasive growth and high heterogeneity are fundamental properties of tumors fueled by a small population of tumor cells..
Sottoriva, A. & Tavaré, S.
(2010). Integrating approximate Bayesian computation with complex agent-based models for cancer research. Compstat 2010 - proceedings in computational statistics, eds. saporta g & lechevallier y. springer, physica verlag,
,
pp. 57-66.
Portegies Zwart, S., McMillan, S., Harfst, S., Groen, D., Fujii, M., Nualláin, B.Ó., Glebbeek, E., Heggie, D., Lombardi, J., Hut, P., et al.
(2009). A multiphysics and multiscale software environment for modeling astrophysical systems. New astronomy,
Vol.14,
pp. 369-378.
show abstract
We present MUSE, a software framework for combining existing computational tools for different astrophysical domains into a single multiphysics, multiscale application. MUSE facilitates the coupling of existing codes written in different languages by providing inter-language tools and by specifying an interface between each module and the framework that represents a balance between generality and computational efficiency. This approach allows scientists to use combinations of codes to solve highly coupled problems without the need to write new codes for other domains or significantly alter their existing codes. MUSE currently incorporates the domains of stellar dynamics, stellar evolution and stellar hydrodynamics for studying generalized stellar systems. We have now reached a “Noah’s Ark” milestone, with (at least) two available numerical solvers for each domain. MUSE can treat multiscale and multiphysics systems in which the time- and size-scales are well separated, like simulating the evolution of planetary systems, small stellar associations, dense stellar clusters, galaxies and galactic nuclei. In this paper we describe three examples calculated using MUSE: the merger of two galaxies, the merger of two evolving stars, and a hybrid N-body simulation. In addition, we demonstrate an implementation of MUSE on a distributed computer which may also include special-purpose hardware, such as GRAPEs or GPUs, to accelerate computations. The current MUSE code base is publicly available as open source at http://muse.li..
Piccirillo, S.G., Sottoriva, A. & Watts, C.
The role of sub-ventricular zone in gliomagenesis. Aging,
Vol.7
(10),
pp. 738-739.
Ellis, H.P., Greenslade, M., Powell, B., Spiteri, I., Sottoriva, A. & Kurian, K.M.
Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence. Frontiers in oncology,
Vol.5.
Werner, B., Traulsen, A., Sottoriva, A. & Dingli, D.
Detecting truly clonal alterations from multi-region profiling of tumours. Scientific reports,
.
Lote, H., Spiteri Sagastume, I., Ermini, L., Vatsiou, A., Roy, A., McDonald, A., Maka, N., Balsitis, M., Bose, N., Simbolo, M., et al.
Carbon dating cancer: defining the chronology of metastatic progression in colorectal cancer. Annals of oncology,
.
Sun, R., Hu, Z., Sottoriva, A., Graham, T.A., Harpak, A., Ma, Z., Fisher, J.M., Shibata, D. & Curtis, C.
Between-Region Genetic Divergence Reflects the Mode and Tempo of Tumor Evolution. Nature genetics,
.
Vlachogiannis, G., Hedayat, S., Vatsiou, A., Jamin, Y., Fernandez Mateos, J., Khan, K., Lampis, A., Eason, K., Burke, R., Rata, M., et al.
Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science,
.
Williams, M., Werner, B., Heide, T., Curtis, C., Sottoriva, A., Barnes, C.P. & Graham, T.A.
Quantification of subclonal selection in cancer from bulk sequencing data. Nature genetics,
.
Werner, B. & Sottoriva, A.
Variation of mutational burden in healthy human tissues suggests non-random strand segregation and allows measuring somatic mutation rates. Plos computational biology,
.
Barry, P., Vatsiou, A., Spiteri Sagastume, I., Nichol, D., Cresswell, G., Acar, A., Trahearn, N., Hrebien, S., Garcia-Murillas, I., Chkhaidze, K., et al.
The spatio-temporal evolution of lymph node spread in early breast cancer. Clinical cancer research,
.
Khan, K., Cunningham, D., Werner, B., Vlachogiannis, G., Spiteri Sagastume, I., Heide, T., Fernandez Mateos, J., Vatsiou, A., Lampis, A., Damavandi, M.D., et al.
Longitudinal liquid biopsy and mathematical modelling of clonal evolution forecast waiting time to treatment failure in the PROSPECT-C phase II colorectal cancer clinical trial. Cancer discovery,
.
Caravagna, G., Giarratano, Y., Ramazzotti, D., Tomlinson, I., Graham, T.A., Sanguinetti, G. & Sottoriva, A.
Detecting repeated cancer evolution from multiregion tumor sequencing data. Nature methods,
.
Sottoriva, A., Chkhaidze, K., Heide, T., Werner, B., Williams, M.J., Huang, W., Caravagna, G. & Graham, T.A.
Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. Plos computational biology,
.
Caravagna, G., Heide, T., Williams, M., Zapata, L., Nichol, D., Chkhaidze, K., Cross, W., Cresswell, G.D., Werner, B., Acar, A., et al.
Model-based tumor subclonal reconstruction. ,
.
show abstract
AbstractThe vast majority of cancer next-generation sequencing data consist of bulk samples composed of mixtures of cancer and normal cells. To study tumor evolution, subclonal reconstruction approaches based on machine learning are used to separate subpopulation of cancer cells and reconstruct their ancestral relationships. However, current approaches are entirely data-driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in subclonal reconstruction if tumor evolution is not accounted for, and that those errors increase when multiple samples are taken from the same tumor. To address this issue, we present a novel approach for model-based subclonal reconstruction that combines data-driven machine learning with evolutionary theory. Using public, synthetic and newly generated data, we show the method is more robust and accurate than current techniques in both single-sample and multi-region sequencing data. With careful data curation and interpretation, we show how the method allows minimizing the confounding factors that affect non-evolutionary methods, leading to a more accurate recovery of the evolutionary history of human tumors..
Caravagna, G., Giarratano, Y., Ramazzotti, D., Graham, T.A., Sanguinetti, G. & Sottoriva, A.
Detecting repeated cancer evolution in human tumours from multi-region sequencing data. ,
.
show abstract
AbstractCarcinogenesis is an evolutionary process driven by the accumulation of genomic aberrations. Recurrent sequences of genomic changes, both between and within patients, reflect repeated evolution that is valuable for anticipating cancer progression. Multi-region sequencing and phylogenetic analysis allow inference of the partial temporal order of genomic changes within a patient’s tumour. However, the inherent stochasticity of the evolutionary process makes phylogenetic trees from different patients appear very distinct, preventing the robust identification of recurrent evolutionary trajectories. Here we present a novel quantitative method based on a machine learning approach called Transfer Learning (TL) that allows overcoming the stochastic effects of cancer evolution and highlighting hidden recurrences in cancer patient cohorts. When applied to multi-region sequencing datasets from lung, breast and renal cancer (708 samples from 160 patients), our method detected repeated evolutionary trajectories that determine novel patient subgroups, which reproduce in large singlesample cohorts (n=2,641) and have prognostic value. Our method provides a novel patient classification measure that is grounded in the cancer evolution paradigm, and which reveals repeated evolution during tumorigenesis, with implications for our ability to anticipate malignant evolution..
Heide, T., Zapata, L., Williams, M.J., Werner, B., Barnes, C.P., Graham, T.A. & Sottoriva, A.
Reply: Neutral tumor evolution?. ,
.
Gatenbee, C.D., Baker, A.-., Schenck, R.O., Neves, M., Hasan, S.Y., Martinez, P., Cross, W.C., Jansen, M., Rodriguez-Justo, M., Sottoriva, A., et al.
Niche engineering drives early passage through an immune bottleneck in progression to colorectal cancer. ,
.
show abstract
Colorectal cancer develops from its precursor lesion, the adenoma. The immune system is hypothesized to be key in modulating progression, but tumor-immune eco-evolutionary dynamics remain uncharacterized. Here, we demonstrate a key role for immune evasion in the progression of human benign disease to colorectal cancer. We constructed a mathematical model of tumor-immune eco-evolutionary dynamics that predicted ecological succession, from an "immune-hot" adenoma immune ecology rich in T cells to an "immune-cold" carcinoma ecology, deficient in T cells and rich in immunosuppressive cells. Using a cross-sectional cohort of adenomas and carcinomas, we validated this prediction by direct measurement of the tumor-immune ecology using whole-slide 10-marker immunohistochemistry (IHC), and analysis of neoantigen clonal architecture multi-region exome sequencing data. Changes in immune ecology relax selection against antigens with high recognition potentials. This study indicates that immune surveillance represents a key evolutionary bottleneck in the evolution of colon cancer..
Werner, B. & Sottoriva, A.
Variation of mutational burden in healthy human tissues suggests non-random strand segregation and allows measuring somatic mutation rates. ,
.
show abstract
AbstractThe immortal strand hypothesis poses that stem cells could produce differentiated progeny while conserving the original template strand, thus avoiding accumulating somatic mutations. However, quantitating the extent of non-random DNA strand segregation in human stem cells remains difficult in vivo. Here we show that the change of the mean and variance of the mutational burden with age in healthy human tissues allows estimating strand segregation probabilities and somatic mutation rates. We analysed deep sequencing data from healthy human colon, small intestine, liver, skin and brain. We found highly effective non-random DNA strand segregation in all adult tissues (mean strand segregation probability: 0.98, standard error bounds (0.97,0.99)). In contrast, non-random strand segregation efficiency is reduced to 0.87 (0.78,0.88) in neural tissue during early development, suggesting stem cell pool expansions due to symmetric self-renewal. Healthy somatic mutation rates differed across tissue types, ranging from 3.5×10−9 /bp/division in small intestine to 1.6×10−7/bp/division in skin.Author SummaryCairn proposed in 1975 that upon proliferation, cells might not segregate DNA strands randomly into daughter cells, but preferentially keep the ancestral (blue print) template strand in stem cells. This mechanism would allow to drastically reduce the rate of mutation accumulation in human tissues. Testing the hypothesis in human stem cells within their natural tissue environment remains challenging. Here we show that the patterns of mutation accumulation in human tissues with age support highly effective non-random DNA strand segregation after adolescence. In contrast, during early development in infants, DNA strand segregation is less effective, likely because stem cell populations are continuing to grow..