Loupakis, F.
Depetris, I.
Biason, P.
Intini, R.
Prete, A.A.
Leone, F.
Lombardi, P.
Filippi, R.
Spallanzani, A.
Cascinu, S.
Bonetti, L.R.
Maddalena, G.
Valeri, N.
Sottoriva, A.
Zapata, L.
Salmaso, R.
Munari, G.
Rugge, M.
Dei Tos, A.P.
Golovato, J.
Sanborn, J.Z.
Nguyen, A.
Schirripa, M.
Zagonel, V.
Lonardi, S.
Fassan, M.
(2020). Prediction of Benefit from Checkpoint Inhibitors in Mismatch Repair Deficient Metastatic Colorectal Cancer: Role of Tumor Infiltrating Lymphocytes. Oncologist,
Vol.25
(6),
pp. 481-487.
show abstract
BACKGROUND: Immunotherapy with immune checkpoint inhibitors (ICIs) is highly effective in microsatellite instability-high (MSI-H) metastatic colorectal cancer (mCRC); however, specific predictive biomarkers are lacking. PATIENTS AND METHODS: Data and samples from 85 patients with MSI-H mCRC treated with ICIs were gathered. Tumor infiltrating lymphocytes (TILs) and tumor mutational burden (TMB) were analyzed in an exploratory cohort of "super" responders and "clearly" refractory patients; TILs were then evaluated in the whole cohort of patients. Primary objectives were the correlation between the number of TILs and TMB and their role as biomarkers of ICI efficacy. Main endpoints included response rate (RR), progression-free survival (PFS), and overall survival (OS). RESULTS: In the exploratory cohort, an increasing number of TILs correlated to higher TMB (Pearson's test, p = .0429). In the whole cohort, median number of TILs was 3.6 in responders compared with 1.8 in nonresponders (Mann-Whitney test, p = .0448). RR was 70.6% in patients with high number of TILs (TILs-H) compared with 42.9% in patients with low number of TILs (odds ratio = 3.20, p = .0291). Survival outcomes differed significantly in favor of TILs-H (PFS: hazard ratio [HR] = 0.42, p = .0278; OS: HR = 0.41, p = .0463). CONCLUSION: A significant correlation between higher TMB and increased number of TILs was shown. A significantly higher activity and better PFS and OS with ICI in MSI-H mCRC were reported in cases with high number of TILs, thus supporting further studies of TIL count as predictive biomarker of ICI efficacy. IMPLICATIONS FOR PRACTICE: Microsatellite instability is the result of mismatch repair protein deficiency, caused by germline mutations or somatic modifications in mismatch repair genes. In metastatic colorectal cancer (mCRC), immunotherapy (with immune checkpoint inhibitors [ICIs]) demonstrated remarkable clinical benefit in microsatellite instability-high (MSI-H) patients. ICI primary resistance has been observed in approximately 25% of patients with MSI-H mCRC, underlining the need for predictive biomarkers. In this study, tumor mutational burden (TMB) and tumor infiltrating lymphocyte (TIL) analyses were performed in an exploratory cohort of patients with MSI-H mCRC treated with ICIs, demonstrating a significant correlation between higher TMB and increased number of TILs. Results also demonstrated a significant correlation between high number of TILs and clinical responses and survival benefit in a large data set of patients with MSI-H mCRC treated with ICI. TMB and TILs could represent predictive biomarkers of ICI efficacy in MSI-H mCRC and should be incorporated in future trials testing checkpoint inhibitors in colorectal cancer..
Werner, B.
Case, J.
Williams, M.J.
Chkhaidze, K.
Temko, D.
Fernández-Mateos, J.
Cresswell, G.D.
Nichol, D.
Cross, W.
Spiteri, I.
Huang, W.
Tomlinson, I.P.
Barnes, C.P.
Graham, T.A.
Sottoriva, A.
(2020). Measuring single cell divisions in human tissues from multi-region sequencing data. Nature communications,
Vol.11
(1).
Cresswell, G.D.
Nichol, D.
Spiteri, I.
Tari, H.
Zapata, L.
Heide, T.
Maley, C.C.
Magnani, L.
Schiavon, G.
Ashworth, A.
Barry, P.
Sottoriva, A.
(2020). Mapping the breast cancer metastatic cascade onto ctDNA using genetic and epigenetic clonal tracking. Nature communications,
Vol.11
(1).
Acar, A.
Nichol, D.
Fernandez-Mateos, J.
Cresswell, G.D.
Barozzi, I.
Hong, S.P.
Trahearn, N.
Spiteri, I.
Stubbs, M.
Burke, R.
Stewart, A.
Caravagna, G.
Werner, B.
Vlachogiannis, G.
Maley, C.C.
Magnani, L.
Valeri, N.
Banerji, U.
Sottoriva, A.
(2020). Exploiting evolutionary steering to induce collateral drug sensitivity in cancer. Nature communications,
Vol.11
(1).
Williams, M.J.
Zapata, L.
Werner, B.
Barnes, C.P.
Sottoriva, A.
Graham, T.A.
(2020). Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios. Elife,
Vol.9.
show abstract
The distribution of fitness effects (DFE) defines how new mutations spread through an evolving population. The ratio of non-synonymous to synonymous mutations (dN/dS) has become a popular method to detect selection in somatic cells. However the link, in somatic evolution, between dN/dS values and fitness coefficients is missing. Here we present a quantitative model of somatic evolutionary dynamics that determines the selective coefficients of individual driver mutations from dN/dS estimates. We then measure the DFE for somatic mutant clones in ostensibly normal oesophagus and skin. We reveal a broad distribution of fitness effects, with the largest fitness increases found for TP53 and NOTCH1 mutants (proliferative bias 1-5%). This study provides the theoretical link between dN/dS values and selective coefficients in somatic evolution, and measures the DFE of mutations in human tissues..
Spiteri, I.
Caravagna, G.
Cresswell, G.D.
Vatsiou, A.
Nichol, D.
Acar, A.
Ermini, L.
Chkhaidze, K.
Werner, B.
Mair, R.
Brognaro, E.
Verhaak, R.G.
Sanguinetti, G.
Piccirillo, S.G.
Watts, C.
Sottoriva, A.
(2019). Evolutionary dynamics of residual disease in human glioblastoma. Ann oncol,
Vol.30
(3),
pp. 456-463.
show abstract
BACKGROUND: Glioblastoma is the most common and aggressive adult brain malignancy against which conventional surgery and chemoradiation provide limited benefit. Even when a good treatment response is obtained, recurrence inevitably occurs either locally (∼80%) or distally (∼20%), driven by cancer clones that are often genomically distinct from those in the primary tumour. Glioblastoma cells display a characteristic infiltrative phenotype, invading the surrounding tissue and often spreading across the whole brain. Cancer cells responsible for relapse can reside in two compartments of residual disease that are left behind after treatment: the infiltrated normal brain parenchyma and the sub-ventricular zone. However, these two sources of residual disease in glioblastoma are understudied because of the difficulty in sampling these regions during surgery. PATIENT AND METHODS: Here, we present the results of whole-exome sequencing of 69 multi-region samples collected using fluorescence-guided resection from 11 patients, including the infiltrating tumour margin and the sub-ventricular zone for each patient, as well as matched blood. We used a phylogenomic approach to dissect the spatio-temporal evolution of each tumour and unveil the relation between residual disease and the main tumour mass. We also analysed two patients with paired primary-recurrence samples with matched residual disease. RESULTS: Our results suggest that infiltrative subclones can arise early during tumour growth in a subset of patients. After treatment, the infiltrative subclones may seed the growth of a recurrent tumour, thus representing the 'missing link' between the primary tumour and recurrent disease. CONCLUSIONS: These results are consistent with recognised clinical phenotypic behaviour and suggest that more specific therapeutic targeting of cells in the infiltrated brain parenchyma may improve patient's outcome..
Turajlic, S.
Sottoriva, A.
Graham, T.
Swanton, C.
(2019). Resolving genetic heterogeneity in cancer. Nat rev genet,
Vol.20
(7),
pp. 404-416.
show abstract
To a large extent, cancer conforms to evolutionary rules defined by the rates at which clones mutate, adapt and grow. Next-generation sequencing has provided a snapshot of the genetic landscape of most cancer types, and cancer genomics approaches are driving new insights into cancer evolutionary patterns in time and space. In contrast to species evolution, cancer is a particular case owing to the vast size of tumour cell populations, chromosomal instability and its potential for phenotypic plasticity. Nevertheless, an evolutionary framework is a powerful aid to understand cancer progression and therapy failure. Indeed, such a framework could be applied to predict individual tumour behaviour and support treatment strategies..
Chkhaidze, K.
Heide, T.
Werner, B.
Williams, M.J.
Huang, W.
Caravagna, G.
Graham, T.A.
Sottoriva, A.
(2019). Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. Plos comput biol,
Vol.15
(7),
p. e1007243.
show abstract
Quantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constraints, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from both bulk and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We also propose a statistical inference framework that incorporates spatial effects within a growing tumour and so represents a further step forwards in the inference of evolutionary dynamics from genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors remains challenging. However, mechanistic model-based approaches have the potential to capture the sources of noise and better interpret the data..
Nawaz, S.
Trahearn, N.A.
Heindl, A.
Banerjee, S.
Maley, C.C.
Sottoriva, A.
Yuan, Y.
(2019). Analysis of tumour ecological balance reveals resource-dependent adaptive strategies of ovarian cancer. Ebiomedicine,
Vol.48,
pp. 224-235.
Williams, M.J.
Sottoriva, A.
Graham, T.A.
(2019). Measuring Clonal Evolution in Cancer with Genomics. Annu rev genomics hum genet,
Vol.20,
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. Ann oncol,
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.
Ermini, L.
Huntingford, I.S.
Cottom, H.
Zabaglo, L.
Koelble, K.
Khalique, S.
Rusby, J.E.
Muscara, F.
Dowsett, M.
Maley, C.C.
Natrajan, R.
Yuan, Y.
Schiavon, G.
Turner, N.
Sottoriva, A.
(2018). The Spatiotemporal Evolution of Lymph Node Spread in Early Breast Cancer. Clin cancer res,
Vol.24
(19),
pp. 4763-4770.
show abstract
Purpose: The most significant prognostic factor in early breast cancer is lymph node involvement. This stage between localized and systemic disease is key to understanding breast cancer progression; however, our knowledge of the evolution of lymph node malignant invasion remains limited, as most currently available data are derived from primary tumors.Experimental Design: In 11 patients with treatment-naïve node-positive early breast cancer without clinical evidence of distant metastasis, we investigated lymph node evolution using spatial multiregion sequencing (n = 78 samples) of primary and lymph node deposits and genomic profiling of matched longitudinal circulating tumor DNA (ctDNA).Results: Linear evolution from primary to lymph node was rare (1/11), whereas the majority of cases displayed either early divergence between primary and nodes (4/11) or no detectable divergence (6/11), where both primary and nodal cells belonged to a single recent expansion of a metastatic clone. Divergence of metastatic subclones was driven in part by APOBEC. Longitudinal ctDNA samples from 2 of 7 subjects with evaluable plasma taken perioperatively reflected the two major evolutionary patterns and demonstrate that private mutations can be detected even from early metastatic nodal deposits. Moreover, node removal resulted in disappearance of private lymph node mutations in ctDNA.Conclusions: This study sheds new light on a crucial evolutionary step in the natural history of breast cancer, demonstrating early establishment of axillary lymph node metastasis in a substantial proportion of patients. Clin Cancer Res; 24(19); 4763-70. ©2018 AACR..
Williams, M.J.
Werner, B.
Heide, T.
Curtis, C.
Barnes, C.P.
Sottoriva, A.
Graham, T.A.
(2018). Quantification of subclonal selection in cancer from bulk sequencing data. Nat genet,
Vol.50
(6),
pp. 895-903.
show abstract
Subclonal architectures are prevalent across cancer types. However, the temporal evolutionary dynamics that produce tumor subclones remain unknown. Here we measure clone dynamics in human cancers by using computational modeling of subclonal selection and theoretical population genetics applied to high-throughput sequencing data. Our method determined the detectable subclonal architecture of tumor samples and simultaneously measured the selective advantage and time of appearance of each subclone. We demonstrate the accuracy of our approach and the extent to which evolutionary dynamics are recorded in the genome. Application of our method to high-depth sequencing data from breast, gastric, blood, colon and lung cancer samples, as well as metastatic deposits, showed that detectable subclones under selection, when present, consistently emerged early during tumor growth and had a large fitness advantage (>20%). Our quantitative framework provides new insight into the evolutionary trajectories of human cancers and facilitates predictive measurements in individual tumors from widely available sequencing data..
Werner, B.
Sottoriva, A.
(2018). Variation of mutational burden in healthy human tissues suggests non-random strand segregation and allows measuring somatic mutation rates. Plos comput biol,
Vol.14
(6),
p. e1006233.
show abstract
The 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..
Caravagna, G.
Giarratano, Y.
Ramazzotti, D.
Tomlinson, I.
Graham, T.A.
Sanguinetti, G.
Sottoriva, A.
(2018). Detecting repeated cancer evolution from multi-region tumor sequencing data. Nat methods,
Vol.15
(9),
pp. 707-714.
show abstract
Recurrent successions of genomic changes, both within and between patients, reflect repeated evolutionary processes that are valuable for the anticipation of cancer progression. Multi-region sequencing allows the temporal order of some genomic changes in a tumor to be inferred, but the robust identification of repeated evolution across patients remains a challenge. We developed a machine-learning method based on transfer learning that allowed us to overcome the stochastic effects of cancer evolution and noise in data and identified hidden evolutionary patterns in cancer cohorts. When applied to multi-region sequencing datasets from lung, breast, renal, and colorectal cancer (768 samples from 178 patients), our method detected repeated evolutionary trajectories in subgroups of patients, which were reproduced in single-sample cohorts (n = 2,935). Our method provides a means of classifying patients on the basis of how their tumor evolved, with implications for the anticipation of disease progression..
Khan, K.H.
Cunningham, D.
Werner, B.
Vlachogiannis, G.
Spiteri, I.
Heide, T.
Mateos, J.F.
Vatsiou, A.
Lampis, A.
Damavandi, M.D.
Lote, H.
Huntingford, I.S.
Hedayat, S.
Chau, I.
Tunariu, N.
Mentrasti, G.
Trevisani, F.
Rao, S.
Anandappa, G.
Watkins, D.
Starling, N.
Thomas, J.
Peckitt, C.
Khan, N.
Rugge, M.
Begum, R.
Hezelova, B.
Bryant, A.
Jones, T.
Proszek, P.
Fassan, M.
Hahne, J.C.
Hubank, M.
Braconi, C.
Sottoriva, A.
Valeri, N.
(2018). Longitudinal Liquid Biopsy and Mathematical Modeling of Clonal Evolution Forecast Time to Treatment Failure in the PROSPECT-C Phase II Colorectal Cancer Clinical Trial. Cancer discov,
Vol.8
(10),
pp. 1270-1285.
show abstract
Sequential profiling of plasma cell-free DNA (cfDNA) holds immense promise for early detection of patient progression. However, how to exploit the predictive power of cfDNA as a liquid biopsy in the clinic remains unclear. RAS pathway aberrations can be tracked in cfDNA to monitor resistance to anti-EGFR monoclonal antibodies in patients with metastatic colorectal cancer. In this prospective phase II clinical trial of single-agent cetuximab in RAS wild-type patients, we combine genomic profiling of serial cfDNA and matched sequential tissue biopsies with imaging and mathematical modeling of cancer evolution. We show that a significant proportion of patients defined as RAS wild-type based on diagnostic tissue analysis harbor aberrations in the RAS pathway in pretreatment cfDNA and, in fact, do not benefit from EGFR inhibition. We demonstrate that primary and acquired resistance to cetuximab are often of polyclonal nature, and these dynamics can be observed in tissue and plasma. Furthermore, evolutionary modeling combined with frequent serial sampling of cfDNA allows prediction of the expected time to treatment failure in individual patients. This study demonstrates how integrating frequently sampled longitudinal liquid biopsies with a mathematical framework of tumor evolution allows individualized quantitative forecasting of progression, providing novel opportunities for adaptive personalized therapies.Significance: Liquid biopsies capture spatial and temporal heterogeneity underpinning resistance to anti-EGFR monoclonal antibodies in colorectal cancer. Dense serial sampling is needed to predict the time to treatment failure and generate a window of opportunity for intervention. Cancer Discov; 8(10); 1270-85. ©2018 AACR.See related commentary by Siravegna and Corcoran, p. 1213This article is highlighted in the In This Issue feature, p. 1195..
Cross, W.
Kovac, M.
Mustonen, V.
Temko, D.
Davis, H.
Baker, A.-.
Biswas, S.
Arnold, R.
Chegwidden, L.
Gatenbee, C.
Anderson, A.R.
Koelzer, V.H.
Martinez, P.
Jiang, X.
Domingo, E.
Woodcock, D.J.
Feng, Y.
Kovacova, M.
Maughan, T.
Jansen, M.
Rodriguez-Justo, M.
Ashraf, S.
Guy, R.
Cunningham, C.
East, J.E.
Wedge, D.C.
Wang, L.M.
Palles, C.
Heinimann, K.
Sottoriva, A.
Leedham, S.J.
Graham, T.A.
Tomlinson, I.P.
(2018). The evolutionary landscape of colorectal tumorigenesis. Nature ecology & evolution,
Vol.2
(10),
pp. 1661-1672.
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'. Nat genet,
Vol.50
(12),
pp. 1628-1630.
Heindl, A.
Khan, A.M.
Rodrigues, D.N.
Eason, K.
Sadanandam, A.
Orbegoso, C.
Punta, M.
Sottoriva, A.
Lise, S.
Banerjee, S.
Yuan, Y.
(2018). Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity. Nature communications,
Vol.9
(1).
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?'. Nat genet,
Vol.50
(12),
pp. 1633-1637.
Vlachogiannis, G.
Hedayat, S.
Vatsiou, A.
Jamin, Y.
Fernández-Mateos, J.
Khan, K.
Lampis, A.
Eason, K.
Huntingford, I.
Burke, R.
Rata, M.
Koh, D.-.
Tunariu, N.
Collins, D.
Hulkki-Wilson, S.
Ragulan, C.
Spiteri, I.
Moorcraft, S.Y.
Chau, I.
Rao, S.
Watkins, D.
Fotiadis, N.
Bali, M.
Darvish-Damavandi, M.
Lote, H.
Eltahir, Z.
Smyth, E.C.
Begum, R.
Clarke, P.A.
Hahne, J.C.
Dowsett, M.
de Bono, J.
Workman, P.
Sadanandam, A.
Fassan, M.
Sansom, O.J.
Eccles, S.
Starling, N.
Braconi, C.
Sottoriva, A.
Robinson, S.P.
Cunningham, D.
Valeri, N.
(2018). Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science,
Vol.359
(6378),
pp. 920-926.
show abstract
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..
Lote, H.
Spiteri, I.
Ermini, L.
Vatsiou, A.
Roy, A.
McDonald, A.
Maka, N.
Balsitis, M.
Bose, N.
Simbolo, M.
Mafficini, A.
Lampis, A.
Hahne, J.C.
Trevisani, F.
Eltahir, Z.
Mentrasti, G.
Findlay, C.
Kalkman, E.A.
Punta, M.
Werner, B.
Lise, S.
Aktipis, A.
Maley, C.
Greaves, M.
Braconi, C.
White, J.
Fassan, M.
Scarpa, A.
Sottoriva, A.
Valeri, N.
(2017). Carbon dating cancer: defining the chronology of metastatic progression in colorectal cancer. Ann oncol,
Vol.28
(6),
pp. 1243-1249.
show abstract
Background: Patients often ask oncologists how long a cancer has been present before causing symptoms or spreading to other organs. The evolutionary trajectory of cancers can be defined using phylogenetic approaches but lack of chronological references makes dating the exact onset of tumours very challenging. Patients and methods: Here, we describe the case of a colorectal cancer (CRC) patient presenting with synchronous lung metastasis and metachronous thyroid, chest wall and urinary tract metastases over the course of 5 years. The chest wall metastasis was caused by needle tract seeding, implying a known time of onset. Using whole genome sequencing data from primary and metastatic sites we inferred the complete chronology of the cancer by exploiting the time of needle tract seeding as an in vivo 'stopwatch'. This approach allowed us to follow the progression of the disease back in time, dating each ancestral node of the phylogenetic tree in the past history of the tumour. We used a Bayesian phylogenomic approach, which accounts for possible dynamic changes in mutational rate, to reconstruct the phylogenetic tree and effectively 'carbon date' the malignant progression. Results: The primary colon cancer emerged between 5 and 8 years before the clinical diagnosis. The primary tumour metastasized to the lung and the thyroid within a year from its onset. The thyroid lesion presented as a tumour-to-tumour deposit within a benign Hurthle adenoma. Despite rapid metastatic progression from the primary tumour, the patient showed an indolent disease course. Primary cancer and metastases were microsatellite stable and displayed low chromosomal instability. Neo-antigen analysis suggested minimal immunogenicity. Conclusion: Our data provide the first in vivo experimental evidence documenting the timing of metastatic progression in CRC and suggest that genomic instability might be more important than the metastatic potential of the primary cancer in dictating CRC fate..
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.
Sun, R.
Hu, Z.
Sottoriva, A.
Graham, T.A.
Harpak, A.
Ma, Z.
Fischer, J.M.
Shibata, D.
Curtis, C.
(2017). Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat genet,
Vol.49
(7),
pp. 1015-1024.
show abstract
Given the implications of tumor dynamics for precision medicine, there is a need to systematically characterize the mode of evolution across diverse solid tumor types. In particular, methods to infer the role of natural selection within established human tumors are lacking. By simulating spatial tumor growth under different evolutionary modes and examining patterns of between-region subclonal genetic divergence from multiregion sequencing (MRS) data, we demonstrate that it is feasible to distinguish tumors driven by strong positive subclonal selection from those evolving neutrally or under weak selection, as the latter fail to dramatically alter subclonal composition. We developed a classifier based on measures of between-region subclonal genetic divergence and projected patient data into model space, finding different modes of evolution both within and between solid tumor types. Our findings have broad implications for how human tumors progress, how they accumulate intratumoral heterogeneity, and ultimately how they may be more effectively treated..
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.
Anderson, K.S.
Gatenby, R.
Swanton, C.
Posada, D.
Wu, C.-.
Schiffman, J.D.
Hwang, E.S.
Polyak, K.
Anderson, A.R.
Brown, J.S.
Greaves, M.
Shibata, D.
(2017). Classifying the evolutionary and ecological features of neoplasms. Nat rev cancer,
Vol.17
(10),
pp. 605-619.
show abstract
Neoplasms change over time through a process of cell-level evolution, driven by genetic and epigenetic alterations. However, the ecology of the microenvironment of a neoplastic cell determines which changes provide adaptive benefits. There is widespread recognition of the importance of these evolutionary and ecological processes in cancer, but to date, no system has been proposed for drawing clinically relevant distinctions between how different tumours are evolving. On the basis of a consensus conference of experts in the fields of cancer evolution and cancer ecology, we propose a framework for classifying tumours that is based on four relevant components. These are the diversity of neoplastic cells (intratumoural heterogeneity) and changes over time in that diversity, which make up an evolutionary index (Evo-index), as well as the hazards to neoplastic cell survival and the resources available to neoplastic cells, which make up an ecological index (Eco-index). We review evidence demonstrating the importance of each of these factors and describe multiple methods that can be used to measure them. Development of this classification system holds promise for enabling clinicians to personalize optimal interventions based on the evolvability of the patient's tumour. The Evo- and Eco-indices provide a common lexicon for communicating about how neoplasms change in response to interventions, with potential implications for clinical trials, personalized medicine and basic cancer research..
Baker, A.-.
Huang, W.
Wang, X.-.
Jansen, M.
Ma, X.-.
Kim, J.
Anderson, C.M.
Wu, X.
Pan, L.
Su, N.
Luo, Y.
Domingo, E.
Heide, T.
Sottoriva, A.
Lewis, A.
Beggs, A.D.
Wright, N.A.
Rodriguez-Justo, M.
Park, E.
Tomlinson, I.
Graham, T.A.
(2017). Robust RNA-based in situ mutation detection delineates colorectal cancer subclonal evolution. Nature communications,
Vol.8
(1).
Werner, B.
Traulsen, A.
Sottoriva, A.
Dingli, D.
(2017). Detecting truly clonal alterations from multi-region profiling of tumours. Sci rep,
Vol.7,
p. 44991.
show abstract
Modern cancer therapies aim at targeting tumour-specific alterations, such as mutations or neo-antigens, and maximal treatment efficacy requires that targeted alterations are present in all tumour cells. Currently, treatment decisions are based on one or a few samples per tumour, creating uncertainty on whether alterations found in those samples are actually present in all tumour cells. The probability of classifying clonal versus sub-clonal alterations from multi-region profiling of tumours depends on the earliest phylogenetic branching event during tumour growth. By analysing 181 samples from 10 renal carcinoma and 11 colorectal cancers we demonstrate that the information gain from additional sampling falls onto a simple universal curve. We found that in colorectal cancers, 30% of alterations identified as clonal with one biopsy proved sub-clonal when 8 samples were considered. The probability to overestimate clonal alterations fell below 1% in 7/11 patients with 8 samples per tumour. In renal cell carcinoma, 8 samples reduced the list of clonal alterations by 40% with respect to a single biopsy. The probability to overestimate clonal alterations remained as high as 92% in 7/10 renal cancer patients. Furthermore, treatment was associated with more unbalanced tumour phylogenetic trees, suggesting the need of denser sampling of tumours at relapse..
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. Nat genet,
Vol.49
(9),
pp. 1289-1291.
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.
Graham, T.A.
Sottoriva, A.
(2016). Functional versus non-functional intratumor heterogeneity in cancer. Molecular & cellular oncology,
Vol.3
(4),
pp. e1162897-e1162897.
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.
Eskilsson, E.
Rosland, G.V.
Talasila, K.M.
Knappskog, S.
Keunen, O.
Sottoriva, A.
Foerster, S.
Solecki, G.
Taxt, T.
Jirik, R.
Fritah, S.
Harter, P.N.
Välk, K.
Al Hossain, J.
Joseph, J.V.
Jahedi, R.
Saed, H.S.
Piccirillo, S.G.
Spiteri, I.
Leiss, L.
Euskirchen, P.
Graziani, G.
Daubon, T.
Lund-Johansen, M.
Enger, P.Ø.
Winkler, F.
Ritter, C.A.
Niclou, S.P.
Watts, C.
Bjerkvig, R.
Miletic, H.
(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.
Venkitaraman, A.R.
Curtis, C.
Marioni, J.C.
Tavaré, S.
Watts, C.
(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.
Curtis, C.
(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.
Oikawa, A.
Reni, C.
Campagnolo, P.
Spinetti, G.
Touloumis, A.
Tavaré, S.
Prandi, F.
Pesce, M.
Hofner, M.
Klemens, V.
Emanueli, C.
Angelini, G.
Madeddu, P.
(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..
Piccirillo, S.G.
Sottoriva, A.
Watts, C.
(2015). The role of sub-ventricular zone in gliomagenesis. Aging (albany ny),
Vol.7
(10),
pp. 738-739.
Kang, H.
Salomon, M.P.
Sottoriva, A.
Zhao, J.
Toy, M.
Press, M.F.
Curtis, C.
Marjoram, P.
Siegmund, K.
Shibata, D.
(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.
Ellis, H.P.
Greenslade, M.
Powell, B.
Spiteri, I.
Sottoriva, A.
Kurian, K.M.
(2015). Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence. Front oncol,
Vol.5,
p. 251.
show abstract
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. .
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.
Chin, S.-.
Provenzano, E.
Turashvili, G.
Green, A.
Ellis, I.
Aparicio, S.
Caldas, C.
(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.
Arnaud, K.
Aslanides, E.
Assis Jesus, A.C.
Astraatmadja, T.
Aubert, J.-.
Auer, R.
Barbarito, E.
Baret, B.
Basa, S.
Bazzotti, M.
Becherini, Y.
Beltramelli, J.
Bersani, A.
Bertin, V.
Beurthey, S.
Biagi, S.
Bigongiari, C.
Billault, M.
Blaes, R.
Bogazzi, C.
de Botton, N.
Bou-Cabo, M.
Boudahef, B.
Bouwhuis, M.C.
Brown, A.M.
Brunner, J.
Busto, J.
Caillat, L.
Calzas, A.
Camarena, F.
Capone, A.
Caponetto, L.
Cârloganu, C.
Carminati, G.
Carmona, E.
Carr, J.
Carton, P.H.
Cassano, B.
Castorina, E.
Cecchini, S.
Ceres, A.
Chaleil, T.
Charvis, P.
Chauchot, P.
Chiarusi, T.
Circella, M.
Compère, C.
Coniglione, R.
Coppolani, X.
Cosquer, A.
Costantini, H.
Cottini, N.
Coyle, P.
Cuneo, S.
Curtil, C.
D Amato, C.
Damy, G.
van Dantzig, R.
De Bonis, G.
Decock, G.
Decowski, M.P.
Dekeyser, I.
Delagnes, E.
Desages-Ardellier, F.
Deschamps, A.
Destelle, J.-.
Di Maria, F.
Dinkespiler, B.
Distefano, C.
Dominique, J.-.
Donzaud, C.
Dornic, D.
Dorosti, Q.
Drogou, J.-.
Drouhin, D.
Druillole, F.
Durand, D.
Durand, R.
Eberl, T.
Emanuele, U.
Engelen, J.J.
Ernenwein, J.-.
Escoffier, S.
Falchini, E.
Favard, S.
Fehr, F.
Feinstein, F.
Ferri, M.
Ferry, S.
Fiorello, C.
Flaminio, V.
Folger, F.
Fritsch, U.
Fuda, J.-.
Galatá, S.
Galeotti, S.
Gay, P.
Gensolen, F.
Giacomelli, G.
Gojak, C.
Gómez-González, J.P.
Goret, P.
Graf, K.
Guillard, G.
Halladjian, G.
Hallewell, G.
van Haren, H.
Hartmann, B.
Heijboer, A.J.
Heine, E.
Hello, Y.
Henry, S.
Hernández-Rey, J.J.
Herold, B.
Hößl, J.
Hogenbirk, J.
Hsu, C.C.
Hubbard, J.R.
Jaquet, M.
Jaspers, M.
de Jong, M.
Jourde, D.
Kadler, M.
Kalantar-Nayestanaki, N.
Kalekin, O.
Kappes, A.
Karg, T.
Karkar, S.
Karolak, M.
Katz, U.
Keller, P.
Kestener, P.
Kok, E.
Kok, H.
Kooijman, P.
Kopper, C.
Kouchner, A.
Kretschmer, W.
Kruijer, A.
Kuch, S.
Kulikovskiy, V.
Lachartre, D.
Lafoux, H.
Lagier, P.
Lahmann, R.
Lahonde-Hamdoun, C.
Lamare, P.
Lambard, G.
Languillat, J.-.
Larosa, G.
Lavalle, J.
Le Guen, Y.
Le Provost, H.
LeVanSuu, A.
Lefèvre, D.
Legou, T.
Lelaizant, G.
Lévéque, C.
Lim, G.
Lo Presti, D.
Loehner, H.
Loucatos, S.
Louis, F.
Lucarelli, F.
Lyashuk, V.
Magnier, P.
Mangano, S.
Marcel, A.
Marcelin, M.
Margiotta, A.
Martinez-Mora, J.A.
Masullo, R.
Mazéas, F.
Mazure, A.
Meli, A.
Melissas, M.
Migneco, E.
Mongelli, M.
Montaruli, T.
Morganti, M.
Moscoso, L.
Motz, H.
Musumeci, M.
Naumann, C.
Naumann-Godo, M.
Neff, M.
Niess, V.
Nooren, G.J.
Oberski, J.E.
Olivetto, C.
Palanque-Delabrouille, N.
Palioselitis, D.
Papaleo, R.
Păvălaş, G.E.
Payet, K.
Payre, P.
Peek, H.
Petrovic, J.
Piattelli, P.
Picot-Clemente, N.
Picq, C.
Piret, Y.
Poinsignon, J.
Popa, V.
Pradier, T.
Presani, E.
Prono, G.
Racca, C.
Raia, G.
van Randwijk, J.
Real, D.
Reed, C.
Réthoré, F.
Rewiersma, P.
Riccobene, G.
Richardt, C.
Richter, R.
Ricol, J.S.
Rigaud, V.
Roca, V.
Roensch, K.
Rolin, J.-.
Rostovtsev, A.
Rottura, A.
Roux, J.
Rujoiu, M.
Ruppi, M.
Russo, G.V.
Salesa, F.
Salomon, K.
Sapienza, P.
Schmitt, F.
Schöck, F.
Schuller, J.-.
Schüssler, F.
Sciliberto, D.
Shanidze, R.
Shirokov, E.
Simeone, F.
Sottoriva, A.
Spies, A.
Spona, T.
Spurio, M.
Steijger, J.J.
Stolarczyk, T.
Streeb, K.
Sulak, L.
Taiuti, M.
Tamburini, C.
Tao, C.
Tasca, L.
Terreni, G.
Tezier, D.
Toscano, S.
Urbano, F.
Valdy, P.
Vallage, B.
Van Elewyck, V.
Vannoni, G.
Vecchi, M.
Venekamp, G.
Verlaat, B.
Vernin, P.
Virique, E.
de Vries, G.
van Wijk, R.
Wijnker, G.
Wobbe, G.
de Wolf, E.
Yakovenko, Y.
Yepes, H.
Zaborov, D.
Zaccone, H.
Zornoza, J.D.
Zúñiga, J.
(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,
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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.
Angelou, V.
Banerjee, S.
Belkus, H.
Fragos, T.
Fregeau, J.
Gaburov, E.
Izzard, R.
Jurić, M.
Justham, S.
Sottoriva, A.
Teuben, P.
van Bever, J.
Yaron, O.
Zemp, M.
(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..
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