Showing papers by "Andrea Sottoriva published in 2018"

Patient-derived organoids model treatment response of metastatic gastrointestinal cancers

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.

Quantification of subclonal selection in cancer from bulk sequencing data.

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.

Longitudinal Liquid Biopsy and Mathematical Modeling of Clonal Evolution Forecast Time to Treatment Failure in the PROSPECT-C Phase II Colorectal Cancer Clinical Trial.

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. 1213 . This article is highlighted in the In This Issue feature, p. 1195

Detecting repeated cancer evolution from multi-region tumor sequencing data

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.

The evolutionary landscape of colorectal tumorigenesis.

Abstract: The evolutionary events that cause colorectal adenomas (benign) to progress to carcinomas (malignant) remain largely undetermined Using multi-region genome and exome sequencing of 24 benign and malignant colorectal tumours, we investigate the evolutionary fitness landscape occupied by these neoplasms Unlike carcinomas, advanced adenomas frequently harbour sub-clonal driver mutations—considered to be functionally important in the carcinogenic process—that have not swept to fixation, and have relatively high genetic heterogeneity Carcinomas are distinguished from adenomas by widespread aneusomies that are usually clonal and often accrue in a ‘punctuated’ fashion We conclude that adenomas evolve across an undulating fitness landscape, whereas carcinomas occupy a sharper fitness peak, probably owing to stabilizing selection

Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity

Abstract: How tumor microenvironmental forces shape plasticity of cancer cell morphology is poorly understood. Here, we conduct automated histology image and spatial statistical analyses in 514 high grade serous ovarian samples to define cancer morphological diversification within the spatial context of the microenvironment. Tumor spatial zones, where cancer cell nuclei diversify in shape, are mapped in each tumor. Integration of this spatially explicit analysis with omics and clinical data reveals a relationship between morphological diversification and the dysregulation of DNA repair, loss of nuclear integrity, and increased disease mortality. Within the Immunoreactive subtype, spatial analysis further reveals significantly lower lymphocytic infiltration within diversified zones compared with other tumor zones, suggesting that even immune-hot tumors contain cells capable of immune escape. Our findings support a model whereby a subpopulation of morphologically plastic cancer cells with dysregulated DNA repair promotes ovarian cancer progression through positive selection by immune evasion.

The Spatiotemporal Evolution of Lymph Node Spread in Early Breast Cancer.

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-naive 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.

Variation of mutational burden in healthy human tissues suggests non-random strand segregation and allows measuring somatic mutation rates.

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.

Reply to 'Neutral tumor evolution?'

Abstract: Impact of clonal copy number alterations In our previous study2, we assessed the cumulative variant allele frequency (VAF) distribution M(f) over the frequency range f = [0.12,0.24] to restrict our analysis to subclonal variants within a range applicable to the diverse datasets that we considered. Tarabichi and colleagues note that tumors with a tetraploid genome will have an additional ‘peak’ of clonal mutations at f ~0.25 (mutations in a single allele, Supplementary Fig. 1a), thus causing incorrect rejection of neutrality (Supplementary Fig. 1b). The integration range that we chose was based on a triploid tumor with read depth of 100× , thereby resulting in an upper threshold of 0.26 (Supplementary Methods). Although this procedure is suitable in most cases, it is not suitable for a tetraploid tumor, thus suggesting that the number of tumors consistent with neutral evolution could be larger than we reported. We show how this problem can be avoided by adjusting the range for tetraploid tumors (Supplementary Fig. 1c). We do acknowledge that the 1/f integration method is more accurate when applied to the entire VAF spectrum of subclonal mutations only. Moreover, we have recently developed a Bayesian model selection framework that compares the neutral model against models with selection, using the entire VAF distribution3. We do stress, however, that most cancers analyzed in our original manuscript were not neutral and showed signs of subclonal selection.

Reply to ‘Currently available bulk sequencing data do not necessarily support a model of neutral tumor evolution’

Abstract: Werner et al. reply — In their correspondence, McDonald et al.1 question our assertion that the distribution of mutations in tumor bulk sequencing data suggests an underlying neutral evolutionary process in a proportion of cancers2 and instead propose alternative explanations that incorporate subclonal selection. We agree with the authors’ demonstration that it is possible, in principle, to construct models of selection that produce patterns similar to the neutral model. However, the key issue is whether the proposed models of selection are realistic, meaningful and, most importantly, more appropriate than the null neutral model. Before examining this issue, we first note that we extensively stressed in the original manuscript2 that the majority of cases we examined were not consistent with neutral evolution (~70% appeared non-neutral), and we did specifically cite Gerlinger et al.3 as an example of data dominated by selection2. Our finding that the majority of cancers do show evidence of subclonal selection is consistent with previous literature, including the cases highlighted by McDonald et al.3,4. Arguably, clonal evolution results from the interplay of three fundamental processes: random alterations (genetic, epigenetic, etc.), random drift and nonrandom selection, the third of which is the most complex to define and model. In the established field of population genetics, extensive effort has been dedicated to modeling the first two processes without selection, the so-called neutral dynamics5–7. This includes the development of entire statistical frameworks based on neutrality, such as coalescent theory8. On the contrary, models that include selection, especially in growing populations, have been much harder to derive analytically owing to the large number of assumptions in the definition of selection, including whether selection is clone intrinsic or clone extrinsic (microenvironmentally defined) and whether the magnitude of selection is constant or fluctuates in response to population dynamics. Importantly, most models of selection describe cancer dynamics in terms of time9,10 (for example, time to fixation of a selected mutant) and therefore, although insightful, are hard to apply to cancer genomic data where temporal dynamics are often unobservable. In light of this complexity, in our study, we asked the simple question of what happens to the mutations in a growing tumor in the case where only the first two processes above, namely random mutations and drift, are operating. This leads to a relatively simple model that is analytically tractable, wherein subclonal mutations accumulate following a 1/f cumulative distribution2. We note that this is the underlying solution of the fully stochastic Luria–Delbrück model, as previously demonstrated11,12. Importantly, this model is based on the ‘null hypothesis’ of molecular evolution in cancer13–15 and predicts what the absence of subclonal selection should look like in a growing tumor. We tested this hypothesis against subclonal mutations from a large body of sequencing data and found that in about 30% of cases we could not reject this null hypothesis, at least within the resolution of the currently available data. In their correspondence, McDonald et al.1 propose a more complex scenario that includes ongoing selection and report that in some cases their model also fits the 1/f cumulative distribution. First, we examine the fit of their proposed model to the data and highlight that considering the stochastic nature of selected mutants would change the interpretation of their analysis. Second, we discuss the distinction between evaluating the power of a test and the limitations of the information content in the data to which the test is applied, in this case singlesample bulk sequencing. Third, we analyze the plausibility of the authors’ biological assumptions underlying their model. In the correspondence by McDonald et al.1, neutrality was correctly rejected in a considerable proportion of simulations with subclonal selection (R2 < 0.98; their Fig. 1b). The exact proportion of cases incorrectly classified as neutral is not reported, but a few specific examples are shown in their Fig. 1c–f. Importantly, in those cases, the mutant proportion at the time of sampling is not reported nor is the time when the mutant was introduced. Both are key factors in judging the strength of the selection signal for two reasons: (i) in the case of strong and early selection, wherein a selected mutant sweeps to fixation, the evolutionary dynamics revert to neutral, and hence accepting the null for the final tumor is correct (as all cells in the tumor bear the selected mutation, so there is no subclonal selection) and (ii) because of the inherent stochasticity of the evolutionary process, selected mutants can either occur too late to grow to a detectable size or be weakly selected such that the clonal population of the tumor remains virtually unchanged with respect to the neutral expectation. Judging from Fig. 2a, this seems to be what happens often: most mutants have fitness slightly higher than 1 (where 1 is neutral) and many have fitness even lower than 1 (should be negatively selected), but all persist in the population. In such a model, it is clear that selection is not sculpting the population by removing unfit clones and benefitting fitter ones, as any mutant—fit or unfit—seems to survive. Thus, the dynamics described in the models of McDonald et al.1 are ‘effectively neutral’, and relatedly, it is not surprising that deviations from neutrality are undetectable. We highlight that it is fundamentally important to consider the size of differentially selected subclones when considering whether or not a tumor can be classified as neutrally evolving. In the authors’ second simulation model (their Fig. 2), many clones arise very late and are therefore undetectable in the data (high frequency of red dots representing a clone size of one cell in their Fig. 2a). We argue that no test will ever be able to detect a subclone made of a single cell in a whole malignancy—and indeed, it is debatable whether a clone of size 1 can even be considered to have been selected. We discuss the detection limits imposed by current data in our original manuscript (Fig. 5)2, as well as in subsequent work16,17. To demonstrate the impact of subclone size in determining whether a tumor is classified as (effectively) neutral or not, we performed a more thorough analysis of our previous model of a stochastic branching process under selection (Fig. 1 in this letter). These simulations show that, in the presence of a subclone of detectable size in the data (for example, one that is not too small to be out of the detectable range of the variant

Reply: Is the evolution of tumors Darwinian or non-Darwinian?

Abstract: 1 Evolution and Cancer Laboratory, Barts Cancer Institute, Queen Marry University of London, London, UK. 2 Department of Cell and Developmental Biology, University College London, London, UK. 3 Centre for Mathematics and Physics in the Life Sciences and Experimental Biology (CoMPLEX), University College London, London, UK. 4 Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK. 5 Department of Genetics, Evolution and Environment, University College London, London, UK.

Variation of mutational burden in healthy human tissues suggests non-random strand segregation and allows measuring somatic mutation rates

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.

Reply to 'Revisiting signatures of neutral tumor evolution in the light of complexity of cancer genomic data'.

Abstract: Williams et al. reply — Balaparya and De1 question the applicability of the power-law neutral-evolution model to adequately describe the pattern of subclonal somatic mutations in bulk cancer sequencing data. The authors’ letter focuses on the issues of the inherent noise in next-generation sequencing data, whereby random sampling of alleles, PCR amplification during library preparation, limited depth sequencing, and subclonal copy number changes may cause considerable uncertainty in variantallele frequency (VAF) measurement. The authors suggest that these errors lead to VAF measurements that, owing to overdispersion, follow a beta-binomial and not a binomial distribution. We thank Balaparya and De for the insightful comments and address their points in the response below. The issue of VAF measurement accuracy is a very important point and something that concerned us in our original study2. For this reason, we provided extensive error-propagation analysis in our original manuscript to identify the inherent biases that affect VAF estimation (Methods and equations (12)–(14) in ref. 2). We aimed at starting from the analytical form of neutral evolution (equation (7) in ref. 2) as the expected signal (S) and adding the different sources of noise (N), such as purity and allele sampling during library preparation, to generate the expected pattern S + N reported by the data. Our results demonstrate that the signature of neutral evolution is detectable with moderately high sequencing depth (≥ 100× ; Methods and Supplementary Fig. 10 in ref. 2), and we fully acknowledged that the signature of neutral evolution versus selection cannot be reasonably extracted (or rejected) from lower-depth datasets. In an effort to address Balaparya and De’s concerns, we tested the ability of our model to recover neutral evolutionary dynamics in the presence of beta-binomially distributed noise, and we found no significant differences with respect to the binomial noise used in our original manuscript, although with very high dispersion (ρ = 0.1), a degree of difference was appreciable (Fig. 1a). Moreover, we estimated the degree of overdispersion in the data that we analyzed in ref. 2 by fitting a beta-binomial model to the clonal cluster by using Markovchain Monte Carlo inference. In both the 100× whole-genome gastric cancer3 and whole-exome colon cancer4 data, we estimated the dispersion parameter ρ to be < 0.005 (Fig. 1b,c, respectively), a value notably 10× lower than postulated by Balaparya and De (Fig. 1c,d in ref. 1). Given that as ρ→ 0, the beta-binomial distribution converges to a binomial distribution, we argue that using a binomial distribution to model noise in sequencing data was appropriate in our original analysis. Balaparya and De also suggest that, because copy number alterations affect VAF distributions, very strict thresholds are necessary to ensure that regions analyzed with our method are truly diploid. This is an important point, and we concur that the original threshold of absolute log R ratio ≤ 0.5 may have been too lenient. To test the effect of this confounding factor, we reanalyzed the TCGA pan-cancer dataset by using the new publicly accessible Genomic Data Commons Data Portal (see URLs) variant calls, which were not available at the time of our original manuscript.

Reply: Neutral tumor evolution?

Abstract: Mutation, selection and neutral drift shape the cancer evolutionary process. The role of selection has received particular interest, but inferring the presence and strength of selection during tumour growth remains challenging. Recently, we analysed the frequency distribution of subclonal mutations in many cancers and found that in approximately 30% of cases the observed distribution was entirely consistent with a simple model of neutral evolution. Thus, we concluded that neutral evolution, perhaps surprisingly, provides an adequate explanation of the intra-tumour heterogeneity present in a significant proportion of cancers. Tarabichi and colleagues [bioRxiv: 2017/06/30/158006] question the robustness of the method we presented in Williams et al. 2016 to identify neutral cancer evolution from variant allele frequency (VAF) distributions. Their critique has four main points that we address in this document using a simulation approach and a reanalysis of public datasets.

Author Correction: Quantification of subclonal selection in cancer from bulk sequencing data

Abstract: In the version of this article originally published, in the “Theoretical framework of subclonal selection” section of the main text, ref. 11 instead of ref. 19 should have been cited at the end of the phrase “Our previously presented frequentist approach to detect subclonal selection from bulk sequencing data involves an R2 test statistic.” The error has been corrected in the HTML and PDF versions of the article.