Inference of Tumor Evolution during Chemotherapy by Computational Modeling and In Situ Analysis of Genetic and Phenotypic Cellular Diversity

Almendro, Vanessa; Cheng, Yuanda; Randles, Amanda; Itzkovitz, Shalev; Marusyk, Andriy; Ametller, Elisabet; González-Farré, X.; Muñoz, M.; Russnes, Hege G.; Helland, Åslaug; Rye, Inga Hansine; Børresen-Dale, Anne Lise; Maruyama, Reo; Oudenaarden, Alexander van; Dowsett, Mitchell; Jones, Robin L.; Reis‐Filho, Jorge S.; Gascón, Pere; Gönen, Mithat; Michor, Franziska; Polyák, Kornélia

doi:10.1016/j.celrep.2013.12.041

Cited by 244 publications

(239 citation statements)

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“…This is of particular relevance for cancer treatment, as most patients will 8 eventually succumb to the disease owing to the appearance of resistant tumour subclones. Despite the considerable clinical impact of tumour heterogeneity 15 , little is known about how it affects response to cancer therapy and how it may change during treatment at both the genomic and the phenotypic levels [16][17][18][19][20] . These issues highlight the need for preclinical models that capture the heterogeneous nature of human cancers and their on-going evolution.…”

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confidence: 99%

Interrogating open issues in cancer precision medicine with patient-derived xenografts

et al. 2017

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Patient-derived xenografts (PDXs) have emerged as an important platform to elucidate new treatments and biomarkers in oncology. PDX models are used to address clinically relevant questions, including the contribution of tumour heterogeneity to therapeutic responsiveness, the patterns of cancer evolutionary dynamics during tumour progression and under drug pressure, and the mechanisms of resistance to treatment. The ability of PDX models to predict clinical outcomes is being improved through mouse humanization strategies and implementation of co-clinical trials, within which patients and PDXs reciprocally inform therapeutic decisions. This Opinion article discusses aspects of PDX modelling that are relevant to these questions and highlights the merits of shared PDX resources to advance cancer medicine from the 6 perspective of EurOPDX, an international initiative devoted to PDX-based research.Response to anticancer therapies varies owing to the substantial molecular heterogeneity of human tumours and to poorly defined mechanisms of drug efficacy and resistance 1 . Immortalized cancer cell lines, either cultured in vitro or grown as xenografts, cannot interrogate the complexity of human tumours, and only provide determinate insights into human disease, as they are limited in number and diversity, and have been cultured on plastic over decades 2 .This disconnection in scale and biological accuracy contributes considerably to attrition in drug development [3][4][5] .Surgically derived clinical tumour samples that are implanted in mice (known as patient-derived xenografts (PDXs)) are expected to better inform therapeutic development strategies. As intact tissue -in which the tumour architecture and the relative proportion of cancer cells and stromal cells are both maintained -is directly implanted into recipient animals, the alignment with human disease is enhanced. More importantly, PDXs retain the idiosyncratic characteristics of different tumours from different patients; hence, they can effectively recapitulate the intra-tumour and inter-tumour heterogeneity that typifies human cancer 6-9 . 7 Exhaustive information on the key characteristics and the practical applications of PDXs can be found in recent reviews [10][11][12][13] . In this Opinion article, we discuss basic methodological concepts, as well as challenges and opportunities in developing "next-generation" models to improve the reach of PDXs as preclinical tools for in vivo studies (TABLE 1). We also elaborate on the merits of PDXs for exploring the intrinsic heterogeneity and subclonal genetic evolution of individual tumours, and discuss how this may influence therapeutic resistance. Finally, we examine the utility of PDXs in navigating complex variables in clinical decision-making, such as the discovery of predictive and prognostic biomarkers, and the categorization of genotype-drug response correlations in high-throughput formats. Being primarily co-authored by leading members of the EurOPDX Consortium (see Further information), we provide...

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confidence: 99%

Interrogating open issues in cancer precision medicine with patient-derived xenografts

et al. 2017

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“…Interestingly, recent genomic profiling studies 24, 25, 26, 27, 28, 29, 30, 31 suggested a substantial degree of intra‐ and inter‐tumour heterogeneity fuelling selection processes during evolution of breast cancer 32, 33, which may complicate prognostication as well as prediction and impede cancer precision medicine approaches 34. In this context, it is worth recalling that just as the molecular phenotype, the morphological phenotype of the tumour, including tumour grade and tumour size is essentially a result of accumulated genetic aberrations over time, thereby reflecting tumour evolution at the phenotypic level.…”

Section: Introductionmentioning

confidence: 99%

Classical pathology and mutational load of breast cancer – integration of two worlds

Budczies

Bockmayr

Denkert

et al. 2015

The Journal of Pathology CR

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Breast cancer is a complex molecular disease comprising several biological subtypes. However, daily routine diagnosis is still based on a small set of well‐characterized clinico‐pathological variables. Here, we try to link the two worlds of surgical pathology and multilayered molecular profiling by analyzing the relationships between clinico‐pathological phenotypes and mutational loads of breast cancer. We evaluated the number of mutated genes with somatic non‐silent mutations in different subgroups of breast cancer based on clinico‐pathological, including immunohistochemical and tumour characteristics. The analysis was performed for a cohort of 687 primary breast cancer patients with mutational profiling, gene expression and clinico‐pathological data available from The Cancer Genome Atlas (TCGA) project. The number of mutated genes was strongly positively associated with higher tumour grade (p = 1.4e−14) and with the different immunohistochemical and PAM50 molecular subtypes of breast cancer (p = 1.4e−10 and p = 4.3e−10, respectively). We observed significant associations (|R| > 0.4) between the abundance of mutated genes and expression levels of genes related to proliferation in the overall cohort and hormone receptor positive cohort, including the Recurrence Score gene signature (e.g., MYBL2 and BIRC5). Specific mutated genes (TP53, NCOR1, NF1, PTPRD and RB1) were highly significantly associated with high loads of mutated genes. Multivariate analysis for overall survival (OS) revealed a worse survival for patients with high numbers of mutated genes (hazard ratio = 4.6, 95% CI: 1.0 – 20.0, p = 0.044). Here, we report a strong association of the number of mutated genes with immunohistochemical and PAM50 subtypes and tumour grade in breast cancer. We provide evidence that specific levels of the mutational load underlie different morphological and biological phenotypes, which collectively constitute the current basis of pathological diagnosis. Our study is a step towards genomics‐informed breast pathology and will provide a basis for future studies in this field bridging the gap between morphology, tumour biology and medical oncology.

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“…[24][25][26]57,73 Thus, the genome theory calls into question the current standard protocols of chemotherapy, as drug intervention could paradoxically promote cancer evolution when applied in the wrong phase. 75,76 Therapeutic strategies should include the aim to reduce system stress to avoid triggering fast cancer evolution.…”

Section: Discussionmentioning

confidence: 99%

Evolutionary mechanism unifies the hallmarks of cancer

Horne

Pollick

Heng

2014

Intl Journal of Cancer

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The basis for the gene mutation theory of cancer that dominates current molecular cancer research consists of: the belief that gene‐level aberrations such as mutations are the main cause of cancers, the concept that stepwise gene mutation accumulation drives cancer progression, and the hallmarks of cancer. The research community swiftly embraced the hallmarks of cancer, as such synthesis has supported the notions that common cancer genes are responsible for the majority of cancers and the complexity of cancer can be dissected into simplified molecular principles. The gene/pathway classification based on individual hallmarks provides explanation for the large number of diverse gene mutations, which is in contrast to the original estimation that only a handful of gene mutations would be discovered. Further, these hallmarks have been highly influential as they also provide the rationale and research direction for continued gene‐based cancer research. While the molecular knowledge of these hallmarks is drastically increasing, the clinical implication remains limited, as cancer dynamics cannot be summarized by a few isolated/fixed molecular principles. Furthermore, the highly heterogeneous genetic signature of cancers, including massive stochastic genome alterations, challenges the utility of continuously studying each individual gene mutation under the framework of these hallmarks. It is therefore necessary to re‐evaluate the concept of cancer hallmarks through the lens of cancer evolution. In this analysis, the evolutionary basis for the hallmarks of cancer will be discussed and the evolutionary mechanism of cancer suggested by the genome theory will be employed to unify the diverse molecular mechanisms of cancer.

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Inference of Tumor Evolution during Chemotherapy by Computational Modeling and In Situ Analysis of Genetic and Phenotypic Cellular Diversity

Cited by 244 publications

References 50 publications

Interrogating open issues in cancer precision medicine with patient-derived xenografts

Interrogating open issues in cancer precision medicine with patient-derived xenografts

Classical pathology and mutational load of breast cancer – integration of two worlds

Evolutionary mechanism unifies the hallmarks of cancer

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