Clonal cooperativity in heterogenous cancers

Zhou, Hengbo; Neelakantan, Deepika; Ford, Heide L.

doi:10.1016/j.semcdb.2016.08.028

Cited by 63 publications

(54 citation statements)

References 182 publications

(202 reference statements)

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“…However, it is worthwhile to mention here that even subclonal mutations may present with drug targeting opportunities if the given subclone is important for the progression of the entire tumor. Cooperativity between distinct subclones was described for some metastasizing tumors [42][43][44] , and it was proposed that disruption of clonal cooperation might be an interesting therapeutic approach. Especially the targeting of Wnt and Hedgehog seems to be promising 42 , the pathway which we show here to be frequently mutated in MF (genes MACF1 , PTCH1 , RNF43 ).…”

Section: Discussionmentioning

confidence: 99%

Branched evolution and genomic intratumor heterogeneity in the pathogenesis of cutaneous T-cell lymphoma

Iyer

Hennessey

OʼKeefe

et al. 2019

Preprint

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Mycosis fungoides (MF) is a slowly progressive cutaneous T-cell lymphoma (CTCL) for which there is no cure. In the early plaque stage the disease is indolent, but development of tumors heralds an increased risk of metastasis and death. Previous research into the genomic landscape of CTCL revealed a complex pattern of >50 driver mutations implicated in more than a dozen of signaling pathways. However, the genomic mechanisms governing disease progression and treatment resistance remain unknown. Building on our previous discovery of the clonotypic heterogeneity of MF, we hypothesized that this lymphoma does not progress in a linear fashion as currently thought, but comprises heterogeneous mutational subclones. We sequenced exomes of 49 cases of MF and identified 28 previously unreported putative driver genes. MF exhibited extensive intratumoral heterogeneity (ITH) of a median of six subclones showing branched pattern of phylogenetic relationships. Stage progression was correlated with an increase in ITH and redistribution of mutations from the stem to the clades. The pattern of clonal driver mutations was highly variable with no consistent mutations between patients. A similar intratumoral heterogeneity was detected in leukemic CTCL (Sézary syndrome). Based on these findings we propose a model of the pathogenesis of MF comprising neutral, divergent evolution of cancer subclones and discuss how ITH impacts the efficacy of targeted drug therapies and immunotherapies of CTCL.

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Section: Discussionmentioning

confidence: 99%

Branched evolution and genomic intratumor heterogeneity in the pathogenesis of cutaneous T-cell lymphoma

Iyer

Hennessey

OʼKeefe

et al. 2019

Preprint

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“…For instance a tumor can be clonal because it is not very aggressive, or on the contrary this might be the result of a selective sweep after a phase of new clonal expansion. Moreover, several authors discuss the consequences and the interplay of the presence of distinct subclonal populations, in terms of cooperation [61,62], competition [63,64], or even neutral evolution [65,66]. Hence, the same level of ITH might uncover very diverse situations, and may not be a prognostic factor by itself.…”

Section: Association With Survival Link With Other Variablesmentioning

confidence: 99%

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Abecassis

Hamy

Laurent

et al. 2018

Preprint

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Tumors are made of evolving and heterogeneous populations of cells which arise from successive appearance and expansion of subclonal populations, following acquisition of mutations conferring them a selective advantage. Those subclonal populations can be sensitive or resistant to different treatments, and provide information about tumor aetiology and future evolution. Hence, it is important to be able to assess the level of heterogeneity of tumors with high reliability for clinical applications.In the past few years, a large number of methods have been proposed to estimate intra-tumor heterogeneity from whole exome sequencing (WES) data, but the accuracy and robustness of these methods on real data remains elusive. Here we systematically apply and compare 6 computational methods to estimate tumor heterogeneity on 1,697 WES samples from the cancer genome atlas (TCGA) covering 3 cancer types (breast invasive carcinoma, bladder urothelial carcinoma, and head and neck squamous cell carcinoma), and two distinct input mutation sets. We observe significant differences between the estimates produced by different methods, and identify several likely confounding factors in heterogeneity assessment for the different methods. We further show that the prognostic value of tumor heterogeneity for survival prediction is limited in those datasets, and find no evidence that it improves over prognosis based on other clinical variables.In conclusion, heterogeneity inference from WES data on a single sample, and its use in cancer prognosis, should be considered with caution. Other approaches to assess intra-tumoral heterogeneity such as those based on multiple samples may be preferable for clinical applications.

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“…Within this vast literature, there is a growing appreciation for how the molecular and biophysical mechanisms promoting metastasis can be understood in an ecological setting. In a number of model systems, interactions between subclones, which separately do not exhibit metastatic potential, can induce EMT and metastases when introduced together to the same animal 20,21 . These observations indicate a kind of “division of labor” involved in development of metastasis, with some cells destabilizing extracellular matrix, others recruiting blood vessels, and others taking advantage of these conditions by detaching from the tumor mass and entering the circulation 18 .…”

Section: Adaptive Radiation and Ecological Niches: Understanding Tmentioning

confidence: 99%

“…Many previous reviews have discussed how heterogeneous tumors evolve 2,3,11-16 , highlighting in particular the complexity of tumor ecosystems 17,18 , the relationship between tumor heterogeneity and progression to metastasis 19-21 , and the importance of considering tumor evolution when designing therapy 6-10,21 . Many computational methods have been designed to grapple with the onslaught of data produced by genomic 22 and epigenomic 23 studies.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary scalpels for dissecting tumor ecosystems

Rosenbloom

Cámara

Chu

et al. 2017

Biochimica et Biophysica Acta (BBA) - Reviews on Cancer

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Amidst the growing literature on cancer genomics and intratumor heterogeneity, essential principles in evolutionary biology recur time and time again. Here we use these principles to guide the reader through major advances in cancer research, highlighting issues of “hit hard, hit early” treatment strategies, drug resistance, and metastasis. We distinguish between two frameworks for understanding heterogeneous tumors, both of which can inform treatment strategies: (1) The tumor as diverse ecosystem, a Darwinian population of sometimes-competing, sometimes-cooperating cells; (2) The tumor as tightly integrated, self-regulating organ, which may hijack developmental signals to restore functional heterogeneity after treatment. While the first framework dominates literature on cancer evolution, the second framework enjoys support as well. Throughout this review, we illustrate how mathematical models inform understanding of tumor progression and treatment outcomes. Connecting models to genomic data faces computational and technical hurdles, but high-throughput single-cell technologies show promise to clear these hurdles.

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Clonal cooperativity in heterogenous cancers

Cited by 63 publications

References 182 publications

Branched evolution and genomic intratumor heterogeneity in the pathogenesis of cutaneous T-cell lymphoma

Branched evolution and genomic intratumor heterogeneity in the pathogenesis of cutaneous T-cell lymphoma

Assessing reliability of intra-tumor heterogeneity estimates from single sample whole exome sequencing data

Evolutionary scalpels for dissecting tumor ecosystems

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