Timing the Landmark Events in the Evolution of Clear Cell Renal Cell Cancer: TRACERx Renal

Mitchell, Thomas J.; Turajlic, Samra; Rowan, Andrew; Nicol, David; Farmery, James H.R.; O’Brien, Tim; Martincorena, Iñigo; Tarpey, Patrick; Angelopoulos, Nicos; Yates, Lucy R.; Butler, Adam; Raine, Keiran; Stewart, Grant D.; Challacombe, Ben; Fernando, A.; López, José I.; Hazell, Steve; Chandra, Ashish; Chowdhury, Simon; Rudman, Sarah; Soultati, Aspasia; Stamp, Gordon; Fotiadis, Nicos; Pickering, Lisa; Au, Lewis; Spain, Lavinia; Lynch, Joanna; Stares, Mark; Teague, Jon W.; Maura, Francesco; Wedge, David C.; Horswell, Stuart; Chambers, Tim; Litchfield, Kevin; Xu, Hang; Stewart, Grant D.; Elaidi, Réza; Oudard, Stéphane; McGranahan, Nicholas; Csabai, István; Gore, Martin; Futreal, P. Andrew; Larkin, James; Lynch, Andy G.; Szállási, Zoltán; Swanton, Charles; Campbell, Peter J.

doi:10.1016/j.cell.2018.02.020

Cited by 436 publications

(462 citation statements)

References 68 publications

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“…More systematic experimental efforts were accomplished in some tumour types by studying data from premalignant lesions and manually compiling these progression models 5,35,36 . Several recent studies explored intratumour heterogeneity in primary and later stage tumours, including in lung and kidney cancer (TRACERx) 23,37,38,39 .…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment

Leshchiner

Livitz

et al. 2018

Preprint

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Driver mutations alter cells from normal to cancer through several evolutionary epochs: premalignancy, early malignancy, subclonal diversification, metastasis and resistance to therapy. Later stages of disease can be explored through analyzing multiple samples collected longitudinally, on or between successive treatments, and finally at time of autopsy. It is also possible to study earlier stages of cancer development through probabilistic reconstruction of developmental trajectories based on mutational information preserved in the genome. Here we present a suite of tools, called Phylogic N-Dimensional with Timing (PhylogicNDT), that statistically model phylogenetic and evolutionary trajectories based on mutation and copy-number data representing samples taken at single or multiple time points. PhylogicNDT can be used to infer: (i) the order of clonal driver events (including in pre-cancerous stages); (ii) subclonal populations of cells and their phylogenetic relationships; and (iii) cell population dynamics. We demonstrate the use of PhylogicNDT by applying it to whole-exome and whole-genome data of 498 lung adenocarcinoma samples (434 previously available and 64 of newly generated data). We identify significantly different progression trajectories across subtypes of lung adenocarcinoma (EGFR mutant, KRAS mutant, fusion-driven and EGFR/KRAS wild type cancers). In addition, we study the progression of fusiondriven lung cancer in 21 patients by analyzing samples from multiple timepoints during treatment with 1st and next generation tyrosine kinase inhibitors. We characterize their subclonal diversification, dynamics, selection, and changes in mutational signatures and neoantigen load. This methodology will enable a systematic study of tumour initiation, progression and resistance across cancer types and therapies.

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Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment

Leshchiner

Livitz

et al. 2018

Preprint

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“…Given the ability of our mathematical model to link observed cancer evasion and recognition cycle timing to underlying tumor-immune co-evolutionary dynamics, we next assessed the extent of immune evasion and clearance observed in cancer evolutionary data. The TRACERx renal clear cell carcinoma (RCCC) dataset (Mitchell et al, 2018) was particularly useful, providing estimates of the timing of landmark evolutionary events. Surprisingly, and consistent with model behavior under sustained control (Fig.…”

Section: Evolutionary Timing Of Renal Clear Cell Carcinoma Predicts Pmentioning

confidence: 99%

Sustained Co-evolution in a Stochastic Model of the Cancer-Immune Interaction

George

Levine

2019

Preprint

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The dynamical interaction between a growing cancer population and the adaptive immune system generates diverse evolutionary trajectories which ultimately result in tumor clearance or immune escape. Here, we create a simple mathematical model coupling T-cell recognition with an evolving cancer population which may randomly produce evasive subclones, imparting transient protection against the effector T-cells. We demonstrate that T-cell turnover declines and evasion rates together explain differential probabilities in early incidence data for almost all cancer types. Fitting the model to TRACERx evolutionary data argues in favor of substantial and sustained immune pressure exerted on a developing tumor, suggesting that measured incidence is a small proportion of all cancer initiation events. Most generally, dynamical models promise to increase our quantitative understanding of many immune escape contexts, with applications to cancer and intracellular pathogenic infections.

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“…8,20,23 With regard to SCNA in ccRCC, 5q gains are the second most frequent alterations following 3p deletions with the SQSTM1 gene, thought to be the pathogenic target for this gain. 24,25 SCNA are common in ccRCC, with alterations in nearly all chromosomes being observed; however, loss of 14q and chromosome 9 have been specifically associated with a poorer prognosis in ccRCC. [26][27][28] Studies have shown that VHL inactivation alone is insufficient for tumorigenesis, 29 highlighting the importance of the SCNA and secondary driver genes for formation and progression of ccRCC.…”

Section: The Vhl Gene In Ccrccmentioning

confidence: 99%

Clear cell renal cell carcinoma with wild‐type von Hippel‐Lindau gene: a non‐existent or new tumour entity?

2018

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The current World Health Organisation (WHO) classification of renal tumours is based on characteristic histological features or specific molecular alterations. von Hippel‐Lindau (VHL) alteration is the hallmark of clear cell renal cell carcinoma (RCC). After identification of the MiT translocation family of tumours, clear cell papillary renal cancer and others, the group of ccRCC with wild‐type VHL is small. TCEB1 mutation combined with chromosome 8q loss is an emerging tumour entity with wild‐type VHL. Inactivation of TCEB1 increases HIF stabilisation via the same mechanism as VHL inactivation. Importantly, recent molecular analyses suggest the existence of another ‘VHL wild‐type’ evolutionary subtype of clear cell RCC in addition to TCEB1 mutated RCC and clear cell papillary renal cancer. These tumours are characterised by an aggressive behaviour, high tumour cell proliferation rate, elevated chromosomal instability and frequent presence of sarcomatoid differentiation. Future clinicopathological studies will have to provide data to determine whether TCEB1 tumours and clear cell RCC with wild‐type VHL are separate tumour entities or represent variants of a clear cell RCC tumour family.

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Timing the Landmark Events in the Evolution of Clear Cell Renal Cell Cancer: TRACERx Renal

Cited by 436 publications

References 68 publications

Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment

Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment

Sustained Co-evolution in a Stochastic Model of the Cancer-Immune Interaction

Clear cell renal cell carcinoma with wild‐type von Hippel‐Lindau gene: a non‐existent or new tumour entity?

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