This tab shows an overview of the selected study/paper [more details]
Reference

Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.

Paper Id
COSP28300
Authors
Gerlinger M,Rowan AJ,Horswell S,Larkin J,Endesfelder D,Gronroos E,Martinez P,Matthews N,Stewart A,Tarpey P,Varela I,Phillimore B,Begum S,McDonald NQ,Butler A,Jones D,Raine K,Latimer C,Santos CR,Nohadani M,Eklund AC,Spencer-Dene B,Clark G,Pickering L,Stamp G,Gore M,Szallasi Z,Downward J,Futreal PA and Swanton C
Affiliation
Cancer Research UK London Research Institute, London, United Kingdom.
Journal
The New England journal of medicine 2012;366(10):883-92
ISSN:1533-4406
PUBMED:22397650
Abstract
Background: Intratumor heterogeneity may foster tumor evolution and adaptation and hinder personalized-medicine strategies that depend on results from single tumor-biopsy samples.Methods: To examine intratumor heterogeneity, we performed exome sequencing, chromosome aberration analysis, and ploidy profiling on multiple spatially separated samples obtained from primary renal carcinomas and associated metastatic sites. We characterized the consequences of intratumor heterogeneity using immunohistochemical analysis, mutation functional analysis, and profiling of messenger RNA expression.Results: Phylogenetic reconstruction revealed branched evolutionary tumor growth, with 63 to 69% of all somatic mutations not detectable across every tumor region. Intratumor heterogeneity was observed for a mutation within an autoinhibitory domain of the mammalian target of rapamycin (mTOR) kinase, correlating with S6 and 4EBP phosphorylation in vivo and constitutive activation of mTOR kinase activity in vitro. Mutational intratumor heterogeneity was seen for multiple tumor-suppressor genes converging on loss of function; SETD2, PTEN, and KDM5C underwent multiple distinct and spatially separated inactivating mutations within a single tumor, suggesting convergent phenotypic evolution. Gene-expression signatures of good and poor prognosis were detected in different regions of the same tumor. Allelic composition and ploidy profiling analysis revealed extensive intratumor heterogeneity, with 26 of 30 tumor samples from four tumors harboring divergent allelic-imbalance profiles and with ploidy heterogeneity in two of four tumors.Conclusions: Intratumor heterogeneity can lead to underestimation of the tumor genomics landscape portrayed from single tumor-biopsy samples and may present major challenges to personalized-medicine and biomarker development. Intratumor heterogeneity, associated with heterogeneous protein function, may foster tumor adaptation and therapeutic failure through Darwinian selection. (Funded by the Medical Research Council and others.).
Paper Status
Curated
Genes Analysed
309
Mutated Samples
2
Total No. of Samples
2
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Genes Samples CDS Mutation AA Mutation
This tab shows genes without mutations in the selected study/paper [more details]
Non-Mutant Genes Gene Id (COSG)
This tab shows samples without mutations in the selected study/paper [more details]
Non-Mutant Samples Sample Id (COSS)
This tab shows mutated samples in the selected study/paper [more details]
Sample Name Mutation Count
This tab shows non coding variant in the selected study/paper [more details]
Sample ID Sample Name ID NCV Annotation Zygosity Chromosome Genome start Genome stop Genome version Strand WT seq Mut seq
This tab shows the gene expression and copy number variation data for this study. [more details]

Table Information

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The table currently shows only high value (numeric) copy number data. Copy number segments are excluded if the total copy number and minor allele values are unknown.

Click here to include all copy number data. For more detailed information about copy number data and gain/loss definitions click here.

Sample Gene Expression Expr Level (Z-Score)

Over Expressed; Z-Score > 2.0

Under Expressed; Z-Score < -2.0

Normal; Z-Score within the range -2.0 to 2.0

CN Type Minor Allele Copy Number CN Segment Posn. Average Ploidy

1. N/A represents cases where the average ploidy value is not available( mostly ICGC samples). For some TCGA samples where the minor allele information is not available the average ploidy value could not be calculated.

2. For TCGA samples, the ASCAT algorithm was used to calculate the average ploidy.

3. For CGP samples, the PICNIC algorithm was used to calculate the average ploidy.

CNV
This tab shows a summary table with counts (number of samples) for CNV gain/loss and under/over expression for all genes. [more details]

The results shown in this table are derived from all copy number data. This includes non-numeric data with descriptive definitions of gain/loss.

  Copy Number Expression
Gene Gain Loss Tested Over Under Tested
This tab shows the fusion mutations observed in this sample [more details]
Gene Sample Name Id Sample(COSS) CDS Mutation Somatic status Zygosity Validated Type