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Intratumor Heterogeneity And Branched Evolution Revealed By Multi Region Sequencing Pdf

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Blood Adv ; 4 11 : — MF exhibits substantial genetic subclones, with a median of 6 subclones across samples. Stage progression was correlated with an increase in intratumoral heterogeneity and redistribution of mutations from stem to clades.

Intratumor heterogeneity of prognostic DNA-based molecular markers in adrenocortical carcinoma

Either your web browser doesn't support Javascript or it is currently turned off. In the latter case, please turn on Javascript support in your web browser and reload this page. Free to read. Intratumor heterogeneity may foster tumor evolution and adaptation and hinder personalized-medicine strategies that depend on results from single tumor-biopsy samples. 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. 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. 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. L arge-scale sequencing analyses of solid cancers have identified extensive heterogeneity between individual tumors.

Intratumor heterogeneity may have important consequences for personalized-medicine approaches that commonly rely on single tumorbiopsy samples to portray tumor mutational landscapes. Studies comparing mutational profiles of primary tumors and associated metastatic lesions 16 , 17 or local recurrences 18 have provided evidence of intratumor heterogeneity at nucleotide resolution. Intratumor heterogeneity within primary tumors and associated metastatic sites has not been systematically characterized by next-generation sequencing.

We applied exome sequencing, chromosome aberration analysis, and DNA ploidy profiling to study multiple spatially separated biopsy samples from primary renal-cell carcinomas and associated metastatic sites. We investigated the phenotypic consequences of genetic intratumor heterogeneity and the representation of the tumor genomic landscape by a single tumorbiopsy sample, the current basis for most biomarker discovery and personalized-medicine approaches.

Biopsy samples were obtained before the initiation of 6 weeks of treatment with everolimus. After a 1-week washout period in which patients did not receive everolimus, a nephrectomy was performed. Everolimus treatment was continued after recovery from surgery until tumor progression. Figure 1 shows biopsy and treatment timelines. Exon-capture sequencing was performed on tumor DNA from pretreatment biopsy samples of the primary tumor PreP and chest-wall metastasis PreM , primary-tumor regions of the nephrectomy specimen R1 to R9 , a perinephric metastasis in the nephrectomy specimen M1 , and two regions of the excised chest-wall metastasis M2a and M2b.

LM denotes liver metastasis, and PD progressive disease. Green boxes indicate periods of everolimus treatment, with the treatment duration provided in weeks. Dotted lines indicate time points of biopsies, and the asterisk indicates a delay in nephrectomy because of toxicity.

We performed whole-exome multiregion spatial sequencing on DNA that was extracted from freshfrozen samples obtained from Patients 1 and 2, as described previously, 19 with paired-end reads of 72 bp and 75 bp, respectively, on Illumina Genome Analyzer IIx and HiSeq platforms. All four patients provided written informed consent.

Details regarding materials and methods are provided in the Supplementary Appendix , available with the full text of this article at NEJM. The study protocol is also available at NEJM. Patient 1 had a clear-cell carcinoma, pulmonary metastases, and a chest-wall metastasis. Sequencing detected a 2-bp deletion in the von Hippel—Lindau tumor-suppressor gene VHL leading to mutational inactivation, which is characteristic of clear-cell carcinoma.

After 6 weeks of everolimus treatment and a 1-week washout period, a nephrectomy was performed. The patient restarted everolimus for 6 weeks and after another 1-week washout period proceeded to surgery of the chest-wall mass Fig.

Computed tomography CT did not reveal any change in the dimensions of the primary tumor or chest-wall metastasis during everolimus treatment.

For Patient 1, we performed exon-capture multiregion sequencing on DNA from pretreatment biopsy samples of the primary tumor PreP and chestwall metastasis PreM , nine primary-tumor regions of the nephrectomy specimen R1 to R9 , a metastasis in the perinephric fat of the nephrectomy specimen M1 , two regions of the excised chestwall metastasis M2a and M2b , and germline DNA 19 Fig.

This sequencing resulted in a median coverage of 74 reads Table 1 in the Supplementary Appendix. Nonsynonymous somatic point mutations and insertions and deletions indels that change the protein amino acid sequence were filtered and manually reviewed to remove sequencing and alignment errors and to determine the regional distribution of mutations. Regions R6 and R7 were excluded from analyses since only one nonsynonymous variant passed filtering.

We identified nonsynonymous point mutations and 32 indels Table 2 in the Supplementary Appendix and mapped their regional distributions across the tumor Fig. Sanger sequencing was used to validate 42 mutations. Panel A shows sites of core biopsies and regions harvested from nephrectomy and metastasectomy specimens.

G indicates tumor grade. Panel B shows the regional distribution of nonsynonymous point mutations and 32 indels in seven primary-tumor regions of the nephrectomy specimen R1 through R5 and R8 through R9 , in the perinephric fat of the nephrectomy specimen M1 , and in two regions of the excised chestwall metastasis M2a and M2b , as detected by exome sequencing including the VHL mutation detected by Sanger sequencing.

The heat map indicates the presence of a mutation gray or its absence dark blue in each region. The color bars above the heat map indicate classification of mutations according to whether they are ubiquitous, shared by primary-tumor regions, shared by metastatic sites, or unique to the region private. Among the gene names, purple indicates that the mutation was validated, and orange indicates that the validation of the mutation failed.

Panel C shows phylogenetic relationships of the tumor regions. R4a and R4b are the subclones detected in R4. A question mark indicates that the detected SETD2 splice-site mutation probably resides in R4a, whereas R4b most likely shares the SETD2 frameshift mutation also found in other primary-tumor regions.

Branch lengths are proportional to the number of nonsynonymous mutations separating the branching points. Potential driver mutations were acquired by the indicated genes in the branch arrows.

Panel D shows regional ploidy profiling analysis. All other primary-tumor regions were diploid not shown. A low false negative mutation call rate is required to avoid overestimation of intratumor heterogeneity. We performed ultradeep exon-capture sequencing of R4 and R9 median coverage of and reads, respectively to investigate whether heterogeneous mutations that were not found in R4 or R9 could be detected by increasing the sequencing depth i.

This identified all 64 mutations known to be present in R4 and 75 mutations in R9 and detected only 2 additional mutations in ITGB3 and AKAP8 , both in R4 present in other primary regions, indicating a low false negative rate of 2 in 1. We excluded 5 mutations that were not validated and classified the remaining mutations into 40 ubiquitous mutations, 59 mutations shared by several but not all regions, and 29 mutations that were unique to specific regions so-called private mutations that were present in a single region.

We subdivided shared mutations into 31 mutations shared by most of the primary tumor regions of the nephrectomy specimen R1 to R3, R5, and R8 to R9 , pretreatment biopsy samples of the primary tumor, and 28 mutations shared by most of the metastatic regions. The detection of private mutations suggested ongoing regional clonal evolution.

We inferred ancestral relationships and constructed a phylogenetic tree of the tumor regions by clonal ordering, as described by Merlo et al. One branch evolved into the clones present in metastatic sites, and the other diversified into primary tumor regions. R4 shared some, but not all, primary-tumor and metastatic mutations, which suggested the presence of at least two clonal populations in this region that arose from progenitor cells of the metastases and of other primary tumor sites.

Variant frequencies in the R4 ultradeep-sequencing data revealed that mutations shared with metastatic sites were detected at higher frequencies than were mutations shared with other primary-tumor regions, further supporting the presence of two subclones in R4 Fig. For an exploratory phylogenetic analysis of the synonymous mutations, see Fig. To address whether everolimus exposure may contribute to intratumor heterogeneity, we compared the phylogenetic relationships of pretreatment samples with those obtained after treatment samples Fig.

Of 71 mutations in pretreatment samples of the primary tumor, 67 were also present in post-treatment primary-tumor regions, and 64 of 66 mutations in the chest-wall metastases were present in post-treatment metastatic regions, indicating that the two main branches of the phylogenetic tree were present before drug treatment. Clones in R4 are unlikely to have evolved from pretreatment samples of the primary tumor or chest-wall metastases during therapy, since such evolution would have required the reversion of a large number of somatic mutations to wild-type, further supporting the presence of intratumor heterogeneity before treatment.

Finally, samples taken before and after 6 and 12 weeks of everolimus exposure had similar numbers of nonsynonymous mutations Fig. Thus, everolimus does not appear to increase the mutational load, and the main phylogenetic branches were present in the tumor before treatment, indicating that intratumor heterogeneity was not a consequence of everolimus treatment.

Ploidy profiling 21 revealed a diploid profile for the majority of primary regions, whereas region m2b of the excised chest-wall metastasis harbored two subtetraploid populations Fig. R4, the region most resembling the metastatic sites through clonal-ordering analysis, had a tetraploid profile, which suggests that the subtetraploid population in the chest-wall metastasis may have developed from a tetraploid intermediate in R4.

Tumor regions were subjected to SNP-array—based allelicimbalance detection to identify chromosomal aberrations. Pretreatment samples of the primary tumor and metastasis were excluded because of insufficient DNA, and R1, R3, and R5 failed quality control.

Sections of allelic imbalance on chromosome 3p were the only ubiquitous abnormalities Fig. Taken together with the corresponding reduced array signal intensities on chromosome 3p Fig. No tumor regions shared identical allelic-imbalance profiles, and heterogeneity of allelic imbalance within metastases, which is probably driven by aneuploidy, indicates that chromosomal aberrations contribute to genetic intratumor heterogeneity.

Of these driver genes, only VHL was mutated ubiquitously in all analyzed regions. Since SETD2 trimethylates H3K36, we stained several tumor regions with an antibody for trimethylated H3K36 to identify the consequences of mutational intratumor heterogeneity on protein function. Trimethylated H3K36 was reduced in cancer cells but positive in most stromal cells and in a SETD2 wild-type control clear-cell carcinoma Fig. These data support phenotypic convergent evolution through loss of SETD2 methyltransferase function driven by three distinct, regionally separated mutations on a background of ubiquitous loss of the other SETD2 allele on chromosome 3p.

Convergent evolution was observed for the X-chromosome—encoded histone H3K4 demethylase KDM5C, harboring disruptive mutations in R1 through R3, R5, and R8 through R9 missense and frameshift deletion and a splice-site mutation in the metastases Fig. The mammalian target of rapamycin mTOR kinase carried a kinase-domain missense mutation LP in all primary tumor regions except R4. It is unlikely that everolimus would affect activity in the mTOR pathway in these specimens, which were acquired 7 days after drug discontinuation drug half-life, 30 hours.

Transient transfection of renal-cell carcinoma lines with the mutant mTOR construct did not affect everolimus sensitivity in vitro data not shown. A structural model derived from this alignment suggests that L maps to a hydrophobic pocket within an autoinhibitory domain adjacent to the activation loop. The substitution of L by proline may affect the conformation of the mTOR activation loop.

These data suggest that genetic intratumor heterogeneity is associated with functional heterogeneity of kinase activity. Panel C shows hierarchical clustering of samples on the basis of prognostic signature genes of two molecular subgroups: clear-cell A ccA , which indicates a good prognosis, and clear-cell B ccB , which indicates a poor prognosis.

The metastatic sites M2a and M2b and the primary-tumor site R4 segregated together, enriched for genes in the clear-cell A subgroup, in contrast to the remaining tumor regions that were enriched for the clear-cell B subgroup, showing that gene-expression signatures may not correctly predict outcomes if samples are obtained from a single biopsy.

The brackets on the right side of the heat map dendrogram indicate the hierarchical clustering of the samples according to the expression of the analyzed genes. The z scores indicate the difference in standard deviations between the mRNA expression of a gene in a sample and its mean mRNA expression across all samples.

We determined the intratumoral expression of a gene signature shown to classify clear-cell carcinoma into two molecular subgroups: clear-cell A associated with a good prognosis and clear-cell B associated with a poor prognosis.

Thus, prognostic gene-expression signatures may not correctly predict outcomes if they are assessed from a single region of a heterogeneous tumor. To determine whether intratumor heterogeneity was present in consecutive clear-cell carcinomas from the E-PREDICT trial, we performed multiregion exome sequencing on the primary tumor and a metastasis from Patient 2 and ploidy and allelicimbalance profiling on primary tumors from Patients 2, 3, and 4.

CT imaging showed no change in tumor dimensions during 6 weeks of everolimus treatment. Patient 2 had a metastatic tumor with a 1-bp deletion in VHL. Primary-tumor regions from R1 through R9 were harvested from the nephrectomy specimen.

Tumour evolution: from linear paths to branched trees

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The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. Comprehensive genomic analyses of cancers have revealed substantial intrapatient molecular heterogeneities that may explain some instances of drug resistance and treatment failures. Examination of the clonal composition of an individual tumor and its evolution through disease progression and treatment may enable identification of precise therapeutic targets for drug design. Multi-region and single-cell sequencing are powerful tools that can be used to capture intratumor heterogeneity. We collected over tumor samples from patients corresponding to 45 different types of cancer. Patient-specific tumor phylogenetic trees were constructed based on somatic mutations or copy number alterations identified in multiple biopsies. Using the structured heterogeneity data, researchers can identify common driver events shared by all tumor regions, and the heterogeneous somatic events present in different regions of a tumor of interest.

Tumor Heterogeneity View all 7 Articles. Today, clinical evaluation of tumor heterogeneity is an emergent issue to improve clinical oncology. In particular, intra-tumor heterogeneity ITH is closely related to cancer progression, resistance to therapy, and recurrences. It is interconnected with complex molecular mechanisms including spatial and temporal phenomena, which are often peculiar for every single patient. It is important to consider the different types of ITH as a whole for any patient to investigate on cancer progression, prognosis, and treatment opportunities.

Overview on Clinical Relevance of Intra-Tumor Heterogeneity

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Gerlinger and Andrew J.

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. The advent of next-generation sequencing technologies enabled the characterization of cancer genomes at unprecedented resolution Milestone 7.

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Intratumor Heterogeneity and Branched Evolution Revealed. by Multiregion Sequencing. Marco Gerlinger, M.D., Andrew J. Rowan.

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