Intratumor heterogeneity and branched evolution revealed by multiregion sequencing

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.).

Conflict of interest statement

Disclosure forms provided by the authors are available with the full text of this article at NEJM.org.

Figures

Figure 1. Biopsy and Treatment Timelines for the Four Patients.

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.

Figure 2. Genetic Intratumor Heterogeneity and Phylogeny in Patient 1.

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 101 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). Regions R6 and R7 were excluded from analyses since only one nonsynonymous variant passed filtering. 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. Because of limited DNA availability, only six mutations were validated in pretreatment samples of the primary tumor (PreP) and chest-wall metastases (PreM) (in VHL, MTOR, SOX9, ALKBH8, SETD2, and KDM5C splice sites). 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). DI denotes DNA index of the aneuploid peak, indicating the DNA content as compared with a diploid genome.

Figure 3. Correlations between Genotype and Phenotype in Patient 1.

Panel A shows phospho-S6 (Ser235/236) and phospho-4EBP (Thr37/46) staining. All tumor regions harboring mTOR (L2431P) had increased staining of the downstream mTOR-pathway targets phospho-S6 and phospho-4EBP. Regions harboring wild-type mTOR had absent phospho-S6 and phospho-4EBP staining in tumor cells. Panel B shows immunoblotting of Caki1 cells (derived from a human renal-cell carcinoma) that were transiently transfected with green fluorescent protein (GFP) vector alone (mock), GFP-mTOR (wild type), or GFP-mTOR (L2431P) with and without serum starvation. 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.

Figure 4. Genetic Intratumor Heterogeneity and Phylogeny in Patient 2.

Panel A shows the regional distribution of somatic mutations detected by exome sequencing in a heat map, with gray indicating the presence of a mutation and dark blue the absence of a mutation. The color bars above the heat map indicate classification of mutations according to whether they are ubiquitous, shared by primary-tumor regions, or unique to the region (private). For gene names, purple indicates that the mutation was validated, and orange indicates that the validation of the mutation failed. Panel B shows phylogenetic relationships of the tumor regions. Branch lengths are proportional to the number of somatic mutations separating the branching points. Potential driver mutations were acquired by the indicated genes in the branch (arrows).