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Spatial intratumoral heterogeneity and temporal clonal evolution in esophageal squamous cell carcinoma

Nature Genetics volume 48, pages 1500–1507 (2016)Cite this article

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Abstract

Esophageal squamous cell carcinoma (ESCC) is among the most common malignancies, but little is known about its spatial intratumoral heterogeneity (ITH) and temporal clonal evolutionary processes. To address this, we performed multiregion whole-exome sequencing on 51 tumor regions from 13 ESCC cases and multiregion global methylation profiling for 3 of these 13 cases. We found an average of 35.8% heterogeneous somatic mutations with strong evidence of ITH. Half of the driver mutations located on the branches of tumor phylogenetic trees targeted oncogenes, including PIK3CA, NFE2L2 and MTOR, among others. By contrast, the majority of truncal and clonal driver mutations occurred in tumor-suppressor genes, including TP53, KMT2D and ZNF750, among others. Interestingly, phyloepigenetic trees robustly recapitulated the topological structures of the phylogenetic trees, indicating a possible relationship between genetic and epigenetic alterations. Our integrated investigations of spatial ITH and clonal evolution provide an important molecular foundation for enhanced understanding of tumorigenesis and progression in ESCC.

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Figure 1: ITH of somatic mutations in 13 ESCCs generated by M-WES.
Figure 2: Clonal status of putative driver mutations in ESCC tumors.
Figure 3: Temporal dissection of mutational signatures in ESCC tumors.
Figure 4: Epigenetic ITH in ESCC.

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Acknowledgements

We thank H. Shen and D. Weisenberger as well as A.D. Jeyasekharan for their kind help on analysis and discussion. This work was funded by the Singapore Ministry of Health's National Medical Research Council (NMRC) through its Singapore Translational Research (STaR) Investigator Award to H.P.K., an NMRC Individual Research Grant (NMRC/1311/2011) and the NMRC Centre Grant awarded to the National University Cancer Institute of Singapore, the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiatives to H.P.K. D.-C.L. was supported by the American Society of Hematology Fellow Scholar Award, the National Natural Science Foundation of China (81672786) and National Center for Advancing Translational Sciences UCLA CTSI Grant UL1TR000124. M.-R.W. was supported by the National Natural Science Foundation of China (81330052, 81520108023 and 81321091). Y.Z. was supported by the Beijing Natural Science Foundation (7151008). This study was partially supported by a generous donation from the Melamed family and NIH/NCI grant 1U01CA184826 as well as institutional support from the Samuel Oschin Comprehensive Cancer Institute to B.P.B. and H.Q.D.

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  1. Jia-Jie Hao, De-Chen Lin, Huy Q Dinh, Anand Mayakonda and Yan-Yi Jiang: These authors contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Molecular Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Jia-Jie Hao, Chen Chang, Ye Jiang, Chen-Chen Lu, Xin Xu, Yu Zhang, Yan Cai, Qi-Min Zhan & Ming-Rong Wang

  2. Division of Hematology/Oncology, Cedars-Sinai Medical Center, UCLA School of Medicine, Los Angeles, California, USA

    De-Chen Lin & H Phillip Koeffler

  3. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China

    De-Chen Lin

  4. Center for Bioinformatics and Functional Genomics, Biomedical Sciences, Cedars-Sinai Medical Center, UCLA School of Medicine, Los Angeles, California, USA

    Huy Q Dinh & Benjamin P Berman

  5. Cancer Science Institute of Singapore, National University of Singapore, Singapore

    Anand Mayakonda, Yan-Yi Jiang & H Phillip Koeffler

  6. Faculty of Medicine, Kunming University of Science and Technology, Kunming, China

    Zhi-Zhou Shi

  7. Department of Pathology, Linzhou Cancer Hospital, Henan, China

    Jin-Wu Wang

  8. Department of Cancer Epidemiology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Wen-Qiang Wei

  9. National University Cancer Institute, National University Hospital Singapore, Singapore

    H Phillip Koeffler

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Contributions

M.-R.W., D.-C.L., B.P.B. and H.P.K. conceived and designed the experiments. J.-J.H., D.-C.L., H.Q.D., W.-Q.W., B.P.B., M.-R.W. and H.P.K. wrote the manuscript. J.-J.H., D.-C.L., Y.J., C.C., C.-C.L., X.X. and Y.C. performed the experiments. J.-J.H., H.Q.D., A.M., B.P.B. and Z.-Z.S. performed statistical analysis. J.-J.H., D.-C.L., H.Q.D., Y.-Y.J., B.P.B. and H.P.K. analyzed the data. X.X. contributed reagents. W.-Q.W. contributed materials. J.-W.W. and J.-J.H. read slides with hematoxylin and eosin staining. D.-C.L., Y.Z., Q.-M.Z. and H.P.K. jointly supervised research.

Corresponding authors

Correspondence to De-Chen Lin, Wen-Qiang Wei, Benjamin P Berman or Ming-Rong Wang.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Fitting somatic variants to evolutionary models based on phylogenetic trees.

Mutations that were mapped to the shared and private branches of trees and were 100% compatible with tree structure are shown on the branch where they occurred; those incompatible with tree structure are not shown. As trunk variants were defined by their presence in all regions, they were by definition consistent and are not shown because of space limitations. The total number of mutations in each case and the percentage that were compatible with the evolutionary tree model are provided above each tree (in parentheses).

Source data

Supplementary Figure 2 Correlation of early and late somatic mutations with potential targeting approaches.

Candidate potential targeting approaches, which have been developed or are under evaluation, were selected on the basis of The Cancer Genome Analysis (TCGA; http://www.broadinstitute.org/cancer/cga/target).

Source data

Supplementary Figure 3 ITH of CNAs in ESCC.

Heat maps displaying recurrent putative driver CNAs for each tumor region (driver CNAs were taken from our previous results; Nat. Genet. 46, 467–473, 2014). Chromosome segments with a log2 ratio between 0.5 and 1.0 were classified as gains, greater than 1.0 were classified as amplifications, less than –0.5 were classified as losses and less than –1.0 were classified as deletions.

Source data

Supplementary Figure 4 The number of clonal and subclonal mutations in ESCC cases.

Clonal/subclonal status is described in the Online Methods.

Source data

Supplementary Figure 5 The 96 trinucleotide mutational spectra of truncal (bottom) and branched (top) mutations in ESCC10 and ESCC12.

Supplementary Figure 6 Reconstruction of phyloepigenetic trees with four different probe selection cutoffs (0.2, 0.3, 0.4 and 0.5).

The probe selection cutoff defines the minimum delta methylation level (β value) used for variable selection (‘shared’ versus ‘private’ status) to choose the methylation probes to be used in construction of trees. Each row shows one case, and each column shows the resulting tree for each cutoff. The cutoff used in the results section (Fig. 4a) was 0.3. The number of probes selected (n) and Robinson-Foulds (RF) distance to the original tree (cutoff = 0.3) are provided for each tree (except for the one with cutoff = 0.3, which is the reference). ESCC05 did not have enough probes to construct a tree at cutoff = 0.5.

Supplementary Figure 7 Immune cells make up most of the non-cancer cell fraction in ESCC samples.

Representative IHC photos of the four spatially distinct regions for case ESCC05, showing that immune cells (LCA/CD45 positive staining; highlighted within the yellow squares) make up most of the non-cancer cells in this tumor. Cancer cells are shown in the red squares). Scale bar, 200 μm.

Supplementary Figure 8 Reconstruction of the phyloepigenetic trees with mitigation for the effects of immune cell content.

We recalculated each phyloepigenetic tree using one of two methods to mitigate the effects of immune cells within the samples. For the first method, immune cell adjusted, we estimated the fraction of leukocytes in each sample using profiles of immune-specific methylation probes, as described previously (Nat. Biotechnol. 30, 413–421, 2012; Nat. Genet. 45, 1134–1140, 2013). We then used the methylation profile of pure leukocytes, along with the estimated leukocyte/cancer cell mixture, to infer the cancer cell methylation value for every probe (probe numbers are not identical because of different probes being below the detection limit in different experiments). For the second method, dichotomized, we used only probes that had a fully unmethylated state in pure leukocytes and dichotomized/binarized each probe on the basis of the minimum methylation level in tumor samples (Online Methods). For each of the immune cell adjusted and dichotomized trees, the RF distance shows similarity to the reference version.

Supplementary Figure 9 Unsupervised hierarchical clustering of methylation values at shared probes.

‘Shared’ probes were those selected as having similar values across different regions of the same tumor (the number of shared probes is given above each heat map as n). The shared probes were compared to normal adjacent tissue and divided into those that had consistently higher methylation values (hypermethylated) or lower methylation values (hypomethylated) in the tumor samples. The probes in each heat map were clustered using hierarchical clustering with the Euclidean distance metric.

Supplementary Figure 10 Analysis of tumor cell content in ESCC samples.

Representative hematoxylin and eosin photographs of case ESCC02. Scale bar, 2 mm.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–10 and Supplementary Tables 1, 5 and 7. (PDF 2039 kb)

Supplementary Table 2

Detailed information of all somatic mutations in 51 tumor regions from 13 patients with ESCC. (XLSX 1129 kb)

Supplementary Table 3

Validation by PCR and Sanger sequencing. (XLSX 13 kb)

Supplementary Table 4

Copy number of each chromosomal segment in 51 tumor regions from 13 patients with ESCC. (XLSX 994 kb)

Supplementary Table 6

Mutations incompatible with the phylogenetic tree. (XLSX 17 kb)

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Hao, JJ., Lin, DC., Dinh, H. et al. Spatial intratumoral heterogeneity and temporal clonal evolution in esophageal squamous cell carcinoma. Nat Genet 48, 1500–1507 (2016). https://doi.org/10.1038/ng.3683

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