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Clonal evolution of chemotherapy-resistant urothelial carcinoma

Nature Genetics volume 48, pages 1490–1499 (2016)Cite this article

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Abstract

Chemotherapy-resistant urothelial carcinoma has no uniformly curative therapy. Understanding how selective pressure from chemotherapy directs the evolution of urothelial carcinoma and shapes its clonal architecture is a central biological question with clinical implications. To address this question, we performed whole-exome sequencing and clonality analysis of 72 urothelial carcinoma samples, including 16 matched sets of primary and advanced tumors prospectively collected before and after chemotherapy. Our analysis provided several insights: (i) chemotherapy-treated urothelial carcinoma is characterized by intra-patient mutational heterogeneity, and the majority of mutations are not shared; (ii) both branching evolution and metastatic spread are very early events in the natural history of urothelial carcinoma; (iii) chemotherapy-treated urothelial carcinoma is enriched with clonal mutations involving L1 cell adhesion molecule (L1CAM) and integrin signaling pathways; and (iv) APOBEC-induced mutagenesis is clonally enriched in chemotherapy-treated urothelial carcinoma and continues to shape the evolution of urothelial carcinoma throughout its lifetime.

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Figure 1: Clinical characteristics of the study cohort.
Figure 2: Clonal mutational heterogeneity in chemotherapy-treated urothelial carcinoma.
Figure 3: Early branching evolution in urothelial carcinoma.
Figure 4: Reconstructing the spatiotemporal evolution of urothelial carcinoma over time and through different treatments.
Figure 5: Hierarchical clustering of 44 urothelial carcinoma tumor samples by copy number alterations.
Figure 6: Clonal enrichment of mutations in chemotherapy-treated urothelial carcinoma.
Figure 7: Mutagenesis in advanced urothelial carcinoma is shaped by chemotherapy and APOBECs.

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Acknowledgements

We would like to thank our patients and their families for participation in this study. We would like to thank B. Sleckman for constructive review of the manuscript. We would also like to acknowledge D. Scherr and C. Barbieri for contributing samples, our research and clinical pathology fellows J. Fontugne, M. Kossai, C. Pauli, K. Hennrick and K. Park for their assistance during rapid autopsies, and S.S. Chae and D. Wilkes for technical assistance and constructive comments. We would also like to thank T.Y. MacDonald, J. Padilla and T. Fedrizzi for technical assistance. Work was partially supported by the Translational Research Program at WCMC Pathology and Laboratory Medicine. This work was supported by a Conquer Cancer Foundation and the John and Elizabeth Leonard Family Foundation Young Investigator Award (B.M.F.), NIH/NCATS KL2TR000458 (B.M.F.), Early Detection Research Network US NCI 5U01 CA111275-09 (J.M.M., M.A.R. and F.D.), Damon Runyon Cancer Research Foundation Clinical Investigator Award CI-67-13 (H.B.), and H2020 European Research Council ERC-CoG 648670 (F.D.).

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Author notes

  1. Bishoy M Faltas and Davide Prandi: These authors contributed equally to this work.

  2. Himisha Beltran, Francesca Demichelis and Mark A Rubin: These authors jointly directed this work.

Authors and Affiliations

  1. Caryl and Israel Englander Institute for Precision Medicine, New York Presbyterian Hospital–Weill Cornell Medicine, New York, New York, USA

    Bishoy M Faltas, Scott T Tagawa, Ana M Molina, David M Nanus, Juan Miguel Mosquera, Brian Robinson, Olivier Elemento, Andrea Sboner, Himisha Beltran, Francesca Demichelis & Mark A Rubin

  2. Department of Medicine, Division of Hematology and Medical Oncology, Weill Cornell Medicine, New York, New York, USA

    Bishoy M Faltas, Scott T Tagawa, Ana M Molina, David M Nanus & Himisha Beltran

  3. Sandra and Edward Meyer Cancer Center at Weill Cornell Medicine, New York, New York, USA

    Bishoy M Faltas, Scott T Tagawa, David M Nanus, Himisha Beltran & Mark A Rubin

  4. Centre for Integrative Biology, University of Trento, Trento, Italy

    Davide Prandi & Francesca Demichelis

  5. Department of Medical Oncology, San Camillo–Forlanini Hospital, Rome, Italy

    Cora Sternberg

  6. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA

    Jonathan Rosenberg

  7. Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, New York, USA

    Juan Miguel Mosquera, Brian Robinson, Andrea Sboner & Mark A Rubin

  8. Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, USA

    Olivier Elemento

  9. Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York, USA

    Olivier Elemento, Andrea Sboner & Francesca Demichelis

Authors
  1. Bishoy M Faltas
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Contributions

Initiation and design of study: B.M.F., H.B., M.A.R. and F.D. Subject enrollment and sample and clinical data collection: B.M.F., H.B., D.M.N., S.T.T., A.M.M., C.S., J.M.M. and B.R. Statistical and bioinformatics analyses: D.P., O.E., A.S. and F.D. Supervision of research: B.M.F., M.A.R., J.R. and F.D. Writing of the first draft of the manuscript: B.M.F., D.P., H.B., M.A.R. and F.D. All authors contributed to the writing and editing of the revised manuscript and approved the manuscript.

Corresponding authors

Correspondence to Francesca Demichelis or Mark A Rubin.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Analysis of mutated genes as reported in TCGA bladder study.

Each row represents a gene harboring frequent SNVs (middle panel) or somatic copy number (bottom panel). Annotation rows include, from top to bottom, tumor ploidy, treatment information, biopsy site, patient’s gender, matched samples. Top bars report the otal number of non-silent SNVs in each sample. Bars on the left indicate the per-sample frequency of aberration in the cohort. NA, not available.

Supplementary Figure 2 SNVs burden comparison.

Per sample number of all SNVs (left boxplot) and only non-silent SNVs (right boxplot) in pre-chemotherapy and post-chemotherapy tumors. No statistical difference.

Supplementary Figure 3 Single nucleotide variants (SNV) validation by targeted sequencing (N250 panel).

Each dot represents a genomic position corresponding to an SNV sequenced with WES and with N250 approaches. Reported values are the ratios between the number of reads supporting the alternative base and the coverage. SNVs from the same patient are represented with the same color.

Supplementary Figure 4 Discordance in the SNVs between chemotherapy-naive and chemotherapy-treated UC tumors.

(a) Percentage of shared and unique SNVs in matched chemotherapy-naive and chemotherapy-treated UC tumors (same of Fig. 2a). (b) Detail of the specific amino acid change in matched chemotherapy-naive and chemotherapy-treated UC tumors. Each column represents a matched pair of pre- and post- chemotherapy tumors. Row reports the specific amino acid change. The figure highlights the wide divergence in the mutational landscape of pre- and post-chemotherapy samples.

Supplementary Figure 5 Phylogenetic trees of patients with two sequenced tumor samples.

Phylogenetic trees from 15 patients with two samples per patient.

Supplementary Figure 6 Hierarchical clusters of 131 UC tumor samples by copy-number alterations.

The plot extends Fig. 5a considering samples from both WCM and TCGA cohorts. Copy number gains are represented in red and copy number losses are represented in blue. Each column represents one tumor sample. Clinical annotations include treatment, biopsy site, cohort, and the presence of TP53 SNV.

Supplementary Figure 7 Hierarchical clusters of 86 UC tumor samples by copy-number alterations.

The plot shows allele specific copy number clusters as in Fig. 5a while considering samples from the TCGA cohort. Copy number gains are represented in red and copy number losses are represented in blue. Each column represents one tumor sample. Each row represents one of the 2503 genes used to compute TCGA clusters (Fig. S4.1 from Nature 507, 315–322, 2014)). Annotations include presence of TP53 SNV and TCGA clusters.

Supplementary Figure 8 Pairwise comparison of SNVs clonality.

Top right scatter plot reports the clonality of SNVs in matched pre-chemotherapy and post-chemotherapy samples. Bottom and left boxplots show clonality of SNVs that are private to pre-chemotherapy and post-chemotherapy samples, respectively.

Supplementary Figure 9 Comparison of FFPE and Fresh samples.

(left) Boxplot of the purity in FFPE and Fresh samples (p>0.05, Wilcoxon test). (right) Boxplot of the number of non-silent SNVs in FFPE and Fresh samples (p>0.05, Wilcoxon test).

Supplementary Figure 10 Landscape of somatic SNVs identified.

Landscape of somatic SNVs identified.

Supplementary Figure 11 Comparison of shared and local SNVs considering silent and non-silent SNVs.

Comparison of the percentage of shared and local SNVs in matched chemotherapy-naive and chemotherapy-treated UC tumors when considering only non-silent SNVs (top) or all SNVs (bottom).

Supplementary Figure 12 Early branching evolution in UC using silent and non-silent SNVs.

Phylogenetic trees (top), shared and private clonally-adjusted mutations (bottom) from 6 patients with three or more tumor samples per patient.

Supplementary Figure 13 Phylogenetic tree of case WCM117 using silent and non-silent SNVs.

Silent and non-silent SNVs across 12 tumor samples (top) collected at various time points and from various anatomical locations. Fractions of tumor cells harboring each mutation represented by shades of green. Reconstruction of evolutionary tree (bottom).

Supplementary Figure 14 Clonal enrichment of mutations in chemotherapy-treated UC considering silent and non-silent SNVs.

Clonal enrichment of mutations in chemotherapy-treated UC considering silent and non-silent SNVs.

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Supplementary Figures 1–14 and Supplementary Note (PDF 3323 kb)

Supplementary Tables 1–11

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Faltas, B., Prandi, D., Tagawa, S. et al. Clonal evolution of chemotherapy-resistant urothelial carcinoma. Nat Genet 48, 1490–1499 (2016). https://doi.org/10.1038/ng.3692

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