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Multiomic analysis of homologous recombination-deficient end-stage high-grade serous ovarian cancer

Nature Genetics volume 55, pages 437–450 (2023)Cite this article

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Abstract

High-grade serous ovarian cancer (HGSC) is frequently characterized by homologous recombination (HR) DNA repair deficiency and, while most such tumors are sensitive to initial treatment, acquired resistance is common. We undertook a multiomics approach to interrogate molecular diversity in end-stage disease, using multiple autopsy samples collected from 15 women with HR-deficient HGSC. Patients had polyclonal disease, and several resistance mechanisms were identified within most patients, including reversion mutations and HR restoration by other means. We also observed frequent whole-genome duplication and global changes in immune composition with evidence of immune escape. This analysis highlights diverse evolutionary changes within HGSC that evade therapy and ultimately overwhelm individual patients.

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Fig. 1: End-stage HGSC cohort, samples and experimental modalities.
Fig. 2: Clonal composition of end-stage HGSC.
Fig. 3: Summary of HGSC genomic landscape at autopsy, as assessed by WGS.
Fig. 4: Known resistance mechanisms in HR-deficient HGSC.
Fig. 5: Multisite and multitemporal immune profiling of HR-deficient HGSC.
Fig. 6: Proteomic-level data analysis.
Fig. 7: Summary of evolutionary events and resistance mechanisms detected for each patient.

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Data availability

ICGC dataset: previously published WGS and RNA-seq data generated as part of the ICGC Ovarian Cancer project are available from the European Genome-phenome Archive (EGA) repository (https://ega-archive.org) as a single bam file for each sample type (tumor/normal), under accession code EGAD00001000877. Due to the sensitive nature of these patient datasets, access is subject to approval from the ICGC Data Access Compliance Office (https://docs.icgc.org/download/data-access/), an independent body that authorizes controlled access to ICGC sequencing data. ICGC SNP array and methylation datasets have been deposited in Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under accession code GSE65821, without access restrictions. ICGC gene count-level transcriptomic data have been deposited in the GEO under accession code GSE209964. CASCADE dataset: WGS, RNA-seq and SNP array data from participants in a rapid research autopsy generated as part of this CASCADE study have been deposited in the EGA repository under accession code EGAS00001006789. WGS (no. EGAD00001009746) and RNA-seq data (no. EGAD00010002398) are available as raw FASTQ files for each sample type (tumor/normal and tumor, respectively), and SNP array data are available as raw signal intensity files in text format for each sample type (tumor/normal, no. EGAD00010002398). Targeted sequencing data for the same participants are available as raw FASTQ files for both tumor and normal (no. EGAD00001009747). Due to the sensitive nature of these patient datasets, access can be gained for academic use through application to the independent Data Access Committee. Further information on how to apply for access is available at EGA under accession code EGAS00001006789. Transcriptomic count-level data and methylation data for the same participants has been deposited in the GEO under accession code GSE217672 (RNA) and GSE217673 (methylation), available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE217674, without access restrictions. Mutational signature reference databases can be accessed via COSMIC (https://cancer.sanger.ac.uk/signatures/) and Signal (https://signal.mutationalsignatures.com/). The LM22 signature matrix used for immune cell deconvolution can be downloaded from https://cibersortx.stanford.edu/. The COSMIC Cancer Gene Census can be accessed from https://cancer.sanger.ac.uk/census. MSigDB hallmark gene sets can be accessed from https://www.gsea-msigdb.org/gsea/msigdb/. Illumina methylation probes that were filtered out due to poor performance (for example, cross-reactive or nonspecific probes) can be found at https://github.com/sirselim/illumina450k_filtering. Germline polymorphic sites for reference and variant allele read counts used in FACETS analysis can be found at ftp://ftp.ncbi.nih.gov/snp/organisms/human_9606_b151_GRCh37p13/VCF/common_all_20180423.vcf.gz. The GTF used for annotation and RNA-seq counts are available at ftp://ftp.ensembl.org/pub/grch37/release-92/. All other data are available within the article and its Supplementary Information files. Multicolor immunofluorescence: final cell counts have been uploaded to Synapse under accession no. syn49448783, and are available publicly at https://www.synapse.org/#!Synapse:syn44043685/files/. Proteomics: MS proteomics data have been deposited at the ProteomeXchange Consortium via the PRIDE partner repository, with dataset identifier PXD030034. Detailed methods of data processing from raw files are available in the Supplementary Note. Source data are provided with this paper.

Code availability

R code for all genomics and mcIF analyses and figure generation has been deposited in Synapse (no. syn44044508), available publicly. No custom code was generated for this study.

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Acknowledgements

This study was financially supported by grants from the Department of Health and Human Services through the National Health and Medical Research Council of Australia (NHMRC, nos. APP1124309 to E.L.C., APP1161198 to D.D.L.B. and E.L.C., APP1117044 to D.D.L.B., APP1092856 to D.D.L.B., APP1186505 to D.W.G. and APP1189939 to N.L.B.), the US National Cancer Institute U54 program (no. U54CA209978 to A.B., J.T.C. and D.D.L.B.), Victorian Cancer Agency (no. ECSG15012 to E.L.C.), Tour de Cure (no. RSP-274-18/19 to E.L.C.) and Goldman Sachs Gives through the Peter MacCallum Cancer Foundation and Cancer Australia (no. APP1004673 to D.D.L.B.). Part of this study was supported by the US Department of Defense – Uniformed Services University of the Health Sciences (nos. HU0001-16-2-0006 and HU0001-16-2-0014 to G.L.M.). We gratefully acknowledge additional support from M. Rose and the Rose family, The WeirAnderson Foundation, Border Ovarian Cancer Awareness Group, donors to the Garvan Institute of Medical Research’s Ovarian Cancer Research Program, W. Taylor and A. Coombs and family. The CASCADE study was supported by the Peter MacCallum Cancer Centre Foundation, and in kind by the Victorian Institute of Forensic Medicine and Tobin Brothers Funerals. The CASCADE investigators thank the CASCADE Management Committee, all staff at the Victorian Institute of Forensic Medicine, D. Stevens and Tobin Brothers Funerals. We acknowledge and thank the women and their families who generously participated in the CASCADE program making this work possible. We acknowledge the vital role of the Australian Ovarian Cancer Study (AOCS) in this study. AOCS was supported by the US Army Medical Research and Materiel Command (no. DAMD17-01-1-0729 to D.D.L.B.), The Cancer Council Victoria, Queensland Cancer Fund, The Cancer Council New South Wales, The Cancer Council South Australia, The Cancer Foundation of Western Australia and The Cancer Council Tasmania and NHMRC (nos. ID400413 and ID400281 to D.D.L.B.). AOCS acknowledges additional support from Ovarian Cancer Australia and the Peter MacCallum Cancer Centre Foundation. AOCS gratefully acknowledges the cooperation of the participating institutions in Australia, and acknowledges the contribution of the study nurses, research assistants and all clinical and scientific collaborators, in particular L. Bowes, D. Ariyaratne and N. Traficante. We thank E. Niedermayr, S. Guo, all the kConFab research nurses and staff, the heads and staff of the Family Cancer Clinics and the Clinical Follow Up Study (which has received funding from NHMRC, the National Breast Cancer Foundation, Cancer Australia and the National Institute of Health (USA)) for their contributions to this resource, and the many families who contribute to kConFab. We thank Peter MacCallum Cancer Centre Molecular Genomics core facility, supported by the Australian Cancer Research Foundation, and the Peter MacCallum Cancer Centre Bioinformatics core facility, in particular M. Jayawardana, for statistical advice. The WHIRC team acknowledge contributions by P. Akowuah, J. Loffredo, U. Rao, S. Makohon-Moore, J. Oliver, D. Mitchell and G. Gist. We thank the BC Cancer Foundation, Genome BC, Canadian Institutes for Health Research, Canadian Cancer Society, Terry Fox Research Institute and Canada Foundation for Innovation. Disclaimer: the views expressed herein are those of the authors and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense or US Government.

Author information

Authors and Affiliations

  1. Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia

    Nikki L. Burdett, Madelynne O. Willis, Kathryn Alsop, Ahwan Pandey, Therese Hoang, Stuart Craig, Sian Fereday, Joy Hendley, Dale W. Garsed, Kathleen I. Pishas, Sumitra Ananda, Clare L. Scott, Linda Mileshkin, Anne Hamilton, George Au-Yeung, Lisa Devereux, Heather Thorne, David D. L. Bowtell & Elizabeth L. Christie

  2. Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia

    Nikki L. Burdett, Kathryn Alsop, Sian Fereday, Dale W. Garsed, Kathleen I. Pishas, Linda Mileshkin, Anne Hamilton, George Au-Yeung, Lisa Devereux, Heather Thorne, David D. L. Bowtell & Elizabeth L. Christie

  3. Department of Medical Oncology, Eastern Health, Box Hill, Victoria, Australia

    Nikki L. Burdett

  4. Women’s Health Integrated Research Center, Inova Women’s Service Line, Inova Health System, Annandale, Victoria, USA

    Allison L. Hunt & G. Larry Maxwell

  5. Gynecologic Cancer Center of Excellence, Department of Obstetrics and Gynecology, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA

    Allison L. Hunt, Tamara Abulez, Brian L. Hood, Katlin N. Wilson, Kelly A. Conrads, Nicholas W. Bateman, G. Larry Maxwell & Thomas P. Conrads

  6. Deeley Research Centre, BC Cancer, Victoria, British Columbia, Canada

    Phineas T. Hamilton, Katy Milne, Shreena Kalaria, Ashley Marshall & Brad H. Nelson

  7. The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA

    Tamara Abulez, Brian L. Hood, Katlin N. Wilson, Kelly A. Conrads, Nicholas W. Bateman & Thomas P. Conrads

  8. Department of Integrative Biology and Pharmacology, The University of Texas Health Science Center, Houston, TX, USA

    Xuan Liu & Jeffrey T. Chang

  9. Department of Medical Oncology, Western Health, St Albans, Victoria, Australia

    Sumitra Ananda

  10. Department of Medicine, Western Health, The University of Melbourne, St Albans, Victoria, Australia

    Sumitra Ananda

  11. Epworth Healthcare, East Melbourne, Victoria, Australia

    Sumitra Ananda

  12. Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia

    Clare L. Scott

  13. Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia

    Yoland Antill

  14. Cabrini Health, Malvern, Victoria, Australia

    Yoland Antill

  15. Department of Medical Oncology, Peninsula health, Frankston, Victoria, Australia

    Yoland Antill

  16. The Royal Women’s Hospital, Parkville, Victoria, Australia

    Orla McNally & Anne Hamilton

  17. Department of Obstetrics and Gynaecology, The University of Melbourne, Melbourne, Victoria, Australia

    Orla McNally

  18. Department of Medical Oncology and Therapeutics, City of Hope Comprehensive Cancer Center, Monrovia, CA, USA

    Andrea Bild

  19. The John P. Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University, Bethesda, MD, USA

    Nicholas W. Bateman, G. Larry Maxwell & Thomas P. Conrads

  20. Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada

    Brad H. Nelson

  21. Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada

    Brad H. Nelson

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  1. Nikki L. Burdett
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Contributions

E.L.C., K.A. and D.D.L.B. conceived the project and directed the study, with input from all authors. A.H., O.M., L.M., S.A., C.L.S., Y.A. and G.A.-Y. consented patients to CASCADE. N.L.B., M.O.W., K.A., K.P., J.H., L.D., H.T., D.D.L.B. and E.L.C. collected and/or processed patient tissues. N.L.B., K.A. and S.F. analyzed clinical information. For DNA- and RNA-seq, E.L.C., G.A.-Y. and D.D.L.B. contributed to the experimental design, M.O.W., S.C. and N.L.B. performed library preparation, A.P. performed data processing and N.L.B., M.O.W., T.H., S.C., K.I.P., D.W.G. and E.L.C. analyzed and interpreted data. X.L. and J.T.C. performed clonal analysis and interpretation. A.L.H., T.A., B.L.H., K.N.W., K.A.C., N.W.B., G.L.M. and T.P.C. optimized, performed and interpreted proteomics experiments and wrote the relevant section of the manuscript. P.T.H., K.M., S.K., A.M. and B.H.N. optimized, performed and interpreted mcIF experiments. A.B. and D.W.G. provided intellectual input and guidance on DNA- and RNA-seq analyses. N.L.B., E.L.C. and D.D.L.B. wrote the manuscript, with input from all authors.

Corresponding author

Correspondence to Elizabeth L. Christie.

Ethics declarations

Competing interests

T.P.C. is a ThermoFisher Scientific, Inc. SAB member and receives research funding from AbbVie. G.A.-Y. receives institutional grant funding from AstraZeneca and Roche-Genentech for unrelated work. AOCS (D.D.L.B., K.A., S.F. and J.H.) has received grant funding from AstraZeneca for an unrelated study. C.L.S. receives grant or research support from AstraZeneca, Clovis Oncology, Eisai, Inc., Sierra Oncology, Roche and Beigene for unrelated work, and sits on advisory boards for AstraZeneca, Clovis Oncology, Eisai, Inc., Sierra Oncology, Roche, Takeda and MSD. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Geoff Macintyre and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 CA125 and treatment data.

The clinical journey is shown for each patient, in 100 day intervals (x-axis), with the first tick referring to the day of first diagnostic information (surgery or CA125). Red line in the top plot indicates CA125 levels (y-axis), and triangles directly below indicate timing of primary surgery and death. The colored bars below indicate the treatments received and treatment duration.

Extended Data Fig. 2 Spatial sampling and distribution of mutations across samples.

(a) Top: simulated data demonstrating chance of detecting a variant present at frequencies between 0–1 (x axis) by the number of samples analyzed (y axis) (p < 0.0000000000000002, glm, t-value 10.62). Bottom: probability of accurate sampling based on the balance of tumor sampling (x axis) per number of samples (y axis) (p = <0.0000000000000002, glm, t-value 11.86). (b) Dendogram with oncoprint for autopsy only mutations within patients with a resistance mechanism detected in the targeted sequencing (that is, reversions and TP53BP1 INDEL), demonstrating their subclonal distribution. Each row represents a mutation within that patient, pink squares indicate mutation present, blue mutation absent. Only patients with a primary sample (to thereby distinguish mutations exclusive to autopsy setting) are shown.

Extended Data Fig. 3 Heterogeneity across WGS analyses.

(a) Copy number profiles for all samples ordered by chromosome (x-axis). Dendogram colored by first cluster split; pink encapsulates all non-whole genome duplicated samples. Copy number increases (red) and decreases (blue). (b) Example of the SV landscape as shown by circos plots for 6 autopsy samples from patient 15292. Lines indicate rearrangements and colors represent chromosomes. (c) Four cases with CCNE1 amplification in at least one tumor sample (all samples from those cases shown); the level of amplification is shown in each sample with the total copy number listed adjacently, with the total reversion allele frequency from the targeted sequencing (right). (d) Scatterplots of percent of autosomal genome duplicated vs TMB (top) and SV count (bottom). Samples in the red ellipse have WGD (>50% autosomal genome duplicated, n = 43), samples in the blue ellipse do not (n = 11).

Extended Data Fig. 4 Transcriptomic QC and analyses.

(a) Principal component analysis of RNAseq samples by QC parameters to assess for outlying samples with substandard parameters. Dimensions include = Dimension 1 < 2, Dimension 2 −0.4 > × < 1.8. Each panel shows the same PCA plot with samples colored by the following variables: RIN – RNA integrity number, TTA – time to autopsy, Uniquely mapped – number of uniquely mapped reads, Median CV coverage – variability of coverage over single genes, 3 prime bias (Picard tools), 5 prime bias (Picard tools). Color bar represents the numerical value of that graph title. (b) Sashimi plot schematic for case 66462 at BRCA1 exons 10–12 (Human genome reference build GRCh38 nomenclature used), showing the proportion of RNAseq reads comprising the canonical and delta 11q splice isoforms. Red arrow marks position of germline BRCA1 mutation. (c) MRE11 expression by sample MRE11 copy number state – RNAseq Z scores (p = 0.0002, glmm, z-statistic −3.75; amplified samples n = 3, non-amplified n = 17) and MRE11 protein abundance (p = 0.23, glm; t-value −1.25; amplified samples n = 5, non-amplified samples n = 97); blue represents copy neutral or loss, pink represents any level amplification. Lower and upper whisker terminates at the minimum and maximum values no further than 1.5 times the interquartile range; center line represents median (50th percentile); lower and upper boundary of box represent the 1st (25th percentile) and 3rd (75th percentile) quartiles respectively; outlying values are plotted as individual points beyond whiskers.

Source data

Extended Data Fig. 5 Evolution of immune composition in HGSC samples.

(a) McIF sample numbers by cores and metastatic sites per patient. (b) Stromal immune cell abundance (percentage of total cell detections) by timepoint. Left and right whisker terminates at the minimum and maximum values no further than 1.5 times interquartile range; center line represents median (50th percentile); left and right boundary of box represents the 1st (25th percentile) and 3rd (75th percentile) quartiles respectively. P values calculated with glmm, adjusted for multiple testing correction. Cut-off for significance accepted as p < 0.01; full statistics reported in Supplementary table 4. NA values occur where modelling could not be appropriately fitted due to small eigenvalues. X axis is logarithmic for improved visualization. Cores N = 282 primary, 488 autopsy (T&B cell) and 283 primary, 500 autopsy (PD1/MΦ). (c) Top: Boxplots of immune cell percentages within autopsy samples by patient. Bottom: variance of immune cell abundance between autopsy metastases per patient. For top and bottom, cores n = 488 (T&B cell) and 501 (PD1/MΦ). Lower and upper whisker terminates at minimum and maximum values no further than 1.5 times interquartile range; lower and upper boundary of box represent the 1st and 3rd quartiles; outlying values are plotted as individual points beyond whiskers; all others as in (b). (d) For 45 samples with both WGS and mcIF, bars represent the number of samples with or without loss of heterozygosity (LOH) of HLA alleles in WGS by mcIF cluster; samples which have cores in multiple mcIF clusters represented multiple times (p = 0.19, Chi sq, one-sided test).

Extended Data Fig. 6 Immune response and epigenetic regulation.

(a) Transcriptomic correlations between constitutive chemokines CCL5 and CXCL9 and T cell marker CD8A. N = 29 samples. Correlation statistics (R of repeated measures (rrm)) are labelled above plots. CCL5 vs CXCL9 Confidence interval (CI) = −0.03–0.78, 16 degrees of freedom; CCL5 vs CD8A CI = 0.20–0.86, 16 degrees of freedom; CXCL9 vs CD8A CI = 0.39–0.91, 16 degrees of freedom. (b) CCL5 expression (left; p = 0.0000000005; glmm, z-value = 6.2)), and M2 macrophage prevalence (right; p = 0.001; glmm, z-value = −3.191) by CCL5 methylation status (n = 21). Lower and upper whisker terminates at the minimum and maximum values no further than 1.5 times the interquartile range; center line represents median (50th percentile); lower and upper boundary of box represent the 1st (25th percentile) and 3rd (75th percentile) quartiles respectively; outlying values are plotted as individual points beyond whiskers. (c) PCA plot constructed from CCL5 and CXCL9 expression, mcIF CD8 T cell percentage and M2 macrophage abundance; left = annotated by RNA sample, right = annotated by anatomical site, demonstrating the overlap. RNAseq and methylation n = 19, corresponding to 42 mcIF cores. Dim = Principal component dimension.

Extended Data Fig. 7 Technical qualitative analyses of LMD enriched proteomics data.

(a) Workflow diagram depicting cell type enrichment via LMD and proteomic data analysis. Representative images from an H&E-stained tissue section and following LMD enrichment of tumor (blue) and stroma (orange) are shown (4 mm scale bar). (b) Supervised analysis of 107 differentially expressed proteins relating to the delay between patient death and specimen acquisition (p < 0.01, unadjusted for multiple testing correction, limma regression) between autopsy specimens (patient-blocked) relative to time to autopsy in LMD enriched tumor samples. Individual proteins are reported in Supplementary table 42. (c) Pearson correlation values of proteins related to previously defined signatures of necrosis and/or hypoxia in LMD enriched tumor samples. Shaded grey area represents 95% confidence interval.

Extended Data Fig. 8 Dendrograms demonstrating temporo-spatial proteomic differences.

Patient-specific phylogenetic analyses for 9 cases using Pearson correlations between early versus autopsy samples in LMD enriched tumor (left) or LMD enriched stroma (right). Timepoint abbreviations are primary debulking surgery (P), interval debulking surgery (I), recurrent sample (R), and autopsy sample (A). Patients with at least 2 early (non-autopsy) samples are included: (a) 15317, (b) 65659, (c) 15292, (d) 65682, (e) 15306, (f) 65913, (g) 66142, (h) 65938, and (i) 66462.

Supplementary information

Supplementary Information

Supplementary note, figures and associated references.

Reporting Summary

Supplementary Table 1

Supplementary tables for manuscript and text.

Source data

Source Data Fig. 5b

Clustering mcIF image (T and B cell panel, Imm-des) from composite image.

Source Data Fig. 5b

Clustering mcIF image (T and B cell panel, Ep/S infiltrated) from composite image

Source Data Fig. 5b

Clustering mcIF image (T and B cell panel, St-re) from composite image.

Source Data Fig. 5b

Clustering mcIF image (no. 65,914 PD1 panel) from composite image.

Source Data Fig. 5b

Clustering mcIF image (no. 65,914 PD1 panel) from composite image.

Source Data Extended Data Fig. 4b

IGV screenshot showing alternative splicing of BRCA1, from which schematic was created.

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Burdett, N.L., Willis, M.O., Alsop, K. et al. Multiomic analysis of homologous recombination-deficient end-stage high-grade serous ovarian cancer. Nat Genet 55, 437–450 (2023). https://doi.org/10.1038/s41588-023-01320-2

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