ABSTRACT Fewer than half of all patients with advanced-stage high-grade serous ovarian cancers (HGSCs) survive more than five years after diagnosis, but those who have an exceptionally long

survival could provide insights into tumor biology and therapeutic approaches. We analyzed 60 patients with advanced-stage HGSC who survived more than 10 years after diagnosis using

whole-genome sequencing, transcriptome and methylome profiling of their primary tumor samples, comparing this data to 66 short- or moderate-term survivors. Tumors of long-term survivors were

more likely to have multiple alterations in genes associated with DNA repair and more frequent somatic variants resulting in an increased predicted neoantigen load. Patients clustered into

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PRIMARY AND METASTATIC HIGH-GRADE SEROUS OVARIAN CANCER TUMORS REVEALS DISTINCT FEATURES ASSOCIATED WITH SURVIVAL Article Open access 03 July 2023 GENOMIC LANDSCAPE AND IMMUNE-RELATED GENE

EXPRESSION PROFILING OF EPITHELIAL OVARIAN CANCER AFTER NEOADJUVANT CHEMOTHERAPY Article Open access 27 January 2022 MULTIOMIC ANALYSIS OF HOMOLOGOUS RECOMBINATION-DEFICIENT END-STAGE

HIGH-GRADE SEROUS OVARIAN CANCER Article 27 February 2023 DATA AVAILABILITY ICGC datasets: Previously published WGS and RNA-seq data generated as part of the ICGC Ovarian Cancer project14

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 the accession code

EGAD00001000877. Due to the sensitive nature of these patient data sets, access is subject to approval from the ICGC Data Access Compliance Office

(https://docs.icgc.org/download/data-access/), an independent body who authorizes controlled access to ICGC sequencing data. ICGC SNP array and methylation data sets have been deposited into

the 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 into the GEO under accession code GSE209964. MOCOG datasets: WGS, RNA-seq and SNP array data from long-term survivors generated as part of the MOCOG study have been deposited in

the EGA repository under accession code EGAS00001005984. WGS and RNA-seq data are available as raw FASTQ files for each sample type (tumor/normal) and SNP array data are available as raw

signal intensity files in text format for each sample type (tumor/normal). Access to patient sequence data can be gained for academic use through application to the independent Data Access

Committee ([email protected]). Responses to data requests will be provided within two weeks. Information on how to apply for access is available at the EGA under accession code

EGAS00001005984. The MOCOG cohort raw methylation data sets have been submitted to the GEO under accession code GSE211687, with no access restrictions. Uniformly processed somatic variant

data from the ICGC and MOCOG cohorts have been deposited in Synapse under accession code syn34616347, and processed expression and methylation data from both cohorts have been submitted into

the GEO under accession code GSE211687, without access restrictions. Population frequencies of genetic variants can be accessed via the Genome Aggregation Database (gnomAD) at

https://gnomad.broadinstitute.org/. Supporting evidence for pathogenicity of genomic alterations can be accessed via ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/), BRCA Exchange

(https://brcaexchange.org/) and the _TP53_ Database (https://tp53.isb-cgc.org/). The Ensembl ranked order of severity of variant consequences is available at:

https://m.ensembl.org/info/genome/variation/prediction/predicted_data.html. Precomputed TCGA ovarian serous cystadenocarcinoma survival analysis data can be downloaded from OncoLnc

(http://www.oncolnc.org/). 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 at https://cibersortx.stanford.edu/. The COSMIC Cancer Gene Census

can be accessed at https://cancer.sanger.ac.uk/census. MSigDB hallmark gene sets can be accessed at 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 gene transfer format

used for annotation and RNA-seq counts is available at ftp://ftp.ensembl.org/pub/grch37/release-92/. All other data are available within the article and its supplementary information files.

CODE AVAILABILITY No custom code or software was used in the data analyses. All results can be replicated using publicly available tools and software. The tools and versions used are fully

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other unwanted variation in high-throughput experiments. _Bioinformatics_ 28, 882–883 (2012). Article  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS We thank P. Webb, K. Byth,

R. Lupat, J. Ellul and the Peter MacCallum Cancer Centre Research Computing Facility for their contributions to the study. This work was supported by the U.S. Army Medical Research and

Materiel Command Ovarian Cancer Research Program (Award No. W81XWH-16-2-0010 and W81XWH-21-1-0401), the National Health and Medical Research Council of Australia (1092856, 1117044 and

2008781 to D.D.L.B., and 1186505 to D.W.G.), and the U.S. National Cancer Institute (P30CA046592 for C.L.P. and P30CA008748 for M.C.P.). This research was made possible by generous support

from the Border Ovarian Cancer Awareness Group, the Garvan Research Foundation, the Graf Family Foundation, Mrs Margaret Rose AM, Arthur Coombs and family, and the Piers K Fowler Fund. The

Australian Ovarian Cancer Study (AOCS) gratefully acknowledges the cooperation of participating institutions in Australia and the contribution of study nurses, research assistants and all

clinical and scientific collaborators. The complete AOCS Group can be found at www.aocstudy.org. We would like to thank all of the women who participated in the study. AOCS was supported by

the U.S. Army Medical Research and Materiel Command (DAMD17-01-1-0729), The Cancer Council Victoria, Queensland Cancer Fund, The Cancer Council New South Wales, The Cancer Council South

Australia, The Cancer Council Tasmania, The Cancer Foundation of Western Australia and the National Health and Medical Research Council of Australia (NHMRC; ID199600, ID400413, ID400281).

AOCS gratefully acknowledges additional support from Ovarian Cancer Australia and the Peter MacCallum Cancer Foundation. We thank all the women who participated in the GynBiobank and

gratefully acknowledge the Departments of Gynaecological Oncology, Medical Oncology and Anatomical Pathology at Westmead Hospital, Sydney. The Gynaecological Oncology Biobank at Westmead was

funded by the NHMRC (ID310670, ID628903), the Cancer Institute NSW (12/RIG/1-17, 15/RIG/1-16) and the Department of Gynaecological Oncology, Westmead Hospital, and acknowledges financial

support from the Sydney West Translational Cancer Research Centre, funded by the Cancer Institute NSW (15/TRC/1-01). E.L.C. was supported by NHMRC grant APP1161198. F.A.M.S. was supported by

a Swiss National Foundation EarlyPostdoc Fellowship (P2BEP3-172246), Swiss Cancer Research Foundation grant BIL KFS-3942-08-2016 and a Professor Dr Max Cloëtta and Uniscientia Foundation

grant. A.M.P. and J.D.B. were supported by Cancer Research UK (A22905). B.H.N. was supported by the BC Cancer Foundation, Canada’s Networks of Centres of Excellence (BioCanRx), Genome BC and

the Canada Foundation for Innovation. D.D.L.B. was supported by the U.S. National Cancer Institute U54 program (U54CA209978). AUTHOR INFORMATION Author notes * These authors contributed

equally: Anna DeFazio, David D. L. Bowtell. AUTHORS AND AFFILIATIONS * Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia Dale W. Garsed, Ahwan Pandey, Sian Fereday, Kazuaki

Takahashi, Kathryn Alsop, Joy Hendley, Nadia Traficante, Dinuka Ariyaratne, George Au-Yeung, Leanne Bowes, Elizabeth L. Christie, Orla McNally, Flurina A. M. Saner & David D. L. Bowtell

* Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia Dale W. Garsed, Sian Fereday, Kathryn Alsop, Nadia Traficante, George Au-Yeung, 

Elizabeth L. Christie & David D. L. Bowtell * The Westmead Institute for Medical Research, Sydney, New South Wales, Australia Catherine J. Kennedy, Yoke-Eng Chiew, Pamela Provan, Jillian

Hung & Anna DeFazio * Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia Catherine J. Kennedy, Yoke-Eng Chiew, Pamela Provan, Alison Brand, 

Jillian Hung & Anna DeFazio * The University of Sydney, Sydney, New South Wales, Australia Catherine J. Kennedy, Yoke-Eng Chiew, Pamela Provan, Alison Brand, Paul Harnett & Anna

DeFazio * Department of Obstetrics and Gynecology, The Jikei University School of Medicine, Tokyo, Japan Kazuaki Takahashi * The Deeley Research Centre, BC Cancer, Victoria, British

Columbia, Canada Phineas T. Hamilton & Brad H. Nelson * Women’s Health Integrated Research Center, Gynecologic Cancer Center of Excellence, Uniformed Services University and Walter Reed

National Military Medical Center, Bethesda, MD, USA Nicholas W. Bateman, Thomas P. Conrads & George L. Maxwell * Henry M. Jackson Foundation for the Advancement of Military Medicine,

Inc., Bethesda, MD, USA Nicholas W. Bateman * The Royal Women’s Hospital, Parkville, Victoria, Australia Leanne Bowes & Orla McNally * Department of Laboratory Medicine and Pathology,

Mayo Clinic, Rochester, MN, USA Julie M. Cunningham * Prince of Wales Clinical School, University of New South Wales, Sydney, New South Wales, Australia Michael Friedlander * Sydney, New

South Wales, Australia Bronwyn Grout * Crown Princess Mary Cancer Centre, Westmead Hospital, Sydney, New South Wales, Australia Paul Harnett * Division of Clinical Trials and Biostatistics,

Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA Bryan McCauley & Robert A. Vierkant * Department of Obstetrics and Gynaecology, The University of Melbourne,

Parkville, Victoria, Australia Orla McNally * Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK Anna M. Piskorz & James D. Brenton * Department of Obstetrics

and Gynecology, Bern University Hospital and University of Bern, Bern, Switzerland Flurina A. M. Saner * Division of Computational Biology, Department of Quantitative Health Sciences, Mayo

Clinic, Rochester, MN, USA Chen Wang & Stacey J. Winham * Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK Paul D. P. Pharoah * Department of

Oncology, University of Cambridge, Cambridge, UK Paul D. P. Pharoah & James D. Brenton * Women’s Health Integrated Research Center, Women’s Service Line, Inova Health System, Falls

Church, VA, USA Thomas P. Conrads & George L. Maxwell * School of Clinical Medicine, Faculty of Medicine and Health, University of NSW, Sydney, New South Wales, Australia Susan J. Ramus

* Adult Cancer Program, Lowy Cancer Research Centre, University of NSW, Sydney, New South Wales, Australia Susan J. Ramus * Department of Epidemiology and Rogel Cancer Center, University of

Michigan, Ann Arbor, MI, USA Celeste Leigh Pearce * Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA Malcolm C. Pike * Department of

Medical Genetics, The University of British Columbia, Vancouver, British Columbia, Canada Brad H. Nelson * Department of Biochemistry and Microbiology, University of Victoria, Victoria,

British Columbia, Canada Brad H. Nelson * Division of Epidemology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA Ellen L. Goode * The Daffodil Centre, The

University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia Anna DeFazio Authors * Dale W. Garsed View author publications You can also search for this

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* David D. L. Bowtell View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS D.W.G., S.F., A.D. and D.D.L.B. conceived the project. C.J.K., K.A.,

N.T., G.A.-Y., A.B., M.F., P.R.H., O.M., E.L.G., A.D. and D.D.L.B. provided patient samples and clinical information, and D.W.G., J. Hendley, Y.C. and E.L.C. prepared patient samples.

D.W.G., A.P., S.F., C.J.K., K.A., D.A., L.B., J.M.C., J. Hung, B.M., R.A.V., C.W., S.J.W. and E.L.G. acquired data. A.P. managed and processed genomic data and performed data analysis

together with D.W.G., S.F., K.T., P.P., D.A., E.L.C., B.M., F.A.M.S., R.A.V., C.W. and S.J.W. D.W.G., S.F., K.A., C.L.P., M.C.P. and D.D.L.B. coordinated the study and interpreted the

results along with A.P., K.T., P.T.H., N.W.B., E.L.C., B.G., A.M.P., F.A.M.S., P.D.P.P., J.D.B., T.P.C., G.L.M., S.J.R., B.H.N. and A.D. D.W.G., A.D. and D.D.L.B. supervised the work and

wrote the manuscript together with A.P. and K.T. All authors discussed the results and read the manuscript. CORRESPONDING AUTHORS Correspondence to Dale W. Garsed or David D. L. Bowtell.

ETHICS DECLARATIONS COMPETING INTERESTS S.F., K.A., N.T. and A.D. received grant funding from AstraZeneca for unrelated work. G.A.-Y. received grant funding from AstraZeneca and

Roche-Genentech for unrelated work. M.F. declares honoraria for advisory boards AstraZeneca, GSK, Incyclix, Lilly, MSD, Novartis and Takeda; consultancy for AstraZeneca, Eisai and Novartis;

speaker’s fee and travel from AstraZeneca; speaker’s fee from ACT Genomics; and institutional research funding from AstraZeneca, BeiGene, Novartis; all for unrelated work. J.D.B. received

funding from Aprea and Clovis Oncology for unrelated work. D.D.L.B. received funding from AstraZeneca, Genentech-Roche and BeiGene for unrelated work. The remaining authors declare no

competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Genetics_ thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are

available. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA

EXTENDED DATA FIG. 1 PATIENT COHORT. A, Overview of patients (_n_ = 126) and tumor samples analyzed in this study. In addition to paired germline and primary tumor samples in all patients, 5

relapse tumor samples were also analyzed from 4 long-term survivor patients. OS, overall survival. B, Clinical characteristics of patients by survival group. All patients received primary

platinum therapy. aKruskal–Wallis, bChi-square, or clog-rank Mantel–Cox test _P_ values comparing survival groups reported. EXTENDED DATA FIG. 2 FREQUENTLY ALTERED CANCER GENES ACROSS

SURVIVAL GROUPS. A, Overview of somatic alterations in driver genes detected by GRIN, dNdScv, GISTIC, and/or in cancer-associated genes (COSMIC Cancer Gene Census) that are enriched in a

survival group relative to another survival group. From left: two-sided Fisher’s test of the difference in proportions of altered samples between survival groups, triangles and color

indicate direction of the log odds ratio (LOR; blue = down, pink = up), asterisks indicate _P_ value < 0.05 (see Supplementary Table 6 for _P_ values), _P_ values were not adjusted for

multiple comparisons; role of gene in COSMIC Cancer Gene Census (TSG, tumor suppressor gene); genomic alterations split by survival groups, bars at the top indicate the number of alterations

in each listed gene per patient; bar plot of the number of samples with an alteration (alteration type indicated by color); bar plots showing the proportion of alteration types per gene;

_P_ values were calculated using the genomic random interval (GRIN) statistical model (one-sided) for recurrent structural variants (SV) (see Supplementary Data 2 for GRIN _P_ values), the

dNdScv likelihood-ratio test (two-sided) for recurrent base substitutions and small-scale deletions and insertions (see Supplementary Data 1 for dNdScv _P_ values), and GISTIC2

permutation-of-markers test (one-sided) for recurrent copy-number variants (CNV) with red indicating amplification and blue indicating deletion (see Supplementary Data 3 for GISTIC2 _P_

values), _P_ values were adjusted for multiple comparisons using the Benjamini-Hochberg procedure (dNdScv, GISTIC2) or the robust false discovery rate procedure (GRIN) and are shown as

negative log10 _P_ values and capped at 0.001 for display purposes. Each patient (column) is annotated with survival group (LTS, long-term survivor; MTS, moderate-term survivor; STS,

short-term survivor). Below the alterations are bar plots indicating somatic mutation burden in variants per megabase (Mb); SV count including duplications, deletions, inversions and

intrachromosomal rearrangements; and the proportion of the tumor genome that is duplicated (WGD) or lost (WGL). B, Pairwise comparison of the alteration frequencies between survival groups

for genes in the COSMIC Cancer Gene Census. The difference in relative alteration frequency is shown on the x-axis and the _P_ value (Fisher’s test, two-sided) is shown on the y-axis.

Symbols of genes with _P_ values < 0.05 are displayed. Multiple hypothesis correction was not applied in this analysis as adjusted _P_ values were all greater than 0.1. Alterations in

this analysis included non-synonymous mutations, homozygous deletions, amplifications and structural variants in coding genes that are expressed. EXTENDED DATA FIG. 3 KEY FEATURES OF

MUTATIONAL SIGNATURE CLUSTERS AND ASSOCIATED SURVIVAL OUTCOMES. A, Summary of the key clinical and genomic features of each mutational signature cluster. Clusters are ordered top to bottom

by lowest to highest proportion of long-term survivors (LTS) in each cluster. HR, homologous recombination; LOH, loss-of-heterozygosity; SV, structural variant; MTS, moderate-term survivor;

STS, short-term survivor; DUP, duplications; DEL, deletions; INV, inversions. B, Kaplan–Meier analysis of progression-free and C, overall survival in patients stratified by signature

clusters. _P_ values calculated by Mantel–Cox log-rank test and dotted lines indicate median survival. D, Boxplots summarize the proportion (y-axis) of clustered and nonclustered

rearrangements by size (x-axis) and type, for each mutational signature cluster (SIG.1 _n_ = 14, SIG.2 _n_ = 25, SIG.3 _n_ = 13, SIG.4 _n_ = 27, SIG.5 _n_ = 22, SIG.6 _n_ = 9, SIG.7 _n_ = 

16); boxes show the interquartile range (25–75th percentiles), central lines indicate the median, whiskers show the smallest/largest values within 1.5 times the interquartile range and

values outside it are shown as individual data points. Del, deletions; tds, tandem duplications; inv, inversions, tra, interchromosomal translocations; Kb, kilobase; Mb, megabase. EXTENDED

DATA FIG. 4 CATEGORICAL FEATURES OF MUTATIONAL SIGNATURE CLUSTERS. A, Proportion of patients affected by gene alterations per mutational signature cluster. Genes are ordered by significance

using Fisher’s exact test (two-sided) and clusters are ordered by the proportion of long-term survivors. B, Proportion of patients with categorical features per cluster. Features are ordered

by significance using Fisher’s exact test (two-sided) and the clusters are arranged by the proportion of long-term survivors. The Fisher’s test _P_ values displayed in (A) and (B) are

Benjamini-Hochberg adjusted _P_ values. Features include homologous recombination (HR) status, homologous recombination deficiency (HRD) type, number of DNA repair pathway alterations,

survival group (LTS, long-term survivor; MTS, moderate-term survivor; STS, short-term survivor), status at last follow-up (D, dead; P, progressed and alive; PF, progression-free and alive),

self-reported smoking status, DeepCC molecular subtype (C1, mesenchymal; C2, immunoreactive; C4, differentiated; C5, proliferative), and neoadjuvant treatment (Y, yes; N, no). EXTENDED DATA

FIG. 5 CLINICAL AND GENOMIC FEATURES OF MUTATIONAL SIGNATURE CLUSTERS. A, Boxplots summarize numerical, clinical and genomic features by mutational signature cluster; points represent each

sample, boxes show the interquartile range (25–75th percentiles), central lines indicate the median, whiskers show the smallest/largest values within 1.5 times the interquartile range, red

triangles indicate the mean, and dotted lines join the means of each cluster to visualize the trend. The Kruskal–Wallis test _P_ values displayed are Benjamini-Hochberg adjusted _P_ values.

Features are ordered by their significance and clusters are ordered by the proportion of long-term survivors. CD8 scores were available for _n_ = 54 primary tumors as previously measured by

immunohistochemistry23 and scored as density of CD8+ T cells (average cells/mm2, y axis) in the tumor epithelium (TE). HRD, homologous recombination deficiency; DEL, deletions; DUP;

duplications; SV, structural variants; Mb, megabase; ITX, intrachromosomal rearrangements; LOH, loss-of-heterozygosity; INV, inversions. B, Bubble plot summary of mutational signature

enrichment across signature clusters. The dendrogram is reused from the signature clustering (Fig. 3) to order the mutational signature types (columns). Mutational signature clusters (rows)

are sorted by the proportion of long-term survivors in each cluster, indicated in brackets. The color and size of bubbles indicate the z-score scaled values of the mean signature exposure

per cluster. Bubbles with a z-score of greater than or equal to 1 have a black border and bubbles with a z-score of greater than 0.5 but less than 1 have a gray border. Bordered bubbles have

asterisks filled in to indicate Kruskal–Wallis test _P_ values adjusted for multiple testing using Benjamini-Hochberg correction. EXTENDED DATA FIG. 6 DNA METHYLATION CLUSTERING OF PRIMARY

TUMOR GENOMES. A, Heatmap of methylation data following consensus clustering of primary tumors (columns) based on the standardized CpG probe intensities (M-values) of the 1% most variable

CpG probes (rows; number of probes = 3,645) across all primary tumor samples (_n_ = 126). The heatmap scale shows the beta values. Five methylation clusters were identified (MET.1—MET.5),

and each patient (column) is annotated with survival group (LTS, long-term survivor; MTS, moderate-term survivor; STS, short-term survivor), age at diagnosis (quartiles), and self-reported

smoking history. Tumor samples are also classified according to _CCNE1_ amplification (amp) status, _BRCA1_ alteration status, CIBERSORTx absolute (abs) immune scores (quartiles), and

molecular subtype11 (C1, mesenchymal; C2, immunoreactive; C4, differentiated; C5, proliferative). Bars in the bottom panel represent the _BRCA1_ (orange) and _BRCA2_ (blue) type homologous

recombination deficiency (CHORD28) scores of each tumor sample. B, Kaplan–Meier analysis of progression-free (PFS) and overall survival (OS) in patients stratified by methylation clusters.

_P_ values calculated by Mantel–Cox log-rank test and dotted lines indicate median survival in years since diagnosis. EXTENDED DATA FIG. 7 TRANSCRIPTIONAL PHENOTYPES IN LONG-TERM SURVIVORS.

A, Clustered heatmap summarizing gene set enrichment analysis (GSEA) using the hallmark Molecular Signatures Database (MSigDB) gene sets. Direction and color of triangles relate to the

normalized enrichment score (NES) as generated by FGSEA. _P_ values (two-sided) were calculated using the FGSEA default Monte Carlo method; the size of the triangles corresponds to the

negative log10 Benjamini-Hochberg adjusted _P_ value (_P_adj). The columns are split by survival groups (STS, short-term survivor; MTS, moderate-term survivor; LTS, long-term survivor), with

the direction of enrichment denoted by the group in the heading (numerator) versus the two other groups labeled below. B, Boxplots summarize expression of _MKI67_ and _PCNA_ proliferation

gene markers across the survival groups (left; STS _n_ = 34, MTS _n_ = 32, LTS _n_ = 60); points represent each sample, boxes show the interquartile range (25–75th percentiles), central

lines indicate the median, and whiskers show the smallest/largest values within 1.5 times the interquartile range. Differential expression analysis was performed using DESeq2 to determine

fold change (right) of gene expression between survival groups (two-tailed Wald test, both unadjusted _P_ values and Benjamini-Hochberg adjusted _P_ values (_P_adj) are shown). C, Forest

plot (left) indicates the hazard ratio (HR, squares) and 95% confidence interval (CI; whiskers) for overall survival calculated using a univariate Cox proportional hazard regression model

based on the LM22 immune cell types detected by CIBERSORTx analysis (_n_ = 126 patients). Cell types are arranged by HR. _P_ values < 0.05 are colored red (*_P_ < 0.05, **_P_ < 

0.01) and were not adjusted for multiple comparisons. Absolute enrichment scores per cell type across the cohort are shown in boxplots (right); boxes show the interquartile range (25–75th

percentiles), central lines indicate the median, whiskers show the smallest/largest values within 1.5 times the interquartile range and values outside it are shown as individual data points.

EXTENDED DATA FIG. 8 GENOMIC AND CLINICAL FEATURES OF IMMUNE CLUSTERS. A, A condensed bubble plot of the various LM22 cell types used for the immune clustering (IMM.1 _n_ = 32, IMM.2 _n_ = 

23, IMM.3 _n_ = 22, IMM.4 _n_ = 24, IMM.5 _n_ = 25). The dendrogram is reused from the immune clustering (Fig. 5a) to order the cell types. Immune clusters (rows) are sorted by the

proportion of long-term survivors indicated in brackets. The color and size of bubbles indicate z-score scaled values of the mean abundance of cell types per cluster. Bubbles with a z-score

of greater than or equal to 1 have a black border, and those with a z-score of greater than 0.5 but less than 1 have a gray border. Asterisks indicate Kruskal–Wallis test _P_ values adjusted

for multiple testing using Benjamini-Hochberg correction. Boxplots (right) summarize CIBERSORTx absolute scores of each cluster; points represent each sample, boxes show the interquartile

range (25–75th percentiles), central lines indicate the median, and whiskers show the smallest/largest values within 1.5 times the interquartile range. B, Boxplots summarize numerical,

clinical and genomic features by immune cluster (IMM.1 _n_ = 32, IMM.2 _n_ = 23, IMM.3 _n_ = 22, IMM.4 _n_ = 24, IMM.5 _n_ = 25); points represent each sample, boxes show the interquartile

range (25–75th percentiles), central lines indicate the median, whiskers show the smallest/largest values within 1.5 times the interquartile range, red triangles indicate the mean, and

dotted lines join the means of each cluster to visualize the trend. The Kruskal–Wallis test _P_ values displayed are Benjamini-Hochberg adjusted. Features are ordered by their significance

and clusters are ordered by the proportion of long-term survivors. CD8 scores were available for _n_ = 54 primary tumors as previously measured by immunohistochemistry23 and scored as

density of CD8+ T cells (average cells/mm2, y axis) in the tumor epithelium (TE). HRD, homologous recombination deficiency; DEL, deletions; DUP; duplications; SV, structural variants; Mb,

megabase; ITX, intrachromosomal rearrangements; LOH, loss-of-heterozygosity; INV, inversions. EXTENDED DATA FIG. 9 CATEGORICAL FEATURES OF IMMUNE CLUSTERS. A, Proportion of patients with

categorical features per cluster. Features are ordered by significance using Fisher’s exact test (two-sided) and the clusters are arranged by the proportion of long-term survivors. Features

include homologous recombination (HR) status, homologous recombination deficiency (HRD) type, number of DNA repair pathway alterations, survival group (LTS, long-term survivor; MTS,

moderate-term survivor; STS, short-term survivor), status at last follow-up (D, dead; P, progressed and alive; PF, progression-free and alive), self-reported smoking status, DeepCC molecular

subtype (C1, mesenchymal; C2, immunoreactive; C4, differentiated; C5, proliferative), and neoadjuvant treatment (Y, yes; N, no). B, Proportion of patients affected by gene alterations per

immune cluster. Genes are ordered by significance using Fisher’s exact test (two-sided) and clusters are ordered by the proportion of long-term survivors. The Fisher’s test _P_ values

displayed in (A) and (B) are Benjamini-Hochberg adjusted _P_ values. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Note, Figures 1–21 and Tables 18–20. REPORTING SUMMARY

PEER REVIEW FILE SUPPLEMENTARY DATA 1 dNdScv cancer driver gene detection results. The table contains the output from the dNdScv R package that uses maximum-likelihood models (two sided) to

detect genes under selection in the study cohort (_n_ = 126). Only high-confidence base substitutions and small-scale deletions and insertions were used for the analysis. Columns include the

gene symbol (gene_name) and global adjusted _P_ value (qglobal_cv). More details regarding the interpretation of all the columns are available at https://github.com/im3sanger/dndscv.

SUPPLEMENTARY DATA 2 Genomic random interval (GRIN) statistical model (one-sided) for recurrent structural variants. The table contains the output from the gene level GRIN analysis on the

study cohort (_n_ = 126) for high-confidence structural variants filtered for expressed protein coding genes. Columns include the gene symbol (gene.label), the adjusted _P_ value of the

cohort level overlap statistic (q.subjects) for all genes, as well as a re-calculation of the adjusted _P_ value after filtering for the expressed protein coding genes

(q.subjects.after_filter). The table has also been annotated for blacklisted genes for SNVs and indels (snv_indel_blacklist), fragile sites (fragile_site), the fragile site database

(fragile_site.list), presence in the COSMIC database (cosmic), the tier in the COSMIC database (cosmic.tier), and its role in cancer in the COSMIC database (cosmic.tier). More details

regarding the interpretation of the columns output by GRIN are available at https://www.stjude.org/research/departments/biostatistics/software/grin.html. SUPPLEMENTARY DATA 3 Regions of

recurrent copy-number change detected by GISTIC2 (permutation-of-markers test, one-sided). Sheet 1 contains cytoband level results for amplifications and deletions in the overall cohort of

126 primary tumors. Columns include the cytoband (Descriptor) and the adjusted _P_ value of the region (q values). Sheet 2 contains the cytoband level analysis with adjusted _P_ values (q

values) for all 126 primary tumors as well as for tumors grouped by survival category. Sheet 3 contains GISTIC2 results at the gene level, and columns include the gene symbol

(genes.in.wide.peak) and adjusted _P_ values (q values) for all 126 primary tumors well as for tumors grouped by survival category. More details regarding the interpretation of the columns

output by GISTIC2 are available at https://www.genepattern.org/modules/docs/GISTIC_2.0. SUPPLEMENTARY DATA 4 RNA-seq differential expression (DE) and gene set enrichment analysis (GSEA)

across survival groups. Sheet 1 contains GSEA output from the FGSEA tool using a pre-ranked list of differentially expressed genes for each survival group comparison. The analysis was run

using the Molecular Signatures Database (MSigDB) hallmark gene sets. _P_ values (two-sided) were calculated using the FGSEA default Monte Carlo method. Columns include the tumor comparison

set (COMPARISON), hallmark pathways (pathway), adjusted _P_ values (padj) and Normalized Enrichment Scores (NES). Sheets 2–4 contain the DE analyses performed using the R package DESeq2

(two-tailed Wald test); comparisons include long-term survivors versus short-term survivors (Sheet2_LTS_vs_STS), long-term survivors versus moderate-term survivors (Sheet3_LTS_vs_MTS) and

moderate-term survivors versus short-term survivors (Sheet4_MTS_vs_STS). The first survival group in the name of each sheet corresponds to the numerator of the DE analysis. Columns include

the gene symbol (SYMBOL), logged and unlogged fold changes (log2FoldChange, foldChange), and adjusted _P_ values (padj) as output by DESeq2. More details regarding the output of FGSEA can be

found at https://github.com/ctlab/fgsea, and DESeq2 can be found at http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html. SUPPLEMENTARY DATA 5 RNA-seq high- and

medium-confidence gene fusion results from the Arriba tool. Columns include the fusion gene pair (gene1, gene2), the breakpoints in the gene pair (breakpoint1, breakpoint2), the confidence

of the fusion assigned by Arriba (confidence) and coordinates of any breakpoint support detected in the whole-genome sequencing list of high-confidence structural variants

(closest_genomic_breakpoint1, closest_genomic_breakpoint2). In-frame fusion gene pairs that were identified in more than one patient are indicated in the column ‘Recurrent in-frame gene

fusion’. More details regarding the interpretation of the Arriba output can be found at https://arriba.readthedocs.io/en/latest/output-files/. SUPPLEMENTARY DATA 6 Differential methylation

(DM) analysis results using the Limma R package in order of long-term survivors versus moderate-term survivors (LTS - MTS), long-term survivors versus short-term survivors (LTS - STS) and

moderate-term survivors versus short-term survivors (MTS - STS). The first survival group in the name of each sheet corresponds to the numerator of the DM analysis. Columns include the

genomic coordinates of the methylation probe (seqname, start, end), the identifier of the probe (Name), the symbol of the gene target (GeneSymbol), the distance to the transcription start

site (TSS) of the gene (distance2TSS), if the probe is in the promoter region of the gene (PROMOTER), if the probe intersects the gene body (GENEBODY), the minimum, maximum, mean, and median

beta value of the probe in the cohort (min_beta, max_beta, mean_beta, median_beta), the minimum, maximum, mean and median RNA expression of the gene in the cohort (min_exp, max_exp,

mean_exp, median_exp), the two-sided Pearson correlation test with gene expression (pearson_cor), the unadjusted Pearson correlation _P_ value (pearson_pval), the Benjamini-Hochberg adjusted

Pearson correlation _P_ value (pearson_qval), the direction of Pearson correlation (pearson_cor_dir), the logged fold-change as output by Limma (logFC), the adjusted _P_ value of the DM

result as output by Limma (adj.P.Val) and a column to indicate a filter for genes that were deemed to be turned off in the numerator comparison group (EXP_TURNED_OFF_IN_LTS or

EXP_TURNED_OFF_IN_MTS). Further details in Supplementary Note. More details regarding the interpretation of the Limma output can be found at

https://bioconductor.org/packages/release/bioc/html/limma.html. SUPPLEMENTARY DATA 7 RNA-seq eifferential expression (DE) and fast gene set enrichment analysis (FGSEA), comparing

transcriptomes of _CCNE1_ amplified tumors in each survival group (short-term survivors _n_ = 11, moderate-term survivors _n_ = 4, long-term survivors _n_ = 6) with a reference group of

tumors with no _CCNE1_ amplification or loss that had no homologous recombination (HR) alterations and were classified as HR proficient (reference _n_ = 21). Sheet 1 contains the output from

the FGSEA tool using a pre-ranked list of differentially expressed genes for each survival group comparison. The analysis was run using the Molecular Signatures Database (MSigDB) hallmark

gene sets. _P_ values (two-sided) were calculated using the FGSEA default Monte Carlo method. Columns include the tumor comparison set (COMPARISON), hallmark pathways (pathway), adjusted _P_

values (padj) and Normalized Enrichment Scores (NES). Sheets 2–4 contain the DE analyses performed using the R package DESeq2 (two-tailed Wald test); comparisons include _CCNE1_ amplified

tumors in long-term survivors versus the reference tumors (Sheet2_LTS_vs_reference), _CCNE1_ amplified tumors in moderate-term survivors versus the reference tumors (Sheet3_MTS_vs_reference)

and _CCNE1_ amplified tumors in short-term survivors versus the reference tumors (Sheet4_STS_vs_reference). The first survival group in the name of each sheet corresponds to the numerator

of the DE analysis. Columns include the gene symbol (SYMBOL), logged and unlogged fold changes (log2FoldChange, foldChange), and adjusted _P_ values (padj) as output by DESeq2. SUPPLEMENTARY

TABLE 1 Supplementary Tables 1–17 RIGHTS AND PERMISSIONS Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing

agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement

and applicable law. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Garsed, D.W., Pandey, A., Fereday, S. _et al._ The genomic and immune landscape of long-term survivors of

high-grade serous ovarian cancer. _Nat Genet_ 54, 1853–1864 (2022). https://doi.org/10.1038/s41588-022-01230-9 Download citation * Received: 06 January 2022 * Accepted: 17 October 2022 *

Published: 01 December 2022 * Issue Date: December 2022 * DOI: https://doi.org/10.1038/s41588-022-01230-9 SHARE THIS ARTICLE Anyone you share the following link with will be able to read

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