Early-onset ovarian cancer: a comprehensive analysis

Early-onset ovarian cancer: a comprehensive analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Early-onset ovarian cancer: a comprehensive analysis Klara Horackova, Petra Zemankova, Petr Nehasil, Michal Vocka, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3972616/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The subset of ovarian cancer (OC) diagnosed ≤ 30yo represents a distinct subgroup exhibiting disparities from late-onset OC in many aspects, including indefinite germline cancer predisposition. We performed DNA/RNA whole exome sequencing together with human leukocyte antigen(HLA) typing, polygenic risk score(PRS) assessment and survival analysis in 123 early-onset OC patients compared to histology/stage-matched late-onset and unselected OC patients, and population-matched controls. Only 6/123(4.9%) early-onset OC patients carried a germline pathogenic variant(GPV) in high-penetrance OC predisposition genes, including a single carrier of GPV in BRCA1 and BRCA2 each. Nevertheless, our comprehensive germline analysis of early-onset OC patients revealed two divergent trajectories of potential germline susceptibility. Firstly, overrepresentation analysis highlighted a connection to breast cancer(BC) that was supported by the enrichment of GPV in CHEK2 in early-onset OC( p = 1.2×10 − 4 ), and the presumably BC-specific PRS 313 , which successfully stratified early-onset OC from controls( p = 0.03). The second avenue pointed towards the impaired immune response, indicated by GPV in LY75-CD302 ( p = 8.3×10 − 4 ) and coupled with diminished HLA diversity compared with controls( p = 3×10 − 7 ). Furthermore, we found a significantly higher GPV burden in early-onset OC patients compared to controls( p = 3.8×10 − 4 ). We observed survival advantage in early-onset OC patients compared with both age-unselected and histology/stage-matched late-onset OC patients lacking gBRCA1/2 . The genetic predisposition to early-onset OC appears to be a heterogeneous and complex process that goes beyond the traditional Mendelian monogenic understanding of hereditary cancer predisposition, with a significant role of the immune system. We speculate that rather a cumulative GPV burden than specific GPV may potentially increase OC risk, concomitantly with reduced HLA diversity. Ovarian cancer early-onset germline genetic testing whole exome sequencing DNA RNA polygenic risk score HLA mutation burden Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 INTRODUCTION Ovarian cancer (OC; including tumors of ovary, fallopian tube and peritoneum) remains the deadliest gynecological malignancy [ 1 ]. Besides its anatomical heterogeneity, OC is characterized by heterogeneity at the cellular and molecular level and includes histologically distinct entities. Majority of the OC patients are diagnosed with epithelial, particularly high-grade serous carcinoma (HGSC) at advanced stages associated with a poor prognosis [ 2 , 3 ]. The lifetime OC risk is about 1.3% [ 1 , 4 ]. Nevertheless, there is a number of known factors, including genetic predisposition, that may substantially modify the OC risk. Germline pathogenic variants (GPV) in OC predisposition genes including the most frequently affected genes BRCA1/BRCA2 are of the highest impact. Identification of the GPV allows to stratify women according to the OC risk and, subsequently, to offer the carriers appropriate surveillance management including targeted therapy. The proportion of hereditary OC associated with GPV in homologous recombination and mismatch repair pathway genes is reported to be around 25%; however, it varies among OC histological subtypes and is the highest in HGSC [ 5 , 6 ]. OC rates are the highest in women aged 55–64 years with a median age of diagnosis at 63 years [ 1 ]. In contrast, OC in young women (≤ 30 years) is rare accounting for less than 5% of all OC cases [ 1 ]. Understanding the genetic basis of early-onset OC is crucial for unraveling the complex factors that contribute to its onset and progression. In many solid malignancies, early onset is a hallmark of hereditary predisposition. However, the proportion of early-onset OC attributable to GPV in OC predisposition genes is uncertain with only a few performed studies analyzing a limited number of early-onset OC patients diagnosed before 30 years of age. Interestingly, all these studies identified no or very low frequency of GPV in OC predisposition genes [ 6 – 11 ]. Despite the limited number of analyzed early-onset OC patients, the lack of GPV in OC predisposition genes, particularly BRCA1/BRCA2 , cannot be simply explained by a different representation of individual histological OC subtypes. Other mechanisms, including polygenic inheritance and immune response, might be involved in pathogenesis of early-onset OC. Polygenic inheritance resulting from the combination of multiple alleles with low impact on OC risk could contribute to early-onset OC development. Several sets of these low-penetrance alleles for stratification of individuals according to their OC risk have been published [ 12 – 21 ]. In addition, certain HLA genotypes have been previously associated with increased susceptibility to various diseases including early-onset OC; tumor neoantigen recognition might also depend on genetic variability in HLA regions. [ 22 ]. Thus, early-onset OC represents a unique and compelling area of investigation within the broader landscape of ovarian malignancies. In our study, we aimed to comprehensively characterize germline genetic landscape of early-onset OC in a set of 123 patients diagnosed before the age of 30 years using DNA/RNA whole exome sequencing (WES) including HLA analysis and polygenic risk score (PRS) analysis. In addition, we analyzed the genotype data in the clinical and histopathological context and in comparison, with population-matched histology/stage-matched late-onset OC patients and non-cancer controls. 2 PATIENTS AND METHODS 2.1 Patients and controls We enrolled 123 patients diagnosed with early-onset (< 30 years) OC (denoted herein as “early-onset OC”). Peripheral-blood derived gDNA of 123 patients and peripheral-blood derived total RNA (available in 71 patients) were analyzed using WES. Mean age at OC diagnosis was 25.4 years (15–30), 93 patients were diagnosed with invasive ovarian tumors (61 epithelial, 15 non-epithelial denoted herein as “other”, and 17 with unspecified histology) and 30 with borderline tumors of ovary (BTO) (Table 1 ; Supplementary Table S1 ). Table 1 Characteristics of 123 early-onset OC patients Histology N (%) Epithelial OC 61 (49.6) Type I 46 (37.4) LGSC 22 (17.9) mucinous 15 (12.2) endometrioid 6 (4.9) clear cell 3 (2.4) Serous NOS 11 (8.9) Type II 4 (3.2) HGSC 2 (1.6) undifferentiated 2 (1.6) NA 17 (13.8) BTO 30 (24.4) other 15 (12.2) Multiple primary tumors No 110 (89.4) Yes 13 (10.6) Family cancer history Positive 88 (71.6) - early onset cancer < 40y in family history 24 (19.5) - hematological malignancy in family history 15 (12.2) Negative 33 (26.8) NA 2 (1.6) BTO, Borderline Tumors of Ovary; LGSC, Low-Grade Serous Carcinoma; HGSC, High-Grade Serous Carcinoma; NA, not available; NOS, not otherwise specified; OC, Ovarian Cancer. For PRS and survival analysis, we employed additional OC patients analyzed previously [ 6 ] (details in Methods; Supplementary Table S4). We employed several sets of population-matched controls: for DNA variant prioritization : 227 healthy females older than 60 years with no personal or first-degree family cancer history (denoted herein as “super-controls”). for RNA splicing event prioritization : 61 unselected non-cancer females with available RNA WES data (denoted herein as “non-cancer controls”). for risk calculation in case-control analysis : 378 unselected individuals provided by the National Center for Medical Genomics [ 23 ] (accessed on 3/2023; denoted herein as “unselected controls”). for HLA analysis : “super-controls” and a second group of 5099 unselected individuals (data for HLA-C and HLA-DQB1 were available for 4669 and 4049 individuals, respectively) from Czech National Marrow Donors Registry [ 24 ] (accessed on 11/2023; denoted herein as “HLA controls”). for PRS analysis : 1403 non-cancer females negative for GPV in hereditary breast, ovarian and pancreatic (HBOP) cancer predisposition genes [ 25 ] (denoted herein as “PRS controls”) [ 26 ]. All patients and controls were Caucasians of Czech origin. Written informed consent was obtained from all patients and controls. The study was approved by the Ethics Committee of the General University Hospital in Prague and performed in accordance with the Declaration of Helsinki. 2.2 NGS Library Preparation Sequencing libraries were prepared as described previously [ 27 – 29 ] with minor modifications (detailed in Supplementary Methods) and targeted whole exome (KAPA HyperExome panel, Roche; capture target 43Mb) and 843 SNP including 65 SNP previously reported to associate with OC PRS (custom HyperChoice panel, Roche; Supplementary Table S2 ) [ 26 ]. The minimal mean coverage was 30× for DNA WES, 200× for RNA WES (with coverage of exon 11 in BRCA1 mRNA as a coverage quality marker) and 20× for PRS genotyping analysis. 2.3 Bioinformatics pipeline for variant analysis The fastq data from DNA WES were analyzed as described previously [ 27 ] with minor modifications (detailed in Supplementary Methods). Copy number variations (CNV) were analyzed using CNVkit as described previously [ 27 ]. The fastq data from RNA WES were mapped to hg19 using STAR aligner to generate BAM files [ 30 ]. Subsequently, duplicates were removed using Picard tools v1.129 [ 31 ] and analyzed by regtools [ 32 ] and SCANVIS [ 33 ]. 2.4 Variant filtration and prioritization for the burden analysis Variant prioritization (considering population frequency or sequencing quality) and variant classification were performed independently for DNA events (separately for substitutions/short-medium length indels and CNV analysis) and RNA events as described in Supplementary Methods. Final list of GPV (Supplementary Table S3 ) was used for the subsequent statistical analysis. The following genes were considered HBOP cancer predisposing: ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CDKN2A, CHEK2, MLH1, MSH2/EPCAM, MSH6, NF1, PALB2, PMS2, PTEN, RAD51C/D, STK11 , and TP53 [ 25 ]. 2.5 Statistical analyses Statistical analysis was performed in R v.4.2.0; p < 0.05 was considered significant. Gene burden analysis of the final list of GPV in patients and unselected controls was performed using Fisher exact test, the 3×10 − 7 exome-wide Bonferroni corrected p -value threshold was considered significant [ 34 ]. Overrepresentation analysis of the final list of GPV in patients was performed using online WEB-based GEne SeT AnaLysis Toolkit ( www.webgestalt.org , accessed on 11/2023) [ 35 ]. 2.6 Human Leukocyte Antigen (HLA) analysis HLA analysis was performed from DNA WES data of 123 early-onset OC patients and 227 super-controls using SpecHLA tool genotyping of HLA-A, B, C, DPA1, DPB1, DQA1, DQB1, and DRB1 [ 36 ]. The HLA genotypes were curated to the level of amino acid (four-digit resolution). The frequency of HLA alleles in early-onset OC and controls (HLA- and super-controls) were compared using Fisher test. Due to unavailability of HLA-DPA1, DPB1, and DQA1 genotypes and information about zygosity in HLA controls, the frequencies of the three genotypes and the zygosity status were statistically evaluated only to super-controls. 2.7 PRS analysis We performed genotyping of 11 SNP sets, including 10 OC [ 12 – 21 ] and one breast cancer (BC) [ 37 ], in 122 early-onset OC patients (PRS in one patient could not be assessed) and 85 histology- and stage-matched late-onset OC patients (aged > 40 years; denoted herein as “histology/stage-matched OC”); and 78 population-matched late-onset HGSC patients negative for GPV in high-penetrance OC predisposition genes (Supplementary Table S1 , S2, S4). Raw NGS data were processed by an inhouse bioinformatics pipeline as described previously [ 27 ]. PRS was calculated as described by Borde et al. [ 38 ] (Supplementary Methods). Differences between the standardized PRS values of patients and PRS controls were assessed in R v.4.2.0 using t -test. 2.8 Survival analysis The survival analysis was performed using the Kaplan-Meier analysis and the log-rank test in R v.4.2.0. Vital status was available in 82 early-onset (median 25.1 years; 14.8–30.8 years) and 917 previously analyzed, late-onset (> 30 years; median age 58.1 years; 31.2–91.8 years) OC patients [ 6 ]. In addition, subgroup analyses were performed using groups stratified by OC histology and by germline BRCA1/BRCA2 ( gBRCA1/2 ) status. Individual OC patients’ subgroups included into the survival analysis are summarized in Supplementary Table S1 , S4, S6. 3 RESULTS 3.1 DNA/RNA WES In our cohort of 123 early-onset OC patients, a total number of 1563 germline GPV (1506 unique variants) in 1390 genes stemmed from DNA (SNV and CNV accounting for 95.3% and 1% of GPV, respectively) and RNA WES (additional 3.7% of GPV). Median number of GPV per patient was 13 (ranged 2–28). The GPV mutation burden was significantly lower in super-controls compared to early-onset OC patients ( p = 3.8×10 − 4 ) and tended to be lower in BTO and patients with other (non-eptihelial) OC compared to type I [low-grade serous (LGSC); endometrioid, clear cell, mucinous] or type II (HGSC, undifferentiated) OC patients (Table 1 ; Supplementary Table S1 , S3; Fig. 1 ) [ 39 ]. Seventeen patients (13.8%) carried a GPV in HBOP cancer predisposition genes, including only a single carrier of GPV in BRCA1 and BRCA2 , respectively (2/123; 1.6%), compared to 30.2% GPV in population-matched OC patients published previously (Table 2 ) [ 6 ]. We found no GPV in established genes associated with the risk of non-epithelial tumors of ovary ( STK11, SMARCA4, DICER1 ). Table 2 GPV in established hereditary breast/ovarian/pancreatic (HBOP) cancer predisposition genes identified in early-onset OC patients and comparison of GPV frequency in unselected OC patients. Established high-penetrance OC predisposition genes and significant p -values are in bold. Gene 123 early-onset OC N (%) 1320 unselected OC [ 6 ]* N (%) p -value ATM 2 (1.6) 6 (0.5) 0.14 BARD1 2 (1.6) 3 (0.2) 0.06 BRCA1 1 (0.8) 229 (17.4) 8.5×10 − 9 BRCA2 1 (0.8) 94 (7.1) 0.003 BRIP1** 1 (0.8) 10 (0.8) 1 CHEK2 6 (4.9) 12 (0.9)*** 0.002 MLH1 0 4 (0.3) 1 MSH2 1 (0.8) 3 (0.2) 0.3 MSH6 0 3 (0.2) 1 PALB2 0 8 (0.6) 1 PMS2 1 (0.8) NA NA RAD51C 2 (1.6) 13 (1) 0.37 RAD51D 0 13 (1) 0.62 TP53** 1 (0.8) 1 (0.1) 0.16 All 17 (13.8) 399 (30.2) 6.6×10 − 5 *multiple GPV carriers (N = 13) were excluded from the analysis; **one concomitant carrier of GPV in BRIP1 and TP53; ***additionally, one initially unrevealed deep intronic GPV in CHEK2 was identified GPV, Germline Pathogenic Variants; NA, not available; OC, Ovarian Cancer The carriership of GPV in established high-penetrance OC predisposition genes (Table 2 ; in bold) was significantly associated with development of multiple primary tumors ( p = 0.016; Fig. 2 A), whereas GPV in established HBOP cancer predisposition genes (Table 2 ; Fig. 2 B) was not, presumably due to the prevalence of moderate penetrance GPV carriers. GPV in established high-penetrance OC predisposition genes (Table 2 ; in bold) associated neither with early-onset (< 40 years) malignancy in family cancer history ( p = 0.054; Fig. 2 C), nor with overall positive family cancer history, nor with hematological malignancies in family history, which was observed in the initial study of Stratton et al. [ 7 ]. On the other hand, multiple primary tumors in early-onset OC patients significantly associated with family history of hematological malignancies ( p = 0.001; Fig. 2 D). It is noteworthy that among early-onset OC patients with known histology, double primaries developed more likely in early-onset OC patients diagnosed with non-epithelial OC (5/15; 33.3%) than with invasive epithelial OC (4/61; 6.6%; p = 0.012; Supplementary Table S1 ). WES gene-based burden analysis revealed CHEK2 and LY75-CD302 (6 carriers each) as the most frequently altered genes in early-onset OC patients ( p = 1.2×10 − 4 and 8.3×10 − 4 , respectively). Altogether, GPV in only five genes ( CHEK2, LY75-CD302, ATP7B, BCHE, MFNG) were enriched in early-onset OC patients compared to unselected controls (Supplementary Table S7); however, the significance did not fall below the exome-wide Bonferroni corrected p -value threshold. Of these genes, CHEK2 was the only one associated with cancer risk. Interestingly, alongside to known founder c.1100delC and exons 9–10 deletion GPV, RNA analysis revealed two intronic GPV, c.1009 − 118_1009-87delinsC and c.1461 + 2301G > T, leading to the aberrant splicing (Supplementary Table S3 ). The recurrent c.1009 − 118_1009-87delinsC variant has been recently reported [ 40 ]. The deep intronic c.1461 + 2301G > T variant has been previously assessed as variant of uncertain significance (VUS) in ClinVar due to its predicted in-frame insertion of 30 amino acids. However, our RNA analysis revealed a new acceptor splice site leading to an inclusion of 89 nucleotides (r.1461_1462ins1461 + 2211_1461 + 2299) and predicted premature termination of translation (p.Asp488SerfsTer34; Supplementary Figure S1 ). Interestingly, the age at OC diagnosis significantly differed between CHEK2 carriers and non-carriers of GPV in HBOP cancer predisposition genes [ 6 ] (median age 33.9; 18–66 years, and 58; 15–92 years, respectively; p = 0.003). The CHEK2 status did not associate with a particular OC histology; most (4/6) CHEK2 GPV carriers had a positive family cancer history (Supplementary Table S1 , S4; Supplementary Figure S2 ). The second most frequently altered gene was LY75-CD302 involved in immune response. GPV in LY75-CD302 gene were unique except for c.4503del identified in two OC patients (Supplementary Table S3 ). Interestingly, four out of six LY75-CD302 GPV affected exon 31 that codes for C-type lectin/C-type lectin-like domain important for antigen binding prior to endocytosis and antigen presentation [ 41 , 42 ]. All the carriers of LY75-CD302 GPV had a positive family cancer history, and none of them developed multiple primary tumors; LY75-CD302 GPV did not associate with any specific OC histology (Supplementary Table S1 , S3). All GPV were present in heterozygous state, with the exception of two patients carrying two GPV in the same gene ( MUSK and MAATS1 ) suggesting possible recessive inheritance; however, their phase was unknown. To determine whether a pre-defined set of genes belonging to certain pathway or disease are over-represented in our OC patients, we performed overrepresentation analysis of the genes from final list of GPV (Supplementary Table S3 ) using functional databases. A combined analysis could be empowered if multiple genes of a predefined set were associated but the effect size is too small to detect individually. The overrepresentation analysis showed highest enrichment for gene set #114480: Breast cancer (disease in OMIM; p = 4.5×10 − 10 ; FDR 3×10 − 9 ; Supplementary Figure S3 ) followed by gene set #HP:0030406 Primary peritoneal carcinoma (phenotype in Human Phenotype Ontology; p = 6.7×10 − 6 ; FDR 0.011). 3.2 HLA genotypization We analyzed HLA of class I (HLA-A, B, C) and class II (HLA-DPA1, DPB1, DQA1, DQB1, and DRB1) in 123 patients (except for HLA-DRB1 not genotyped in two patients due to the insufficient coverage of sequencing data in the HLA-DRB1 region). To increase robustness of the analysis, HLA alleles frequencies were compared to two sets of controls (HLA- and super-controls). Two HLA class I (HLA-A*36:01 and HLA-B*53:01) and three HLA class II (HLA-DRB*11:01, HLA-DQA1*01:03 and HLA-DQA1*03:03) alleles were significantly enriched in patients compared to HLA- and super-controls (Supplementary Table S8). We assessed the ratio of homozygotes in patients compared to super-controls in each locus. Patients were significantly more frequently carriers of at least one homozygous HLA allele ( p = 3×10 − 7 ). The carriers’ frequency of multiple homozygous alleles was also significantly increased (Supplementary Table S9). The rates of homozygotes in HLA class I and II were both statistically significantly higher compared to super-controls (Fig. 3 ). 3.3 PRS analysis In contrast to BC, a single SNP set has not yet been widely adopted for the analysis of PRS in OC, although many have been published and, therefore, we performed PRS analysis using 10 different (rather) non-overlapping SNP sets associated with OC [ 12 – 21 ]. However, PRS of early-onset OC differed significantly neither from PRS controls, nor from histology/stage-matched OC patients in any individual OC SNP set analyzed (Supplementary Table S5; Fig. 4 ). Similarly, we did not observe any difference between PRS of histology/stage-matched OC patients and PRS controls. The SNP sets selection is usually based on studies of patients regardless of OC histology. As HGSC is the prevailing one and the PRS calculated based on the analyzed individual SNP set did not differ in principle between non-HGSC OC patients and PRS controls, we performed the PRS analysis in a group of late-onset HGSC patients who were previously tested negative for a GPV in OC predisposition genes [ 43 ] (Supplementary Table S4) to evaluate the performance of the individual SNP sets for PRS calculation in HGSC patients compared to PRS controls. Contrary to early-onset predominantly non-HGSC OC, PRS of HGSC patients based on 4 individual SNP sets differed significantly from PRS controls (Supplementary Table S5). Additionally, Phelan et al. established OC histology-specific OR for each included SNP (Supplementary Table S2 ). Nevertheless, when we employed LGSC, or mucinous-specific OR for the PRS calculation in 64 and 28 early- and late-onset OC patients with the respective histology, respectively, the PRS did not differ between these patients and PRS controls. Furthermore, we calculated PRS using 313 SNP with OR established for BC. Surprisingly, the difference in PRS between both early-onset and pooled early-onset and histology/stage-matched OC patients (thus, predominantly non-HGSC OC), and PRS controls was significant ( p = 0.03 and 0.009, respectively) whereas non-significant in HGSC (Supplementary Table S5; Fig. 4 ). The representation of early-onset OC patients in each decile according to PRS 313 was quite even. Interestingly, five early-onset OC patients were also diagnosed with BC and PRS 313 categorized them into 1st, 5th ,7th, 9th, 9th decile, respectively; the patient carrying a BRCA2 GPV was categorized in the 1st decile. 3.4 Survival analysis Finally, we performed a survival analysis to pursuit a difference in early- and late-onset OC patients. Regardless of their GPV carriership, the survival analysis of 82 early-onset and 917 late-onset OC patients with available vital status showed significantly improved survival of early-onset OC patients ( p = 8×10 − 9 ; Fig. 5 A). The survival differed significantly according to germline gBRCA1/2 status ( p = 0.002; Fig. 5 B). However, we can only comment in principle on late-onset OC patients as g BRCA1/2 GPV were rare among early-onset OC (216/917; 23.6% vs. 2/82; 2.4%). Survival analysis of 80 gBRCA1/2 -negative early-onset (median age 25.1 years) and 79 histology/stage-matched gBRCA1/2 -negative OC (median age 61.7 years) was also significant although the survival advantage was less pronounced ( p = 0.02; Fig. 5 C). Survival analysis for individual histological subtypes was not significant (LGSC; Fig. 5 D) or cannot be performed due to insufficient number of events in at least one of the patients` groups. 4 DISCUSSION The early-onset OC (diagnosed at < 30 years), represents a distinct subgroup exhibiting striking differences from late-onset OC in many aspects, including germline cancer predisposition. We undertake the most extensive germline analysis of early-onset OC patients so far, employing comprehensive approaches encompassing DNA WES complemented by RNA WES and PRS analysis. Mutation profiles in our early-onset OC patients were dissimilar to unselected predominantly HGSC patients that we analyzed previously [ 6 ], with BRCA1/BRCA2 GPV identified in only two (1.6%) early-onset OC patients. Our observation is in agreement with findings from the few studies investigating at least a few early-onset OC patients that consistently reported either the absence, or unusually low frequency of GPV in established OC predisposition genes, as reviewed in [ 11 ]. It is noteworthy that GPV in established high-penetrance OC predisposition genes in our study significantly associated with multiple primary malignancies in early-onset OC patients who tended to develop invasive OC, and were more likely diagnosed with non-epithelial OC. The increased risk of double primary tumors was observed in a large study by Casper et al. who described the inverse correlation between age at OC diagnosis and double primary cancer risk [ 44 ]. Moreover, the risk of double primary tumors was not increased in patients with BTO in a SEER population-based study [ 45 ]. Notably, early-onset OC patients with double primary tumors in our study had a significantly more likely hematological malignancies in their family cancer history which was in agreement with the initial and so far, the only early-OC patient study by Stratton et al. , who observed increased risk of non-Hodgkin lymphoma and malignant myeloma in first degree relatives of early-onset OC patients with invasive disease [ 7 ]. Most GPV were identified uniquely in our early-onset OC cohort and were enriched in only five genes compared to unselected controls, with the CHEK2 gene coding for checkpoint kinase 2 ranking first. Interestingly, CHEK2 GPV identified in 6/123 (4.9%) early-onset OC patients were significantly associated with earlier age at diagnosis compared to previously analyzed OC patients negative for GPV in HBOP cancer predisposition genes [ 6 ]. Furthermore, GPV in CHEK2 have been identified in early-onset OC patients by other studies [ 6 , 10 , 46 , 47 ], the most prevalently by Carter et al. [ 10 ] who identified CHEK2 GPV in 5/147 (3.4%) early-onset OC patients. Although CHEK2 have not been acknowledged as the OC predisposition gene, some studies pointed to an association of CHEK2 with the OC risk [ 6 , 48 , 49 ]. This hypothesis further supports an indirect evidence potentially influencing prognostic and therapeutic considerations. It has recently been shown that CHEK2 is a master regulator of oocyte survival and modifier of the ovarian cellular response to damage [ 50 ]. The mechanistic link to these functions involves phosphorylation of the transcription factor FoxM1 whose overexpression is associated with a poor OC prognosis [ 51 , 52 ]. GPV in CHEK2 are associated with moderate BC risk [ 53 ]. Interestingly, our overrepresentation analysis, which has greater power when multiple genes of a predefined set are associated with a small effect, identified a BC gene set enrichment in carriers of GPV in our early-onset OC patients. In addition, PRS 313 developed for BC risk stratification significantly differed in early-onset OC patients compared to PRS controls (see below). Moreover, 5/13 early-onset OC patients with a second primary tumor were diagnosed with BC. Consequently, it is plausible to speculate that there may be a shared germline predisposition factor(s) or functional underlying mechanisms common to both BC and early-onset OC, particularly non-HGSC OC. Besides CHEK2 , LY75-CD302 was the second most GPV-enriched gene revealed by the gene burden analysis. This gene consists of LY75 and CD302 that are alternatively transcribed in a readthrough way leading to translation of fusion proteins with high similarity, acting as receptors involved in endocytosis-mediated immune responses including HLA class I-mediated antigen presentation [ 41 , 54 ]. In addition, LY75 was shown to modulate cellular phenotype of epithelial OC cells and their metastatic potential through mediation of mesenchymal-epithelial transition [ 55 , 56 ]. Although the significance of GPV in LY75-CD302 (and other immunity-related genes with identified private GPV) for early-onset OC risk is unknown, it can indicate an attractive direction in early-OC development, especially in view of the results of HLA analysis. HLA molecules are coded by multiple highly polymorphic loci and are crucial for immune reaction activation and progression including anti-tumor immunity [ 57 ]. Previously, specific HLA alleles have been described to predispose to certain cancer types, including OC, as observed by Kubler et al. who identified a significantly higher frequency of carriers of HLA class II haplotypes HLA-DQA1*05:01-DQB1*02:01-DRB1*03:01 and HLA-DQA1*01:01-DQB1*05:01-DRB1*10:01) in OC patients from Germany [ 22 ]. In addition, HLA-DRB1*03:01 (homozygous or heterozygous) and HLA-DQB1*02:01 (only homozygous) were individually enriched in their patients compared to controls. However, we did not observe significant enrichment of either these haplotypes or genotypes in our cohort; however, we identified another HLA-DRB1 (*11:01) allele enriched in our early-onset OC patients. This allele was associated with BC in Italian cohort of patients previously [ 58 ]. The high abundance of HLA-DRB1*11:01 carriers among our early-onset OC patients might indicate a genetic link between BC, immune system, and early-onset OC. In addition, we identified significantly associated HLA-DQA1*01:03 risk allele and HLA-DQA1*03:03 protective allele. However, the cancer risk associations of HLA-DQA1 are contradictory and not very well understood yet [ 22 , 59 , 60 ] In addition to the specific disease-related risk HLA alleles, also heterogeneity of inherited HLA alleles seems to be important with regard to tumor-associated antigen presentation, cancer cell recognition and elimination, as well as to immunoediting in early cancer development through e.g. stronger selective pressure on driver mutations in tumors [ 57 ]. Having analyzed the HLA zygosity, the early-onset OC patients were significantly more frequently homozygotes compared to super-controls. Interestingly, homozygotes in HLA class II were more abundant than homozygotes in HLA class I loci when compared to super-controls. HLA class II homozygosity was previously associated with increased risk of lung and head and neck cancer, and non-Hodgkin lymphomas, thus, particularly with tumors with high mutational burden or infectious etiology, whereas the lower diversity at HLA class I locus was associated with increased risk of Hodgkin lymphoma [ 61 ]. In addition, increased HLA homozygosity rate was also described in colorectal patients by Tsai et al. who also noted that the patients with higher HLA heterozygosity were more likely to manifest higher tumor infiltrating lymphocytes in their tumors [ 62 ]. Polygenic inheritance, arising from the cumulative effect of numerous genetic low-risk variants, might elucidate a portion of the missing heritability in the predisposition to early-onset OC [ 63 ]. However, PRS analysis has currently its limitations, given the lack of consensus on a specific SNP set and uncertain clinical efficacy in OC risk stratification [ 13 , 64 ]. We conducted PRS analysis using 10 different SNP sets [ 12 – 21 ], but none demonstrated the ability to distinguish early-onset from histology/stage-matched OC patients, or from PRS controls. Conversely, PRS based on four SNP sets [ 13 , 15 , 16 , 21 ] were able to discriminate between HGSC patients and PRS controls. This implies that these four SNP sets are specifically associated with the risk of HGSC, the most prevalent OC type in GWAS focused on identifying OC risk loci. Interestingly, PRS 313 , designed specifically for BC [ 37 ], significantly differed in early-onset, predominantly non-HGSC OC patients but not in HGSC. This suggests a potential pleiotropic effect, indicating a common mechanism underlying the development of multiple phenotypes associated with some common variant susceptibility loci. However, evidence supporting a biological function has only been identified for certain loci, highlighting a significant portion of biology that remains unclear. Considering polygenic inheritance from an alternative perspective, we observed significant variability in GPV burden among different OC types and super-controls. Higher GPV burden was identified in patients diagnosed with invasive epithelial OC and, remarkably, GPV burden tended to increase with increasing somatic genomic instability characteristic for each histological OC types [ 39 , 65 ]. Interestingly, Qing et al. noticed strong negative correlation between GPV burden and age, suggesting a greater contribution of GPV to the transformation process in early-onset OC patients compared to their late-onset counterparts, where somatic mutations were hypothesized to play a more predominant role [ 66 ]. This observation supports our hypothesis that, the cumulative GPV burden may potentially elevate the cancer risk rather than GPV in certain genes, particularly when concomitant with reduced HLA diversity, influencing the efficiency of neoantigen recognition. Regarding to the association of clinicopathological and genetic factors with the survival, we observed an improved survival of early-onset OC patients compared to previously analyzed late-onset OC patients [ 6 ]. In addition, our findings revealed survival advantage in early-onset OC patients compared to histology/stage-matched OC patients lacking gBRCA1/2 GPV. This suggests that age is an independent positive prognostic factor and that the survival advantage in early-onset OC primarily stems not solely from the distinct distribution of histological OC types compared to late-onset OC, particularly the lack of prognostically unfavorable HGSC histological subtype in early-onset OC patients. A positive correlation between survival and age as well as improved survival among non-HGSC epithelial OC patients has been described in prior research [ 67 , 68 ]. Nevertheless, certain investigations have delineated a less favorable prognosis and lower 5-year survival in LGSC OC in early-onset OC [ 69 ]. However, we did not observe any difference in survival of LGSC early- and late-onset OC patients. Additionally, our research unveiled a significant initial survival advantage in gBRCA1/2 positive OC patients, consistent with earlier findings [ 70 – 72 ]. While our study stands out as the most intricate and the third-largest investigation focused on early-onset OC patients diagnosed before the age of 30, still a noteworthy limitation lies in the restricted number of patients. The low number of patients hinders our ability to pinpoint potential private causal alleles effectively. These limitations underscore the need for comprehensive data to better understand the complex landscape of early-onset OC and its associated risk factors. In conclusion, our comprehensive germline analysis of early-onset OC patients revealed two divergent trajectories of potential germline susceptibility. Overrepresentation analysis highlighted an association to BC, supported by the enrichment of GPV in CHEK2 and the presumably BC-specific PRS 313 , which successfully stratified early-onset OC from PRS controls. The second avenue pointed towards the impaired immune response, indicated by GPV in the LY75-CD302 gene, coupled with diminished HLA diversity. Furthermore, we found a significantly higher GPV burden in early-onset OC patients compared to super-controls. In summary, the genetic predisposition to early-onset of OC appears to be a very heterogeneous and complex process beyond the conventional Mendelian monogenic understanding of hereditary cancer predisposition with a modifying role of the immune system. Based on our results, we speculate that rather a cumulative GPV burden than GPV in specific genes may increase early-onset OC risk, especially when it is concomitant with reduced HLA diversity, which affects the efficiency of neoantigen recognition. However, it cannot be definitively excluded that the occurrence of early-onset OC is a random event influenced by the chance and varying values of random variables. Abbreviations BC Breast Cancer BTO Borderline Tumors of Ovary CI Confidence Interval CNV Copy Number Variations FDR False Discovery Rate, gBRCA1/2 ,Germline BRCA1/2 GPV Germline Pathogenic Variants HBOP Hereditary Breast,Ovarian and Pancreatic HGSC High-Grade Serous Carcinoma HLA Human Leukocyte Antigen LGSC Low-Grade Serous Carcinoma NA Not Available neg. negative NGS Next Generation Sequencing NS Not Significant NOS Not Otherwise Specified OC Ovarian Cancer OR Odds Ratio PRS Polygenic Risk Score SNP Single Nucleotide Polymorphism SNV Single Nucleotide Variant VUS Variant of Uncertain Significance WES Whole Exome Sequencing Declarations DECLARATION OF INTEREST STATEMENT No potential conflicts of interest were reported. We declare that the results summarized in this manuscript have not been published previously and have not been submitted for consideration to any other journal. FUNDING This work has been supported by the Ministry of Health of the Czech Republic: NU20-03-00016, NU20-09-00355, RVO-VFN 00064165; Charles University: COOPERATIO, SVV260631; UNCE/24/MED/022; and the Ministry of Education Youth and Sports of the Czech Republic: LX22NPO05102, and The National Center for Medical Genomics" (LM2023067). ACKNOWLEDGEMENTS We thank Pavel Pesek and Eva Tureckova for their excellent technical assistance and the National Center for Medical Genomics for providing sequencing data of unselected controls. References SEER, https://seer.cancer.gov/statfacts/html/ovary.html. Labidi-Galy, S.I., et al., High grade serous ovarian carcinomas originate in the fallopian tube. Nature Communications, 2017. 8 (1): p. 1093. Lisio, M.A., et al., High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints. Int J Mol Sci, 2019. 20 (4). Pearce, C.L., et al., Population distribution of lifetime risk of ovarian cancer in the United States. Cancer Epidemiol Biomarkers Prev, 2015. 24 (4): p. 671-676. Flaum, N., et al., Epithelial ovarian cancer risk: A review of the current genetic landscape. Clin Genet, 2019. Lhotova, K., et al., Multigene Panel Germline Testing of 1333 Czech Patients with Ovarian Cancer. Cancers (Basel), 2020. 12 (4). Stratton, J.F., et al., The genetic epidemiology of early-onset epithelial ovarian cancer: a population-based study. Am J Hum Genet, 1999. 65 (6): p. 1725-32. Flaum, N., et al., MSH2 is the very young onset ovarian cancer predisposition gene, not BRCA1. J Med Genet, 2023. Herold, N., et al., Pathogenic germline variants in SMARCA4 and further cancer predisposition genes in early onset ovarian cancer. Cancer Med, 2023. 12 (14): p. 15256-15260. Carter, N.J., et al., Germline pathogenic variants identified in women with ovarian tumors. Gynecol Oncol, 2018. 151 (3): p. 481-488. Horackova, K., et al., Early-Onset Ovarian Cancer <30 Years: What Do We Know about Its Genetic Predisposition? International Journal of Molecular Sciences, 2023. 24 (23): p. 17020. Bolton, K.L., et al., Common variants at 19p13 are associated with susceptibility to ovarian cancer. Nat Genet, 2010. 42 (10): p. 880-4. Dareng, E.O., et al., Polygenic risk modeling for prediction of epithelial ovarian cancer risk. Eur J Hum Genet, 2022. 30 (3): p. 349-362. Goode, E.L., et al., A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24. Nat Genet, 2010. 42 (10): p. 874-9. Jervis, S., et al., A risk prediction algorithm for ovarian cancer incorporating BRCA1, BRCA2, common alleles and other familial effects. J Med Genet, 2015. 52 (7): p. 465-75. Kuchenbaecker, K.B., et al., Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers. J Natl Cancer Inst, 2017. 109 (7). Permuth-Wey, J., et al., Identification and molecular characterization of a new ovarian cancer susceptibility locus at 17q21.31. Nat Commun, 2013. 4 : p. 1627. Pharoah, P.D., et al., GWAS meta-analysis and replication identifies three new susceptibility loci for ovarian cancer. Nat Genet, 2013. 45 (4): p. 362-70, 370e1-2. Phelan, C.M., et al., Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat Genet, 2017. 49 (5): p. 680-691. Yang, X., et al., Evaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study. 2018(1468-6244 (Electronic)). Jia, G., et al., Evaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers. JNCI Cancer Spectr, 2020. 4 (3): p. pkaa021. Kubler, K., et al., HLA-class II haplotype associations with ovarian cancer. Int J Cancer, 2006. 119 (12): p. 2980-5. The NCMG research infrastructure; http://ncmg.cz . National Marrow Donors Registry; www.allelefrequencies.net/pop6001c.asp?pop_id=3258 NCCN. Guidelines Version 2.2024: Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic . 2023. Hovhannisyan, M., et al., Polygenic risk score (PRS) and its potential for breast cancer risk stratification. Klin Onkol, 2023. 36 (3): p. 198-205. Soukupova, J., et al., Validation of CZECANCA (CZEch CAncer paNel for Clinical Application) for targeted NGS-based analysis of hereditary cancer syndromes. PLoS One, 2018. 13 (4): p. e0195761. Horackova, K., et al., Low Frequency of Cancer-Predisposition Gene Mutations in Liver Transplant Candidates with Hepatocellular Carcinoma. LID - 10.3390/cancers15010201 [doi] LID - 201. 2022(2072-6694 (Print)). Walker, L.C., et al., Comprehensive Assessment of BARD1 Messenger Ribonucleic Acid Splicing With Implications for Variant Classification. Front Genet, 2019. 10 : p. 1139. Dobin, A., et al., STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 2013. 29 (1): p. 15-21. https://broadinstitute.github.io/picard/ . Cotto, K.C., et al., Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer. Nat Commun, 2023. 14 (1): p. 1589. Agius, P., H. Geiger, and N. Robine, SCANVIS: a tool for SCoring, ANnotating and VISualizing splice junctions. Bioinformatics, 2019. 35 (22): p. 4843-4845. Fadista, J., et al., The (in)famous GWAS P-value threshold revisited and updated for low-frequency variants. Eur J Hum Genet, 2016. 24 (8): p. 1202-5. Liao, Y., et al., WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res, 2019. 47 (W1): p. W199-W205. Wang, S., et al., SpecHLA enables full-resolution HLA typing from sequencing data. Cell Rep Methods, 2023. 3 (9): p. 100589. Mavaddat, N., et al., Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. Am J Hum Genet, 2019. 104 (1): p. 21-34. Borde, J., et al., Performance of Breast Cancer Polygenic Risk Scores in 760 Female CHEK2 Germline Mutation Carriers. J Natl Cancer Inst, 2021. 113 (7): p. 893-899. Kurman, R.J. and M. Shih Ie, The Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded. Am J Pathol, 2016. 186 (4): p. 733-47. Petra Zemankova, M.C., Klara Horackova, Corinna Ernst, Jana Soukupova, Marianna Borecka, Britta Blumcke, Leona Cerna, Monika Cerna, Vaclava Curtisova, Tatana Dolezalova, Lenka Dvorakova, Petra Duskova, Lenka Foretova, Ondrej Havranek, Jan Hauke, Eric Hahnen, Milena Hovhannisyan, Lucie Hruskova, Stepan Chvojka, Marketa Janatova, Maria Janikova, Sandra Jelinkova, Pavel Just, Marcela Kosarova, Monika Koudova, Vera Krutilkova, Martin Lenicek, Eva Machackova, Katerina Matejkova, Renata Michalovska, Petr Nehasil, Barbora Nemcova, Jan Novotny, Pavel Pesek, Ondrej Scheinost, Drahomira Springer, Barbora Stastna, Viktor Stranecky, Ivan Subrt, Spiros Tavandzis, Eva Tureckova, Kamila Vesela, Zdenka Vlckova, Michal Vocka, Tomas Zima, Zdenek Kleibl, Petra Kleiblova, A deep intronic recurrent CHEK2 variant c.1009-118_1009-87delinsC affects pre-mRNA splicing and contributes to hereditary breast cancer predisposition. BREAST 2024. submitted . Bonifaz, L., et al., Efficient targeting of protein antigen to the dendritic cell receptor DEC-205 in the steady state leads to antigen presentation on major histocompatibility complex class I products and peripheral CD8+ T cell tolerance. J Exp Med, 2002. 196 (12): p. 1627-38. Zelensky, A.N. and J.E. Gready, The C-type lectin-like domain superfamily. FEBS J, 2005. 272 (24): p. 6179-217. Network, N.C.C. Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic (Version 1.2023) . 2022; Available from: https://www.nccn.org/professionals/physician_gls/pdf/genetics_bop.pdf. Casper, A.C., et al., Risk of secondary malignancies in ovarian cancer survivors: 52,680 patients analyzed with over 40 years of follow-up. Gynecol Oncol, 2021. 162 (2): p. 454-460. Wang, J., Y. Du, and Y. Kang, Do survivors of borderline ovarian tumors have susceptibility to secondary primary malignancies? A SEER population-based study. Int J Gynaecol Obstet, 2024. Koczkowska, M., et al., Spectrum and Prevalence of Pathogenic Variants in Ovarian Cancer Susceptibility Genes in a Group of 333 Patients. Cancers (Basel), 2018. 10 (11). Jarhelle, E., et al., Identifying sequence variants contributing to hereditary breast and ovarian cancer in BRCA1 and BRCA2 negative breast and ovarian cancer patients. Sci Rep, 2019. 9 (1): p. 19986. Lawrenson, K., et al., Common variants at the CHEK2 gene locus and risk of epithelial ovarian cancer. Carcinogenesis, 2015. 36 (11): p. 1341-53. Kurian, A.W., et al., Genetic Testing and Results in a Population-Based Cohort of Breast Cancer Patients and Ovarian Cancer Patients. J Clin Oncol, 2019: p. JCO1801854. Emori, C., Z. Boucher, and E. Bolcun-Filas, CHEK2 signaling is the key regulator of oocyte survival after chemotherapy. Sci Adv, 2023. 9 (42): p. eadg0898. Zhang, Z., et al., FoxM1 promotes the migration of ovarian cancer cell through KRT5 and KRT7. Gene, 2020. 757 : p. 144947. Bruckner, L., et al., FOXM1 Inhibition in Ovarian Cancer Tissue Cultures Affects Individual Treatment Susceptibility Ex Vivo. Cancers (Basel), 2021. 13 (5). Stolarova, L., et al., CHEK2 Germline Variants in Cancer Predisposition: Stalemate Rather than Checkmate. Cells, 2020. 9 (12). Lo, T.H., et al., Characterization of the Expression and Function of the C-Type Lectin Receptor CD302 in Mice and Humans Reveals a Role in Dendritic Cell Migration. J Immunol, 2016. 197 (3): p. 885-98. Faddaoui, A., et al., The mannose receptor LY75 (DEC205/CD205) modulates cellular phenotype and metastatic potential of ovarian cancer cells. 2015(1949-2553 (Electronic)). Mehdi, S., et al., LY75 Ablation Mediates Mesenchymal-Epithelial Transition (MET) in Epithelial Ovarian Cancer (EOC) Cells Associated with DNA Methylation Alterations and Suppression of the Wnt/beta-Catenin Pathway. Int J Mol Sci, 2020. 21 (5). Pagliuca, S., et al., Individual HLA heterogeneity and its implications for cellular immune evasion in cancer and beyond. Front Immunol, 2022. 13 : p. 944872. Aureli, A., et al., Breast Cancer Is Associated with Increased HLA-DRB1*11:01 and HLA-DRB1*10:01 Allele Frequency in a Population of Patients from Central Italy. Immunol Invest, 2020. 49 (5): p. 489-497. Tsai, S.C., M.C. Sheen, and B.H. Chen, Association between HLA-DQA1, HLA-DQB1 and oral cancer. Kaohsiung J Med Sci, 2011. 27 (10): p. 441-5. Chen, P.C., et al., HLA-DQA1 and -DQB1 allele typing in southern Taiwanese women with breast cancer. Clin Chem Lab Med, 2007. 45 (5): p. 611-4. Wang, Q.L., et al., Association of HLA diversity with the risk of 25 cancers in the UK Biobank. EBioMedicine, 2023. 92 : p. 104588. Tsai, Y.Y., et al., Heterozygote advantage at HLA class I and II loci and reduced risk of colorectal cancer. Front Immunol, 2023. 14 : p. 1268117. Jones, M.R., et al., Genetic epidemiology of ovarian cancer and prospects for polygenic risk prediction. Gynecol Oncol, 2017. 147 (3): p. 705-713. Kar, S.P., et al., Common Genetic Variation and Susceptibility to Ovarian Cancer: Current Insights and Future Directions. Cancer Epidemiol Biomarkers Prev, 2018. 27 (4): p. 395-404. Struzinska, I., et al., Somatic Genomic and Transcriptomic Characterization of Primary Ovarian Serous Borderline Tumors and Low-Grade Serous Carcinomas. J Mol Diagn, 2024. Qing, T., et al., Germline variant burden in cancer genes correlates with age at diagnosis and somatic mutation burden. Nat Commun, 2020. 11 (1): p. 2438. Chan, J.K., et al., Ovarian cancer in younger vs older women: a population-based analysis. Br J Cancer, 2006. 95 (10): p. 1314-20. Gockley, A., et al., Outcomes of Women With High-Grade and Low-Grade Advanced-Stage Serous Epithelial Ovarian Cancer. Obstet Gynecol, 2017. 129 (3): p. 439-447. Gershenson, D.M., et al., Impact of Age and Primary Disease Site on Outcome in Women With Low-Grade Serous Carcinoma of the Ovary or Peritoneum: Results of a Large Single-Institution Registry of a Rare Tumor. J Clin Oncol, 2015. 33 (24): p. 2675-82. Bolton, K.L., et al., Association between BRCA1 and BRCA2 mutations and survival in women with invasive epithelial ovarian cancer. JAMA, 2012. 307 (4): p. 382-90. McLaughlin, J.R., et al., Long-term ovarian cancer survival associated with mutation in BRCA1 or BRCA2. J Natl Cancer Inst, 2013. 105 (2): p. 141-8. Heemskerk-Gerritsen, B.A.M., et al., Progression-free survival and overall survival after BRCA1/2-associated epithelial ovarian cancer: A matched cohort study. PLoS One, 2022. 17 (9): p. e0275015. Additional Declarations No competing interests reported. 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Soukupova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYBACxgYoQwJC2ciwNyPzidCSxsNznIAWOIAqOczDc56ASub2swc/MPyxSZw5I/fgY56a8zw8zAyMn3l3WDDw9x/A7rCevGQJxra0xNkSecnGPMdug7QwS/OekWCQOIBDS0OOgQRjw+HEeRI5ZpIz2G7z2DMzsDHztkkwGMD9iaal/43xD4Y/MC3/zoFsgWphxu4Xxhk5ZhIMbIeBDgMyPrYdQNLChkvLGzOLxLY045k975INPvYlA7UwNkvObZPgkTiDXYthf47xjQ9/bGRnHM89+CDhm50cD//hgx/ettXJ4QoxQ5AXE8BMHkSQoHDRgTyCiVPNKBgFo2AUjHQAAPnXT1/NLmd4AAAAAElFTkSuQmCC","orcid":"","institution":"Charles University and General University Hospital in Prague","correspondingAuthor":true,"prefix":"","firstName":"Jana","middleName":"","lastName":"Soukupova","suffix":""}],"badges":[],"createdAt":"2024-02-20 11:14:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3972616/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3972616/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51564116,"identity":"d7e0ff31-347d-451b-afd1-ce9dbe97a76d","added_by":"auto","created_at":"2024-02-23 18:54:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49079,"visible":true,"origin":"","legend":"\u003cp\u003eGPV burden in early-onset OC and super-controls\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBTO, Borderline Tumors of Ovary; GPV, Germline Pathogenic Variant\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3972616/v1/6f678da9e2ac1a75f2f3c9f8.png"},{"id":51564117,"identity":"4546b0af-af5e-47f5-a2f7-23a465684334","added_by":"auto","created_at":"2024-02-23 18:54:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132631,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of GPV in OC (\u003cstrong\u003eA\u003c/strong\u003e) and HBOP (\u003cstrong\u003eB\u003c/strong\u003e) cancer predisposition genes with multiple primary tumors in early-onset OC patients, and with family cancer history (\u003cstrong\u003eC\u003c/strong\u003e), and association of positive personal cancer history beyond OC with hematological malignancies in family cancer history (\u003cstrong\u003eD\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCI, Confidence Interval; GPV, Germline Pathogenic Variants; HBOP, Hereditary Breast, Ovarian and Pancreatic; NS, not significant; OC, Ovarian Cancer; OR, Odds Ratio\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3972616/v1/ead72de41dc2c83e977e4970.png"},{"id":51564119,"identity":"69e8de65-dceb-4488-9679-ab48a567f540","added_by":"auto","created_at":"2024-02-23 18:54:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58029,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of HLA loci homozygotes in patients compared to female super-controls.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*p\u0026lt;0.05; **p\u0026lt;0.001;***p \u0026lt;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e-5\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHLA, Human Leukocyte Antigen; OC, Ovarian Cancer\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3972616/v1/6e03fb5e7363cfdfaa9d4d0d.png"},{"id":51564123,"identity":"6178e436-da51-4a2c-a7ce-88df89f088ba","added_by":"auto","created_at":"2024-02-23 18:54:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136539,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of PRS distribution between subgroups of OC patients and PRS controls. Only SNP sets significantly stratifying at least one subgroup of OC patients from PRS controls are shown; (\u003cstrong\u003eA\u003c/strong\u003e) early-onset OC patients, (\u003cstrong\u003eB\u003c/strong\u003e) pooled early-onset and histology/stage-matched OC patients, (\u003cstrong\u003eC\u003c/strong\u003e) late-onset HGSC patients. Details in Supplementary Table S5.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNS, not significant;\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3972616/v1/4afccf0ce764962985978ca2.png"},{"id":51564122,"identity":"2b3cd68e-9b98-4cb8-bd5a-6fac7b5d8d08","added_by":"auto","created_at":"2024-02-23 18:54:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":147376,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of OC patients. Kaplan-Meier survival curves of (\u003cstrong\u003eA\u003c/strong\u003e) early-onset and late-onset OC patients; (\u003cstrong\u003eB\u003c/strong\u003e) \u003cem\u003egBRCA1/2\u003c/em\u003e positive and negative OC patients; (\u003cstrong\u003eC\u003c/strong\u003e) \u003cem\u003egBRCA1/2\u003c/em\u003e negative early-onset and histology/stage-matched OC patients; (\u003cstrong\u003eD\u003c/strong\u003e) LGSC early- and late-onset OC.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003egBRCA1/2, germline BRCA1/2; LGSC, Low-Grade Serous Carcinoma; neg., negative; NS, not significant; OC, Ovarian Cancer\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3972616/v1/845d2b10fd8df9cd999e5988.png"},{"id":51603194,"identity":"2465f47a-1a7e-4142-83f7-8f983d1f10d8","added_by":"auto","created_at":"2024-02-25 12:44:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":977687,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3972616/v1/dfffb12f-1445-442e-9a0a-226e16b83fdc.pdf"},{"id":51564118,"identity":"f806b3d3-8ed1-4368-a67c-8b7cec40b8cb","added_by":"auto","created_at":"2024-02-23 18:54:34","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":274106,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pptx","url":"https://assets-eu.researchsquare.com/files/rs-3972616/v1/b27e43233eaa94b9dd0a3fa0.pptx"},{"id":51564121,"identity":"e51202f3-2236-4e6d-911b-f93f44c2d39d","added_by":"auto","created_at":"2024-02-23 18:54:35","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":80564,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-3972616/v1/8d83eae158598e70ccf6665d.docx"},{"id":51564124,"identity":"50f5ec3f-716c-492c-a0b1-42704bda7d8b","added_by":"auto","created_at":"2024-02-23 18:54:35","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":248649,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3972616/v1/8f09dd63bd22438eedac8b6d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early-onset ovarian cancer: a comprehensive analysis","fulltext":[{"header":"1 INTRODUCTION","content":"\u003cp\u003eOvarian cancer (OC; including tumors of ovary, fallopian tube and peritoneum) remains the deadliest gynecological malignancy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Besides its anatomical heterogeneity, OC is characterized by heterogeneity at the cellular and molecular level and includes histologically distinct entities. Majority of the OC patients are diagnosed with epithelial, particularly high-grade serous carcinoma (HGSC) at advanced stages associated with a poor prognosis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The lifetime OC risk is about 1.3% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nevertheless, there is a number of known factors, including genetic predisposition, that may substantially modify the OC risk. Germline pathogenic variants (GPV) in OC predisposition genes including the most frequently affected genes \u003cem\u003eBRCA1/BRCA2\u003c/em\u003e are of the highest impact. Identification of the GPV allows to stratify women according to the OC risk and, subsequently, to offer the carriers appropriate surveillance management including targeted therapy. The proportion of hereditary OC associated with GPV in homologous recombination and mismatch repair pathway genes is reported to be around 25%; however, it varies among OC histological subtypes and is the highest in HGSC [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. OC rates are the highest in women aged 55\u0026ndash;64 years with a median age of diagnosis at 63 years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In contrast, OC in young women (\u0026le;\u0026thinsp;30 years) is rare accounting for less than 5% of all OC cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnderstanding the genetic basis of early-onset OC is crucial for unraveling the complex factors that contribute to its onset and progression. In many solid malignancies, early onset is a hallmark of hereditary predisposition. However, the proportion of early-onset OC attributable to GPV in OC predisposition genes is uncertain with only a few performed studies analyzing a limited number of early-onset OC patients diagnosed before 30 years of age. Interestingly, all these studies identified no or very low frequency of GPV in OC predisposition genes [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite the limited number of analyzed early-onset OC patients, the lack of GPV in OC predisposition genes, particularly \u003cem\u003eBRCA1/BRCA2\u003c/em\u003e, cannot be simply explained by a different representation of individual histological OC subtypes. Other mechanisms, including polygenic inheritance and immune response, might be involved in pathogenesis of early-onset OC. Polygenic inheritance resulting from the combination of multiple alleles with low impact on OC risk could contribute to early-onset OC development. Several sets of these low-penetrance alleles for stratification of individuals according to their OC risk have been published [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In addition, certain HLA genotypes have been previously associated with increased susceptibility to various diseases including early-onset OC; tumor neoantigen recognition might also depend on genetic variability in HLA regions. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Thus, early-onset OC represents a unique and compelling area of investigation within the broader landscape of ovarian malignancies.\u003c/p\u003e \u003cp\u003eIn our study, we aimed to comprehensively characterize germline genetic landscape of early-onset OC in a set of 123 patients diagnosed before the age of 30 years using DNA/RNA whole exome sequencing (WES) including HLA analysis and polygenic risk score (PRS) analysis. In addition, we analyzed the genotype data in the clinical and histopathological context and in comparison, with population-matched histology/stage-matched late-onset OC patients and non-cancer controls.\u003c/p\u003e"},{"header":"2 PATIENTS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients and controls\u003c/h2\u003e \u003cp\u003eWe enrolled 123 patients diagnosed with early-onset (\u0026lt;\u0026thinsp;30 years) OC (denoted herein as \u0026ldquo;early-onset OC\u0026rdquo;). Peripheral-blood derived gDNA of 123 patients and peripheral-blood derived total RNA (available in 71 patients) were analyzed using WES. Mean age at OC diagnosis was 25.4 years (15\u0026ndash;30), 93 patients were diagnosed with invasive ovarian tumors (61 epithelial, 15 non-epithelial denoted herein as \u0026ldquo;other\u0026rdquo;, and 17 with unspecified histology) and 30 with borderline tumors of ovary (BTO) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of 123 early-onset OC patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpithelial OC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61 (49.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (37.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (17.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emucinous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (12.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eendometrioid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (4.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclear cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerous NOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eundifferentiated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (13.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBTO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (24.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (12.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple primary tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110 (89.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily cancer history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88 (71.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- early onset cancer\u0026thinsp;\u0026lt;\u0026thinsp;40y in family history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (19.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e- hematological malignancy in family history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (12.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (26.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eBTO, Borderline Tumors of Ovary; LGSC, Low-Grade Serous Carcinoma; HGSC, High-Grade Serous Carcinoma; NA, not available; NOS, not otherwise specified; OC, Ovarian Cancer.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eFor PRS and survival analysis, we employed additional OC patients analyzed previously [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] (details in Methods; Supplementary Table S4).\u003c/p\u003e \u003cp\u003eWe employed several sets of population-matched controls:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003efor DNA variant prioritization\u003c/em\u003e: 227 healthy females older than 60 years with no personal or first-degree family cancer history (denoted herein as \u0026ldquo;super-controls\u0026rdquo;).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003efor RNA splicing event prioritization\u003c/em\u003e: 61 unselected non-cancer females with available RNA WES data (denoted herein as \u0026ldquo;non-cancer controls\u0026rdquo;).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003efor risk calculation in case-control analysis\u003c/em\u003e: 378 unselected individuals provided by the National Center for Medical Genomics [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (accessed on 3/2023; denoted herein as \u0026ldquo;unselected controls\u0026rdquo;).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003efor HLA analysis\u003c/em\u003e: \u0026ldquo;super-controls\u0026rdquo; and a second group of 5099 unselected individuals (data for HLA-C and HLA-DQB1 were available for 4669 and 4049 individuals, respectively) from Czech National Marrow Donors Registry [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] (accessed on 11/2023; denoted herein as \u0026ldquo;HLA controls\u0026rdquo;).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003efor PRS analysis\u003c/em\u003e: 1403 non-cancer females negative for GPV in hereditary breast, ovarian and pancreatic (HBOP) cancer predisposition genes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] (denoted herein as \u0026ldquo;PRS controls\u0026rdquo;) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAll patients and controls were Caucasians of Czech origin. Written informed consent was obtained from all patients and controls. The study was approved by the Ethics Committee of the General University Hospital in Prague and performed in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 NGS Library Preparation\u003c/h2\u003e \u003cp\u003eSequencing libraries were prepared as described previously [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] with minor modifications (detailed in Supplementary Methods) and targeted whole exome (KAPA HyperExome panel, Roche; capture target 43Mb) and 843 SNP including 65 SNP previously reported to associate with OC PRS (custom HyperChoice panel, Roche; Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The minimal mean coverage was 30\u0026times; for DNA WES, 200\u0026times; for RNA WES (with coverage of exon 11 in BRCA1 mRNA as a coverage quality marker) and 20\u0026times; for PRS genotyping analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Bioinformatics pipeline for variant analysis\u003c/h2\u003e \u003cp\u003eThe fastq data from DNA WES were analyzed as described previously [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] with minor modifications (detailed in Supplementary Methods). Copy number variations (CNV) were analyzed using CNVkit as described previously [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The fastq data from RNA WES were mapped to hg19 using STAR aligner to generate BAM files [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Subsequently, duplicates were removed using Picard tools v1.129 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and analyzed by regtools [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and SCANVIS [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Variant filtration and prioritization for the burden analysis\u003c/h2\u003e \u003cp\u003eVariant prioritization (considering population frequency or sequencing quality) and variant classification were performed independently for DNA events (separately for substitutions/short-medium length indels and CNV analysis) and RNA events as described in Supplementary Methods. Final list of GPV (Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e) was used for the subsequent statistical analysis. The following genes were considered HBOP cancer predisposing: \u003cem\u003eATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CDKN2A, CHEK2, MLH1, MSH2/EPCAM, MSH6, NF1, PALB2, PMS2, PTEN, RAD51C/D, STK11\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed in R v.4.2.0; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Gene burden analysis of the final list of GPV in patients and unselected controls was performed using Fisher exact test, the 3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e exome-wide Bonferroni corrected \u003cem\u003ep\u003c/em\u003e-value threshold was considered significant [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Overrepresentation analysis of the final list of GPV in patients was performed using online WEB-based GEne SeT AnaLysis Toolkit (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.webgestalt.org\" target=\"_blank\"\u003ewww.webgestalt.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.webgestalt.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 11/2023) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Human Leukocyte Antigen (HLA) analysis\u003c/h2\u003e \u003cp\u003eHLA analysis was performed from DNA WES data of 123 early-onset OC patients and 227 super-controls using SpecHLA tool genotyping of HLA-A, B, C, DPA1, DPB1, DQA1, DQB1, and DRB1 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The HLA genotypes were curated to the level of amino acid (four-digit resolution). The frequency of HLA alleles in early-onset OC and controls (HLA- and super-controls) were compared using Fisher test. Due to unavailability of HLA-DPA1, DPB1, and DQA1 genotypes and information about zygosity in HLA controls, the frequencies of the three genotypes and the zygosity status were statistically evaluated only to super-controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 PRS analysis\u003c/h2\u003e \u003cp\u003eWe performed genotyping of 11 SNP sets, including 10 OC [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and one breast cancer (BC) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], in 122 early-onset OC patients (PRS in one patient could not be assessed) and 85 histology- and stage-matched late-onset OC patients (aged\u0026thinsp;\u0026gt;\u0026thinsp;40 years; denoted herein as \u0026ldquo;histology/stage-matched OC\u0026rdquo;); and 78 population-matched late-onset HGSC patients negative for GPV in high-penetrance OC predisposition genes (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S2, S4).\u003c/p\u003e \u003cp\u003eRaw NGS data were processed by an inhouse bioinformatics pipeline as described previously [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. PRS was calculated as described by Borde \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] (Supplementary Methods). Differences between the standardized PRS values of patients and PRS controls were assessed in R v.4.2.0 using \u003cem\u003et\u003c/em\u003e-test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Survival analysis\u003c/h2\u003e \u003cp\u003eThe survival analysis was performed using the Kaplan-Meier analysis and the log-rank test in R v.4.2.0. Vital status was available in 82 early-onset (median 25.1 years; 14.8\u0026ndash;30.8 years) and 917 previously analyzed, late-onset (\u0026gt;\u0026thinsp;30 years; median age 58.1 years; 31.2\u0026ndash;91.8 years) OC patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, subgroup analyses were performed using groups stratified by OC histology and by germline \u003cem\u003eBRCA1/BRCA2\u003c/em\u003e (\u003cem\u003egBRCA1/2\u003c/em\u003e) status. Individual OC patients\u0026rsquo; subgroups included into the survival analysis are summarized in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S4, S6.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 RESULTS","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 DNA/RNA WES\u003c/h2\u003e\n \u003cp\u003eIn our cohort of 123 early-onset OC patients, a total number of 1563 germline GPV (1506 unique variants) in 1390 genes stemmed from DNA (SNV and CNV accounting for 95.3% and 1% of GPV, respectively) and RNA WES (additional 3.7% of GPV). Median number of GPV per patient was 13 (ranged 2\u0026ndash;28). The GPV mutation burden was significantly lower in super-controls compared to early-onset OC patients (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;3.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and tended to be lower in BTO and patients with other (non-eptihelial) OC compared to type I [low-grade serous (LGSC); endometrioid, clear cell, mucinous] or type II (HGSC, undifferentiated) OC patients (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, S3; Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eSeventeen patients (13.8%) carried a GPV in HBOP cancer predisposition genes, including only a single carrier of GPV in \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e, respectively (2/123; 1.6%), compared to 30.2% GPV in population-matched OC patients published previously (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]. We found no GPV in established genes associated with the risk of non-epithelial tumors of ovary (\u003cem\u003eSTK11, SMARCA4, DICER1\u003c/em\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGPV in established hereditary breast/ovarian/pancreatic (HBOP) cancer predisposition genes identified in early-onset OC patients and comparison of GPV frequency in unselected OC patients. Established high-penetrance OC predisposition genes and significant \u003cem\u003ep\u003c/em\u003e-values are in bold.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e123 early-onset OC\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1320 unselected OC [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e]*\u003c/p\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eATM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBARD1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRCA1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.5\u0026times;10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;9\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRCA2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRIP1**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCHEK2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (0.9)***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMLH1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMSH2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMSH6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePALB2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePMS2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAD51C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAD51D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTP53**\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e17 (13.8)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e399 (30.2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.6\u0026times;10\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e\u0026minus;\u0026thinsp;5\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003e*multiple GPV carriers (N\u0026thinsp;=\u0026thinsp;13) were excluded from the analysis; **one concomitant carrier of GPV in BRIP1 and TP53; ***additionally, one initially unrevealed deep intronic GPV in CHEK2 was identified\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cem\u003eGPV, Germline Pathogenic Variants; NA, not available; OC, Ovarian Cancer\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe carriership of GPV in established high-penetrance OC predisposition genes (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; in bold) was significantly associated with development of multiple primary tumors (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.016; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA), whereas GPV in established HBOP cancer predisposition genes (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB) was not, presumably due to the prevalence of moderate penetrance GPV carriers. GPV in established high-penetrance OC predisposition genes (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; in bold) associated neither with early-onset (\u0026lt;\u0026thinsp;40 years) malignancy in family cancer history (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.054; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC), nor with overall positive family cancer history, nor with hematological malignancies in family history, which was observed in the initial study of Stratton \u003cem\u003eet al.\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. On the other hand, multiple primary tumors in early-onset OC patients significantly associated with family history of hematological malignancies (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). It is noteworthy that among early-onset OC patients with known histology, double primaries developed more likely in early-onset OC patients diagnosed with non-epithelial OC (5/15; 33.3%) than with invasive epithelial OC (4/61; 6.6%; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.012; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWES gene-based burden analysis revealed \u003cem\u003eCHEK2\u003c/em\u003e and \u003cem\u003eLY75-CD302\u003c/em\u003e (6 carriers each) as the most frequently altered genes in early-onset OC patients (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1.2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e and 8.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, respectively). Altogether, GPV in only five genes (\u003cem\u003eCHEK2, LY75-CD302, ATP7B, BCHE, MFNG)\u003c/em\u003e were enriched in early-onset OC patients compared to unselected controls (Supplementary Table S7); however, the significance did not fall below the exome-wide Bonferroni corrected \u003cem\u003ep\u003c/em\u003e-value threshold.\u003c/p\u003e\n \u003cp\u003eOf these genes, \u003cem\u003eCHEK2\u003c/em\u003e was the only one associated with cancer risk. Interestingly, alongside to known founder c.1100delC and exons 9\u0026ndash;10 deletion GPV, RNA analysis revealed two intronic GPV, c.1009\u0026thinsp;\u0026minus;\u0026thinsp;118_1009-87delinsC and c.1461\u0026thinsp;+\u0026thinsp;2301G\u0026thinsp;\u0026gt;\u0026thinsp;T, leading to the aberrant splicing (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). The recurrent c.1009\u0026thinsp;\u0026minus;\u0026thinsp;118_1009-87delinsC variant has been recently reported [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. The deep intronic c.1461\u0026thinsp;+\u0026thinsp;2301G\u0026thinsp;\u0026gt;\u0026thinsp;T variant has been previously assessed as variant of uncertain significance (VUS) in ClinVar due to its predicted in-frame insertion of 30 amino acids. However, our RNA analysis revealed a new acceptor splice site leading to an inclusion of 89 nucleotides (r.1461_1462ins1461\u0026thinsp;+\u0026thinsp;2211_1461\u0026thinsp;+\u0026thinsp;2299) and predicted premature termination of translation (p.Asp488SerfsTer34; Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Interestingly, the age at OC diagnosis significantly differed between \u003cem\u003eCHEK2\u003c/em\u003e carriers and non-carriers of GPV in HBOP cancer predisposition genes [\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e] (median age 33.9; 18\u0026ndash;66 years, and 58; 15\u0026ndash;92 years, respectively; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.003). The \u003cem\u003eCHEK2\u003c/em\u003e status did not associate with a particular OC histology; most (4/6) \u003cem\u003eCHEK2\u003c/em\u003e GPV carriers had a positive family cancer history (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, S4; Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe second most frequently altered gene was \u003cem\u003eLY75-CD302\u003c/em\u003e involved in immune response. GPV in \u003cem\u003eLY75-CD302\u003c/em\u003e gene were unique except for c.4503del identified in two OC patients (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). Interestingly, four out of six \u003cem\u003eLY75-CD302\u003c/em\u003e GPV affected exon 31 that codes for C-type lectin/C-type lectin-like domain important for antigen binding prior to endocytosis and antigen presentation [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. All the carriers of \u003cem\u003eLY75-CD302\u003c/em\u003e GPV had a positive family cancer history, and none of them developed multiple primary tumors; \u003cem\u003eLY75-CD302\u003c/em\u003e GPV did not associate with any specific OC histology (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, S3).\u003c/p\u003e\n \u003cp\u003eAll GPV were present in heterozygous state, with the exception of two patients carrying two GPV in the same gene (\u003cem\u003eMUSK\u003c/em\u003e and \u003cem\u003eMAATS1\u003c/em\u003e) suggesting possible recessive inheritance; however, their phase was unknown.\u003c/p\u003e\n \u003cp\u003eTo determine whether a pre-defined set of genes belonging to certain pathway or disease are over-represented in our OC patients, we performed overrepresentation analysis of the genes from final list of GPV (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e) using functional databases. A combined analysis could be empowered if multiple genes of a predefined set were associated but the effect size is too small to detect individually. The overrepresentation analysis showed highest enrichment for gene set #114480: Breast cancer (disease in OMIM; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;4.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e; FDR 3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e; Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e) followed by gene set #HP:0030406 Primary peritoneal carcinoma (phenotype in Human Phenotype Ontology; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;6.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e; FDR 0.011).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 HLA genotypization\u003c/h2\u003e\n \u003cp\u003eWe analyzed HLA of class I (HLA-A, B, C) and class II (HLA-DPA1, DPB1, DQA1, DQB1, and DRB1) in 123 patients (except for HLA-DRB1 not genotyped in two patients due to the insufficient coverage of sequencing data in the HLA-DRB1 region). To increase robustness of the analysis, HLA alleles frequencies were compared to two sets of controls (HLA- and super-controls). Two HLA class I (HLA-A*36:01 and HLA-B*53:01) and three HLA class II (HLA-DRB*11:01, HLA-DQA1*01:03 and HLA-DQA1*03:03) alleles were significantly enriched in patients compared to HLA- and super-controls (Supplementary Table S8).\u003c/p\u003e\n \u003cp\u003eWe assessed the ratio of homozygotes in patients compared to super-controls in each locus. Patients were significantly more frequently carriers of at least one homozygous HLA allele (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e). The carriers\u0026rsquo; frequency of multiple homozygous alleles was also significantly increased (Supplementary Table S9). The rates of homozygotes in HLA class I and II were both statistically significantly higher compared to super-controls (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 PRS analysis\u003c/h2\u003e\n \u003cp\u003eIn contrast to BC, a single SNP set has not yet been widely adopted for the analysis of PRS in OC, although many have been published and, therefore, we performed PRS analysis using 10 different (rather) non-overlapping SNP sets associated with OC [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, PRS of early-onset OC differed significantly neither from PRS controls, nor from histology/stage-matched OC patients in any individual OC SNP set analyzed (Supplementary Table S5; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, we did not observe any difference between PRS of histology/stage-matched OC patients and PRS controls.\u003c/p\u003e\n \u003cp\u003eThe SNP sets selection is usually based on studies of patients regardless of OC histology. As HGSC is the prevailing one and the PRS calculated based on the analyzed individual SNP set did not differ in principle between non-HGSC OC patients and PRS controls, we performed the PRS analysis in a group of late-onset HGSC patients who were previously tested negative for a GPV in OC predisposition genes [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] (Supplementary Table S4) to evaluate the performance of the individual SNP sets for PRS calculation in HGSC patients compared to PRS controls. Contrary to early-onset predominantly non-HGSC OC, PRS of HGSC patients based on 4 individual SNP sets differed significantly from PRS controls (Supplementary Table S5). Additionally, Phelan \u003cem\u003eet al.\u003c/em\u003e established OC histology-specific OR for each included SNP (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). Nevertheless, when we employed LGSC, or mucinous-specific OR for the PRS calculation in 64 and 28 early- and late-onset OC patients with the respective histology, respectively, the PRS did not differ between these patients and PRS controls.\u003c/p\u003e\n \u003cp\u003eFurthermore, we calculated PRS using 313 SNP with OR established for BC. Surprisingly, the difference in PRS between both early-onset and pooled early-onset and histology/stage-matched OC patients (thus, predominantly non-HGSC OC), and PRS controls was significant (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.03 and 0.009, respectively) whereas non-significant in HGSC (Supplementary Table S5; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The representation of early-onset OC patients in each decile according to PRS\u003csub\u003e313\u003c/sub\u003e was quite even. Interestingly, five early-onset OC patients were also diagnosed with BC and PRS\u003csub\u003e313\u003c/sub\u003e categorized them into 1st, 5th ,7th, 9th, 9th decile, respectively; the patient carrying a \u003cem\u003eBRCA2\u003c/em\u003e GPV was categorized in the 1st decile.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Survival analysis\u003c/h2\u003e\n \u003cp\u003eFinally, we performed a survival analysis to pursuit a difference in early- and late-onset OC patients. Regardless of their GPV carriership, the survival analysis of 82 early-onset and 917 late-onset OC patients with available vital status showed significantly improved survival of early-onset OC patients (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). The survival differed significantly according to germline \u003cem\u003egBRCA1/2\u003c/em\u003e status (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.002; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB). However, we can only comment in principle on late-onset OC patients as g\u003cem\u003eBRCA1/2\u003c/em\u003e GPV were rare among early-onset OC (216/917; 23.6% vs. 2/82; 2.4%). Survival analysis of 80 \u003cem\u003egBRCA1/2\u003c/em\u003e-negative early-onset (median age 25.1 years) and 79 histology/stage-matched \u003cem\u003egBRCA1/2\u003c/em\u003e-negative OC (median age 61.7 years) was also significant although the survival advantage was less pronounced (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.02; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC). Survival analysis for individual histological subtypes was not significant (LGSC; Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD) or cannot be performed due to insufficient number of events in at least one of the patients` groups.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eThe early-onset OC (diagnosed at \u0026lt;\u0026thinsp;30 years), represents a distinct subgroup exhibiting striking differences from late-onset OC in many aspects, including germline cancer predisposition. We undertake the most extensive germline analysis of early-onset OC patients so far, employing comprehensive approaches encompassing DNA WES complemented by RNA WES and PRS analysis.\u003c/p\u003e \u003cp\u003eMutation profiles in our early-onset OC patients were dissimilar to unselected predominantly HGSC patients that we analyzed previously [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], with \u003cem\u003eBRCA1/BRCA2\u003c/em\u003e GPV identified in only two (1.6%) early-onset OC patients. Our observation is in agreement with findings from the few studies investigating at least a few early-onset OC patients that consistently reported either the absence, or unusually low frequency of GPV in established OC predisposition genes, as reviewed in [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It is noteworthy that GPV in established high-penetrance OC predisposition genes in our study significantly associated with multiple primary malignancies in early-onset OC patients who tended to develop invasive OC, and were more likely diagnosed with non-epithelial OC. The increased risk of double primary tumors was observed in a large study by Casper \u003cem\u003eet al.\u003c/em\u003e who described the inverse correlation between age at OC diagnosis and double primary cancer risk [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Moreover, the risk of double primary tumors was not increased in patients with BTO in a SEER population-based study [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Notably, early-onset OC patients with double primary tumors in our study had a significantly more likely hematological malignancies in their family cancer history which was in agreement with the initial and so far, the only early-OC patient study by Stratton \u003cem\u003eet al.\u003c/em\u003e, who observed increased risk of non-Hodgkin lymphoma and malignant myeloma in first degree relatives of early-onset OC patients with invasive disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost GPV were identified uniquely in our early-onset OC cohort and were enriched in only five genes compared to unselected controls, with the \u003cem\u003eCHEK2\u003c/em\u003e gene coding for checkpoint kinase 2 ranking first. Interestingly, \u003cem\u003eCHEK2\u003c/em\u003e GPV identified in 6/123 (4.9%) early-onset OC patients were significantly associated with earlier age at diagnosis compared to previously analyzed OC patients negative for GPV in HBOP cancer predisposition genes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, GPV in \u003cem\u003eCHEK2\u003c/em\u003e have been identified in early-onset OC patients by other studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], the most prevalently by Carter \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] who identified \u003cem\u003eCHEK2\u003c/em\u003e GPV in 5/147 (3.4%) early-onset OC patients. Although \u003cem\u003eCHEK2\u003c/em\u003e have not been acknowledged as the OC predisposition gene, some studies pointed to an association of \u003cem\u003eCHEK2\u003c/em\u003e with the OC risk [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This hypothesis further supports an indirect evidence potentially influencing prognostic and therapeutic considerations. It has recently been shown that \u003cem\u003eCHEK2\u003c/em\u003e is a master regulator of oocyte survival and modifier of the ovarian cellular response to damage [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The mechanistic link to these functions involves phosphorylation of the transcription factor FoxM1 whose overexpression is associated with a poor OC prognosis [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. GPV in \u003cem\u003eCHEK2\u003c/em\u003e are associated with moderate BC risk [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Interestingly, our overrepresentation analysis, which has greater power when multiple genes of a predefined set are associated with a small effect, identified a BC gene set enrichment in carriers of GPV in our early-onset OC patients. In addition, PRS\u003csub\u003e313\u003c/sub\u003e developed for BC risk stratification significantly differed in early-onset OC patients compared to PRS controls (see below). Moreover, 5/13 early-onset OC patients with a second primary tumor were diagnosed with BC. Consequently, it is plausible to speculate that there may be a shared germline predisposition factor(s) or functional underlying mechanisms common to both BC and early-onset OC, particularly non-HGSC OC.\u003c/p\u003e \u003cp\u003eBesides \u003cem\u003eCHEK2\u003c/em\u003e, \u003cem\u003eLY75-CD302\u003c/em\u003e was the second most GPV-enriched gene revealed by the gene burden analysis. This gene consists of \u003cem\u003eLY75\u003c/em\u003e and \u003cem\u003eCD302\u003c/em\u003e that are alternatively transcribed in a readthrough way leading to translation of fusion proteins with high similarity, acting as receptors involved in endocytosis-mediated immune responses including HLA class I-mediated antigen presentation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In addition, \u003cem\u003eLY75\u003c/em\u003e was shown to modulate cellular phenotype of epithelial OC cells and their metastatic potential through mediation of mesenchymal-epithelial transition [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Although the significance of GPV in \u003cem\u003eLY75-CD302\u003c/em\u003e (and other immunity-related genes with identified private GPV) for early-onset OC risk is unknown, it can indicate an attractive direction in early-OC development, especially in view of the results of HLA analysis.\u003c/p\u003e \u003cp\u003eHLA molecules are coded by multiple highly polymorphic loci and are crucial for immune reaction activation and progression including anti-tumor immunity [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Previously, specific HLA alleles have been described to predispose to certain cancer types, including OC, as observed by Kubler \u003cem\u003eet al.\u003c/em\u003e who identified a significantly higher frequency of carriers of HLA class II haplotypes HLA-DQA1*05:01-DQB1*02:01-DRB1*03:01 and HLA-DQA1*01:01-DQB1*05:01-DRB1*10:01) in OC patients from Germany [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, HLA-DRB1*03:01 (homozygous or heterozygous) and HLA-DQB1*02:01 (only homozygous) were individually enriched in their patients compared to controls. However, we did not observe significant enrichment of either these haplotypes or genotypes in our cohort; however, we identified another HLA-DRB1 (*11:01) allele enriched in our early-onset OC patients. This allele was associated with BC in Italian cohort of patients previously [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The high abundance of HLA-DRB1*11:01 carriers among our early-onset OC patients might indicate a genetic link between BC, immune system, and early-onset OC. In addition, we identified significantly associated HLA-DQA1*01:03 risk allele and HLA-DQA1*03:03 protective allele. However, the cancer risk associations of HLA-DQA1 are contradictory and not very well understood yet [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn addition to the specific disease-related risk HLA alleles, also heterogeneity of inherited HLA alleles seems to be important with regard to tumor-associated antigen presentation, cancer cell recognition and elimination, as well as to immunoediting in early cancer development through e.g. stronger selective pressure on driver mutations in tumors [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Having analyzed the HLA zygosity, the early-onset OC patients were significantly more frequently homozygotes compared to super-controls. Interestingly, homozygotes in HLA class II were more abundant than homozygotes in HLA class I loci when compared to super-controls. HLA class II homozygosity was previously associated with increased risk of lung and head and neck cancer, and non-Hodgkin lymphomas, thus, particularly with tumors with high mutational burden or infectious etiology, whereas the lower diversity at HLA class I locus was associated with increased risk of Hodgkin lymphoma [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In addition, increased HLA homozygosity rate was also described in colorectal patients by Tsai \u003cem\u003eet al.\u003c/em\u003e who also noted that the patients with higher HLA heterozygosity were more likely to manifest higher tumor infiltrating lymphocytes in their tumors [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePolygenic inheritance, arising from the cumulative effect of numerous genetic low-risk variants, might elucidate a portion of the missing heritability in the predisposition to early-onset OC [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. However, PRS analysis has currently its limitations, given the lack of consensus on a specific SNP set and uncertain clinical efficacy in OC risk stratification [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. We conducted PRS analysis using 10 different SNP sets [\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], but none demonstrated the ability to distinguish early-onset from histology/stage-matched OC patients, or from PRS controls. Conversely, PRS based on four SNP sets [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] were able to discriminate between HGSC patients and PRS controls. This implies that these four SNP sets are specifically associated with the risk of HGSC, the most prevalent OC type in GWAS focused on identifying OC risk loci. Interestingly, PRS\u003csub\u003e313\u003c/sub\u003e, designed specifically for BC [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], significantly differed in early-onset, predominantly non-HGSC OC patients but not in HGSC. This suggests a potential pleiotropic effect, indicating a common mechanism underlying the development of multiple phenotypes associated with some common variant susceptibility loci. However, evidence supporting a biological function has only been identified for certain loci, highlighting a significant portion of biology that remains unclear.\u003c/p\u003e \u003cp\u003eConsidering polygenic inheritance from an alternative perspective, we observed significant variability in GPV burden among different OC types and super-controls. Higher GPV burden was identified in patients diagnosed with invasive epithelial OC and, remarkably, GPV burden tended to increase with increasing somatic genomic instability characteristic for each histological OC types [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Interestingly, Qing \u003cem\u003eet al.\u003c/em\u003e noticed strong negative correlation between GPV burden and age, suggesting a greater contribution of GPV to the transformation process in early-onset OC patients compared to their late-onset counterparts, where somatic mutations were hypothesized to play a more predominant role [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. This observation supports our hypothesis that, the cumulative GPV burden may potentially elevate the cancer risk rather than GPV in certain genes, particularly when concomitant with reduced HLA diversity, influencing the efficiency of neoantigen recognition.\u003c/p\u003e \u003cp\u003eRegarding to the association of clinicopathological and genetic factors with the survival, we observed an improved survival of early-onset OC patients compared to previously analyzed late-onset OC patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, our findings revealed survival advantage in early-onset OC patients compared to histology/stage-matched OC patients lacking \u003cem\u003egBRCA1/2\u003c/em\u003e GPV. This suggests that age is an independent positive prognostic factor and that the survival advantage in early-onset OC primarily stems not solely from the distinct distribution of histological OC types compared to late-onset OC, particularly the lack of prognostically unfavorable HGSC histological subtype in early-onset OC patients. A positive correlation between survival and age as well as improved survival among non-HGSC epithelial OC patients has been described in prior research [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Nevertheless, certain investigations have delineated a less favorable prognosis and lower 5-year survival in LGSC OC in early-onset OC [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. However, we did not observe any difference in survival of LGSC early- and late-onset OC patients. Additionally, our research unveiled a significant initial survival advantage in \u003cem\u003egBRCA1/2\u003c/em\u003e positive OC patients, consistent with earlier findings [\u003cspan additionalcitationids=\"CR71\" citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile our study stands out as the most intricate and the third-largest investigation focused on early-onset OC patients diagnosed before the age of 30, still a noteworthy limitation lies in the restricted number of patients. The low number of patients hinders our ability to pinpoint potential private causal alleles effectively. These limitations underscore the need for comprehensive data to better understand the complex landscape of early-onset OC and its associated risk factors.\u003c/p\u003e \u003cp\u003eIn conclusion, our comprehensive germline analysis of early-onset OC patients revealed two divergent trajectories of potential germline susceptibility. Overrepresentation analysis highlighted an association to BC, supported by the enrichment of GPV in \u003cem\u003eCHEK2\u003c/em\u003e and the presumably BC-specific PRS\u003csub\u003e313\u003c/sub\u003e, which successfully stratified early-onset OC from PRS controls. The second avenue pointed towards the impaired immune response, indicated by GPV in the \u003cem\u003eLY75-CD302\u003c/em\u003e gene, coupled with diminished HLA diversity. Furthermore, we found a significantly higher GPV burden in early-onset OC patients compared to super-controls.\u003c/p\u003e \u003cp\u003eIn summary, the genetic predisposition to early-onset of OC appears to be a very heterogeneous and complex process beyond the conventional Mendelian monogenic understanding of hereditary cancer predisposition with a modifying role of the immune system. Based on our results, we speculate that rather a cumulative GPV burden than GPV in specific genes may increase early-onset OC risk, especially when it is concomitant with reduced HLA diversity, which affects the efficiency of neoantigen recognition. However, it cannot be definitively excluded that the occurrence of early-onset OC is a random event influenced by the chance and varying values of random variables.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBreast Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBTO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBorderline Tumors of Ovary\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCopy Number Variations\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse Discovery Rate,\u003cem\u003egBRCA1/2\u003c/em\u003e,Germline \u003cem\u003eBRCA1/2\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGPV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGermline Pathogenic Variants\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHBOP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHereditary Breast,Ovarian and Pancreatic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHGSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Grade Serous Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHLA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Leukocyte Antigen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGSC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-Grade Serous Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNot Available\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eneg.\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enegative\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNext Generation Sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNot Significant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNot Otherwise Specified\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOvarian Cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePolygenic Risk Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Nucleotide Polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle Nucleotide Variant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariant of Uncertain Significance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWhole Exome Sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDECLARATION OF INTEREST STATEMENT\u003c/h2\u003e\n\u003cp\u003eNo potential conflicts of interest were reported. We declare that the results summarized in this manuscript have not been published previously and have not been submitted for consideration to any other journal.\u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e\n\u003cp\u003eThis work has been supported by the Ministry of Health of the Czech Republic: NU20-03-00016, NU20-09-00355, RVO-VFN 00064165; Charles University: COOPERATIO, SVV260631; UNCE/24/MED/022; and the Ministry of Education Youth and Sports of the Czech Republic: LX22NPO05102, and The National Center for Medical Genomics\u0026quot; (LM2023067).\u003c/p\u003e\n\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e\n\u003cp\u003eWe thank Pavel Pesek and Eva Tureckova for their excellent technical assistance and the National Center for Medical Genomics for providing sequencing data of unselected controls.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSEER, \u003cem\u003ehttps://seer.cancer.gov/statfacts/html/ovary.html.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eLabidi-Galy, S.I., et al., \u003cem\u003eHigh grade serous ovarian carcinomas originate in the fallopian tube.\u003c/em\u003e Nature Communications, 2017. \u003cstrong\u003e8\u003c/strong\u003e(1): p. 1093.\u003c/li\u003e\n \u003cli\u003eLisio, M.A., et al., \u003cem\u003eHigh-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints.\u003c/em\u003e Int J Mol Sci, 2019. \u003cstrong\u003e20\u003c/strong\u003e(4).\u003c/li\u003e\n \u003cli\u003ePearce, C.L., et al., \u003cem\u003ePopulation distribution of lifetime risk of ovarian cancer in the United States.\u003c/em\u003e Cancer Epidemiol Biomarkers Prev, 2015. \u003cstrong\u003e24\u003c/strong\u003e(4): p. 671-676.\u003c/li\u003e\n \u003cli\u003eFlaum, N., et al., \u003cem\u003eEpithelial ovarian cancer risk: A review of the current genetic landscape.\u003c/em\u003e Clin Genet, 2019.\u003c/li\u003e\n \u003cli\u003eLhotova, K., et al., \u003cem\u003eMultigene Panel Germline Testing of 1333 Czech Patients with Ovarian Cancer.\u003c/em\u003e Cancers (Basel), 2020. \u003cstrong\u003e12\u003c/strong\u003e(4).\u003c/li\u003e\n \u003cli\u003eStratton, J.F., et al., \u003cem\u003eThe genetic epidemiology of early-onset epithelial ovarian cancer: a population-based study.\u003c/em\u003e Am J Hum Genet, 1999. \u003cstrong\u003e65\u003c/strong\u003e(6): p. 1725-32.\u003c/li\u003e\n \u003cli\u003eFlaum, N., et al., \u003cem\u003eMSH2 is the very young onset ovarian cancer predisposition gene, not BRCA1.\u003c/em\u003e J Med Genet, 2023.\u003c/li\u003e\n \u003cli\u003eHerold, N., et al., \u003cem\u003ePathogenic germline variants in SMARCA4 and further cancer predisposition genes in early onset ovarian cancer.\u003c/em\u003e Cancer Med, 2023. \u003cstrong\u003e12\u003c/strong\u003e(14): p. 15256-15260.\u003c/li\u003e\n \u003cli\u003eCarter, N.J., et al., \u003cem\u003eGermline pathogenic variants identified in women with ovarian tumors.\u003c/em\u003e Gynecol Oncol, 2018. \u003cstrong\u003e151\u003c/strong\u003e(3): p. 481-488.\u003c/li\u003e\n \u003cli\u003eHorackova, K., et al., \u003cem\u003eEarly-Onset Ovarian Cancer \u0026lt;30 Years: What Do We Know about Its Genetic Predisposition?\u003c/em\u003e International Journal of Molecular Sciences, 2023. \u003cstrong\u003e24\u003c/strong\u003e(23): p. 17020.\u003c/li\u003e\n \u003cli\u003eBolton, K.L., et al., \u003cem\u003eCommon variants at 19p13 are associated with susceptibility to ovarian cancer.\u003c/em\u003e Nat Genet, 2010. \u003cstrong\u003e42\u003c/strong\u003e(10): p. 880-4.\u003c/li\u003e\n \u003cli\u003eDareng, E.O., et al., \u003cem\u003ePolygenic risk modeling for prediction of epithelial ovarian cancer risk.\u003c/em\u003e Eur J Hum Genet, 2022. \u003cstrong\u003e30\u003c/strong\u003e(3): p. 349-362.\u003c/li\u003e\n \u003cli\u003eGoode, E.L., et al., \u003cem\u003eA genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24.\u003c/em\u003e Nat Genet, 2010. \u003cstrong\u003e42\u003c/strong\u003e(10): p. 874-9.\u003c/li\u003e\n \u003cli\u003eJervis, S., et al., \u003cem\u003eA risk prediction algorithm for ovarian cancer incorporating BRCA1, BRCA2, common alleles and other familial effects.\u003c/em\u003e J Med Genet, 2015. \u003cstrong\u003e52\u003c/strong\u003e(7): p. 465-75.\u003c/li\u003e\n \u003cli\u003eKuchenbaecker, K.B., et al., \u003cem\u003eEvaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers.\u003c/em\u003e J Natl Cancer Inst, 2017. \u003cstrong\u003e109\u003c/strong\u003e(7).\u003c/li\u003e\n \u003cli\u003ePermuth-Wey, J., et al., \u003cem\u003eIdentification and molecular characterization of a new ovarian cancer susceptibility locus at 17q21.31.\u003c/em\u003e Nat Commun, 2013. \u003cstrong\u003e4\u003c/strong\u003e: p. 1627.\u003c/li\u003e\n \u003cli\u003ePharoah, P.D., et al., \u003cem\u003eGWAS meta-analysis and replication identifies three new susceptibility loci for ovarian cancer.\u003c/em\u003e Nat Genet, 2013. \u003cstrong\u003e45\u003c/strong\u003e(4): p. 362-70, 370e1-2.\u003c/li\u003e\n \u003cli\u003ePhelan, C.M., et al., \u003cem\u003eIdentification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.\u003c/em\u003e Nat Genet, 2017. \u003cstrong\u003e49\u003c/strong\u003e(5): p. 680-691.\u003c/li\u003e\n \u003cli\u003eYang, X., et al., \u003cem\u003eEvaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study.\u003c/em\u003e 2018(1468-6244 (Electronic)).\u003c/li\u003e\n \u003cli\u003eJia, G., et al., \u003cem\u003eEvaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers.\u003c/em\u003e JNCI Cancer Spectr, 2020. \u003cstrong\u003e4\u003c/strong\u003e(3): p. pkaa021.\u003c/li\u003e\n \u003cli\u003eKubler, K., et al., \u003cem\u003eHLA-class II haplotype associations with ovarian cancer.\u003c/em\u003e Int J Cancer, 2006. \u003cstrong\u003e119\u003c/strong\u003e(12): p. 2980-5.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eThe NCMG research infrastructure; http://ncmg.cz\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eNational Marrow Donors Registry; www.allelefrequencies.net/pop6001c.asp?pop_id=3258\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eNCCN. \u003cem\u003eGuidelines Version 2.2024: Genetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic\u003c/em\u003e. 2023.\u003c/li\u003e\n \u003cli\u003eHovhannisyan, M., et al., \u003cem\u003ePolygenic risk score (PRS) and its potential for breast cancer risk stratification.\u003c/em\u003e Klin Onkol, 2023. \u003cstrong\u003e36\u003c/strong\u003e(3): p. 198-205.\u003c/li\u003e\n \u003cli\u003eSoukupova, J., et al., \u003cem\u003eValidation of CZECANCA (CZEch CAncer paNel for Clinical Application) for targeted NGS-based analysis of hereditary cancer syndromes.\u003c/em\u003e PLoS One, 2018. \u003cstrong\u003e13\u003c/strong\u003e(4): p. e0195761.\u003c/li\u003e\n \u003cli\u003eHorackova, K., et al., \u003cem\u003eLow Frequency of Cancer-Predisposition Gene Mutations in Liver Transplant Candidates with Hepatocellular Carcinoma. LID - 10.3390/cancers15010201 [doi] LID - 201.\u003c/em\u003e 2022(2072-6694 (Print)).\u003c/li\u003e\n \u003cli\u003eWalker, L.C., et al., \u003cem\u003eComprehensive Assessment of BARD1 Messenger Ribonucleic Acid Splicing With Implications for Variant Classification.\u003c/em\u003e Front Genet, 2019. \u003cstrong\u003e10\u003c/strong\u003e: p. 1139.\u003c/li\u003e\n \u003cli\u003eDobin, A., et al., \u003cem\u003eSTAR: ultrafast universal RNA-seq aligner.\u003c/em\u003e Bioinformatics, 2013. \u003cstrong\u003e29\u003c/strong\u003e(1): p. 15-21.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003ehttps://broadinstitute.github.io/picard/\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eCotto, K.C., et al., \u003cem\u003eIntegrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer.\u003c/em\u003e Nat Commun, 2023. \u003cstrong\u003e14\u003c/strong\u003e(1): p. 1589.\u003c/li\u003e\n \u003cli\u003eAgius, P., H. Geiger, and N. Robine, \u003cem\u003eSCANVIS: a tool for SCoring, ANnotating and VISualizing splice junctions.\u003c/em\u003e Bioinformatics, 2019. \u003cstrong\u003e35\u003c/strong\u003e(22): p. 4843-4845.\u003c/li\u003e\n \u003cli\u003eFadista, J., et al., \u003cem\u003eThe (in)famous GWAS P-value threshold revisited and updated for low-frequency variants.\u003c/em\u003e Eur J Hum Genet, 2016. \u003cstrong\u003e24\u003c/strong\u003e(8): p. 1202-5.\u003c/li\u003e\n \u003cli\u003eLiao, Y., et al., \u003cem\u003eWebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs.\u003c/em\u003e Nucleic Acids Res, 2019. \u003cstrong\u003e47\u003c/strong\u003e(W1): p. W199-W205.\u003c/li\u003e\n \u003cli\u003eWang, S., et al., \u003cem\u003eSpecHLA enables full-resolution HLA typing from sequencing data.\u003c/em\u003e Cell Rep Methods, 2023. \u003cstrong\u003e3\u003c/strong\u003e(9): p. 100589.\u003c/li\u003e\n \u003cli\u003eMavaddat, N., et al., \u003cem\u003ePolygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.\u003c/em\u003e Am J Hum Genet, 2019. \u003cstrong\u003e104\u003c/strong\u003e(1): p. 21-34.\u003c/li\u003e\n \u003cli\u003eBorde, J., et al., \u003cem\u003ePerformance of Breast Cancer Polygenic Risk Scores in 760 Female CHEK2 Germline Mutation Carriers.\u003c/em\u003e J Natl Cancer Inst, 2021. \u003cstrong\u003e113\u003c/strong\u003e(7): p. 893-899.\u003c/li\u003e\n \u003cli\u003eKurman, R.J. and M. Shih Ie, \u003cem\u003eThe Dualistic Model of Ovarian Carcinogenesis: Revisited, Revised, and Expanded.\u003c/em\u003e Am J Pathol, 2016. \u003cstrong\u003e186\u003c/strong\u003e(4): p. 733-47.\u003c/li\u003e\n \u003cli\u003ePetra Zemankova, M.C., Klara Horackova, Corinna Ernst, Jana Soukupova, Marianna Borecka, Britta Blumcke, Leona Cerna, Monika Cerna, Vaclava Curtisova, Tatana Dolezalova, Lenka Dvorakova, Petra Duskova, Lenka Foretova, Ondrej Havranek, Jan Hauke, Eric Hahnen, Milena Hovhannisyan, Lucie Hruskova, Stepan Chvojka, Marketa Janatova, Maria Janikova, Sandra Jelinkova, Pavel Just, Marcela Kosarova, Monika Koudova, Vera Krutilkova, Martin Lenicek, Eva Machackova, Katerina Matejkova, Renata Michalovska, Petr Nehasil, Barbora Nemcova, Jan Novotny, Pavel Pesek, Ondrej Scheinost, Drahomira Springer, Barbora Stastna, Viktor Stranecky, Ivan Subrt, Spiros Tavandzis, Eva Tureckova, Kamila Vesela, Zdenka Vlckova, Michal Vocka, Tomas Zima, Zdenek Kleibl, Petra Kleiblova, \u003cem\u003eA deep intronic recurrent CHEK2 variant c.1009-118_1009-87delinsC affects pre-mRNA splicing and contributes to hereditary breast cancer predisposition.\u003c/em\u003e BREAST 2024. \u003cstrong\u003esubmitted\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eBonifaz, L., et al., \u003cem\u003eEfficient targeting of protein antigen to the dendritic cell receptor DEC-205 in the steady state leads to antigen presentation on major histocompatibility complex class I products and peripheral CD8+ T cell tolerance.\u003c/em\u003e J Exp Med, 2002. \u003cstrong\u003e196\u003c/strong\u003e(12): p. 1627-38.\u003c/li\u003e\n \u003cli\u003eZelensky, A.N. and J.E. Gready, \u003cem\u003eThe C-type lectin-like domain superfamily.\u003c/em\u003e FEBS J, 2005. \u003cstrong\u003e272\u003c/strong\u003e(24): p. 6179-217.\u003c/li\u003e\n \u003cli\u003eNetwork, N.C.C. \u003cem\u003eGenetic/Familial High-Risk Assessment: Breast, Ovarian, and Pancreatic (Version 1.2023)\u003c/em\u003e. 2022; Available from: https://www.nccn.org/professionals/physician_gls/pdf/genetics_bop.pdf.\u003c/li\u003e\n \u003cli\u003eCasper, A.C., et al., \u003cem\u003eRisk of secondary malignancies in ovarian cancer survivors: 52,680 patients analyzed with over 40 years of follow-up.\u003c/em\u003e Gynecol Oncol, 2021. \u003cstrong\u003e162\u003c/strong\u003e(2): p. 454-460.\u003c/li\u003e\n \u003cli\u003eWang, J., Y. Du, and Y. Kang, \u003cem\u003eDo survivors of borderline ovarian tumors have susceptibility to secondary primary malignancies? A SEER population-based study.\u003c/em\u003e Int J Gynaecol Obstet, 2024.\u003c/li\u003e\n \u003cli\u003eKoczkowska, M., et al., \u003cem\u003eSpectrum and Prevalence of Pathogenic Variants in Ovarian Cancer Susceptibility Genes in a Group of 333 Patients.\u003c/em\u003e Cancers (Basel), 2018. \u003cstrong\u003e10\u003c/strong\u003e(11).\u003c/li\u003e\n \u003cli\u003eJarhelle, E., et al., \u003cem\u003eIdentifying sequence variants contributing to hereditary breast and ovarian cancer in BRCA1 and BRCA2 negative breast and ovarian cancer patients.\u003c/em\u003e Sci Rep, 2019. \u003cstrong\u003e9\u003c/strong\u003e(1): p. 19986.\u003c/li\u003e\n \u003cli\u003eLawrenson, K., et al., \u003cem\u003eCommon variants at the CHEK2 gene locus and risk of epithelial ovarian cancer.\u003c/em\u003e Carcinogenesis, 2015. \u003cstrong\u003e36\u003c/strong\u003e(11): p. 1341-53.\u003c/li\u003e\n \u003cli\u003eKurian, A.W., et al., \u003cem\u003eGenetic Testing and Results in a Population-Based Cohort of Breast Cancer Patients and Ovarian Cancer Patients.\u003c/em\u003e J Clin Oncol, 2019: p. JCO1801854.\u003c/li\u003e\n \u003cli\u003eEmori, C., Z. Boucher, and E. Bolcun-Filas, \u003cem\u003eCHEK2 signaling is the key regulator of oocyte survival after chemotherapy.\u003c/em\u003e Sci Adv, 2023. \u003cstrong\u003e9\u003c/strong\u003e(42): p. eadg0898.\u003c/li\u003e\n \u003cli\u003eZhang, Z., et al., \u003cem\u003eFoxM1 promotes the migration of ovarian cancer cell through KRT5 and KRT7.\u003c/em\u003e Gene, 2020. \u003cstrong\u003e757\u003c/strong\u003e: p. 144947.\u003c/li\u003e\n \u003cli\u003eBruckner, L., et al., \u003cem\u003eFOXM1 Inhibition in Ovarian Cancer Tissue Cultures Affects Individual Treatment Susceptibility Ex Vivo.\u003c/em\u003e Cancers (Basel), 2021. \u003cstrong\u003e13\u003c/strong\u003e(5).\u003c/li\u003e\n \u003cli\u003eStolarova, L., et al., \u003cem\u003eCHEK2 Germline Variants in Cancer Predisposition: Stalemate Rather than Checkmate.\u003c/em\u003e Cells, 2020. \u003cstrong\u003e9\u003c/strong\u003e(12).\u003c/li\u003e\n \u003cli\u003eLo, T.H., et al., \u003cem\u003eCharacterization of the Expression and Function of the C-Type Lectin Receptor CD302 in Mice and Humans Reveals a Role in Dendritic Cell Migration.\u003c/em\u003e J Immunol, 2016. \u003cstrong\u003e197\u003c/strong\u003e(3): p. 885-98.\u003c/li\u003e\n \u003cli\u003eFaddaoui, A., et al., \u003cem\u003eThe mannose receptor LY75 (DEC205/CD205) modulates cellular phenotype and metastatic potential of ovarian cancer cells.\u003c/em\u003e 2015(1949-2553 (Electronic)).\u003c/li\u003e\n \u003cli\u003eMehdi, S., et al., \u003cem\u003eLY75 Ablation Mediates Mesenchymal-Epithelial Transition (MET) in Epithelial Ovarian Cancer (EOC) Cells Associated with DNA Methylation Alterations and Suppression of the Wnt/beta-Catenin Pathway.\u003c/em\u003e Int J Mol Sci, 2020. \u003cstrong\u003e21\u003c/strong\u003e(5).\u003c/li\u003e\n \u003cli\u003ePagliuca, S., et al., \u003cem\u003eIndividual HLA heterogeneity and its implications for cellular immune evasion in cancer and beyond.\u003c/em\u003e Front Immunol, 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 944872.\u003c/li\u003e\n \u003cli\u003eAureli, A., et al., \u003cem\u003eBreast Cancer Is Associated with Increased HLA-DRB1*11:01 and HLA-DRB1*10:01 Allele Frequency in a Population of Patients from Central Italy.\u003c/em\u003e Immunol Invest, 2020. \u003cstrong\u003e49\u003c/strong\u003e(5): p. 489-497.\u003c/li\u003e\n \u003cli\u003eTsai, S.C., M.C. Sheen, and B.H. Chen, \u003cem\u003eAssociation between HLA-DQA1, HLA-DQB1 and oral cancer.\u003c/em\u003e Kaohsiung J Med Sci, 2011. \u003cstrong\u003e27\u003c/strong\u003e(10): p. 441-5.\u003c/li\u003e\n \u003cli\u003eChen, P.C., et al., \u003cem\u003eHLA-DQA1 and -DQB1 allele typing in southern Taiwanese women with breast cancer.\u003c/em\u003e Clin Chem Lab Med, 2007. \u003cstrong\u003e45\u003c/strong\u003e(5): p. 611-4.\u003c/li\u003e\n \u003cli\u003eWang, Q.L., et al., \u003cem\u003eAssociation of HLA diversity with the risk of 25 cancers in the UK Biobank.\u003c/em\u003e EBioMedicine, 2023. \u003cstrong\u003e92\u003c/strong\u003e: p. 104588.\u003c/li\u003e\n \u003cli\u003eTsai, Y.Y., et al., \u003cem\u003eHeterozygote advantage at HLA class I and II loci and reduced risk of colorectal cancer.\u003c/em\u003e Front Immunol, 2023. \u003cstrong\u003e14\u003c/strong\u003e: p. 1268117.\u003c/li\u003e\n \u003cli\u003eJones, M.R., et al., \u003cem\u003eGenetic epidemiology of ovarian cancer and prospects for polygenic risk prediction.\u003c/em\u003e Gynecol Oncol, 2017. \u003cstrong\u003e147\u003c/strong\u003e(3): p. 705-713.\u003c/li\u003e\n \u003cli\u003eKar, S.P., et al., \u003cem\u003eCommon Genetic Variation and Susceptibility to Ovarian Cancer: Current Insights and Future Directions.\u003c/em\u003e Cancer Epidemiol Biomarkers Prev, 2018. \u003cstrong\u003e27\u003c/strong\u003e(4): p. 395-404.\u003c/li\u003e\n \u003cli\u003eStruzinska, I., et al., \u003cem\u003eSomatic Genomic and Transcriptomic Characterization of Primary Ovarian Serous Borderline Tumors and Low-Grade Serous Carcinomas.\u003c/em\u003e J Mol Diagn, 2024.\u003c/li\u003e\n \u003cli\u003eQing, T., et al., \u003cem\u003eGermline variant burden in cancer genes correlates with age at diagnosis and somatic mutation burden.\u003c/em\u003e Nat Commun, 2020. \u003cstrong\u003e11\u003c/strong\u003e(1): p. 2438.\u003c/li\u003e\n \u003cli\u003eChan, J.K., et al., \u003cem\u003eOvarian cancer in younger vs older women: a population-based analysis.\u003c/em\u003e Br J Cancer, 2006. \u003cstrong\u003e95\u003c/strong\u003e(10): p. 1314-20.\u003c/li\u003e\n \u003cli\u003eGockley, A., et al., \u003cem\u003eOutcomes of Women With High-Grade and Low-Grade Advanced-Stage Serous Epithelial Ovarian Cancer.\u003c/em\u003e Obstet Gynecol, 2017. \u003cstrong\u003e129\u003c/strong\u003e(3): p. 439-447.\u003c/li\u003e\n \u003cli\u003eGershenson, D.M., et al., \u003cem\u003eImpact of Age and Primary Disease Site on Outcome in Women With Low-Grade Serous Carcinoma of the Ovary or Peritoneum: Results of a Large Single-Institution Registry of a Rare Tumor.\u003c/em\u003e J Clin Oncol, 2015. \u003cstrong\u003e33\u003c/strong\u003e(24): p. 2675-82.\u003c/li\u003e\n \u003cli\u003eBolton, K.L., et al., \u003cem\u003eAssociation between BRCA1 and BRCA2 mutations and survival in women with invasive epithelial ovarian cancer.\u003c/em\u003e JAMA, 2012. \u003cstrong\u003e307\u003c/strong\u003e(4): p. 382-90.\u003c/li\u003e\n \u003cli\u003eMcLaughlin, J.R., et al., \u003cem\u003eLong-term ovarian cancer survival associated with mutation in BRCA1 or BRCA2.\u003c/em\u003e J Natl Cancer Inst, 2013. \u003cstrong\u003e105\u003c/strong\u003e(2): p. 141-8.\u003c/li\u003e\n \u003cli\u003eHeemskerk-Gerritsen, B.A.M., et al., \u003cem\u003eProgression-free survival and overall survival after BRCA1/2-associated epithelial ovarian cancer: A matched cohort study.\u003c/em\u003e PLoS One, 2022. \u003cstrong\u003e17\u003c/strong\u003e(9): p. e0275015.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ovarian cancer, early-onset, germline genetic testing, whole exome sequencing, DNA, RNA, polygenic risk score, HLA, mutation burden","lastPublishedDoi":"10.21203/rs.3.rs-3972616/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3972616/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe subset of ovarian cancer (OC) diagnosed\u0026thinsp;\u0026le;\u0026thinsp;30yo represents a distinct subgroup exhibiting disparities from late-onset OC in many aspects, including indefinite germline cancer predisposition.\u003c/p\u003e \u003cp\u003eWe performed DNA/RNA whole exome sequencing together with human leukocyte antigen(HLA) typing, polygenic risk score(PRS) assessment and survival analysis in 123 early-onset OC patients compared to histology/stage-matched late-onset and unselected OC patients, and population-matched controls.\u003c/p\u003e \u003cp\u003eOnly 6/123(4.9%) early-onset OC patients carried a germline pathogenic variant(GPV) in high-penetrance OC predisposition genes, including a single carrier of GPV in \u003cem\u003eBRCA1\u003c/em\u003e and \u003cem\u003eBRCA2\u003c/em\u003e each. Nevertheless, our comprehensive germline analysis of early-onset OC patients revealed two divergent trajectories of potential germline susceptibility. Firstly, overrepresentation analysis highlighted a connection to breast cancer(BC) that was supported by the enrichment of GPV in \u003cem\u003eCHEK2\u003c/em\u003e in early-onset OC(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.2\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), and the presumably BC-specific PRS\u003csub\u003e313\u003c/sub\u003e, which successfully stratified early-onset OC from controls(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03). The second avenue pointed towards the impaired immune response, indicated by GPV in \u003cem\u003eLY75-CD302\u003c/em\u003e(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and coupled with diminished HLA diversity compared with controls(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e). Furthermore, we found a significantly higher GPV burden in early-onset OC patients compared to controls(\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). We observed survival advantage in early-onset OC patients compared with both age-unselected and histology/stage-matched late-onset OC patients lacking \u003cem\u003egBRCA1/2\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe genetic predisposition to early-onset OC appears to be a heterogeneous and complex process that goes beyond the traditional Mendelian monogenic understanding of hereditary cancer predisposition, with a significant role of the immune system. We speculate that rather a cumulative GPV burden than specific GPV may potentially increase OC risk, concomitantly with reduced HLA diversity.\u003c/p\u003e","manuscriptTitle":"Early-onset ovarian cancer: a comprehensive analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-23 18:54:29","doi":"10.21203/rs.3.rs-3972616/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30fc027d-2c54-4476-b77c-1d1628432d27","owner":[],"postedDate":"February 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-25T12:36:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-23 18:54:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3972616","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3972616","identity":"rs-3972616","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}