Key Points
Questions
What is the incidence rate of germline alterations in CDH1, which has been reported as a susceptibility gene present in 25% to 50% of patients with hereditary diffuse gastric cancer (HDGC), and is there a genetic basis underlying disease susceptibility in the remaining 50% to 75% of patients with HDGC?
Findings
In this cohort study of 284 Chinese patients with HDGC, the frequency of CDH1 germline alterations was low (2.8%), and germline alterations, insertions, and deletions were most frequently found in MUC4, ABCA13, ZNF469, FCGBP, IGFN1, RNF213, and SSPO. Double-hit events in genes such as CACNA1D were observed among patients with HDGC.
Meaning
This study’s findings challenge the previously reported high frequency of CDH1 germline alterations in HDGC and suggest that double-hit events may serve as important mechanisms for HDGC tumorigenesis; the study also provided a genetic landscape and identified new susceptibility genes for HDGC.
Abstract
Importance
The E-cadherin gene, CDH1, and the α-E-catenin gene, CTNNA1, were previously identified as hereditary diffuse gastric cancer (HDGC) susceptibility genes, explaining 25% to 50% of HDGC cases. The genetic basis underlying disease susceptibility in the remaining 50% to 75% of patients with HDGC is still unknown.
Objective
To assess the incidence rate of CDH1 germline alterations in HDGC, identify new susceptibility genes that can be used for screening of HDGC, and provide a genetic landscape for HDGC.
Design, Setting, and Participants
This cohort study conducted retrospective whole-exome and targeted sequencing of 284 leukocyte samples and 186 paired tumor samples from Chinese patients with HDGC over a long follow-up period (median, 21.7 [range, 0.6-185.9] months). Among 10 431 patients diagnosed with gastric cancer between January 1, 2002, and August 31, 2018, 284 patients who met the criteria for HDGC were included. Data were analyzed from August 1 to 30, 2020.
Main Outcomes and Measures
Incidence rate of CDH1 germline alterations, identification of new HDGC susceptibility genes, and genetic landscape of HDGC.
Results
Among 284 Chinese patients, 161 (56.7%) were female, and the median age was 35 (range, 20-75) years. The frequency of CDH1 germline alterations was 2.8%, whereas the frequency of CDH1 somatic alterations was 25.3%. The genes with the highest incidence (>10%) of private germline alterations (including insertions and deletions) in the HDGC cohort were MUC4, ABCA13, ZNF469, FCGBP, IGFN1, RNF213, and SSPO, whereas previously reported germline alterations of CTNNA1, BRCA2, STK11, PRSS1, ATM, MSR1, PALB2, BRCA1, and RAD51C were observed at low frequencies (median, 4 [range, 1-12] cases). Furthermore, enrichment of the somatic variant signature of exposure to aflatoxin suggested potential interaction between genetics and environment in HDGC. Double-hit events in genes such as CACNA1D were observed, which suggested that these events might serve as important mechanisms for HDGC tumorigenesis. In addition, germline variants of FSIP2, HSPG2, and NCKAP5 and somatic alterations of FGFR3, ASPSCR1, CIC, DGCR8, and LZTR1 were associated with poor overall survival among patients with HDGC.
Conclusions and Relevance
This study provided a genetic landscape for HDGC. The study’s findings challenged the previously reported high germline alteration rate of CDH1 in HDGC and identified new potential susceptibility genes. Analyses of variant signatures and double-hit events revealed potentially important mechanisms for HDGC tumorigenesis. Findings from the present study may provide helpful information for further investigations of HDGC.
This cohort study uses whole-exome and targeted sequencing to assess the incidence rate of CDH1 germline alterations in hereditary diffuse gastric cancer, provide a genetic landscape for the disease, and identify new susceptibility genes among Chinese patients with hereditary diffuse gastric cancer.
Introduction
Gastric cancer (GC) is the fifth most common cancer and the third most common cause of cancer-related death worldwide.1 The disease is particularly common in East Asia.2 Although most GCs occur sporadically, aggregation within families occurs in approximately 10% of cases.3 It has now been established that 3% to 5% of GCs are associated with inherited GC predisposition syndromes, including hereditary diffuse GC (HDGC), gastric adenocarcinoma and proximal polyposis of the stomach, and familial intestinal GC.4,5,6
Hereditary diffuse GC is thought to be associated with inherited genetic susceptibility. The E-cadherin gene, CDH1, was previously identified as an HDGC susceptibility gene, explaining 25% to 50% of HDGC cases.7,8,9 Carriage of the abnormal CDH1 gene confers a greater than 80% lifetime risk of developing GC.5 Alterations in the α-E-catenin gene, CTNNA1, have been identified as additional genetic factors associated with HDGC.10 In a large HDGC pedigree, a germline truncating allele of CTNNA1 was present in 2 family members with invasive diffuse GC and 4 family members in which intramucosal signet ring cells were detected as part of endoscopic surveillance.10 Furthermore, increased risk of GC is associated with other previously reported HDGC susceptibility genes, including BRCA1, MLH1, MSH2, MSH6, PMS2, and PALB2.11 However, the alteration rate of CDH1 in East Asian patients and the genetic basis underlying susceptibility in the remaining 50% to 75% of patients with HDGC are still unknown.
Advances in sequencing technology have led to pioneering studies profiling the genomic landscape of diffuse-type and non–diffuse-type GCs.12,13,14,15,16,17 The Cancer Genome Atlas (TCGA) project has further provided multinomics-based molecular characterization of gastric adenocarcinoma.14 Roberts et al18 defined the genetic heterogeneity of familial pancreatic cancer through whole-genome sequencing. The diagnosis of HDGC is based on clinical criteria, including age at diagnosis, family history, and Lauren classification,3 but the rationale for including individuals diagnosed with diffuse GC before age 40 years who do not have a family history of the disease deserves further consideration. Thus, characterization of the molecular portrait is needed to provide further understanding of the tumorigenesis of clinically defined HDGC. This cohort study aimed to assess the incidence rates of CDH1 germline alterations in HDGC, identify new HDGC susceptibility genes, and provide a genetic landscape for HDGC.
Methods
This retrospective cohort study was performed in accordance with the Declaration of Helsinki19 and was approved by the ethics committee of Sun Yat-sen University Cancer Center. All participants provided consent to report and publish their individual patient data. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.
We screened the clinical data of 10 431 patients who were diagnosed with GC at Sun Yat-sen University Cancer Center between January 1, 2002, and August 31, 2018 (eFigure 1 in Supplement 1). Data were analyzed from August 1 to 30, 2020. Patients were included if they met at least 1 of 4 criteria for HDGC: (1) a diagnosis of 2 or more cases of GC or 1 confirmed case of diffuse GC in first- or second-degree relatives before age 50 years; (2) 3 or more confirmed cases of diffuse GC among first- or second-degree relatives, independent of age at onset; (3) diagnosis of diffuse GC before age 40 years and without a family history of diffuse GC; or (4) personal or family history of diffuse GC and lobular breast cancer, 1 of which was diagnosed before age 50 years. Overall, 542 of 10 431 patients (5.2%) from Sun Yat-sen University Cancer Center met the criteria for HDGC; of those, 177 patients with insufficient leukocyte samples and 83 patients without detailed follow-up data were excluded. The remaining 282 patients with HDGC were included, and 2 patients with HDGC from The First People’s Hospital of Foshan were added to the sample, for a total of 284 participants.
Whole-exome sequencing was performed on 284 HDGC leukocyte samples and 186 paired tumor samples (eTable 4 in Supplement 2). Because HDGC is a rare disease, the analysis of germline alterations focused on private variants. Private variants were defined as (1) nonsense alterations, splice-site alterations, and frameshift insertions and deletions (indels); (2) variants heterozygous in the germline; (3) variants with less than 0.5% minor allele frequency in the 1000 Genomes Project20 database or the Chinese Millionome Database21; (4) variants present in only 1 patient; (5) variants with a mappability score greater than 0.5; and (6) variants with no additional genomic locus based on queries in the BLAT alignment tool.22 Given that most of the known high-penetrance disease–associated variants are located in the coding regions, only genetic alterations located in these regions were considered.
The MutSigCV algorithm, version 1.41,23 was used to detect significantly altered genes (with significance set at Q < .05). Somatic copy number alterations (SCNAs) were detected using the Control-FREEC algorithm, version 11.1.24 The GISTIC2.0 algorithm25 was used to identify recurrently amplified or deleted genomic regions in the HDGC cohort. G scores were calculated for sequencing regions based on the frequency and amplitude of amplification or deletions in each gene. A double-hit event was defined as a heterozygous germline alteration that became homozygous and may expand the impact of germline alterations and potentially be associated with tumorigenesis. Additional details on target capture sequencing; detection of somatic single-nucleotide variants (SSNVs), indels, and SCNAs; double-hit event analysis; and drug target analysis are provided in the eMethods in Supplement 1.
Statistical Analysis
For comparison between categorical variables, 2-sided P values were calculated using a Fisher exact or χ2 test. For multiple testing corrections, the false discovery rate was calculated using the Benjamini-Hochberg procedure. Survival function estimation was performed using Kaplan-Meier estimates and a log-rank test. All statistical analyses were performed using R software, version 3.5.0 (R Foundation for Statistical Computing). Statistical significance was set at 2-sided P < .05.
Results
Among 284 Chinese patients, 161 (56.7%) were female, and the median age was 35 (range, 20-75) years. Most patients had stage III or stage IV GC (190 patients [66.9%]) and were diagnosed with diffuse GC before age 40 years, with no family history of diffuse GC (254 patients [89.4%]). Median follow-up was 21.7 (range, 0.6-185.9) months. Only 8 patients (2.8%) had positive results for Epstein-Barr virus infection, which was lower than previously reported infection rates among patients with GC.26 The 5-year survival rate was 61.4% (95% CI, 53.2%-68.6%), which was higher than the survival rate of 44.1% reported in a previous study of patients with sporadic diffuse GC.27 Additional clinicopathological characteristics of enrolled patients are shown in the Table, and further details about the cohort and exome sequencing of patient samples are provided in the eResults in Supplement 1.
Table. Clinicopathological Characteristics of Patients With Hereditary Diffuse Gastric Cancer Included in Sequenced Blood or Tumor Sample Analysis.
| Characteristic | Patients, No. (%) | |
|---|---|---|
| Blood sample analysis (n = 284) | Tumor sample analysis (n = 186) | |
| Age, y | ||
| <40 | 267 (94.0) | 178 (95.7) |
| ≥40 | 17 (6.0) | 8 (4.3) |
| Sex | ||
| Male | 123 (43.3) | 82 (44.1) |
| Female | 161 (56.7) | 104 (55.9) |
| Family history of diffuse GC | ||
| Yes | 30 (10.6) | 15 (8.1) |
| No | 254 (89.4) | 171 (91.9) |
| Definition of HDGC | ||
| Condition 1a | 23 (8.1) | 13 (7.0) |
| Condition 2b | 7 (2.5) | 2 (1.1) |
| Condition 3c | 254 (89.4) | 171 (91.9) |
| Condition 4d | 0 | 0 |
| AJCC tumor categorye | ||
| T1 or T2 | 65 (22.9) | 39 (21.0) |
| T3 or T4 | 188 (66.2) | 146 (78.5) |
| Unknown | 31 (10.9) | 1 (0.5) |
| AJCC node categorye | ||
| N0 or N1 | 91 (32.0) | 55 (29.6) |
| N2 or N3 | 162 (57.0) | 130 (69.9) |
| Unknown | 31 (10.9) | 1 (0.5) |
| AJCC metastasis categorye | ||
| M0 | 201 (70.8) | 146 (78.5) |
| M1 | 83 (29.2) | 40 (21.5) |
| AJCC cancer stagee | ||
| I or II | 94 (33.1) | 57 (30.6) |
| III or IV | 190 (66.9) | 129 (69.4) |
| Differentiation status | ||
| Moderate | 14 (4.9) | 8 (4.3) |
| Poor | 264 (93.0) | 172 (92.5) |
| Unknown | 6 (2.1) | 6 (3.2) |
| EBV status | ||
| Positive | 8 (2.8) | 5 (2.7) |
| Negative | 209 (73.6) | 148 (79.6) |
| Unknown | 63 (22.2) | 33 (17.7) |
| ERBB2 (formerly HER2 or HER2/neu) status | ||
| Positive | 6 (2.1) | 3 (1.6) |
| Negative | 215 (75.7) | 155 (83.3) |
| Unknown | 63 (22.2) | 28 (15.1) |
| Relapse | ||
| Yes | 46 (16.2) | 32 (17.2) |
| No | 178 (62.7) | 128 (68.8) |
| Unknown | 60 (21.1) | 26 (14.0) |
Abbreviations: AJCC, American Joint Committee on Cancer; EBV, Epstein-Barr virus; GC, gastric cancer; HDGC, hereditary diffuse gastric cancer.
Condition 1 was defined as 2 or more cases of GC and 1 confirmed case of diffuse GC before age 50 years.
Condition 2 was defined as 3 or more cases of confirmed diffuse GC in first-degree or second-degree relatives, independent of age at onset.
Condition 3 was defined as diffuse GC before age 40 years without a family history of diffuse GC.
Condition 4 was defined as a personal or family history of diffuse GC and lobular breast cancer, one of which was diagnosed before age 50 years.
Based on the AJCC Cancer Staging Manual, Seventh edition.
Germline Variant Profiling
Based on the identified private germline variants (Figure 1), we examined genes potentially associated with susceptibility to HDGC (variant validation is shown in eTable 5 in Supplement 2). A previous study reported that 40% of HDGC cases had CDH1 germline alterations.28 However, we detected CDH1 germline missense alterations and indels in only 8 of 284 HDGC cases (2.8%) (Figure 2A), and all of the 6 missense alterations and 2 indels had not been previously reported. Even in patients with a family history of diffuse GC in this HDGC cohort, the frequency of CDH1 germline alterations was still low (3.3%). The low frequency of CDH1 germline variants suggested different genetic backgrounds and HDGC carcinogenesis mechanisms in East Asian populations. Furthermore, previous studies proposed that CTNNA1, BRCA2, STK11, SDHB, PRSS1, ATM, MSR1, PALB2, BRCA1, and RAD51C may be associated with susceptibility to non–CDH1-variant HDGC.28,29 In our study, germline single-nucleotide polymorphisms and indels of CTNNA1 were observed in 2 HDGC cases, BRCA2 in 12 cases, STK11 in 2 cases, PRSS1 in 2 cases, ATM in 5 cases, MSR1 in 4 cases, PALB2 in 6 cases, BRCA1 in 6 cases, and RAD51C in 1 case. No alteration was found in SDHB. In addition, the observed germline single-nucleotide polymorphisms and indels in these genes were different from those reported in previous studies.28,29 The genes with the highest incidence rates (>10%) of private germline variants in the HDGC cohort were MUC4 (19%), ABCA13 (10%), ZNF469 (10%), FCGBP (10%), IGFN1 (10%), RNF213 (10%), and SSPO (10%) (Figure 1); these alterations had not been previously reported. Previously reported alterations and indels of CTNNA1, BRCA2, STK11, PRSS1, ATM, MSR1, PALB2, BRCA1, and RAD51C were observed at low frequencies (median, 4 [range, 1-12] cases). As the most frequently altered gene, private germline variants of MUC4 were observed in 53 of 284 patients (18.7%) with HDGC (Figure 2B). These genes were novel candidates and revealed a different spectrum of germline alterations for HDGC.
Figure 1. Germline Alteration Landscape of Hereditary Diffuse Gastric Cancer.
Oncoplot showing the germline alteration landscape among 284 Chinese patients with hereditary diffuse gastric cancer. The top panel shows the total of all nonsilent germline alterations for each sample. The middle panel shows the alteration details; only genes with alteration frequencies of 8% or greater are shown. The bottom panel shows clinical information. Cancer staging was based on the AJCC Cancer Staging Manual, 7th edition. EBER indicates Epstein-Barr virus–encoded small RNAs.
Figure 2. Germline Alterations of CDH1 and MUC4.
Because the functional significance of most missense variants is unknown, we focused our analysis on premature truncating variants (PTVs) of private variants; these PTVs included frameshift indels, nonsense alterations, and splice-site alterations according to findings of a previous study.18 In total, 2715 private heterozygous PTVs in 2278 genes were identified. The most altered gene, MUC4, had 7 PTVs, whereas WDR87, CRIPAK, DNAH7, EFCAB13, and ITGAV each had 4 PTVs (eFigure 2 in Supplement 1). Previous studies30,31 proposed that MUC4 may induce oncogenic transformation of fibroblast cells in mice30 and that overexpression of MUC4 might promote aggressive properties via ERBB2 (formerly HER2 or HER2/neu) signaling in gastric adenocarcinoma.31 Alterations of ITGAV, which is the regulator of PI3K signaling, were also previously observed in GC tumor tissues.32 Thus, although the functional consequences for most of the private PTVs were unknown, part of the identified private PTVs have been found to be involved in the development of GC, and these private PTVs might be associated with susceptibility to HDGC and serve as founder alterations.
Somatic Variant Analysis
In addition to germline alterations, we profiled the somatic variants for the 186 paired HDGC samples (variant validation is shown in eTable 5 in Supplement 2). The most altered gene was TP53, with a frequency of 32.3% (60 of 186 samples) (Figure 3). Of note, although the frequency of germline variants was low in our data set, the frequency of CDH1 somatic alterations was as high as 25.3% (47 of 186 samples). Because only 5 CDH1 somatic alterations were found in paired peripheral blood, with only 1 supporting read for each, the potential for constitutional mosaicism, which is a well-known mechanism for multiple hereditary cancer–associated genes, was not supported in our data set. In addition to genes with significant alterations and indels, including TP53 (Q < 2.2 × 1016), CDH1 (Q < 2.2 × 1016), and ARID1A (Q = 8.7 × 108; 29 of 186 samples [15.6%] with alterations) (eFigure 4 in Supplement 1), which were previously identified in a TCGA cohort of patients with stomach adenocarcinoma (STAD) (11% frequency),12 the MutSigCV algorithm identified several SMG genes, including HRCT1, KRTAP5-4, RAB21, PIGR, FAM136A, TGFBR2, BAP1, and ELF3, based on our data set (Figure 4A; eTable 1 in Supplement 2).
Figure 3. Somatic Alteration Landscape of Hereditary Diffuse Gastric Cancer.
Oncoplot showing the somatic alteration landscape among 186 Chinese patients with hereditary diffuse gastric cancer. The top panel shows the total of all nonsilent somatic alterations for each sample. The middle panel shows the alteration details; only the 24 most altered genes with alteration frequencies of 7% or greater are shown, with red font indicating genes with significant alterations. The bottom panel shows clinical information. Cancer staging was based on the AJCC Cancer Staging Manual, 7th edition. EBER indicates Epstein-Barr virus–encoded small RNAs.
Figure 4. Significantly Altered Genes and Somatic Copy Number Variations.
A, Significantly altered genes (Q < .05) were detected using the MutSigCV algorithm. The low Q values were limited to a fixed value because of the processing precision limit. B, The G scores of genomic regions were plotted along the genome, with orange spikes representing significant copy number gains and dark gray spikes representing significant copy number losses. Light gray spikes indicate insignificant gain and loss. Key genes associated with gastric cancer are shown in red type, and cancer census genes (from the Catalogue of Somatic Mutations in Cancer signature database) associated with other tumor types are shown in green type.
Furthermore, we compared the alteration burden between HDGC and sporadic diffuse STAD (D-STAD) in the TCGA and found that the alteration burden of HDGC was comparable with that of D-STAD (eFigure 3A in Supplement 1); HDGC and D-STAD had similar gene alteration frequencies (R = 0.24; P < 2.2 × 1016) (eFigure 3B in Supplement 1). We found several highly altered genes in D-STAD that had significantly lower alteration frequencies in HDGC (eFigure 3B-C in Supplement 1). For example, the alteration frequency of FAT4 was 22.9% in D-STAD but only 4.3% in HDGC (eFigure 3B in Supplement 1). In addition, the alteration frequency of MLLT4 was 10.0% in D-STAD but 0% in HDGC (eFigure 3C in Supplement 1). Although HDGC had copy number variation frequencies that were relatively similar to those of D-STAD in different genomic regions (R = 0.37; P < 2.2 × 1016) (eFigure 3D in Supplement 1), copy number variation frequencies in many parts were significantly different, including several differences in chromosome arm-level regions (eg, more deletions of chr16p, chr17, chr19, chr20, and chr22) and many focal regions (eg, more amplification frequency in chr6p21.32-33 and chr6p22.1-2 and more deletion frequency in chr7p11, the starts of chromosomes 1 and 7, and the ends of chromosomes 8, 9, 10, and 12) (eFigure 3E in Supplement 1).
With the help of the sequencing coverage information from whole-exome sequencing, the SCNAs in HDGC were also evaluated. The chromosome-level SCNA (Figure 4B) calculated by the GISTIC2.0 algorithm suggested that a large number of SCNA events occurred in HDGC tumor tissues. Genome regions such as 6p22.2 and 19p13.2 were recurrently amplified, whereas regions such as 1q21.3 and 10q21.3 were recurrently deleted. Results of further gene-level analysis revealed that the SCNAs of known cancer-related genes, such as PTK6 (44.6%), ERBB3 (13.4%), PIK3CA (11.8%), and UBR5 (11.8%), were relatively frequent (eFigure 4 in Supplement 1). A previous study identified the metastasis-promoting roles of PTK6,33 and Jha et al34 proposed that PTK6 was under positive selection and correlated with Helicobacter pylori invasion; the finding that both amplification and deletion of PTK6 were observed in the tumor was controversial (eFigure 4 in Supplement 1). Well-known oncogenes, ERBB3 and PIK3CA, were reported to be involved in GC,35,36 and highly frequent amplification of these genes was observed in the cohort (eFigure 4 in Supplement 1). As an E3 ligase that regulates protein ubiquitination and degradation, UBR5 has been reported to destabilize the tumor suppressor GKN in GC,37 and the frequent amplification of UBR5 found in the present study further suggested its potential oncogenic role (eFigure 4 in Supplement 1). Although the Control-FREEC and GISTIC2.0 algorithms have been widely used and reported to be reliable,38,39,40 additional methods and further studies are needed to validate the SCNA results and reveal the mechanisms.
Signaling pathways, including PI3K-Akt, MAPK, cell cycle, Wnt, and TGF-β, were previously identified as important in the carcinogenesis and development of GC.41 In this study, we identified the somatic variant frequencies of these pathways for both the HDGC cohort (eFigure 5 in Supplement 1) and the TCGA STAD cohort (eFigure 6 in Supplement 1), which revealed distinct profiles. Generally, the somatic variant frequencies of these pathways in the TCGA cohort were substantially higher than those of the HDGC cohort. For example, the alteration rates for PIK3CA, KRAS, SMAD4, MYC, and APC in the TCGA STAD cohort were higher than alteration rates in the HDGC cohort (eFigure 5A-C and eFigure 6A-C in Supplement 1). The alteration rates in the cell cycle pathway were both substantially lower in the TCGA STAD cohort than the HDGC cohort (eFigure 5D and eFigure 6D in Supplement 1). Furthermore, the frequencies of SCNAs in the HDGC cohort were higher than those in the TCGA STAD cohort. Taken together, the pathways important for sporadic GC might have a smaller role in the carcinogenesis of HDGC.
Etiologic Analysis
To investigate the etiology of HDGC, variant signature analysis was performed and correlated with the Catalogue of Somatic Mutations in Cancer (COSMIC) signature database (eMethods in Supplement 1). The results revealed that signature 1 (etiology: spontaneous deamination of 5-methylcytosine), signature 5 (etiology: unknown), and signature 24 (etiology: exposure to aflatoxin) were enriched in the cohort (eFigure 7A-B in Supplement 1), presenting a different alteration spectrum from the TCGA STAD cohort. Furthermore, the enrichment of signature 24 was consistent when the number of clusters was 4 (eFigure 7C in Supplement 1) or 5 (eFigure 7D in Supplement 1). The distribution of the 3 variant signatures among samples is presented in eFigure 7E in Supplement 1. Signature 24 was not detected in the TCGA STAD cohort but was found as a dominant signature in 11 of 186 samples (5.9%) from patients with HDGC in our study (eFigure 7F-G in Supplement 1). Patients with signature 24 predominance were enriched in the Epstein-Barr virus–positive subgroup (2 of 3 patients vs 8 of 69 patients in the virus-negative subgroup; Fisher P = .04) (eFigure 7H in Supplement 1). Furthermore, signature 24 was important for alterations in genes, including TP53, WDR87, PCDH9, SP8, CNTN4, PIK3CA, and TMEM132C (eFigure 7I in the Supplement), which might be associated with exposure to aflatoxin. These results suggested that environmental factors may have a large role in HDGC, and there might be substantial interaction between genetics and the environment.
To further elucidate the tumorigenesis mechanism, we analyzed double-hit events in HDGC. A total of 406 potential double-hit events were identified in 396 genes from 66 patients (mean, 2 [range, 0-46] events) (eTable 2 in Supplement 2). The detected count of double-hit events was associated with nonsilent germline variant count, loss of heterozygosity event count, and loss of heterozygosity region size (eFigure 8A-C in Supplement 1). In total, 35 of 66 cases (53.0%) had only 1 or 2 double-hit events, whereas there were more than 20 double-hit events from 5 cases. Only 1 double-hit event was observed in 386 genes, whereas there were 2 double-hit events for the other 10 genes. For example, 1 patient had the most double-hit events (46 events), among which CACNA1D was observed as a p.I264V alteration, and another patient had 2 double-hit events, among which CACNA1D was observed as a p.R2101Q alteration (eFigure 8D-G in Supplement 1). The double-hit events detected in another 14 COSMIC cancer census genes from patients with HDGC are shown in eFigure 9 in Supplement 1. These genes had only 1 double-hit event, suggesting insufficient evidence to support their potential roles as tumorigenesis mechanisms. Although further studies are needed to clarify the functions and mechanisms of the double-hit events, these results suggested that double-hit events might be important mechanisms in HDGC.
Clinical Outcome Alterations and Actionable Variants
Beyond oncogenesis and cancer development, we examined clinical outcome–related variants in HDGC. The results revealed that nonsilent germline alterations in 7 genes, including SDK1, HSPG2, FSIP2, CUBN, NCKAP5, FLNB, and MUC16, were significantly associated with overall survival (OS) (eFigure 10A in Supplement 1). Among these genes, MUC16 (eFigure 10B in Supplement 1) is a known oncogene annotated in the COSMIC database, and multivariate Cox proportional hazzards regression analysis revealed that the germline alterations of NCKAP5 (eFigure 10C in Supplement 1), HSPG2 (eFigure 10D in Supplement 1), and FSIP2 (eFigure 10E in Supplement 1) were independent factors associated with clinical outcomes of patients with HDGC (eFigure 10F-H in Supplement 1). Furthermore, somatic alterations (including nonsilent alterations and SCNAs) in a large number of genes were found to be associated with OS (eFigure 11A in Supplement 1). Among them, COSMIC cancer census genes, including FGFR3 (hazard ratio [HR], 2.2; 95% CI, 1.2-4.2), ASPSCR1 (HR, 2.2; 95% CI, 1.2-4.1), CIC (HR, 2.4; 95% CI, 1.2-4.7), DGCR8 (HR, 2.2; 95% CI, 1.1-4.5), and LZTR1 (HR, 2.5; 95% CI, 1.2-5.3), were altered in high frequencies (>10%), and their alterations were independently associated with worse OS (eFigure 11B-F in Supplement 1). Because FGFR3 is a well-known cancer-related gene, we further compared its alteration profile between the HDGC and TCGA STAD cohorts. We found that the alteration frequencies, especially the deletion frequencies, were substantially higher among those in the HDGC cohort (25.8%) vs the TCGA STAD cohort (1.4%) (eFigure 11G in Supplement 1).
Furthermore, pathway analyses found that the genes with somatic alterations that were associated with poor OS enriched interferon-related pathways, including interferon signaling, interferon α and β signaling, regulation of interferon α signaling, DDX58- and IFIH1-mediated induction of interferon α and β, antiviral response–related pathway of TRAF6-mediated IRF7 activation, and factors involved in megakaryocyte development and platelet production (eFigure 11H in Supplement 1). The genes with alterations that were associated with better OS enriched tyrosine kinase–regulated pathways, including signaling by nonreceptor tyrosine kinases, signaling by PTK6, downregulation of ERBB2 and ERBB3 signaling, ERBB2-activating PTK6 signaling, the canonical retinoid cycle in rods (twilight vision), and IKK complex recruitment mediated by RIP1 (eFigure 11I in Supplement 1). These results suggest that aberrant interferon signaling and PTK6 signaling might play important roles in the progression of HDGC.
In addition to associations with OS, the potential clinical actionability of the somatic variants was assessed according to the ESMO Scale of Clinical Actionability for Molecular Targets by integrating data from the OncoKB database,42 the Cancer Genome Interpreter,43 and the CIViC (Clinical Interpretation of Variants in Cancer) database.44 In total, 263 of 21 345 SSNVs (1.2%) from 125 patients with HDGC and 338 of 469 582 SCNAs (0.07%) from 153 patients with HDGC were annotated as actionable (eTable 6 in Supplement 2). The annotation results are shown in eTable 3 in Supplement 2 and eFigure 12A-B in Supplement 1. These findings suggested that actionable variants were rare for both SSNVs and SCNAs, and the distributions of evidence levels were different. According to the DrugCVar system,45 the evidence levels for most of the actionable SSNVs (203 of 263 [77.2%]) were higher than tier 4, whereas most of the actionable SCNAs (239 of 338 [70.7%]) were tier 4. These results might be helpful for the further development of therapies for patients with HDGC.
Discussion
In this cohort study, we retrospectively screened more than 10 000 patients with GC over the last 16 years and established a cohort of 284 patients with HDGC, finding that 542 of 10 431 patients (5.2%) with GC met the criteria for HDGC, which was consistent with a previous estimation.3 Most patients in the HDGC cohort (89.4%) had no family history of diffuse GC, and they were included according to the criterion of a diffuse GC diagnosis before age 40 years. Although a previous study found that young patients, especially those younger than 35 years, had a poor prognosis,46 the present study found a higher 5-year OS rate in patients with HDGC (61.4%) than in patients with sporadic diffuse GC (44.1%), which suggested different biological behaviors between HDGC and sporadic diffuse GC.
In this study, the profiled genetic landscape for the HDGC cohort challenged previous observations that CDH1 germline alteration is present in 25% to 50% of families with HDGC28; those incidence rates were not applicable to Chinese patients with HDGC in the current study. Even in patients with a family history of diffuse GC in this HDGC cohort, the frequency of CDH1 germline alterations was still low (3.3%). It was previously reported that CDH1 alterations were significantly associated with poor OS,47 which was consistent with the findings of our study. In addition, no highly common germline alteration was observed in this cohort, which suggested that the genetic factors associated with HDGC need to be further investigated.
In addition to the gene alterations potentially associated with carcinogenesis, we identified OS-associated gene alterations in HDGC, several of which were previously reported. For example, Li et al48 reported an association between MUC16 alteration and outcomes in patients with GC, another study12 found that the CIC gene was highly altered among patients in a TCGA STAD cohort, and Yang et al49 found that the alteration frequencies of MUC16 and FSIP2 were high in East Asian patients with GC. Furthermore, we assessed the potential clinical actionability of the somatic variants. Based on analysis of SCNAs, drugs targeting amplification of mTOR, CDK4 and CDK6, fibroblast growth factor receptor, MET, and ERBB2 were most common. Somatic single-nucleotide variants as drug candidates included variants of ARID1A, PI3KCA, SMARCA4, EGFR, and BAP. Until now, only drugs targeting ERBB2 and VEGF have been approved for the treatment of patients with metastatic GC, and the results have been unsatisfactory. The present study provides information that could be helpful for future clinical trials of drugs targeting gene alterations in patients with HDGC.
Limitations
This study has several limitations. First, the study is retrospective and included Chinese patients from only 2 sites. There are likely to be differences in the genetic backgrounds of East Asian vs Western populations. Further international multicenter prospective studies will be helpful to better understand the genomic features of HDGC. Second, 94.0% of patients in this study were younger than 40 years and did not have a family history of diffuse GC, which may explain the low alteration rate of CDH1. Additional studies including patients with different conditions, especially those with a family history or personal history of lobular breast cancer, are warranted because patients with different conditions were not included in the present cohort. Third, the incidence rate of actionable altered genes was low, which may limit the application of this study’s findings, and the diverse chemotherapy treatments received by patients with HDGC in this study made it difficult to control for factors to assess chemotherapy prognosis-related events, such as homologous recombination repair gene alterations.
Conclusions
This cohort study provides a genetic landscape for HDGC that challenges the previously reported high germline alteration rate of CDH1 in HDGC and identified new potential susceptibility genes. Analyses of variant signatures and double-hit events revealed important mechanisms for HDGC tumorigenesis. To our knowledge, the present study is the first to provide such a genetic picture for HDGC, which may be helpful for the development of diagnostic assessments and therapies for patients with GC.
eMethods. Patient Enrollment, Whole-Exome Sequencing of HDGC Samples, Target Capture Sequencing, SNV/INDEL and Somatic Copy Number Alterations, Double-Hit Event Analysis of HDGC, Drug Target Analysis, and Statistical Analyses
eResults. HDGC Cohort and Exome Sequencing
eFigure 1. Flowchart Illustrating Filter-Based Strategy to Identify Novel Candidate HDGC Susceptibility Genes at Both Germline and Somatic Levels
eFigure 2. Truncating Germline Alteration Landscape of HDGC
eFigure 3. Comparison Between HDGC and TCGA Data
eFigure 4. Oncoplot Illustrating Somatic Alterations and Copy Number Alterations of the COSMIC Cancer Gene Census
eFigure 5. Variant Summary Diagram of Gastric Cancer–Related Pathways in HDGC
eFigure 6. Variant Summary Diagram of Gastric Cancer–Related Pathways in TCGA Stomach Cancer Cohort
eFigure 7. Somatic Alteration Signatures of HDGC
eFigure 8. Correlation Analyses and Examples of Double-Hit Events
eFigure 9. Double-Hit Events Detected in COSMIC Cancer Census Genes From Patients With HDGC
eFigure 10. Clinical-Associated Variants in HDGC
eFigure 11. Clinical-Associated Somatic Alterations (Nonsilent Alterations and CNAs) in HDGC
eFigure 12. Summary of Actionable Variants of HDGC
eReferences
eTable 1. Results of MutSigCV Analysis of Somatic Alterations
eTable 2. Double-Hit Events in HDGC
eTable 3. Actionable Somatic SNVs in HDGC Cohort
eTable 4. Complete List of Whole-Exome Sequencing Data With Detailed Information
eTable 5. Variant Validation Results by Target Sequencing
eTable 6. Mapping Details Among ESCAT, OncoKB, CIViC, and CGI
Data Sharing Statement
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eMethods. Patient Enrollment, Whole-Exome Sequencing of HDGC Samples, Target Capture Sequencing, SNV/INDEL and Somatic Copy Number Alterations, Double-Hit Event Analysis of HDGC, Drug Target Analysis, and Statistical Analyses
eResults. HDGC Cohort and Exome Sequencing
eFigure 1. Flowchart Illustrating Filter-Based Strategy to Identify Novel Candidate HDGC Susceptibility Genes at Both Germline and Somatic Levels
eFigure 2. Truncating Germline Alteration Landscape of HDGC
eFigure 3. Comparison Between HDGC and TCGA Data
eFigure 4. Oncoplot Illustrating Somatic Alterations and Copy Number Alterations of the COSMIC Cancer Gene Census
eFigure 5. Variant Summary Diagram of Gastric Cancer–Related Pathways in HDGC
eFigure 6. Variant Summary Diagram of Gastric Cancer–Related Pathways in TCGA Stomach Cancer Cohort
eFigure 7. Somatic Alteration Signatures of HDGC
eFigure 8. Correlation Analyses and Examples of Double-Hit Events
eFigure 9. Double-Hit Events Detected in COSMIC Cancer Census Genes From Patients With HDGC
eFigure 10. Clinical-Associated Variants in HDGC
eFigure 11. Clinical-Associated Somatic Alterations (Nonsilent Alterations and CNAs) in HDGC
eFigure 12. Summary of Actionable Variants of HDGC
eReferences
eTable 1. Results of MutSigCV Analysis of Somatic Alterations
eTable 2. Double-Hit Events in HDGC
eTable 3. Actionable Somatic SNVs in HDGC Cohort
eTable 4. Complete List of Whole-Exome Sequencing Data With Detailed Information
eTable 5. Variant Validation Results by Target Sequencing
eTable 6. Mapping Details Among ESCAT, OncoKB, CIViC, and CGI
Data Sharing Statement