Whole genome sequencing identifies rare genetic variants in familial pancreatic cancer patients

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive and fatal malignancies worldwide with an estimated 5-year survival of just 5% (Naghavi et al., 2019; McGuigan et al., 2018). Familial pancreatic cancer (FPC) is defined as having two or more first-degree relatives (FDRs) with PDAC without known inherited cancer syndrome, and is responsible for up to 10% of all cases of PDAC (Diaz & Lucas, 2019). Families fulfilling the FPC criteria represent up to 80% of all families with PDAC aggregation (Llach et al., 2020). We have recently estimated the heritability of FPC as high as 0.51 (Tan et al., 2021a). The estimated relatively high genetic contribution to FPC calls for efforts to find the genetic variants underlying the genetic predisposition to FPC. Studies using next-generation sequencing (NGS) technique have detected rare sequence variations in BRCA1, BRCA2, CDKN2A, PALB2, and ATM genes to be related to FPC (Roberts et al., 2016; Zhen et al., 2015). However, those variants are only observed in about 12% of all FPC cases. The suspected germline contribution to over 80% of all FPC cases still remains unknown (Chaffee et al., 2018).

Whole genome sequencing (WGS) can be used to explore genomic alterations in cancer and help us to better understand the whole landscape of mutational signatures in the cancer genomes and to elucidate their functional or clinical implications (Nakagawa & Fujita, 2018). Using WGS analysis, Roberts et al. (2016) demonstrated that the genetic architecture of FPC is highly heterogeneous. Genetic heterogeneity refers to: (1) allelic heterogeneity, where different variants at a single gene locus cause the same or similar phenotypic expressions of a disease and (2) locus heterogeneity, where variants at different gene loci cause the same or similar phenotypes of a disease (McClellan & King, 2010). The genetic heterogeneity of FPC means that susceptibility variants could be private to certain individuals or families. The situation renders the traditional association analysis for common variants underpowered. Both allelic and locus heterogeneity impose challenges in identifying the relevant genetic variants for FPC. More high-coverage sequencing analyses are required to uncover the genetic diversity in FPC.

We have performed a WGS on PDAC patients from 27 FPC predisposed families from a recently established national cohort—with a focus on detecting rare genetic variants for the disease. We report findings from the WGS study and compare with published results from previous studies to validate and verify the detected genetic alterations as potential hotspots of functional variations for FPC.

In total, benign FFPE nonpancreatic tissue samples were available from 35 FPC patients (14 males, 21 females) for DNA extraction and sequencing analysis (Table 1, Table S1). The median age of FPC patients at diagnosis was 61.9 years (range: 33.5–86.9 years); the median age at death was 62.3 years (range: 35.4–87.2 years). The minimum and maximum survival times were 11 and 3696 days with a median of 241 days (Table 1). Two patients were censored at 2148 days (female) and 3696 days (male) respectively after diagnosis. No sex difference was found for age at death (p = 0.52), nor for time from diagnosis to death (p = 0.83) in the patient samples.

In contrast to common variants identified in genome-wide association studies, rare variants revealed by sequencing analysis have played unique roles in the genetics of complex diseases in humans due to their distinctive features. Their unique roles are hypothesis-free evidence for gene causality, serving as precise targets of functional analysis for understanding disease mechanisms as well as genetic markers for personalized medicine (Momozawa & Mizukami, 2021). By performing whole genome sequencing on benign, noncancerous tissues of FPC patients, we were able to focus on rare germline variants, aiming to characterize the hereditary basis of FPC. Our finding of a large number of genes hosting loss-of-function variants (PTVs, Table S2) and genes enriched by multiple PTVs (Table 2) revealed a high genetic heterogeneity in the form of both locus and allelic heterogeneity (16). The genetic diversity of FPC patients was observed not only across samples in our cohort, but also within individual FPC patients showing intraindividual allelic heterogeneity (multiple PTVs of the same gene in the same patient) and locus heterogeneity (multiple PTV genes in the same patient). High genetic heterogeneity of FPC patients has been reported by previous sequencing studies in cohorts from the United States and Germany (Roberts et al., 2016; Slater et al., 2021). Our results provide new evidence reconfirming FPC as a genetically heterogeneous disease associated with rare germline variants.

The high diversity in the genetic architecture of FPC imposes a challenge to current strategies for screening predisposed individuals. Traditional genetic screening is based on analyzing classical high penetrance genes that only explain the genetic predisposition in a reduced number of families. It is estimated that the currently identified variants in FPC susceptibility genes including BRCA1/2, ATM, CDKN2A, PALB2, etc. explain less than 20% of FPC cases leaving the genetic basis of more than 80% of FPC patients unknown (Roberts et al., 2016). A very recent WGS study on FPC patients failed to detect pathogenic variants in BRCA1/2, CDKN2A, or PALB2 (Slater et al., 2021). Likewise, association testing of our recent WGS on first-degree relatives of FPC patients did not find rare variants in any of the reported FPC candidate genes (Tan et al., 2021b). Meanwhile, a comprehensive analysis of 35 candidate genes associated with hereditary cancer revealed that variants in previously described cancer-predisposition genes including MLH1, CDKN2A, POLQ, TET2, and FANCM are found in 19% of FPC cases (Earl et al., 2020). In a recent genome-wide meta-analysis, NOC2L was also suggested as a pancreatic cancer susceptibility gene (Klein et al., 2018). The situation implies that traditional genetic testing based on classical high penetrance genes will most likely miss the majority of genetically predisposed individuals to FPC. More individualized testing strategies such as the NGS-based panel testing (Nagahashi et al., 2019) that take genetic heterogeneity into account are called for.

The extracellular matrix (ECM) is a major structural component of the tumor microenvironment that provides both structural and biochemical support to regulate proliferation, self-renewal, and differentiation of cancer stem cells (Nallanthighal et al., 2019). Among the 20 gene sets in Table 3, seven are related to ECM involving organization, assembly, remodeling, degradation, and coding of ECM-associated proteins. PDAC has an extraordinarily dense fibrotic stroma primarily made of ECM whose stiffness confers mechanical properties of the tumor microenvironment and provides important biochemical and physical cues that promote survival, proliferation, and metastasis of cancer cells (Weniger et al., 2018). Nonsense and frameshift variants like PTVs and nonsynonymous variants that change the sequence and structure of coding proteins reduce the production of ECM proteins to impair matrix integrity, composition, and assembly due to quantitative ECM defects (Lamandé & Bateman, 2020). In a recent WGS study, we have observed a significant enrichment of the ECM pathway by genes carrying rare nonsynonymous variants in first-degree relatives of FPC patients (Tan et al., 2021b). Moreover, a recent network-based analysis of gene expression data on FPC and sporadic pancreatic cancer patients reported increased activity in extracellular structure and ECM organization (Tan et al., 2020). Our previous and current results from pathway analysis concerning ECM may help functionally characterizing the identified rare variants in ECM composition, assembly, and degradation as accomplices in the development and progression of FPC.

Guanosine triphosphate (GTP) is one of the building blocks needed for the synthesis of RNA during the transcription process. It is also used as a source of energy for protein synthesis and gluconeogenesis or has the role of an activator of substrates in metabolic reactions. Its binding proteins (Rho GTPases) play central roles in numerous cellular processes with dysregulation of Rho GTPase signaling observed in a broad range of human cancers (Jung et al., 2020). Although large scale sequencing efforts have revealed that variants in the Rho GTPase family are rare (Pajic et al., 2015), our results showed significant overrepresentation of PTV genes in the Rho GTPase pathways (Table 3) involving genes such as ROCK1 (Rho Associated Coiled-Coil Containing Protein Kinase 1) (Figure S2). In a study by Nakashima et al. (2011), a suppressive role of ROCK in pancreatic cancer cell proliferation was characterized. The PTV we observed in ROCK1 is a frameshift variant (chr18:18566913, C:-) that may result in a complete loss of protein structure and functionality, with the latter potentially beneficial for FPC development and progression. This observation serves as an example of support to Nakashima et al. (2011).

Extremely significant overlaps of our detected PTV genes have been found in cancer driver genes and previously reported cancer genes. Among the overlapping genes, the roles of ATM, BRCA2 in hereditary breast and ovarian cancers have been well characterized (Kobayashi et al., 2013). Variants in BRCA and ATM genes occur in both hereditary and sporadic PDAC causing deficiency in DNA repair pathways and provoke genomic instability (Perkhofer et al., 2021). In a recent study of 130 families with 2,227 family members with FPC predisposition, it was shown that individuals with an ATM variant had a cumulative risk for pancreatic cancer of 6.3% by age 70 and 9.5% by age 80 (Hsu et al., 2021). Both ATM and BRCA2 variants have previously been identified as inherited germline variants related to pancreatic cancer (Hu et al., 2018; Huang et al., 2018; Zhen et al., 2015).

Other overlapping genes that we found include POLE, TYRO3, PABPC1, and SSC5D. Variants in POLE have been found in individuals with early onset colorectal cancer, large numbers of adenomatous colorectal polyps and/or significant family history of colorectal cancer (Bellido et al., 2016). Some families with POLE variants include individuals with a wide range of cancers including pancreatic cancer (Hansen et al., 2015; Mur et al., 2020). TYRO3 is constitutively expressed in pancreatic cancer cells and is required for cell proliferation and invasion of pancreatic cancer (Morimoto et al., 2020). PABPC1 (Poly(A) Binding Protein Cytoplasmic 1) encodes PABP1 protein which binds mRNA and facilitates a variety of functions such as transport into and out of the nucleus, degradation, translation, and stability. A recent whole exome sequencing study reported that sequence variations in PABPC1 are associated with familial prostate cancer (Schaid et al., 2021). The SSC5D gene codes for soluble scavenger receptor cysteinerich domain-containing protein (SSC5D), which binds to extracellular matrix proteins as a pattern recognition receptor and may play a role in the innate defense and homeostasis of certain epithelial surfaces. Al-Sukhni et al. (2012) reported gain in DNA copy number in SSC5D gene region in familial pancreatic cancer. The RICTOR gene has recently been shown to be amplified in cancer, highlighting its role in cancer development and its potential as a therapeutic target (Jebali & Dumaz, 2018). Overall, previously published studies show that variations in the overlapping PTV genes have been associated with cancer development or directly with pancreatic cancer or FPC.

In addition to cancer related genes, our identified PTV genes also overlap significantly with genes reported by previously published WGS studies on FPC or FPC families. For example, enrichment of PTVs was also observed in BRCA2 and ATM in a WGS on FPC patients (Roberts et al., 2016). Such nonrandom overlaps could indicate that these genes are mutation hotspots serving as oncogenic drivers in FPC development. Moreover, it is interesting that the PTV genes found in FPC patients in this study also overlap significantly with the PTV genes detected in unaffected first-degree relatives of FPC patients (Tan et al., 2021b). This is important as, with cancer events available from follow-up, the burden of private variants in these genes can be calculated for each individual and used to build models for PDAC risk prediction and prognosis in the predisposed relatives of FPC patients.

Although with limited sample size of FPC patients, our gene-based association test using ProxECAT was able to identify six genes (MORN1, MYO16, PIEZO1, KLHL5, PTS, and CEP95) with nominal significance of p < 0.05. Among the genes, KLHL5 (Kelch Like Family Member 5) was previously shown to represent an eligible prognostic predictor for gastric malignancy (Wu et al., 2020), and knockdown of the gene increases cellular sensitivity to anticancer drugs (Schleifer et al., 2018). PIEZO1 (Piezo Type Mechanosensitive Ion Channel Component 1) encodes a protein that induces mechanically activated currents in various cell types. The gene has been demonstrated to play oncogenic roles in gastric cancer cell proliferation, migration and invasion to promote gastric cancer progression (Zhang et al., 2018). Multiple studies have shown that the expression of PIEZO1 is related to the clinical characteristics of senescence and cancer, making the gene a new biomarker for diagnosis and prognosis of a variety of human cancers (Yu & Liao, 2021). Although some of the genes found by association test have been reported in cancer studies, their roles in pancreatic cancer need further verification and validation.

As mentioned, a big limitation of the study is the small sample size of FPC patients, which has limited the statistical power of our association test and the ability in detecting PTVs. Another limitation is the quality of DNA samples from formalin-fixed paraffin-embedded benign tissues of FPC patients. As full blood samples were not available for the FPC cases, FFPE samples of non-pancreatic benign tissues were used for DNA extraction. The latter led to low sequencing coverage or depth due to an insufficient number of sequencing reads. As a result, the number of genes available for association testing was also limited, as ProxECAT requires both nonsynonymous and synonymous SNVs to conduct an association test. This is exacerbated by the sample size issue which resulted in a low number of genes found by association analysis. Moreover, ProxECAT assumes that the cases and the external controls match by ancestral population. The included FFPE samples were retrieved from cancer-free, nonpancreatic sites and have been carefully examined by an experienced pathologist at the time of retrieval—eliminating the risk of potential contamination of samples from tumor cells. Although this ensures that the detected PTVs are germline variants, the big number of over 800 PTV genes should be treated with caution as the majority of them carry one PTV (only 40 genes carry multiple PTVs, see Table 2) from a small-scale study.

With the establishment of a nation-wide cohort of FPC families, high quality DNA samples from all families (including first-degree relatives included in our nationwide FPC screening program) have been collected, sequenced, or stored to correlate with future cancer events (Tan et al., 2021a, b)—which will eventually help validate our current findings.

Whole genome sequencing on FPC patients detected a multitude of rare variants displaying a high degree of allelic and locus heterogeneity in FPC. The hosting genes of detected variants significantly over-represent cancer driver genes and/or cancer-related genes that mediate cancer cell proliferation, migration, and invasion. The genetic heterogeneity of FPC is functionally characterized by significant enrichment of multiple biological pathways including the ECM and Rho GTPase pathways that jointly may contribute to the development and progression of FPC.

Conceptualization, M.T., M.T.J. and O.B.S.M.; methodology, M.T. and KB; software, M.T., K.B., and M.J.L.; validation, M.T., K.B. and S.D.; formal analysis, M.T.; investigation, M.T.; resources, M.T.J., O.B.S.M., K.B. and S.D.; data curation, M.T.; writing—original draft preparation, M.T.; writing—review and editing, M.T., M.T.J., O.B.S.M., K.B., S.D., M.B.M., A.-M.G. and M.J.L.; visualization, M.T.; supervision, M.T.J., O.B.S.M., K.B. and S.D.; project administration, M.T., M.T.J., O.B.S.M.; funding acquisition, M.T., M.T.J., O.B.S.M. All authors have read and agreed to the published version of the manuscript.

The authors declare no conflicts of interest.