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Article

Whole Exome-Wide Association Identifies Rare Variants in APC Associated with High-Risk Colorectal Cancer in the Middle East

1
Human Cancer Genomic Research, King Faisal Specialist Hospital and Research Center, P.O. Box 3354, Riyadh 11211, Saudi Arabia
2
Department of Pathology, King Faisal Specialist Hospital and Research Center, P.O. Box 3354, Riyadh 11211, Saudi Arabia
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Cancers 2024, 16(21), 3720; https://doi.org/10.3390/cancers16213720
Submission received: 30 September 2024 / Revised: 29 October 2024 / Accepted: 3 November 2024 / Published: 4 November 2024
(This article belongs to the Section Cancer Pathophysiology)

Simple Summary

This study focused on the identification of rare variants that are associated with high-risk colorectal cancer (CRC) from the Middle Eastern region. This study analyzed DNA samples from 146 patients with CRC and from 1395 healthy individuals. We identified rare inactivating variants in the APC gene that are strongly linked to CRC, increasing the risk approximately 60-fold. Other significant genes harboring rare damaging variants were also identified. These results may have implications for genetic counseling and the early detection of CRC in the Middle Eastern population.

Abstract

Background: Colorectal cancer (CRC) displays a complex pattern of inheritance. It is postulated that much of the missing heritability of CRC is enriched in high-impact rare alleles, which might play a crucial role in the etiology and susceptibility of CRC. Methods: In this study, an exome-wide association analysis was performed in 146 patients with high-risk CRC in the Middle East and 1395 healthy controls. The aim was to identify rare germline variants in coding regions and their splicing sites associated with high-risk CRC in the Middle Eastern population. Results: Rare inactivating variants (RIVs) in APC had the strongest association with high-risk CRC (6/146 in cases vs. 1/1395 in controls, OR = 59.7, p = 5.13 × 10−12), whereas RIVs in RIMS1, an RAS superfamily member, were significantly associated with high-risk CRC (5/146 case vs. 2/1395 controls, OR = 24.7, p = 2.03 × 10−8). Rare damaging variants in 17 genes were associated with high-risk CRC at the exome-wide threshold (p < 2.5 × 10−6). Based on the sequence kernel association test, nonsynonymous variants in six genes (TNXB, TAP2, GPSM3, ADGRG4, TMEM229A, and ANKRD33B) had a significant association with high-risk CRC. RIVs in APC—the most common high-penetrance genetic factor—were associated with patients with high-risk CRC in the Middle East. Individuals who inherited APC RIVs had an approximate 60-fold increased risk of developing CRC and were likely to develop the disease earlier. Conclusions: We identified new potential CRC predisposition variants in other genes that could play a role in CRC inheritance. However, large collaborative studies are needed to confirm the association of these variants with high-risk CRC. These results provide information for counseling patients with high-risk CRC and their families in our population.

1. Introduction

Colorectal cancer (CRC) is the most frequently diagnosed cancer among males in Saudi Arabia and ranks as the third most common cancer in females, with a median age of 60 years at diagnosis [1]. However, 23% of all cases occur in individuals under the age of 50 [1]. The incidence of CRC in this group is expected to increase because patients aged ≤50 years do not routinely undergo CRC screening [2,3,4].
The genetic landscape of CRC susceptibility spans from highly penetrant germline mutations linked to well-known syndromes to more common polymorphisms. However, in >30% of the cases of familial CRC, the heritable cause is unknown [5,6].
Recent genomic studies have suggested that missing heritability may be attributed to rare, high-impact variants. These rare variants might play crucial roles in human diseases, as evolutionary purifying selection causes damaging alleles to remain rare [7]. Numerous rare variants have been linked to various types of cancer, including thyroid [8], pancreatic [9], and lung [10] cancers as well as CRC [11,12,13,14,15].
Despite the importance of this susceptibility and its clinical relevance to the clinical management of familial CRC, rare variants have not been studied in CRC from Middle Eastern populations in which the incidence of young-onset CRC is higher than that in Western populations.
Therefore, rare variants responsible for CRC susceptibility in Middle Eastern populations must be investigated. Identifying cancer predisposition genes through pathogenic rare variants could provide new insights into the genetic foundations of CRC in Middle Eastern populations. This approach could prove valuable for discovering preventive markers and advancing precision medicine strategies.
In the current study, we utilized whole-exome sequencing (WES) to uncover rare damaging variants (RDVs) and rare inactivating variants (RIVs) linked to high-risk colorectal cancer (CRC) among Middle Eastern populations. The Saudi Cancer Registry indicates that the onset age for colorectal cancer (CRC) is between 58 and 60 years [1], which is significantly younger than the age of 65 years or older reported in Western countries [16]. This earlier onset prompted us to define high-risk individuals in our study as those aged ≤56 years. Consequently, our criteria for identifying high-risk individuals include either ≤56 years or a positive family history of CRC. Using exome data derived from 146 patients with high-risk CRC and 1395 patients without cancer, we confirmed RIVs in APC, which is the most prevalent high-penetrance genetic factor linked to high-risk CRC patients in the Middle East. In addition, we identified other candidate variants and/or genes. Our study could facilitate genetic counseling and the tailoring of prevention strategies in these CRC patients.

2. Materials and Methods

2.1. Patient Selection

Archival samples from 146 patients with high-risk CRC diagnosed between 2000 and 2015 at King Faisal Specialist Hospital and Research Center (KFSHRC) Riyadh, Saudi Arabia, were included in this study. Relaxed criteria were used to select patients at high risk, based on our previous publication [17]. Age ≤ 56 years or a positive family history of cancer were considered high risk. Clinicopathological data were gathered from the medical records of the patients (Table 1). Familial antecedent data for colorectal cancer patients with positive family history (n = 53) has been provided in Supplementary Table S1. This study received approval from the hospital’s Institutional Review Board. As only retrospective patient data were used, the Research Advisory Council (RAC) granted a waiver of consent for the project RAC # 2190 016.

2.2. DNA Extraction

DNA was extracted from blood or fresh tissues without tumors using the DNA extraction kit (Gentra, Minneapolis, MN, USA) in accordance with the product protocol, as previously detailed [18]. To ensure the integrity of the DNA from tissues or blood samples, we evaluated its quality using a Nanodrop spectrophotometer and Invitrogen Qubit Fluorometer, ensuring it is suitable for downstream applications.

2.3. Whole Exome Sequencing

WES was conducted for 146 cases using the SureSelectXT Target Enrichment kit (Agilent Technologies, Inc., Santa Clara, CA, USA) on the Illumina sequencing platform, as described previously [8]. GATK and FastQC were utilized to acquire all quality metrics [19]. We performed germline variant calling using GATK’s HaplotypeCaller and variant annotation with ANNOVAR, following the same methodology as outlined in our previous work [8]. This included filtering variants based on minor allele frequency thresholds, quality metrics, and Hardy–Weinberg equilibrium, as well as validating results through the Integrated Genomics Viewer. The control population in our cohort consists of 1395 non-cancer samples from our in-house data gathered from exome sequencing, sequenced at different times. All samples were processed using the same WES methodology.
RIVs defined as deleterious variants, including stop-loss, stop-gain, and splice-site variants, as well as frameshift insertions and deletions with allele frequencies of less than 0.01, were observed in our control cohort as well as in the Exome Aggregation Consortium (ExAC) database. RDVs are classified as either damaging or inactivating and are predicted to be damaging or pathogenic, exhibiting M-CAP classifier scores of more than 0.025 [9,20].
Non-synonymous variants comprised those having allele frequencies below 0.1 in both the ExAC and our cohort. This category included damaging variants, missense variants, as well as non-frameshift insertions and deletions. These variants were utilized in the sequence kernel association test (SKAT) [9].
A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID) platform [21]. The list of genes was analyzed by using default parameters and results were significant if p < 0.05. A flowchart illustrating the employed methodology has been presented in Figure 1.

2.4. Statistical Analysis

Genes carrying a minimum of five RIVs in the cohort were considered for association analysis. The association of genes between cases and the control population were assessed using χ2 test. Associations with a p-value of p < 2.5 × 10−6 were considered to be exome-wide significant, while associations were considered as suggestive if p < 0.001 [9]. All statistical tests were conducted as two-sided. We also employed a filter-based methodology for comparison, ranking genes according to the frequency of RIVs present in the cases.
The SKAT method, implemented in the R package, was used to perform the analysis of association. Standard parameters were utilized to determine this association. The p-values for nonsynonymous variants were calculated using efficient resampling techniques integrated within the “SKATBinary_Single” algorithm. [9].

3. Results

The participants in the study had an average age of 41 years, with slightly more males than females. Most cases were adenocarcinoma, while a smaller fraction of cases were mucinous CRC. A significant number of tumors were found to be moderately differentiated and primarily located on the left side. Detailed clinicopathological characteristics have been mentioned in Table 1.
We identified a total of 266,030 variants across 17,300 genes, which included 218,056 rare damaging variants (24,010 in cases and 194,046 in controls) and 17,040 rare inactivating variants (1585 in cases and 15,455 in controls). The median number of RIVs was 10 (interquartile range: 6–14) in cases and 11 (interquartile range: 7–14) in controls, respectively (p = 0.510 by Wilcoxon rank sum test).
Our primary analysis was focused on RIVs, where the strongest association was observed in APC with an approximate 60-fold increased risk of developing high-risk CRC (odds ratio adjusted OR = 59.7, p = 5.08 × 10−8), meeting exome-wide significance (p < 2.5 × 10−6). In total, 6/146 (4.1%) cases carried RIVs compared with 1/1395 (0.1%) controls. This association was driven by five variants as follows: chr5: 112154963C>T, the most common missense variant, was observed in 3/146 cases (2.1%) and 0/1395 controls (OR = 68.1, p = 8.36 × 10−8); three other variants were identified in one case each and were not present in the controls (OR = 28.8, p = 0.002) (Table 2); and the last variant, a frameshift deletion of chr5:112175077Tdel, was detected in one control but was absent in cases (OR = 0.31, p = 0.746).
In addition to APC, RIMS1, an RAS superfamily member, passed the exome-wide significance threshold and was the second most significant gene with a ~24-fold increased risk of developing high-risk CRC (OR = 24.7, p = 2.03 × 10−8) when comparing cases with controls (Table 2). RIMS1 was positive in 5/146 (3.4%) vs. 2/1395 controls (0.1%). The most significant variant in RIMS1 was chr6: 72974704T>G, a missense variant observed in 4/146 cases (2.7%) and 0/1395 controls (OR = 88.1, p = 6.01 × 10−10) (Table 2).
At the suggested threshold, RIVs in ST6 N-acetylgalactosaminide alpha-2, 6-sialyltransferase 2 (ST6GALNAC2) were found to be significant (OR = 14.6, p = 1.12 × 10−4), observed in 3/146 cases (2%), with a 14-fold increased risk of developing high-risk CRC, compared with 2/1395 controls (0.1%) (Table 2). Two cases (1.4%) carried a frameshift deletion (chr17: 74566661del) compared with 1/1395 controls (0.1%) (OR = 19.4, p = 0.001) (Table 2).
Furthermore, we used a filter-based method to examine 10 genes carrying the highest number of RIVs in cases, determined by counting. Out of these 10 genes, APC was ranked at the top with six RIVs, whereas the lowest number was found in ITGA10 with two RIVs. When comparing cases vs. controls, five genes showed significant associations (p < 0.05), including APC (p = 5.08 × 10−12), RIMS1 (p = 2.03 × 10−8), ACOT4 (p = 0.003), ST6GALNAC2 (p = 1.12 × 10−4), and FSIP2 (p = 0.026) (Table 3).
A KEGG pathway analysis was conducted to evaluate the signaling pathways. However, no significant association was observed for any pathways with an elevated risk of CRC development.
We also concentrated on RDVs in both cases and controls for the secondary analysis. A total of 17 genes achieved exome-wide significance (Table 4). The associations were driven by multiple RDVs, and 14 variants were identified among two or three individuals. The highest association was observed for SPRED1 chr15: 38545392C>A, SHANK1 chr19:51206940G>A, OR5K4 chr3: 98073028G>T, and COL11A2 chr6: 33154366G>T variants present in 3/146 cases (2.1%) each and absent in controls (OR = 68.1 p = 8.36 × 10−8) (Supplementary Table S2). Interestingly, on comparing the clinicopathological characteristics of these three cases, we found that 66.7% (2/3) were female and all three cases had lymph node metastasis, with one of the patients also exhibiting distant metastasis involving the liver. Two of the three patients had tumor in the left colon, while the other patient had right colon tumor. In addition, there were no other germline pathogenic variants in the cancer-related genes identified in these cases.
At the suggestive threshold (p < 0.001), we found RDVs in 117 genes significantly associated with high-risk CRC (Supplementary Table S3).
For tertiary analyses, we utilized SKAT to assess the combined impact of all non-synonymous variants having cohort allele frequency <0.1. It was observed that seven genes had exome-wide significant associations as follows: tenascin XB (TNXB); transporter 2; ATP binding cassette subfamily B member (TAP2); G protein signaling modulator 3 (GPSM3); adhesion G protein-coupled receptor G4 (ADGRG4); transmembrane protein 229A (TMEM229A); and ankyrin repeat domain 33B (ANKRD33B) (Table 5). Each gene contained a minimum of one variant having p < 0.001. Three variants showed significant associations at the exome-wide threshold after Bonferroni’s correction (Supplementary Table S4).

4. Discussion

New strategies to prevent CRC are urgently needed, especially in understudied ethnicities, such as Middle Eastern populations. Identifying inherited, rare germline genetic variants in this ethnicity that increase the risk of CRC may improve prevention strategies, helping to reduce the high incidence of high-risk CRC.
Next-generation sequencing has opened new avenues for exploring the genetic causes of cancer and has added new unbiased approaches to facilitate the identification of new genes and/or variants responsible for predisposition to human disease.
In this study, we conducted WES to identify novel rare variants and genes associated with CRC susceptibility, potentially explaining the high-risk of CRC observed in these patients. For this purpose, we analyzed germline data from 146 unrelated cases diagnosed with high-risk CRC and 1395 cancer-free controls. The analysis focused exclusively on patients from the Middle Eastern population, with stringent quality control measures implemented to ensure data integrity.
In the primary analysis, we concentrated on RIVs. APC demonstrated the most significant association with an approximate 60-fold increased risk of developing high-risk CRC, meeting the exome-wide threshold. Our primary analyses led to the identification of four RIVs in APC in six cases, with a significant association with high-risk CRC in our research population.
Interestingly, our analysis detected one frameshift deletion in one control, but it was absent in cases, contrary to reports that deleterious variants in APC increased the risk of CRC. One explanation might be the limited sample size in this study. Therefore, large-scale studies must be used to investigate the role of this deleterious variant in the development of high-risk CRC in our population.
Patients with germline pathogenic variants in the APC gene develop multiple adenomatous polyps in their colon ranging from tens in attenuated familial polyposis [AFAP]) to countless in classic FAP, which significantly elevates their lifetime risk of developing CRC [22,23,24]. It is not surprising that APC emerged as the most interesting gene in our analysis. Hence, the role of APC pathogenic germline mutations in CRC susceptibility was firmly established, with a prevalence of 5–18% [25,26,27,28].
In our primary analyses, RIVs in RIMS1 were significantly associated with an approximate 25-fold increased risk of developing high-risk CRC. RIMS1 is an RAS superfamily member. It is one of the genes that is significantly downregulated in the classical multidrug resistance gastric carcinoma cell line [29]. RIMS1 mutations impacted survival in patients with pancreatic cancer [30] and gastric cancer [31]. Recently, mutations in RIMS1 were identified as a potential causal mutation in Chinese familial hemangioblastoma [32].
The enrichment of RDVs in the other 17 genes further bolsters our insight of the inherited genetic basis of colorectal cancer. All of these 17 genes, except COL11A2, was reported to be associated with several types of cancer, such as ovarian, gastrointestinal, lung, and pancreatic cancers [33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49]. However, studies on the roles of COL11A2 in the progression of cancer are lacking.
We attempted to combine the rare variants to pathways throughout the genome to explore the associations with increased CRC risk through KEGG pathway analysis. No signal pathways were found to be significantly linked to CRC.
A limitation of the study is the inclusion of cases from a specific population, which precludes the applicability of the results to the general population. Despite this limitation, our results using WES data provide valuable insights into deleterious and disruptive rare coding variants and susceptibility genes for CRC cancer risk in Middle Eastern populations.
Future studies with larger sample sizes and collaborative multicenter samples could allow deep targeted sequencing to reveal other promising variants and additional disease- susceptible genes for CRC in Middle Eastern populations.

5. Conclusions

The current study utilized whole-exome sequencing to identify rare variants linked to high-risk CRC. These findings provide important insights into the genetic foundations of CRC in this understudied demographic. The identification of these susceptibility variants may inform the development of targeted prevention strategies, potentially reducing the burden of CRC in Middle Eastern communities. Future large sample studies with broader geographic representation are necessary to further elucidate the genetic landscape of CRC in this region and to refine targeted prevention approaches.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cancers16213720/s1, Table S1: Familial antecedent data for colorectal cancer patients with positive family history (n = 53); Table S2: List of rare damaging variants associations with CRC risk; Table S3: List of RDV genes significant at the suggestive level (p < 0.001); Table S4: List of variants significantly related to CRC risk in seven SKAT genes.

Author Contributions

Conceptualization: K.S.A.-K.; Methodology: A.K.S. and R.B.; Formal analysis and investigation: S.A., Z.Q. and K.I.; Writing—original draft preparation: K.S.A.-K., A.K.S. and R.B.; Writing—review and editing: K.I., S.A. and S.K.P.; Resources: F.A.-D.; Supervision: K.S.A.-K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (IRB) of King Faisal Specialist Hospital and Research Center under the RAC # 2190 016.

Informed Consent Statement

Since only retrospective patient data were utilized, the Research Advisory Council (RAC) provided a waiver of consent under project RAC # 2190 016.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Council, S.H. Cancer Incidence Report 2020: Saudi Arabia; Saudi Cancer Registry: Riyadh, Saudi Arabia, 2020. [Google Scholar]
  2. Araghi, M.; Soerjomataram, I.; Bardot, A.; Ferlay, J.; Cabasag, C.J.; Morrison, D.S.; De, P.; Tervonen, H.; Walsh, P.M.; Bucher, O. Changes in colorectal cancer incidence in seven high-income countries: A population-based study. Lancet Gastroenterol. Hepatol. 2019, 4, 511–518. [Google Scholar] [CrossRef] [PubMed]
  3. Bailey, C.E.; Hu, C.-Y.; You, Y.N.; Bednarski, B.K.; Rodriguez-Bigas, M.A.; Skibber, J.M.; Cantor, S.B.; Chang, G.J. Increasing disparities in the age-related incidences of colon and rectal cancers in the United States, 1975–2010. JAMA Surg. 2015, 150, 17–22. [Google Scholar] [CrossRef] [PubMed]
  4. Yeo, H.; Betel, D.; Abelson, J.S.; Zheng, X.E.; Yantiss, R.; Shah, M.A. Early-onset colorectal cancer is distinct from traditional colorectal cancer. Clin. Color. Cancer 2017, 16, 293–299.e296. [Google Scholar] [CrossRef] [PubMed]
  5. Armelao, F.; de Pretis, G. Familial colorectal cancer: A review. World J. Gastroenterol. WJG 2014, 20, 9292. [Google Scholar]
  6. Chubb, D.; Broderick, P.; Frampton, M.; Kinnersley, B.; Sherborne, A.; Penegar, S.; Lloyd, A.; Ma, Y.P.; Dobbins, S.E.; Houlston, R.S. Genetic diagnosis of high-penetrance susceptibility for colorectal cancer (CRC) is achievable for a high proportion of familial CRC by exome sequencing. J. Clin. Oncol. 2015, 33, 426–432. [Google Scholar] [CrossRef]
  7. Lee, S.; Abecasis, G.R.; Boehnke, M.; Lin, X. Rare-variant association analysis: Study designs and statistical tests. Am. J. Hum. Genet. 2014, 95, 5–23. [Google Scholar] [CrossRef]
  8. Bu, R.; Siraj, A.K.; Azam, S.; Iqbal, K.; Qadri, Z.; Al-Rasheed, M.; Al-Sobhi, S.S.; Al-Dayel, F.; Al-Kuraya, K.S. Whole Exome-Wide Association Identifies Rare Variants in GALNT9 Associated with Middle Eastern Papillary Thyroid Carcinoma Risk. Cancers 2023, 15, 4235. [Google Scholar] [CrossRef]
  9. Grant, R.C.; Denroche, R.E.; Borgida, A.; Virtanen, C.; Cook, N.; Smith, A.L.; Connor, A.A.; Wilson, J.M.; Peterson, G.; Roberts, N.J. Exome-wide association study of pancreatic cancer risk. Gastroenterology 2018, 154, 719–722.e713. [Google Scholar] [CrossRef]
  10. Liu, Y.; Xia, J.; McKay, J.; Tsavachidis, S.; Xiao, X.; Spitz, M.R.; Cheng, C.; Byun, J.; Hong, W.; Li, Y. Rare deleterious germline variants and risk of lung cancer. NPJ Precis. Oncol. 2021, 5, 12. [Google Scholar] [CrossRef]
  11. Li, J.; Zou, L.; Zhou, Y.; Li, L.; Zhu, Y.; Yang, Y.; Gong, Y.; Lou, J.; Ke, J.; Zhang, Y. A low-frequency variant in SMAD7 modulates TGF-β signaling and confers risk for colorectal cancer in Chinese population. Mol. Carcinog. 2017, 56, 1798–1807. [Google Scholar] [CrossRef]
  12. Chubb, D.; Broderick, P.; Dobbins, S.E.; Frampton, M.; Kinnersley, B.; Penegar, S.; Price, A.; Ma, Y.P.; Sherborne, A.L.; Palles, C. Rare disruptive mutations and their contribution to the heritable risk of colorectal cancer. Nat. Commun. 2016, 7, 11883. [Google Scholar] [CrossRef] [PubMed]
  13. Esteban-Jurado, C.; Vila-Casadesús, M.; Garre, P.; Lozano, J.J.; Pristoupilova, A.; Beltran, S.; Muñoz, J.; Ocaña, T.; Balaguer, F.; López-Cerón, M. Whole-exome sequencing identifies rare pathogenic variants in new predisposition genes for familial colorectal cancer. Genet. Med. 2015, 17, 131–142. [Google Scholar] [CrossRef]
  14. Bouras, A.; Fabre, A.; Zattara, H.; Handallou, S.; Desseigne, F.; Kientz, C.; Prieur, F.; Peysselon, M.; Legrand, C.; Calavas, L. Hereditary Colorectal Cancer and Polyposis Syndromes Caused by Variants in Uncommon Genes. Genes Chromosomes Cancer 2024, 63, e23263. [Google Scholar] [CrossRef] [PubMed]
  15. Hassanin, E.; Spier, I.; Bobbili, D.R.; Aldisi, R.; Klinkhammer, H.; David, F.; Dueñas, N.; Hüneburg, R.; Perne, C.; Brunet, J. Clinically relevant combined effect of polygenic background, rare pathogenic germline variants, and family history on colorectal cancer incidence. BMC Med. Genom. 2023, 16, 42. [Google Scholar] [CrossRef] [PubMed]
  16. Siegel, R.L.; Wagle, N.S.; Cercek, A.; Smith, R.A.; Jemal, A. Colorectal cancer statistics, 2023. CA Cancer J. Clin. 2023, 73, 233–254. [Google Scholar] [CrossRef]
  17. Siraj, A.K.; Masoodi, T.; Bu, R.; Parvathareddy, S.K.; Al-Badawi, I.A.; Al-Sanea, N.; Ashari, L.H.; Abduljabbar, A.; Alhomoud, S.; Al-Sobhi, S.S. Expanding the spectrum of germline variants in cancer. Hum. Genet. 2017, 136, 1431–1444. [Google Scholar] [CrossRef]
  18. Abubaker, J.; Jehan, Z.; Bavi, P.; Sultana, M.; Al-Harbi, S.; Ibrahim, M.; Al-Nuaim, A.; Ahmed, M.; Amin, T.; Al-Fehaily, M. Clinicopathological analysis of papillary thyroid cancer with PIK3CA alterations in a Middle Eastern population. J. Clin. Endocrinol. Metab. 2008, 93, 611–618. [Google Scholar] [CrossRef]
  19. Masoodi, T.; Siraj, A.K.; Siraj, S.; Azam, S.; Qadri, Z.; Parvathareddy, S.K.; Al-Sobhi, S.S.; AlDawish, M.; Alkuraya, F.S.; Al-Kuraya, K.S. Evolution and impact of subclonal mutations in papillary thyroid cancer. Am. J. Hum. Genet. 2019, 105, 959–973. [Google Scholar] [CrossRef]
  20. Jagadeesh, K.A.; Wenger, A.M.; Berger, M.J.; Guturu, H.; Stenson, P.D.; Cooper, D.N.; Bernstein, J.A.; Bejerano, G. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat. Genet. 2016, 48, 1581–1586. [Google Scholar] [CrossRef]
  21. Laskowski, R.A. PDBsum new things. Nucleic Acids Res. 2009, 37, D355–D359. [Google Scholar] [CrossRef]
  22. Galiatsatos, P.; Foulkes, W.D. Familial adenomatous polyposis. Off. J. Am. Coll. Gastroenterol. ACG 2006, 101, 385–398. [Google Scholar] [CrossRef] [PubMed]
  23. Burt, R.W.; Leppert, M.F.; Slattery, M.L.; Samowitz, W.S.; Spirio, L.N.; Kerber, R.A.; Kuwada, S.K.; Neklason, D.W.; DiSario, J.A.; Lyon, E. Genetic testing and phenotype in a large kindred with attenuated familial adenomatous polyposis. Gastroenterology 2004, 127, 444–451. [Google Scholar] [CrossRef] [PubMed]
  24. Ibrahim, A.; Barnes, D.R.; Dunlop, J.; Barrowdale, D.; Antoniou, A.C.; Berg, J.N. Attenuated familial adenomatous polyposis manifests as autosomal dominant late-onset colorectal cancer. Eur. J. Hum. Genet. 2014, 22, 1330–1333. [Google Scholar] [CrossRef] [PubMed]
  25. Stoffel, E.M.; Koeppe, E.; Everett, J.; Ulintz, P.; Kiel, M.; Osborne, J.; Williams, L.; Hanson, K.; Gruber, S.B.; Rozek, L.S. Germline genetic features of young individuals with colorectal cancer. Gastroenterology 2018, 154, 897–905.e891. [Google Scholar] [CrossRef] [PubMed]
  26. Samadder, N.J.; Giridhar, K.V.; Baffy, N.; Riegert-Johnson, D.; Couch, F.J. Hereditary cancer syndromes—A primer on diagnosis and management: Part 1: Breast-ovarian cancer syndromes. In Mayo Clinic Proceedings; Elsevier: Amsterdam, The Netherlands, 2019; pp. 1084–1098. [Google Scholar]
  27. Frostberg, E.; Petersen, A.H.; Bojesen, A.; Rahr, H.B.; Lindebjerg, J.; Rønlund, K. The prevalence of pathogenic or likely pathogenic germline variants in a nationwide cohort of young colorectal cancer patients using a panel of 18 genes associated with colorectal cancer. Cancers 2021, 13, 5094. [Google Scholar] [CrossRef]
  28. Stanich, P.P.; Pearlman, R.; Hinton, A.; Gutierrez, S.; LaDuca, H.; Hampel, H.; Jasperson, K. Prevalence of germline mutations in polyposis and colorectal cancer–associated genes in patients with multiple colorectal polyps. Clin. Gastroenterol. Hepatol. 2019, 17, 2008–2015.e2003. [Google Scholar] [CrossRef]
  29. Heim, S.; Lage, H. Transcriptome analysis of different multidrug-resistant gastric carcinoma cells. In Vivo 2005, 19, 583–590. [Google Scholar]
  30. Jones, S.; Zhang, X.; Parsons, D.W.; Lin, J.C.-H.; Leary, R.J.; Angenendt, P.; Mankoo, P.; Carter, H.; Kamiyama, H.; Jimeno, A. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 2008, 321, 1801–1806. [Google Scholar] [CrossRef]
  31. Dai, J.; Li, Z.-X.; Zhang, Y.; Ma, J.-L.; Zhou, T.; You, W.-C.; Li, W.-Q.; Pan, K.-F. Whole genome messenger RNA profiling identifies a novel signature to predict gastric cancer survival. Clin. Transl. Gastroenterol. 2019, 10, e00004. [Google Scholar] [CrossRef]
  32. Shinriki, S.; Maeshiro, M.; Shimamura, K.; Kawashima, J.; Araki, E.; Ibusuki, M.; Yamamoto, Y.; Iwase, H.; Miyamoto, Y.; Baba, H. Evaluation of an amplicon-based custom gene panel for the diagnosis of hereditary tumors. Neoplasma 2020, 67, 898. [Google Scholar] [CrossRef]
  33. Ablain, J.; Xu, M.; Rothschild, H.; Jordan, R.; Mito, J.; Daniels, B. SPRED1 Is a Tumor Suppressor in Mucosal Melanoma. Cancer Discov. 2018, 8, 1507. [Google Scholar]
  34. Ayanlaja, A.A.; Hong, X.; Cheng, B.; Zhou, H.; Kanwore, K.; Alphayo-Kambey, P.; Zhang, L.; Tang, C.; Adeyanju, M.M.; Gao, D. Susceptibility of cytoskeletal-associated proteins for tumor progression. Cell Mol. Life Sci. 2022, 79, 13. [Google Scholar] [CrossRef] [PubMed]
  35. Basak, A.J.; Maiti, S.; Hansda, A.; Mahata, D.; Duraivelan, K.; Kundapura, S.V.; Lee, W.; Mukherjee, G.; De, S.; Samanta, D. Structural insights into N-terminal IgV domain of BTNL2, a T cell inhibitory molecule, suggests a non-canonical binding interface for its putative receptors. J. Mol. Biol. 2020, 432, 5938–5950. [Google Scholar] [CrossRef] [PubMed]
  36. Chen, B.; Zhao, H.; Li, M.; She, Q.; Liu, W.; Zhang, J.; Zhao, W.; Huang, S.; Wu, J. SHANK1 facilitates non-small cell lung cancer processes through modulating the ubiquitination of Klotho by interacting with MDM2. Cell Death Dis. 2022, 13, 403. [Google Scholar] [CrossRef] [PubMed]
  37. Chien, W.; Lee, K.; Ding, L.; Wuensche, P.; Kato, H.; Doan, N.; Poellinger, L.; Said, J.; Koeffler, H. PIAS4 is an activator of hypoxia signalling via VHL suppression during growth of pancreatic cancer cells. Br. J. Cancer 2013, 109, 1795–1804. [Google Scholar] [CrossRef]
  38. Chiu, C.-F.; Chang, H.-Y.; Huang, C.-Y.; Mau, C.-Z.; Kuo, T.-T.; Lee, H.-C.; Huang, S.-Y. Betulinic acid affects the energy-related proteomic profiling in pancreatic ductal adenocarcinoma cells. Molecules 2021, 26, 2482. [Google Scholar] [CrossRef]
  39. Fejzo, M.S.; Chen, H.-W.; Anderson, L.; McDermott, M.S.; Karlan, B.; Konecny, G.E.; Slamon, D.J. Analysis in epithelial ovarian cancer identifies KANSL1 as a biomarker and target gene for immune response and HDAC inhibition. Gynecol. Oncol. 2021, 160, 539–546. [Google Scholar] [CrossRef]
  40. Hanieh, H.; Ahmed, E.A.; Vishnubalaji, R.; Alajez, N.M. SOX4: Epigenetic regulation and role in tumorigenesis. In Seminars in Cancer Biology; Academic Press: Cambridge, MA, USA, 2010; pp. 91–104. [Google Scholar]
  41. Lapkina-Gendler, L.; Rotem, I.; Pasmanik-Chor, M.; Gurwitz, D.; Sarfstein, R. Identification of signaling pathways associated with cancer protection in Laron syndrome. Endocr. Relat. Cancer 2016, 23, 399–410. [Google Scholar] [CrossRef]
  42. Liot, S.; Aubert, A.; Hervieu, V.; El Kholti, N.; Schalkwijk, J.; Verrier, B.; Valcourt, U.; Lambert, E. Loss of Tenascin-X expression during tumor progression: A new pan-cancer marker. Matrix Biol. Plus 2020, 6, 100021. [Google Scholar] [CrossRef]
  43. Mauri, G.; Patelli, G.; Roazzi, L.; Valtorta, E.; Amatu, A.; Marrapese, G.; Bonazzina, E.; Tosi, F.; Bencardino, K.; Ciarlo, G. Clinicopathological characterisation of MTAP alterations in gastrointestinal cancers. J. Clin. Pathol. 2024; ahead of print. [Google Scholar] [CrossRef]
  44. Ren, H.; Zhu, J.; Yu, H.; Bazhin, A.V.; Westphalen, C.B.; Renz, B.W.; Jacob, S.N.; Lampert, C.; Werner, J.; Angele, M.K. Angiogenesis-related gene expression signatures predicting prognosis in gastric cancer patients. Cancers 2020, 12, 3685. [Google Scholar] [CrossRef] [PubMed]
  45. Sharma, M.; Verma, S.; Angurana, S.L.; Tufail, Z.; Bhagat, V.; Nagyal, S.; Jamwal, R.S.; Sharma, B.; Shah, R.; Bhat, A. Exome sequencing identifies ADGRG4 G-protein-coupled receptors gene as a novel cancer biomarker in ovarian cancer patients from North India. J. Biochem. Mol. Toxicol. 2024, 38, e23672. [Google Scholar] [CrossRef] [PubMed]
  46. Wang, X.; Zhang, W.; Guo, Y.; Zhang, Y.; Bai, X.; Xie, Y. Identification of critical prognosis signature associated with lymph node metastasis of stomach adenocarcinomas. World J. Surg. Oncol. 2023, 21, 61. [Google Scholar] [CrossRef] [PubMed]
  47. Yang, Z.; Liu, Z.; Deng, D.; Fu, Z.; Chen, J.; Cui, Y.; Zhao, Z.; Zhang, X. Effect of MPP2 and its methylation levels on prognosis of colorectal cancer patients. World J. Surg. Oncol. 2024; in review. [Google Scholar] [CrossRef]
  48. Zhao, W.; Yang, L.; Chen, X.; Huang, W. Cardiac-Specific Gene TNNI3 as a Potential Oncogene for Kidney Cancer and Its Involvement in Wnt Signaling Pathway. Res. Sq. 2021; in review. [Google Scholar] [CrossRef]
  49. Zhu, L.; Li, Y.; Xie, X.; Zhou, X.; Gu, M.; Jie, Z.; Ko, C.-J.; Gao, T.; Hernandez, B.E.; Cheng, X. TBKBP1 and TBK1 form a growth factor signalling axis mediating immunosuppression and tumourigenesis. Nat. Cell Biol. 2019, 21, 1604–1614. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Flowchart illustrating the methodology employed in the study. (Made with Whimsical 2024, online version available at https://whimsical.com).
Figure 1. Flowchart illustrating the methodology employed in the study. (Made with Whimsical 2024, online version available at https://whimsical.com).
Cancers 16 03720 g001
Table 1. Clinicopathological characteristics of the study cohort.
Table 1. Clinicopathological characteristics of the study cohort.
N = 146
Age (years)
Median (IQR), years41.0 (34.0–48.9)
<50 119 (81.5)
≥5027 (18.5)
Gender
Female64 (43.8)
Male82 (56.2)
Family history of cancer
Positive53 (36.3)
Negative93 (63.7)
Family history of colon cancer
Positive28 (19.2)
Negative118 (80.8)
Body mass index (kg/m2)
<3092 (63.0)
≥3040 (27.4)
Unknown14 (9.6)
History of diabetes mellitus
Present19 (13.0)
Absent97 (66.4)
Unknown30 (20.5)
Histologic subtype
Adenocarcinoma127 (87.0)
Mucinous19 (13.0)
Tumor location
Left colon113 (77.4)
Right colon26 (17.8)
Transverse colon7 (4.8)
Histologic grade
Well differentiated8 (5.5)
Moderately differentiated113 (77.4)
Poorly differentiated14 (9.6)
Unknown11 (7.5)
pT
T14 (2.8)
T217 (11.7)
T390 (61.6)
T424 (16.4)
Unknown11 (7.5)
pN
N056 (38.4)
N1/N279 (54.1)
Nx11 (7.5)
pM
M0111 (76.0)
M128 (19.2)
Mx7 (4.8)
TNM Stage
I 17 (11.7)
II38 (26.0)
III55 (37.7)
IV28 (19.2)
Unknown8 (5.4)
Table 2. List of rare inactivating variants associated with CRC risk.
Table 2. List of rare inactivating variants associated with CRC risk.
S NoGeneChrPositionRefAltVariant TypeNo. of
Cases
%No. of
Controls
%p-ValueOdds Ratio
1APCchr5112,154,963CTMissense32.100.08.36 × 10−868.1
2APCchr5112,128,191CTMissense10.700.00.00228.8
3APCchr5112,155,042GCMissense10.700.00.00228.8
4APCchr5112,174,112GTMissense10.700.00.00228.8
5APCchr5112,175,077T-Frameshift
Deletion
00.710.00.746−3.1
6RIMS1chr672,974,704TGMissense42.700.06.01 × 10−1088.1
7RIMS1chr672,975,696-TCFrameshift
Insertion
10.700.00.00228.8
8RIMS1chr672,945,397TCMissense00.010.10.7463.2
9RIMS1chr672,984,083CTMissense00.010.10.7463.2
10ST6GALNAC2chr1774,566,661T-Frameshift
Deletion
21.410.10.00119.4
11ST6GALNAC2chr1774,568,782G-Frameshift
Deletion
10.710.10.0509.6
Table 3. List of top 10 genes by number of cases in RIVs.
Table 3. List of top 10 genes by number of cases in RIVs.
S NoGeneNo. of Cases% CasesNo. of Controls% Controlsp-ValueOdds Ratio
1APC *64.110.15.08 × 10−1259.7
2CD3664.1251.80.0582.3
3RIMS1 *53.420.12.03 × 10−824.7
4ACOT432.140.30.0037.3
5ST6GALNAC2 #32.120.11.12 × 10−414.6
6FSIP232.170.50.0264.2
7PNPLA732.190.60.0653.2
8TTN21.4322.30.4700.6
9TTLL1021.450.40.0843.9
10ITGA1021.4523.70.1400.4
* Genes passing exome-wide significance level (p < 2.5 × 10−6). # Gene passing suggestive significance level (p < 0.001).
Table 4. List of genes with significant RDVs at the exome-wide level (p < 2.5 × 10−6).
Table 4. List of genes with significant RDVs at the exome-wide level (p < 2.5 × 10−6).
S NoGeneNo. of Cases% CasesNo. of Controls% Controlsp-ValueOdds Ratio
1TNXB106.810.10.00 × 10−0102.5
2GPR11274.800.00.00 × 10−0150.1
3COL11A253.400.04.42 × 10−12108.5
4ANKRD33B42.710.16.91 × 10−839.3
5TBKBP153.420.12.03 × 10−824.7
6OR5K453.420.12.03 × 10−824.7
7MTAP53.420.12.03 × 10−824.7
8SHANK1106.8130.92.02 × 10−87.8
9SPRED142.710.16.91 × 10−839.3
10MPP242.710.16.91 × 10−839.3
11KANSL142.710.16.91 × 10−839.3
12PIAS442.710.16.91 × 10−839.3
13TNNI342.710.16.91 × 10−839.3
14BTNL242.710.16.91 × 10−839.3
15CAPZA142.720.11.64 × 10−619.6
16OSTC42.720.11.64 × 10−619.6
17SOX442.720.11.64 × 10−619.6
Table 5. List of genes significant at the exome-wide level analyzed by SKAT.
Table 5. List of genes significant at the exome-wide level analyzed by SKAT.
S NoGeneNo. of Cases% CasesNo. of Controls% Controlsp-ValueOdds Ratio
1TNXB1611.010.10.00 × 10−0171.6
2TAP285.500.00.00 × 10−0171.3
3GPSM374.800.00.00 × 10−0150.1
4ADGRG474.800.00.00 × 10−0150.1
5TMEM229A85.540.31.11 × 10−1120.2
6ANKRD33B64.110.15.08 × 10−1259.7
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Siraj, A.K.; Bu, R.; Azam, S.; Qadri, Z.; Iqbal, K.; Parvathareddy, S.K.; Al-Dayel, F.; Al-Kuraya, K.S. Whole Exome-Wide Association Identifies Rare Variants in APC Associated with High-Risk Colorectal Cancer in the Middle East. Cancers 2024, 16, 3720. https://doi.org/10.3390/cancers16213720

AMA Style

Siraj AK, Bu R, Azam S, Qadri Z, Iqbal K, Parvathareddy SK, Al-Dayel F, Al-Kuraya KS. Whole Exome-Wide Association Identifies Rare Variants in APC Associated with High-Risk Colorectal Cancer in the Middle East. Cancers. 2024; 16(21):3720. https://doi.org/10.3390/cancers16213720

Chicago/Turabian Style

Siraj, Abdul Khalid, Rong Bu, Saud Azam, Zeeshan Qadri, Kaleem Iqbal, Sandeep Kumar Parvathareddy, Fouad Al-Dayel, and Khawla S. Al-Kuraya. 2024. "Whole Exome-Wide Association Identifies Rare Variants in APC Associated with High-Risk Colorectal Cancer in the Middle East" Cancers 16, no. 21: 3720. https://doi.org/10.3390/cancers16213720

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