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Cited by 16Cancer Genomics and DiagnosticsGenetic factors in colorectal cancerCancer Immunotherapy and Biomarkers

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Exploring Co-occurring POLE Exonuclease and Non-exonuclease Domain Mutations and Their Impact on Tumor Mutagenicity

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Shah, Shreya M., Demidova, Elena V., Ringenbach, Salena, Faezov, Bulat, Andrake, Mark, Gandhi, Arjun, Mur, Pilar, Viana-Errasti, Julen, Xiu, Joanne, Swensen, Jeffrey, Valle, Laura, Dunbrack, Roland L., Hall, Michael J., Arora, Sanjeevani·2024·Cancer Research Communications
DOI 10.1158/2767-9764.crc-23-0312PMC PMC10812383

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Abstract

POLE driver mutations in the exonuclease domain (ExoD driver) are prevalent in several cancers, including colorectal cancer and endometrial cancer, leading to dramatically ultra-high tumor mutation burden (TMB). To understand whether POLE mutations that are not classified as drivers (POLE Variant) contribute to mutagenesis, we assessed TMB in 447 POLE-mutated colorectal cancers, endometrial cancers, and ovarian cancers classified as TMB-high ≥10 mutations/Mb (mut/Mb) or TMB-low <10 mut/Mb. TMB was significantly highest in tumors with “POLE ExoD driver plus POLE Variant” (colorectal cancer and endometrial cancer, P < 0.001; ovarian cancer, P < 0.05). TMB increased with additional POLE variants (P < 0.001), but plateaued at 2, suggesting an association between the presence of these variants and TMB. Integrated analysis of AlphaFold2 POLE models and quantitative stability estimates predicted the impact of multiple POLE variants on POLE functionality. The prevalence of immunogenic neoepitopes was notably higher in the “POLE ExoD driver plus POLE Variant” tumors. Overall, this study reveals a novel correlation between POLE variants in POLE ExoD-driven tumors, and ultra-high TMB. Currently, only select pathogenic ExoD mutations with a reliable association with ultra-high TMB inform clinical practice. Thus, these findings are hypothesis-generating, require functional validation, and could potentially inform tumor classification, treatment responses, and clinical outcomes. Significance: Somatic POLE ExoD driver mutations cause proofreading deficiency that induces high TMB. This study suggests a novel modifier role for POLE variants in POLE ExoD-driven tumors, associated with ultra-high TMB. These data, in addition to future functional studies, may inform tumor classification, therapeutic response, and patient outcomes.

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FIGURE 1 Characterization of POLE mutations in the CLS and TCGA dataset. A, Flowchart and analysis tree for colorectal cancer (CRC), endometrial cancer (EC), and ovarian cancer (OC) tumors by POLE mutations, TMB, and MSI/MSS status. Among 1,870 colorectal cancer, 4,481 endometrial cancers, and 8,910 ovarian cancer tumor genomic profiles, a total of 447 carried POLE mutations. Clinically relevant TMB cut-off points were used to define the TMB-H (≥10 mut/Mb) and TMB-L (<10 mut/Mb) cohorts. POLE mutation cohorts along with TMB and MSI/MSS status were defined. TMB-L tumors with POLE variants but no established POLE ExoD driver are referred to as “POLE Variants TMB-L” (Group 1, MSS or MSI). TMB-H tumors with known POLE ExoD driver only were referred to as “POLE ExoD Driver” (Group 2, MSS or MSI). TMB-H tumors with co-occurring POLE ExoD driver and POLE variant(s) were referred to as “POLE ExoD Driver + POLE ExoD Variant” (Group 3, MSS or MSI). TMB-H tumors with only POLE variant(s) and no POLE ExoD driver were referred to as “POLE Variant TMB-H” (Group 4, MSS or MSI). B, Age distribution of patients in the CLS cohort with POLE-mutated tumors (n = 447) designated as Group 1 (green), Group 2 (red), Group 3 (purple), and Group 4 (blue). mTMB comparisons between Group 2 and 3 colorectal cancers (C), endometrial cancers (D), and ovarian cancers (E). mTMB comparisons between Group 2 and 3 genomic profiles of colorectal cancer (F), endometrial cancer (G), and ovarian cancer (H). MSI-H tumor profiles were removed from this analysis. TCGA cohort mTMB comparisons between Group 2 and 3 tumors, in I MSI-H or MSS tumor profiles were included and in J only MSS tumor profiles were included. Because of smaller sample size per tumor type, analyses were pooled. A Mann–Whitney test was performed and ***, P < 0.001; *, P < 0.05; NS, nonsignificant.
FIGURE 1 Characterization of POLE mutations in the CLS and TCGA dataset. A, Flowchart and analysis tree for colorectal cancer (CRC), endometrial cancer (EC), and ovarian cancer (OC) tumors by POLE mutations, TMB, and MSI/MSS status. Among 1,870 colorectal cancer, 4,481 endometrial cancers, and 8,910 ovarian cancer tumor genomic profiles, a total of 447 carried POLE mutations. Clinically relevant TMB cut-off points were used to define the TMB-H (≥10 mut/Mb) and TMB-L (<10 mut/Mb) cohorts. POLE mutation cohorts along with TMB and MSI/MSS status were defined. TMB-L tumors with POLE variants but no established POLE ExoD driver are referred to as “POLE Variants TMB-L” (Group 1, MSS or MSI). TMB-H tumors with known POLE ExoD driver only were referred to as “POLE ExoD Driver” (Group 2, MSS or MSI). TMB-H tumors with co-occurring POLE ExoD driver and POLE variant(s) were referred to as “POLE ExoD Driver + POLE ExoD Variant” (Group 3, MSS or MSI). TMB-H tumors with only POLE variant(s) and no POLE ExoD driver were referred to as “POLE Variant TMB-H” (Group 4, MSS or MSI). B, Age distribution of patients in the CLS cohort with POLE-mutated tumors (n = 447) designated as Group 1 (green), Group 2 (red), Group 3 (purple), and Group 4 (blue). mTMB comparisons between Group 2 and 3 colorectal cancers (C), endometrial cancers (D), and ovarian cancers (E). mTMB comparisons between Group 2 and 3 genomic profiles of colorectal cancer (F), endometrial cancer (G), and ovarian cancer (H). MSI-H tumor profiles were removed from this analysis. TCGA cohort mTMB comparisons between Group 2 and 3 tumors, in I MSI-H or MSS tumor profiles were included and in J only MSS tumor profiles were included. Because of smaller sample size per tumor type, analyses were pooled. A Mann–Whitney test was performed and ***, P < 0.001; *, P < 0.05; NS, nonsignificant.CC BY · PubMed Central
FIGURE 2 A, mTMB comparisons in Group 2 and 3 tumors by ExoD driver alone [P286R, V411L, or other driver(s) combined] or in conjunction with POLE variants. Each filled round circle represents a tumor genomic profile; data are shown for the driver alone and/or plus POLE variant. Data in “other drivers” were combined because of lower numbers. The data are segregated for MSS or MSI status where relevant. A few statistical comparisons were not performed because of ≤2 datapoints. B, mTMB comparisons in Group 2 and 3 tumors by the increasing number of POLE variants. Data are combined analysis of colorectal cancer, endometrial cancer, and ovarian cancer genomic profiles in the Caris dataset. Each filled round circle represents a tumor genomic profile; data are shown for Group 2 ExoD drivers, and for Group 3 by the ExoD driver plus the number of variants [1, 2, and >2 variant(s)]. A and B, A Mann–Whitney test was performed and ****, P < 0.0001; ***, P < 0.001; **, P < 0.01; NS, nonsignificant. Corrections for multiple comparisons were performed using the Benjamini–Hochberg FDR test. C–E, 3-nucleotide context of POLE variants in Group 3 tumors. All COSMIC mutational signatures associated with POLE ExoD driver defects (SBS 10a, SBS 10b, SBS 14, and SBS 28) were assessed (39). Pie chart distribution of SBS 10a, SBS 10b, SBS 14, and SBS 28 in colorectal cancer (C), endometrial cancer (D), and ovarian cancer (E). Each of these signatures has a primary mutation which has been described as a “hotspot” (39); SBS 10a is C>A in TCT context; SBS 10b is C>T in the TCG context; SBS 14 is C>A in the NCT context (N is any base); and SBS 28 is T>G in the TTT context. In addition to these primary “hotspots,” all mutations that comprise >1% of the genome signature of interest were counted, capturing 88%–90% of each signature in the analysis.
FIGURE 2 A, mTMB comparisons in Group 2 and 3 tumors by ExoD driver alone [P286R, V411L, or other driver(s) combined] or in conjunction with POLE variants. Each filled round circle represents a tumor genomic profile; data are shown for the driver alone and/or plus POLE variant. Data in “other drivers” were combined because of lower numbers. The data are segregated for MSS or MSI status where relevant. A few statistical comparisons were not performed because of ≤2 datapoints. B, mTMB comparisons in Group 2 and 3 tumors by the increasing number of POLE variants. Data are combined analysis of colorectal cancer, endometrial cancer, and ovarian cancer genomic profiles in the Caris dataset. Each filled round circle represents a tumor genomic profile; data are shown for Group 2 ExoD drivers, and for Group 3 by the ExoD driver plus the number of variants [1, 2, and >2 variant(s)]. A and B, A Mann–Whitney test was performed and ****, P < 0.0001; ***, P < 0.001; **, P < 0.01; NS, nonsignificant. Corrections for multiple comparisons were performed using the Benjamini–Hochberg FDR test. C–E, 3-nucleotide context of POLE variants in Group 3 tumors. All COSMIC mutational signatures associated with POLE ExoD driver defects (SBS 10a, SBS 10b, SBS 14, and SBS 28) were assessed (39). Pie chart distribution of SBS 10a, SBS 10b, SBS 14, and SBS 28 in colorectal cancer (C), endometrial cancer (D), and ovarian cancer (E). Each of these signatures has a primary mutation which has been described as a “hotspot” (39); SBS 10a is C>A in TCT context; SBS 10b is C>T in the TCG context; SBS 14 is C>A in the NCT context (N is any base); and SBS 28 is T>G in the TTT context. In addition to these primary “hotspots,” all mutations that comprise >1% of the genome signature of interest were counted, capturing 88%–90% of each signature in the analysis.CC BY · PubMed Central
FIGURE 3 Molecular features of colorectal cancer (A) and endometrial cancer (B). The mutational landscape and patient demographic/clinical characteristics (PD-L1 by IHC, MSI comprehensive, age, and sex) of the four cohorts for each cancer type were plotted using the GenVisR package of R.
FIGURE 3 Molecular features of colorectal cancer (A) and endometrial cancer (B). The mutational landscape and patient demographic/clinical characteristics (PD-L1 by IHC, MSI comprehensive, age, and sex) of the four cohorts for each cancer type were plotted using the GenVisR package of R.CC BY · PubMed Central
FIGURE 4 Comparison of mTMB and ΔΔG values in Group 2 and 3 tumors in the CLS dataset. With AF2 DNA unbound model and Rosetta ddG_monomer, we generated 25 repacked decoys for each mutation and compared the average energy score for these decoys with an average for 25 decoys of the WT protein. For mutations in the NTL, we performed calculations on both models (with and without DN). For those in the CTL, we performed calculations only on the DNA unbound model. We used a cutoff of ±1.45 kcal/mol for significant ΔΔG, corresponding to approximately 3 SDs of the differences of the mean Rosetta scores for WT and mutant structures. A, Comparison of mTMB and ΔΔG values in Group 2 and Group 3 tumors by the number of POLE variants. Data for colorectal cancer, endometrial cancer, and ovarian cancer genomic profiles were combined, and ΔΔG values were plotted against the mTMB. For Group 2 or 3 data with + 1 POLE variant plots, each filled round circle represents a single tumor genomic profile. B, Comparison of mTMB and ΔΔG values in Group 2 and Group 3 tumors by POLE ExoD driver. Data for colorectal cancer, endometrial cancer, and ovarian cancer genomic profiles were combined, and ΔΔG values were plotted against the mTMB. A and B, Group 3 tumors with multiple variants, a circle next to another circle (without any space) represents a single tumor. For clarity, ΔΔG values for ExoD drivers in Group 3 tumors are not shown (they are same as in Group 2). Color in each filled circle—green and shades of green, structure-destabilizing variants (positive ΔΔG); white, variants that are within the SDs of ±1.45 kcal/mol and are structure neutral; red and shades of red, structure-stabilizing variants (negative ΔΔG). Yellow, nonsense, or frameshift variants.
FIGURE 4 Comparison of mTMB and ΔΔG values in Group 2 and 3 tumors in the CLS dataset. With AF2 DNA unbound model and Rosetta ddG_monomer, we generated 25 repacked decoys for each mutation and compared the average energy score for these decoys with an average for 25 decoys of the WT protein. For mutations in the NTL, we performed calculations on both models (with and without DN). For those in the CTL, we performed calculations only on the DNA unbound model. We used a cutoff of ±1.45 kcal/mol for significant ΔΔG, corresponding to approximately 3 SDs of the differences of the mean Rosetta scores for WT and mutant structures. A, Comparison of mTMB and ΔΔG values in Group 2 and Group 3 tumors by the number of POLE variants. Data for colorectal cancer, endometrial cancer, and ovarian cancer genomic profiles were combined, and ΔΔG values were plotted against the mTMB. For Group 2 or 3 data with + 1 POLE variant plots, each filled round circle represents a single tumor genomic profile. B, Comparison of mTMB and ΔΔG values in Group 2 and Group 3 tumors by POLE ExoD driver. Data for colorectal cancer, endometrial cancer, and ovarian cancer genomic profiles were combined, and ΔΔG values were plotted against the mTMB. A and B, Group 3 tumors with multiple variants, a circle next to another circle (without any space) represents a single tumor. For clarity, ΔΔG values for ExoD drivers in Group 3 tumors are not shown (they are same as in Group 2). Color in each filled circle—green and shades of green, structure-destabilizing variants (positive ΔΔG); white, variants that are within the SDs of ±1.45 kcal/mol and are structure neutral; red and shades of red, structure-stabilizing variants (negative ΔΔG). Yellow, nonsense, or frameshift variants.CC BY · PubMed Central

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Metadata

Authors
Shah, S. M., Demidova, E. V., Ringenbach, S., Faezov, B., Andrake, M., Gandhi, A., Mur, P., Viana-Errasti, J., Xiu, J., Swensen, J., Valle, L., Dunbrack, R. L., Hall, M. J., & Arora, S.
Affiliations
  • Fox Chase Cancer Center (US)
  • University College Dublin (IE)
  • Institut d'Investigació Biomédica de Bellvitge (ES)
  • Caris Life Sciences (United States) (US)
Corresponding author
Arora, Sanjeevani
ORCID
Venue
Cancer Research Communications
Publisher
American Association for Cancer Research (AACR)
Published
2024-01-26
Volume
4
Issue
1
Pages
213-225
ISSN
2767-9764 (ISSN-L)
DOI
10.1158/2767-9764.crc-23-0312
PMC
PMC10812383
OpenAlex
W4390733351
Cited by
16
References
3
Citations by year
2026: 5 · 2025: 10 · 2024: 1
FWCI
3.21
Type
journal article
Language
en
Update notices
correction (2024-05-31)
Open access
Licence
Locations
  • Cancer Research Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • TUScholarShare (Temple University) (submittedVersion, cc-by)
  • Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, cc-by)
Funders
  • HHS | National Institutes of Health (P30 CA006927, R35 GM122517, UH2CA271230-01, CEP Award (Yale Head and Neck Cancer NIH SPORE))
  • American Cancer Society
  • U.S. Department of Defense (W81XWH-18-1-0148)
  • Spanish Ministry of Science and Innovation (PID2020-112595RB-I00 and predoctoral fellowship to JV-E)
  • MEC | Instituto de Salud Carlos III (CIBERONC CB16/12/00234)
  • Generalitat de Catalunya (AGAUR 2017SGR1282,CERCA Program)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Health Sciences › Medicine › Pathology and Forensic Medicine › Genetic factors in colorectal cancer
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Keywords
POLE mutation, exonuclease domain, tumor mutational burden, mutagenesis, colorectal cancer, endometrial cancer, ovarian cancer, neoantigens, protein stability

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