Mutation Data Research Articles

DeepCBS: shedding light on the impact of mutations occurring at CTCF binding sites.

CTCF-mediated chromatin loops create insulated neighborhoods that constrain promoter-enhancer interactions, serving as a unit of gene regulation. Disruption of the CTCF binding sites (CBS) will lead to the destruction of insulated neighborhoods, which in turn can cause dysregulation of the contained genes. In a recent study, it is found that CTCF/cohesin binding sites are a major mutational hotspot in the cancer genome. Mutations can affect CTCF binding, causing the disruption of insulated neighborhoods. And our analysis reveals a significant enrichment of well-known proto-oncogenes in insulated neighborhoods with mutations specifically occurring in anchor regions. It can be assumed that some mutations disrupt CTCF binding, leading to the disruption of insulated neighborhoods and subsequent activation of proto-oncogenes within these insulated neighborhoods. To explore the consequences of such mutations, we develop DeepCBS, a computational tool capable of analyzing mutations at CTCF binding sites, predicting their influence on insulated neighborhoods, and investigating the potential activation of proto-oncogenes. Futhermore, DeepCBS is applied to somatic mutation data of liver cancer. As a result, 87 mutations that disrupt CTCF binding sites are identified, which leads to the identification of 237 disrupted insulated neighborhoods containing a total of 135 genes. Integrative analysis of gene expression differences in liver cancer further highlights three genes: ARHGEF39, UBE2C and DQX1. Among them, ARHGEF39 and UBE2C have been reported in the literature as potential oncogenes involved in the development of liver cancer. The results indicate that DQX1 may be a potential oncogene in liver cancer and may contribute to tumor immune escape. In conclusion, DeepCBS is a promising method to analyze impacts of mutations occurring at CTCF binding sites on the insulator function of CTCF, with potential extensions to shed light on the effects of mutations on other functions of CTCF.

Large-Scale Cancer Genomic Analysis Reveals Significant Disparities between Microsatellite Instability and Tumor Mutational Burden.

Microsatellite instability (MSI) and tumor mutational burden (TMB) are predictive biomarkers for pan-cancer immunotherapy. The interrelationship between MSI-high (MSI-H) and TMB-high (TMB-H) in human cancers and their predictive value for immunotherapy in lung cancer remain unclear. We analyzed somatic mutation data from the Genomics Evidence Neoplasia Information Exchange (n = 46,320) to determine the relationship between MSI-H and TMB-H in human cancers using adjusted multivariate regression models. Patient survival was examined using the Cox proportional hazards model. The association between MSI and genetic mutations was assessed. Patients (31-89%) with MSI-H had TMB-low phenotypes across 22 cancer types. Colorectal and stomach cancers showed the strongest association between TMB and MSI. TMB-H patients with lung cancer who received immunotherapy exhibited significantly higher overall survival [HR, 0.61; 95% confidence interval (CI), 0.44-0.86] and progression-free survival (HR, 0.65; 95% CI, 0.47-0.91) compared to the TMB-low group; no significant benefit was observed in the MSI-H group. Patients with TMB and MSI phenotypes showed further improvement in overall survival and PFS. We identified several mutated genes associated with MSI-H phenotypes, including known mismatch repair genes and novel mutated genes, such as ARID1A and ARID1B. Our results demonstrate that TMB-H and/or a combination of MSI-H can serve as biomarkers for immunotherapies in lung cancer. These findings suggest that distinct or combined biomarkers should be considered for immunotherapy in human cancers because notable discrepancies exist between MSI-H and TMB-H across different cancer types.

Epistasis between mutator alleles contributes to germline mutation spectrum variability in laboratory mice.

Maintaining germline genome integrity is essential and enormously complex. Although many proteins are involved in DNA replication, proofreading, and repair, mutator alleles have largely eluded detection in mammals. DNA replication and repair proteins often recognize sequence motifs or excise lesions at specific nucleotides. Thus, we might expect that the spectrum of de novo mutations - the frequencies of C>T, A>G, etc. - will differ between genomes that harbor either a mutator or wild-type allele. Previously, we used quantitative trait locus mapping to discover candidate mutator alleles in the DNA repair gene Mutyh that increased the C>A germline mutation rate in a family of inbred mice known as the BXDs (Sasani et al., 2022, Ashbrook et al., 2021). In this study we developed a new method to detect alleles associated with mutation spectrum variation and applied it to mutation data from the BXDs. We discovered an additional C>A mutator locus on chromosome 6 that overlaps Ogg1, a DNA glycosylase involved in the same base-excision repair network as Mutyh (David et al., 2007). Its effect depends on the presence of a mutator allele near Mutyh, and BXDs with mutator alleles at both loci have greater numbers of C>A mutations than those with mutator alleles at either locus alone. Our new methods for analyzing mutation spectra reveal evidence of epistasis between germline mutator alleles and may be applicable to mutation data from humans and other model organisms.

Epistasis between mutator alleles contributes to germline mutation spectrum variability in laboratory mice

Maintaining germline genome integrity is essential and enormously complex. Although many proteins are involved in DNA replication, proofreading, and repair, mutator alleles have largely eluded detection in mammals. DNA replication and repair proteins often recognize sequence motifs or excise lesions at specific nucleotides. Thus, we might expect that the spectrum of de novo mutations – the frequencies of C>T, A>G, etc. – will differ between genomes that harbor either a mutator or wild-type allele. Previously, we used quantitative trait locus mapping to discover candidate mutator alleles in the DNA repair gene Mutyh that increased the C>A germline mutation rate in a family of inbred mice known as the BXDs (Sasani et al., 2022, Ashbrook et al., 2021). In this study we developed a new method to detect alleles associated with mutation spectrum variation and applied it to mutation data from the BXDs. We discovered an additional C>A mutator locus on chromosome 6 that overlaps Ogg1, a DNA glycosylase involved in the same base-excision repair network as Mutyh (David et al., 2007). Its effect depends on the presence of a mutator allele near Mutyh, and BXDs with mutator alleles at both loci have greater numbers of C>A mutations than those with mutator alleles at either locus alone. Our new methods for analyzing mutation spectra reveal evidence of epistasis between germline mutator alleles and may be applicable to mutation data from humans and other model organisms.

Clinical and genetic factors associated with tumor response to neoadjuvant (chemo)radiotherapy, survival and recurrence risk in rectal cancer.

Rectal cancer poses challenges in preoperative treatment response, with up to 30% achieving a complete response (CR). Personalized treatment relies on accurate identification of responders at diagnosis. This study aimed to unravel CR determinants, overall survival (OS), and time to recurrence (TTR) using clinical and targeted sequencing data. Analyzing 402 patients undergoing preoperative treatment, tumor stage, size, and treatment emerged as robust response predictors. CR rates were higher in smaller, early-stage, and intensively treated tumors. Targeted sequencing analyzed 216 cases, while 120 patients provided hotspot mutation data. KRAS mutation dramatically reduced CR odds by over 50% (odds ratio [OR] = 0.3 in the targeted sequencing and OR = 0.4 hotspot cohorts, respectively). In contrast, SMAD4 and SYNE1 mutations were associated with higher CR rates (OR = 6.0 and 6.8, respectively). Favorable OS was linked to younger age, CR, and low baseline carcinoembryonic antigen levels. Notably, CR and an APC mutation increased TTR, while a BRAF mutation negatively affected TTR. Beyond tumor burden, SMAD4 and SYNE1 mutations significantly influenced CR. KRAS mutations independently correlated with radiotherapy resistance, and BRAF mutations heightened recurrence risk. Intriguingly, non-responding tumors with initially small sizes carried a higher risk of recurrence. The findings, even if limited in addition to the imperfect clinical factors, offer insights into rectal cancer treatment response, guiding personalized therapeutic strategies. By uncovering factors impacting CR, OS, and TTR, this study underscores the importance of tailored approaches for rectal cancer patients. These findings, based on extensive analysis and mutation data, pave the way for personalized interventions, optimizing outcomes in the challenges of rectal cancer preoperative treatment.

Predicting hosts and cross-species transmission of Streptococcus agalactiae by interpretable machine learning

BackgroundStreptococcus agalactiae, commonly known as Group B Streptococcus (GBS), exhibits a broad host range, manifesting as both a beneficial commensal and an opportunistic pathogen across various species. In humans, it poses significant risks, causing neonatal sepsis and meningitis, along with severe infections in adults. Additionally, it impacts livestock by inducing mastitis in bovines and contributing to epidemic mortality in fish populations. Despite its wide host spectrum, the mechanisms enabling GBS to adapt to specific hosts remain inadequately elucidated. Therefore, the development of a rapid and accurate method differentiates GBS strains associated with particular animal hosts based on genome-wide information holds immense potential. Such a tool would not only bolster the identification and containment efforts during GBS outbreaks but also deepen our comprehension of the bacteria's host adaptations spanning humans, livestock, and other natural animal reservoirs. Methods and resultsHere, we developed three machine learning models—random forest (RF), logistic regression (LR), and support vector machine (SVM) based on genome-wide mutation data. These models enabled precise prediction of the host origin of GBS, accurately distinguishing between human, bovine, fish, and pig hosts. Moreover, we conducted an interpretable machine learning using SHapley Additive exPlanations (SHAP) and variant annotation to uncover the most influential genomic features and associated genes for each host. Additionally, by meticulously examining misclassified samples, we gained valuable insights into the dynamics of host transmission and the potential for zoonotic infections. ConclusionsOur study underscores the effectiveness of random forest (RF) and logistic regression (LR) models based on mutation data for accurately predicting GBS host origins. Additionally, we identify the key features associated with each GBS host, thereby enhancing our understanding of the bacteria's host-specific adaptations.

Salivary Duct Carcinoma Arising in the Submandibular Gland in a Patient with Neurofibromatosis Type 1.

A 71-year-old man with neurofibromatosis type 1 (NF1) presented to our department with a 1-week history of a painful mass in the left submandibular area. Computed tomography (CT) and magnetic resonance imaging revealed an irregular-shaped tumor with a diameter of 2.0 cm in the left submandibular gland and a metastatic lymph node with a diameter of 1.0 cm adjacent to the tumor. Fluorodeoxyglucose-positron emission tomography/CT revealed increased uptake in the tumor. Fine-needle aspiration cytology revealed atypical cells, suggesting salivary duct carcinoma (SDC). Left neck dissection with resection of the tumor and submandibular gland was performed under general anesthesia. Histologic examination revealed ductal formation with a solid, cystic, cribriform, and papillary structure with intraductal comedonecrosis, diagnosing as SDC originating in the submandibular gland (pT3N1M0 pStage III). Mutational analysis of 160 cancer-related genes by next-generation sequencing (NGS) revealed a germline and frameshift mutation in the NF1 gene (p.R2408Kfs*14) and a somatic and frameshift mutation in the TP53 gene (p.C176Wfs*22). The patient received postoperative radiotherapy to the left neck area at 66 Gy. No evidence of recurrence or metastasis has been observed as of 10 months postoperatively. This is the first reported case of SDC in the submandibular gland in a patient with NF1. The mutational data by NGS may contribute to a better understanding of the oncogenesis of SDC in patients with NF1.

Development of a novel lncRNA-derived immune gene score using machine learning-based ensembles for predicting the survival of HCC

BackgroundLong noncoding RNAs (lncRNAs) are implicated in the tumor immunology of hepatocellular carcinoma (HCC). MethodsHCC mRNA and lncRNA expression profiles were used to extract immune-related genes with the ImmPort database, and immune-related lncRNAs with the ImmLnc algorithm. The MOVICS package was used to cluster immune-related mRNA, immune-related lncRNA, gene mutation and methylation data on HCC from the TCGA. GEO and ICGC datasets were used to validate the model. Data from single-cell sequencing was used to determine the expression of genes from the model in various immune cell types. ResultsWith this model, the area under the curve (AUC) for 1-, 3- and 5-year survival of HCC patients was 0.862, 0.869 and 0.912, respectively. Single-cell sequencing showed EREG was significantly expressed in a variety of immune cell types. Knockdown of the EREG target gene resulted in significant anti-apoptosis, pro-proliferation and pro-migration effects in HepG2 and HUH7 cells. Moreover, serum and liver tissue EREG levels in HCC patients were significantly higher than those of healthy control patients. ConclusionWe built a prognostic model with good accuracy for predicting HCC patient survival. EREG is a potential immunotherapeutic target and a promising prognostic biomarker.

Short tandem repeat mutations regulate gene expression in colorectal cancer

Short tandem repeat (STR) mutations are prevalent in colorectal cancer (CRC), especially in tumours with the microsatellite instability (MSI) phenotype. While STR length variations are known to regulate gene expression under physiological conditions, the functional impact of STR mutations in CRC remains unclear. Here, we integrate STR mutation data with clinical information and gene expression data to study the gene regulatory effects of STR mutations in CRC. We confirm that STR mutability in CRC highly depends on the MSI status, repeat unit size, and repeat length. Furthermore, we present a set of 1244 putative expression STRs (eSTRs) for which the STR length is associated with gene expression levels in CRC tumours. The length of 73 eSTRs is associated with expression levels of cancer-related genes, nine of which are CRC-specific genes. We show that linear models describing eSTR-gene expression relationships allow for predictions of gene expression changes in response to eSTR mutations. Moreover, we found an increased mutability of eSTRs in MSI tumours. Our evidence of gene regulatory roles for eSTRs in CRC highlights a mostly overlooked way through which tumours may modulate their phenotypes. Future extensions of these findings could uncover new STR-based targets in the treatment of cancer.

Enhanced dynamic state estimation of regional new energy power system under different abnormal scenarios

AbstractThe high proportion of renewable energy sources in the power grid increases the failure probability of the system, which becomes a new challenge for the safe and stable operation of the regional power grid. To ensure stable control of the power network with substantial renewable energy integration, this article proposes a new method that combines the long short‐term memory (LSTM) neural networks and adaptive cubature Kalman filter (ACKF) to improve the prediction accuracy of mutation data inherited from the renewable generation. Four abnormal scenarios, including low voltage ride‐through (LVRT), high voltage ride‐through (HVRT), continuous fault ride‐through and bad data injection of the regional power grid are investigated through extensive case studies. The proposed method is implemented on the IEEE 30‐node system for performance verification. The simulation results demonstrate that the proposed method has considerably higher robustness than the traditional Kalman filter algorithm and can effectively improve the overall state estimation accuracy of the renewable energy power system under different scenarios.

CYP21A2 Gene Analysis in Southern Iranian CAH Patients and a Brief Review of the Mutation Spectrum.

CYP21A2 gene mutations are responsible for more than 95% of Congenital Adrenal Hyperplasia (CAH) disorders with autosomal recessive inheritance. Most of these pathogenic mutations originate from the CYP21A1P, a neighboring pseudogene with 98% homology, due to unequal crossing over or gene conversion events. Mutation identification of the gene could be beneficial for accurate diagnosis and outcome prediction. Twelve unrelated patients with CAH diagnosis were recruited for genetic counseling. To ensure distinct amplification of the CYP21A2 gene rather than its pseudogene, the complete sequence of the gene was amplified through two overlapping fragments by specific primers. The entire sequences were screened by direct Sanger sequencing using new sequencing primers. Only two pathogenic point mutations were identified. The c.293-13C>G, also known as In2G, and the c.955C>T mutations were found in 37.5 and 33.3% of alleles, respectively. One patient showed homozygous gene deletion. We also reviewed recent reports on CYP21A2 gene mutations in Iran. Evaluating the ethnicity-specific gene mutation data is significant for populations with diverse ethnic groups including the Iranian population. Although several common mutations have been reported as causative mutations among CAH patients, identifying only two common point mutations in Fars province would help prioritize exon sequencing and reduce the cost and time of genotyping.

ICDM-GEHC: identifying cancer driver module based on graph embedding and hierarchical clustering

Due to the high heterogeneity of cancers, it is rather essential to explore driver modules with the help of gene mutation data as well as known interactions between genes/proteins. Unfortunately, latent false positive interactions are inevitable in the Protein-Protein Interaction (PPI) network. Hence in the presented method, a new weight evaluation index, based on the gene-microRNA network as well as somatic mutation profile, is introduced for weighting the PPI network first. Subsequently, the vertices in the weighted PPI network are hierarchically clustered by measuring the Mahalanobis distance of their feature vectors, extracted with the graph embedding method Node2vec. Finally, a heuristic process with dropping and extracting is conducted on the gene clusters to produce a group of gene modules. Numerous experiment results demonstrate that the proposed method exhibits superior performance to four cutting-edge identification methods in most cases regarding the capability of recognizing the acknowledged cancer-related genes, generating modules having relatively high coverage and mutual exclusivity, and are significantly enriched for specific types of cancers. The majority of the genes in the identified modules are involved in cancer-related signaling pathways, or have been reported to be carcinogenic in the literature. Furthermore, many cancer related genes detected by the proposed method are actually omitted by the four comparison methods, which has been verified in the experiments.

A two-step passenger flow anomaly detection scheme based on machine learning methods

Subway is an important transportation means for residents due to its large volume, punctuality and environmental friendliness. However, weather factors, sports events, concerts and some unexpected events can lead to a surge or abnormality in passenger flow, which brings enormous pressure to the management of stations and passenger flow guidance. Inspired by this, this paper formulates the abnormal passenger flows into different categories in terms of the characteristics and periodical trends, and proposes a two-step abnormal detection scheme to identify the anomalies and their type, and locate abnormal positions. First, two abnormal passenger flows recognition methods based on Jensen–Shannon divergence, dynamic time warping, and density-based spatial clustering of applications with noise are established to identify the station-level abnormal passenger flow. Then, a triple standard deviation algorithm based on sliding window is further proposed to identify the abnormal type and position. Real-world smart card data of the Beijing subway in China, and the manual mutation data of the real data are employed to evaluate effectiveness of our framework. The results show that our two-step scheme is superior to the state-of-the-art algorithms, which can detect out and locate abnormal passenger flows with various characteristics. On more mutation data, this paper discusses the performances on various anomalies of different types of stations in depth, which further indicates our framework is robust and effective in practice.

MDSubSampler: A Python library for a posteriori sampling of important protein conformations, automated workflows for mutation engineering and data processing for machine learning prediction

MDSubSampler: A Python library for a posteriori sampling of important protein conformations, automated workflows for mutation engineering and data processing for machine learning prediction

Biochemical characterization and mutational analysis of the NurA protein from the hyperthermophilic euryarchaeon Thermococcus barophilus Ch5

Archaeal NurA protein plays a key role in producing 3′-single stranded DNA used for homologous recombination repair, together with HerA, Mre11, and Rad50. Herein, we describe biochemical characteristics and roles of key amino acid residues of the NurA protein from the hyperthermophilic euryarchaeon Thermococcus barophilus Ch5 (Tba-NurA). Tba-NurA possesses 5′–3′ exonuclease activity for degrading DNA, displaying maximum efficiency at 45 °C–65 °C and at pH 8.0 in the presence of Mn2+. The thermostable Tba-NurA also possesses endonuclease activity capable of nicking plasmid DNA and circular ssDNA. Mutational data demonstrate that residue D49 of Tba-NurA is essential for exonuclease activity and is involved in binding ssDNA since the D49A mutant lacked exonuclease activity and reduced ssDNA binding. The R96A and R129A mutants had no detectable dsDNA binding, suggesting that residues R96 and R129 are important for binding dsDNA. The abolished degradation activity and reduced dsDNA binding of the D120A mutant suggest that residue D120 is essential for degradation activity and dsDNA binding. Additionally, residues Y392 and H400 are important for exonuclease activity since these mutations resulted in exonuclease activity loss. To our knowledge, it is the first report on biochemical characterization and mutational analysis of the NurA protein from Thermococcus.

Predicting colorectal cancer prognosis based on long noncoding RNAs of disulfidptosis genes.

A recently hypothesized cause of cell death called disulfidptosis has been linked to the expansion, emigration, and vascular rebuilding of cancer cells. Cancer can be treated by targeting the pathways that trigger cell death. To discover the long non-coding RNA of the disulfidaptosis-related lncRNAs (DRLs), prognosis clinical survival, and treat patients with colorectal cancer with medications. Initially, we queried the Cancer Genome Atlas database to collect transcriptome, clinical, and genetic mutation data for colorectal cancer (CRC). Training and testing sets for CRC patient transcriptome data were generated randomly. Key long non-coding RNAs (lncRNAs) related to DRLs were then identified and evaluated using a least absolute shrinkage and selection operator procedure, as well as univariate and multivariate Cox regression models. A prognostic model was then created after risk scoring. Also, Immune infiltration analysis, immune checkpoint analysis, and medication susceptibility analysis were used to investigate the causes of the different prognoses between high and low risk groups. Finally, we validated the differential expression and biomarker potential of risk-predictive lncRNAs through induction using both NCM460 and HT-29 cell lines, as well as a disulfidptosis model. In this work, eight significant lncRNAs linked to disulfidptosis were found. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of differentially expressed genes between high- and low-risk groups from the prognostic model showed a close relationship with the immune response as well as significant enrichment in neutrophil extracellular trap formation and the IL-17 signaling pathway. Furthermore, significant immune cell variations between the high-risk and low-risk groups were seen, as well as a higher incidence of immunological escape risk in the high-risk group. Finally, Epirubicin, bortezomib, teniposide, and BMS-754807 were shown to have the lowest sensitivity among the four immunotherapy drugs. Our findings emphasizes the role of disulfidptosis in regulating tumor development, therapeutic response, and patient survival in CRC patients. For the clinical treatment of CRC, these important LncRNAs could serve as viable therapeutic targets.

Is Metal Stabilization of the Leaving Group Required or Can Lysine Facilitate Phosphodiester Bond Cleavage in Nucleic Acids? A Computational Study of EndoV.

Endonuclease V (EndoV) is a single-metal-dependent enzyme that repairs deaminated DNA nucleobases in cells by cleaving the phosphodiester bond, and this enzyme has proven to be a powerful tool in biotechnology and medicine. The catalytic mechanism used by EndoV must be understood to design new disease detection and therapeutic solutions and further exploit the enzyme in interdisciplinary applications. This study has used a mixed molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) approach to compare eight distinct catalytic pathways and provides the first proposed mechanism for bacterial EndoV. The calculations demonstrate that mechanisms involving either direct or indirect metal coordination to the leaving group of the substrate previously proposed for other nucleases are unlikely for EndoV, regardless of the general base (histidine, aspartate, and substrate phosphate moiety). Instead, distinct catalytic pathways are characterized for EndoV that involve K139 stabilizing the leaving group, a metal-coordinated water stabilizing the transition structure, and either H214 or a substrate phosphate group activating the water nucleophile. In silico K139A and H214A mutational results support the newly proposed roles of these residues. Although this is a previously unseen combination of general base, general acid, and metal-binding architecture for a one-metal-dependent endonuclease, our proposed catalytic mechanisms are fully consistent with experimental kinetic, structural, and mutational data. In addition to substantiating a growing body of literature, suggesting that one metal is enough to catalyze P-O bond cleavage in nucleic acids, this new fundamental understanding of the catalytic function will promote the exploration of new and improved applications of EndoV.

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.

Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .

Molecular and clinical characteristics of POLE/POLD1 alterations among patients with colorectal cancer.

184 Background: POLE and POLD1 alterations (DNA polymerase ε and DNA polymerase δ) are key biomarkers of immune response for patients with MSS colorectal cancers (CRC). The clinical and molecular characteristics of POLE/POLD1 alterations are not yet well defined across CRCs. In this study, we investigated the clinical/molecular features of POLE and POLD1 alterations and its association with MSI-H CRC. Methods: All the patients and mutation data were selected from the cBioPortal database (https://www.cbioportal.org). All nonsynonymous mutations including missense, frameshift, nonsense, nonstop, splice site, and translation start site changes of POLE/POLD1 were considered. 21 studies were used in the analysis. 7179 data samples were obtained from 7179 patients. Tumors included were from all stages of CRCs. POLE/POLD1 mutations were from mutation tables, only protein-changing mutations were kept. MSI-H status was based on mutation data (presence of MLH1/PMS2/MSH2/MSH6 mutations). Results: Of 7179 patients with all stage CRCs, 6.1% (439) were found to harbor mutated POLE/POLD1 genes. Among those, 3446 patients had known MSI status and 14.9% (514/3446) had MSI-H disease. Notably, 3.3% patients with POLE/POLD1 mutations were found to have concurrent MSI-H disease (co-existence) and only 1.1% of patients with POLE/POLD1 mutations had MSS CRC. MSI status was unknown for 1.7% of patients with POLE/POLD mutant CRC. The mean age for patients with POLE/POLD1 + with or without MSI-H disease was 67. POLE/POLD mutations were exceedingly more common in colon than rectum regardless of MSI-H status (85% vs 15% overall and 88% vs 12% in MSI-H subgroup). No difference was seen among genders for the distribution of POLE/POLD1 mutations. Conclusions: POLE/POLD1 mutations frequently co-exist with MSI-H disease in CRC. Patients with MSS-CRC harboring POLE/POLD1 mutations represent a smaller subgroup of CRC (~1%). The incidence of POLE/POLD1 somatic mutations was more common among patients with colon cancer than those with rectal cancer.

Natural history and patterns of progression for dMMR/MSI-H colorectal cancer treated with immune checkpoint blockade: A single center retrospective analysis.

145 Background: Deficiency in mismatch repair or high microsatellite instability (dMMR/MSI-H), occurring in approximately 20% of all colorectal cancer (CRC) cases and approximately 4% of metastatic CRC (mCRC), predicts clinical benefit of immune checkpoint blockade (ICB). With response rates of about 50% in mCRC and higher in localized cases, ICB is providing durable benefit and even cure in a meaningful portion of cases. However, further understanding of those patients who progress and treatment options following progression are needed. Methods: A single institution retrospective review was performed on 151 dMMR/MSI-H CRC patients receiving ICB at MD Anderson Cancer Center from 2014 to 2023. Data was collected including patient demographics, tumor and mutational data, ICB treatment data, progression patterns, and treatment following progression. Progression patterns were classified as intrinsic (progression at initial restaging with pseudoprogression excluded) or adaptive progression and oligometastatic (3 or less sites of metastatic disease regardless of organs involved) or systemic progression. Results: The median time to progression (TTP) of all patients treated with ICB was 88 months (m). Median follow up was 40 m. Of the 151 patients, 59 (39%) progressed on ICB. The majority of these were treated with monotherapy anti-PD1 (49, 83%) while only 10 (17%) were treated with combination ICB. Of those that progressed, 29 (49%) were considered intrinsic while 30 (51%) were considered adaptive. In the adaptive group, average TTP was 21 m. The majority progressed at a metastatic site (51, 86%) and at a prior site of disease (47, 80%). Progression was oligometastatic in 30 (51%) patients and systemic in 29 (49%) patients. The median TTP for oligometastatic progression was 9 m while median TTP for systemic progression was 4 m (p=0.005). The median OS for those with systemic progression was 17 m while the median OS for oligometastatic progression was not reached (p=0.027). Of those with progression, 12 (20%) had non-surgical local therapy to progressing sites, 6 (10%) underwent surgery to progressing sites, and 31 (52%) received alternative systemic therapy (chemotherapy or ICB). Of the 16 patients who received local therapy (non-surgical, surgical, or both), 7 (44%) remain with no evidence of disease. Conclusions: This is one of the largest reviews of dMMR/MSI-H CRC progressors on ICB. Adaptive progression represented approximately one half of all progression events and local-modality therapy was utilized in 27% of patients. Long-term disease control was seen in oligometastatic adaptive progressors who received local modality therapy.