Cancer Gene Research Articles

Abstract B100: Improving access to hereditary cancer genetic testing in diverse populations: Outcomes in over 33,000 patients tested by genetics and non-genetics providers in Kaiser Permanente Southern California

Abstract Kaiser Permanente Southern California is an integrated health care organization serving over 4.7 million members in 14 medical centers. KPSC members are 42% Hispanic/Latino, 28 % Non-Hispanic White, 11% Asian/Pacific Islander, 8% Black/African American, and 12% Other/Unk. To facilitate detection of hereditary cancer syndromes among patients with personal and/or family histories of breast, colorectal, ovarian, pancreas and other cancers, we offered a 48-gene hereditary cancer multigene-panel test designed to detect all the most common hereditary cancer conditions. Patients seen by genetic counselors or geneticists (Gen providers) receive pre-test genetic counseling and include those with a family history of cancer with or without personal history of cancer. Our mainstreaming program allows non-genetics (non-Gen) providers including medical and surgical oncologists, gastroenterologists, and other specialists to order the test at the point of care (based on NCCN criteria) on their patients with cancer without pre-test genetic counseling. Providers place the order in the EMR system and patients receive an email/text message with information about the test, potential results, links to educational videos and a Genetics phone number for questions. When results are ready patients receive an automatic email/text notifying them and providing a link to their test report. Those with a positive test result receive post-test genetic counseling. Patients with negative or uncertain significance (VUS) results receive a message with a link to their test report, information about their result and the option to schedule a visit with genetics. From April 2021 to May 2024, we completed 33,173 tests: 13,076 (39%) ordered by non-Gen and 20,097 (61%) by Gen providers. The results distribution was similar in both groups: pathogenic/likely pathogenic (PV/LPV): 15% for Gen orders, 12% for non-Gen; VUS: 28% Gen, 30% non-Gen, and negative: 56% Gen, 58% Non-Gen. The patients tested closely reflected the diversity of our population, except for under-representation of Hisp/Latino patients and over-representation of Non-Hisp Whites: 14,400 (42%); Hisp/Latino 9,863 (29%); Asian/PI 4,279 (13%); AA/Black 3,209 (9%); and Oth/Unk 2,415 (7%). Eliminating single MUTYH variants the top 5 genes with the highest number and percentage of PV/LPV results for each of the race/ethnicity categories were as follows: AA/Black: BRCA2=60, BRCA1=38, ATM=36, MUTYH=18; Asian/PI: BRCA2=101, ATM=40, BRCA1=37, PMS2=24; Hisp/Latino: BRCA2=250, BRCA1=171, ATM=127, CHEK2=125; CDKN2A=113; Non Hisp White: CHEK2=346; BRCA2=304; ATM=200, BRCA1=196, FH=100; Oth/Unk: BRCA2=50, BRCA1=41, CHEK2=40, PALB2=30, ATM=20. Our results represent one of the largest and most diverse patient cohorts completing hereditary cancer testing from a single institution. They indicate that mainstreaming hereditary cancer testing can improve access for patients from under-represented groups and suggest differences in the frequency of deleterious variants in different hereditary cancer genes across race/ethnic groups. Citation Format: Monica Alvarado, Trevor Hoffman, Reina Haque, Hilary Kershberg, John Goff. Improving access to hereditary cancer genetic testing in diverse populations: Outcomes in over 33,000 patients tested by genetics and non-genetics providers in Kaiser Permanente Southern California [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B100.

Abstract C083: Meta-analysis reveals differences in somatic alterations by genetic ancestry across common cancers

Abstract Genetic similarity of populations (or genetic ancestry) contributes to cancer driving alterations. We performed a meta-analysis leveraging two real-world cohorts of targeted gene panel sequencing, comprising 275,605 tumor samples, to investigate ancestry-associated somatic alterations across 14 common cancers. We focused on five continental ancestries – European (EUR), African (AFR), East Asian (EAS), South Asian (SAS), and admixed American (AMR). Genetic ancestry was inferred using single nucleotide polymorphism markers from captured regions of gene panel sequencing in each cohort. The combined cohort from Memorial Sloan Kettering – Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) and Foundation Medicine (FMI) contains a substantial number (45,131) of non-EUR samples. Within each cohort, we performed multivariate logistic regression to test association of each cancer gene alteration with proportion of genetic ancestry as a continuous variable, controlling for relevant covariates (age, sex, gene panel version, and histological subtypes) for every combination of ancestry and cancer type. Our analysis focused on oncogenic or likely oncogenic alterations spanning mutations, copy number alterations, and structural rearrangements for each gene. We then aggregated the logistic regression results from the two cohorts using a fixed effects model of meta-analysis weighted by cohort sample size. We found 444 significant associations involving 116 unique genes with consistent logistic regression coefficients between cohorts and False Discovery Rate (FDR)-adjusted p-value < 0.1. These genes were involved in cancer-specific and cross-cancer associations. TP53, KRAS, and TERT were the top three recurrent ancestry-associated genes respectively. TERT promoter mutations were depleted in patients of AFR or EAS ancestry, but enriched in EUR ancestry in bladder urothelial carcinoma, glioblastoma, cutaneous melanoma, and hepatocellular carcinoma. Additionally, TP53 mutations were enriched in AFR and EAS ancestry across multiple cancers, consistent with previous reports, but depleted in prostate cancer in both AFR and EAS ancestries. Among the novel cancer-specific associations with FDA-approved targeted therapies, we found that CDK12 mutations in prostate adenocarcinoma were enriched in EAS ancestry while ERBB2 mutations in lung adenocarcinoma were enriched in AMR ancestry. We also found novel potential drug targets/biomarkers associated with AFR ancestry, such as enrichment of BAP1 mutations in head and neck squamous cell carcinoma and depletion of FGFR2 mutations in uterine endometrioid carcinoma. Overall, our work identified ancestry-associated somatic alteration differences of cancer genes in a combined cohort with a large number of non-European samples. This work underscores the value of studying under-represented populations to replicate or reveal new potential targets for precision oncology. Citation Format: Setor Amuzu, Amy Xie, Xuechun Bai, Kelly R. Pekala, David Ma, Tomin Perea-Chamblee, Kanika Aurora, Garrett Frampton, Michael Berger, Nikolaus Schultz, Justin Y. Newberg, Jian Carrot-Zhang. Meta-analysis reveals differences in somatic alterations by genetic ancestry across common cancers [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C083.

Survival prediction and analysis of drug-resistance genes in HER2-positive breast cancer

Despite the approval of several therapeutic agents for HER2-positive breast cancer, drug resistance remains a significant challenge, hindering the patient's prognosis. Thus, our study aimed to establish a risk model to predict the prognosis of patients and identify key genes regulating drug resistance in HER2-positive breast cancer. Utilizing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), a predictive model was constructed based on 5 drug resistance-related genes, which demonstrated a notable capacity to indicate the survival rates of patients. Besides, through eccDNA and transcriptome sequencing of drug-sensitive and resistant cancer cells, 3 significant DEGs were identified: MED1, MED24, and NMD3. Among them, MED1 showed the most significant elevation in drug-resistance cells, highlighting its crucial role in mediating drug resistance. MED1 may serve as a valuable target for alleviating drug resistance in HER2-positive breast cancer.

Identification and Validation of an Invasion-Related Disease-Free Survival Prognostic Model for Tongue Squamous Cell Carcinoma

Introduction: Tongue squamous cell carcinoma (TSCC) is a common malignant tumour type with aggressive invasion and a poor prognosis. To date, invasion-related gene expression signatures for the prognostic stratification of TSCC patients are unavailable in clinical practice. This study aimed to assess the impact of invasion-related genes on the prognosis of TSCC patients. Methods: We obtained mRNA profiles and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (TCGA-TSCC and GSE41116, respectively). The TSCC samples from the TCGA-TSCC cohort were randomly divided into TCGA training and TCGA test datasets at a 7:3 ratio. Next, a disease-free survival (DFS) prognostic risk model was established on the basis of univariate and stepwise multivariate Cox regression analyses of the TCGA training cohort. Moreover, prognostic genes were screened. The model was subsequently evaluated and validated using the TCGA test and GSE41116 datasets. In addition, the prognostic genes were validated in the human TSCC cell line UM1 and the human oral keratinocyte (HOK) cell line using quantitative real-time polymerase chain reaction (qRT-PCR) analysis. Results: A total of 70 candidate genes related to invasion were identified in the TCGA-TSCC cohort. DFS data were subsequently constructed, and 6 prognostic genes, HMGN2, MYL12B, ACTB, PPP1CA, PSMB9, and IFITM3, were identified. The TSCC samples were divided into high- and low-risk groups in the TCGA training, TCGA test, and GSE41116 cohorts, respectively. In particular, patients with TSCC in the low-risk group had longer DFS than those in the high-risk group. Furthermore, qRT-PCR analysis confirmed that the expression levels of the 6 prognostic genes were significantly greater in the TSCC cell line UM1 than in the HOK cell line. Conclusion: This study identified new invasion-related target genes related to poor prognosis in TSCC patients, providing new insights into the underlying mechanisms of TSCC invasion.

Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnostic model

PurposeThis study aims to utilize bioinformatics methods to systematically screen and identify susceptibility genes for cervical cancer, as well as to construct and validate an mitophagy-related genes (MRGs) diagnostic model. The objective is to increase the understanding of the disease’s pathogenesis and improve early diagnosis and treatment.MethodWe initially collected a large amount of genomic data, including gene expression profile and single nucleotide polymorphism (SNP) data, from the control group and Cervical cancer (CC) patients. Through bioinformatics analysis, which employs methods such as differential gene expression analysis and pathway enrichment analysis, we identified a set of candidate susceptibility genes associated with cervical cancer.ResultsMRGs were extracted from single-cell RNA sequencing data, and a network graph was constructed on the basis of intercellular interaction data. Furthermore, using machine learning algorithms, we constructed a clinical prognostic model and validated and optimized it via extensive clinical data. Through bioinformatics analysis, we successfully identified a group of genes whose expression significantly differed during the development of CC and revealed the biological pathways in which these genes are involved. Moreover, our constructed clinical prognostic model demonstrated excellent performance in the validation phase, accurately predicting the clinical prognosis of patients.ConclusionThis study delves into the susceptibility genes of cervical cancer through bioinformatics approaches and successfully builds a reliable clinical prognostic model. This study not only helps uncover potential pathogenic mechanisms of cervical cancer but also provides new directions for early diagnosis and treatment of the disease.

The role of KRT7 in metastasis and prognosis of pancreatic cancer

PurposeThe aim of this study is to delve into the value of N6-Methyladenosine (m6A)-associated genes (MAGs) in pancreatic cancer (PC) prognosis.MethodsPC sequencing data and corresponding clinicopathological information were retrieved from GEO and TCGA databases. We filtered 19 MAGs in PC specimens and implemented functional annotation in biology. Later, the m6A modification pattern was stratified into m6Acluster A-B according to MAG expression levels, and further categorized into genecluster A-C based on differentially expressed genes between m6Acluster A and B. Next, a MAG-based prognostic prediction model was established by the least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis. At last, the role of KRT7 in PC were explored.ResultsWe found m6Acluster A pattern presented enrichment pathways associated with cell apoptosis, proliferation, migration, and cancer pathways. Additionally, high-risk group displayed more dismal prognosis and a higher programmed death-ligand 1 expression. The survival prediction ability of the model was verified in three independent PC GEO datasets. KRT7 is the most momentous risk gene in the established prognostic model. Among 18 clinical samples, the KRT7 protein in the surviving patient samples is lower than that in the deceased patient samples. We also identified elevated expression of KRT7 in PC tumor tissues compared to normal tissues using GEPIA 2. Then, the metastasis of PC cells was promoted by overexpressed KRT7 in vitro and in vivo. And IGF2BP3 upregulated KRT7 by increasing the mRNA stability of KRT7.ConclusionsThe PPM built based on CXCL5, LY6K and KRT7 is an encouraging biomarker to define the prognosis. Additionally, IGF2BP3 promoted KRT7 by stabilizing mRNA of KRT7. And KRT7 promoted the metastasis of PC cells by promoting EMT.

Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis

BackgroundBreast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear.MethodThis study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells.ResultsOne hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan–Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels.ConclusionARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.

Y-box binding protein 1/cyclin A1 axis specifically promotes cell cycle progression at G2/M phase in ovarian cancer

Y-box binding protein 1 (YBX1) promotes oncogenic transformation and tumor growth. YBX1 plays a role in regulation of cell cycle promotion via upregulation of cell cycle-related genes. In ovarian cancer, YBX1 also promotes tumor growth, but the mechanisms of YBX1 in cell growth and cell cycle in ovarian cancer remain not to be fully understood. Here, we investigated whether YBX1-dependent cancer cell proliferation was specifically associated with expression of cell cycle related genes in ovarian cancer. Protein and mRNA expression levels of YBX1 and cell cycle-related genes in ovarian cancer cell lines and tissues were determined by western blot analysis, immunohistochemical analysis and reverse transcription-quantitative PCR. Cell cycle analysis was performed by flow cytometry. Luciferase assay and Chromatin immunoprecipitation assay were used to investigate a transcriptional function of YBX1. YBX1 silencing induced marked growth suppression in 4 cell lines (group A), moderate suppression in 5 cell lines (group B), and no suppression in 3 cell lines (group C) among 12 ovarian cancer cell lines in culture. The YBX1 silencing induced cell cycle arrest at G2/M phase and suppressed expression of cyclin A1 gene in group A and B cell lines, but not in group C cell lines. Cyclin A1 silencing specifically suppressed cell proliferation in group A cell lines and partially in group B cell lines, but not at all in group C cell lines. YBX1 mRNA levels were significantly correlated with cyclin A1 mRNA levels in patients with high-grade serous carcinoma. Augmented YBX1 expression plays a key role in tumor growth promotion in ovarian cancer in its close association with cyclin A1.

Elucidating the molecular markers and biological pathways associated with extrahepatic cholangiocarcinoma: a transcriptome sequencing study.

Cholangiocarcinoma is a malignancy with high aggressiveness, and extrahepatic cholangiocarcinoma (ECCA) represents the predominant subtype. However, the molecular architecture and underlying pathogenic mechanisms of ECCA remain poorly understood. The objective of this study is to elucidate the molecular markers and biological pathways associated with ECCA. In order to identify the factors influencing ECCA, we conducted transcriptome sequencing on a cohort of 8 surgically resected ECCA specimens. To validate our findings, we integrated data from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases using batch integration analysis. Finally, we confirmed our results using clinical samples. The findings of this study reveal that through the analysis of sequencing data, we have successfully identified the genes that are differentially expressed and have a significant role in the development of ECCA. Utilizing the Weighted Gene Co-expression Network Analysis approach, we have integrated these identified gene modules with the GEO dataset, leading to the identification of four key genes (PTGDS, ITIH2, LSAMP, HBB) that are strongly associated with the progression-free survival of ECCA. We screened a key gene LSAMP from four genes using immunohistochemistry. The gene primarily participate in crucial biological processes such as the ECCA cell cycle and DNA replication. The qRT-PCR reaction and Western Blot conducted on the tissues provided confirmation of the expression levels of the gene, which exhibited consistency with the outcomes of our analysis. Our study has successfully identified potential biomarkers LSAMP for ECCA, which can serve as valuable tools for early detection and targeted therapeutic interventions in clinical settings.

Development and validation of a prognostic signature of breast cancer based on drug absorption, distribution, metabolism and excretion (ADME)-related genes

The individual variation of carcinogenesis and drug response is influenced by the absorption, distribution, metabolism, and excretion (ADME) of drugs. The utilization of signatures derived from ADME-related genes holds potential for predicting prognosis and treatment response across diverse cancer types. Further investigation is required to completely understand the role of ADME-associated genes in breast cancer. A signature was constructed through the application of a least absolute shrinkage and selection operator regression model, employing prognostic differentially expressed genes found in both cancer tissue and normal tissue. To assess the robustness of the signature, verification analyses were carried out. RT-qPCR was utilized for the validation of gene expression related to risk. Subsequently, a nomogram was developed to enhance the clinical utility of our prognostic tool. The ADME signature, comprising four genes, was established and exhibited a robust association with the prognoses of individuals diagnosed with breast cancer. The nomogram was created by fusing the clinicopathological characteristics with the ADME signature. The ADME signature demonstrated remarkable superiority when compared to the performance of the other individual predictors. Additionally, the analysis of the immune microenvironment revealed that the ImmuneScores of the low-risk group were elevated. The variation in both the infiltration of immune cells and the expression of immune-related genes in the tissues differed among the two groups. For patients with breast cancer, the utilization of ADME signatures as biomarkers presents a significant reference point for prognosis and individualized treatment strategies.

Characteristics of Oxidative Phosphorylation-Related Subtypes and Construction of a Prognostic Signature in Ovarian Cancer.

Ovarian cancer is associated with a high mortality rate. Oxidative Phosphorylation (OXPHOS) is an active metabolic pathway in cancer; nevertheless, its role in ovarian cancer continues to be ambiguous. Therefore, the objective of this study was to identify the prognostic value of OXPHOS-related genes and the immune landscape in ovarian cancer. We obtained public ovarian cancer-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and recognized OXPHOS-related genes from the GeneCards database and literature. Cox regression analyses were conducted to identify prognostic OXPHOS-related genes and develop a prognostic nomogram based on the OXPHOS score and clinicopathological features of patients. Functional enrichment analyses were employed to identify related processes. A 12-gene signature was identified to classify the ovarian cancer patients into high- and low-risk groups. The Immunophenoscore (IPS) was higher in the OXPHOS score-high group than in the OXPHOS score-low group, suggesting a better response to immune checkpoint inhibitors. Functional enrichment analyses unveiled that OXPHOS-related genes were considerably abundant in a series of immune processes. The calibration curves of the constructed prognostic nomograms at 1, 2, and 3 years exhibited strong concordance between the anticipated and observed survival probabilities of ovarian cancer patients. We have constructed a prognostic model containing 12 OXPHOS-related genes and demonstrated its strong predictive value in ovarian cancer patients. OXPHOS has been found to be closely linked to immune infiltration and the reaction to immunotherapy, which may contribute to improving individualized treatment and prognostic evaluation in ovarian cancer.

Fanconi Anemia a Rare Disease

Fanconi anemia (FA) is a genetically and phenotypically recessive autosomal illness. Fanconi anemia (FA), a rare genetic illness, is currently receiving more attention from hematologists, cancer biologists, and fundamental scientists studying DNA repair and ubiquitin biology. Chromosome instability, progressive bone marrow failure, cancer susceptibility, and several other congenital anomalies are its defining characteristics. All three blood cell lines are included. This is a fatal illness that typically strikes children under the age of five. One of the fastest-growing fields of medical study is FA. The discovery of 15 distinct FA genes and the clarification of the FA molecular pathways have contributed to our knowledge of the pathogenic mechanism and, in many cases, the development of treatment guidelines. Because FA possesses distinct traits in many different biological areas, investigations on FA provided significant material for studies on malignancies. Research has demonstrated a genetic relationship between FA and cancer, showing that both cancer genes and FA genes are present in malignancies. FA is therefore identified as a prototypical illness for the comprehension of aging and cancer. Here we review the incidence of FA, Genetics, Pathophysiology, impact of FA, Identification of FA genes and delineation of FA pathways, Symptoms and indication, Diagnosis, Management, Complication and Prevention, and patient education about FA.

Abstract A031: Identification of therapeutic vulnerabilities in the subset of pancreatic ductal adenocarcinoma (PDAC) with homologous recombination deficiency

Abstract The genetic landscape of pancreatic ductal adenocarcinoma (PDAC) is characterized by mutations in the KRAS, TP53, CDKN2A and SMAD4 genes alongside multiple low-frequency mutations, which may offer opportunities for precision medicine. The success of poly-ADP-ribose polymerase (PARP) inhibitors in metastatic PDAC characterized by germline mutations in the Breast Cancer (BRCA) genes has led to the hypothesis that mutations in other BRCAness genes may sensitize PDAC to PARP inhibitors. To test this hypothesis, we used KPC mice as a backbone (Pdx1-Cre; LSL-KrasG12D/+; LSL-Trp53R172H/+) to generate models of PDAC driven by the loss of Ataxia Telangiectasia mutated (Atm, KPCA) or Brca1 (KPCB). Loss of Atm or Brca1 resulted in homologous recombination deficiency and genomic instability in vivo. In vitro cultures of KPCA and KPCB PDAC cells had higher numbers of micronuclei and were more radiosensitive than KPC cells. Next, we performed bulk and single-cell RNA sequencing to identify therapeutic vulnerabilities associated with Atm or Brca1 loss. We found that loss of Atm upregulated ATR signaling, suggesting ATR as a therapeutic target in KPCA mice. In agreement, ATR inhibition improved the survival of KPCA mice, with evidence of tumor growth delay. However, though PARP inhibition significantly improved the survival of KPCB mice, with delayed tumor growth, it was ineffective in KPCA mice. In addition, transcriptomic analysis showed an enrichment of immune cells in KPCB PDAC compared to KPC and KPCA PDAC. Notably, we found greater levels of STING signaling, a more diverse and abundant cytokine profile and more CD8 T cells in KPCB PDAC, suggesting sensitivity to immune checkpoint blockade (ICB). However, ICB was ineffective in KPCB mice as a single agent or in combination with PARP inhibition. Based on our transcriptomic data, we hypothesized that boosting antigen priming using a CD40 agonist (CD40a) in combination with FLT3-L may improve ICB efficacy in our mouse models of BRCAness. Immunotherapy consisting of combined ICB, CD40a and FLT-3L had little efficacy in either KPCA or KPCB mice. However, ATR inhibition improved the efficacy of combined ICB, CD40a and FLT-3L in KPCA mice, as indicated by increased CD8 T cell infiltration in the tumor, delayed tumor growth and longer survival. In contrast, we found that PARP inhibition combined with ICB, CD40a and FLT-3L was ineffective in KPCB mice. In summary, we present evidence to suggest that PDAC characterized by BRCAness is not a homogeneous group with respect to sensitivity to PARP inhibition, composition of the tumor microenvironment and response to immunotherapy. We highlight ATM loss as a potential predictive biomarker of ATR inhibitor efficacy and immunotherapy in PDAC. Future directions include the evaluation of inflammation as a therapeutic vulnerability in murine PDAC with loss of Brca1. Ultimately, we aim to inform clinical trials to refine precision medicine of PDAC characterized by BRCAness. Citation Format: Mathias Tesson, Kay Kong, Peter Repsicak, Aldo Bader, Ya-Ching Hsieh, Craig Nourse, Kristina Kirschner, Crispin Miller, Ed Roberts, David Chang, Jen Morton. Identification of therapeutic vulnerabilities in the subset of pancreatic ductal adenocarcinoma (PDAC) with homologous recombination deficiency [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A031.

Prognosis and immunotherapeutic implications of molecular classification of cervical cancer based on immunophenoscore-related genes

The immunophenoscore (IPS) is an important indicator for evaluating immunotherapy response. This work was designed to establish a prognostic model based on IPS-related genes in cervical cancer. Weighted correlation network analysis (WGCNA) was utilized to identify key modules related to IPS in cervical cancer data from The Cancer Genome Atlas (TCGA). The results show that the yellow module (158 genes) had a high correlation with both IPS_CTLA4_blocker and IPS_CTLA4_and PC1/PDL1/PDL2 blocker. Univariate cox regression analysis and LASSO regression analysis were performed based on 158 genes, and 9 characteristic genes were finally identified to construct the model. According to the differentially expressed genes, cervical cancer samples were divided into high-risk and low-risk groups and cluster 1.2.3. Higher risk scores associated with poorer prognosis. cluster2 and cluster3 were identified as cervical cancer subtypes with significant survival differences. cluster2 had higher immune cell infiltration levels and better prognosis, with greater sensitivity to Cyclopamine, Imatinib, MG-13, Paclitaxel, PHA-665752, Rapamycin, Sorafenib, Sunitinib, and VX-680. In contrast, cluster3 had higher TTN and PIK3CA mutations and greater sensitivity to AZ628, Dasatinib, Doxorubicin, HG-6-64-1, JQ12, Midostaurin, PF-562271, TAE684, and WH-4-023. In conclusion, we developed a feasible risk score model based on IPS-related genes for cervical cancer prognosis and identified potential drugs for different cervical cancer subtypes.

A Circadian Rhythm-related Signature to Predict Prognosis, Immunei Infiltration, and Drug Response in Breast Cancer.

Circadian rhythm genes (CRRGs) play essential roles in cancer occurrence and development. However, the prognostic significance of CRRGs in breast cancer (BC) has not been fully elucidated. Our study aimed to develop a prognostic gene signature based on CRRGs that can accurately and stably predict the prognosis of BC. The transcriptome data and clinical information for BC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A consensus unsupervised clustering analysis was carried out to investigate the roles of CRRGs in BC. A CRRGs-related prognostic risk model was established by using logistic least absolute shrinkage and selection operator (LASSO) Cox regression and univariate Cox regression analyses. Kaplan-Meier (KM) curves analysis, time-dependent receptor operation characteristics (ROC) curves analysis, and nomogram were plotted to evaluate the predictive efficacy of the model. The relevance of risk score to the immune cell infiltration, tumor burden mutation (TMB), and therapeutic response was assessed. A risk model comprising six CRRGs (SLC44A4, SLC16A6, TPRG1, FABP7, GLYATL2, and FDCSP) was constructed and validated, demonstrating a good predictor of BC. The low-risk group displayed a higher number of immune activities and immune checkpoint expression and a lower burden of tumor mutation. Additionally, drug sensitivity analysis demonstrated the prognostic signature may serve as a potential chemosensitivity predictor. We established 6 CRRGs-related risk signatures for the prognosis of BC, which is of great value in predicting the prognosis of patients with BC and guiding the treatment for BC.

Pyroptosis-associated genes and tumor immune response in endometrial cancer

The occurrence and progression of tumors are linked to the process of pyroptosis. However, the precise involvement of pyroptosis-associated genes (PRGs) in endometrial cancer (EC) remains uncertain. 29 PRGs were identified as being either up-regulated or down-regulated in EC. PRGs subgroup analysis demonstrated distinct survival outcomes and diverse responses to chemotherapy and immune checkpoint blockade therapy. A higher expression of GPX4 and NOD2, coupled with lower levels of CASP6, PRKACA, and NLRP2, were found to be significantly associated with higher overall survival (OS) rates (p < 0.05). Conversely, lower expression of NOD2 was linked to lower progression-free survival (p = 0.021) and advanced tumor stage(p = 0.0024). NOD2, NLRP2, and TNM stages were identified as independent prognostic factors (p < 0.001). The LASSO prognostic model exhibited a notable decrease in OS among EC patients in the high-risk score group (ROC-AUC10-years: 0.799, p = 0.00644). Furthermore, NOD2 displayed a positive correlation with the infiltration of immune cells and the expression of immune checkpoints (p < 0.001). GPX4 and CASP6 are significantly associated with TMB and MSI (RTMB = 0.39; RMSI = 0.23). Additionally, a substantial upregulation of NOD2 was confirmed in both EC cells and tissue, indicating a positive relationship between advanced TNM stage (p < 0.0001) and infiltration of M1 phenotype macrophages. Nonetheless, its impact on patient OS did not reach statistical significance (p = 0.141). Our findings have contributed to the advancement of a prognostic model for EC patients. NOD2 receptor-mediated pyroptosis mechanism potentially regulates tumor immunity and promotes the transformation of macrophages from the M2 phenotype to the M1 phenotype, which significantly impacts the progression of EC.

PerturbDB for unraveling gene functions and regulatory networks.

Perturb-Seq combines CRISPR (clustered regularly interspaced short palindromic repeats)-based genetic screens with single-cell RNA sequencing readouts for high-content phenotypic screens. Despite the rapid accumulation of Perturb-Seq datasets, there remains a lack of a user-friendly platform for their efficient reuse. Here, we developed PerturbDB (http://research.gzsys.org.cn/perturbdb), a platform to help users unveil gene functions using Perturb-Seq datasets. PerturbDB hosts 66 Perturb-Seq datasets, which encompass 4518521 single-cell transcriptomes derived from the knockdown of 10194 genes across 19 different cell lines. All datasets were uniformly processed using the Mixscape algorithm. Genes were clustered by their perturbed transcriptomic phenotypes derived from Perturb-Seq data, resulting in 421 gene clusters, 157 of which were stable across different cellular contexts. Through integrating chemically perturbed transcriptomes with Perturb-Seq data, we identified 552 potential inhibitors targeting 1409 genes, including an mammalian target of rapamycin (mTOR)signaling inhibitor, retinol, which was experimentally verified. Moreover, we developed a 'Cancer' module to facilitate the understanding of the regulatory role of genes in cancer using Perturb-Seq data. An interactive web interface has also been developed, enabling users to visualize, analyze and download all the comprehensive datasets available in PerturbDB. PerturbDB will greatly drive gene functional studies and enhance our understanding of the regulatory roles of genes in diseases such as cancer.

Targeting GPX4-mediated ferroptosis protection sensitizes BRCA1-deficient cancer cells to PARP inhibitors

BRCA1 is one of the most frequently-mutated tumor suppressor genes in ovarian and breast cancers. Loss of BRCA1 triggers homologous recombination (HR) repair deficiency, consequently leading to genomic instability and PARP inhibitors (PARPi)-associated synthetic lethality. Although, the roles of BRCA1 in DNA repair and replication have been extensively investigated, its tumor suppressive functions beyond genome safeguard remain poorly understood. Here, we report that BRCA1 promotes ferroptosis susceptibility through catalyzing K6-linked polyubiquitination of GPX4 and subsequently accelerating GPX4 degradation. Depletion of BRCA1 induces ferroptosis resistance in ovarian cancer cells due to elevated GPX4 protein, and silence of GPX4 significantly suppresses the growth of BRCA1-deficient ovarian cancer xenografts. Importantly, we found that PARPi triggers ferroptosis in ovarian cancer cells, inhibition of GPX4 markedly increase PARPi-induced ferroptosis in BRCA1-deficient ovarian cancer cells. Combined treatment of GPX4 inhibitor and PARPi produces synergistic anti-tumor efficacy in BRCA1-deficient ovarian cancer cells, patient derived organoid (PDO) and xenografts. Thus, our study uncovers a novel mechanism via which BRCA1 exerts tumor suppressive function through regulating ferroptosis, and demonstrates the potential of GPX4 as a therapeutic target for BRCA1-mutant cancers.

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes

Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. Laboratory-based experimental methods for assessing these effects are time-consuming and often impractical, highlighting the importance of in-silico tools for variant impact prediction. However, the performance metrics of currently available tools on breast cancer missense variants from benchmarking databases have not been thoroughly investigated, creating a knowledge gap in the accurate prediction of pathogenicity. In this study, the benchmarking datasets ClinVar and HGMD were used to evaluate 21 Artificial Intelligence (AI)-derived in-silico tools. Missense variants in breast cancer genes were extracted from ClinVar and HGMD professional v2023.1. The HGMD dataset focused on pathogenic variants only, to ensure balance, benign variants for the same genes were included from the ClinVar database. Interestingly, our analysis of both datasets revealed variants across genes with varying penetrance levels like low and moderate in addition to high, reinforcing the value of disease-specific tools. The top-performing tools on ClinVar dataset identified were MutPred (Accuracy = 0.73), Meta-RNN (Accuracy = 0.72), ClinPred (Accuracy = 0.71), Meta-SVM, REVEL, and Fathmm-XF (Accuracy = 0.70). While on HGMD dataset they were ClinPred (Accuracy = 0.72), MetaRNN (Accuracy = 0.71), CADD (Accuracy = 0.69), Fathmm-MKL (Accuracy = 0.68), and Fathmm-XF (Accuracy = 0.67). These findings offer clinicians and researchers valuable insights for selecting, improving, and developing effective in-silico tools for breast cancer pathogenicity prediction. Bridging this knowledge gap contributes to advancing precision medicine and enhancing diagnostic and therapeutic approaches for breast cancer patients with potential implications for other conditions.

The NRF2-KEAP1 pathway and epigenetic control in colorectal cancer: A promising prognostic and predictive biomarker

In colorectal cancer (CRC), important genes in the NRF2/KEAP1 pathway may undergo methylation, yet the exact genes that control this pathway in CRC are not entirely understood. Targeting the NRF2/KEAP1 pathway could be a valuable therapeutic strategy for colorectal cancer. Our research examined the methylation and expression of the KEAP1 gene in precancerous lesions and colorectal cancer, as well as investigated this pathway in the HT-29 cell line for potential CRC treatment. In this study, 208 frozen colorectal tissue samples were analysed, including 34 tumours, 60 precancerous lesions with adjacent normal tissues, and 20 normal tissue samples. The methylation-specific PCR and quantitative PCR were used to examine KEAP1 gene promoter methylation, and NRF2 and KEAP1 gene expressions, respectively. Furthermore, the HT 29 cell line was treated with 5-Azacytidine, followed by quantitative real-time PCR to evaluate post-demethylation KEAP1 and NRF2 mRNA expression. Unlike NRF2, our research revealed a notable reduction in KEAP1 gene expression in CRC tissues compared to adjacent normal tissues (p=0.02). Intriguingly, treating the HT-29 cell line with 5-Azacytidine led to a significant increase in KEAP1 gene expression compared to untreated cells (P=0.03), with no significant alteration observed in NRF2 gene expression. Examination of CpG islands within the KEAP1 gene promoter indicated extensive hypermethylation in 58.8% of tumour tissues compared to adjacent normal samples. These findings indicate that decreased KEAP1 gene expression resulting from promoter hypermethylation could interfere with the NRF2/KEAP1 pathway, a crucial mechanism in colorectal carcinogenesis. The study proposes that KEAP1 may play a significant role in the progression of CRC and could potentially serve as a biomarker for diagnosing and monitoring therapy in CRC