Multiple Independent Cohorts Research Articles

Senescence-related genes as prognostic indicators in breast cancer survival.

Breast cancer is a leading cause of cancer-related mortality among women worldwide, particularly affecting those in their later years. As the incidence of breast cancer increases with age, understanding the biological mechanisms that link aging and cancer becomes crucial. Cellular senescence, a hallmark of aging, plays a dual role in cancer by inhibiting tumorigenesis while also contributing to tumor progression through the senescence-associated secretory phenotype (SASP). This study aims to investigate the prognostic significance of senescence-related genes in breast cancer. We utilized the SenMayo gene list, a comprehensive set of senescence-related genes, to analyze gene expression data from a large cohort of breast cancer samples. The data was sourced from the Kaplan-Meier plotter, an integrated database that compiles gene expression information from multiple independent cohorts. Cox proportional hazards regression and false discovery rate (FDR) corrections were employed to evaluate the correlation between gene expression and survival outcomes, aiming to establish a prognostic signature. Our findings demonstrate that higher expression levels of senescence-related genes are significantly associated with improved survival, while lower expression levels correlate with shorter survival outcomes. These results suggest that senescence-related pathways play a protective role in breast cancer, potentially serving as valuable prognostic indicators. The identification of a prognostic signature based on senescence-related genes underscores the importance of cellular senescence in breast cancer progression and survival. Our study highlights the potential of senescence-related biomarkers in enhancing patient stratification and informing treatment strategies, contributing to the growing body of literature on the intersection of aging and cancer.

A lactate metabolism-related gene signature to diagnose osteoarthritis based on machine learning combined with experimental validation.

Lactate is gradually proved as the essential regulator in intercellular signal transduction, energy metabolism reprogramming, and histone modification. This study aims to clarify the diagnosis value of lactate metabolism-related genes in osteoarthritis (OA). Lactate metabolism-related genes were retrieved from the MSigDB. GSE51588 was downloaded from the Gene Expression Omnibus (GEO) as the training dataset. GSE114007, GSE117999, and GSE82107 datasets were adopted for external validation. Genomic difference detection, protein-protein interaction network analysis, LASSO, SVM-RFE, Boruta, and univariate logistic regression (LR) analyses were used for feature selection. Multivariate LR, Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB) were used to develop the multiple-gene diagnosis models. 12 control and 12 OA samples were collected from the local hospital for re-verification. The transfection assays were conducted to explore the regulatory ability of the gene to the apoptosis and vitality of chondrocytes. Through the bioinformatical analyses and machine learning algorithms, SLC2A1 and NDUFB9 of the 273 lactate metabolism-related genes were identified as the significant diagnosis biomarkers. The LR, RF, SVM, and XGB models performed impressively in the cohorts (AUC > 0.7). The local clinical samples indicated that SLC2A1 and NDUFB9 were both down-regulated in the OA samples (both P < 0.05). The knockdown of NDUFB9 inhibited the viability and promoted the apoptosis of the CHON-001 cells treated with IL-1beta (both P < 0.05). A lactate metabolism-related gene signature was constructed to diagnose OA, which was validated in multiple independent cohorts, local clinical samples, and cellular functional experiments.

Integrative Multiomics in the Lung Reveals a Protective Role of Asporin in Pulmonary Arterial Hypertension.

Integrative multiomics can elucidate pulmonary arterial hypertension (PAH) pathobiology, but procuring human PAH lung samples is rare. We leveraged transcriptomic profiling and deep phenotyping of the largest multicenter PAH lung biobank to date (96 disease and 52 control) by integration with clinicopathologic data, genome-wide association studies, Bayesian regulatory networks, single-cell transcriptomics, and pharmacotranscriptomics. We identified 2 potentially protective gene network modules associated with vascular cells, and we validated ASPN, coding for asporin, as a key hub gene that is upregulated as a compensatory response to counteract PAH. We found that asporin is upregulated in lungs and plasma of multiple independent PAH cohorts and correlates with reduced PAH severity. We show that asporin inhibits proliferation and transforming growth factor-β/phosphorylated SMAD2/3 signaling in pulmonary artery smooth muscle cells from PAH lungs. We demonstrate in Sugen-hypoxia rats that ASPN knockdown exacerbated PAH and recombinant asporin attenuated PAH. Our integrative systems biology approach to dissect the PAH lung transcriptome uncovered asporin as a novel protective target with therapeutic potential in PAH.

A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade

Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance frequently develops. To explore ICB resistance mechanisms, we develop Immunotherapy Resistance cell-cell Interaction Scanner (IRIS), a machine learning model aimed at identifying cell-type-specific tumor microenvironment ligand-receptor interactions relevant to ICB resistance. Applying IRIS to deconvolved transcriptomics data of the five largest melanoma ICB cohorts, we identify specific downregulated interactions, termed resistance downregulated interactions (RDI), as tumors develop resistance. These RDIs often involve chemokine signaling and offer a stronger predictive signal for ICB response compared to upregulated interactions or the state-of-the-art published transcriptomics biomarkers. Validation across multiple independent melanoma patient cohorts and modalities confirms that RDI activity is associated with CD8 + T cell infiltration and highly manifested in hot/brisk tumors. This study presents a strongly predictive ICB response biomarker, highlighting the key role of downregulating chemotaxis-associated ligand-receptor interactions in inhibiting lymphocyte infiltration in resistant tumors.

Hypoxia-Associated GPNMB+ Macrophages Promote Malignant Progression of Colorectal Cancer and Its RelatedRisk Signature Are Powerful Predictive Tool for theTreatment of Colorectal Cancer Patients.

Colorectal cancer (CRC) is a highly malignant tumor with hypoxia being a crucial feature during its progression. This study utilized multiple independent CRC cohorts for bioinformatics analysis and in vitro experiments to investigate the role of hypoxia-related subgroups in CRC. Machine learning was employed to construct risk features associated with this subgroup and further explore its therapeutic value in CRC. The study identified the GPNMB+ Macrophage (GPNMB+ Macr) subgroup as most relevant to hypoxia. GPNMB+ Macr showed significantly higher infiltration in tumor tissues compared to non-tumor tissues, increasing with CRC stage. High infiltration of GPNMB+ Macr was associated with poor prognosis in terms of overall and recurrence-free survival in CRC patients. GPNMB+ Macrophages exhibit M2-like characteristics and have the ability to promote 5-FU resistance, proliferation, and metastasis of CRC cells. The study developed the Hypoxia-Related Macrophage Risk Score (HMRS), which not only served as an independent prognostic factor for CRC patients but also demonstrated robust prognostic performance compared to 84 previously published prognostic features. Patients with low HMRS were sensitive to fluorouracil, oxaliplatin (FOLFOX), and anti-PD-1 immunotherapy, while those with high HMRS showed resistance. Additionally, HMRS was identified as an independent prognostic factor in other digestive tract tumors (hepatocellular carcinoma, pancreatic cancer, esophageal cancer, and gastric cancer), indicating potential extrapolation to other tumor types. In conclusion, GPNMB+ Macr promotes the malignant progression of CRC, and HMRS serves as a powerful predictive tool for prognosis, chemotherapy, and immunotherapy in CRC patients, aiding in improving the quality of survival.

Next-generation precision medicine for suicidality prevention

Suicidality remains a clear and present danger in society in general, and for mental health patients in particular. Lack of widespread use of objective and/or quantitative information has hampered treatment and prevention efforts. Suicidality is a spectrum of severity from vague thoughts that life is not worth living, to ideation, plans, attempts, and completion. Blood biomarkers that track suicidality risk provide a window into the biology of suicidality, as well as could help with assessment and treatment. Previous studies by us were positive. Here we describe new studies we conducted transdiagnostically in psychiatric patients, starting with the whole genome, to expand the identification, prioritization, validation and testing of blood gene expression biomarkers for suicidality, using a multiple independent cohorts design. We found new as well as previously known biomarkers that were predictive of high suicidality states, and of future psychiatric hospitalizations related to them, using cross-sectional and longitudinal approaches. The overall top increased in expression biomarker was SLC6A4, the serotonin transporter. The top decreased biomarker was TINF2, a gene whose mutations result in very short telomeres. The top biological pathways were related to apoptosis. The top upstream regulator was prednisolone. Taken together, our data supports the possibility that biologically, suicidality is an extreme stress-driven form of active aging/death. Consistent with that, the top subtypes of suicidality identified by us just based on clinical measures had high stress and high anxiety. Top therapeutic matches overall were lithium, clozapine and ketamine, with lithium stronger in females and clozapine stronger in males. Drug repurposing bioinformatic analyses identified the potential of renin-angiotensin system modulators and of cyclooxygenase inhibitors. Additionally, we show how patient reports for doctors would look based on blood biomarkers testing, personalized by gender. We also integrated with the blood biomarker testing social determinants and psychological measures (CFI-S, suicidal ideation), showing synergy. Lastly, we compared that to machine learning approaches, to optimize predictive ability and identify key features. We propose that our findings and comprehensive approach can have transformative clinical utility.

Decoding functional impact of epigenetic regulator mutations on ligand-receptor interaction perturbations for evaluation of cancer immunotherapy.

Cellular crosstalk mediated by ligand-receptor interactions largely complicates the tumour ecosystem, resulting in heterogeneous tumour microenvironments that affect immune response and clinical benefits from immunotherapy. Epigenetic mechanisms are pivotal to expression changes of immune-related genes and can modulate the anti-tumour immune response. However, the functional consequences of disrupted epigenetic regulators (ERs) on ligand-receptor interactions in the tumour microenvironment remain largely unexplored. Here, we proposed mutations of ERs in perturbed interactions (MERIN), a molecular network-based approach that incorporates multi-omics data, to infer the potential consequences of ER mutations on ligand-receptor interaction perturbations. Leveraging cancer genomic profiles and molecular interaction data, we comprehensively decoded the functional consequences of ER mutations on dysregulated ligand-receptor interactions across 33 cancers. The dysregulated ligand-receptor genes were indeed enriched in cancer and immune-related function. We demonstrated the potential significance of PD1-PDL1 interaction-related ER mutations in stratifying cancer patients from multiple independent data cohorts. The ER mutation group showed distinct immunological characterizations and prognoses. Furthermore, we highlighted that the ER mutations could potentially predict clinical outcomes of immunotherapy. Our computational and clinical assessment underscore the utility of MERIN for elucidating the functional relevance of ER mutations in cancer immune response, potentially aiding patients' stratification for immunotherapy.

Towards Digital Quantification of Ploidy from Pan-Cancer Digital Pathology Slides using Deep Learning.

Abnormal DNA ploidy, found in numerous cancers, is increasingly being recognized as a contributor in driving chromosomal instability, genome evolution, and the heterogeneity that fuels cancer cell progression. Furthermore, it has been linked with poor prognosis of cancer patients. While next-generation sequencing can be used to approximate tumor ploidy, it has a high error rate for near-euploid states, a high cost and is time consuming, motivating alternative rapid quantification methods. We introduce PloiViT, a transformer-based model for tumor ploidy quantification that outperforms traditional machine learning models, enabling rapid and cost-effective quantification directly from pathology slides. We trained PloiViT on a dataset of fifteen cancer types from The Cancer Genome Atlas and validated its performance in multiple independent cohorts. Additionally, we explored the impact of self-supervised feature extraction on performance. PloiViT, using self-supervised features, achieved the lowest prediction error in multiple independent cohorts, exhibiting better generalization capabilities. Our findings demonstrate that PloiViT predicts higher ploidy values in aggressive cancer groups and patients with specific mutations, validating PloiViT potential as complementary for ploidy assessment to next-generation sequencing data. To further promote its use, we release our models as a user-friendly inference application and a Python package for easy adoption and use.

A comprehensive meta-analysis of tissue resident memory T cell shaping non-small-cell lung cancer immune microenvironment and patient prognosis.

210 Background: Tissue-resident memory T cells (TRM) are a specialized subset of long-lived memory T cells that reside in peripheral tissues. However, the impact of TRM-related immunosurveillance on the tumor-immune microenvironment (TIME) and tumor progression across various non-small-cell lung cancer (NSCLC) patient populations is yet to be elucidated. Methods: Our comprehensive analysis of multiple independent single-cell and bulk RNA-seq datasets of patient NSCLC samples generated reliable, unique TRM signatures, through which we inferred the abundance of TRM in NSCLC. A machine learning model was developed to prognosticate survival, tested in multiple independent NSCLC cohorts. Model performance was corroborated through Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, principal component analysis, and t-SNE analyses. Results: We discovered that TRM abundance is consistently positively correlated with CD4+ T helper 1 cells, M1 macrophages, and resting dendritic cells in the TIME. In addition, TRM signatures are strongly associated with immune checkpoint genes and the prognosis of NSCLC patients. A TRM-based machine learning model to predict patient survival was validated and an 18-gene risk score was further developed to effectively stratify patients into low-risk and high-risk categories, wherein patients with high-risk scores had significantly lower overall survival than patients with low-risk. The prognostic value of the risk score was independently validated by the TCGA dataset and multiple independent NSCLC patient datasets. Notably, low-risk NSCLC patients with higher TRM infiltration exhibited enhanced T-cell immunity, nature killer cell activation, and other TIME immune responses related pathways, indicating a more active immune profile benefitting from immunotherapy. However, the TRM signature revealed low TRM abundance and a lack of prognostic association among lung squamous cell carcinoma patients in contrast to adenocarcinoma, indicating that the two NSCLC subtypes are driven by distinct TIMEs. Conclusions: Altogether, this study provides valuable insights into the complex interactions between TRM and TIME and their impact on NSCLC patient prognosis. The development of a simplified 18-gene risk score provides a practical prognostic marker for risk stratification. Keywords: Tissue resident memory T cell, non-small-cell lung cancer, prognosis, tumor immune microenvironment, machine learning.

Exploring Novel Genome Instability-associated lncRNAs and their Potential Function in Pan-Renal Cell Carcinoma.

Genomic instability can drive clonal evolution, continuous modification of tumor genomes, and tumor genomic heterogeneity. The molecular mechanism of genomic instability still needs further investigation. This study aims to identify novel genome instabilityassociated lncRNAs (GI-lncRNAs) and investigate the role of genome instability in pan-Renal cell carcinoma (RCC). A mutator hypothesis was employed, combining the TCGA database of somatic mutation (SM) information, to identify GI-lncRNAs. Subsequently, a training cohort (n = 442) and a testing cohort (n = 439) were formed by randomly dividing all RCC patients. Based on the training cohort dataset, a multivariate Cox regression analysis lncRNAs risk model was created. Further validations were performed in the testing cohort, TCGA cohort, and different RCC subtypes. To confirm the relative expression levels of lncRNAs in HK-2, 786-O, and 769-P cells, qPCR was carried out. Functional pathway enrichment analyses were performed for further investigation. A total of 170 novel GI-lncRNAs were identified. The lncRNA prognostic risk model was constructed based on LINC00460, AC073218.1, AC010789.1, and COLCA1. This risk model successfully differentiated patients into distinct risk groups with significantly different clinical outcomes. The model was further validated in multiple independent patient cohorts. Additionally, functional and pathway enrichment analyses revealed that GI-lncRNAs play a crucial role in GI. Furthermore, the assessments of immune response, drug sensitivity, and cancer stemness revealed a significant relationship between GI-lncRNAs and tumor microenvironment infiltration, mutational burden, microsatellite instability, and drug resistance. In this study, we discovered four novel GI-lncRNAs and developed a novel signature that effectively predicted clinical outcomes in pan-RCC. The findings provide valuable insights for pan-RCC immunotherapy and shed light on potential underlying mechanisms.

EBF1-COX4I2 signaling axis promotes a myofibroblast-like phenotype in cancer-associated fibroblasts (CAFs) and is associated with an immunosuppressive microenvironment

Immunotherapy has limited response rates in colorectal cancer (CRC) due to an immunosuppressive tumor microenvironment (TME). Combining transcriptome sequencing, clinical specimens, and functional experiments, we identified a unique group of CAF subpopulations (COX4I2 + ) with inhibited mitochondrial respiration and enhanced glycolysis. Through bioinformatics predictions and luciferase reporter assays, we determined that EBF1 can upstreamly regulate COX4I2 transcription. COX4I2 + CAFs functionally and phenotypically resemble myofibroblasts, are important for the formation of the fibrotic TME, and are capable of activating the M2 phenotype of macrophages. In vitro experiments demonstrated that COX4I2 + CAFs promote immunosuppressive TME by blocking CD8 + T cell infiltration and inducing CD8 + T cell dysfunction. Using multiple independent cohorts, we also found a strong correlation between the immunotherapy response rate of CRC patients and COX4I2 expression in their tumors. Our results identify a CAF subpopulation characterized by activation of the EBF1-COX4I2 axis, and this group of CAFs can be targeted to improve cancer immunotherapy outcomes.

Integrative Multiomics Analyses Identify Molecular Subtypes of Head and Neck Squamous Cell Carcinoma with Distinct Therapeutic Vulnerabilities.

Substantial heterogeneity in molecular features, patient prognoses, and therapeutic responses in head and neck squamous cell carcinomas (HNSCC) highlights the urgent need to develop molecular classifications that reliably and accurately reflect tumor behavior and inform personalized therapy. Here, we leveraged the similarity network fusion bioinformatics approach to jointly analyze multiomics datasets spanning copy number variations, somatic mutations, DNA methylation, and transcriptomic profiling and derived a prognostic classification system for HNSCC. The integrative model consistently identified three subgroups (IMC1-3) with specific genomic features, biological characteristics, and clinical outcomes across multiple independent cohorts. The IMC1 subgroup included proliferative, immune-activated tumors and exhibited a more favorable prognosis. The IMC2 subtype harbored activated EGFR signaling and an inflamed tumor microenvironment with cancer-associated fibroblast/vascular infiltrations. Alternatively, the IMC3 group featured highly aberrant metabolic activities and impaired immune infiltration and recruiting. Pharmacogenomics analyses from in silico predictions and from patient-derived xenograft model data unveiled subtype-specific therapeutic vulnerabilities including sensitivity to cisplatin and immunotherapy in IMC1 and EGFR inhibitors (EGFRi) in IMC2, which was experimentally validated in patient-derived organoid models. Two signatures for prognosis and EGFRi sensitivity were developed via machine learning. Together, this integrative multiomics clustering for HNSCC improves current understanding of tumor heterogeneity and facilitates patient stratification and therapeutic development tailored to molecular vulnerabilities. Significance: Head and neck squamous cell carcinoma classification using integrative multiomics analyses reveals subtypes with distinct genetics, biological features, clinicopathological traits, and therapeutic vulnerabilities, providing insights into tumor heterogeneity and personalized treatment strategies.

Quantifiable TCR repertoire changes in prediagnostic blood specimens among patients with high-grade ovarian cancer

High-grade ovarian cancer (HGOC) is a major cause of death in women. Early detection of HGOC usually leads to a cure, yet it remains a clinical challenge with over 90% HGOCs diagnosed at advanced stages. This is mainly because conventional biomarkers are not sensitive enough to detect the microscopic yet metastatic early lesions. In this study, we sequence the blood Tcell receptor (TCR) repertoires of 466 patients with ovarian cancer and controls and systematically investigate the immune repertoire signatures in HGOCs. We observe quantifiable changes of selected TCRs in HGOCs that are reproducible in multiple independent cohorts. Importantly, these changes are stronger during stage I. Using pre-diagnostic patient blood samples from the Nurses' Health Study, we confirm that HGOC signals can be detected in the blood TCR repertoire up to 4 years preceding conventional diagnosis. Our findings may provide the basis for future immune-based HGOC early detection criteria.

A potassium-chloride co-transporter with altered genome architecture functions as a suppressor in glioma.

Gliomas, the most lethal tumours in brain, have a poor prognosis despite accepting standard treatment. Limited benefits from current therapies can be attributed to genetic, epigenetic and microenvironmental cues that affect cell programming and drive tumour heterogeneity. Through the analysis of Hi-C data, we identified a potassium-chloride co-transporter SLC12A5 associated with disrupted topologically associating domain which was downregulated in tumour tissues. Multiple independent glioma cohorts were included to analyse the characterization of SLC12A5 and found it was significantly associated with pathological features, prognostic value, genomic alterations, transcriptional landscape and drug response. We constructed two SLC12A5 overexpression cell lines to verify the function of SLC12A5 that suppressed tumour cell proliferation and migration invitro. In addition, SLC12A5 was also positively associated with GABAA receptor activity and negatively associated with pro-tumour immune signatures and immunotherapy response. Collectively, our study provides a comprehensive characterization of SLC12A5 in glioma and supports SLC12A5 as a potential suppressor of disease progression.

Robust and replicable functional brain signatures of 22q11.2 deletion syndrome and associated psychosis: a deep neural network-based multi-cohort study.

A major genetic risk factor for psychosis is 22q11.2 deletion (22q11.2DS). However, robust and replicable functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis remain elusive due to small sample sizes and a focus on small single-site cohorts. Here, we identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis, and their links with idiopathic early psychosis, using one of the largest multi-cohort data to date. We obtained multi-cohort clinical phenotypic and task-free fMRI data from 856 participants (101 22q11.2DS, 120 idiopathic early psychosis, 101 idiopathic autism, 123 idiopathic ADHD, and 411 healthy controls) in a case-control design. A novel spatiotemporal deep neural network (stDNN)-based analysis was applied to the multi-cohort data to identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis. Next, stDNN was used to test the hypothesis that the functional brain signatures of 22q11.2DS-associated psychosis overlap with idiopathic early psychosis but not with autism and ADHD. stDNN-derived brain signatures distinguished 22q11.2DS from controls, and 22q11.2DS-associated psychosis with very high accuracies (86-94%) in the primary cohort and two fully independent cohorts without additional training. Robust distinguishing features of 22q11.2DS-associated psychosis emerged in the anterior insula node of the salience network and the striatum node of the dopaminergic reward pathway. These features also distinguished individuals with idiopathic early psychosis from controls, but not idiopathic autism or ADHD. Our results reveal that individuals with 22q11.2DS exhibit a highly distinct functional brain organization compared to controls. Additionally, the brain signatures of 22q11.2DS-associated psychosis overlap with those ofidiopathic early psychosis in the salience network and dopaminergic reward pathway, providing substantial empirical support for the theoretical aberrant salience-based model of psychosis. Collectively, our findings, replicated across multiple independent cohorts, advance the understanding of 22q11.2DS and associated psychosis, underscoring the value of 22q11.2DS as a genetic model for probing the neurobiological underpinnings of psychosis and its progression.

NDRGs in Breast Cancer: A Review and In Silico Analysis.

The N-myc downstream regulated gene family (NDRGs) includes four members: NDRG1, NDRG2, NDRG3, and NDRG4. These members exhibit 53-65% amino acid identity. The role of NDRGs in tumor growth and metastasis appears to be tumor- and context-dependent. While many studies have reported that these family members have tumor suppressive roles, recent studies have demonstrated that NDRGs, particularly NDRG1 and NDRG2, function as oncogenes, promoting tumor growth and metastasis. Additionally, NDRGs are involved in regulating different signaling pathways and exhibit diverse cellular functions in breast cancers. In this review, we comprehensively outline the oncogenic and tumor suppressor roles of the NDRG family members in breast cancer, examining evidence from in vitro and in vivo breast cancer models as well as tumor tissues from breast cancer patients. We also present analyses of publicly available genomic and transcriptomic data from multiple independent cohorts of breast cancer patients.

Abstract 2011: MAOA Drives Anti Androgen Therapy Resistance in Advanced Prostate Cancer by Conferring Neuroendocrine Plasticity via a Twist1 BRN2 Signaling Pathway

Abstract Background: Therapy resistance in cancer is often linked to cancer cell state or lineage changes. In prostate cancer (PCa), a subset of castration-resistant prostate cancer (CRPC) loses reliance on the androgen receptor (AR) signaling and gains neuroendocrine (NE) traits through a lineage switch along disease progression. However, the molecular mechanisms driving such cellular lineage plasticity remain unclear. This study investigated the functional and mechanistic role of monoamine oxidase A (MAOA) in driving adenocarcinoma (adeno)-to-NE lineage conversion in CRPC cells upon enzalutamide (ENZ), a FDA-approved newer-generation antiandrogen drug, and evaluated the efficacy of MAOA inhibitors for treating CRPC and overcoming ENZ resistance.Methods: ENZ-resistant C4-2B and CWR-R1 cells (C4-2BENZR and CWR-R1ENZR) CRPC cells were transfected with a PSA promoter-driven GFP expression construct for isolating a PSAlo/− cell population distinct from PSAhi cells, which was used for manipulation of MAOA expression levels and subsequent functional assays in vitro and in vivo. The MAOA expression levels were determined and correlated with NE parameters in PCa clinical samples by immunohistochemistry and data mining of multiple publicly available clinical datasets. Results: MAOA protein expression was significantly upregulated in the PSAlo/− ENZ-resistant cell population, accompanied by concomitant activation of NE markers and downregulation of luminal markers including AR and AR targets, compared to the PSAhi counterpart. Elevated MAOA expression was also found in PCa patient samples associated with NE features from multiple independent cohorts. Silencing MAOA suppressed the proliferation, migration, invasion, stem-like properties and NE plasticity of both PSAlo/- C4-2BENZR and CWR-R1ENZR cells. Moreover, co-culturing adeno LNCaP or C4-2B cells with MAOA-silenced PSAlo/- cells exhibiting a repressed secretory phenotype attenuated acquired induction of NE markers and proliferation of adeno cells as compared to controls. Mechanistically, MAOA induces Twist1, which directly activates the transcription and expression of BRN2, a master neural transcription factor known to confer NE differentiation and anti-AR therapy resistance in CRPC. Further, pharmacological inhibition of MAOA greatly enhanced the growth-inhibitory efficacy of ENZ with ENZ sensitivity restored in ENZ-resistant LuCaP PC PDX-derived organoids as well as a PSAlo/− CWR-R1ENZR xenograft mouse model.Conclusion: Our results suggest MAOA as a potential therapeutic target for reversing ENZ resistance in CRPC treatment by blockade of NE transdifferentiation.Funding Acknowledgements: This work was supported by the NIH/NCI grants R37CA233658 and R01CA258634 (to B. Wu) and the DOD Department of Defense EIRA award W81XWH-21-1-0218 (to J. Wei) Citation Format: Jing Wei, Jing Wang, Chia-hui Chen, Wen Guan, Boyang Wu. MAOA Drives Anti Androgen Therapy Resistance in Advanced Prostate Cancer by Conferring Neuroendocrine Plasticity via a Twist1 BRN2 Signaling Pathway [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2011.

Abstract 2520: CD133 expression is associated with less DNA repair, better response to chemotherapy, and survival in ER-positive/ER-negative breast cancer

Abstract Introduction: Cancer stem cells (CSC) constitute only 0.1-1% of all the cells in a bulk tumor, yet they play a crucial role in treatment resistance and relapses. Its role in breast cancer (BC) has been studied but a consensus remains elusive. We focused on gene expression of a CSC marker CD133 and studied its clinical significance in ER-positive/HER2-negative (ER+/HER2-) BC, the most prevalent subtype. We utilized multiple independent large patient cohorts of gene expression and clinical variables for transcriptome analysis. Methods: A total of 2969 patients from The Cancer Genome Atlas (TCGA, n=1065) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC, n=1904) BC cohorts, as well as BC patients who underwent neoadjuvant chemotherapy (NAC; GSE25066, GSE20194, GSE32646 cohorts) were analyzed. Results: By analyzing SCP1039 and SCP1106 single-cell sequencing cohorts, we found that epithelial cells were the predominant source of CD133 gene expression in the tumor microenvironment. High expression of CD133 in ER+/HER2- BC correlated with elevated gene expressions of the other CSC markers; CD24, NOTCH1, DLL1, and ALDH1A1 (all p&amp;lt;0.001), and significantly enriched WNT/β-Catenin, Hedgehog, and Notch signaling (all FDR&amp;lt;0.25), which are all known pathways related with CSC in TCGA and validated in METABRIC cohort. Consistent with CSC phenotype, CD133-high BC was negatively enriched with Hallmark cell proliferation-related gene sets, such as E2F Targets, G2M Checkpoint, MYC Targets V1, Ki67 gene expression and proliferation score (FDR&amp;lt;0.25 and p&amp;lt;0.001, respectively) in both cohorts. CD133-high BC was negatively associated with DNA repair genes, such as BRCA1, E2F7 in both cohorts (all p&amp;lt;0.001). In agreement, CD133-high BC was associated with less homologous recombination deficiency, lower silent and non-silent mutation rates. Surprisingly, CD133-high BC exhibited a proinflammatory microenvironment, as represented by higher cytolytic activity in the METABRIC and higher lymphocyte infiltrations, TGF-beta response, and TCR richness in TCGA (both p&amp;lt;0.001), but less Th1 cells, M1 macrophages and dendritic cell infiltrations consistently in both cohorts. It also enriched signaling pathways related to inflammation and immune responses such as inflammatory response, allograft rejection, TNF-alpha, IL-6, TGF-beta signaling, and complement signaling (all FDR&amp;lt;0.25). CD133-high tumors had better pathological complete response after NAC in GSE25066 cohort and improved Disease-Free Survival and Overall Survival in both TCGA and METABRIC cohorts (all p&amp;lt;0.05). Conclusion: CD133-high ER+/HER2- BC is associated with CSC phenotype, reduced cell proliferation, and impaired DNA repair, but enhanced inflammation, potentially explaining the better response to NAC and favorable patient prognosis. Citation Format: Takumi Sato, Masanori Oshi, Jing Lee, Arya M. Roy, Kohei Chida, Itaru Endo, Masato Obayashi, Kazuaki Takabe. CD133 expression is associated with less DNA repair, better response to chemotherapy, and survival in ER-positive/ER-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2520.

Abstract 5064: Wild type KRAS amplification as de novo oncogenic driver in non-small cell lung cancer

Abstract Introduction: Oncogenic KRAS mutations define subsets of non-small cell lung cancer (NSCLC) with unique clinicopathologic and genomic features, and the recent development of direct KRAS inhibitors has changed the treatment landscape of these patients (pts). However, whether KRAS amplification is oncogenic and defines unique subset of potentially targetable NSCLCs is currently unknown. Methods: Clinicopathologic and genomic data were abstracted from multiple independent cohorts of NSCLCs which underwent tumor genomic profiling at the Dana-Farber Cancer Institute (Cohort 1), AACR-GENIE v.13, TCGA, and other 212 studies (Cohort 2). Whole transcriptome sequencing and quantitative proteomics data from the The Cancer Genome Atlas (TCGA) and the Cancer Cell Lines Encyclopedia (CCLE) were used to correlate KRAS amplification with KRAS RNA levels and protein expression. KRAS doxycycline-inducible models were generated to determine in vitro and in vivo oncogenicity of wild-type KRAS overexpression. Results: Among 15,341 pts with NSCLC identified, KRAS amplification was identified in 355 (2.4%) cases. Compared to KRAS not amplified cases, pts with KRAS amplified NSCLC were more likely to be males (52.4% vs 45.6%, p=0.01), have a history of smoking (82.1% vs 64.2%, p&amp;lt;0.01), and higher median pack-years (35 vs 23.5, p&amp;lt;0.01). KRAS amplification was also associated with higher median aneuploidy levels (as fraction of genome altered) (30.4% vs 14.3%, p&amp;lt;0.01), tumor mutational burden (p&amp;lt;0.01), and increased median PD-L1 expression (20% vs 5%, p=0.01). KRAS amplified cases were enriched with KRAS, POLE2, SLC34A2 and PALB2 mutations, while EGFR mutations were mutually exclusive (Q&amp;lt;0.1). Among NSCLC samples with transcriptomic and proteomic profiling from the TCGA and CCLE cohorts, KRAS amplification was associated with significantly increased KRAS mRNA (p&amp;lt;0.01) and protein expression (p&amp;lt;0.01), compared to KRAS diploid samples. Among pts with clinical outcomes data available (N=9,335), median overall survival (OS) from the date of diagnosis was significantly shorter in pts with KRAS amplified vs KRAS diploid genotype in both KRAS mutant (HR: 0.71, P=0.02) and KRAS WT (HR: 0.74, P=0.02) NSCLCs. In KRAS doxycycline-inducible models (BEAS-2B cells) we demonstrated increased cell proliferation and MAPK activation upon doxycycline-induced KRAS expression. In vivo, sustained expression of KRAS in animals constantly fed with doxycycline-diet triggered tumor formation and development of metastasis, indicating that WT KRAS overexpression is oncogenic. Conclusion: KRAS amplification defines a novel subset of NSCLCs characterized by distinct clinicopathologic and genomic features, and worse survival. Developing novel therapeutic strategies to target KRAS amplification may improve outcomes in pts with NSCLC. Citation Format: Biagio Ricciuti, Sandra V. Michelina, Joao Alessi, Xinan Wang, Federica Pecci, Alessandro Di Federico, Malini Gandhi, Giuseppe Lamberti, Enrico Patrucco, Lynette Sholl, Andrew Aguirre, Chiara Ambrogio, Mark Magdi Awad. Wild type KRAS amplification as de novo oncogenic driver in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5064.

GPX8+ cancer-associated fibroblast, as a cancer-promoting factor in lung adenocarcinoma, is related to the immunosuppressive microenvironment

BackgroundCancer-associated fibroblasts (CAFs) play a crucial role in the tumor microenvironment of lung adenocarcinoma (LUAD) and are often associated with poorer clinical outcomes. This study aimed to screen for CAF-specific genes that could serve as promising therapeutic targets for LUAD.MethodsWe established a single-cell transcriptional profile of LUAD, focusing on genetic changes in fibroblasts. Next, we identified key genes associated with fibroblasts through weighted gene co-expression network analysis (WGCNA) and univariate Cox analysis. Then, we evaluated the relationship between glutathione peroxidase 8 (GPX8) and clinical features in multiple independent LUAD cohorts. Furthermore, we analyzed immune infiltration to shed light on the relationship between GPX8 immune microenvironment remodeling. For clinical treatment, we used the tumor immune dysfunction and exclusion (TIDE) algorithm to assess the immunotherapy prediction efficiency of GPX8. After that, we screened potential therapeutic drugs for LUAD by the connectivity map (cMAP). Finally, we conducted a cell trajectory analysis of GPX8+ CAFs to show their unique function.ResultsFibroblasts were found to be enriched in tumor tissues. Then we identified GPX8 as a key gene associated with CAFs through comprehensive bioinformatics analysis. Further analysis across multiple LUAD cohorts demonstrated the relationship between GPX8 and poor prognosis. Additionally, we found that GPX8 played a role in inducing the formation of an immunosuppressive microenvironment. The TIDE method indicated that patients with low GPX8 expression were more likely to be responsive to immunotherapy. Using the cMAP, we identified beta-CCP as a potential drug-related to GPX8. Finally, cell trajectory analysis provided insights into the dynamic process of GPX8+ CAFs formation.ConclusionsThis study elucidates the association between GPX8+ CAFs and poor prognosis, as well as the induction of immunosuppressive formation in LUAD. These findings suggest that targeting GPX8+ CAFs could potentially serve as a therapeutic strategy for the treatment of LUAD.