Molecular Subtypes Research Articles

Molecular subtypes and prognostic models for predicting prognosis of lung adenocarcinoma based on miRNA-related genes.

MicroRNAs (miRNAs) are crucial in cancer development and progression, and therapies targeting miRNAs demonstrate great therapeutic promise. We sought to predict the prognosis and therapeutic response of lung adenocarcinoma (LUAD) by classifying molecular subtypes and constructing a prognostic model based on miRNA-related genes. This study was based on miRNA-mRNA action pairs and ceRNA networks in the Cancer Genome Atlas (TCGA) database. Three molecular subtypes were determined based on 64 miRNA-associated target genes identified in the ceRNA network. The S3 subtype had the best prognosis, and the S2 subtype had the worst prognosis. The S2 subtype had a higher tumor mutational load (TMB) and a lower immune score. The S2 subtype was more suitable for immunotherapy and sensitive to chemotherapy. The least absolute shrinkage and selection operator (LASSO) algorithm was performed to determine eight miRNA-associated target genes for the construction of prognostic models. High-risk patients had a poorer prognosis, lower immune score, and lower response to immunotherapy. Robustness was confirmed in the Gene-Expression Omnibus (GEO) database cohort (GSE31210, GSE50081, and GSE37745 datasets). Overall, our study deepened the understanding of the mechanism of miRNA-related target genes in LUAD and provided new ideas for classification. Such miRNA-associated target gene characterization could be useful for prognostic prediction and contribute to therapeutic decision-making in LUAD.

Identification of Necroptosis-related Molecular Subtypes and Construction of Necroptosis-related Gene Signature for Glioblastoma Multiforme.

Necroptosis is a highly regulated and genetically controlled process, and therefore, attention has been paid to the exact effects of this disorder on a variety of diseases, including cancer. An in-depth understanding of the key regulatory factors and molecular events that trigger necroptosis can not only identify patients at risk of cancer development but can also help to develop new treatment strategies. This study aimed to increase understanding of the complex role of necroptosis in glioblastoma multiforme (GBM) and provide a new perspective and reference for accurate prediction of clinical outcomes and gene-targeted therapy in patients with GBM. The objective of this study was to analyze the gene expression profile of necroptosis regulatory factors in glioblastoma multiforme (GBM) and establish a necroptosis regulatory factor-based GBM classification and prognostic gene signature to recognize the multifaceted impact of necroptosis on GBM. The necroptosis score of the glioblastoma multiforme (GBM) sample in TCGA was calculated by ssGSEA, and the correlation between each gene and the necroptosis score was calculated. Based on necroptosis score-related genes, unsupervised consensus clustering was employed to classify patients. The prognosis, tumor microenvironment (TME), genomic changes, biological signal pathways and gene expression differences among clusters were analyzed. The gene signature of GBM was constructed by Cox and LASSO regression analysis of differentially expressed genes (DEGs). Based on 34 necroptosis score-related genes, GBM was divided into two clusters with different overall survival (OS) and TME. A necroptosis-related gene signature (NRGS) containing 8 genes was developed, which could stratify the risk of GBM in both the training set and verification set and had good prognostic value. NRGS and age were both independent prognostic indicators of GBM, and a nomogram developed by the integration of both of them showed a better predictive effect than traditional clinical features. In this study, patients from public data sets were divided into two clusters and the unique TME and molecular characteristics of each cluster were described. Furthermore, an NRGS was constructed to effectively and independently predict the survival outcome of GBM, which provides some insights for the implementation of personalized precision medicine in clinical practice.

Distinct pathway activities are associated with prognosis and response to bortezomib-containing treatment in MCL1-M based molecular subtypes of multiple myeloma

Distinct pathway activities are associated with prognosis and response to bortezomib-containing treatment in MCL1-M based molecular subtypes of multiple myeloma

Abstract A032: Disparities in molecular subtypes of acute lymphoblastic leukemia incidence by age, race/ethnicity, and sex in the United States

Abstract A032: Disparities in molecular subtypes of acute lymphoblastic leukemia incidence by age, race/ethnicity, and sex in the United States

Molecular detection and subtype distribution of Blastocystis sp. from shelter dogs and cats in Van, Turkey: First report of ST10 in cats and ST1, ST10 and ST30 in dogs

Molecular detection and subtype distribution of Blastocystis sp. from shelter dogs and cats in Van, Turkey: First report of ST10 in cats and ST1, ST10 and ST30 in dogs

Proteogenomic characterization identifies clinical subgroups in EGFR and ALK wild-type never-smoker lung adenocarcinoma

AbstractPatients with lung adenocarcinoma who have never smoked (NSLA) and lack key driver mutations, such as those in the EGFR and ALK genes, face limited options for targeted therapies. They also tend to have poorer outcomes with immune checkpoint inhibitors than lung cancer patients who have a history of smoking. The proteogenomic profile of nonsmoking lung adenocarcinoma patients without these oncogenic driver mutations is poorly understood, which complicates the precise molecular classification of these cancers and highlights a significant area of unmet clinical need. This study analyzed the genome, transcriptome, and LC‒MS/MS-TMT-driven proteome data of tumors obtained from 99 Korean never-smoker lung adenocarcinoma patients. NSLA tumors without EGFR or ALK driver oncogenes were classified into four proteogenomic subgroups: proliferation, angiogenesis, immune, and metabolism subgroups. These 4 molecular subgroups were strongly associated with distinct clinical outcomes. The proliferation and angiogenesis subtypes were associated with a poorer prognosis, while the immune subtype was associated with the most favorable outcome, which was validated in an external lung cancer dataset. Genomic-wide impacts were analyzed, and significant correlations were found between copy number alterations and both the transcriptome and proteome for several genes, with enrichment in the ERBB, neurotrophin, insulin, and MAPK signaling pathways. Proteogenomic analyses suggested several targetable genes and proteins, including CDKs and ATR, as potential therapeutic targets in the proliferation subgroup. Upregulated cytokines, such as CCL5 and CXCL13, in the immune subgroup may serve as potential targets for combination immunotherapy. Our comprehensive proteogenomic analysis revealed the molecular subtypes of EGFR- and ALK-wild-type NSLA with significant unmet clinical needs.

Glutamine metabolism-related genes predict the prognostic risk of acute myeloid leukemia and stratify patients by subtype analysis

BackgroundAcute myeloid leukemia (AML) is a genetically heterogeneous disease in which glutamine (Gln) contributes to AML progression. Therefore, this study aimed to identify potential prognostic biomarkers for AML based on Gln metabolism-related genes.MethodsGln-related genes that were differentially expressed between Cancer Genome Atlas-based AML and normal samples were analyzed using the limma package. Univariate, least absolute shrinkage, selection operators, and stepwise Cox regression analyses were used to identify prognostic signatures. Risk score-based prognostic and nomogram models were constructed to predict the prognostic risk of AML. Subsequently, consistent cluster analysis was performed to stratify patients into different subtypes, and subtype-related module genes were screened using weighted gene co-expression network analysis.ResultsThrough a series of regression analyses, HGF, ANGPTL3, MB, F2, CALR, EIF4EBP1, EPHX1, and PDHA1 were identified as potential prognostic biomarkers of AML. Prognostic and nomogram models constructed based on these genes could significantly differentiate between high- and low-risk AML with high predictive accuracy. The eight-signature also stratified patients with AML into two subtypes, among which Cluster 2 was prone to a high risk of AML prognosis. These two clusters exhibited different immune profiles. Of the subtype-related module genes, the HOXA and HOXB family genes may be genetic features of AML subtypes.ConclusionEight Gln metabolism-related genes were identified as potential biomarkers of AML to predict prognostic risk. The molecular subtypes clustered by these genes enabled prognostic risk stratification.

Clinical characteristics and genomic profiling of outpatients with endometrial cancer at a Chinese tertiary cancer center

ObjectiveEndometrial cancer is stepping into the era of precision therapy. Genomic test is recommended for newly diagnostic patients. However, outpatients displayed more complex characteristics. Here, we elucidated the clinical characteristics and genomic profiling of outpatients with endometrial cancer at our institution.MethodsBetween 2018 and 2023, 68 endometrial cancer received genomic tests at outpatient department of Fudan University Shanghai Cancer Center. Data, including age, pathological histology, FIGO stage and treatment strategy were collected. Germline mutations, molecular subtypes and other somatic mutations were also summarized.ResultsOverall, 72.1% (49/68) of patients receive genomic tests at primary diagnosis, while 27.9% (19/68) of patients received tests at recurrence. Nine patients had deleterious germline mutations, including BRCA1(2), MLH1(1), MSH2(2, including one with co-mutation of RAD50), MSH6(2), FANCA(1), MUTYH(1). Molecular subtypes were recognized among 62 patients, as POLE super-mutation(4, 6.5%), MSI-H(7, 11.3%), CN-Low(36, 58.1%) and CN-High(15, 24.2%). Ten patients received anti-PD1 monotherapy or in combination with chemotherapy or anti-angiogenic therapy, with the duration of disease control of 1 to 35 months. The ORR rate was 30%, and six patients had stable disease. The median (range) follow-up time was 18(2–160) months. 23(33.8%) relapses were recorded, and CN-High subtype displayed worst PFS compared with other subtypes (P < 0.01). 6 deaths were reported including 2(5.6%) of CN-Low and 4(26.7%) of CN-High.ConclusionOutpatients department gathered a considerable proportion of recurrent patients with complex genomic features. Patients with worse prognosis could be well studied, and anti-PD1 therapy was a promising salvage therapy in the real world.

The utility of ultrafast MRI and conventional DCE-MRI for predicting histologic aggressiveness in patients with breast cancer.

Prediction of histologic prognostic markers is important for determining management strategy and predicting prognosis. To identify important features of ultrafast and conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) that can predict histopathologic prognostic markers in patients with breast cancer. Preoperative MRI scans of 158 consecutive women (mean age = 54.0 years; age range = 29-86 years) with 163 breast cancers between February 2021 and August 2022 were retrospectively reviewed. Inter-observer agreements for ultrafast MRI parameters were analyzed by two radiologists. The qualitative and quantitative MRI parameters were correlated with histopathologic prognostic markers including molecular subtypes and histologic invasiveness. Inter-observer agreements for ultrafast MRI parameters were excellent (intraclass correlation coefficients of area under the kinetic curve [AUC], maximum slope [MS], maximum enhancement [ME], and slope = 0.987, 0.844, 0.822, and 0.760, respectively). Triple-negative breast cancers (TNBC) were significantly associated with rim enhancement (odds ratio [OR] = 9.4, P = 0.003) and peritumoral edema (OR = 17.9, P = 0.002), compared to luminal cancers. Invasive cancers were associated with lesion type-mass, increased delayed washout, angiovolume, ME, slope, MS, and AUC, compared to in situ cancers. In regression analysis, the combination of MS (>46.2%/s) (OR = 5.7, P = 0.046) and delayed washout (>17.5%) (OR = 17.6, P = 0.01), and that of AUC (>27,410.3) (OR = 9.6, P = 0.04), delayed washout (>17.5%) (OR = 8.9, P = 0.009), and lesion-type mass (OR = 4.6, P = 0.04) were predictive of histologic invasiveness. Conventional DCE-MRI with ultrafast imaging can provide useful information for predicting histologic underestimation and aggressive molecular subtype. MS and AUC on ultrafast MRI can be potential imaging markers for predicting histologic upgrade from DCIS to invasive cancer with high reliability.

Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study.

While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes. A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas. Statistical analysis of data from standards showed significant differences between sites and individual users. However, the multivariate classification models created from breast cancer data elicited 97.1% and 98.6% correct classification for leave-one-site-out and leave-one-patient-out cross validation. Molecular subtypes represented by PIK3CA mutation gave consistent results across sites. The results clearly demonstrate the feasibility of creating and using global classification models for a REIMS-based margin assessment tool, supporting the clinical translatability of the approach.

The Landscape of Breast Cancer Molecular and Histologic Subtypes in Canada

Purpose: To characterize the histologic and molecular subtype distribution of, and survival from, breast cancer (BC) among Canadian women overall, and by stage and age at diagnosis. Methods: Invasive BC cases from the Canadian Cancer Registry for women aged 15–99 years between 2012 and 2017 in Canada, excluding Quebec, were examined using pre-existing mortality linkages. Stage at diagnosis, molecular, and histologic subtypes, and 5-year net survival (NS) by age, subtype, and stage were determined. Results: 107,271 women with BC were included. Luminal A was the most common subtype, present in increasing proportions as women aged, up to a maximum of 55% of cases in 70–74. Ductal and luminal A were most likely to be diagnosed at stage I, while HER2+ had the highest proportion of diagnosis at stage III; triple negative (TN) and unknown had the highest proportion of stage IV. For all stages combined, luminal A had a five-year NS of 98%, while TN was 74%. NS for stage I BC was 99–100% for all subtypes, excepting TN, which was 96%. Survival decreased with advancing stage, most markedly for TN, for which stage III was 47% and stage IV 7%. Survival by equivalent stage and subtype was comparable across age groups but declined in older age categories. Conclusions: The varying natural histories of BC subtypes and histologies can inform prognoses, health system economics, and screening practices. The NS of 96% or greater for stage I, regardless of subtype, highlights the importance of early detection for all subtypes of BC, especially in aggressive subtypes.

Molecular profiling of risk factors for relapse in Japanese patients with stage II colorectal cancer: a retrospective cohort study.

The association between the molecular profiles and prognosis of Stage II colorectal cancer remains unclear. This study aimed to examine the risk factors for relapse of Stage II colorectal cancer using molecular profiling. We retrospectively enrolled patients with pStage II colorectal cancer who did not receive perioperative adjuvant therapy and whose surgically resected specimens were evaluated using gene expression and whole-exome analyses between January 2014 and December 2018. We evaluated the long-term outcomes and examined the risk factors for relapse-free survival. We evaluated 322 patients with pStage II colorectal cancer, including 126 (39.1%) with right colon cancer. Eighty-seven patients (27.0%) had pT4 tumor, 175 (54.3%) had positive venous invasion, 120 (37.3%) had positive lymphatic invasion, and 68 (21.1%) had perineural invasion. The presence of mutations in key genes for colorectal cancer development based on whole-exome analyses was as follows: APC, 245 (76.1%); TP53, 208 (64.6%); and KRAS, 134 (41.6%). According to the consensus molecular subtype classification based on gene expression, 76 patients (23.6%) had consensus molecular subtype 4 and a significantly lower relapse-free survival than the other patients (5-year relapse-free survival: 83.8% vs. 92.9%, p = 0.017). Perineural invasion (hazard ratio: 5.316, p < 0.001) and consensus molecular subtype 4 (hazard ratio: 2.399, p = 0.020) were identified as independent risk factors for relapse-free survival. Molecular profiling of Stage II colorectal cancer to assess the risk factors for relapse may contribute to the indication and drug selection for adjuvant chemotherapy.

Improved diagnostic efficiency of CRC subgroups revealed using machine learning based on intestinal microbes

BackgroundColorectal cancer (CRC) is a common cancer that causes millions of deaths worldwide each year. At present, numerous studies have confirmed that intestinal microbes play a crucial role in the process of CRC. Additionally, studies have shown that CRC can be divided into several consensus molecular subtypes (CMS) based on tumor gene expression, and CRC microbiomes have been reported related to CMS. However, most previous studies on intestinal microbiome of CRC have only compared patients with healthy controls, without classifying of CRC patients based on intestinal microbial composition.ResultsIn this study, a CRC cohort including 339 CRC samples and 333 healthy controls was selected as the discovery set, and the CRC samples were divided into two subgroups (234 Subgroup1 and 105 Subgroup2) using PAM clustering algorithm based on the intestinal microbial composition. We found that not only the microbial diversity was significantly different (Shannon index, p-value < 0.05), but also 129 shared genera altered (p-value < 0.05) between the two CRC subgroups, including several marker genera in CRC, such as Fusobacterium and Bacteroides. A random forest algorithm was used to construct diagnostic models, which showed significantly higher efficiency when the CRC samples were divided into subgroups. Then an independent cohort including 187 CRC samples (divided into 153 Subgroup1 and 34 Subgroup2) and 123 healthy controls was chosen to validate the models, and confirmed the results.ConclusionsThese results indicate that the divided CRC subgroups can improve the efficiency of disease diagnosis, with various microbial composition in the subgroups.

Clinicopathological characteristics and survival analysis of different molecular subtypes of breast invasive ductal carcinoma achieving pathological complete response through neoadjuvant chemotherapy

BackgroundTo investigate the prognostic differences following the achievement of a pathological complete response (pCR) through neoadjuvant chemotherapy across different molecular subtypes of breast invasive ductal carcinoma.MethodsData from the Surveillance, Epidemiology, and End Results (SEER) were identified for patients undergoing neoadjuvant chemotherapy who achieved pathological complete response for invasive ductal carcinoma of the breast between 2010 and 2019.Comparing the clinicopathological characteristics of patients across different molecular subtypes. Univariate and Cox multivariate analyses were utilized to identify independent predictors of overall survival (OS) and cancer-specific survival (CSS). The Kaplan–Meier method is used to compare OS and CSS among different molecular subtypes. After propensity score matching, subgroup analysis results were presented through forest plots.ResultsThis study included 9,380 patients diagnosed with invasive ductal carcinoma, who were categorized into four molecular subtypes: 2,721 (29.01%) HR + /HER-2 + , 1,661 (17.71%) HR + /HER2-, 2,082 (22.20%) HR-/HER2 + , and 2,916 (31.08%) HR-/HER-2-. HR + /HER-2- subgroup exhibited a significantly higher proportion of patients under 50 years old than the other subtype groups (54.67% vs 40.2%, 50.35% and 51.82%, p < 0.01), and had a higher N2 + N3 stage (11.2% vs 7.24%, 8.69% and 7.48%, p < 0.01). Univariate and multivariate analysis revealed that molecular subtype was the independent risk factor for OS and CSS in patients(p < 0.05). The Kaplan–Meier curves indicated that the HR + /HER-2 + subtype had the highest OS and CSS(p < 0.05). Next, were the HR-/HER-2 + and HR-/HER-2- subtypes, with the HR + /HER-2- group having the lowest OS and CSS(p < 0.05). After propensity score matching, the OS and CSS of patients in the HR + /HER-2 + group remained higher compared to HR + /HER-2- group(p < 0.05).ConclusionsPatients with invasive ductal carcinoma of different molecular subtypes exhibit varying prognoses after achieving pCR to neoadjuvant chemotherapy. Those in the HR + /HER-2- group are younger, have a higher lymph node stage, and the lowest OS and CSS, whereas patients in the HR + /HER-2 + group have the highest OS and CSS.

Integrated analysis of N6-methyladenosine- and 5-methylcytosine-related long non-coding RNAs for predicting prognosis in cervical cancer

BackgroundN6-methyladenosine (m6A) and 5-methylcytosine (m5C) play a role in modifying long non-coding RNAs (lncRNAs) implicated in tumorigenesis and progression. This study was performed to evaluate prognostic value of m6A- and m5C-related lncRNAs and develop an efficient model for prognosis prediction in cervical cancer (CC).MethodsUsing gene expression data of TCGA set, we identified m6A- and m5C-related lncRNAs. Consensus Clustering Analysis was performed for samples subtyping based on survival-related lncRNAs, followed by analyzing tumor infiltrating immune cells (TIICs). Optimal signature lncRNAs were obtained using lasso Cox regression analysis for constructing a prognostic model and a nomogram to predict prognosis.ResultsWe built a co-expression network of 23 m6A-related genes, 15 m5C-related genes, and 62 lncRNAs. Based on 9 m6A- and m5C-related lncRNAs significantly associated with overall survival (OS) time, two molecular subtypes were obtained, which had significantly different OS time and fractions of TIICs. A prognostic model based on six m6A- and m5C-related signature lncRNAs was constructed, which could dichotomize patients into two risk subgroups with significantly different OS time. Prognostic power of the model was successfully validated in an independent dataset. We subsequently constructed a nomogram which could accurately predict survival probabilities. Drug sensitivity analysis found preferred chemotherapeutic agents for high and low-risk patients, respectively.ConclusionOur study reveals that m6A- and m5C-related lncRNAs are associated with prognosis and immune microenvironment of CC. The m6A- and m5C-related six-lncRNA signature may be a useful tool for survival stratification in CC and open new avenues for individualized therapies.

Abstract PR-14: Meta-analysis of spatial transcriptomics data from pancreatic cancer precursors proposes a common molecular framework for PanIN and IPMN

Abstract INTRODUCTION Pancreatic ductal adenocarcinoma (PDAC) arises from either pancreatic intraepithelial neoplasia (PanIN) or intraductal papillary mucinous neoplasm (IPMN). Transcriptomic studies of PanIN and IPMN conducted in isolation both aim to improve early detection, risk stratification, and therapeutic target selection. Here, we curated existing and new whole transcriptome digital spatial profiles (DSP-RNA) from normal to malignant pancreatic epithelium to map the molecular landscape of PDAC progenitors. METHODS New NanoString GeoMx DSP-RNA datasets were generated from 1) surgical IPMN specimens resected at Duke University and 2) deceased donor pancreas specimens collected by the Gift of Life Michigan Donor Care Center that could not be used for transplantation and thus allocated to the University of Michigan for approved research. Published studies were downloaded from GEO (GSE229752, GSE226829, GSE199102) and the NanoString Spatial Organ Atlas. Epithelial areas of interest (AOIs) were processed and analyzed with stgeomx (https://github.com/mkiyer/stgeomx) and R/Bioconductor packages. Quality control (QC) procedures removed low quality AOIs. Batch effects were mitigated by defining highly variable genes within each dataset and merging them into a common set for downstream analysis. Molecular subtype gene signatures were obtained from the pdacR package and quantified using Gene Set Variation Analysis. RESULTS We assembled a pancreatic epithelial DSP-RNA cohort consisting of 768 AOIs from 62 patients. AOIs were annotated as normal duct (n=193), acini (n=53), acinar-ductal metaplasia (ADM) (n=57), PanIN (n=108), IPMN (n=92), and PDAC (n=265). Principal component analysis (PCA) revealed compelling associations between DSP-RNA profiles and the established classical, basal-like, and exocrine molecular subtypes of PDAC. The first principal component (PC1) stratified the exocrine and basal-like PDAC subtypes on opposite ends of its axis (PC1 vs exocrine Pearson r=0.929, p &amp;lt; 0.001, PC1 vs basal-like Pearson r=-0.775, padj &amp;lt; 0.001). PC2 further distinguished classical from basal-like AOIs, and PC3 separated normal (CRISP3+) from neoplastic (MUC5AC+) ductal epithelium. Unsupervised clustering of AOIs proposed four epithelial subtypes: “acinar/ADM” (cAcinar), “normal duct” (cDuct), “low-risk neoplasm” (cLR), and “high-risk/malignant” (cHR). PanIN and IPMN lesions exhibited remarkably similar global gene expression patterns, suggesting that they arise from a common lineage. CONCLUSIONS Meta-analysis of pancreatic epithelial DSP-RNA datasets provided a unifying molecular context for PanIN, IPMN, and PDAC. The established classical, basal-like, and exocrine PDAC subtypes explained global patterns of gene expression variation. Intriguingly, PanIN and IPMN transcriptomes appeared similar, suggesting that early detection and therapeutic selection strategies may be broadly applicable to PDAC precursors. Citation Format: Matthew K Iyer, Ashley Fletcher, Elishama Kanu, Chanjuan Shi, Rosina Carr, Ahmed M. Elhossiny, Daniel P. Nussbaum, Eileen S. Carpenter, Marina Pasca di Magliano, Arul M. Chinnaiyan, Peter J. Allen, Timothy L. Frankel. Meta-analysis of spatial transcriptomics data from pancreatic cancer precursors proposes a common molecular framework for PanIN and IPMN [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 PR-14.

Abstract B078: A comprehensive spatial atlas of neoadjuvant chemoradiation therapy in resected pancreatic cancer identifies cellular &amp; microenvironmental determinants of persister populations

Abstract Background The molecular pathways involved in the response to radiation therapy (RT) in pancreatic ductal adenocarcinoma (PDAC) remain poorly understood. Despite extensive molecular stratification efforts, improvements in molecularly informed RT-based therapeutic strategies in the neoadjuvant setting have been limited. This study aims to elucidate the adaptive mechanisms and interactions within PDAC to identify new combinatorial therapeutic targets with RT. MethodsWe constructed a high-resolution molecular landscape of the cellular subtypes and spatial communities that constitute PDAC. Single-cell RNA sequencing (scRNA- seq) was performed on 16 PDAC biopsies of matched pre- and post-treatment samples from a Phase I/II dose-escalation clinical trial of pancreatic stereotactic body radiation therapy (SBRT). Additionally, Visium spatial transcriptomics (ST) was conducted on biopsies from primary PDAC patients collected at the time of surgery. These patients received either no neoadjuvant therapy (n = 8) or neoadjuvant chemoradiation (chemoRT) (n = 20). Cell type profiles were determined from scRNA-seq to compare changes in cell type proportions and signatures before and after RT. From these profiles, we employed robust cell type decomposition to analyze cell type signatures in our ST dataset. ResultsChemoRT induced several significant effects compared to controls within the tumor microenvironment, including an increase in the proportion of myofibroblasts, specifically senescent cancer-associated fibroblasts (CAFs), with signatures consistent with immune cell dysfunction. We observed an increase in CD8 and CD4 effector memory cells with RT; however, this occurred alongside a rise in immunosuppressive alternatively activated tumor-associated macrophages. Additionally, RT upregulated pathways related to HIF, cytokine production, B cell proliferation, and negative regulation of lymphocytes. Patients with increased infiltration of NK, memory B, and CD8 effector memory cells demonstrated improved overall survival outcomes. Inference of copy number variations enabled us to identify cancer subclones as either responsive or resistant in matched pre and post RT biopsies. Resistance was characterized by augmented hypoxia, KRAS activation, epithelial- mesenchymal transition, and interferon-gamma response signaling. Conversely, responders exhibited increased oxidative phosphorylation signaling and pathway enrichment associated with cell proliferation and cell cycle progression. Interestingly, RT induced a decrease in cancer cells characterized as harboring a chemoresistant basal-like molecular subtype. ConclusionThrough both longitudinal and therapy stratified tumor biopsies, we were able to observe significant selection pressures on stromal and cancer cells following RT. This includes upregulation of innate and adaptive immune pathways with a compensatory immunosuppressive response driven by myeloid cells and CAFs. This may provide opportunities for development of clinical trials to therapeutically target specific cellular phenotypes and their interactions. Citation Format: Vincent Bernard, Galia Jacobson, Kimal Rajapakshe, Jimin Min, Guangsheng Pei, Daniel Lin, Ayush Suresh, Dadi Jiang, Ching-Wei Tzeng, Manoop S Bhutani, Linghua Wang, Paola A Guerrero, Ethan B Ludmir, Albert C Koong, Anirban Maitra, Cullen M Taniguchi, Eugene J Koay. A comprehensive spatial atlas of neoadjuvant chemoradiation therapy in resected pancreatic cancer identifies cellular &amp; microenvironmental determinants of persister populations [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 B078.

Abstract C071: Transcription factor switching drives progression of the classical subtype of pancreatic cancer

Abstract Despite recent advances in the treatment of pancreatic adenocarcinoma (PDAC), the median survival remains &amp;lt;12 months. Patients typically present with late-stage disease and are often unable to tolerate drug combination regimens. Large-scale genomic approaches categorise PDAC into two distinct molecular subtypes, based on transcriptomic signatures, and these are termed the classical and squamous (or basal) subtypes. Although it is well-described that PDAC harbours gene mutations (i.e. KRAS G12D, p53 R172H) that initiate the disease, little is known about the influence of lineage-defining Transcription Factors (TF) on PDAC subtypes and their contribution to tumor progression. We took an unbiased approach to discover driving TFs in PDAC, we performed ChIP-seq of the active enhancer mark H3K27Ac from surplus tissue following resection of primary PDAC tumors (n=6) and normal adjacent tissue (n=4). Analysis of the H3K27Ac sites showed that the most enriched motifs within all experimentally mapped enhancer elements were HNF motifs (HNF4G &amp; HNF4A) and Forkhead (i.e., FOXA1), confirming that these two classes of TFs constitute the lineage-defining factors in these clinical samples. Investigation of publicly available clinical datasets revealed HNF4G to be amplified in 9% of PDAC patients. Using Rapid Immunoprecipitation and Mass Spectrometry of Endogenous protein complexes (RIME), our method for unbiased discovery of endogenous protein complexes, we investigated the fundamental differences in the FOXA1 interactome between models of classical and squamous subtypes. HNF4A and HNF4G were only seen as FOXA1-interactors in models of the classical subtype. HNF4G Knockout (KO) clones injected orthotopically into the pancreas of mice, showed a significant increase (p-value&amp;lt;0.0006) in median survival and slower tumor growth compared to Control (Cas-9) (p-value &amp;lt;0.0001). HNF4G-KO dependent genes were specifically expressed in primary tumors (with decreased EMT signalling) versus metastatic samples (PanCRux dataset), implying that the HNF4G target genes were mostly limited to primary tumor contexts. We found Protein Arginine Methylate Transferase (PRMT1) to be a significant and reproducible interactor of FOXA1 and HNF4G. We observed a significant global reduction in PRMT1 chromatin binding in both the HNF4G-KO and FOXA1 depleted cells compared to the control. This data suggests a dependency on both HNF4G and FOXA1 for recruitment of PRMT1 to chromatin. We find HNF4G to be the key driver of classical subtype primary disease, in part by recruiting the methyltransferase PRMT1 to regulatory regions revealing a therapeutically exploitable opportunity. A molecular switch occurs in advanced disease, whereby HNF4G expression decreases, unmasking FOXA1’s transcriptional activity. De-repressed FOXA1 drives late-stage disease, by orchestrating metastasis-specific enhancer-promoter loops to direct metastatic gene regulation. We suggest the existence of stage-dependent TF activity, triggered by molecular compartmentalisation that mediates multistage progression of PDAC. Citation Format: Shalini Vasudev Rao, Lisa V Young, Danya Cheeseman, Stephanie V Mack, Dominique-Laurent Couturier, Sean V Flynn, Rebecca Brias, Amy Smith, Jill Temple, Duncan Jodrell, Alasdair Russell V Russell, Igor Chernukhin, Jason Carroll. Transcription factor switching drives progression of the classical subtype of pancreatic cancer [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 C071.

Abstract C078: Chromatin accessibility profiling of human pancreatic tumors reveals epigenetic features of malignant and stromal cell subtypes

Abstract Pancreatic ductal adenocarcinoma (PDAC) disease progression involves complex cell state transitions in both malignant and non-malignant populations within the tumor microenvironment. To uncover PDAC cell states and epigenetic features associated with clinical outcome, we profiled 40 tumor samples (33 primary tumors and 7 metastases) from 39 patients with single cell ATAC-seq, along with 10 sample-matched single cell RNA-seq profiles. Through topic modeling of high dimensional chromatin data, we uncovered distinct cis-regulatory networks underlying the classical and basal-like molecular subtypes of PDAC, which showed differential activity in basal-classical hybrid cells. Furthermore, delineation of epigenetic alterations by basal, classical, and hybrid subtypes revealed a core set of chromatin regions opened in all PDAC subtypes relative to ductal and acinar cells. These universally open regions showed striking overlap with gastrointestinal enhancers, highlighting a common endoderm lineage infidelity process across pancreatic tumors. Within the stromal compartment, we identified novel regulators of myofibroblastic cancer-associated fibroblasts (myCAFs) and inflammatory CAFs (iCAFs) from enhancer networks active in each CAF subtype. Importantly, our data uncover a highly prognostic gene program active in only one of two distinct myCAF chromatin states found in our data, highlighting a putative tumor-promoting myCAF subpopulation. Our work provides a high-resolution description of epigenetic cell state heterogeneity in PDAC, revealing novel regulators and gene programs associated with PDAC subtypes and clinical outcome. Citation Format: Kevin MacPherson-Hawthorne, Jason M. Link, Patrick Worth, Brett Sheppard, Andrew Fields, Colin Daniel, Andrew Nishida, Carl Pelz, Trent Waugh, Ethan Agritelle, John Muschler, Andrew Adey, Rosalie Sears. Chromatin accessibility profiling of human pancreatic tumors reveals epigenetic features of malignant and stromal cell subtypes [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 C078.

Abstract B068: Human pancreatic cancer single cell atlas reveals association of CXCL10 positive fibroblasts and basal subtype tumor cells

Abstract Pancreatic ductal adenocarcinoma (PDAC) patients with tumors enriched for the basal-like molecular subtype exhibit severe resistance to standard of care treatments and have significantly worse overall survival, compared to patients with classical subtype enriched tumors. It is therefore important to develop resources that allow identification of novel targets in a statistically rigorous way to overcome therapeutic challenges. Here we compiled a single cell RNA sequencing (scRNAseq) atlas of the human pancreas with 229 individual patient samples, aggregated from raw data obtained from published studies. We mapped cell-type specific scRNAseq gene signatures in newly generated (n=12) and existing (n=13) spatial transcriptomics (ST) data from PDAC samples. Analysis of tumor cells from our scRNAseq atlas revealed nine transcriptionally distinct populations, two of which strongly align with a basal gene signature, correlating with worse overall survival in PDAC bulk RNAseq datasets. Deconvolution showed one of the basal populations identified to be the predominant tumor subtype in non- dissociated ST tissues and in vitro tumor cell and organoid PDAC lines. In bulk RNAseq datasets, we uncovered that myofibroblasts (myFb) expressing CXCL10 consistently correlated with both basal subtypes and were found near basal tumor cells using immunostaining. We also identified that besides immune cells, ductal cells also express CXCR3, the receptor for CXCL10, further suggesting a relationship between these cell types in PDAC tumor microenvironment (TME). Overall, we show that our newly built scRNAseq atlas, containing over 700,000 cells, integrated with ST data is a powerful resource, given that it corroborates numerous PDAC TME observations in the literature, with increased statistical power. Moreover, we uncovered a novel association between CXCL10+ myFb and basal tumor cells, underscoring the presence of unique spatial niches in PDAC that should be explored for the development of targeted therapies for this dismal disease. Citation Format: Ian M Loveless, Samantha B Kempt, Yuesong Wu, Daniel J Salas-Escabillas, Kailee Hartway, Madison George, Yetunde Makinwa, Thais Stockdale, Samuel D Zwernik, Kendyll Gartrelle, Rohit G Reddy, Daniel Long, Allison Wombell, Julie M Clark, Albert M Levin, David Kwon, Ling Huang, Ralph Francescone, Débora B Vendramini-Costa, Ben Stanger, Adam Alessio, Andrew M Waters, Yuehua Cui, Brian Theisen, Howard C Crawford, Nina G Steele. Human pancreatic cancer single cell atlas reveals association of CXCL10 positive fibroblasts and basal subtype tumor cells [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 B068.