Adenocarcinoma Cohort Research Articles

Clinicopathological and molecular characteristics of microsecretory adenocarcinoma in salivary gland

Objective: To investigate the clinicopathological, immunohistochemical, and molecular genetic characteristics of microsecretory adenocarcinoma (MSA) of the salivary gland, and to improve the understanding of this rare tumor. Methods: Cases originally diagnosed as MSA at the Department of Oral Pathology, the Ninth People's Hospital of Shanghai Jiao Tong University School of Medicine were retrospectively collected. The cases of polymorphous adenocarcinoma and adenocarcinoma, not otherwise specified from January 2000 to January 2020 were reviewed to identify potential misdiagnosed MSA cases. Clinicopathological analysis and follow-up of all confirmed MSA cases were performed, and relevant literature was reviewed. Results: A total of 4 MSA cases were identified, including 2 screened from the polymorphous adenocarcinoma cohort. Of the 4 MSA patients, 3 were male and 1 was female, with an average age of 53 years (range, 37-67 years). Three cases occurred in the palate, and one in the buccal region. The clinical manifestation was usually a slow-growing painless mass. Tumors were generally small, with a maximum diameter ranging from 0.7 to 1.8 cm (average, 1.2 cm). Microscopically, the tumor was unencapsulated and showed an infiltrative growth pattern. The tumor cells appeared small in size and showed bland, cubic and flattened cytological features, forming microcystic lumens and glandular tubes. Significant basophilic secretions were seen in the lumens. Between the tumor nests there was fibro-myxoid stroma. Immunohistochemistry showed diffusely or partially positive staining for cytokeratin 7, S-100, SOX-10, p63 and vimentin and negative staining for p40, mammaglobin, and calponin. The proliferation index of Ki-67 was relatively low (1%-3%). Four MSA cases all harbored SS18 gene rearrangement as shown by fluorescence in situ hybridization (FISH), including 2 cases with MEF2C::SS18 fusion gene through RNA-targeted next generation sequencing. All 4 patients underwent surgical resection without any adjuvant treatments. Three patients were followed up for a period of 2 to 203 months. No tumor recurrence, metastasis, or disease-related death was found. Conclusions: Salivary gland MSA is a novel and rare low-grade carcinoma with unique and consistent histological morphology, immunophenotype, and molecular changes. Immunohistochemical staining and SS18 break apart FISH are useful for the diagnosis of the tumor with atypical morphology and high-grade transformation.

Stereologic consequences of iatrogenic collapse: The morphology of adenocarcinoma in situ overlaps with invasive patterns. Proposal for a necessary modified classification of pulmonary adenocarcinomas

Recognizing non-invasive growth patterns is necessary for correct diagnosis, invasive size determination and pT-stage in resected non-small cell lung carcinoma. Due to iatrogenic collapse after resection, the distinction between adenocarcinoma in-situ (AIS) and invasive adenocarcinoma may be difficult. The aim of this study is to investigate the complex morphology of non-mucinous non-invasive patterns of AIS in resection specimen with iatrogenic collapse, and to relate this to follow-up.The effects of iatrogenic collapse on the morphology of collapsed AIS were simulated in a mathematical model. Three dimensional related criteria applied in a modified classification, using also cytokeratin 7 and elastin as additional stains, in two independent retrospective cohorts of primary pulmonary adenocarcinomas ≤3 cm resection specimen with available follow-up information.The model demonstrated that infolding of alveolar walls occurs during iatrogenic collapse and lead to a significant increase in tumor cell heights in maximal collapse areas, compared to less collapsed areas. The morphology of infolded AIS overlaps with patterns described as papillary and acinar adenocarcinoma according to the WHO classification, necessitating an adaptation.The modified classification incorporates recognition of iatrogenic and biologic collapse, tangential cutting effect true invasion and surrogate markers of invasion i.e. grey zone, covering a multilayering falling short of micropapillary, cribriform and solid alveolar filling growth. The use of elastin and CK7 staining aids in the morphologic recognition of iatrogenic collapsed AIS and the distinction from invasive adenocarcinoma. Out of a total of 70 resection specimens 1 case was originally classified as AIS and 9 were reclassified as iatrogenic collapsed AIS. Patients with collapsed AIS showed a 100 % recurrence-free survival after a mean follow-up time of 69.5 months.With the current WHO classification, AIS is overdiagnosed as invasive adenocarcinoma due to infolding. The modified classification facilitates the diagnosis of AIS.

A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma

Background and ObjectiveIn Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel quantitative prognostic factors. We realized a pipeline that relies on survival machine-learning (SML) classifiers and explainability based on patients’ follow-up (FU) to stratify prognosis from the public-available multi-omic datasets of the CPTAC-PDA project. Materials and MethodsAnalyzed datasets included tumor-annotated radiologic images, clinical, and mutational data. A feature selection was based on univariate (UV) and multivariate (MV) survival analyses according to Overall Survival (OS) and recurrence (REC). In this study, we considered seven multi-omic datasets and compared four SML classifiers: Cox, survival random forest, generalized boosted, and support vector machines (SVM). For each classifier, we assessed the concordance (C) index on the validation set. The best classifiers for the validation set on both OS and REC underwent explainability analyses using SurvSHAP(t), which extends SHapley Additive exPlanations (SHAP). ResultsAccording to OS, after UV and MV analyses we selected 18/37 and 10/37 multi-omic features, respectively. According to REC, based on UV and MV analyses we selected 10/35 and 5/35 determinants, respectively. Generally, SML classifiers including radiomics outperformed those modelled on clinical or mutational predictors. For OS, the Cox model encompassing radiomic, clinical, and mutational features reached 75 % of C index, outperforming other classifiers. On the other hand, for REC, the SVM model including only radiomics emerged as the best-performing, with 68 % of C index. For OS, SurvSHAP(t) identified the first order Median Gray Level (GL) intensities, the gender, the tumor grade, the Joint Energy GL Co-occurrence Matrix (GLCM), and the GLCM Informational Measures of Correlations of type 1 as the most important features. For REC, the first order Median GL intensities, the GL size zone matrix Small Area Low GL Emphasis, and first order variance of GL intensities emerged as the most discriminative. ConclusionsIn this work, radiomics showed the potential for improving patients’ risk stratification in PDA. Furthermore, a deeper understanding of how radiomics can contribute to prognosis in PDA was achieved with a time-dependent explainability of the top multi-omic predictors.

Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study

IntroductionMolecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability. MethodsThis retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples. Validation is performed in the publicly available TCGA-LUAD cohort. ResultsModels trained on the larger HLCC cohort generalized well to the TCGA dataset for mutations in EGFR (AUC 0.76), STK11 (AUC 0.71) and TP53 (AUC 0.75), in line with the hypothesis that larger cohort sizes improve model robustness. Variation in performance due to pre-processing and modeling choices, such as mutation variant calling, affected EGFR prediction accuracy by up to 7 %. DiscussionModel explanations suggest that acinar and papillary growth patterns are critical for the detection of EGFR mutations, whereas solid growth patterns and large nuclei are indicative of TP53 mutations. These findings highlight the importance of specific morphological features in mutation detection and the potential of deep learning models to improve mutation prediction accuracy. ConclusionAlthough deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they may support patient pre-selection for clinical trials and deepen the insight in genotype-phenotype relationships.

Defining the Soluble and Extracellular Vesicle Protein Compartments of Plasma Using In-Depth Mass Spectrometry-Based Proteomics.

Plasma-derived extracellular vesicles (pEVs) are a potential source of diseased biomarker proteins. However, characterizing the pEV proteome is challenging due to its relatively low abundance and difficulties in enrichment. This study presents a streamlined workflow to identify EV proteins from cancer patient plasma using minimal sample input. Starting with 400 μL of plasma, we generated a comprehensive pEV proteome using size exclusion chromatography (SEC) combined with HiRIEF prefractionation-based mass spectrometry (MS). First, we compared the performance of HiRIEF and long gradient MS workflows using control pEVs, quantifying 2076 proteins with HiRIEF. In a proof-of-concept study, we applied SEC-HiRIEF-MS to a small cohort (12) of metastatic lung adenocarcinoma (LUAD) and malignant melanoma (MM) patients. We also analyzed plasma samples from the same patients to study the relationship between plasma and pEV proteomes. We identified and quantified 1583 proteins in cancer pEVs and 1468 proteins in plasma across all samples. While there was substantial overlap, the pEV proteome included several unique EV markers and cancer-related proteins. Differential analysis revealed 30 DEPs in LUAD vs the MM group, highlighting the potential of pEVs as biomarkers. This work demonstrates the utility of a prefractionation-based MS for comprehensive pEV proteomics and EV biomarker discovery. Data are available via ProteomeXchange with the identifiers PXD039338 and PXD038528.

Glycolysis-related genes predict prognosis and indicate immune microenvironment features in gastric cancer.

Gastric cancer (GC) is a major contributor to cancer-related mortality. Glycolysis plays a pivotal role in tumor microenvironment (TME) reprogramming. In this research, the functions of glycolysis-associated genes (GRGs) were evaluated to predict the outcome and reveal the characteristics of the immune microenvironment in individuals with stomach cancer. The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort provided gene expression and clinical data for gastric cancer (GC) patients, which were further authenticated using datasets sourced from the Gene Expression Omnibus (GEO). By referencing the Molecular Signatures Database (MSigDB), a total of 326 GRGs were pinpointed. The various subtypes of GC were outlined through consensus clustering, derived from the expression patterns of these GRGs. Utilizing multivariate Cox regression analysis, a multigene risk score model was formulated. Both the CIBERSORT and ESTIMATE algorithms played a pivotal role in assessing the immune microenvironment. To delve into the biological functions of the key genes, wound healing, transwell invasion, and MTT assays were conducted. Based on the expression patterns of GRGs, patients were categorized into two distinct groups: the metabolic subtype, designated as cluster A, and the immune subtype, labeled as cluster B. Patients belonging to cluster B exhibited a poorer prognosis. A prognostic risk score model, formulated upon the expression levels of six key GRGs - ME1, PLOD2, NUP50, CXCR4, SLC35A3, and SRD35A3 - emerged as a viable tool for predicting patient outcomes. The downregulation of CXCR4 notably diminished the glycolytic capacity of gastric cancer (GC) cells, alongside their migratory, invasive, and proliferative capabilities. Intriguingly, despite the adverse prognostic implications associated with both the immune subtype (cluster B) and the high-risk cohort, these groups exhibited a favorable immune microenvironment coupled with elevated expression of immune checkpoint genes. Our investigations revealed a positive correlation between high CXCR4 expression and low ME1 expression with the infiltration of CD8+ T cells, as well as an enhanced responsiveness to treatment with an anti-PD-1 immune checkpoint inhibitor. In this study, we discovered that the expression profiles of GRGs hold the potential to forecast the prognosis of gastric cancer (GC) patients, thereby possibly aiding in clinical treatment decision-making.

Exploration of bacterial lipopolysaccharide-related genes signature based on T cells for predicting prognosis in colorectal cancer.

The intratumoral microorganisms participates in the progression and immunotherapy of colorectal cancer (CRC). However, due to technical limitations, the impact of microorganisms on CRC has not been fully understood. Therefore, we conducted a systematic analysis of relationship between bacterial lipopolysaccharide (LPS)-associated genes and immune cells to explore new biomarkers for predicting the prognosis of CRC. The single-cell RNA sequencing data and the Comparative Toxicogenomics Database were used to screen T cells-associated LPS-related genes (TALRGs). Then, we established and validated the TALRGs risk signature in The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) cohort and GSE39582 cohort. Besides, we compared the differences in tumor-infiltrating immune cell types, immunotherapeutic response, somatic mutation profiles, and tumor mutation burden (TMB) between high-risk group and low-risk group. In addition, the immunotherapeutic cohort (Imvigor210) treated with an anti-PD-L1 agent was performed to explore the potential value of the TALRGs signature on immunotherapy. Five prognostic TALRGs were identified and selected to build the prognostic model. The high-risk group had poor prognosis in both TCGA-COAD cohort (P < 0.0001) and GSE39582 cohort (P = 0.00019). The areas under the curves (AUCs) of TALRGs signature were calculated (TCGA-COAD cohort: 0.624 at 1 years, 0.639 at 3 years, 0.648 at 5 years; anti-PD-L1 cohort was 0.59). The high-risk group had advanced pathological stages and higher TMN stages in both TCGA-COAD cohort and GSE39582 cohort. The high-risk group had the higher infiltration of immunosuppressive cells, the expressions of immune checkpoint molecules, the IC50 values of chemotherapy drugs, and TP53 mutation rate (P < 0.05). In addition, patients with high TMB had worse prognosis (P < 0.05). Furthermore, the Imvigor210 also showed patients with high-risk scores had poor prognosis (platinum-treated cohort: P = 0.0032; non-platinum-treated cohort: P = 0.00017). Microorganisms are closely related to the tumor microenvironment to influence the progression and immune response of CRC via stimulating T cells through LPS-related genes. The TALRGs signature contributed to predict the prognosis and immunotherapy of CRC, and became new therapeutic targets and biomarkers of CRC.

Identifying an immunosenescence-associated gene signature in gastric cancer by integrating bulk and single-cell sequencing data

It has been believed that immunosenescence plays a crucial role in tumorigenesis and cancer therapy. Nevertheless, there is still a lack of understanding regarding its role in determining clinical outcomes and therapy selection for gastric cancer patients, due to the lack of a feasible immunosenescence signature. Therefore, this research aims to develop a gene signature based on immunosenescence, which is used for stratification of gastric cancer. By integrative analysis of bulk transcriptome and single-cell data, we uncovered immunosenescence features in gastric cancer. Random forest algorithm was used to select hub genes and multivariate Cox algorithm was applied to construct a scoring system to evaluate the prognosis and the response to immunotherapy and chemotherapy. The Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD) cohort was implemented as the training cohort and two independent cohorts from the Gene Expression Omnibus (GEO) database were used for validation. The model was further tested by our Fudan cohort. In this study, immunosenescence was identified as a hallmark of gastric cancer that is linked with transcriptomic features, genomic variations, and distinctive tumor microenvironment (TME). Four immunosenescence genes, including APOD, ADIPOR2, BRAF, and C3, were screened out to construct a gene signature for risk stratification. Higher risk scores indicated strong predictive power for poorer overall survival. Notably, the risk score signature could reliably predict response to chemotherapy and immunotherapy, with patients with high scores benefiting from immunotherapy and patients with low scores responding to chemotherapy. We report immunosenescence as a hitherto unheralded hallmark of gastric cancer that affects prognosis and treatment efficiency.

PARP4 interacts with hnRNPM to regulate splicing during lung cancer progression

BackgroundThe identification of cancer driver genes from sequencing data has been crucial in deepening our understanding of tumor biology and expanding targeted therapy options. However, apart from the most commonly altered genes, the mechanisms underlying the contribution of other mutations to cancer acquisition remain understudied. Leveraging on our whole-exome sequencing of the largest Asian lung adenocarcinoma (LUAD) cohort (n = 302), we now functionally assess the mechanistic role of a novel driver, PARP4.MethodsIn vitro and in vivo tumorigenicity assays were used to study the functional effects of PARP4 loss and mutation in multiple lung cancer cell lines. Interactomics analysis by quantitative mass spectrometry was conducted to identify PARP4’s interaction partners. Transcriptomic data from cell lines and patient tumors were used to investigate splicing alterations.ResultsPARP4 depletion or mutation (I1039T) promotes the tumorigenicity of KRAS- or EGFR-driven lung cancer cells. Disruption of the vault complex, with which PARP4 is commonly associated, did not alter tumorigenicity, indicating that PARP4’s tumor suppressive activity is mediated independently. The splicing regulator hnRNPM is a potentially novel PARP4 interaction partner, the loss of which likewise promotes tumor formation. hnRNPM loss results in splicing perturbations, with a propensity for dysregulated intronic splicing that was similarly observed in PARP4 knockdown cells and in LUAD cohort patients with PARP4 copy number loss.ConclusionsPARP4 is a novel modulator of lung adenocarcinoma, where its tumor suppressive activity is mediated not through the vault complex—unlike conventionally thought, but in association with its novel interaction partner hnRNPM, thus suggesting a role for splicing dysregulation in LUAD tumorigenesis.

Construction and validation of a TTN mutation associated immune prognostic model for evaluating immune microenvironment and outcomes of gastric cancer: An observational study.

Gastric cancer (GC) is a prevalent form of cancer worldwide, and TTN (titin) mutations are frequently observed in GC. However, the association between TTN mutations and immunotherapy for GC remains unclear, necessitating the development of novel prognostic models. The prognostic value and potential mechanisms of TTN in stomach adenocarcinoma were evaluated by TCGA (The Cancer Genome Atlas)-stomach adenocarcinoma cohort analysis, and an immune prognostic model was constructed based on TTN status. We validated it using the GSE84433 dataset. We performed Gene Set Enrichment Analysis and screened for differentially expressed genes, and used lasso (least absolute shrinkage and selection operator) regression analysis to screen for survival genes to construct a multifactorial survival model. In addition, we evaluated the relative proportions of 22 immune cells using the CIBERSORT algorithm for immunogenicity analysis. Finally, we constructed the nomogram integrating immune prognostic model and other clinical factors. GESA showed enrichment of immune-related phenotypes in patients with TTN mutations. We constructed an immune prognostic model based on 16 genes could identify gastric cancer patients with higher risk of poor prognosis. Immuno-microenvironmental analysis showed increased infiltration of naive B cells, plasma cells, and monocyte in high-risk patients. In addition, Nomo plots predicted the probability of 1-year, 3-year, and 5-year OS (overall survival) in GC patients, showing good predictive performance. In this study, we identified that TTN gene may be a potential clinical biomarker for GC and TTN mutations may be a predictor of immunotherapy in patients. We constructed and validated a new model for prognosis of GC patients based on immune characteristics associated with TTN mutations. This study may provide potential therapeutic strategies for gastric cancer.

Three biomarkers (HER2, PD-L1, and microsatellite status) in a large cohort of metastatic gastroesophageal adenocarcinomas: The MD Anderson Cancer Center experience.

Human epidermal growth factor receptor-2 (HER2), programmed death-ligand 1 (PD-L1), and microsatellite (MS) status are well-established biomarkers in gastroesophageal adenocarcinomas (GEAs). However, it is unclear how the combination of these biomarkers is associated with clinicopathological factors and prognosis. This retrospective study included baseline metastatic GEA patients who were tested for all three biomarkers (HER2, PD-L1, and MS status) at the MD Anderson Cancer Center between 2012 and 2022. Stratification was performed according to the combination of biomarker profiles: triple negative (TN), single positive (SP), and multiple positive (MP). Comparative analyses of clinicopathological factors and survival using combinations of biomarkers were performed. Among the 698 GEA patients analyzed, 251 (36.0%) were classified as TN, 334 (47.9%) as SP, and 113 (16.1%) as MP. The MP group showed a significant association with tumors located in the esophagus (p < .001), well to moderate differentiation (p < .001), and the absence of signet ring cells (p < .001). In the survival analysis, MP group had a significantly longer overall survival (OS) compared to the other groups (MP vs. TN, p < .001 and MP vs. SP, p < .001). Multivariate Cox regression analysis revealed that MP serves as an independent positive prognostic indicator for OS (hazard ratio = 0.63, p < .01). Our findings indicate that MP biomarkers are associated with a favorable prognosis in metastatic GEA. These results are reflective of clinical practice and offer valuable insights into how therapeutics and future biomarkers could influence therapy/prognosis.

A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.Graphical

Role of TAP1 in the identification of immune-hot tumor microenvironment and its prognostic significance for immunotherapeutic efficacy in gastric carcinoma.

Gastric cancer (GC), a multifaceted gastrointestinal malignancy, is the fourth most prevalent contributor to cancer-related fatalities globally. As a member of the ATP-binding cassette (ABC) family, transporter associated with antigen processing 1 (TAP1) is crucial for conveying antigen peptides from the cytoplasm to the lumen of the endoplasmic reticulum and subsequently loading them onto the major histocompatibility complex (MHC) class I molecules. Recent studies have established the biological significance of TAP1 in upholding tumor survival and facilitating immune evasion by remodeling the tumor microenvironment (TME) and orchestrating immune infiltration. The study was conducted to elucidate the association of TAP1 expression with immunological characteristics, and sought to exploit the value of TAP1 as a biomarker reflecting the inflamed TME and immunotherapeutic response. RNA-sequencing profiles and clinical annotations were obtained from The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) cohort and Gene Expression Omnibus (GEO) portal. Preprocessing was conducting using the limma package. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules and TAP1 co-expressed genes (CEGs) based on correlation patterns. Consensus clustering and silhouette analysis determined the optimal number of TAP1-related groups. Gene expression profiles were integrated and classified using the pamr package. The Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate immunological characteristics. Differential expression analysis was conducted using the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Single-cell RNA sequencing (scRNA-seq) datasets were analyzed using the Seurat toolkit to characterize cell types. Within this investigation, no significant differences in TAP1 expression were observed among patients exhibiting various clinicopathological features, indicating that TAP1 expression was not specific to molecular subtypes. Subsequent analysis revealed a positive correlation between TAP1 and diverse immunological traits, encompassing immunomodulators, tumor-infiltrating immune cells, as well as immune checkpoints across multiple datasets. Besides, within a GC immunotherapy cohort, individuals displaying high TAP1 expression demonstrated an increased likelihood of achieving complete remission (CR) post-treatment, suggesting heightened sensitivity to immunotherapy. In the clinical cohort, TAP1 overexpression in GC patients was positively correlated with CD8. TAP1 appears linked to an inflamed TME and serves as a prospective biomarker for discerning immunological attributes and gauging immunotherapeutic responses in GC, particularly in identifying immune-reactive tumors.

Senescence-related signatures predict prognosis and response to immunotherapy in colon cancer.

Colorectal cancer (CRC) is one of the most common cancers. Cellular senescence plays a vital role in carcinogenesis by activating many pathways. In this study, we aimed to identify biomarkers for predicting the survival and recurrence of CRC through cellular senescence-related genes. Utilizing The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, RNA-sequencing data and clinical information for CRC were collected. A risk model for predicting overall survival was established based on five differentially expressed genes using least absolute shrinkage and selection operator-Cox regression (LASSO-Cox regression), receiver operating characteristic (ROC), and Kaplan-Meier analyses. The study also delved into both the tumor microenvironment and the response to immunotherapy. Moreover, we gathered clinical sample data from our center in order to confirm the findings of public database analysis. Through ROC and Kaplan-Meier analyses, a risk model was developed using five cellular senescence-related genes [i.e., CDKN2A, SERPINE1, SNAI1, CXCL1, and ETS2] to categorize patients into high- and low-risk groups. In the TCGA-colon adenocarcinoma (COAD) and GEO-COAD cohorts, the high-risk group was associated with a bleaker forecast (P<0.05), immune cell inactivation, and insensitivity to immunotherapy in IMvigor210 database (http://research-pub.gene.com/IMvigor210CoreBiologies/). Clinical samples were then used to confirm that ETS2 and CDKN2A could serve as independent prognostic biomarkers in CRC. Gene signatures related to cellular senescence, specifically involving CDKN2A and ETS2, are emerging as promising biomarkers for predicting CRC prognosis and guiding immunotherapy.

A heterogeneous tumor immune microenvironment of uncommon epidermal growth factor receptor mutant non-small cell lung cancer.

Common epidermal growth factor receptor (EGFR) mutations are usually not considered for immunotherapy in non-small cell lung cancer (NSCLC) due to poor efficacy. However, whether uncommon EGFR mutations are suitable for immunotherapy has not been thoroughly studied. Thus, we explored the tumor immune microenvironment (TME) features in uncommon EGFR mutant NSCLC. In this study, a total of 41 patients with EGFR mutations were included, the majority (85.4%) of whom were stage I. Among them, 22 patients harbored common mutations, while 19 patients presented with uncommon mutations. Compared with common mutations, uncommon mutations exhibited more infiltrating T cells and fewer M2 macrophages, upregulated expression of antigen processing and a presentation pathway. Unsupervised clustering based on the mIF profile identified two classes with heterogeneous TME in uncommon mutations. Class 1 featured the absence of PD-1+ cytotoxic T cell infiltration, and class 2 displayed a hotter TME because of the downregulated expression of hypoxia (p < 0.001), oxidative phosphorylation (p = 0.009), and transforming growth factor beta signaling (p = 0.01) pathways as well as increased expression of CTLA4 (p = 0.001) and PDCD1 (p = 0.004). The association of CTLA4 and PDCD1 with TME profiles was validated in a TCGA lung adenocarcinoma cohort with uncommon EGFR mutations. Our study reveals the distinct and heterogeneous TME features in uncommon EGFR mutant NSCLC.

Abstract PR013: Detecting pairwise and higher-order antagonistic epistatic effects among somatic cancer genotypes to discover synthetic lethality

Abstract The mutual exclusivity of somatic variants in cancer suggests that current somatic variants can exert antagonistic epistatic effects on the selection of new mutations, contributing to a complex landscape of evolutionary trajectories. However, quantifying pairwise and higher-order epistatic effects—which are essential to estimation of the trajectory of likely cancer genotypes—has been a challenge. We have developed a continuous-time Markov chain model that enables the estimation of mutation origination and fixation, dependent on somatic cancer genotype. We coupled the continuous-time Markov chain model with an approach that deconvolutes the underlying mutation rates and selection intensities across trajectories of oncogenesis. By elucidating the routes of variant evolution and the strengths of selective forces at play, our approach lays the groundwork for quantifying antagonistic epistatic effects of specific somatic genotypes. We applied our approach to report the variant mutation rates, selection intensities, and consequent substitution rates for lung adenocarcinoma, comparing smoker and nonsmoker tumor cohorts. We describe the distinct influences of smoking on underlying mutation rates and on physiology affecting the selective regime of lung tissue. Inference of the most likely routes of site-specific variant evolution, as well as estimation of the selection strength operating on each step along the route represents a key component for prioritization, development, and implementation of personalized cancer therapies that target synthetic lethality. Citation Format: Jorge A. Alfaro-Murillo, Krishna Dasari, Jeffrey P. Townsend. Detecting pairwise and higher-order antagonistic epistatic effects among somatic cancer genotypes to discover synthetic lethality [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr PR013.

Machine learning-based integration develops an immunogenic cell death-derived lncRNA signature for predicting prognosis and immunotherapy response in lung adenocarcinoma.

Accumulating evidence demonstrates that lncRNAs are involved in the regulation of the immune microenvironment and early tumor development. Immunogenic cell death occurs mainly through the release or increase of tumor-associated antigen and tumor-specific antigen, exposing "danger signals" to stimulate the body's immune response. Given the recent development of immunotherapy in lung adenocarcinoma, we explored the role of tumor immunogenic cell death-related lncRNAs in lung adenocarcinoma for prognosis and immunotherapy benefit, which has never been uncovered yet. Based on the lung adenocarcinoma cohorts from the TCGA database and GEO database, the study developed the immunogenic cell death index signature by several machine learning algorithms and then validated the signature for prognosis and immunotherapy benefit of lung adenocarcinoma patients, which had a more stable performance compared with published signatures in predicting the prognosis, and demonstrated predictive value for benefiting from immunotherapy in multiple cohorts of multiple cancers, and also guided the utilization of chemotherapy drugs.

Stroma AReactive Invasion Front Areas (SARIFA) predict poor survival in adenocarcinomas of the stomach and gastrooesophageal junction: a validation study

Recently, the presence of “Stroma AReactive Invasion Front Areas” (SARIFA) has been described as a promising adverse prognostic factor in gastric cancer. However, the validity of this approach still needs to be tested. The aim of this study was to independently assess the utility of the proposed method in a well-characterised cohort of primary resected adenocarcinomas of stomach and gastrooesophageal junction (n = 392). SARIFA status was analysed on routine slides of resection specimens. Cases were divided into SARIFA-positive and negative groups and analysed in relation to clinicopathological and survival data. SARIFA positivity was found in 15.1% (n = 59) cases and was significantly associated with Lauren phenotype (p < 0.001), pT (p = 0.001), pN (p = 0.018), UICC stage (p = 0.031), tumour budding (p = 0.002), overall survival (p < 0.001) and cancer-specific survival (p < 0.001). SARIFA-positive tumours had a worse prognosis in the multivariate setting (HR = 1.847, 95% CI: 1.300–2.624, p = 0.001). SARIFA status is an independent prognostic factor in gastric cancer, in particular in locally advanced tumours.

1,25-Dihydroxyvitamin D3 Suppresses Prognostic Survival Biomarkers Associated with Cell Cycle and Actin Organization in a Non-Malignant African American Prostate Cell Line.

Vitamin D3 is a steroid hormone that confers anti-tumorigenic properties in prostate cells. Serum vitamin D3 deficiency has been associated with advanced prostate cancer (PCa), particularly affecting African American (AA) men. Therefore, elucidating the pleiotropic effects of vitamin D on signaling pathways, essential to maintaining non-malignancy, may provide additional drug targets to mitigate disparate outcomes for men with PCa, especially AA men. We conducted RNA sequencing on an AA non-malignant prostate cell line, RC-77N/E, comparing untreated cells to those treated with 10 nM of vitamin D3 metabolite, 1α,25(OH)2D3, at 24 h. Differential gene expression analysis revealed 1601 significant genes affected by 1α,25(OH)2D3 treatment. Pathway enrichment analysis predicted 1α,25(OH)2D3- mediated repression of prostate cancer, cell proliferation, actin cytoskeletal, and actin-related signaling pathways (p < 0.05). Prioritizing genes with vitamin D response elements and associating expression levels with overall survival (OS) in The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA PRAD) cohort, we identified ANLN (Anillin) and ECT2 (Epithelial Cell Transforming 2) as potential prognostic PCa biomarkers. Both genes were strongly correlated and significantly downregulated by 1α,25(OH)2D3 treatment, where low expression was statistically associated with better overall survival outcomes in the TCGA PRAD public cohort. Increased ANLN and ECT2 mRNA gene expression was significantly associated with PCa, and Gleason scores using both the TCGA cohort (p < 0.05) and an AA non-malignant/tumor-matched cohort. Our findings suggest 1α,25(OH)2D3 regulation of these biomarkers may be significant for PCa prevention. In addition, 1α,25(OH)2D3 could be used as an adjuvant treatment targeting actin cytoskeleton signaling and actin cytoskeleton-related signaling pathways, particularly among AA men.

BCAT1, IKZF1 and SEPT9: methylated DNA biomarkers for detection of pan-gastrointestinal adenocarcinomas

Introduction Methylated circulating tumour DNA (ctDNA) blood tests for BCAT1/IKZF1 (COLVERA) and SEPT9 (Epi proColon) are used to detect colorectal cancer (CRC). However, there are no ctDNA assays approved for other gastrointestinal adenocarcinomas. We aimed to characterize BCAT1, IKZF1 and SEPT9 methylation in different gastrointestinal adenocarcinoma and non-gastrointestinal tumours to determine if these validated CRC biomarkers might be useful for pan-gastrointestinal adenocarcinoma detection. Methods Tissue DNA methylation data from colorectal (COAD, READ), gastroesophageal (ESCA, STAD), pancreatic (PAAD) and cholangiocarcinoma (CHOL) adenocarcinoma cohorts within The Cancer Genome Atlas were used for differential methylation analyses. Clinicodemographic predictors of BCAT1, IKZF1 and SEPT9 methylation, and the selectivity of hypermethylated BCAT1, IKZF1 and SEPT9 for colorectal adenocarcinomas in comparison to other cancers were each explored with beta regression. Results Hypermethylated BCAT1, IKZF1 and SEPT9 were each differentially methylated in colorectal and gastroesophageal adenocarcinomas. IKZF1 was differentially methylated in pancreatic adenocarcinoma. Hypermethylated DNA biomarkers BCAT1, IKZF1 and SEPT9 were largely stable across different stages of disease and were highly selective for gastrointestinal adenocarcinomas relative to other cancer types. Discussion Existing CRC methylated ctDNA blood tests for BCAT1/IKZF1 and SEPT9 might be usefully repurposed for use in other gastrointestinal adenocarcinomas and warrant further prospective ctDNA studies.