Subtype Of Adenocarcinoma Research Articles

Construction and Evaluation of a Prognostic Risk Prediction Model of Pancreatic Ductal Adenocarcinoma Based on Immune-Related Genes

Objective To construct a risk prediction model by integrating the molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) and immune-related genes.Methods With GSE71729 data set (n=145) as the training set,the differentially expressed genes and differential immune-related genes between the squamous and non-squamous subtypes of PDAC were integrated to construct a regulatory network,on the basis of which five immune marker genes regulating the squamous subtype were screened out.An integrated immune score (IIS) model was constructed based on patient survival information and immune marker genes to predict the clinical prognosis of PDAC patients,and its predictive performance was tested with 5 validation sets (n=758).Results PDAC patients were assigned into high risk and low risk groups according to the IIS.In both training and validation sets,the overall survival of patients in the high risk group was shorter than that in the low risk group (both P<0.001).The multivariable Cox regression showed that IIS was an independent prognostic factor for PDAC (HR=2.16,95%CI=1.50-3.10,P<0.001).Conclusion IIS can be used for risk stratification of PDAC patients and may become a potential prognostic marker for PDAC.

Efficacy of systemic treatments in patients with metastatic lung invasive mucinous adenocarcinoma.

e20609 Background: Invasive mucinous adenocarcinoma (IMA) is a relatively rare histological subtype of lung adenocarcinomas that differs in its clinical behavior, radiologic features, histopathological, and genomic characteristics than invasive non-mucinous adenocarcinomas (INMA). Methods: We retrospectively investigated medical records of patients diagnosed with lung IMA at four oncology centers in Turkey. Patients who were ≥18 years of age, pathologically diagnosed with lung IMA, and who received at least one course of treatment for metastatic or inoperable disease were included in the study. Results: Forty-one patients were included in the study. The median follow-up was 16.6 months. Among the included patients, 24 (58.5%) were male, 17 (38.5%) were female, and their average age was 62. Six (14.6%) patients had a targetable mutation. Three (7.3%) patients were epidermal growth factor receptor (EGFR), two (4.8%) anaplastic lymphoma kinase (ALK) mutation, and one (2.4%) neurotrophic tyrosine receptor kinase (NTRK2) fusion. Most patients (83.1%) had received platinum-based chemotherapy as a first-line treatment. Of the patients who received cytotoxic chemotherapy, 9 (25.7%) had a partial response, 14 (40%) had a stable response, and 12 (34.3%) had progression. The median PFS was 8.24 months (95% CI: 5.9-10.5 months), and the median OS was 20.4 months (95% CI: 14.4-26.3 months) in all study populations. In those receiving cytotoxic chemotherapy, median PFS and OS were 8.11 months (95% CI: 5.02-11.2) and 17.5 months (95% CI: 11.7-23.3 months), respectively. While the PFS of two EGFR-positive patients were similar at 18.9 months and 20.6 months, the third patient's PFS was quite prolonged (66.5 months). The ALK-positive patients receiving brigatinib and crizotinib PFS were 15.8 months and 2.73 months, respectively. The PFS of the patient receiving entrectinib was only two months. Among the entire patient population, only one patient used oxaliplatin and capecitabine combination (XELOX) as chemotherapy. In this patient, who progressed after four cycles of gemcitabine and platinum first-line treatment, a partial response and 7.23 months PFS were achieved with second line XELOX. Conclusions: Standard-of-care treatments have the same efficacy in IMA and INMA patients. Targetable mutation is less common in IMA patients compared to INMA. 5-FU-based chemotherapies also appear to be effective in lung IMA patients.

A novel nomogram model for lung adenocarcinoma subtypes based on RNA-modification regulatory genes

BackgroundIn non-small cell lung cancer (NSCLC), lung adenocarcinoma (LUAD) is the most common subtype. RNA modification has become the frontier and hotspot of current tumor research. ResultsIn this study, 109 genes that regulate RNA modifications were identified according to The Cancer Genome Atlas (TCGA). A differential gene expression analysis identified 46 differentially expressed RNA modification regulatory genes (DERRGs). LUAD samples were stratified into two distinct clusters based on the expression of these DERRGs. A significant correlation was observed between these clusters and patient survival rates, as well as clinical features. Furthermore, a four-DERRG signature (EIF3B, HNRNPC, IGF2BP1, and METTL3) developed using LASSO regression. According to the calculated risk scores from this signature, LUAD patients were categorized into high-risk and low-risk groups. Patients in the low-risk group exhibited a more favorable prognosis. A prognostic nomogram was crafted, integrating the four-DERRGs signature with clinical parameters. The nomogram was revealed that OS, age, clinical stage, immune cell infiltration, and immune checkpoint molecule expression were significantly linked to the OS of LUAD. GSEA analysis found that the DERRGs were primarily regulated immune pathways. ConclusionsThis study developed four DERRGs signatures and formulated a nomogram model for precise prognosis estimation in LUAD patients. The study's insights are instrumental for advancing diagnosis, prognosis, and therapeutic strategies for LUAD.

The road to tailored adjuvant chemotherapy for all four non-pancreatic periampullary cancers: An international multimethod cohort study.

Despite differences in tumour behaviour and characteristics between duodenal adenocarcinoma (DAC), the intestinal (AmpIT) and pancreatobiliary (AmpPB) subtype of ampullary adenocarcinoma and distal cholangiocarcinoma (dCCA), the effect of adjuvant chemotherapy (ACT) on these cancers, as well as the optimal ACT regimen, has not been comprehensively assessed. This study aims to assess the influence of tailored ACT on DAC, dCCA, AmpIT, and AmpPB. Patients after pancreatoduodenectomy for non-pancreatic periampullary adenocarcinoma were identified and collected from 36 tertiary centres between 2010 - 2021. Per non-pancreatic periampullary tumour type, the effect of adjuvant chemotherapy and the main relevant regimens of adjuvant chemotherapy were compared. The primary outcome was overall survival (OS). The study included a total of 2866 patients with DAC (n = 330), AmpIT (n = 765), AmpPB (n = 819), and dCCA (n = 952). Among them, 1329 received ACT, and 1537 did not. ACT was associated with significant improvement in OS for AmpPB (P = 0.004) and dCCA (P < 0.001). Moreover, for patients with dCCA, capecitabine mono ACT provided the greatest OS benefit compared to gemcitabine (P = 0.004) and gemcitabine - cisplatin (P = 0.001). For patients with AmpPB, no superior ACT regime was found (P > 0.226). ACT was not associated with improved OS for DAC and AmpIT (P = 0.113 and P = 0.445, respectively). Patients with resected AmpPB and dCCA appear to benefit from ACT. While the optimal ACT for AmpPB remains undetermined, it appears that dCCA shows the most favourable response to capecitabine monotherapy. Tailored adjuvant treatments are essential for enhancing prognosis across all four non-pancreatic periampullary adenocarcinomas.

A novel subtype based on driver methylation–transcription in lung adenocarcinoma

AimsTo identify driver methylation genes and a novel subtype of lung adenocarcinoma (LUAD) by multi-omics and elucidate its molecular features and clinical significance.MethodsWe collected LUAD patients from public databases, and identified driver methylation genes (DMGs) by MethSig and MethylMix algrothms. And novel driver methylation multi-omics subtypes were identified by similarity network fusion (SNF). Furthermore, the prognosis, tumor microenvironment (TME), molecular features and therapy efficiency among subtypes were comprehensively evaluated.Results147 overlapped driver methylation were identified and validated. By integrating the mRNA expression and methylation of DMGs using SNF, four distinct patterns, termed as S1-S4, were characterized by differences in prognosis, biological features, and TME. The S2 subtype showed unfavorable prognosis. By comparing the characteristics of the DMGs subtypes with the traditional subtypes, S3 was concentrated in proximal-inflammatory (PI) subtype, and S4 was consisted of terminal respiratory unit (TRU) subtype and PI subtype. By analyzing TME and epithelial mesenchymal transition (EMT) features, increased immune infiltration and higher expression of immune checkpoint genes were found in S3 and S4. While S4 showed higher EMT score and expression of EMT associated genes, indicating S4 may not be as immunosensitive as the S3. Additionally, S3 had lower TIDE and higher IPS score, indicating its increased sensitivity to immunotherapy.ConclusionThe driver methylation-related subtypes of LUAD demonstrate prognostic predictive ability that could help inform treatment response and provide complementary information to the existing subtypes.

Identification of NR3C2 as a functional diagnostic and prognostic biomarker and potential therapeutic target in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC), including the lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) subtypes, is a malignant tumor type with a poor 5-year survival rate. The identification of new powerful diagnostic biomarkers, prognostic biomarkers, and potential therapeutic targets in NSCLC is urgently required. The UCSC Xena, UALCAN, and GEO databases were used to screen and analyze differentially expressed genes, regulatory modes, and genetic/epigenetic alterations in NSCLC. The UCSC Xena database, GEO database, tissue microarray, and immunohistochemistry staining analyses were used to evaluate the diagnostic and prognostic values. Gain-of-function assays were performed to examine the roles. The ESTIMATE, TIMER, Linked Omics, STRING, and DAVID algorithms were used to analyze potential molecular mechanisms. NR3C2 was identified as a potentially important molecule in NSCLC. NR3C2 is expressed at low levels in NSCLC, LUAD, and LUSC tissues, which is significantly related to the clinical indexes of these patients. Receiver operating characteristic curve analysis suggests that the altered NR3C2 expression patterns have diagnostic value in NSCLC, LUAD, and especially LUSC patients. Decreased NR3C2 expression levels can help predict poor prognosis in NSCLC and LUAD patients but not in LUSC patients. These results have been confirmed both with database analysis and real-world clinical samples on a tissue microarray. Copy number variation contributes to low NR3C2 expression levels in NSCLC and LUAD, while promoter DNA methylation is involved in its downregulation in LUSC. Two NR3C2 promoter methylation sites have high sensitivity and specificity for LUSC diagnosis with clinical application potential. NR3C2 may be a key participant in NSCLC development and progression and is closely associated with the tumor microenvironment and immune cell infiltration. NR3C2 co-expressed genes are involved in many cancer-related signaling pathways, further supporting a potentially significant role of NR3C2 in NSCLC. NR3C2 is a novel potential diagnostic and prognostic biomarker and therapeutic target in NSCLC.

Establishment of artificial intelligence model for precise histological subtyping of lung adenocarcinoma and its application to quantitative and spatial analysis.

The histological subtype of lung adenocarcinoma is a major prognostic factor. We developed a new artificial intelligence model to classify lung adenocarcinoma images into seven histological subtypes and adopted the model for whole-slide images to investigate the relationship between the distribution of histological subtypes and clinicopathological factors. Using histological subtype images, which are typical for pathologists, we trained and validated an artificial intelligence model. Then, the model was applied to whole-slide images of resected lung adenocarcinoma specimens from 147 cases. The model achieved an accuracy of 99.7% in training sets and 90.4% in validation sets consisting of typical tiles of histological subtyping for pathologists. When the model was applied to whole-slide images, the predominant subtype according to the artificial intelligence model classification matched that determined by pathologists in 75.5% of cases. The predominant subtype and tumor grade (using the WHO fourth and fifth classifications) determined by the artificial intelligence model resulted in similar recurrence-free survival curves to those determined by pathologists. Furthermore, we stratified the recurrence-free survival curves for patients with different proportions of high-grade components (solid, micropapillary and cribriform) according to the physical distribution of the high-grade component. The results suggested that tumors with centrally located high-grade components had a higher malignant potential (P<0.001 for 5-20% high-grade component). The new artificial intelligence model for histological subtyping of lung adenocarcinoma achieved high accuracy, and subtype quantification and subtype distribution analyses could be achieved. Artificial intelligence model therefore has potential for clinical application for both quantification and spatial analysis.

Identification of collagen subtypes of gastric cancer for distinguishing patient prognosis and therapeutic response.

Gastric cancer is a highly heterogeneous disease, presenting a major obstacle to personalized treatment. Effective markers of the immune checkpoint blockade response are needed for precise patient classification. We, therefore, divided patients with gastric cancer according to collagen gene expression to indicate their prognosis and treatment response. We collected data for 1250 patients with gastric cancer from four cohorts. For the TCGA-STAD cohort, we used consensus clustering to stratify patients based on expression levels of 44 collagen genes and compared the prognosis and clinical characteristics between collagen subtypes. We then identified distinct transcriptomic and genetic alteration signatures for the subtypes. We analyzed the associations of collagen subtypes with the responses to chemotherapy, immunotherapy, and targeted therapy. We also established a platform-independent collagen-subtype predictor. We verified the findings in three validation cohorts (GSE84433, GSE62254, and GSE15459) and compared the collagen subtyping method with other molecular subtyping methods. We identified two subtypes of gastric adenocarcinoma: a high-expression collagen subtype (CS-H) and a low-expression collagen subtype (CS-L). Collagen subtype was an independent prognostic factor, with better overall survival in the CS-L subgroup. The inflammatory response, angiogenesis, and phosphoinositide 3-kinase (PI3K)/Akt pathways were transcriptionally active in the CS-H subtype, while DNA repair activity was significantly greater in the CS-L subtype. PIK3CA was frequently amplified in the CS-H subtype, while PIK3C2A, PIK3C2G, and PIK3R1 were frequently deleted in the CS-L subtype. CS-H subtype tumors were more sensitive to fluorouracil, while CS-L subtype tumors were more sensitive to immune checkpoint blockade. CS-L subtype was predicted to be more sensitive to HER2-targeted drugs, and CS-H subtype was predicted to be more sensitive to vascular endothelial growth factor and PI3K pathway-targeting drugs. Collagen subtyping also has the potential to be combined with existing molecular subtyping methods for better patient classification. We classified gastric cancers into two subtypes based on collagen gene expression and validated these subtypes in three validation cohorts. The collagen subgroups differed in terms of prognosis, clinical characteristics, transcriptome, and genetic alterations. The subtypes were closely related to patient responses to chemotherapy, immunotherapy, and targeted therapy.

Deciphering fatty acid biosynthesis-driven molecular subtypes in pancreatic ductal adenocarcinoma with prognostic insights.

Pancreatic ductal adenocarcinoma (PDAC) poses a significant challenge due to its high heterogeneity and aggressiveness. Recognizing the urgency to delineate molecular subtypes, our study focused on the emerging field of lipid metabolism remodeling in PDAC, particularly exploring the prognostic potential and molecular classification associated with fatty acid biosynthesis. Gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (ssGSEA) were performed to evaluate the dysregulation of lipid metabolism in PDAC. Univariate cox analysis and the LASSO module were used to build a prognostic risk score signature. The distinction of gene expression in different risk groups was explored by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The biological function of Acyl-CoA Synthetase Long Chain Family Member 5 (ACSL5), a pivotal gene within 7-hub gene signature panel, was validated through in vitro assays. Our study identified a 7-hub gene signature associated with fatty acid biosynthesis-related genes (FRGs), providing a robust tool for prognosis prediction. The high-FRGs score group displayed a poorer prognosis, decreased immune cell infiltration, and a higher tumor mutation burden. Interestingly, this group exhibited enhanced responsiveness to various compounds according to the Genomics of Drug Sensitivity in Cancer (GDSC) database. Notably, ACSL5 was upregulated in PDAC and essential for tumor progression. In conclusion, our research defined two novel fatty acid biosynthesis-based subtypes in PDAC, characterized by distinct transcriptional profiles. These subtypes not only served as prognostic indicator, but also offered valuable insights into their metastatic propensity and therapeutic potential.

Spatial transcriptomic analysis reveals local effects of intratumoral fusobacterial infection on DNA damage and immune signaling in rectal cancer

ABSTRACT Mucinous colorectal cancer (CRC) is a common histological subtype of colorectal adenocarcinoma, associated with a poor response to chemoradiotherapy. The commensal facultative anaerobes fusobacteria, have been associated with poor prognosis specifically in mesenchymal CRC. Interestingly, fusobacterial infection is especially prevalent in mucinous CRC. The objective of this study was therefore to increase our understanding of beneficial and detrimental effects of fusobacterial infection, by contrasting host cell signaling and immune responses in areas of high vs. low infection, using mucinous rectal cancer as a clinically relevant example. We employed spatial transcriptomic profiling of 106 regions of interest from 8 mucinous rectal cancer samples to study gene expression in the epithelial and immune segments across regions of high versus low fusobacterial infection. Fusobacteria high regions were associated with increased oxidative stress, DNA damage, and P53 signaling. Meanwhile regions of low fusobacterial prevalence were characterized by elevated JAK-STAT, Il-17, Il-1, chemokine and TNF signaling. Immune masks within fusobacterial high regions were characterized by elevated proportions of cytotoxic (CD8+) T cells (p = 0.037), natural killer (NK) cells (p < 0.001), B-cells (p < 0.001), and gamma delta T cells (p = 0.003). Meanwhile, fusobacteria low regions were associated with significantly greater M2 macrophage (p < 0.001), fibroblast (p < 0.001), pericyte (p = 0.002), and endothelial (p < 0.001) counts.

Retrospective Study of Claudin 18 Isoform 2 Prevalence and Prognostic Association in Gastric and Gastroesophageal Junction Adenocarcinoma.

Claudin 18 isoform 2 (CLDN18.2) is an emerging biomarker and therapeutic target in gastric and gastroesophageal junction (G/GEJ) adenocarcinoma. This study aimed to obtain deeper understanding of CLDN18.2 positivity patterns, prognostic implications, and associations with various demographic, clinical, and molecular characteristics in G/GEJ adenocarcinoma. Archived tumor tissue samples from 304 patients with G/GEJ adenocarcinoma in the United States were assessed for CLDN18.2 positivity by immunohistochemistry. CLDN18.2 positivity was defined as ≥50% or ≥75% of tumor cells with CLDN18 staining intensity ≥2+. CLDN18.2 positivity patterns were analyzed for association with prognosis and clinicopathologic/demographic characteristics. Where possible, CLDN18.2 positivity was analyzed for matched tissue samples to assess concordance between primary and metastatic tumors and concordance before and after chemotherapy. The overall prevalence of CLDN18.2-positive tumors (with ≥75% cutoff) was 44.4% (n = 135 of 304). CLDN18.2-positive tumors had a prevalence of 51.4% (n = 91 of 177) in gastric and 34.6% (n = 44 of 127) in GEJ adenocarcinoma. With a ≥50% cutoff, the prevalence of CLDN18.2-positive tumors was 64.4% (n = 114 of 177) in gastric adenocarcinoma and 44.9% (n = 57 of 127) in GEJ adenocarcinoma. There was no association between overall survival and CLDN18.2 positivity using either threshold. Statistically significant associations were noted between CLDN18.2 positivity and sex, histologic type of G/GEJ adenocarcinoma, and adenocarcinoma subtype (≥75% cutoff), and metastasis site and tumor grade (≥50% cutoff). The overall concordance of CLDN18.2 positivity (≥75% cutoff) was 73% (27 of 37) for matched primary versus metastatic tumor samples and 74% (29 of 39) for matched samples before and after chemotherapy. This study demonstrated that CLDN18.2 positivity did not correlate with survival in G/GEJ adenocarcinoma, consistent with published data. On the basis of matched sample analysis, CLDN18.2 appears to demonstrate >70% concordance as a biomarker. Observed correlations with certain patient/tumor characteristics warrant further study.

Enhancement Patterns in Differentiated and Undifferentiated Gastric Carcinoma: Multiphasic Contrast-Enhanced Computed Tomography Versus Histopathology.

Background Gastric adenocarcinoma (GCA) poses a significant global health burden due to its prevalence and high morbidity and mortality rates. GCA is classified into three main histological types: well-differentiated (intestinal type), poorly differentiated (diffuse type), and mixed or indeterminate forms. These types vary in causes, epidemiology, and genetics, with the diffuse type often associated with the worst prognosis. Endoscopic biopsy is the primary method for characterization, but it has its limitations. There is potential in using contrast-enhanced computed tomography (CT) to differentiate between histological subtypes of gastric adenocarcinoma, which could aid subtype differentiation. Building on this, our study aims to assess CT's effectiveness in distinguishing between broad histological groups of gastric adenocarcinoma based on enhancement patterns, contributing to improved diagnostic accuracy Objective Our research focuses on evaluating the effectiveness of multiphasic contrast-enhanced computed tomography (CECT) in distinguishing between the three broad histopathological subtypes of gastrointestinal cancers. Methods This study was a prospective, analytical observational study that was approvedand carried out in our institutional tertiary care hospital. Consecutive individuals who had undergone endoscopic-guided biopsy and demonstrated histological evidence of GCA were taken into consideration for participation in the study. In order to complete the clinical staging process, further multiphasic CT scans were carried out on each of the fifty patients and were categorised accordingly based on the findings of histopathology. Results In the differentiated type, segmental distribution was: 5.5% upper segment, 16.7% middle segment, 66.7% lower segment, and 11.1% diffuse type. Esophageal involvement was 5.6%, duodenal involvement was similar, and lymph node involvement was approximately 38.8%. TNM staging: 38.8% IIIB, 22.2% III, 27.8% IVA, and 11.1% IVB. In the undifferentiated type, segmental distribution: 6.2% upper segment, 31.2% middle segment, 50.0% lower segment, and 12.5% diffuse type. Esophageal involvement was around 6.25%, duodenal involvement was 18.75%, and lymph node involvement was about 71.8%. TNM staging: 34.4% IIIB, 21.8% III, 28.1% IVA, and 15.6% IVB. Conclusion Multiphasic CT evaluations provide valuable insights into the prognostic aspects of gastric carcinomas by assessing peak enhancement. Differentiated tumors typically exhibit arterial phase enhancement, while undifferentiated tumors show venous phase enhancement, reflecting their microvascular architecture. Recent studies emphasize the importance of understanding gastric carcinoma characteristics for diagnosis and prognosis. Our research aligns with this, revealing distinct contrast enhancement patterns between differentiated and undifferentiated types. However, discrepancies in histological classifications and contrast enhancement patterns across studies warrant further investigation. Integrating histopathological and radiological insights is essential for accurate diagnosis and treatment planning.

Efficacy of Systemic Treatments in Patients With Metastatic Lung Invasive Mucinous Adenocarcinoma

BackgroundInvasive mucinous adenocarcinoma (IMA) is a rare histological subtype of lung invasive adenocarcinoma with unique clinical, radiological, histopathological, and genomic characteristics. There have been limited studies on the effectiveness of systemic therapy for lung IMA, with conflicting results reported. MethodsWe retrospectively investigated the medical records of patients diagnosed with lung IMA. Patients who were ≥ 18 years of age and received at least 1 course of treatment for metastatic or locally advanced inoperable disease were included in the study. Archive records of 113 patients diagnosed with IMA were screened for the study. ResultsA total of 41 patients with lung IMA were included. The targetable mutation rate was 20.6% (in 6 of 29 patients). Most patients (83.1%) had received platinum-based chemotherapy as a first-line treatment. The objective response rate (ORR) was 25.7%, and median progression-free survival (PFS) and overall survival (OS) were 8.1 months (95% CI, 5.02-11.2) and 17.5 months (95% CI, 11.7-23.3 months), respectively, in the patients who received chemotherapy. The median PFS and ORR were 20.6 (95% CI, 18.9-66.5) and 66.6%, respectively, in epidermal growth factor receptor (EGFR) mutation-positive patients (n = 3) with relevant targeted therapy. Only 1 patient used oxaliplatin and capecitabine combination (XELOX) as chemotherapy in the second-line treatment and achieved a partial response (PR) at 7.2 months. ConclusionPlatinum-based chemotherapies moderately enhance IMA patients’ survival rates. Anti-EGFR-targeted drugs are seen as potentially effective in patients with EGFR driver mutation positive. Large, prospective studies are needed to confirm our findings.

Genomic Profiling of Rare Undifferentiated Sarcomatoid Subtypes of Pancreatic Carcinomas: In Search of Therapeutic Targets

PURPOSE The highly aggressive undifferentiated sarcomatoid carcinoma (USC) subtype of pancreatic ductal adenocarcinoma (PDAC) remains poorly characterized because of its rarity. Previous case reports suggest that immune checkpoint inhibitors could be a promising treatment strategy, but the prevalence of established predictive biomarkers of response is largely unknown. The objective of this study was to leverage comprehensive genomic profiling of USC PDAC tumors to determine the prevalence of biomarkers associated with potential response to targeted therapies. METHODS USC tumors (n = 20) underwent central pathology review by a board-certified gastrointestinal pathologist to confirm the diagnosis. These samples were compared with non-USC PDAC tumors (N = 5,562). Retrospective analysis of DNA and RNA next-generation sequencing data was performed. RESULTS USC PDACs were more frequently PD-L1+ by immunohistochemistry than non-USC PDAC (63% v 16%, respectively, P &lt; .001). Furthermore, USC PDAC had an increase in neutrophils (8.99% v 5.55%, P = .005) and dendritic cells (1.08% v 0.00%, q = 0.022) and an increased expression of PDCD1LG2 (4.6% v 1.3%, q = 0.001), PDCD1 (2.0% v 0.8%, q = 0.060), and HAVCR2 (45.9% v 21.7%, q = 0.107) than non-USC PDAC. Similar to non-USC PDAC, KRAS was the most commonly mutated gene (86% v 90%, respectively, P = 1). CONCLUSION To our knowledge, this work represents the largest molecular analysis of USC tumors to date and showed an increased expression of immune checkpoint genes in USC tumors. These findings provide evidence for further investigation into immune checkpoint inhibitors in USC tumors.

Comments and illustrations of the European Federation of Societies for Ultrasound in Medicine contrast-enhanced ultrasound guidelines. Rare pancreatic tumors, imaging features on transabdominal ultrasound and EUS with contrast enhancement: Rare epithelial pancreatic tumors: solid pseudopapillary neoplasm, acinar cell carcinoma, mixed neuroendocrine-non-neuroendocrine neoplasms, some rare subtypes of pancreatic adenocarcinoma and pancreatoblastoma.

Rare malignant pancreatic lesions are systematically reported in this review. The focus is on the imaging appearance of the rare epithelial pancreatic tumors such as the solid pseudopapillary neoplasm, acinar cell carcinoma, rare subtypes of adenocarcinoma, and pancreatoblastoma as seen on ultrasound, EUS, and contrast-enhanced ultrasound or EUS. The present overview summarizes the data and shows that not every pancreatic tumor is likely to be the most common entities of ductal adenocarcinoma or neuroendocrine tumor.

Patient-derived organoids of pancreatic ductal adenocarcinoma for subtype determination and clinical outcome prediction

BackgroundRecently, two molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) have been proposed: the “Classical” and “Basal-like” subtypes, with the former showing better clinical outcomes than the latter. However, the “molecular” classification has not been applied in real-world clinical practice. This study aimed to establish patient-derived organoids (PDOs) for PDAC and evaluate their application in subtype classification and clinical outcome prediction.MethodsWe utilized tumor samples acquired through endoscopic ultrasound-guided fine-needle biopsy and established a PDO library for subsequent use in morphological assessments, RNA-seq analyses, and in vitro drug response assays. We also conducted a prospective clinical study to evaluate whether analysis using PDOs can predict treatment response and prognosis.ResultsPDOs of PDAC were established at a high efficiency (> 70%) with at least 100,000 live cells. Morphologically, PDOs were classified as gland-like structures (GL type) and densely proliferating inside (DP type) less than 2 weeks after tissue sampling. RNA-seq analysis revealed that the “morphological” subtype (GL vs. DP) corresponded to the “molecular” subtype (“Classical” vs. “Basal-like”). The “morphological” classification predicted the clinical treatment response and prognosis; the median overall survival of patients with GL type was significantly longer than that with DP type (P < 0.005). The GL type showed a better response to gemcitabine than the DP type in vitro, whereas the drug response of the DP type was improved by the combination of ERK inhibitor and chloroquine.ConclusionsPDAC PDOs help in subtype determination and clinical outcome prediction, thereby facilitating the bench-to-bedside precision medicine for PDAC.

Expression of Selected miRNAs in Undifferentiated Carcinoma with Osteoclast-like Giant Cells (UCOGC) of the Pancreas: Comparison with Poorly Differentiated Pancreatic Ductal Adenocarcinoma.

Undifferentiated carcinoma with osteoclast-like giant cells (UCOGC) of the pancreas represents a rare subtype of pancreatic ductal adenocarcinoma (PDAC). Despite a distinct morphology and specific clinical behavior, UCOGCs exhibit unexpected similarities in regard to DNA mutational profiles with conventional PDAC. Treating pancreatic ductal adenocarcinoma is particularly challenging, with limited prospects for cure. As with many other malignant neoplasms, the exploration of microRNAs (miRNAs, miRs) in regulating the biological characteristics of pancreatic cancer is undergoing extensive investigation to enhance tumor diagnostics and unveil the therapeutic possibilities. Herein, we evaluated the expression of miR-21, -96, -148a, -155, -196a, -210, and -217 in UCOGCs and poorly differentiated (grade 3, G3) PDACs. The expression of miR-21, miR-155, and miR-210 in both UCOGCs and G3 PDACs was significantly upregulated compared to the levels in normal tissue, while the levels of miR-148a and miR-217 were downregulated. We did not find any significant differences between cancerous and normal tissues for the expression of miR-96 and miR-196a in G3 PDACs, whereas miR-196a was slightly, but significantly, downregulated in UCOGCs. On the other hand, we have not observed significant differences in the expression of the majority of miRNAs between UCOGC and G3 PDAC, with the exception of miR-155. UCOGC samples demonstrated lower mean levels of miR-155 in comparison with those in G3 PDACs.

First insight about the ability of specific glycerophospholipids to discriminate non-small cell lung cancer subtypes.

Introduction: Discrimination between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) subtypes in non-small cell lung cancer (NSCLC) patients is a significant challenge in oncology. Lipidomics analysis provides a promising approach for this differentiation. Methods: In an accompanying paper, we explored oxPCs levels in a cohort of 200 NSCLC patients. In this research, we utilized liquid chromatography coupled with mass spectrometry (LC-MS) to analyze the lipidomics profile of matching tissue and plasma samples from 25 NSCLC patients, comprising 11 ADC and 14 SCC cases. This study builds upon our previous findings, which highlighted the elevation of oxidised phosphatidylcholines (oxPCs) in NSCLC patients. Results: We identified eight lipid biomarkers that effectively differentiate between ADC and SCC subtypes using an untargeted approach. Notably, we observed a significant increase in plasma LPA 20:4, LPA 18:1, and LPA 18:2 levels in the ADC group compared to the SCC group. Conversely, tumour PC 16:0/18:2, PC 16:0/4:0; CHO, and plasma PC 16:0/18:2; OH, PC 18:0/20:4; OH, PC 16:0/20:4; OOH levels were significantly higher in the ADC group. Discussion: Our study is the first to report that plasma LPA levels can distinguish between ADC and SCC patients in NSCLC, suggesting a potential role for LPAs in NSCLC subtyping. This finding warrants further investigation into the mechanisms underlying these differences and their clinical implications.

Integrated immunogenomic analysis of single-cell and bulk profiling reveals novel tumor antigens and subtype-specific therapeutic agents in lung adenocarcinoma

BackgroundIn recent years, mRNA-based vaccines with promising safety and functional characteristics have gained significant momentum in cancer immunotherapy. However, stable immunological molecular subtypes of lung adenocarcinoma (LUAD) and novel tumor antigens for LUAD mRNA vaccine development remain elusive. Therefore, a novel approach is urgently needed to identify suitable LUAD subtypes and potential tumor antigens. MethodsThe Cancer Genome Atlas (TCGA), the Genotype Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases were utilized to retrieve gene expression data. The LUAD Immunological Multi-Omics Classification (LIMOC) system was developed using seven machine learning (ML) algorithms by performing integrative immunogenomic analysis of single-cell and bulk tissue transcriptome profiling. Subsequently, a panel of approaches was applied to identify novel tumor antigens. ResultsFirst, the LIMOC system was construct to identify three subtypes: LIMOC1, LIMOC2, and LIMOC3. Second, we identified CHIT1, LILRA4, and MEP1A as novel tumor antigens in LUAD; these genes were up-regulated, amplified, and mutated, and showed a positive association with APC infiltration, making them promising candidates for designing mRNA vaccines. Notably, the LIMOC2 subtype had the worst prognosis and could benefit most from mRNA immunization. Furthermore, we performed a comprehensive in silico screening of approximately 2000 compounds and identified Sorafenib and Azacitidine as potential subtype-specific therapeutic agents. ConclusionsOverall, our study established a robust LIMOC system and identified CHIT1, LILRA4, and MEP1A as promising tumor antigen candidates for development of anti-LUAD mRNA vaccines, particularly for the LIMOC2 subtype.

A Pilot Immunohistochemical Study Identifies Hedgehog Pathway Expression in Sinonasal Adenocarcinoma.

Tumors of the head and neck, more specifically the squamous cell carcinoma, often show upregulation of the Hedgehog signaling pathway. However, almost nothing is known about its role in the sinonasal adenocarcinoma, either in intestinal or non-intestinal subtypes. In this work, we have analyzed immunohistochemical staining of six Hedgehog pathway proteins, sonic Hedgehog (SHH), Indian Hedgehog (IHH), Patched1 (PTCH1), Gli family zinc finger 1 (GLI1), Gli family zinc finger 2 (GLI2), and Gli family zinc finger 3 (GLI3), on 21 samples of sinonasal adenocarcinoma and compared them with six colon adenocarcinoma and three salivary gland tumors, as well as with matching healthy tissue, where available. We have detected GLI2 and PTCH1 in the majority of samples and also GLI1 in a subset of samples, while GLI3 and the ligands SHH and IHH were generally not detected. PTCH1 pattern of staining shows an interesting pattern, where healthy samples are mostly positive in the stromal compartment, while the signal shifts to the tumor compartment in tumors. This, taken together with a stronger signal of GLI2 in tumors compared to non-tumor tissues, suggests that the Hedgehog pathway is indeed activated in sinonasal adenocarcinoma. As Hedgehog pathway inhibitors are being tested in combination with other therapies for head and neck squamous cell carcinoma, this could provide a therapeutic option for patients with sinonasal adenocarcinoma as well.