Subtype Of Adenocarcinoma Research Articles

Comparison of long-term outcomes for neoadjuvant chemoradiation (NA-CRT) followed by surgery versus (V) definitive chemoradiation (D-CRT) in localized gastroesophageal cancer (GEC): A single-center analysis.

371 Background: The therapeutic approach in locally advanced GEC is evolving. NA-CRT followed by surgery has been recommended based on the CROSS trial. The SANO trial reports active surveillance may be an alternative in clinically complete response after D-CRT in both squamous cell carcinoma (SCC) and adenocarcinoma (AC). SCC and AC in GEC are different diseases with differences in tumor location, etiology, and biology. Despite the evolving landscape, there still may be a role for both CROSS and SANO approaches. In our large observational cohort study, we sought to compare the long-term outcomes in localized GEC across all histological subtypes treated with NA-CRT+Surgery V D-CR+Surveillance. Methods: A retrospective analysis comparing NA-CRT + surgery V D-CRT in localized GEC between 2007-2023 treated at the Princess Margaret Cancer Centre was completed. Baseline characteristics including age, gender, race, performance status and Charlson Comorbidity index (CCI) were reviewed. The primary and secondary endpoints were overall survival (OS) and disease-free survival (DFS), respectively and to compare these outcomes in both histologies. Cox proportional hazards analysis and Kaplan-Meier methods were employed. Outcomes were adjusted for baseline characteristics. Results: There were 529 patients (pts) included, 321 (61%) received NA-CRT+Surgery and 208 (39%) D-CR+Surveillance. Median age was 64yrs, 75% were male and 66% had N1+ disease. The proportion of pts with SCC treated with NA-CRT+Surgery and D-CR+Surveillance was 25% and 63%, respectively. There was a statistically significant difference in median OS and DFS between pts who received NA-CRT+surgery and D-CRT+surveillance (Table). Multivariate regression analysis shows a significant association between OS and age (adjusted HR 1.02, p=0.005), male sex (HR 1.55, p=0.003) but no significant association between OS and CCI (HR 1.1 p=0.36). In our subgroup analyses, there was no statistically significant difference in OS (p=0.33) and DFS (p=0.18) between SCC and AC. However, this was significant in D-CRT (OS p<0.0001, DFS p<0.001) favoring SCC. Conclusions: In contrast to the SANO trial, we found that NA-CRT+surgery was associated with a significantly better OS and DFS compared to D-CRT when subtypes were combined. However, in the D-CRT subgroup, SCC was associated with improved OS and DFS compared to AC. Thus, we continue to recommend current approaches in SCC. Awaited updates from SANO will inform whether active surveillance may also be appropriate in pts with AC subtype. Median (95% CI)NA-CRT Surgery Median (95% CI)D-CRT Log-rank test p-value OS (mo) 28.2 (15.1, 57.6) 15.6 (7.7, 33.7) P=<0.0001 DFS (mo) 17.9 (9.6, 40.8) 9.4 (5.0, 20.7) P=<0.0001 pathCR (%) 14 - R0 resection (%) 89 -

HCST Expression Distinguishes Immune-hot and Immune-cold Subtypes in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is the most prevalent malignancy of the pancreas, and the incidence of this disease is approximately equivalent to the mortality rate. Immunotherapy has made a remarkable breakthrough in numerous cancers, while its efficacy in PDAC remains limited due to the immunosuppressive microenvironment. Immunotherapy efficacy is highly correlated with the abundance of immune cells, particularly cytotoxic T cells. Therefore, molecular classifier is needed to identify relatively hot tumors that may benefit from immunotherapy. In this study, we carried out a transcriptome analysis of 145 pancreatic tumors to define the underlying immune regulatory mechanism driving the PDAC immunosuppressive microenvironment. The immune subtype was identified by consensus clustering, and the underlying PDAC immune activation mechanism was thoroughly examined using single sample gene set enrichment analysis (ssGSEA). Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the accuracy of the molecular classifier in differentiating immunological subgroups of PDAC.5. The protein level of molecular classifier was verified by immunohistochemistry in human PDAC tissue. Immune-hot tumors displayed higher levels of immune cell infiltration and immune checkpoint, in line with enriched immune escape pathways. Hematopoietic cell signal transducer (HCST), a molecular classifier used to differentiate immunological subtypes of PDAC, has shown a substantial link with the expression levels of cytotoxic markers, such as CD8A and CD8B. At the single cell level, we found that HCST was predominantly expressed in CD8T cells. By immunohistochemistry and survival analysis, we further demonstrated the prognostic value of HCST in PDAC. We identified HCST as a molecular classifier to distinguish PDAC immune subtypes, which may be useful for early diagnosis and targeted therapy of PDAC.

Integrating multiomics analysis and machine learning to refine the molecular subtyping and prognostic analysis of stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a common malignancy with high heterogeneity and a lack of highly precise treatment options. We downloaded the multiomics data of STAD patients in The Cancer Genome Atlas (TCGA)-STAD cohort, which included mRNA, microRNA, long non-coding RNA, somatic mutation, and DNA methylation data, from the sxdyc website. We synthesized the multiomics data of patients with STAD using 10 clustering methods, construct a consensus machine learning-driven signature (CMLS)-related prognostic models by combining 10 machine learning methods, and evaluated the prognosis models using the C-index. The prognostic relationship between CMLS and STAD was assessed using Kaplan-Meier curves, and the independent prognostic value of CMLS was determined by univariate and multivariate regression analyses. we also evaluated the immune characteristics, immunotherapy response, and drug sensitivity of different CMLS groups. The results of the multiomics analysis classified STAD into three subtypes, with CS1 resulting in the best survival outcome. In total, 10 hub genes (CES3, AHCYL2, APOD, EFEMP1, CYP1B1, ASPN, CPE, CLIP3, MAP1B, and DKK1) were screened and constructed the CMLS was significantly correlated with prognosis in patients with STAD and was an independent prognostic factor for patients with STAD. Using the CMLS risk score, all patients were divided into a high CMLS group and a low CMLS group. Patients in the low-CMLS group had better survival, more enriched immune cells, and higher tumor mutation load scores, suggesting better immunotherapy responsiveness and a possible “hot tumor” phenotype. Patients in the high-CMLS group had a significantly poorer prognosis and were less sensitive to immunotherapy but were likely to benefit more from chemotherapy and targeted therapy. In this study, 10 clustering methods and 10 machine learning methods were combined to analyze the multiomics of STAD, classify STAD into three subtypes, and constructed CMLS-related prognostic model features, which are important for accurate management and effective treatment of STAD.

Invasive stratified mucin-producing carcinoma of the cervix: A case report and review of the literature

ABSTRACT Invasive stratified mucin-producing carcinoma (ISMC) is a rare, recently described subtype of HPV-associated cervical adenocarcinoma. It shows varied morphological patterns, making it difficult to diagnose, especially the mixed forms. A 57-year-old female, P7L7, presented with postmenopausal bleeding for the past five months. On per-vaginal examination, a friable cervical growth was noted. Microscopic examination of the cervical biopsy showed an invasive tumor composed of nests of stratified columnar cells with peripheral palisading and a variable amount of (alcian blue-positive) intracytoplasmic mucin. Another component of the tumor showed sheets and nests of undifferentiated tumor cells. IHC on both components showed diffuse positivity for P16, PAX8, and keratin 7. Based on these findings, a diagnosis of ISMC was made. Awareness of the morphological features and ancillary stain results of ISMC can aid in picking up the diagnosis in a small biopsy. These findings have prognostic value, as most cases of ISMC behave aggressively.

Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods

Classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) poses significant challenges for cytopathologists, often necessitating clinical tests and biopsies that delay treatment initiation. To address this, we developed a machine learning-based approach utilizing resected lung-tissue microbiome of AC and SCC patients for subtype classification. Differentially enriched taxa were identified using LEfSe, revealing ten potential microbial markers. Linear discriminant analysis (LDA) was subsequently applied to enhance inter-class separability. Next, benchmarking was performed across six different supervised-classification algorithms viz. logistic-regression, naïve-bayes, random-forest, extreme-gradient-boost (XGBoost), k-nearest neighbor, and deep neural network. Noteworthy, XGBoost, with an accuracy of 76.25%, and AUROC (area-under-receiver-operating-characteristic) of 0.81 with 69% specificity and 76% sensitivity, outperform the other five classification algorithms using LDA-transformed features. Validation on an independent dataset confirmed its robustness with an AUROC of 0.71, with minimal false positives and negatives. This study is the first to classify AC and SCC subtypes using lung-tissue microbiome.

The X-Linked Tumor Suppressor TSPX Regulates Genes Involved in the EGFR Signaling Pathway and Cell Viability to Suppress Lung Adenocarcinoma.

Background: TSPX is an X-linked tumor suppressor that was initially identified in non-small cell lung cancer (NSCLC) cell lines. However, its expression patterns and downstream mechanisms in NSCLC remain unclear. This study aims to investigate the functions of TSPX in NSCLC by identifying its potential downstream targets and their correlation with clinical outcomes. Methods: RNA-seq transcriptome and pathway enrichment analyses were conducted on the TSPX-overexpressing NSCLC cell lines, A549 and SK-MES-1, originating from lung adenocarcinoma and squamous cell carcinoma subtypes, respectively. In addition, comparative analyses were performed using the data from clinical NSCLC specimens (515 lung adenocarcinomas and 502 lung squamous cell carcinomas) in the Cancer Genome Atlas (TCGA) database. Results: TCGA data analysis revealed significant downregulation of TSPX in NSCLC tumors compared to adjacent non-cancerous tissues (Wilcoxon matched pairs signed rank test p < 0.0001). Notably, the TSPX expression levels were inversely correlated with the cancer stage, and higher TSPX levels were associated with better clinical outcomes and improved survival in lung adenocarcinoma, a subtype of NSCLC (median survival extended by 510 days; log-rank test, p = 0.0025). RNA-seq analysis of the TSPX-overexpressing NSCLC cell lines revealed that TSPX regulates various genes involved in the cancer-related signaling pathways and cell viability, consistent with the suppression of cell proliferation in cell culture assays. Notably, various potential downstream targets of TSPX that correlated with patient survival (log-rank test, p = 0.016 to 4.3 × 10-10) were identified, including EGFR pathway-related genes AREG, EREG, FOSL1, and MYC, which were downregulated. Conclusions: Our results suggest that TSPX plays a critical role in suppressing NSCLC progression by downregulating pro-oncogenic genes, particularly those in the EGFR signaling pathway, and upregulating the tumor suppressors, especially in lung adenocarcinoma. These findings suggest that TSPX is a potential biomarker and therapeutic target for NSCLC management.

Comparative Analysis of Transcriptomic and Proteomic Expression between Two Non-Small Cell Lung Cancer Subtypes.

Non-small cell lung cancer (NSCLC) is frequently diagnosed late and has poor survival. The two predominant subtypes of NSCLC, adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), are currently differentially diagnosed using immunohistochemical markers; however, they are increasingly recognized as very different cancer types suggestive of potential for new, more targeted therapies. There are extensive efforts to find more precise and noninvasive differential diagnostic tools. Here, we examined these two NSCLC subtypes for differences that may inform treatment and identify potential novel therapeutic pathways. We presented a comparative analysis of transcriptomic and proteomic expression in tumors from a cohort of 22 NSCLC patients: 8 LUSC and 14 LUAD. Comparing NSCLC subtypes, we found differential gene expression related to cell differentiation for LUSC and cellular structure and immune response regulation for LUAD. Differential protein expression between NSCLC subtypes was related to extracellular structure for LUSC and metabolic processes, including glucose metabolism for LUAD. This direct comparison was more informative about subtype-specific pathways than between each subtype and control (nontumor) tissues. Many of our observations between NSCLC subtypes support and inform existing observations and reveal differences that may aid research seeking to identify and validate novel subtype biomarkers or druggable targets.

Intratumor Heterogeneity Predicts Prognosis in Lepidic Predominant Lung Adenocarcinoma.

Among the different subtypes of invasive lung adenocarcinoma, lepidic predominant adenocarcinoma (LPA) has been recognized as the lowest-risk subtype with good prognosis. The aim of this study is to provide insight into the heterogeneity within LPA tumors and to better understand the influence of other sub-histologies on survival outcome. Overall, 75 consecutive patients with LPA in pathologic stage I (TNM 8th edition) who underwent resection between 2010 and 2022 were included into this retrospective, single center analysis. The proportions of different growth patterns were reported in 5% increments according to the WHO classification. All tumors exhibited a predominantly lepidic growth pattern (median proportion 70%, IQR 60%-85%). The invasive component included acinar (n = 66, 88%), papillary (n = 41, 55%), micropapillary (n = 14, 19%), and solid growth patterns (n = 4, 5%), with most tumors exhibiting more than one invasive growth pattern. The presence of high-risk growth, that is, micropapillary and solid, was associated with higher T stage (r = 0.423, p = 0.0002). A classification of patients as lepidic/high-risk or lepidic/low-risk based on the presence of micropapillary and solid growth patterns resulted in a significantly worse disease-free survival (p = 0.0169, 5-year DFS: lepidic/high-risk 73% vs. lepidic/low-risk: 95%) for the lepidic/high-risk group, while the groups did not differ in age, gender, smoking status, or extent of resection. Patients with stage I LPA exhibit considerable intratumor heterogeneity regarding growth patterns, which can be used for prognostic stratification. The occurrence of micropapillary and solid growth patterns in LPA is associated with poorer disease-free survival.

Identification of immune characteristics of two lung adenocarcinoma subtypes based on immune- and pyroptosis-related genes to improve immunotherapy.

Lung adenocarcinoma (LUAD) is the most prevalent histological subtype of lung cancer. Pyroptosis is a programmatic cell death linked to inflammation. The data information of 541 LUAD samples and 59 normal samples were obtained from TCGA database. The analysis of differentially expressed genes (DEGs) was carried out on LUAD patients. The intersection of integrated PRGs and IRGs with DEGs yielded IPRGs. We utilized univariate Cox regression to determine IPRGs linked to overall survival (OS). Based on their expression levels, unsupervised clustering of LUAD was conducted. Patients were divided into two clusters. Analyses of immunity and drugs were performed in two clusters. One hundred and thirty-two IPRGs were linked with OS. Cluster 1 had a longer OS. Two thousand two hundred and fifty-six DEGs were detected in various subtypes. The results of immune analysis showed that most of the immune cells in cluster 2, which had a worse prognosis, had a low degree of infiltration. High Th2 cell infiltration may be related to poor prognosis in LUAD patients. Higher tumor immune dysfunction and exclusion (TIDE) and immunophenotypic scores in Cluster 1 indicated that these patients may have a better response to immunotherapy. There were significant differences in human leukocyte antigen (HLA), immune checkpoints, immunophenoscore (IPS), and TIDE scores in the two subtypes. The mutation frequencies of the top 10 genes in cluster 2 were higher than those in cluster 1. Different subtypes also had distinct sensitivities to different drugs. IPRGs can be utilized for LUAD subtyping. Different subtypes have varied immune landscapes and immunotherapy responses.

Hypoxia drives the formation of lung micropapillary adenocarcinoma-like structure through hypoxia-inducible factor-1α

Micropapillary adenocarcinoma (MPC) is an aggressive histological subtype of lung adenocarcinoma (LUAD). MPC is composed of small clusters of cancer cells exhibiting inverted polarity. However, the mechanism underlying its formation is poorly understood. Here we show that hypoxia is involved in MPC formation. Hypoxia induced the formation of MPC-like structures (MLSs) in a three-dimensional culture system using A549 human LUAD cells, and HIF-1α was indispensable for MLS formation. RNA sequencing analysis demonstrated that A549 cells forming MLSs exhibited a gene expression signature similar to that of lung MPC. Moreover, MLS formation enhanced the resistance of A549 cells to natural killer cell cytotoxicity. Our findings suggest that hypoxia drives lung MPC formation through HIF-1α and that immune escape from natural killer cells might underlie the aggressiveness of MPC.

HMGA2 Expression Predicts Subtype, Survival, and Treatment Outcome in Pancreatic Ductal Adenocarcinoma.

To establish HMGA2 as a marker of basal-like disease in pancreatic ductal adenocarcinoma (PDAC) and explore its use as a biomarker for prognosis and treatment resistance. We identified high expression of HMGA2 in basal PDAC cells in a scRNAseq Atlas of 172 patient samples. We then analyzed HMGA2 expression, along with expression of the classical marker GATA6, in a cohort of 580 PDAC samples with multiplex immunohistochemistry. We further supplemented these data with an additional 30 diverse patient samples and multiple independent single-cell RNAseq databases. We found that expression of HMGA2, but not previously described basal markers CK5 or CK17, predicted overall survival in our cohort. Combining HMGA2 and GATA6 status allowed for identification of two key study groups: an HMGA2+/GATA6- cohort with worse survival, low tumor-infiltrating CD8+ T cells, increased FAP+ fibroblasts, and poorer response to gemcitabine-based chemotherapies (n=94, median survival=11.2 months post-surgery); and an HMGA2-/GATA6+ cohort with improved survival, increased CD8+ T-cell infiltrate, decreased FAP+ fibroblasts, and improved survival with gemcitabine-based chemotherapy (n=198, median survival=21.7 months post-surgery). HMGA2 was also prognostic for overall survival in RNA sequencing from an independent cohort. IHC stratification of primary tumors by HMGA2 and GATA6 status in pancreatic cancer is associated with differential outcomes, survival following chemotherapy, and tumor microenvironments. As a nuclear marker for basal disease, HMGA2 complements GATA6 to identify disease subtypes in PDAC.

Gastric Cytology: A Supplement to Early Diagnosis of Gastric Malignancies.

Gastric malignancies are one of the leading causes of morbidity and mortality globally. Rapid accurate interpretation of gastric cytology aids in early diagnosis and management. This study evaluates the utility of gastric cytology in diagnosing gastric malignancies. This retrospective, cross-sectional study was conducted in the Department of Pathology for a period of 3.5 years. The cases with clinical suspicion of gastric malignancy and those who have had both cytology and histopathology examinations were included. Cytology results were reported as positive for malignancy, suspicious for malignancy, atypical cells-favor reactive, and negative for malignancy. The cytology and histopathology results were correlated, and descriptive statistics were used to analyze data. Among 118 patients included in the study, 103 cases were malignant and 15 were nonmalignant. Out of 103 malignant cases, 89 cases were detected in cytology. False positive cases consisted of four gastritis cases with florid reactive atypia and one with moderate dysplasia. False negative cases were of diffuse and intestinal subtypes of adenocarcinoma, followed by non-Hodgkin lymphoma. Sensitivity and specificity were found to be 86.41% and 66.67%, respectively. The positive predictive value is 94.68% with 41.67% negative predictive value and a diagnostic accuracy of 83.90%. Gastric cytology is a reliable screening and diagnostic tool with a high positive predictive value and acceptable sensitivity. Negative cytology in suspected cases of gastric malignancy should always be correlated with biopsy reports. Diffuse type, intestinal type gastric adenocarcinoma and non-Hodgkin lymphoma were the major pitfall on cytology. The cells in the background must be meticulously observed for the malignant features. Gastric cytology is cost-effective and yields rapid diagnosis with a high positive predictive value, sensitivity, and diagnostic accuracy.

PIK3CA Mutations and Co-Mutations in Operated Non-Small Cell Lung Carcinoma.

Background: Understanding PIK3CA mutations and co-mutations in non-small cell lung carcinoma (NSCLC) is critical to developing personalized treatment strategies. Therefore, this study aims to investigate PIK3CA mutations and the accompanying somatic variations in NSCLC. Methods: This retrospective study included 98 patients over 18 years of age who were diagnosed with NSCLC, operated on, and referred to the Molecular Pathology Laboratory between January 2019 and June 2024 for next-generation sequencing panel tests and ALK-ROS1 FISH analysis. Results: All patients were found to carry PIK3CA mutations. Among the 98 NSCLC patients analyzed, 16 (16.33%) were female and 82 (83.67%) were male. The average age of the patients was 64.53 ± 9.63 years, with an age range of 38-84 years, and the majority were 50 years or older. Of the cases, 51 presented the adenocarcinoma subtype, while the remaining 47 showed the squamous cell carcinoma subtype. A smoking history was present in 77 (78.57%) patients, while 21 (21.43%) had no smoking history. The most frequently detected pathogenic or likely pathogenic PIK3CA variations were c.1633G>A p.E545K (32.65%), c.1624G>A p.E542K (11.22%), c.3140A>G p.H1047R (11.22%), c.3140A>T p.H1047L (5.10%), c.1357G>C p.E453Q (4.08%), and c.3143A>G p.H1048R (2.04%). The top 10 mutations that most commonly accompanied PIK3CA variations were KRAS, NF1, TP53, EGFR, PTEN, BRAF, KIT, CDKN2A, SMARCA4, and ATM mutations, respectively. Conclusions:PIK3CA variations, along with other gene variations, may influence cancer progression and thus may play a crucial role in the determination of targeted treatment strategies.

Correlation between [18F]-FDG PET/CT findings and pathological subtypes of lung adenocarcinoma presenting as ground-glass opacity.

The aim of this study is to analyze the correlation between [18F]-FDG PET/CT (positron emission tomography/computed tomography) findings and pathological subtypes of lung adenocarcinoma with ground-glass opacity (GGO). 88 patients were included in this study, which underwent [18F]-FDG PET/CT and were finally diagnosed with lung adenocarcinoma. A total of 90 GGO lesions were analyzed. The size and SUVmax of all lesions were measured, the proportion of solid components of GGO in lesions was calculated, and quantitative classification was performed. The above GGO lesions were divided into three groups based on the 2011 IASLC/ATS/ERS lung adenocarcinoma pathological classification, namely good prognosis group, relatively good prognosis group and poor prognosis group. Chi-square test, independent sample t test, and analysis of variance were used for statistical analysis. There was a negative correlation between the SUVmax and quantitative classification value (r = -0.638, P < 0.001). Atypical adenomatous hyperplasia (AAH), acinar predominant adenocarcinoma (APA), lepidic predominant adenocarcinoma (LPA), papillary predominant adenocarcinoma (PPA), and solid predominant adenocarcinoma (SPA) had significant differences in GGO lesion size, SUVmax, and quantitative classification value (F = 3.849, P = 0.019; F = 27.420, P < 0.001; F = 4.353, P = 0.002). There were significant differences in GGO lesion size, SUVmax, and quantitative classification value among the good prognosis group, relatively good prognosis group, and poor prognosis group (F = 5.626, P = 0.011; F = 37.587, P < 0.001; F = 5.119, P = 0.008). GGO lesion size, SUVmax, and quantitative classification value are correlated with different pathological subtypes and can be used toevaluate the prognosis of lung adenocarcinoma with GGO.

CT-based radiomics analysis for prediction of pathological subtypes of lung adenocarcinoma

ObjectiveLung cancer is a leading cause of cancer-related deaths worldwide, and its early and accurate diagnosis is crucial for improving patient survival. This paper developed an artificial intelligence (AI) model based on machine learning combined with radiomics for predicting the pathologic type of ground glass nodules (GGN). It explored its potential and challenges in practical applications. MethodsA total of 179 GGN patients with postoperative pathologically confirmed lung adenocarcinoma from June 2022 to June 2024 were collected from a hospital, including 22 cases of atypical adenomatous hyperplasia (AAH), 61 cases of adenocarcinoma in situ (AIS), 55 cases of invasive adenocarcinoma (IAC), and 41 cases of minimally invasive adenocarcinoma (MIA). Two experienced radiologists outlined the imaging data's regions of interest (ROI). Radiomic features were extracted and selected through normalization, mutual information, Spearman correlation coefficient, recursive feature elimination, and LASSO regression. Different machine learning models were developed and the best model was determined based on classification accuracy. ResultsDifferent machine learning models were trained and tested, and a variety of deep learning models were selected for comparison, among which Random Forest had the highest accuracy of 83.3%, and the AUC value of 0.892(95%CI, 0.846-0.923) in recognizing lung adenocarcinoma types. The AUC values reached 0.92 and 0.94 respectively in diagnosing AIS and IAC. ConclusionRadiomics combined with machine learning models, such as Random Forest, outperform average physician diagnostic accuracy in identifying lung adenocarcinoma types. The model is valuable for early and precise lung adenocarcinoma diagnosis, enhancing clinical decision-making.

Sex-based differences in CEACAM5 expression in lung cancer.

Carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) is expressed in 20-25% of non-small cell lung cancer (NSCLC) and there is interest in CEACAM5 as a biomarker given its potential for blood-based detection and investigational study as a drug target. Increased expression of CEACAM5 has been observed in semi-solid lung adenocarcinoma lesions, which have an increased prevalence in women and never smokers. Given this association, sex-based differences in CEACAM5 were evaluated. The Cancer Genome Atlas (TCGA) data on CEACAM5 expression in NSCLC tumors (n=994) in women (n=398) and men (n=596) were analyzed for differences in expression of CEACAM5 based on sex and histologic subtypes of adenocarcinoma and for correlations with overall survival (OS). Among all stages of NSCLC, mean expression of CEACAM5 was 143.3 fragments per kilobase of transcript per million (FPKM) with differences observed between female and male patients (194.5 vs. 109.2 FPKM, P<0.0001) and between adenocarcinoma and squamous cell carcinoma (239.3 vs. 46.2 FPKM, P<0.0001). Differences persisted among combined sex and histology subgroups. High CEACAM5 was not predictive of survival in NSCLC or adenocarcinoma overall, but was associated with worse survival among stage I female patients with adenocarcinoma (5-year OS CEACAM5 high =33% vs. low =64%, log-rank P=0.008). Higher levels of CEACAM5 expression are observed in NSCLC tumors in female patients and adenocarcinoma histology. High CEACAM5 expression in lung adenocarcinoma is associated with worse survival among female patients. The biologic impact of sex on CEACAM5 as a biomarker warrants further study.

Minimally invasive determination of pancreatic ductal adenocarcinoma (PDAC) subtype by means of circulating cell-free RNA.

Pancreatic ductal adenocarcinoma (PDAC) comprises two clinically relevant molecular subtypes that are currently determined using tissue biopsies, which are spatially biased and highly invasive. We used whole transcriptome sequencing of 10 plasma samples with tumor-informed subtypes, complemented by proteomic analysis for minimally invasive identification of PDAC subtype markers. Data were validated in independent large cohorts and correlated with treatment response and patient outcome. Differential transcript abundance analyses revealed 32 subtype-specific, protein-coding cell-free RNA (cfRNA) transcripts. The subtype specificity of these transcripts was validated in two independent tissue cohorts comprising 195 and 250 cases, respectively. Three disease-relevant cfRNA-defined subtype markers (DEGS1, KDELC1, and RPL23AP7) that consistently associated with basal-like tumors across all cohorts were identified. In both tumor and liquid biopsies, the overexpression of these markers correlated with poor survival. Moreover, elevated levels of the identified markers were linked to a poor response to systemic therapy and early relapse in resected patients. Our data indicate clinical applicability of cfRNA markers in determining tumor subtypes and monitoring disease recurrence.

Explainable 18F-FDG PET/CT radiomics model for predicting EGFR mutation status in lung adenocarcinoma: a two-center study

PurposeTo establish an explainable 18F-FDG PET/CT-derived prediction model to identify EGFR mutation status and subtypes (EGFR wild, EGFR-E19, and EGFR-E21) in lung adenocarcinoma (LUAD).MethodsBaseline 18F-FDG PET/CT images of 478 patients with LUAD from 2 hospitals were collected. Data from hospital A (n = 390) was randomly split into a training group (n = 312) and an internal test group (n = 78), with data from hospital B (n = 88) utilized for external test. Further, a total of 4,760 handcrafted radiomics features (HRFs) were extracted from PET/CT scans. Candidates for the prediction model were constructed by cross-combinations of 11 feature selection methods and 7 classifiers. The optimal model was determined by combining the results of cross-center data validation and model visualization (Yellowbrick). The predictive performance was assessed via receiver operating characteristic curve, confusion matrix and classification report. Four explainable artificial intelligence technologies were used for optimal model interpretation.ResultsSex and SUVmax were selected as clinical risk factors, which were then combined with 8 robust PET/CT HRFs to establish the models. The optimal performance was obtained by combining a light gradient boosting machine classifier with random forest feature selection method achieving an optimal performance with a macro-average AUC of 0.75 in the internal test group and 0.81 in the external test group.ConclusionThe explainable EGFR mutation status prediction model have certain clinical practicability and good generalization performance, which may help in the timely selection of treatment options and prognosis prediction in patients with LUAD.

Identifying Clusters of Curatively-Treated Esophageal Cancer Patients Using Patient-Reported Outcome Measures at Three Months from Discharge: A Secondary Analysis from a Longitudinal Single Center Study.

This study aims to identify meaningful clusters based on Patient-Reported Outcome Measures (PROMs) in curatively-treated esophageal cancer patients at three months post-discharge. This secondary analysis of a longitudinal single-center study included 46 esophageal cancer patients who underwent curative surgery. Patients were selected based on their completion of PROMs surveys at three months post-discharge, were aged 18 years or older, and had undergone surgical resection (esophagectomy) with or without neoadjuvant chemotherapy and/or radiotherapy. The analysis utilized t-distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction and hierarchical clustering to analyze PROMs data collected three months post-discharge. Clustering was performed on physical, emotional, cognitive, and social functioning variables, symptom burden, and health literacy. Three distinct clusters were identified: Cluster 1 (n = 24) with higher functioning and moderate symptoms, Cluster 2 (n = 14) with moderate functioning, higher symptoms, and lower health literacy, and Cluster 3 (n = 8) with the highest functioning, lowest symptoms, and highest health literacy. Significant differences between squamous cell carcinoma and adenocarcinoma subtypes were observed across several PROMs domains, including critical health literacy, general health status/quality of life, nausea and vomiting, and insomnia. These clusters provide an exploratory framework for tailoring post-operative interventions to enhance patient recovery, which necessitates further confirmatory investigations, including outcomes such as complications and mortality, in the analysis. This study fills a research gap by demonstrating the utility of PROMs in identifying distinct recovery patterns in esophageal cancer patients post-surgery. The findings support the use of PROMs to guide personalized post-operative care, potentially improving patient outcomes and quality of life. Further research is needed to validate these findings in larger, diverse populations.

Classification of histological subtypes of non-small cell lung cancer using computerized tomography texture analysis

Objective: This study aimed to differentiate between the two main histological subtypes of non-small cell lung cancer using a non-invasive technique, computerized tomography texture analysis. Method: We included 53 patients. All patients were histopathologically proven non-small cell lung cancer cases. All patients underwent thorax CT scans. In CT images, the differences present in the texture features of adenocarcinoma and squamous cell carcinoma, which are the two main histological subtypes of non-small cell lung cancer, were determined by the consensus of two radiologists for computerized tomography-based texture analysis. Results: A total of 44 texture features were extracted, including 12 first-order features and 32 second-order features derived from gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), neighborhood gray-level different matrix (NGLDM), and gray-level zone length matrix (GLZLM) features in 51 CT images. None of the evaluated texture parameters were statistically significant. However, in patients with squamous cell lung cancer, the values of Intensity Histogram, NGTDM Complexity, and Intensity Based Robust Mean Absolute Deviation higher from adenocarcinoma patients and had the highest area under the curve in roc analyses (0.727, 0.664, 0.666 respectively) Conclusion: Intensity Histogram, NGTDM Complexity, and Intensity Based Robust Mean Absolute Deviation features can be used to differentiate between the subtypes of non-small cell lung cancer, adenocarcinoma and squamous cell carcinoma. These features were highly associated with the high intratumoral heterogeneity of squamous cell lung cancer.