Purpose: While fluoropyrimidine-based chemotherapy regimens are recommended second-line treatment for patients with advanced biliary tract cancer (BTC), there have been no studies comparing different regimens head-to-head. Materials and Methods: We performed individual patient-level meta-analysis based on data from the intention-to-treat population of the phase 2b NIFTY trial (liposomal irinotecan [nal-IRI] plus fluorouracil and leucovorin [5-FU/LV] vs. 5-FU/LV; NCT03542508) and the phase 2 FIReFOX trial (modified oxaliplatin plus 5-FU/LV [mFOLFOX] vs. modified irinotecan plus 5-FU/LV [mFOLFIRI]; NCT03464968). Pairwise log-rank tests and multivariable analysis using Cox proportional hazards modeling with shared frailty to account for the trial's effect were used to compare overall survival (OS) between regimens. Results: A total of 277 patients were included. The nal-IRI plus 5-FU/LV group (n=88) showed significantly better OS compared to the mFOLFOX group (n=49, pairwise log-rank, p=0.02), and mFOLFIRI group (n=50, p=0.03). Multivariable analysis showed consistent trends in OS with adjusted hazard ratios of 1.39 (mFOLFOX vs. nal-IRI plus 5-FU/LV: 95% confidence interval [CI], 0.93 to 2.07; p=0.11) and 1.36 (mFOLFIRI vs. nal-IRI plus 5-FU/LV: 95% CI, 0.92 to 2.03; p=0.13), respectively. Compared to the 5-FU/LV group, the mFOLFOX group and the mFOLFIRI group did not show differences in terms of OS (pairwise log-rank p=0.83 and p=0.58, respectively). The nal-IRI plus 5-FU/LV group experienced more frequent diarrhea, while the mFOLFOX group experienced peripheral neuropathy. Conclusion Nal-IRI plus 5-FU/LV showed favorable survival outcomes compared to mFOLFOX, mFOLFIRI, or 5-FU/LV. The safety profiles of these regimens should be considered along with efficacy.

Purpose: While fluoropyrimidine-based chemotherapy regimens are recommended second-line treatment for patients with advanced biliary tract cancer (BTC), there have been no studies comparing different regimens head-to-head. Materials and Methods: We performed individual patient-level meta-analysis based on data from the intention-to-treat population of the phase 2b NIFTY trial (liposomal irinotecan [nal-IRI] plus fluorouracil and leucovorin [5-FU/LV] vs. 5-FU/LV; NCT03542508) and the phase 2 FIReFOX trial (modified oxaliplatin plus 5-FU/LV [mFOLFOX] vs. modified irinotecan plus 5-FU/LV [mFOLFIRI]; NCT03464968). Pairwise log-rank tests and multivariable analysis using Cox proportional hazards modeling with shared frailty to account for the trial's effect were used to compare overall survival (OS) between regimens. Results: A total of 277 patients were included. The nal-IRI plus 5-FU/LV group (n=88) showed significantly better OS compared to the mFOLFOX group (n=49, pairwise log-rank, p=0.02), and mFOLFIRI group (n=50, p=0.03). Multivariable analysis showed consistent trends in OS with adjusted hazard ratios of 1.39 (mFOLFOX vs. nal-IRI plus 5-FU/LV: 95% confidence interval [CI], 0.93 to 2.07; p=0.11) and 1.36 (mFOLFIRI vs. nal-IRI plus 5-FU/LV: 95% CI, 0.92 to 2.03; p=0.13), respectively. Compared to the 5-FU/LV group, the mFOLFOX group and the mFOLFIRI group did not show differences in terms of OS (pairwise log-rank p=0.83 and p=0.58, respectively). The nal-IRI plus 5-FU/LV group experienced more frequent diarrhea, while the mFOLFOX group experienced peripheral neuropathy. Conclusion Nal-IRI plus 5-FU/LV showed favorable survival outcomes compared to mFOLFOX, mFOLFIRI, or 5-FU/LV. The safety profiles of these regimens should be considered along with efficacy.

Purpose: While fluoropyrimidine-based chemotherapy regimens are recommended second-line treatment for patients with advanced biliary tract cancer (BTC), there have been no studies comparing different regimens head-to-head. Materials and Methods: We performed individual patient-level meta-analysis based on data from the intention-to-treat population of the phase 2b NIFTY trial (liposomal irinotecan [nal-IRI] plus fluorouracil and leucovorin [5-FU/LV] vs. 5-FU/LV; NCT03542508) and the phase 2 FIReFOX trial (modified oxaliplatin plus 5-FU/LV [mFOLFOX] vs. modified irinotecan plus 5-FU/LV [mFOLFIRI]; NCT03464968). Pairwise log-rank tests and multivariable analysis using Cox proportional hazards modeling with shared frailty to account for the trial's effect were used to compare overall survival (OS) between regimens. Results: A total of 277 patients were included. The nal-IRI plus 5-FU/LV group (n=88) showed significantly better OS compared to the mFOLFOX group (n=49, pairwise log-rank, p=0.02), and mFOLFIRI group (n=50, p=0.03). Multivariable analysis showed consistent trends in OS with adjusted hazard ratios of 1.39 (mFOLFOX vs. nal-IRI plus 5-FU/LV: 95% confidence interval [CI], 0.93 to 2.07; p=0.11) and 1.36 (mFOLFIRI vs. nal-IRI plus 5-FU/LV: 95% CI, 0.92 to 2.03; p=0.13), respectively. Compared to the 5-FU/LV group, the mFOLFOX group and the mFOLFIRI group did not show differences in terms of OS (pairwise log-rank p=0.83 and p=0.58, respectively). The nal-IRI plus 5-FU/LV group experienced more frequent diarrhea, while the mFOLFOX group experienced peripheral neuropathy. Conclusion Nal-IRI plus 5-FU/LV showed favorable survival outcomes compared to mFOLFOX, mFOLFIRI, or 5-FU/LV. The safety profiles of these regimens should be considered along with efficacy.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. Materials and Methods: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data.The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. Results: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4 + and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. Conclusion In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: We aimed to comprehensively analyze the immune cell and stromal components of tumor microenvironment at the single-cell level and identify tumor heterogeneity among the major top-derived oncogene mutations in non-small cell lung cancer (NSCLC) using single-cell RNA sequencing (scRNA-seq) data. Materials and Methods: The scRNA-seq dataset utilized in this study comprised 64369 primary tumor tissue cells from 21 NSCLC patients, focusing on mutations in EGFR, ALK, BRAF, KRAS, TP53, and the wild-type. Results: Tumor immune microenvironment (TIM) analysis revealed differential immune responses across NSCLC mutation subtypes. TIM analysis revealed different immune responses across the mutation subtypes. Two mutation clusters emerged: KRAS, TP53, and EGFR+TP53 mutations (MC1); and EGFR, BRAF, and ALK mutations (MC2). MC1 showed higher tertiary lymphoid structures signature scores and enriched populations of C2-T-IL7R, C3-T/NK-CXCL4, C9-T/NK-NKG, and C1-B-MS4A1 clusters than cluster 2. Conversely, MC2 cells exhibited higher expression levels of TNF, IL1B, and chemokines linked to alternative immune pathways. Remarkably, co-occurring EGFR and TP53 mutations were grouped as MC1. EGFR+TP53 mutations showed upregulation of peptide synthesis and higher synthetic processes, as well as differences in myeloid and T/NK cells compared to EGFR mutations. In T/NK cells, EGFR+TP53 mutations showed a higher expression of features related to cell activity and differentiation, whereas EGFR mutations showed the opposite. Conclusion Our research indicates a close association between mutation types and tumor microenvironment in NSCLC, offering insights into personalized approaches for cancer diagnosis and treatment.

Purpose: Biliary tract cancers (BTCs) are rare and show a dismal prognosis with limited treatment options. To improve our understanding of these heterogeneous tumors and develop effective therapeutic agents, suitable preclinical models reflecting diverse tumor characteristics are needed. We established and characterized new patient-derived cancer cell cultures and patient-derived xenograft (PDX) models using malignant ascites from five patients with BTC. Materials and Methods: Five patient-derived cancer cell cultures and three PDX models derived from malignant ascites of five patients with BTC, AMCBTC-01, -02, -03, -04, and -05, were established. To characterize the models histogenetically and confirm whether characteristics of the primary tumor were maintained, targeted sequencing and histopathological comparison between primary tissue and xenograft tumors were performed. Results: From malignant ascites of five BTC patients, five patient-derived cancer cell cultures (100% success rate), and three PDXs (60% success rate) were established. The morphological characteristics of three primary xenograft tumors were compared with those of matched primary tumors, and they displayed a similar morphology. The mutated genes in samples (models, primary tumor tissue, or both) from more than one patient were TP53 (n=2), KRAS (n=2), and STK11 (n=2). Overall, the pattern of commonly mutated genes in BTC cell cultures was different from that in commercially available BTC cell lines. Conclusion We successfully established the patient-derived cancer cell cultures and xenograft models derived from malignant ascites in BTC patients. These models accompanied by different genetic characteristics from commercially available models will help better understand BTC biology.