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Intratumoral heterogeneity (ITH) is a fundamental property of cancer; however, the origins of ITH remain poorly understood. We performed single-cell transcriptome profiling of peritoneal carcinomatosis (PC) from 15 patients with gastric adenocarcinoma (GAC), constructed a map of 45,048 PC cells, profiled the transcriptome states of tumor cell populations, incisively explored ITH of malignant PC cells and identified significant correlates with patient survival. The links between tumor cell lineage/state compositions and ITH were illustrated at transcriptomic, genotypic, molecular and phenotypic levels. We uncovered the diversity in tumor cell lineage/state compositions in PC specimens and defined it as a key contributor to ITH. Single-cell analysis of ITH classified PC specimens into two subtypes that were prognostically independent of clinical variables, and a 12-gene prognostic signature was derived and validated in multiple large-scale GAC cohorts. The prognostic signature appears fundamental to GAC carcinogenesis and progression and could be practical for patient stratification. Single-cell analysis of gastric cancer samples tracks the cell of origin of metastatic lesions and identifies an independent prognostic signature of the clinical outcome.

Recent advances in spatial transcriptomics (ST) techniques provide valuable insights into cellular interactions within the tumor microenvironment (TME). However, most analytical tools lack consideration of histological features and rely on matched single-cell RNA sequencing data, limiting their effectiveness in TME studies. To address this, we introduce the M orphology- E nhanced Spatial T ranscriptome Analysis I ntegrator (METI), an end-to-end framework that maps cancer cells and TME components, stratifies cell types and states, and analyzes cell co-localization. By integrating spatial transcriptomics, cell morphology, and curated gene signatures, METI enhances our understanding of the molecular landscape and cellular interactions within the tissue. We evaluate the performance of METI on ST data generated from various tumor tissues, including gastric, lung, and bladder cancers, as well as premalignant tissues. We also conduct a quantitative comparison of METI with existing clustering and cell deconvolution tools, demonstrating METI’s robust and consistent performance. Integrating tissue histology with spatial transcriptomics (ST) can significantly enhance the analysis of tumor heterogeneity and the tumor microenvironment (TME). Here, the authors present METI, a computational framework to analyze cancer cells and the complex TME by integrating ST with histology imaging.

We conducted a phase I trial to determine the optimal dose of triplet therapy with the tyrosine kinase inhibitor sitravatinib plus nivolumab plus ipilimumab in 22 previously untreated patients with advanced clear cell renal cell carcinoma. The primary endpoint was safety. Secondary endpoints were objective response rate (ORR), disease control rate (DCR), duration of response (DOR), progression-free survival (PFS), overall survival (OS), 1-year survival probability, and sitravatinib pharmacokinetics. Sitravatinib dose of 35 mg daily plus nivolumab 3 mg/kg and ipilimumab 1 mg/kg resulted in high frequency of immune-related adverse events. Subsequent dose reduction of ipilimumab to 0.7 mg/kg allowed safe escalation of sitravatinib up to 100 mg daily. Overall, the triplet combination achieved ORR 45.5%, DCR 86.4%, median PFS 14.5 months, and 1-year survival 80.8%. Median OS and DOR were not reached. Sitravatinib exposure increased dose-dependently. Single-cell RNA-seq of longitudinally collected tumor biopsies from 12 patients identified a tumor cell-specific epithelial-mesenchymal transition-like program associated with treatment resistance and poor outcomes. Treatment resistance was characterized by a transition from cytotoxic to exhausted T cell state and enrichment for M2-like myeloid cells. The observed hypothesis-generating changes in gene expression dynamics and cellular states may help inform future strategies to optimize immunotherapy efficacy. Clinical Trials.gov identifier: NCT04518046 In patients with advanced clear cell renal cell carcinoma (ccRCC), sitravatinib (tyrosine kinase inhibitor) has shown efficacy both alone and in combination with nivolumab (anti-PD-1). Here, the authors investigate triplet combination of sitravatinib with nivolumab and ipilimumab (anti-CTLA4) in patients with metastatic ccRCC and longitudinal single-cell transcriptomic analysis.

Background Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios between tumor-normal paired samples serve as the inferring proxy. Existing SCNA based subclonal population inferring tools consider the GC bias of tumor and normal sample is of the same fature, and could be fully offset by read count ratio. However, we found that, the read count ratio on SCNA segments presents a Log linear biased pattern, which influence existing read count ratios based subclonal inferring tools performance. Currently no correction tools take into account the read ratio bias. Results We present Pre-SCNAClonal, a tool that improving tumor subclonal population inferring by correcting GC-bias at SCNAs level. Pre-SCNAClonal first corrects GC bias using Markov chain Monte Carlo probability model, then accurately locates baseline DNA segments (not containing any SCNAs) with a hierarchy clustering model. We show Pre-SCNAClonal’s superiority to exsiting GC-bias correction methods at any level of subclonal population. Conclusions Pre-SCNAClonal could be run independently as well as serving as pre-processing/gc-correction step in conjuntion with exsiting SCNA-based subclonal inferring tools.

Tumor-infiltrating T cells offer a promising avenue for cancer treatment, yet their states remain to be fully characterized. Here we present a single-cell atlas of T cells from 308,048 transcriptomes across 16 cancer types, uncovering previously undescribed T cell states and heterogeneous subpopulations of follicular helper, regulatory and proliferative T cells. We identified a unique stress response state, T STR , characterized by heat shock gene expression. T STR cells are detectable in situ in the tumor microenvironment across various cancer types, mostly within lymphocyte aggregates or potential tertiary lymphoid structures in tumor beds or surrounding tumor edges. T cell states/compositions correlated with genomic, pathological and clinical features in 375 patients from 23 cohorts, including 171 patients who received immune checkpoint blockade therapy. We also found significantly upregulated heat shock gene expression in intratumoral CD4/CD8 + cells following immune checkpoint blockade treatment, particularly in nonresponsive tumors, suggesting a potential role of T STR cells in immunotherapy resistance. Our well-annotated T cell reference maps, web portal and automatic alignment/annotation tool could provide valuable resources for T cell therapy optimization and biomarker discovery. A single-cell analysis of tumor-infiltrating T cells from 16 cancer types identifies new T cell subsets and a stress response cell state enriched in tumors resistant to immunotherapy.

Immune checkpoint inhibitors are associated with immune-related adverse events (irAEs), including arthritis (arthritis-irAE). Management of arthritis-irAE is challenging because immunomodulatory therapy for arthritis should not impede antitumor immunity. Understanding of the mechanisms of arthritis-irAE is critical to overcome this challenge, but the pathophysiology remains unknown. Here, we comprehensively analyze peripheral blood and/or synovial fluid samples from 20 patients with arthritis-irAE, and unmask a prominent Th1-CD8 + T cell axis in both blood and inflamed joints. CX3CR1 hi CD8 + T cells in blood and CXCR3 hi CD8 + T cells in synovial fluid, the most clonally expanded T cells, significantly share TCR repertoires. The migration of blood CX3CR1 hi CD8 + T cells into joints is possibly mediated by CXCL9/10/11/16 expressed by myeloid cells. Furthermore, arthritis after combined CTLA-4 and PD-1 inhibitor therapy preferentially has enhanced Th17 and transient Th1/Th17 cell signatures. Our data provide insights into the mechanisms, predictive biomarkers, and therapeutic targets for arthritis-irAE. Arthritis is the most common rheumatic immune-related adverse event (irAE) occurring in cancer patients receiving immune checkpoint inhibitors. Here the authors study the immune landscape of blood and synovial fluid samples from patients with arthritis-irAE, reporting immunological differences and similarities with classic autoimmune arthritis.

It is unclear how cells counteract the potentially harmful effects of uncoordinated DNA replication in the context of oncogenic stress. Here, we identify the WRAD (WDR5/RBBP5/ASH2L/DPY30) core as a modulator of DNA replication in pancreatic ductal adenocarcinoma (PDAC) models. Molecular analyses demonstrated that the WRAD core interacts with the replisome complex, with disruption of DPY30 resulting in DNA re-replication, DNA damage, and chromosomal instability (CIN) without affecting cancer cell proliferation. Consequently, in immunocompetent models, DPY30 loss induced T cell infiltration and immune-mediated clearance of highly proliferating cancer cells with complex karyotypes, thus improving anti-tumor efficacy upon anti-PD-1 treatment. In PDAC patients, DPY30 expression was associated with high tumor grade, worse prognosis, and limited response to immune checkpoint blockade. Together, our findings indicate that the WRAD core sustains genome stability and suggest that low intratumor DPY30 levels may identify PDAC patients who will benefit from immune checkpoint inhibitors.

The recent advance of spatial transcriptomics (ST) technique provides valuable insights into the organization and interactions of cells within the tumor microenvironment (TME). While various analytical tools have been developed for tasks such as spatial clustering, spatially variable gene identification, and cell type deconvolution, most of them are general methods lacking consideration of histological features in spatial data analysis. This limitation results in reduced performance and interpretability of their results when studying the TME. Here, we present a computational framework named, Morphology-Enhanced Spatial Transcriptome Analysis Integrator (METI) to address this gap. METI is an end-to-end framework capable of spatial mapping of both cancer cells and various TME cell components, robust stratification of cell type and transcriptional states, and cell co-localization analysis. By integrating both spatial transcriptomics, cell morphology and curated gene signatures, METI enhances our understanding of the molecular landscape and cellular interactions within the tissue, facilitating detailed investigations of the TME and its functional implications. The performance of METI has been evaluated on ST data generated from various tumor tissues, including gastric, lung, and bladder cancers, as well as premalignant tissues. Across all these tissues and conditions, METI has demonstrated robust performance with consistency.

This guideline aims to provide evidence-based recommendations for the prevention, screening, assessment, diagnosis, treatment decision, and survivorship care of older adults with cancer. The CACA Geriatric Oncology Society convened expert panels to plan, write, and revise the manuscript. These experts engaged in in-depth discussions to propose clinical practice recommendations for geriatric oncology. The guideline highlights the increasing prevalence and mortality of cancer among the older adults in China, driven by an aging population. It emphasizes that a comprehensive geriatric assessment is critical for optimal treatment decision-making. Additionally, the use of traditional Chinese Medicine is suggested to minimize toxicity, alleviate symptoms, and enhance immune function. In conclusion, integrative and multidisciplinary approaches are essential to achieving both increased longevity and improved quality of life for older patients with cancer.