Single-cell tumor heterogeneity landscape of hepatocellular carcinoma: unraveling the pro-metastatic subtype and its interaction loop with fibroblasts.

BACKGROUND: Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear. METHODS: Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed. RESULTS: We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-ß signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-ß-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis. CONCLUSION: By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.

Combined High-Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer.

Colorectal cancer (CRC) is a frequent, worldwide tumor described for its huge complexity, including inter-/intra-heterogeneity and tumor microenvironment (TME) variability. Intra-tumor heterogeneity and its connections with metabolic reprogramming and epithelial-mesenchymal transition (EMT) were investigated with explorative shotgun proteomics complemented by a Random Forest (RF) machine-learning approach. Deep and superficial tumor regions and distant-site non-tumor samples from the same patients (n = 16) were analyzed. Among the 2009 proteins analyzed, 91 proteins, including 23 novel potential CRC hallmarks, showed significant quantitative changes. In addition, a 98.4% accurate classification of the three analyzed tissues was obtained by RF using a set of 21 proteins. Subunit E1 of 2-oxoglutarate dehydrogenase (OGDH-E1) was the best classifying factor for the superficial tumor region, while sorting nexin-18 and coatomer-beta protein (beta-COP), implicated in protein trafficking, classified the deep region. Down- and up-regulations of metabolic checkpoints involved different proteins in superficial and deep tumors. Analogously to immune checkpoints affecting the TME, cytoskeleton and extracellular matrix (ECM) dynamics were crucial for EMT. Galectin-3, basigin, S100A9, and fibronectin involved in TME-CRC-ECM crosstalk were found to be differently variated in both tumor regions. Different metabolic strategies appeared to be adopted by the two CRC regions to uncouple the Krebs cycle and cytosolic glucose metabolism, promote lipogenesis, promote amino acid synthesis, down-regulate bioenergetics in mitochondria, and up-regulate oxidative stress. Finally, correlations with the Dukes stage and budding supported the finding of novel potential CRC hallmarks and therapeutic targets.

A single-cell and spatially resolved atlas of human osteosarcomas.

Osteosarcomas are intricate cellular ecosystems, where heterotypic interactions significantly influence disease progression and therapeutic outcomes. Despite their importance, a detailed understanding of their cellular composition and organizational structure remains elusive. In this study, we provide a comprehensive single-cell and spatially resolved transcriptomics analysis of human osteosarcomas. We construct a cellular meta-map to dissect spatial transcriptomic data, unveiling a detailed atlas of osteosarcoma compositional subgroups. We meticulously characterize the unique gene signatures and functional states of each subgroup and investigate the impact of chemotherapy on these cellular subpopulations. Additionally, our spatial transcriptomics analysis identifies a distinct spatial niche, located at the forefront of tumor necrotic zones, potentially associated with chemotherapy resistance. We also delve into the crosstalk between different cellular subgroups. This study furnishes a comprehensive transcriptional atlas of osteosarcoma's cellular architecture, enriching our comprehension of its complexity and laying the groundwork for more targeted therapeutic approaches.

MRI-based habitat imaging in cancer treatment: current technology, applications, and challenges.

Extensive efforts have been dedicated to exploring the impact of tumor heterogeneity on cancer treatment at both histological and genetic levels. To accurately measure intra-tumoral heterogeneity, a non-invasive imaging technique, known as habitat imaging, was developed. The technique quantifies intra-tumoral heterogeneity by dividing complex tumors into distinct sub- regions, called habitats. This article reviews the following aspects of habitat imaging in cancer treatment, with a focus on radiotherapy: (1) Habitat imaging biomarkers for assessing tumor physiology; (2) Methods for habitat generation; (3) Efforts to combine radiomics, another imaging quantification method, with habitat imaging; (4) Technical challenges and potential solutions related to habitat imaging; (5) Pathological validation of habitat imaging and how it can be utilized to evaluate cancer treatment by predicting treatment response including survival rate, recurrence, and pathological response as well as ongoing open clinical trials.

Suppressive immune microenvironment and CART therapy for glioblastoma: Future prospects and challenges.

Glioblastoma, a highly malignant intracranial tumor, has acquired slow progress in treatment. Previous clinical trials involving targeted therapy and immune checkpoint inhibitors have shown no significant benefits in treating glioblastoma. This ineffectiveness is largely due to the complex immunosuppressive environment of glioblastoma. Glioblastoma cells exhibit low immunogenicity and strong heterogeneity and the immune microenvironment is replete with inhibitory cytokines, numerous immunosuppressive cells, and insufficient effective T cells. Fortunately, recent Phase I clinical trials of CART therapy for glioblastoma have confirmed its safety, with a small subset of patients achieving survival benefits. However, CART therapy continues to face challenges, including blood-brain barrier obstruction, antigen loss, and an immunosuppressive tumor microenvironment (TME). This article provides a detailed examination of glioblastoma's immune microenvironment, both from intrinsic and extrinsic tumor cell factors, reviews current clinical and basic research on multi-targets CART treatment, and concludes by outlining the key challenges in using CART cells for glioblastoma therapy.

Single-cell transcriptomics reveals tumor landscape in ovarian carcinosarcoma. / 单细胞转录组学揭示卵巢癌肉瘤的肿瘤特征.

OBJECTIVES: The present study used single-cell RNA sequencing (scRNA-seq) to characterize the cellular composition of ovarian carcinosarcoma (OCS) and identify its molecular characteristics. METHODS: scRNA-seq was performed in resected primary OCS for an in-depth analysis of tumor cells and the tumor microenvironment. Immunohistochemistry staining was used for validation. The scRNA-seq data of OCS were compared with those of high-grade serous ovarian carcinoma (HGSOC) tumors and other OCS tumors. RESULTS: Both malignant epithelial and malignant mesenchymal cells were observed in the OCS patient of this study. We identified four epithelial cell subclusters with different biological roles. Among them, epithelial subcluster 4 presented high levels of breast cancer type 1 susceptibility protein homolog (BRCA1) and DNA topoisomerase 2-α (TOP2A) expression and was related to drug resistance and cell cycle. We analyzed the interaction between epithelial and mesenchymal cells and found that fibroblast growth factor (FGF) and pleiotrophin (PTN) signalings were the main pathways contributing to communication between these cells. Moreover, we compared the malignant epithelial and mesenchymal cells of this OCS tumor with our previous published HGSOC scRNA-seq data and OCS data. All the epithelial subclusters in the OCS tumor could be found in the HGSOC samples. Notably, the mesenchymal subcluster C14 exhibited specific expression patterns in the OCS tumor, characterized by elevated expression of cytochrome P450 family 24 subfamily A member 1 (CYP24A1), collagen type XXIII α1 chain (COL23A1), cholecystokinin (CCK), bone morphogenetic protein 7 (BMP7), PTN, Wnt inhibitory factor 1 (WIF1), and insulin-like growth factor 2 (IGF2). Moreover, this subcluster showed distinct characteristics when compared with both another previously published OCS tumor and normal ovarian tissue. CONCLUSIONS: This study provides the single-cell transcriptomics signature of human OCS, which constitutes a new resource for elucidating OCS diversity.

CAR-NK Cell Therapy: A Transformative Approach to Overcoming Oncological Challenges.

The use of chimeric antigen receptor (CAR) in natural killer (NK) cells for cancer therapy is gaining momentum, marking a significant shift in cancer treatment. This review aims to explore the potential of CAR-NK cell therapy in cancer immunotherapy, providing a fresh perspective. It discusses the innovative approaches in CAR-NK cell design and engineering, particularly targeting refractory or recurrent cancers. By comparing CAR-NK cells with traditional therapies, the review highlights their unique ability to tackle tumor heterogeneity and immune system suppression. Additionally, it explains how novel cytokines and receptors can enhance CAR-NK cell efficacy, specificity, and functionality. This review underscores the advantages of CAR-NK cells, including reduced toxicity, lower cost, and broader accessibility compared to CAR-T cells, along with their potential in treating both blood cancers and solid tumors.

Unveil Intrahepatic Cholangiocarcinoma Heterogeneity through the Lens of Omics and Multi-Omics Approaches.

Intrahepatic cholangiocarcinoma (iCCA) is recognized worldwide as the second leading cause of morbidity and mortality among primary liver cancers, showing a continuously increasing incidence rate in recent years. iCCA aggressiveness is revealed through its rapid and silent intrahepatic expansion and spread through the lymphatic system leading to late diagnosis and poor prognoses. Multi-omics studies have aggregated information derived from single-omics data, providing a more comprehensive understanding of the phenomena being studied. These approaches are gradually becoming powerful tools for investigating the intricate pathobiology of iCCA, facilitating the correlation between molecular signature and phenotypic manifestation. Consequently, preliminary stratifications of iCCA patients have been proposed according to their "omics" features opening the possibility of identifying potential biomarkers for early diagnosis and developing new therapies based on personalized medicine (PM). The focus of this review is to provide new and advanced insight into the molecular pathobiology of the iCCA, starting from single- to the latest multi-omics approaches, paving the way for translating new basic research into therapeutic practices.

3D histology reveals that immune response to pancreatic precancers is heterogeneous and depends on global pancreas structure.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer for which few effective therapies exist. Immunotherapies specifically are ineffective in pancreatic cancer, in part due to its unique stromal and immune microenvironment. Pancreatic intraepithelial neoplasia, or PanIN, is the main precursor lesion to PDAC. Recently it was discovered that PanINs are remarkably abundant in the grossly normal pancreas, suggesting that the vast majority will never progress to cancer. Here, through construction of 48 samples of cm3-sized human pancreas tissue, we profiled the immune microenvironment of 1,476 PanINs in 3D and at single-cell resolution to better understand the early evolution of the pancreatic tumor microenvironment and to determine how inflammation may play a role in cancer progression. We found that bulk pancreatic inflammation strongly correlates to PanIN cell fraction. We found that the immune response around PanINs is highly heterogeneous, with distinct immune hotspots and cold spots that appear and disappear in a span of tens of microns. Immune hotspots generally mark locations of higher grade of dysplasia or locations near acinar atrophy. The immune composition at these hotspots is dominated by naïve, cytotoxic, and regulatory T cells, cancer associated fibroblasts, and tumor associated macrophages, with little similarity to the immune composition around less-inflamed PanINs. By mapping FOXP3+ cells in 3D, we found that regulatory T cells are present at higher density in larger PanIN lesions compared to smaller PanINs, suggesting that the early initiation of PanINs may not exhibit an immunosuppressive response. This analysis demonstrates that while PanINs are common in the pancreases of most individuals, inflammation may play a pivotal role, both at the bulk and the microscopic scale, in demarcating regions of significance in cancer progression.

Integrative Drug Screening and Multiomic Characterization of Patient-derived Bladder Cancer Organoids Reveal Novel Molecular Correlates of Gemcitabine Response.

BACKGROUND AND OBJECTIVE: Predicting response to therapy for each patient's tumor is critical to improving long-term outcomes for muscle-invasive bladder cancer. This study aims to establish ex vivo bladder cancer patient-derived organoid (PDO) models that are representative of patients' tumors and determine the potential efficacy of standard of care and curated experimental therapies. METHODS: Tumor material was collected prospectively from consented bladder cancer patients to generate short-term PDO models, which were screened against a panel of clinically relevant drugs in ex vivo three-dimensional culture. Multiomic profiling was utilized to validate the PDO models, establish the molecular characteristics of each tumor, and identify potential biomarkers of drug response. Gene expression (GEX) patterns between paired primary tissue and PDO samples were assessed using Spearman's rank correlation coefficients. Molecular correlates of therapy response were identified using Pearson correlation coefficients and Kruskal-Wallis tests with Dunn's post hoc pairwise comparison testing. KEY FINDINGS AND LIMITATIONS: A total of 106 tumors were collected from 97 patients, with 65 samples yielding sufficient material for complete multiomic molecular characterization and PDO screening with six to 32 drugs/combinations. Short-term PDOs faithfully represent the tumor molecular characteristics, maintain diverse cell types, and avoid shifts in GEX-based subtyping that accompany long-term PDO cultures. Utilizing an integrative approach, novel correlations between ex vivo drug responses and genomic alterations, GEX, and protein expression were identified, including a multiomic signature of gemcitabine response. The positive predictive value of ex vivo drug responses and the novel multiomic gemcitabine response signature need to be validated in future studies. CONCLUSIONS AND CLINICAL IMPLICATIONS: Short-term PDO cultures retain the molecular characteristics of tumor tissue and avoid shifts in expression-based subtyping that have plagued long-term cultures. Integration of multiomic profiling and ex vivo drug screening data identifies potential predictive biomarkers, including a novel signature of gemcitabine response. PATIENT SUMMARY: Better models are needed to predict patient response to therapy in bladder cancer. We developed a platform that uses short-term culture to best mimic each patient's tumor and assess potential sensitivity to therapeutics.

Forming Single-Cell-Derived Colon Cancer Organoid Arrays on a Microfluidic Chip for High Throughput Tumor Heterogeneity Analysis.

Single-cell-derived tumor organoids (STOs) possess a distinct genetic background, making them valuable tools for demonstrating tumor heterogeneity. In order to fulfill the high throughput demands of STO assays, we have developed a microfluidic chip containing 30 000 microwells, which is dedicated to a single cell culture approach for selective expansion and differential induction of cancer stem cells. The microwells are coated with a hydrophilic copolymer to eliminate cell adhesion, and the cell culture is supported by poly(ethylene glycol) (PEG) to establish a nonadhesive culture environment. By utilizing an input cell density of 7 × 103·mL-1, it is possible to construct a 4000 single cell culture system through stochastic cell occupation. We demonstrate that the addition of 15% PEG10000 in the cell culture medium effectively prevents cell loss while facilitating tumor stem cell expansion. As were demonstrated by HCT116, HT29, and SW480 colon cancer cells, the microfluidic approach achieved a STO formation rate of ∼20%, resulting in over 800 STOs generated from a single culture. Comprehensive analysis through histomorphology, immunohistochemistry, drug response evaluation, assessment of cell invasion, and biomarker detection reveals the heterogeneity among individual STOs. Specifically, the smaller STOs exhibited higher invasion and drug resistance capabilities compared with the larger ones. The developed microfluidic approach effectively facilitates STO formation and offers promising prospects for investigating tumor heterogeneity, as well as conducting personalized therapy-focused drug screening.

Primary tumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET/CT for the prediction of biochemical recurrence in prostate cancer.

PURPOSE: The aim of this study is to research the value of the texture analysis of primary tumors in pre-treatment [68Ga]Ga-PSMA PET in the prediction of the development of biochemical recurrence (BCR) in prostate cancer patients who underwent definitive therapies. METHODS: 51 patients with prostate adenocarcinoma who had a pre-treatment [68Ga]Ga-PSMA-11 PET/CT and underwent definitive radiotherapy (RT) or radical prostatectomy (RP) were included in the study. Demographics, clinicopathologic features, the presence of BCR, and the last follow-up date of patients were recorded. Textural and conventional PET parameters (maximum standardized uptake value (SUVmax), total lesion-PSMA (TL-PSMA), and PSMA-tumor volume (PSMA-TV)) were obtained from PET/CT images using LifeX program. Parameters were grouped using the Youden index in ROC analysis. Factors predicting the BCR were determined using Cox regression analyses. RESULTS: 29 (56.9%) patients have received primary curative RT, while the remaining 22 (43.1%) patients have undergone RP. 5 (22.7%) patients with RP and 3 (10.3%) patients with curative RT have developed BCR during the follow-up. INTENSITY-BASED-minimum grey level (P=.050), GLCM-sum variance (P=.019), and GLCM-cluster prominence (P=.050) were associated with BCR in univariate analysis. INTENSITY-BASED-minimum grey level (P=.009) and GLCM-sum variance (P=.004) were found as independent predictors of BCR in the multivariate analysis. CONCLUSION: Tumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET is associated with a high risk of BCR in PCa patients who underwent definitive therapies.

Elucidating the multifaceted role of MGAT1 in hepatocellular carcinoma: integrative single-cell and spatial transcriptomics reveal novel therapeutic insights.

Background: Glycosyltransferase-associated genes play a crucial role in hepatocellular carcinoma (HCC) pathogenesis. This study investigates their impact on the tumor microenvironment and molecular mechanisms, offering insights into innovative immunotherapeutic strategies for HCC. Methods: We utilized cutting-edge single-cell and spatial transcriptomics to examine HCC heterogeneity. Four single-cell scoring techniques were employed to evaluate glycosyltransferase genes. Spatial transcriptomic findings were validated, and bulk RNA-seq analysis was conducted to identify prognostic glycosyltransferase-related genes and potential immunotherapeutic targets. MGAT1's role was further explored through various functional assays. Results: Our analysis revealed diverse cell subpopulations in HCC with distinct glycosyltransferase gene activities, particularly in macrophages. Key glycosyltransferase genes specific to macrophages were identified. Temporal analysis illustrated macrophage evolution during tumor progression, while spatial transcriptomics highlighted reduced expression of these genes in core tumor macrophages. Integrating scRNA-seq, bulk RNA-seq, and spatial transcriptomics, MGAT1 emerged as a promising therapeutic target, showing significant potential in HCC immunotherapy. Conclusion: This comprehensive study delves into glycosyltransferase-associated genes in HCC, elucidating their critical roles in cellular dynamics and immune cell interactions. Our findings open new avenues for immunotherapeutic interventions and personalized HCC management, pushing the boundaries of HCC immunotherapy.

Single-cell atlas profiling revealed cellular characteristics and dynamic changes after PD-1 blockade therapy of brain metastases from laryngeal squamous cell carcinoma.

Brain metastasis (BM) in laryngeal squamous cell carcinoma (LSCC) is uncommon but prognosis is poor. Anti-PD-1 immunotherapy benefits some advanced LSCC cases, yet its efficiency is limited by tumor complexity. We analyzed paired metastatic tumor samples from before and after immunotherapy using single-cell RNA sequencing (scRNA-seq), along with a primary LSCC dataset and bulk RNA sequencing. This identified changes post-immunotherapy and revealed differences in single-cell transcriptomes among LSCC, primBM, and neoBM. Our findings show that anti-PD-1 treatment suppresses metastasis-promoting pathways like VEGF and EMT in cancer cells, and alters immune cell functions. Notably, it upregulates T cell activation, leading to CD8 T cell exhaustion from excess heat shock proteins, notably HSPA8. However, CD8 T cell cytotoxic functions improve post-treatment. In myeloid cells, anti-PD-1 therapy enhances antigen presentation and promotes a proinflammatory shift post-metastasis. Additionally, NUPR1 is linked to BM in LSCC, and NEAT1 is a potential metastatic cancer cell cycle participant. Our study provides insights into cancer heterogeneity and the impact of PD-1 immunotherapy on metastasis, aiding precise diagnosis and prognosis.

Spatial molecular profiling of mixed invasive ductal and lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures, and mutations.

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity - e.g., MDLC with triple-negative breast cancer (TNBC) or basal ductal and estrogen receptor positive (ER+) luminal lobular regions, distinct enrichment of cell cycle arrest/senescence and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular but not ductal regions, and single-cell ductal and lobular subpopulations with unique oncogenic signatures further highlighting intraregional heterogeneity. Altogether, we demonstrated that the intratumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma.

Identification of a pro-protein synthesis osteosarcoma subtype for predicting prognosis and treatment.

Osteosarcoma (OS) is a heterogeneous malignant spindle cell tumor that is aggressive and has a poor prognosis. Although combining surgery and chemotherapy has significantly improved patient outcomes, the prognosis for OS patients with metastatic or recurrent OS has remained unsatisfactory. Therefore, it is imperative to gain a fresh perspective on OS development mechanisms and treatment strategies. After studying single-cell RNA sequencing (scRNA-seq) data in public databases, we identified seven OS subclonal types based on intra-tumor heterogeneity. Subsequently, we constructed a prognostic model based on pro-protein synthesis osteosarcoma (PPS-OS)-associated genes. Correlation analysis showed that the prognostic model performs extremely well in predicting OS patient prognosis. We also demonstrated that the independent risk factors for the prognosis of OS patients were tumor primary site, metastatic status, and risk score. Based on these factors, nomograms were constructed for predicting the 3- and 5-year survival rates. Afterward, the investigation of the tumor immune microenvironment (TIME) revealed the vital roles of γδ T-cell and B-cell activation. Drug sensitivity analysis and immune checkpoint analysis identified drugs that have potential application value in OS. Finally, the jumping translocation breakpoint (JTB) gene was selected for experimental validation. JTB silencing suppressed the proliferation, migration, and invasion of OS cells. Therefore, our research suggests that PPS-OS-related genes facilitate the malignant progression of OS and may be employed as prognostic indicators and therapeutic targets in OS.

Genomic profiling of a multi-lineage and multi-passage patient-derived xenograft biobank reflects heterogeneity of ovarian cancer.

Ovarian cancer (OC) manifests as a complex disease characterized by inter- and intra-patient heterogeneity. Despite enhanced biological and genetic insights, OC remains a recalcitrant malignancy with minimal survival improvement. Based on multi-site sampling and a multi-lineage patient-derived xenograft (PDX) establishment strategy, we present herein the establishment of a comprehensive PDX biobank from histologically and molecularly heterogeneous OC patients. Comprehensive profiling of matched PDX and patient samples demonstrates that PDXs closely recapitulate parental tumors. By leveraging multi-lineage models, we reveal that the previously reported genomic disparities of PDX could be mainly attributed to intra-patient spatial heterogeneity instead of substantial model-independent genomic evolution. Moreover, DNA damage response pathway inhibitor (DDRi) screening uncovers heterogeneous responses across models. Prolonged iterative drug exposure recapitulates acquired drug resistance in initially sensitive models. Meanwhile, interrogation of induced drug-resistant (IDR) models reveals that suppressed interferon (IFN) response and activated Wnt/ß-catenin signaling contribute to acquired DDRi drug resistance.

A Microdissection Protocol for Proteogenomic Analysis of Histological Sections to Advance Drug Development.

Combining proteogenomics with laser capture microdissection (LCM) in cancer research offers a targeted way to explore the intricate interactions between tumor cells and the different microenvironment components. This is especially important for immuno-oncology (IO) research where improvements in the predictability of IO-based drugs are sorely needed, and depends on a better understanding of the spatial relationships involving the tumor, blood supply, and immune cell interactions, in the context of their associated microenvironments. LCM is used to isolate and obtain distinct histological cell types, which may be routinely performed on complex and heterogeneous solid tumor specimens. Once cells have been captured, nucleic acids and proteins may be extracted for in-depth multimodality molecular profiling assays. Optimizing the minute tissue quantities from LCM captured cells is challenging. Following the isolation of nucleic acids, RNA-seq may be performed for gene expression and DNA sequencing performed for the discovery and analysis of actionable mutations, copy number variation, methylation profiles, etc. However, there remains a need for highly sensitive proteomic methods targeting small-sized samples. A significant part of this protocol is an enhanced liquid chromatography mass spectrometry (LC-MS) analysis of micro-scale and/or nano-scale tissue sections. This is achieved with a silver-stained one-dimensional sodium dodecyl sulfate polyacrylamide gel electrophoresis (1D-SDS-PAGE) approach developed for LC-MS analysis of fresh-frozen tissue specimens obtained via LCM. Included is a detailed in-gel digestion method adjusted and specifically designed to maximize the proteome coverage from amount-limited LCM samples to better facilitate in-depth molecular profiling. Described is a proteogenomic approach leveraged from microdissected fresh frozen tissue. The protocols may also be applicable to other types of specimens having limited nucleic acids, protein quantity, and/or sample volume.

Effect of Bioactive Black Phosphorus Nanomaterials on Cancer-Associated Fibroblast Heterogeneity in Pancreatic Cancer.

Tumor-stromal interactions and stromal heterogeneity in the tumor microenvironment are critical factors that influence the progression, metastasis, and chemoresistance of pancreatic ductal adenocarcinoma (PDAC). Here, we used spatial transcriptome technology to profile the gene expression landscape of primary PDAC and liver metastatic PDAC after bioactive black phosphorus nanomaterial (bioactive BP) treatment using a murine model of PDAC (LSL-KrasG12D/+; LSL-Trp53R172H/+; and Pdx-1-Cre mice). Bioinformatic and biochemical analyses showed that bioactive BP contributes to the tumor-stromal interplay by suppressing cancer-associated fibroblast (CAF) activation. Our results showed that bioactive BP contributes to CAF heterogeneity by decreasing the amount of inflammatory CAFs and myofibroblastic CAFs, two CAF subpopulations. Our study demonstrates the influence of bioactive BP on tumor-stromal interactions and CAF heterogeneity and suggests bioactive BP as a potential PDAC treatment.

[Advancements in Single-cell RNA Sequencing Technology
in the Study of the Tumor Microenvironment in Lung Cancer].

The immune microenvironment plays a key role in the development and progression of tumors. In recent years, with the rapid advancement of high-throughput sequencing technologies, researchers have gained a deeper understanding of the composition and function of immune cells in the tumor microenvironment. However, traditional bulk sequencing technologies are limited in resolving heterogeneity at the single-cell level, constraining a comprehensive understanding of the complexity of the tumor microenvironment. The advent of single-cell RNA sequencing technology has brought new opportunities to uncover the heterogeneity of the immune microenvironment in lung cancer. Currently, T-cell-centered immunotherapy in clinical settings is prone to side effects affecting prognosis, such as immunogenic drug resistance or immune-related pneumonia, with the key factor being changes in the interactions between immune cells and tumor cells in the tumor microenvironment. Single-cell RNA sequencing technology can reveal the origins and functions of different subgroups within the tumor microenvironment from perspectives such as intercellular interactions and pseudotime analysis, thereby discovering new cell subgroups or novel biomarkers, providing new avenues for uncovering resistance to immunotherapy and monitoring therapeutic efficacy. This review comprehensively discusses the newest research techniques and advancements in single-cell RNA sequencing technology for unveiling the heterogeneity of the tumor microenvironment after lung cancer immunotherapy, offering insights for enhancing the precision and personalization of immunotherapy.
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