Spatial Transcriptome-Wide Profiling of Small Cell Lung Cancer Reveals Intra-Tumoral Molecular and Subtype Heterogeneity.

Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid growth and early metastasis and is susceptible to treatment resistance and recurrence. Understanding the intra-tumoral spatial heterogeneity in SCLC is crucial for improving patient outcomes and clinically relevant subtyping. In this study, a spatial whole transcriptome-wide analysis of 25 SCLC patients at sub-histological resolution using GeoMx Digital Spatial Profiling technology is performed. This analysis deciphered intra-tumoral multi-regional heterogeneity, characterized by distinct molecular profiles, biological functions, immune features, and molecular subtypes within spatially localized histological regions. Connections between different transcript-defined intra-tumoral phenotypes and their impact on patient survival and therapeutic response are also established. Finally, a gene signature, termed ITHtyper, based on the prevalence of intra-tumoral heterogeneity levels, which enables patient risk stratification from bulk RNA-seq profiles is identified. The prognostic value of ITHtyper is rigorously validated in independent multicenter patient cohorts. This study introduces a preliminary tumor-centric, regionally targeted spatial transcriptome resource that sheds light on previously unexplored intra-tumoral spatial heterogeneity in SCLC. These findings hold promise to improve tumor reclassification and facilitate the development of personalized treatments for SCLC patients.

The Role of Mesenchymal Reprogramming in Malignant Clonal Evolution and Intra-Tumoral Heterogeneity in Glioblastoma.

Glioblastoma (GBM) is the most common yet uniformly fatal adult brain cancer. Intra-tumoral molecular and cellular heterogeneities are major contributory factors to therapeutic refractoriness and futility in GBM. Molecular heterogeneity is represented through molecular subtype clusters whereby the proneural (PN) subtype is associated with significantly increased long-term survival compared to the highly resistant mesenchymal (MES) subtype. Furthermore, it is universally recognized that a small subset of GBM cells known as GBM stem cells (GSCs) serve as reservoirs for tumor recurrence and progression. The clonal evolution of GSC molecular subtypes in response to therapy drives intra-tumoral heterogeneity and remains a critical determinant of GBM outcomes. In particular, the intra-tumoral MES reprogramming of GSCs using current GBM therapies has emerged as a leading hypothesis for therapeutic refractoriness. Preventing the intra-tumoral divergent evolution of GBM toward the MES subtype via new treatments would dramatically improve long-term survival for GBM patients and have a significant impact on GBM outcomes. In this review, we examine the challenges of the role of MES reprogramming in the malignant clonal evolution of glioblastoma and provide future perspectives for addressing the unmet therapeutic need to overcome resistance in GBM.

Targeting Tumor Heterogeneity by Breaking a Stem Cell and Epithelial Niche Interaction Loop.

Tumor heterogeneity, the presence of multiple distinct subpopulations of cancer cells between patients or among the same tumors, poses a major challenge to current targeted therapies. The way these different subpopulations interact among themselves and the stromal niche environment, and how such interactions affect cancer stem cell behavior has remained largely unknown. Here, it is shown that an FGF-BMP7-INHBA signaling positive feedback loop integrates interactions among different cell populations, including mammary gland stem cells, luminal epithelial and stromal fibroblast niche components not only in organ regeneration but also, with certain modifications, in cancer progression. The reciprocal dependence of basal stem cells and luminal epithelium is based on basal-derived BMP7 and luminal-derived INHBA, which promote their respective expansion, and is regulated by stromal-epithelial FGF signaling. Targeting this interaction loop, for example, by reducing the function of one or more of its components, inhibits organ regeneration and breast cancer progression. The results have profound implications for overcoming drug resistance because of tumor heterogeneity in future targeted therapies.

A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory.

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) is a highly fatal cancer characterized by high intra-tumor heterogeneity (ITH). A panoramic understanding of its tumor evolution, in relation to its clinical trajectory, may provide novel prognostic and treatment strategies. METHODS: Through the Asia-Pacific Hepatocellular Carcinoma trials group (NCT03267641), we recruited one of the largest prospective cohorts of patients with HCC, with over 600 whole genome and transcriptome samples from 123 treatment-naïve patients. RESULTS: Using a multi-region sampling approach, we revealed seven convergent genetic evolutionary paths governed by the early driver mutations, late copy number variations and viral integrations, which stratify patient clinical trajectories after surgical resection. Furthermore, such evolutionary paths shaped the molecular profiles, leading to distinct transcriptomic subtypes. Most significantly, although we found the coexistence of multiple transcriptomic subtypes within certain tumors, patient prognosis was best predicted by the most aggressive cell fraction of the tumor, rather than by overall degree of transcriptomic ITH level - a phenomenon we termed the 'bad apple' effect. Finally, we found that characteristics throughout early and late tumor evolution provide significant and complementary prognostic power in predicting patient survival. CONCLUSIONS: Taken together, our study generated a comprehensive landscape of evolutionary history for HCC and provides a rich multi-omics resource for understanding tumor heterogeneity and clinical trajectories. IMPACT AND IMPLICATIONS: This prospective study, utilizing comprehensive multi-sector, multi-omics sequencing and clinical data from surgically resected hepatocellular carcinoma (HCC), reveals critical insights into the role of tumor evolution and intra-tumor heterogeneity (ITH) in determining the prognosis of HCC. These findings are invaluable for oncology researchers and clinicians, as they underscore the influence of distinct evolutionary paths and the 'bad apple' effect, where the most aggressive tumor fraction dictates disease progression. These insights not only enhance prognostic accuracy post-surgical resection but also pave the way for personalized treatment strategies tailored to specific tumor evolutionary and transcriptomic profiles. The coexistence of multiple subtypes within the same tumor prompts a re-appraisal of the utilities of depending on single samples to represent the entire tumor and suggests the need for clinical molecular imaging. This research thus marks a significant step forward in the clinical understanding and management of HCC, underscoring the importance of integrating tumor evolutionary dynamics and multi-omics biomarkers into therapeutic decision-making. CLINICAL TRIAL NUMBER: NCT03267641 (Observational cohort).

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections.

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

Histopathological analysis of tumor microenvironment in adrenocortical carcinoma: Possible effects of in situ disorganized glucocorticoid production on tumor immunity.

Adrenocortical carcinoma (ACC) patients with glucocorticoid excess have been reported to be associated with decreased tumor-infiltrating immune cells, but the effects of in situ glucocorticoid production on tumor immunity have remained unknown. In addition, ACC was also known to harbor marked intra-tumoral heterogeneity of steroidogenesis or disorganized steroidogenesis. Therefore, in this study, we immune-profiled tumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs) and pivotal steroidogenic enzymes of glucocorticoid biosynthesis (CYP17A and CYP11B1) to explore the potential effects of in situ glucocorticoid production and intra-tumoral heterogeneity/disorganized steroidogenesis on tumor immunity of ACC. We also studied the correlations of the status of tumor immunity with that of angiogenesis and tumor grade to further explore the tumor tissue microenvironment of ACC. TILs (CD3, CD4, CD8, and FOXP3), TAMs (CD68 and CD163), key steroidogenic enzymes of glucocorticoid (CYP17A and CYP11B1), angiogenesis (CD31 and vasohibin-1 (VASH-1)), tumor grade (Ki-67 and Weiss score) were immunohistochemically evaluated in 34 ACCs. Increased CYP17A immunoreactivity in the whole tumor area was significantly positively correlated with FOXP3-positive TILs (p = 0.021) and negatively with CD4/CD3 ratio (p = 0.001). Increased CYP11B1 immunoreactivity in the whole tumor area was significantly positively correlated with CD8/CD3 (p = 0.039) and CD163/CD68 ratios (p = 0.006) and negatively with CD4-positive TILs (p = 0.036) and CD4/CD3 ratio (p = 0.001). There were also significant positive correlations between CYP17A and CD8 (r = 0.334, p < 0.001) and FOXP3-positive TILs (r = 0.414, p < 0.001), CD8/CD3 ratio (r = 0.421, p < 0.001), and CD68-positive TAMs (r = 0.298, p < 0.001) in randomly selected areas. Significant positive correlations were also detected between CYP11B1 and CD8/CD3 ratio (r = 0.276, p = 0.001) and negative ones detected between CYP11B1 and CD3- (r = -0.259, p = 0.002) and CD4-positive TILs (r = -0.312, p < 0.001) in those areas above. Increased micro-vessel density (MVD) -VASH-1 was significantly positively correlated with CD68- (p = 0.015) and CD163-positive TAMs (p = 0.009) and CD163/CD68 ratio and the high VASH-1 with CD163-positive TAMs (p = 0.042). Ki-67 labeling index was significantly positively correlated with MAD-VASH-1 (p = 0.006) and VASH-1 (p = 0.006) status. Results of our present study indicated that in situ glucocorticoid production did influence the status of tumor immunity in ACC. In particular, increased levels of CYP17A and CYP11B1, both involved in glucocorticoid producing immunoreactivity played different effects on tumor immunity, i.e., reflecting the involvement of intra-tumoral heterogeneity and disorganized steroidogenesis of ACC, which also did indicate the importance of in situ approaches when analyzing tumor immunity of ACC.

Multi-omics analyses reveal spatial heterogeneity in primary and metastatic oesophageal squamous cell carcinoma.

BACKGROUND: Biopsies obtained from primary oesophageal squamous cell carcinoma (ESCC) guide diagnosis and treatment. However, spatial intra-tumoral heterogeneity (ITH) influences biopsy-derived information and patient responsiveness to therapy. Here, we aimed to elucidate the spatial ITH of ESCC and matched lymph node metastasis (LNmet ). METHODS: Primary tumour superficial (PTsup ), deep (PTdeep ) and LNmet subregions of patients with locally advanced resectable ESCC were evaluated using whole-exome sequencing (WES), whole-transcriptome sequencing and spatially resolved digital spatial profiling (DSP). To validate the findings, immunohistochemistry was conducted and a single-cell transcriptomic dataset was analysed. RESULTS: WES revealed 15.72%, 5.02% and 32.00% unique mutations in PTsup , PTdeep and LNmet , respectively. Copy number alterations and phylogenetic trees showed spatial ITH among subregions both within and among patients. Driver mutations had a mixed intra-tumoral clonal status among subregions. Transcriptome data showed distinct differentially expressed genes among subregions. LNmet exhibited elevated expression of immunomodulatory genes and enriched immune cells, particularly when compared with PTsup (all P < .05). DSP revealed orthogonal support of bulk transcriptome results, with differences in protein and immune cell abundance between subregions in a spatial context. The integrative analysis of multi-omics data revealed complex heterogeneity in mRNA/protein levels and immune cell abundance within each subregion. CONCLUSIONS: This study comprehensively characterised spatial ITH in ESCC, and the findings highlight the clinical significance of unbiased molecular classification based on multi-omics data and their potential to improve the understanding and management of ESCC. The current practices for tissue sampling are insufficient for guiding precision medicine for ESCC, and routine profiling of PTdeep and/or LNmet should be systematically performed to obtain a more comprehensive understanding of ESCC and better inform treatment decisions.

Application of single-cell RNA sequencing in head and neck squamous cell carcinoma.

Single-cell RNA sequencing has been broadly applied to head and neck squamous cell carcinoma (HNSCC) for characterizing the heterogeneity and genomic mutations of HNSCC benefiting from the advantage of single-cell resolution. We summarized most of the current studies and aimed to explore their research methods and ideas, as well as how to transform them into clinical applications. Through single-cell RNA sequencing, we found the differences in tumor cells' expression programs and differentiation tracks. The studies of immune microenvironment allowed us to distinguish immune cell subpopulations, the extensive expression of immune checkpoints, and the complex crosstalk network between immune cells and non-immune cells. For cancer-associated fibroblasts (CAFs), single-cell RNA sequencing had made an irreplaceable contribution to the exploration of their differentiation status, specific CAFs markers, and the interaction with tumor cells and immune cells. In addition, we demonstrated in detail how single-cell RNA sequencing explored the HNSCC epithelial-to-mesenchymal transition (EMT) model and the mechanism of drug resistance, as well as its clinical value.

Cuproptosis engages in c-Myc-mediated breast cancer stemness.

BACKGROUND: Intra-tumoral heterogeneity (ITH) is a distinguished hallmark of cancer, and cancer stem cells (CSCs) contribute to this malignant characteristic. Therefore, it is of great significance to investigate and even target the regulatory factors driving intra-tumoral stemness. c-Myc is a vital oncogene frequently overexpressed or amplified in various cancer types, including breast cancer. Our previous study indicated its potential association with breast cancer stem cell (BCSC) biomarkers. METHODS: In this research, we performed immunohistochemical (IHC) staining on sixty breast cancer surgical specimens for c-Myc, CD44, CD24, CD133 and ALDH1A1. Then, we analyzed transcriptomic atlas of 1533 patients with breast cancer from public database. RESULTS: IHC staining indicated the positive correlation between c-Myc and BCSC phenotype. Then, we used bioinformatic analysis to interrogate transcriptomics data of 1533 breast cancer specimens and identified an intriguing link among c-Myc, cancer stemness and copper-induced cell death (also known as "cuproptosis"). We screened out cuproptosis-related characteristics that predicts poor clinical outcomes and found that the pro-tumoral cuproptosis-based features were putatively enriched in MYC-targets and showed a significantly positive correlation with cancer stemness. CONCLUSION: In addition to previous reports on its oncogenic roles, c-Myc showed significant correlation to stemness phenotype and copper-induced cell toxicity in breast cancer tissues. Moreover, transcriptomics data demonstrated that pro-tumoral cuproptosis biomarkers had putative positive association with cancer stemness. This research combined clinical samples with large-scale bioinformatic analysis, covered description and deduction, bridged classic oncogenic mechanisms to innovative opportunities, and inspired the development of copper-based nanomaterials in targeting highly heterogeneous tumors.

Single cell clonotypic and transcriptional evolution of multiple myeloma precursor disease.

Multiple myeloma remains an incurable disease, and the cellular and molecular evolution from precursor conditions, including monoclonal gammopathy of undetermined significance and smoldering multiple myeloma, is incompletely understood. Here, we combine single-cell RNA and B cell receptor sequencing from fifty-two patients with myeloma precursors in comparison with myeloma and normal donors. Our comprehensive analysis reveals early genomic drivers of malignant transformation, distinct transcriptional features, and divergent clonal expansion in hyperdiploid versus non-hyperdiploid samples. Additionally, we observe intra-patient heterogeneity with potential therapeutic implications and identify distinct patterns of evolution from myeloma precursor disease to myeloma. We also demonstrate distinctive characteristics of the microenvironment associated with specific genomic changes in myeloma cells. These findings add to our knowledge about myeloma precursor disease progression, providing valuable insights into patient risk stratification, biomarker discovery, and possible clinical applications.

Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma.

BACKGROUND: Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. METHODS: We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. RESULTS: In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e-4 for progression-free survival (PFS) and 3.63e-4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fisher's exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e-4 for PFS and 3.66e-4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient using in vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage ( http://www.wang-lab-hkust.com:3838/TMZEP ). CONCLUSIONS: We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs.

Zebrafish Cancer Avatars: A Translational Platform for Analyzing Tumor Heterogeneity and Predicting Patient Outcomes.

The increasing number of available anti-cancer drugs presents a challenge for oncologists, who must choose the most effective treatment for the patient. Precision cancer medicine relies on matching a drug with a tumor's molecular profile to optimize the therapeutic benefit. However, current precision medicine approaches do not fully account for intra-tumoral heterogeneity. Different mutation profiles and cell behaviors within a single heterogeneous tumor can significantly impact therapy response and patient outcomes. Patient-derived avatar models recapitulate a patient's tumor in an animal or dish and provide the means to functionally assess heterogeneity's impact on drug response. Mouse xenograft and organoid avatars are well-established, but the time required to generate these models is not practical for clinical decision-making. Zebrafish are emerging as a time-efficient and cost-effective cancer avatar model. In this review, we highlight recent developments in zebrafish cancer avatar models and discuss the unique features of zebrafish that make them ideal for the interrogation of cancer heterogeneity and as part of precision cancer medicine pipelines.

Perfusion-Based Fluorescent Dye Labeling to Sort Cancer Cells Based on Their Distance from Blood Vessels.

Tumor vasculature is the major extrinsic factor that shapes Intra-tumoral heterogeneity (ITH). Non-uniform exposure of microenvironmental cues greatly impacts cancer cell phenotypes leading to ITH, which exacerbates therapy resistance. This raises a need to study the influence of non-uniform perfusion patterns and the resulting heterogeneity that persists within the tumor microenvironment (TME). A method was developed to identify cancer cells based on their proximity to functional blood vessels (BVs) called perfusion-based fluorescent dye labeling of cells (PFDLC). PFDLC works on the principle of perfusion, where a freely diffusible nuclear binding fluorescent dye (Hoechst 33342) is injected intravenously (i.v.) through a tail vein into atumor-bearing mice. The tumors are retrieved post dye perfusion, dissociated into single cells, and sorted based on their dye uptake proportional to their distance from the nearest blood capillary. This method is amenable to multi-omics as well as functional assays.

Deciphering the role of miR-187-3p/LRFN1 axis in modulating progression, aerobic glycolysis and immune microenvironment of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant genitourinary cancers with high recurrence risk worldwide. Recently, multi-omics data facilitate obtaining a molecular landscape of tumor development, and were implemented to affect pathogenesis, phenotype, and prognosis of ccRCC. In this study, after screening for differential expressed microRNAs based on multiply datasets, we tested expression levels and prognostic value of miR-187-3p in ccRCC samples, and transfected miR-187-3p mimics or negative controls into ccRCC cells. Up-regulation of miR-187-3p restrains proliferation, migration and promotes apoptosis ability in human ccRCC A498 and 786O cells. In addition, Luciferase reporter assay revealed that miR-187-3p directly targets LRFN1-3'-UTR and negatively modulates LRFN1 expression. LRFN1 rescues proliferation and invasion capacities after miR-187-3p mimic transfection in vitro and in subcutaneous xenograft models. We further performed deep-sequencing technology and bioinformatics analyses to evaluate the biological functions and potential clinical implications of LRFN1 expression in ccRCC. Interestingly, LRFN1 could serve as an independent and potential biomarker for prognosis in over 1000 patients with ccRCC from multiply independent cohorts. Besides, the up-regulated LRFN1 expression prominently promoted intra-tumoral heterogeneity and immune-infiltrating microenvironment, represented by elevated M2 macrophage infiltration, CD8+ T cells activity and PD-L1 expression. In conclusion, this study revealed the tumor-specific and immunological role of miR-187-3p/LRFN1 axis in the progression and reshaping of tumor immune microenvironment of ccRCC.

Significance of tumor heterogeneity of p-Smad2 and c-Met in HER2-positive gastric carcinoma with lymph node metastasis.

BACKGROUND: Tumor heterogeneity has frequently been observed in gastric cancer (GC), but the correlation between patients' clinico-pathologic features and the tumoral heterogeneity of GC-associated molecules is unclear. We investigated the correlation between lymph node metastasis and the intra-tumoral heterogeneity of driver molecules in GC. MATERIALS AND METHODS: We retrospectively analyzed the cases of 504 patients who underwent a gastrectomy at the Department of Gastroenterological Surgery, Osaka Metropolitan University and 389 cases drawn from The Cancer Genome Atlas (TCGA) data. We performed a clustering analysis based on eight cancer-associated molecules including HER2, c-Met, and p-Smad2 using the protein expression revealed by our immunohistochemical study of the patients' and TCGA cases. We determined the correlations between HER2 expression and the other molecules based on the degree of lymph node metastasis. RESULTS: Immunohistochemical staining data showed that a 43 of the 504 patients with GC (8.5%) were HER2-positive. In the HER2-positive cases, the expressions of c-Met and p-Smad2 were increased in accord with the lymph-node metastatic level. The overall survival of the HER2-positive GC patients with both p-Smad2 and c-Met expression was significantly (p = 0.030) poorer than that of the patients with p-Smad2-negative and/or c-Met-negative expression. The results of the TCGA data analysis revealed that 58 of the 389 GC cases (14.9%) were ERBB2-positive. MET expression was more frequent in the N1 metastasis group than the N0 group. In the high lymph-node metastasis (N2 and N3) group, SMAD2 expression was more frequent, as was ERBB2 and MET expression. CONCLUSION: p-Smad2 and c-Met signaling might play important roles in lymph node metastasis in HER2-positive GC.

Identification of Transcriptional Heterogeneity and Construction of a Prognostic Model for Melanoma Based on Single-Cell and Bulk Transcriptome Analysis.

Melanoma is one of the most aggressive and heterogeneous life-threatening cancers. However, the heterogeneity of melanoma and its impact on clinical outcomes are largely unknown. In the present study, intra-tumoral heterogeneity of melanoma cell subpopulations was explored using public single-cell RNA sequencing data. Marker genes, transcription factor regulatory networks, and gene set enrichment analysis were further analyzed. Marker genes of each malignant cluster were screened to create a prognostic risk score, and a nomogram tool was further generated to predict the prognosis of melanoma patients. It was found that malignant cells were divided into six clusters by different marker genes and biological characteristics in which the cell cycling subset was significantly correlated with unfavorable clinical outcomes, and the Wnt signaling pathway-enriched subset may be correlated with the resistance to immunotherapy. Based on the malignant marker genes, melanoma patients in TCGA datasets were divided into three groups which had different survival rates and immune infiltration states. Five malignant cell markers (PSME2, ARID5A, SERPINE2, GPC3, and S100A11) were selected to generate a prognostic risk score. The risk score was associated with overall survival independent of routine clinicopathologic characteristics. The nomogram tool showed good performance with an area under the curve value of 0.802.

Application and prospect of single-cell sequencing in cancer metastasis.

Cancer metastasis is a complicated process driven by internal genetic variations and developed through interactions with the external environment. This process usually causes therapeutic resistance and results in a low survival rate. In recent years, single-cell sequencing has become a popular method for revealing the tumor evolutionary genetic lineage, intra-tumoral heterogeneity and tumor microenvironment of the metastasis process. So as to find more therapeutic targets for clinical application, the spatial transcriptomics method has become a new rising field of cancer studies, which promotes the combination between clinical medicine and molecular biology. In future prospects, more accurate and personalized treatment models will come into reality.

Cancer metastasis describes the primary tumor cells' spread to other organs, and is usually hard to cure. Single-cell sequencing methods have revealed the origin and development of this complicated process. They have found different cell populations and their functions, not only for tumor cells themselves, but also the nonmalignant cells. Therefore, research is attempting to find some key points to stop this process and improve the survival rate. In addition, putting these cells and genetic information on a tissue map is a rising technique, so we can learn about where these cells gather. This provides an intuitive view of metastasis. Thus, the future treatments will be more accurate and personalized.

Sensitization of Resistant Breast Cancer Cells with a Jumonji Family Histone Demethylase Inhibitor.

In the present study, we evaluated JIB-04, a small-molecule epigenetic inhibitor initially discovered to inhibit cancer growth, to determine its ability to affect deep intrinsic resistance in a breast cancer model. The model was based on a function-based approach to the selection of cancer cells in a cell culture that can survive a variety of challenges in prolonged, but reversible, quiescence. These resistant cancer cells possessed a variety of mechanisms, including modifications of the epigenome and transcriptome, for generating a high degree of cellular heterogeneity. We found that long pretreatment with JIB-04 sensitized resistant triple-negative inflammatory breast cancer cells and their parental cell line SUM149 to the chemotherapeutic drugs doxorubicin and paclitaxel. Resistant cancer cells derived from another inflammatory breast cancer cell line, FC-IBC02, were considerably more sensitive to JIB-04 than the parental cell line. Investigating a mechanism of sensitization, we found that JIB-04 exposure increased the expression of PD-L1 in resistant cells, suggesting that JIB-04 may also sensitize resistant breast cancer cells to anti-PD-L1 immune therapy. Finally, these results support the usefulness of a cell culture-based experimental strategy for evaluating anticancer agents, such as JIB-04, that may halt cancer evolution and prevent the development of cancer resistance to currently used therapies.

Assessing Tumour Haemodynamic Heterogeneity and Response to Choline Kinase Inhibition Using Clustered Dynamic Contrast Enhanced MRI Parameters in Rodent Models of Glioblastoma.

To investigate the utility of DCE-MRI derived pharmacokinetic parameters in evaluating tumour haemodynamic heterogeneity and treatment response in rodent models of glioblastoma, imaging was performed on intracranial F98 and GL261 glioblastoma bearing rodents. Clustering of the DCE-MRI-based parametric maps (using Tofts, extended Tofts, shutter speed, two-compartment, and the second generation shutter speed models) was performed using a hierarchical clustering algorithm, resulting in areas with poor fit (reflecting necrosis), low, medium, and high valued pixels representing parameters Ktrans, ve, Kep, vp, τi and Fp. There was a significant increase in the number of necrotic pixels with increasing tumour volume and a significant correlation between ve and tumour volume suggesting increased extracellular volume in larger tumours. In terms of therapeutic response in F98 rat GBMs, a sustained decrease in permeability and perfusion and a reduced cell density was observed during treatment with JAS239 based on Ktrans, Fp and ve as compared to control animals. No significant differences in these parameters were found for the GL261 tumour, indicating that this model may be less sensitive to JAS239 treatment regarding changes in vascular parameters. This study demonstrates that region-based clustered pharmacokinetic parameters derived from DCE-MRI may be useful in assessing tumour haemodynamic heterogeneity with the potential for assessing therapeutic response.

Emerging Concepts on the Role of Extracellular Vesicles and Its Cargo Contents in Glioblastoma-Microglial Crosstalk.

Glioblastoma multiforme is the most common, highly aggressive malignant brain tumor which is marked by highest inter- and intra-tumoral heterogeneity. Despite, immunotherapy, and combination therapies developed; the clinical trials often result into large number of failures. Often cancer cells are known to communicate with surrounding cells in tumor microenvironment (TME). Extracellular vesicles (EVs) consisting of diverse cargo mediates this intercellular communication and is believed to modulate the immune function against GBM. Tumor-associated microglia (TAM), though being the resident innate immune cell of CNS, is known to attain pro-tumorigenic M2 phenotype, and this immunomodulation is aided by extracellular vesicle-mediated transfer of oncogenic, immunomodulatory molecules. Besides, oncogenic proteins, long non-coding RNAs (lncRNAs), are believed to carry oncogenic potential, and therefore, understanding the mechanism leading to microglial dysregulation mediated by GBM-derived extracellular vesicle (GDEV) lncRNAs becomes crucial. This review focuses on current understanding of role of GDEV and lncRNA in microglial dysfunction and its potential as a therapeutic target.