Tumor Heterogeneity Research Articles

A patient-derived HCC spheroid system to model the tumor microenvironment and treatment response

Background & AimsHepatocellular carcinoma (HCC) is the third leading and fastest rising cause of cancer-related death worldwide. The discovery and preclinical development of compounds targeting HCC is hampered by the absence of authentic tractable systems recapitulating the heterogeneity of patient HCC tumors and the tumor microenvironment (TME). MethodsWe established a novel and simple patient-derived multicellular tumorspheroid model based on clinical HCC tumor tissues, processed using enzymatic and mechanical dissociation. After quality controls, 22 HCC tissues and 17 HCC serum were selected for tumorspheroid generation and perturbation studies. Cells were grown in 3D in optimized medium in presence of patient serum. Characterization of the tumorspheroid cell populations was performed by flow cytometry, immunohistochemistry and functional assays. As a proof of concept, we treated patient-derived spheroids with FDA approved anti-HCC compounds. ResultsThe model was successfully established independently from cancer etiology and grade from 22 HCC tissues. We show that the use of HCC patient serum was essential for tumorspheroid generation, the TME function and to maintain cell viability. The tumorspheroids comprised the main cell compartments including epithelial cancer cells as well as all major cell populations of the TME (cancer-associated fibroblasts, macrophages and T cells, endothelial cells). Tumorspheroids reflect HCC heterogeneity, including variability in cell type proportions and TME, and mimic the original tumor features. Moreover, we observed differential responses to FDA HCC approved drugs between donors as observed in patients. ConclusionThis patient HCC serum-tumorspheroid model provides novel opportunities for drug discovery and development as well as mechanism of action studies including compounds targeting the TME. This model will likely contribute to improve the dismal outcome of patients with HCC. Impact and implicationsHepatocellular carcinoma (HCC) is a leading and fast rising cause of cancer-related death worldwide. Despite approval of novel therapies, the outcome of advanced HCC remains unsatisfactory. By developing a novel patient-derived tumorspheroid model recapitulating tumor heterogeneity and microenvironment, we provide new opportunities for HCC drug development and analysis of mechanism of action in authentic patient tissues. The application of the patient-derived tumorspheroids combined with other HCC models will likely contribute to drug development and to improve the dismal outcome of patients with HCC.

Spatiotemporal dynamics of tumor - CAR T-cell interaction following local administration in solid cancers.

The success of chimeric antigen receptor (CAR) T-cell therapy in treating hematologic malignancies has generated widespread interest in translating this technology to solid cancers. However, issues like tumor infiltration, the immunosuppressive tumor microenvironment, and tumor heterogeneity limit its efficacy in the solid tumor setting. Recent experimental and clinical studies propose local administration directly into the tumor or at the tumor site to increase CAR T-cell infiltration and improve treatment outcomes. Characteristics of the types of solid tumors that may be the most receptive to this treatment approach remain unclear. In this work, we develop a spatiotemporal model for CAR T-cell treatment of solid tumors, and use numerical simulations to compare the effect of introducing CAR T cells via intratumoral injection versus intracavitary administration in diverse cancer types. We demonstrate that the model can recapitulate tumor and CAR T-cell data from imaging studies of local administration of CAR T cells in mouse models. Our results suggest that locally administered CAR T cells will be most successful against slowly proliferating, highly diffusive tumors, which have the lowest average tumor cell density. These findings affirm the clinical observation that CAR T cells will not perform equally across different types of solid tumors, and suggest that measuring tumor density may be helpful when considering the feasibility of CAR T-cell therapy and planning dosages for a particular patient. We additionally find that local delivery of CAR T cells can result in deep tumor responses, provided that the initial CAR T-cell dose does not contain a significant fraction of exhausted cells.

Ovarian cancer-derived IL-4 promotes immunotherapy resistance.

Ovarian cancer is resistant to immunotherapy, and this is influenced by the immunosuppressed tumor microenvironment (TME) dominated by macrophages. Resistance is also affected by intratumoral heterogeneity, whose development is poorly understood. To identify regulators of ovarian cancer immunity, we employed a spatial functional genomics screen (Perturb-map), focused on receptor/ligands hypothesized to be involved in tumor-macrophage communication. Perturb-map recapitulated tumor heterogeneity and revealed that interleukin-4 (IL-4) promotes resistance to anti-PD-1. We find ovarian cancer cells are the key source of IL-4, which directs the formation of an immunosuppressive TME via macrophage control. IL-4 loss was not compensated by nearby IL-4-expressing clones, revealing short-range regulation of TME composition dictating tumor evolution. Our studies show heterogeneous TMEs can emerge from localized altered expression of cancer-derived cytokines/chemokines that establish immune-rich and immune-excluded neighborhoods, which drive clone selection and immunotherapy resistance. They also demonstrate the potential of targeting IL-4 signaling to enhance ovarian cancer response to immunotherapy.

Enhanced interactions within microenvironment accelerates dismal prognosis in HBV-related HCC after TACE.

Transarterial chemoembolization (TACE) is the first-line treatment for patients with advanced HCC, but there are limited studies on the microenvironment alterations caused by TACE. Six fresh HBV-related HCC specimens with or without TACE intervention were used to perform single-cell RNA sequencing. The 757 bulk samples from 3 large-scale multicenter cohorts were applied for comprehensive analysis. The biological functions of the biomarkers were further validated by phenotypic experiments. Using single-cell RNA sequencing analysis, we delineated the global cell atlas of post-TACE and demonstrated elevated tumor heterogeneity and an enhanced proinflammatory microenvironment induced by TACE. Cell-cell communication analysis revealed that markedly elevated interactions between NABP1+ malignant hepatocytes, neutrophils, and CD8+ T cells after TACE might accelerate the shift from CD8+ effector memory T cells to CD8+ effector T cells. This result was substantiated by the developmental trajectory between the 2 and dramatically decreased resident scores along the pseudotemporal trajectory. Integrating bulk data, we further found that the increased estimated proportion of NABP1+ malignant hepatocytes was related to poor TACE response and dismal prognosis, and its biomarker role could be replaced by NABP1. In vitro, multiple biological experiments consistently verified that NABP1 knockdown significantly inhibited the proliferation and migration of HCC cells. Based on our depicted global map of post-TACE, we confirmed that the enhanced interactions within the microenvironment after TACE may be the culprits for postoperative progression. NABP1 may become an attractive tool for the early identification of patients sensitive to first-line TACE in clinical practice.

The Role of Intracellular and Extracellular Vesicles in the Development of Therapy Resistance in Cancer.

Cancer is the second leading cause of global mortality and claims approximately 10 million lives annually. Despite advances in treatments such as surgery, chemotherapy, and immunotherapy, resistance to these methods has emerged. Multidrug resistance (MDR), where cancer cells resist diverse treatments, undermines therapy effectiveness, escalating mortality rates. MDR mechanisms include, among others, drug inactivation, reduced drug uptake, enhanced DNA repair, and activation of survival pathways such as autophagy. Moreover, MDR mechanisms can confer resistance to other therapies like radiotherapy. Understanding these mechanisms is crucial for improving treatment efficacy and identifying new therapeutic targets. Extracellular vesicles (EVs) have gathered attention for their role in cancer progression, including MDR development through protein transfer and genetic reprogramming. Autophagy, a process balancing cellular resources, also influences MDR. The intersection of EVs and autophagy further complicates the understanding of MDR. Both extracellular (exosomes, microvesicles) and intracellular (autophagic) vesicles contribute to therapy resistance by regulating the tumor microenvironment, facilitating cell communication, and modulating drug processing. While much is known about these pathways, there is still a need to explore their potential for predicting treatment responses and understanding tumor heterogeneity.

Centromeres in cancer: Unraveling the link between chromosomal instability and tumorigenesis.

Centromeres are critical structures involved in chromosome segregation, maintaining genomic stability, and facilitating the accurate transmission of genetic information. They are key in coordinating the assembly and help keep the correct structure, location, and function of the kinetochore, a proteinaceous structure vital for ensuring proper chromosome segregation during cell division. Abnormalities in centromere structure can lead to aneuploidy or chromosomal instability, which have been implicated in various diseases, including cancer. Accordingly, abnormalities in centromeres, such as structural rearrangements and dysregulation of centromere-associated proteins, disrupt gene function, leading to uncontrolled cell growth and tumor progression. For instance, altered expression of CENP-A, CENP-E, and others such as BUB1, BUBR1, MAD1, and INCENP, have been shown to ascribe to centromere over-amplification, chromosome missegregation, aneuploidy, and chromosomal instability; this, in turn, can culminate in tumor progression. These centromere abnormalities also promoted tumor heterogeneity by generating genetically diverse cell populations within tumors. Advanced techniques like fluorescence in situ hybridization (FISH) and chromosomal microarray analysis are crucial for detecting centromere abnormalities, enabling accurate cancer classification and tailored treatment strategies. Researchers are exploring strategies to disrupt centromere-associated proteins for targeted cancer therapies. Thus, this review explores centromere abnormalities in cancer, their molecular mechanisms, diagnostic implications, and therapeutic targeting. It aims to advance our understanding of centromeres' role in cancer and develop advanced diagnostic tools and targeted therapies for improved cancer management and treatment.

A deep-learning model for characterizing tumor heterogeneity using patient-derived organoids

Genotypic and phenotypic diversity, which generates heterogeneity during disease evolution, is common in cancer. The identification of features specific to each patient and tumor is central to the development of precision medicine and preclinical studies for cancer treatment. However, the complexity of the disease due to inter- and intratumor heterogeneity increases the difficulty of effective analysis. Here, we introduce a sequential deep learning model, preprocessing to organize the complexity due to heterogeneity, which contrasts with general approaches that apply a single model directly. We characterized morphological heterogeneity using microscopy images of patient-derived organoids (PDOs) and identified gene subsets relevant to distinguishing differences among original tumors. PDOs, which reflect the features of their origins, can be reproduced in large quantities and varieties, contributing to increasing the variation by enhancing their common characteristics, in contrast to those from different origins. This resulted in increased efficiency in the extraction of organoid morphological features sharing the same origin. Linking these tumor-specific morphological features to PDO gene expression data enables the extraction of genes strongly correlated with intertumor differences. The relevance of the selected genes was assessed, and the results suggest potential applications in preclinical studies and personalized clinical care.

389 (PB377): Sensitization to carboplatin chemotherapy by decoding intra tumor heterogeneity in triple negative breast cancer

389 (PB377): Sensitization to carboplatin chemotherapy by decoding intra tumor heterogeneity in triple negative breast cancer

Chemotherapy Enriches for Proinflammatory Macrophage Phenotypes that Support Cancer Stem-Like Cells and Disease Progression in Ovarian Cancer.

High-grade serous ovarian cancer remains a poorly understood disease with a high mortality rate. Although most patients respond to cytotoxic therapies, a majority will experience recurrence. This may be due to a minority of drug-resistant cancer stem-like cells (CSC) that survive chemotherapy and are capable of repopulating heterogeneous tumors. It remains unclear how CSCs are supported in the tumor microenvironment (TME) particularly during chemotherapy exposure. Tumor-associated macrophages (TAM) make up half of the immune population of the ovarian TME and are known to support CSCs and contribute to cancer progression. TAMs are plastic cells that alter their phenotype in response to environmental stimuli and thus may influence CSC maintenance during chemotherapy. Given the plasticity of TAMs, we studied the effects of carboplatin on macrophage phenotypes using both THP1- and peripheral blood mononuclear cell (PBMC)-derived macrophages and whether this supports CSCs and ovarian cancer progression following treatment. We found that carboplatin exposure induces an M1-like proinflammatory phenotype that promotes SOX2 expression, spheroid formation, and CD117+ ovarian CSCs, and that macrophage-secreted CCL2/MCP-1 is at least partially responsible for this effect. Depletion of TAMs during carboplatin exposure results in fewer CSCs and prolonged survival in a xenograft model of ovarian cancer. This study supports a role for platinum-based chemotherapies in promoting a transient proinflammatory M1-like TAM that enriches for CSCs during treatment. Improving our understanding of TME responses to cytotoxic drugs and identifying novel mechanisms of CSC maintenance will enable the development of better therapeutic strategies for high-grade serous ovarian cancer. Significance: We show that chemotherapy enhances proinflammatory macrophage phenotypes that correlate with ovarian cancer progression. Given that macrophages are the most prominent immune cell within these tumors, this work provides the foundation for future translational studies targeting specific macrophage populations during chemotherapy, a promising approach to prevent relapse in ovarian cancer.

Everything you ever wanted to know about cancer stem cells in neuroendocrine neoplasms but were afraid to ask

While the role of cancer stem cells (CSCs) in tumorigenesis, chemoresistance, metastasis and relapse has been extensively studied in solid tumors, such as adenocarcinomas or sarcomas, the same cannot be said for neuroendocrine neoplasms (NENs). While lagging, CSCs have been described in numerous NENs including gastrointestinal and pancreatic neuroendocrine neoplasms (PanNENs), and they have been found to play critical roles in tumor initiation, progression, and treatment resistance. However, it seems there is still skepticism regarding the role of CSCs in NENs, even in light of studies that support the CSC model in these tumors and the therapeutic benefit of targeting them. For example, in lung neuroendocrine carcinoids, a high percentage of CSCs have been found in atypical carcinoids, suggesting the presence of CSCs in these cancers. In PanNENs, CSCs marked by aldehyde dehydrogenases or CD90 have been identified, and targeting CSCs with inhibitors of molecular pathways has shown therapeutic potential. Overall, while evidence exists for the presence of CSCs in NENs, either the CSC field has neglected NENs or the NEN field has not fully embraced the CSC model. Both might apply and/or may be a consequence of the fact that NENs are a relatively rare and heterogeneous tumor entity, with confusing histology and nomenclature to match. Regardless, this review intends to summarize our current knowledge of CSCs in NENs and highlight the importance of understanding the role of CSCs in the biology of these rare tumors, with a special focus on developing targeted therapies to improve patients' outcomes.

Effect of silibinin on GAS6/sAXL and JAK/STAT pathways in human cholangiocarcinoma cell line

Abstract Objectives Cholangiocarcinoma (CCA) is a highly heterogeneous biliary malignant tumor. Studies have demonstrated that JAK/STAT signaling is activated in many types of cancer. In addition, JAK/STAT is activated downstream of AXL, and the AXL receptor is activated by its ligand, GAS6. In this study, we investigated the anticarcinogenic effect of silibinin and its relationship with the GAS6/AXL ve JAK/STAT pathway in the human EGI-1 cell line. Methods Cell viability, apoptosis, and cell cycle were measured by Muse Cell Analyzer. All the protein levels were determined by the ELISA method. Results We observed that silibinin significantly reduced cell proliferation and colony formation (p<0.05, p<0.001, respectively). Silibinin also significantly induced total apoptosis and the G0/G1 phase of the cell cycle (p<0.01). We observed that silibinin significantly decreased JAK2 levels while increased STAT1 levels compared to the controls respectively (p<0.001; p<0.001). Besides, silibinin statistically decreased the levels of sAXL; however, numerically, but not statistically, it increased the level of GAS6 (p>0.05). Conclusions Silibinin reduces colony formation, inducing apoptosis and arresting cancer cells in the G0/G1 phase, which is an indicator of its anticancer activity. In addition, silibinin decreased the levels of JAK2 and sAXL, which contributes to the development of cancer, and increased the levels of STAT1, suggesting silibinin’s antiproliferative effects.

Integrated analysis to identify biological features and molecular markers of poorly cohesive gastric carcinoma (PCC)

As one of the two main histologic subtypes of gastric cancer (GC), diffuse-type gastric cancer (DGC) containing poorly cohesive gastric carcinoma (PCC) components has a worse prognosis and does not respond well to typical therapies. Despite the large number of studies revealing the complex pathogenic network of DGC, the molecular heterogeneity of DGC is still not fully understood. We obtained single-cell RNA-seq data and bulk data from the tumor immune single cell hub, the public gene expression omnibus, and the cancer genome atlas databases. A series of bioinformatics analyses were performed using R software. Immunofluorescence staining, hematoxylin and eosin staining, western blot, and functional experiments were used for experimental validation. Caudin-3, -4 and -7 were lowly expressed in DGC and their expression levels were further reduced in PCC. The PCC components were mainly located in the deeper layers of the DGC and had a high level of hypoxic Wnt/β-catenin signaling and stemness. We further identified Insulin Like Growth Factor Binding Protein 7 (IGFBP7) as a marker for PCC components in the deep layer. IGFBP7 is stimulated by hypoxia and promotes cancer cell invasiveness and reduced claudin expression. In addition, programmed death-1 ligand (PD-L1) was specifically expressed in the deep layer, reflecting deep layer-specific immunosuppression. The PCC components are predominantly situated in the deeper layers of DGC. Initial molecular characterization of these PCC components revealed distinct features, including low expression of claudin-3, -4, and -7, high expression of IGFBP7, and the presence of PD-L1. These molecular traits may partially account for the pronounced tumor heterogeneity observed in GC.

The chromatin landscape of high-grade serous ovarian cancer metastasis identifies regulatory drivers in post-chemotherapy residual tumour cells

Disease recurrence following chemotherapy is a major clinical challenge in ovarian cancer (OC), but little is known regarding how the tumour epigenome regulates transcriptional programs underpinning chemoresistance. We determine the single cell chromatin accessibility landscape of omental OC metastasis from treatment-naïve and neoadjuvant chemotherapy-treated patients and define the chromatin accessibility profiles of epithelial, fibroblast, myeloid and lymphoid cells. Epithelial tumour cells display open chromatin regions enriched with motifs for the oncogenic transcription factors MEIS and PBX. Post chemotherapy microenvironments show profound tumour heterogeneity and selection for cells with accessible chromatin enriched for TP53, TP63, TWIST1 and resistance-pathway-activating transcription factor binding motifs. An OC chemoresistant tumour subpopulation known to be present prior to treatment, and characterised by stress-associated gene expression, is enriched post chemotherapy. Nuclear receptors RORa, NR2F6 and HNF4G are uncovered as candidate transcriptional drivers of these cells whilst closure of binding sites for E2F2 and E2F4 indicate post-treated tumour having low proliferative capacity. Delineation of the gene regulatory landscape of ovarian cancer cells surviving chemotherapy treatment therefore reveals potential core transcriptional regulators of chemoresistance, suggesting novel therapeutic targets for improving clinical outcome.

Assessment of dual time point protocols to produce parametric Ki images in FDG PET/CT: A virtual clinical study.

This simulation study investigated the feasibility of generating Patlak Ki images using a dual time point (DTP-Ki) scan protocol involving two 3-min/bed routine static PET scans and, subsequently, assessed DTP-Ki performance for an optimal DTP scan time frame combination, against conventional Patlak Ki estimated from complete 0-93min dynamic PET data. Six realistic heterogeneous tumors of different characteristic spatiotemporal [18F]FDG uptake distributions for three noise levels commonly found in clinical studies and 20 noise realizations (N=360 samples) were produced by analytic simulations of the XCAT phantom. Subsequently, DTP-Ki images were generated by performing standard linear indirect Patlak analysis with t* -min (Patlakt* = 12) using a scaled population-based input function (sPBIF) model on 66 combinations of early and late 3-min/bed static whole-body PET reconstructed images. All DTP-Ki images were evaluated against respective DTP-Ki images estimated with Patlakt* = 12 and 0-93min individual input functions (iIFs) and against gold standard Ki images estimated with Patlakt* = 12, 0-93min iIFs and tissue time activity curves from all reconstructed WB passes 12-93min post injection. The optimal combination of early and late frames, in terms of attaining the highest correlation between DTP-Ki with sPBIF and gold standard Ki was also determined from a set of 66 different combinations of 2-min early and late frames. Moreover, the performance of DTP-Ki with sPBIF was compared against that of the retention index (RI) in terms of their correlation to the gold standard Ki. Finally, the feasibility and practicality of DTP protocol in the clinic were assessed through the analysis of nine patients. High correlations(>0.9) were observed between DTP-Ki values from sPBIF and those from iIFs for all evaluated DTP protocols while the mean AUC difference between sPBIF and iIFs was less than 10%. The percentage difference of mean values between DTP-Ki from sPBIF and from iIFs was less than 1%. DTP Ki from sPBIF exhibited significantly higher correlation with gold standard Ki, in contrast to RI, across all 66 DTP protocols (p<0.05 using the two-tailed t-test by Williams) with the highest correlation attained for the 50-53-min early + 90-93-min late scan time frames (optimal DTP protocol). Feasibility of generating Patlak Ki [18F] FDG images from an early and a late post injection 3-min/bed routine static scan using a population-based input function model was demonstrated and an optimal DTP scan protocol was determined. The results indicated high correlations between DTP-Ki and gold-standard Ki images that are significantly larger than those between RI and gold-standard Ki.

Epithelial-Mesenchymal Plasticity and Epigenetic Heterogeneity in Cancer.

The tumor microenvironment comprises various cell types and experiences dynamic alterations in physical and mechanical properties as cancer progresses. Intratumoral heterogeneity is associated with poor prognosis and poses therapeutic challenges, and recent studies have begun to identify the cellular mechanisms that contribute to phenotypic diversity within tumors. This review will describe epithelial-mesenchymal (E/M) plasticity and its contribution to phenotypic heterogeneity in tumors as well as how epigenetic factors, such as histone modifications, histone modifying enzymes, DNA methylation, and chromatin remodeling, regulate and maintain E/M phenotypes. This review will also report how mechanical properties vary across tumors and regulate epigenetic modifications and E/M plasticity. Finally, it highlights how intratumoral heterogeneity impacts therapeutic efficacy and provides potential therapeutic targets to improve cancer treatments.

Exploring the potential of extrachromosomal DNA as a novel oncogenic driver.

Extrachromosomal DNA (ecDNA) is a form of circular DNA mostly found in tumor cells. Unlike the typical chromosomal DNA, ecDNA is circular, self-replicating, and carries complete or partial gene fragments. Although ecDNA occurrence remains a rare event in cancer, recent studies have shown that oncogene amplification on ecDNA is widespread throughout many types of cancer, implying that ecDNA plays a central role in accelerating tumor evolution. ecDNA has also been associated with increased tumor mutation burden, chromosomal instability, and even tumor microenvironment remodeling. ecDNA may be crucial in influencing tumor heterogeneity, drug sensitivity, oncogenic senescence, and tumor immunogenicity, leading to a worsening prognosis for tumor patients. In this way, several clinical trials have been conducted to investigate the importance of ecDNA in clinical treatment. In this review, we summarize the biogenesis, characteristics, and current research methods of ecDNA, discuss the vital role of ecDNA-caused tumor heterogeneity in cancers, and highlight the potential role of ecDNA in cancer therapy.

Spatial transcriptomics reveal tumor microenvironment and SLCO2A1 correlated with tumor suppression in hypopharyngeal squamous cell carcinoma

BackgroundHypopharyngeal squamous cell carcinoma (HSCC) is a type of head and neck tumor with malignant behavior and poor prognosis. Spatial transcriptomics is a method that spatially analyzes gene expression patterns in tissues and has been used to discover tumor microenvironment and molecular markers in various tumors. However, there are no published reports on spatial transcriptomic analysis of HSCC. MethodsIn this study, spatial transcriptomic analysis was performed on tumor tissues in situ, peritumoral tissues, and lymphatic metastatic tissues of four patients with HSCC. Morphological markers, including panCK, SMA, and CD45, were used to identify epithelial, fibroblast, and immune cells, respectively. By analyzing the expression of more than 18, 000 genes within the transcriptome of all ROIs, differentially expressed genes of three cell types in different tissues were identified, and differentially expressed signaling pathways and immune infiltration were analyzed. ResultsThe spatial distribution of cells suggests that fibroblast cells in tumor tissues may be involved in the genesis and development of tumors, and the immune infiltration of lymphatic tumor metastasis is lower than that of tumors in situ. For epithelial cells, SLCO2A1, which is a favorable prognosis marker in head and neck squamous cell carcinoma (HNSCC), was significantly down-regulated in tumor tissues and lymphatic metastatic tissues compared with adjacent normal tissues. For immune cells, KANK3, which is a favorable prognosis markers in HNSCC, was significantly down-regulated in lymphatic metastatic tissues compared with adjacent normal tissues. For fibroblast cells, AQP1, CLEC3B and SLCO2A1, which are favorable prognosis markers in HNSCC, were significantly down-regulated in tumor tissues compared with adjacent normal tissues. ITGA8, which is a favorable prognosis markers in HNSCC, was significantly down-regulated in lymphatic metastatic tissues compared with normal lymphatic tissues. CSRP1, DES, and SLCO2A1 positively correlate with immune infiltration in HNSCC. Moreover, SLCO2A1 overexpression suppressed Fadu cells proliferation and metastasis and significantly correlated with favorable survival overcome in HSCC. ConclusionsWe investigated tumor and fibroblast heterogeneity, as well as the immune microenvironment in HSCC by using spatial transcriptomics. SLCO2A1 may be a tumor suppressor gene and correlates with immune infiltration for HSCC and could serve as a potential target for its diagnosis and treatment.

High expression of ARHGEF5 predicts unfavorable prognosis in acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly heterogeneous hematological neoplasm, highlighting the need for new molecular markers to improve prognosis prediction and therapeutic strategies. While Rho guanine nucleotide exchange factor 5 (ARHGEF5) is known to be overexpressed in various cancers, its role in AML is not well understood. This study investigates the correlation between ARHGEF5 expression and AML using data from the Cancer Genome Atlas (TCGA). ARHGEF5 expression levels in AML patients and normal samples were compared using the Wilcoxon rank-sum test. The Kaplan–Meier method and Cox regression analysis (CRA) assessed the association between ARHGEF5 expression and patient survival. A prognostic nomogram was constructed using CRA, incorporating patient age and cytogenetic risk.Our findings indicate significant overexpression of ARHGEF5 in AML compared to normal samples. Elevated ARHGEF5 levels were associated with poor prognosis, particularly in patients ≤ 60 years, those with NPM1 mutations, FLT3 mutation-positive, and wild-type RAS (P < 0.05). CRA confirmed that high ARHGEF5 expression independently predicts poor prognosis. Additionally, 412 differentially expressed genes (DEGs) were identified between high and low ARHGEF5 expression groups, with 216 genes upregulated and 196 downregulated. Pathway enrichment analyses using GO and KEGG, along with protein–protein interaction network and single sample gene set enrichment analyses, revealed key pathways and immune cell associations linked to ARHGEF5. These findings suggest that ARHGEF5 overexpression could serve as a biomarker for unfavorable outcomes in AML, providing insights into the underlying mechanisms of AML onset and progression.

Machine learning-based individualized survival prediction model for prognosis in osteosarcoma: Data from the SEER database.

Patient outcomes of osteosarcoma vary because of tumor heterogeneity and treatment strategies. This study aimed to compare the performance of multiple machine learning (ML) models with the traditional Cox proportional hazards (CoxPH) model in predicting prognosis and explored the potential of ML models in clinical decision-making. From 2000 to 2018, 1243 patients with osteosarcoma were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Three ML methods were chosen for model development (DeepSurv, neural multi-task logistic regression [NMTLR]) and random survival forest [RSF]) and compared them with the traditional CoxPH model and TNM staging systems. 871 samples were used for model training, and the rest were used for model validation. The models' overall performance and predictive accuracy for 3- and 5-year survival were assessed by several metrics, including the concordance index (C-index), the Integrated Brier Score (IBS), receiver operating characteristic curves (ROC), area under the ROC curves (AUC), calibration curves, and decision curve analysis. The efficacy of personalized recommendations by ML models was evaluated by the survival curves. The performance was highest in the DeepSurv model (C-index, 0.77; IBS, 0.14; 3-year AUC, 0.80; 5-year AUC, 0.78) compared with other methods (C-index, 0.73-0.74; IBS, 0.16-0.17; 3-year AUC, 0.73-0.78; 5-year AUC, 0.72-0.78). There are also significant differences in survival outcomes between patients who align with the treatment option recommended by the DeepSurv model and those who do not (hazard ratio, 1.88; P < .05). The DeepSurv model is available in an approachable web app format at https://survivalofosteosarcoma.streamlit.app/. We developed ML models capable of accurately predicting the survival of osteosarcoma, which can provide useful information for decision-making regarding the appropriate treatment.

Ultrafast Power Doppler Ultrasound Enables Longitudinal Tracking of Vascular Changes that Correlate with Immune Response After Radiotherapy.

While immunotherapy shows great promise in patients with triple negative breast cancer, many will not respond to treatment. Radiotherapy has the potential to prime the tumor-immune microenvironment for immunotherapy. However, predicting response is difficult due to tumor heterogeneity across patients, which necessitates personalized medicine strategies that incorporate tumor tracking into the therapeutic approach. Here, we investigated the use of ultrasound (US) imaging of the tumor vasculature to monitor the tumor response to treatment. We utilized ultrafast power doppler US to track the vascular response to radiotherapy over time. We used 4T1 (metastatic) and 67NR (non-metastatic) breast cancer models to determine if US measurements corroborate conventional immunostaining analysis of the tumor vasculature. To evaluate the effects of radiation, tumor volume and vascular index were calculated using US, and the correlation between vascular changes and immune cell infiltration was determined. US tumor measurements and the quantified vascular response to radiation were confirmed with caliper measurements and immunostaining, respectively, demonstrating a proof-of-principle method for non-invasive vascular monitoring. Additionally, we found significant infiltration of CD8 + T cells into irradiated tumors 10 days after radiation, which followed a sustained decline in vascular index and an increase in splenic CD8 + T cells that was first observed 1 day post-radiation. Our findings reveal that ultrafast power doppler US can evaluate changes in tumor vasculature that are indicative of shifts in the tumor-immune microenvironment. This work may lead to improved patient outcomes through observing and predicting response to therapy.