Tumor microenvironment heterogeneity: challenges and opportunities

Plasma-derived extracellular matrix for xenofree and cost-effective organoid modeling for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) causes significant cancer mortality worldwide. Cancer organoids can serve as useful disease models by high costs, complexity, and contamination risks from animal-derived products and extracellular matrix (ECM) that limit its applications. On the other hand, synthetic ECM alternatives also have limitations in mimicking native biocomplexity. This study explores the development of a physiologically relevant HCC organoid model using plasma-derived extracellular matrix as a scaffold and nutritive biomatrix with different cellularity components to better mimic the heterogenous HCC microenvironment. Plasma-rich platelet is recognized for its elevated levels of growth factors, which can promote cell proliferation. By employing it as a biomatrix for organoid culture there is a potential to enhance the quality and functionality of organoid models for diverse applications in biomedical research and regenerative medicine and to better replicate the heterogeneous microenvironment of HCC.

METHOD

To generate the liver cancer organoids, HUH-7 hepatoma cells were cultured alone (homogenous model) or with human bone marrow-derived mesenchymal stromal cells and human umbilical vein endothelial cells (heterogeneous model) in plasma-rich platelet extracellular matrix (ECM). The organoids were grown for 14 days and analyzed for cancer properties including cell viability, invasion, stemness, and drug resistance.

RESULTS

HCC organoids were developed comprising HUH-7 hepatoma cells with or without human mesenchymal stromal and endothelial cells in plasma ECM scaffolds. Both homogeneous (HUH-7 only) and heterogeneous (mixed cellularity) organoids displayed viability, cancer hallmarks, and chemoresistance. The heterogeneous organoids showed enhanced invasion potential, cancer stem cell populations, and late-stage HCC genetic signatures versus homogeneous counterparts.

CONCLUSION

The engineered HCC organoids system offers a clinically relevant and cost-effective model to study liver cancer pathogenesis, stromal interactions, and drug resistance. The plasma ECM-based culture technique could enable standardized and reproducible HCC modeling. It could also provide a promising option for organoid culture and scaling up.

Highly expressed of BID indicates poor prognosis and mediates different tumor microenvironment characteristics in clear cell renal cell carcinoma

BACKGROUND

Studies have found that BH3 interacting domain death agonist (BID) is closely related to the occurrence and development of many kinds of tumors. However, little attention has been paid to the situation of BID in clear cell renal cell carcinoma (ccRCC). So, our aim was to explore the effect of BID in ccRCC.

METHODS

Survival analysis, ROC curve, correlation analysis and Cox regression analysis were executed to analyze the prognostic value and clinical correlation of BID in ccRCC. The risk prognosis model was constructed in the training cohort and further validated in the internal testing cohort, ICGC cohort, and GEO cohort. Transcriptome sequencing and immunohistochemical staining of clinical specimens were used to validate the results of bioinformatics analysis. The GSEA, ESTIMATE algorithm, CIBERSORT algorithm, ssGSEA, TIDE score, correlation and difference analysis were used to analyze the effects of BID on immune infiltration in tumor microenvironment (TME).

RESULTS

BID was highly expressed in ccRCC tissues, which was verified by transcriptome sequencing and immunohistochemical staining of clinical specimens. Patients with high expression of BID had a worse prognosis. BID is an independent prognostic factor for ccRCC. The prognostic model based on BID can accurately predict the prognosis of patients in different cohorts. In addition, the expression levels of BID was closely related to immunomodulatory molecules such as PD-1, LAG3, and CTLA4. Enrichment analysis indicated that BID was significantly enriched in immune-related responses and cancer-related pathways. The change of BID expression mediates different characteristics of immune infiltration in TME.

CONCLUSIONS

BID is highly expressed in ccRCC, which is a reliable biomarker of ccRCC prognosis. It is closely related to TME, and may be a potential target for immunotherapy in patients with ccRCC.

Comprehensive analysis of T-cell regulatory factors and tumor immune microenvironment in stomach adenocarcinoma

BACKGROUND

Gastric cancer (GC) is one of the most prevalent malignant tumors worldwide and is associated with high morbidity and mortality rates. However, the specific biomarkers used to predict the postoperative prognosis of patients with gastric cancer remain unknown. Recent research has shown that the tumor microenvironment (TME) has an increasingly positive effect on anti-tumor activity. This study aims to build signatures to study the effect of certain genes on gastric cancer.

METHODS

Expression profiles of 37 T cell-related genes and their TME characteristics were comprehensively analyzed. A risk signature was constructed and validated based on the screened T cell-related genes, and the roles of hub genes in GC were experimentally validated.

RESULTS

A novel T cell-related gene signature was constructed based on CD5, ABCA8, SERPINE2, ESM1, SERPINA5, and NMU. The high-risk group indicated lower overall survival (OS), poorer immune efficacy, and higher drug resistance, with SERPINE2 promoting GC cell proliferation, according to experiments. SERPINE2 and CXCL12 were significantly correlated, indicating poor OS via the Youjiang cohort.

CONCLUSIONS

This study identified T cell-related genes in patients with stomach adenocarcinoma (STAD) for prognosis estimation and proposed potential immunotherapeutic targets for STAD.

A novel transformer-based aggregation model for predicting gene mutations in lung adenocarcinoma

In recent years, predicting gene mutations on whole slide imaging (WSI) has gained prominence. The primary challenge is extracting global information and achieving unbiased semantic aggregation. To address this challenge, we propose a novel Transformer-based aggregation model, employing a self-learning weight aggregation mechanism to mitigate semantic bias caused by the abundance of features in WSI. Additionally, we adopt a random patch training method, which enhances model learning richness by randomly extracting feature vectors from WSI, thus addressing the issue of limited data. To demonstrate the model's effectiveness in predicting gene mutations, we leverage the lung adenocarcinoma dataset from Shandong Provincial Hospital for prior knowledge learning. Subsequently, we assess TP53, CSMD3, LRP1B, and TTN gene mutations using lung adenocarcinoma tissue pathology images and clinical data from The Cancer Genome Atlas (TCGA). The results indicate a notable increase in the AUC (Area Under the ROC Curve) value, averaging 4%, attesting to the model's performance improvement. Our research offers an efficient model to explore the correlation between pathological image features and molecular characteristics in lung adenocarcinoma patients. This model introduces a novel approach to clinical genetic testing, expected to enhance the efficiency of identifying molecular features and genetic testing in lung adenocarcinoma patients, ultimately providing more accurate and reliable results for related studies.

Comprehensive analysis of bulk and single-cell transcriptomic data reveals a novel signature associated with endoplasmic reticulum stress, lipid metabolism, and liver metastasis in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with high probability of recurrence and distant metastasis. Liver metastasis is the predominant metastatic mode developed in most pancreatic cancer cases, which seriously affects the overall survival rate of patients. Abnormally activated endoplasmic reticulum stress and lipid metabolism reprogramming are closely related to tumor growth and metastasis. This study aims to construct a prognostic model based on endoplasmic reticulum stress and lipid metabolism for pancreatic cancer, and further explore its correlation with tumor immunity and the possibility of immunotherapy.

METHODS

Transcriptomic and clinical data are acquired from TCGA, ICGC, and GEO databases. Potential prognostic genes were screened by consistent clustering and WGCNA methods, and the whole cohort was randomly divided into training and testing groups. The prognostic model was constructed by machine learning method in the training cohort and verified in the test, TCGA and ICGC cohorts. The clinical application of this model and its relationship with tumor immunity were analyzed, and the relationship between endoplasmic reticulum stress and intercellular communication was further explored.

RESULTS

A total of 92 characteristic genes related to endoplasmic reticulum stress, lipid metabolism and liver metastasis were identified in pancreatic cancer. We established and validated a prognostic model for pancreatic cancer with 7 signatures, including ADH1C, APOE, RAP1GAP, NPC1L1, P4HB, SOD2, and TNFSF10. This model is considered to be an independent prognosticator and is a more accurate predictor of overall survival than age, gender, and stage. TIDE score was increased in high-risk group, while the infiltration levels of CD8+ T cells and M1 macrophages were decreased. The number and intensity of intercellular communication were increased in the high ER stress group.

CONCLUSIONS

We constructed and validated a novel prognostic model for pancreatic cancer, which can also be used as an instrumental variable to predict the prognosis and immune microenvironment. In addition, this study revealed the effect of ER stress on cell-cell communication in the tumor microenvironment.

A patient-specific lung cancer assembloid model with heterogeneous tumor microenvironments

Cancer models play critical roles in basic cancer research and precision medicine. However, current in vitro cancer models are limited by their inability to mimic the three-dimensional architecture and heterogeneous tumor microenvironments (TME) of in vivo tumors. Here, we develop an innovative patient-specific lung cancer assembloid (LCA) model by using droplet microfluidic technology based on a microinjection strategy. This method enables precise manipulation of clinical microsamples and rapid generation of LCAs with good intra-batch consistency in size and cell composition by evenly encapsulating patient tumor-derived TME cells and lung cancer organoids inside microgels. LCAs recapitulate the inter- and intratumoral heterogeneity, TME cellular diversity, and genomic and transcriptomic landscape of their parental tumors. LCA model could reconstruct the functional heterogeneity of cancer-associated fibroblasts and reflect the influence of TME on drug responses compared to cancer organoids. Notably, LCAs accurately replicate the clinical outcomes of patients, suggesting the potential of the LCA model to predict personalized treatments. Collectively, our studies provide a valuable method for precisely fabricating cancer assembloids and a promising LCA model for cancer research and personalized medicine.

Signature Construction and Disulfidptosis-Related Molecular Cluster Identification for Better Prediction of Prognosis in Glioma

Disulfidptosis is a newly discovered form of regulatory cell death. However, the identification of disulfidptosis-related molecular subtypes and potential biomarkers in gliomas and their prognostic predictive potential need to be further elucidated. RNA sequencing profiles and the relevant clinical data were obtained from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Disulfidptosis-related clusters were identified by unsupervised clustering analysis. Immune cell infiltration analysis and drug sensitivity analysis were used to explore the differences between clusters. Gene set enrichment analysis (GSEA) of differential genes between clusters was performed to explore the potential biological functions and signaling. A disulfidptosis-related scoring system (DRSS) was constructed based on a combined COX and LASSO analysis. Mendelian randomization (MR) analyses were used to further explore the causal relationship between levels of genes in DRSS and an increased risk of glioma. A prognosis nomogram was constructed based on the DRSS and 3 clinical features (age, WHO stage, and IDH status). The accuracy and stability of the prognosis nomogram were also validated in different cohorts. We identified two clusters that exhibited different prognoses, drug sensitivity profiles, and tumor microenvironment infiltration profiles. The overall survival (OS) of Cluster2 was significantly better than Cluster1. Cluster1 had an overall greater infiltration of immune cells compared to Cluster2. However, the Monocytes, activated B cells had higher infiltration abundance in Cluster2. GSEA results showed significant enrichment of immune-related biological processes in Cluster1, while Cluster2 was more enriched for functions related to neurotransmission and regulation. PER3, RAB34, NKX3-2, GPX7, FRA10AC1, and TGIF1 were finally included to construct DRSS. DRSS was independently related to prognosis. There was a significant difference in overall survival between the low-risk score group and the high-risk score group. Among six genes in DRSS, GPX7 levels were demonstrated to have a causal relationship with an increased risk of glioma. GPX7 may become a more promising biomarker for gliomas. The prognosis nomogram constructed based on the DRSS and three clinical features has considerable potential for predicting the prognosis of patients with glioma. Free online software for implementing this nomogram was established:  https://yekun-zhuang.shinyapps.io/DynNomapp/ . Our study established a novel glioma classification based on the disulfidptosis-related molecular subtypes. We constructed the DRSS and the prognosis nomogram to accurately stratify the prognosis of glioma patients. GPX7 was identified as a more promising biomarker for glioma. We provide important insights into the treatment and prognosis of gliomas.

UFObow: A single-wavelength excitable Brainbow for simultaneous multicolor ex-vivo and in-vivo imaging of mammalian cells

Brainbow is a genetic cell-labeling technique that allows random colorization of multiple cells and real-time visualization of cell fate within a tissue, providing valuable insights into understanding complex biological processes. However, fluorescent proteins (FPs) in Brainbow have distinct excitation spectra with peak difference greater than 35 nm, which requires sequential imaging under multiple excitations and thus leads to long acquisition times. In addition, they are not easily used together with other fluorophores due to severe spectral bleed-through. Here, we report the development of a single-wavelength excitable Brainbow, UFObow, incorporating three newly developed blue-excitable FPs. We have demonstrated that UFObow enables not only tracking the growth dynamics of tumor cells in vivo but also mapping spatial distribution of immune cells within a sub-cubic centimeter tissue, revealing cell heterogeneity. This provides a powerful means to explore complex biology in a simultaneous imaging manner at a single-cell resolution in organs or in vivo.

IL-37 dampens immunosuppressive functions of MDSCs via metabolic reprogramming in the tumor microenvironment

Interleukin-37 (IL-37) has been shown to inhibit tumor growth in various cancer types. However, the immune regulatory function of IL-37 in the tumor microenvironment is unclear. Here, we established a human leukocyte antigen-I (HLA-I)-matched humanized patient-derived xenograft hepatocellular carcinoma (HCC) model and three murine orthotopic HCC models to study the function of IL-37 in the tumor microenvironment. We found that IL-37 inhibited HCC growth and promoted T cell activation. Further study revealed that IL-37 impaired the immunosuppressive capacity of myeloid-derived suppressor cells (MDSCs). Pretreatment of MDSCs with IL-37 before adoptive transfer attenuated their tumor-promoting function in HCC tumor-bearing mice. Moreover, IL-37 promoted both glycolysis and oxidative phosphorylation in MDSCs, resulting in the upregulation of ATP release, which impaired the immunosuppressive capacity of MDSCs. Collectively, we demonstrated that IL-37 inhibited tumor development through dampening MDSCs' immunosuppressive capacity in the tumor microenvironment via metabolic reprogramming, making it a promising target for future cancer immunotherapy.

Artificial tumor matrices and bioengineered tools for tumoroid generation

The tumor microenvironment (TME) is critical for tumor growth and metastasis. The TME contains cancer-associated cells, tumor matrix, and tumor secretory factors. The fabrication of artificial tumors, so-called tumoroids, is of great significance for the understanding of tumorigenesis and clinical cancer therapy. The assembly of multiple tumor cells and matrix components through interdisciplinary techniques is necessary for the preparation of various tumoroids. This article discusses current methods for constructing tumoroids (tumor tissue slices and tumor cell co-culture) for pre-clinical use. This article focuses on the artificial matrix materials (natural and synthetic materials) and biofabrication techniques (cell assembly, bioengineered tools, bioprinting, and microfluidic devices) used in tumoroids. This article also points out the shortcomings of current tumoroids and potential solutions. This article aims to promotes the next-generation tumoroids and the potential of them in basic research and clinical application.

The pyroptosis-related signature predicts prognosis and influences the tumor immune microenvironment in dedifferentiated liposarcoma

Background

Dedifferentiated liposarcoma (DDL), a member of malignant mesenchymal tumors, has a high local recurrence rate and poor prognosis. Pyroptosis, a newly discovered programmed cell death, is tightly connected with the progression and outcome of tumor.

Objective

The aim of this study was to explore the role of pyroptosis in DDL.

Methods

We obtained the RNA sequencing data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression databases to identify different pyroptosis-related genes (PRGs) expression pattern. An unsupervised method for clustering based on PRGs was performed. Based on the result of cluster analysis, we researched clinical outcomes and immune microenvironment between clusters. The differentially expressed genes (DEGs) between the two clusters were used to develop a prognosis model by the LASSO Cox regression method, followed by the performance of functional enrichment analysis and single-sample gene set enrichment analysis. All of the above results were validated in the Gene Expression Omnibus (GEO) dataset.

Results

Forty-one differentially expressed PRGs were found between tumor and normal tissues. A consensus clustering analysis based on PRGs was conducted and classified DDL patients into two clusters. Cluster 2 showed a better outcome, higher immune scores, higher immune cells abundances, and higher expression levels in numerous immune checkpoints. DEGs between clusters were identified. A total of 5 gene signatures was built based on the DEGs and divided all DDL patients of the TCGA cohort into low-risk and high-risk groups. The low-risk group indicates greater inflammatory cell infiltration and better outcome. For external validation, the survival difference and immune landscape between the two risk groups of the GEO cohort were also significant. Receiver operating characteristic curves implied that the risk model could exert its function as an outstanding predictor in predicting DDL patients' prognoses.

Conclusion

Our findings revealed the clinical implication and key role in tumor immunity of PRGs in DDL. The risk model is a promising predictive tool that could provide a fundamental basis for future studies and individualized immunotherapy.

Revealing the potential of necroptosis-related genes in prognosis, immune characteristics, and treatment strategies for head and neck squamous cell carcinoma

Necroptosis is a recently discovered apoptotic mechanism that has been linked to tumor formation, prognosis, and treatment response. However, the relationship between the TME and NRGs remains unclear. In this study, we analyzed the expression patterns of NRGs in 769 HNSCC cases from two distinct data sets. Our findings revealed distinct genetic groups and a correlation between patient clinical features, prognosis, TME cell infiltration characteristics, and NRG alterations. We then developed an NRG model to predict OS and confirmed its accuracy in predicting OS in HNSCC patients. Moreover, we have devised a precise nomogram that enhances the clinical utility of the NRG model substantially. The low-risk group had a better OS, and they were associated with immune suppression, more mutated genes, and higher TIDE scores. The risk score also had a significant correlation with the CSC index and susceptibility to anti-tumor agents. Our study provides insights into how NRGs affect prognosis, clinically significant features, TME, and immunotherapy response in HNSCC. With a better knowledge of NRGs in HNSCC, we could assess the prognosis and develop immunotherapy regimens that are more successful at opening up new doors.

Identification of pyroptosis-related subtypes and comprehensive analysis of characteristics of the tumor microenvironment infiltration in clear cell renal cell carcinoma

Pyroptosis is a kind of programmed cell death triggered by the inflammasome. Growing evidence has revealed the crucial utility of pyroptosis in tumors. However, the potential mechanism of pyroptosis in clear cell renal cell carcinoma (ccRCC) is still unclear. In this research, we systematically analyze the genetic and transcriptional alterations of pyroptosis-related genes (PRGs) in ccRCC, identify pyroptosis-related subtypes, analyze the clinical and microenvironmental differences among different subtypes, develop a corresponding prognostic model to predict the prognosis of patients, and interpret the effect of pyroptosis on ccRCC microenvironment. This study provides a new perspective for a comprehensive understanding of the role of pyroptosis in ccRCC and its impact on the immune microenvironment, and a reliable scoring system was established to predict patients' prognosis.

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.

Griddient: a microfluidic array to generate reconfigurable gradients on-demand for spatial biology applications

Biological tissues are highly organized structures where spatial-temporal gradients (e.g., nutrients, hypoxia, cytokines) modulate multiple physiological and pathological processes including inflammation, tissue regeneration, embryogenesis, and cancer progression. Current in vitro technologies struggle to capture the complexity of these transient microenvironmental gradients, do not provide dynamic control over the gradient profile, are complex and poorly suited for high throughput applications. Therefore, we have designed Griddent, a user-friendly platform with the capability of generating controllable and reversible gradients in a 3D microenvironment. Our platform consists of an array of 32 microfluidic chambers connected to a 384 well-array through a diffusion port at the bottom of each reservoir well. The diffusion ports are optimized to ensure gradient stability and facilitate manual micropipette loading. This platform is compatible with molecular and functional spatial biology as well as optical and fluorescence microscopy. In this work, we have used this platform to study cancer progression.

MYCT1 in cancer development: Gene structure, regulation, and biological implications for diagnosis and treatment

Myc target 1 (MYCT1), located at 6q25.2, is a crucial player in cancer development. While widely distributed in cells, its subcellular localization varies across different cancer types. As a novel c-Myc target gene, MYCT1 is subject to regulation by multiple transcription factors. Studies have revealed aberrant expression of MYCT1 in various cancers, impacting pivotal biological processes such as proliferation, apoptosis, migration, genomic instability, and differentiation in cancer cells. Additionally, MYCT1 plays a critical role in modulating tumor angiogenesis and remodeling tumor immune responses within the tumor microenvironment. Despite certain debated functions, MYCT1 undeniably holds significance in cancer development. In this review, we comprehensively examine the relationship between MYCT1 and cancer, encompassing gene structure, regulation of gene expression, gene mutation, and biological function, with the aim of providing valuable insights for cancer diagnosis and treatment.

Model for predicting prognosis and immunotherapy based on CD+8 T cells infiltration in neuroblastoma

BACKGROUND

Neuroblastoma (NBL) is an extracranial malignant tumor in children deriving from the neural crest in the sympathetic nervous system. Although various immunotherapy interventions have made significant breakthroughs in many adult cancers, the efficacy of these immunotherapies was still limited in NBL. NBL has low immunogenicity which results in a lack of tumor-infiltrating T lymphocytes in the tumor microenvironment (TME). Moreover, tumor cells can wield many immune evasion strategies both in the TME and systemically to impede lymphocyte infiltration and activation. All these factors hamper the anti-tumor effects of CD8+ T cells during immunotherapy and the levels of infiltrating CD8+ T cells correlate with therapy response.

MATERIALS AND METHODS

In this study, we utilized multidimensional bioinformatic methods to establish a risk model based on CD8+ T cells -related genes (CD8+ TRGs).

RESULTS

We obtained 33 CD8+ TRGs with well-predictive ability for prognosis in both GSE49711 and E-MTAB-8248 cohorts. Then, 12 CD8+ TRGs including HK2, RP2, HPSE, ELL2, GFI1, SLC22A16, FCGR3A, CTSS, SH2D1A, RBP5, ATF5, and ADAM9 were finally identified for risk model construction and validation. This model revealed a stable performance in prognostic prediction of the overall survival (OS) and event-free survival (EFS) in patients with NBL. Additionally, our research indicated that the immune and stromal scores, immune-related pathways, immune cell infiltration, the expression of major histocompatibility complex (MHC) and immune checkpoint molecules, immunotherapy response, and drug susceptibility revealed significant differences between high and low-risk groups.

CONCLUSIONS

According to our analyses, the constructed CD8+ TRGs-based risk model may be promising for the clinical prediction of anti-tumor therapy responses and prognoses in NBL.

Development and validation of a novel T cell proliferation-related prognostic model for predicting survival and immunotherapy benefits in melanoma

BACKGROUND

T cell plays a crucial role in the occurrence and progression of Skin cutaneous melanoma (SKCM). This research aims to identify the actions of T cell proliferation-related genes (TRGs) on the prognosis and immunotherapy response of tumor patients.

METHOD

The clinical manifestation and gene expression data of SKCM patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. T cell proliferation-related molecular subtypes were identified utilizing consensus clustering. Subsequently, Cox and Lasso regression analysis was conducted to identify six prognostic genes, and a prognostic signature was constructed. A series of experiments, such as qRT-PCR, Western blotting and CCK8 assay, were then conducted to verify the reliability of the six genes.

RESULTS

In this study, a grading system was established to forecast survival time and responses to immunotherapy, providing an overview of the tumoral immune landscape. Meanwhile, we identified six prognostic signature genes. Notably, we also found that C1RL protein may inhibit the growth of melanoma cell lines.

CONCLUSION

The scoring system depending on six prognostic genes showed great efficiency in predicting survival time. The system could help to forecast prognosis of SKCM patients, characterize SKCM immunological condition, assess patient immunotherapy response.

Standardization of organoid culture in cancer research

Establishing a valid in vitro model to represent tumor heterogeneity and biology is critical but challenging. Tumor organoids are self-assembled three-dimensional cell clusters which are of great significance for recapitulating the histopathological, genetic, and phenotypic characteristics of primary tissues. The organoid has emerged as an attractive in vitro platform for tumor biology research and high-throughput drug screening in cancer medicine. Organoids offer unique advantages over cell lines and patient-derived xenograft models, but there are no standardized methods to guide the culture of organoids, leading to confusion in organoid studies that may affect accurate judgments of tumor biology. This review summarizes the shortcomings of current organoid culture methods, presents the latest research findings on organoid standardization, and proposes an outlook for organoid modeling.

Identification of mitochondrial respiratory chain signature for predicting prognosis and immunotherapy response in stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is the third leading cause of cancer-related deaths and the fifth most prevalent malignancy worldwide. Mitochondrial respiratory chain complexes play a crucial role in STAD pathogenesis. However, how mitochondrial respiratory chain complex genes (MRCCGs) affect the prognosis and tumor microenvironment in STAD remains unclear. In this study, we systematically analyzed genetic alterations and copy number variations of different expression densities of MRCCGs, based on 806 samples from two independent STAD cohorts. Then we employed the unsupervised clustering method to classify the samples into three expression patterns based on the prognostic MRCCG expressions, and found that they were involved in different biological pathways and correlated with the clinicopathological characteristics, immune cell infiltration, and prognosis of STAD. Subsequently, we conducted a univariate Cox regression analysis to identify the prognostic value of 1175 subtype-related differentially expressed genes (DEGs) and screened out 555 prognostic-related genes. Principal component analysis was performed and developed the MG score system to quantify MRCCG patterns of STAD. The prognostic significance of MG Score was validated in three cohorts. The low MG score group, characterized by increased microsatellite instability-high (MSI-H), tumor mutation burden (TMB), PD-L1 expression, had a better prognosis. Interestingly, we demonstrated MRCCG patterns score could predict the sensitivity to ferroptosis inducing therapy. Our comprehensive analysis of MRCCGs in STAD demonstrated their potential roles in the tumor-immune-stromal microenvironment, clinicopathological features, and prognosis. Our findings highlight that MRCCGs may provide a new understanding of immunotherapy strategies for gastric cancer and provide a new perspective on the development of personalized immune therapeutic strategies for patients with STAD.

Interpretable attention-based deep learning ensemble for personalized ovarian cancer treatment without manual annotations

Inhibition of pathological angiogenesis has become one of the first FDA approved targeted therapies widely tested in anti-cancer treatment, i.e. VEGF-targeting monoclonal antibody bevacizumab, in combination with chemotherapy for frontline and maintenance therapy for women with newly diagnosed ovarian cancer. Identification of the best predictive biomarkers of bevacizumab response is necessary in order to select patients most likely to benefit from this therapy. Hence, this study investigates the protein expression patterns on immunohistochemical whole slide images of three angiogenesis related proteins, including Vascular endothelial growth factor, Angiopoietin 2 and Pyruvate kinase isoform M2, and develops an interpretable and annotation-free attention based deep learning ensemble framework to predict the bevacizumab therapeutic effect on patients with epithelial ovarian cancer or peritoneal serous papillary carcinoma using tissue microarrays (TMAs). In evaluation with five-fold cross validation, the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 achieves a notably high F-score (0.99±0.02), accuracy (0.99±0.03), precision (0.99±0.02), recall (0.99±0.02) and AUC (1.00±0). Kaplan-Meier progression free survival analysis confirms that the proposed ensemble is able to identify patients in the predictive therapeutic sensitive group with low cancer recurrence (p<0.001), and the Cox proportional hazards model analysis further confirms the above statement (p=0.012). In conclusion, the experimental results demonstrate that the proposed ensemble model using the protein expressions of both Pyruvate kinase isoform M2 and Angiopoietin 2 can assist treatment planning of bevacizumab targeted therapy for patients with ovarian cancer.

Nasopharyngeal Carcinoma Subtype Discovery via Immune Cell Scores from Tumor Microenvironment

BACKGROUND

Nasopharyngeal carcinoma (NPC) is one of the most prevalent cancers with a poor prognosis. Immunotherapy, especially immune checkpoint blockade (ICB), is becoming a potential therapeutic choice for NPC patients. Thus, the identification of patients who could benefit from immunotherapy is clinically significant.

METHODS

The NPC expression profiles from GSE102349 were used to calculate the cell scores of the tumor microenvironment (TME). The consensus clustering method was utilized to identify the potential molecular subtypes among NPC samples. The hub genes were selected from subtype-specific genes by bioinformatics analysis. Machine learning models, including random forest (RF) and support vector machine (SVM) algorithms, were constructed to predict the immune subtype.

RESULTS

In the present study, we identified two TME subtypes among NPC patients. Patients with the S1 subtype have higher levels of immune cells, immune checkpoint genes, and prognosis. Using expression data profiles of NPC patients, we constructed machine learning models for predicting TME subtypes of NPC patients. This model consists of 8 genes (LCK, CD247, FYN, ZAP70, SH2D1A, CD3D, CD3E, and CD3G). Among them, LCK, FYN, SH2D1A, and CD3D were associated with better prognoses. Among the two constructed models, SVM exhibited a higher area under curve (AUC) of 0.977, when compared with RF (AUC = 0.966). The web server based on the constructed machine learning models will contribute to the identification of NPC patients likely to benefit from ICB therapies.

CONCLUSIONS

This study identified NPC subtypes and provided an accurate model to select individuals who are most likely to respond to ICB.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

Leveraging a cuproptosis-based signature to predict the prognosis and drug sensitivity of cutaneous melanoma

Immunotherapy is a vital treatment for patients with cutaneous melanoma (CM), but effective predictors to guide clinical immunotherapy are lacking. Cuproptosis is a newly discovered mode of cell death related to tumorigenesis. Exploring the relationship between the mode of cuproptosis and the effect of immunotherapy on CM could better guide clinical management. We clustered all patients with CM in the Cancer Genome Atlas (TCGA) database based on cuproptosis-related genes (CRGs). Prognosis, immunotherapeutic effect, tumor microenvironment score, expression of CD274, CTLA4, and PDCD1, and abundance of CD8 + T infiltration in group A were higher than in group B. Using a combination of LASSO and COX regression analysis, we identified 10 molecules significant to prognosis from differentially expressed genes between the two groups and constructed a cuproptosis-related scoring system (CRSS). Compared with the American Joint Committee on Cancer (AJCC) staging system, CRSS more accurately stratified CM patient risk and guided immunotherapy. CRSS successfully stratified risk and predicted the effect of immunotherapy in 869 patients with eight CM immunotherapy datasets and multiple other tumor immunotherapy cohorts. The nomogram model, which combined AJCC stage and CRSS, greatly improved the ability and accuracy of prognosis prediction. In general, our cuproptosis-related scoring system and nomogram model accurately stratified risk in CM patients and effectively predicted prognosis and the effect of immunotherapy in CM patients.

A novel RNA modification prognostic signature for predicting the characteristics of the tumor microenvironment in gastric cancer

Gastric cancer (GC) is one of the most common neoplastic malignancies, which permutes a fourth of cancer-related mortality globally. RNA modification plays a significant role in tumorigenesis, the underlying molecular mechanism of how different RNA modifications directly affect the tumor microenvironment (TME) in GC is unclear. Here, we profiled the genetic and transcriptional alterations of RNA modification genes (RMGs) in GC samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Through the unsupervised clustering algorithm, we identified three distinct RNA modification clusters and found that they participate in different biological pathways and starkly correlate with the clinicopathological characteristics, immune cell infiltration, and prognosis of GC patients. Subsequently, univariate Cox regression analysis unveiled 298 of 684 subtype-related differentially expressed genes (DEGs) are tightly interwoven to prognosis. In addition, we conducted the principal component analysis to develop the RM_Score system, which was used to quantify and predict the prognostic value of RNA modification in GC. Our analysis indicated that patients with high RM_Score were characterized by higher tumor mutational burden, mutation frequency, and microsatellite instability which were more susceptible to immunotherapy and had a favorable prognosis. Altogether, our study uncovered RNA modification signatures that may have a potential role in the TME and prediction of clinicopathological characteristics. Identification of these RNA modifications may provide a new understanding of immunotherapy strategies for gastric cancer.

Identification of angiogenesis-related subtypes, the development of prognostic models, and the landscape of tumor microenvironment infiltration in colorectal cancer

Background: Angiogenesis is one of the most prominent markers of cancer progression and contributes to tumor metastasis and prognosis. Anti-angiogenic drugs have proven effective in treating metastatic colorectal cancer. However, there is some uncertainty regarding the potential role of angiogenesis-related genes in the tumor microenvironment. Methods: We analyzed 1,214 colorectal cancer samples to identify alterations in angiogenesis-related genes (ARGs), and then correlated angiogenesis with clinical features, prognosis, and TME. The ARGs expression profiles in colorectal cancer were analyzed using three computational methods (CIBERSORT, ssGSEA, and MCPcounter) and provided a systematic immune landscape. Patients with CRC were classified into two subtypes based on consensus clustering analysis of angiogenesis-related genes. The revealed differentially expressed genes between the two subtypes were used to create and validate ARGscore prognostic models. In addition, we collected eight colorectal cancer patient specimens and performed RT-qPCR to validate the signature gene expression. Results: We assessed the expression patterns of ARGs in colorectal cancer. We identified two molecular subtypes and confirmed that the expression of ARGs was associated with prognosis and TME characteristics. Based on differentially expressed genes between subtypes, we constructed ARGscore and evaluated their predictive power for the survival of colorectal cancer patients. We also developed an accurate nomogram to make the ARGscore more clinically useful. In addition, ARGscore was significantly correlated with microsatellite instability, cancer stem cells, and chemotherapeutic drug sensitivity. Patients with ARGscore-low characterized by immune activation and microsatellite instability high had a better prognosis. Conclusion: ARGs expression influenced the prognosis, clinicopathological features, and tumor stromal immune microenvironment in colorectal cancer. We developed a new risk model, ARGscore, for the treatment and prognosis of CRC patients and validated its promising predictive power. These findings will enable us to understand colorectal cancer better, assess prognoses, and develop more effective treatment options.

Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer

Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear. Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features. Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore. Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination.

Identification of Cuproptosis-Related Subtypes in Lung Cancer, Characterization of Tumor Microenvironment Infiltration, and Establishment of a Prognostic Model

Cuproptosis, a recently found kind of programmed cell death, has been linked to tumor development, prognosis, and therapeutic response. The roles of cuproptosis-related genes (CRG) in the tumor microenvironment (TME) are, nevertheless, unknown. We evaluated alterations in CRG and assessed the related expression patterns in 1445 lung cancer (LC) samples from three separate datasets, analyzing genetic, and transcriptional domains. We discovered two separate molecular subtypes of CRG and discovered that various subtypes of CRG were connected with patient clinical features and prognosis. Furthermore, we discovered connections between distinct CRG subtypes and TME cell infiltration features. The CRG_score was then developed and validated for predicting overall survival (OS). Following that, we investigated the relationship between CRG_score and the cancer stem cell (CSC) index and chemotherapeutic treatment sensitivity. In addition, we created a very accurate nomogram to increase the clinical usefulness of CRG_score. The potential roles of CRG in the tumor-immune-microenvironment, clinical characteristics, and prognosis in LC are demonstrated by our multiplex study. These findings expand our understanding of CRG in LC and may open up new options for assessing LC patients' prognosis and generating more effective immunotherapeutic treatments.

Vascular Niche Facilitates Acquired Drug Resistance to c-Met Inhibitor in Originally Sensitive Osteosarcoma Cells

Osteosarcoma (OS) is the most common primary bone tumor in children and adolescents characterized by drug resistance and poor prognosis. As one of the key oncogenes, c-Met is recognized as a promising therapeutic target for OS. In this report, we show that c-Met inhibitor PF02341066 specifically killed OS cells with highly phosphorylated c-Met in vitro. However, the inhibitory effect of PF02341066 was abrogated in vivo due to interference from the vascular niche. OS cells adjacent to microvessels or forming vascular mimicry suppressed c-Met expression and phosphorylation. Moreover, VEGFR2 was activated in OS cells and associated with acquired drug resistance. Dual targeting of c-Met and VEGFR2 could effectively shrink the tumor size in a xenograft model. c-Met-targeted therapy combined with VEGFR2 inhibition might be beneficial to achieve an ideal therapeutic effect in OS patients. Together, our results confirm the pivotal role of tumor heterogeneity and the microenvironment in drug response and reveal the molecular mechanism underlying acquired drug resistance to c-Met-targeted therapy.

Engineering chimeric antigen receptor T cells for solid tumour therapy

Cell-based immunotherapy, for example, chimeric antigen receptor T (CAR-T) cell immunotherapy, has revolutionized cancer treatment, particularly for blood cancers. However, factors such as insufficient T cell tracking, tumour heterogeneity, inhibitory tumour microenvironment (TME) and T cell exhaustion limit the broad application of CAR-based immunotherapy for solid tumours. In particular, the TME is a complex and evolving entity, which is composed of cells of different types (e.g., cancer cells, immune cells and stromal cells), vasculature, soluble factors and extracellular matrix (ECM), with each component playing a critical role in CAR-T immunotherapy. Thus, developing approaches to mitigate the inhibitory TME factors is critical for future success in applying CAR-T cells for solid tumour treatment. Accordingly, understanding the bilateral interaction of CAR-T cells with the TME is in pressing need to pave the way for more efficient therapeutics. In the following review, we will discuss TME-associated aspects with an emphasis on T cell trafficking, ECM barriers, abnormal vasculature, solid tumour heterogenicity and immune suppressive microenvironment. We will then summarize current engineering strategies to overcome the challenges posed by the TME-associated factors. Lastly, the future directions for engineering efficient CAR-T cells for solid tumour therapy will be discussed.

Targeting cell death pathways for cancer therapy: recent developments in necroptosis, pyroptosis, ferroptosis, and cuproptosis research

Many types of human cells self-destruct to maintain biological homeostasis and defend the body against pathogenic substances. This process, called regulated cell death (RCD), is important for various biological activities, including the clearance of aberrant cells. Thus, RCD pathways represented by apoptosis have increased in importance as a target for the development of cancer medications in recent years. However, because tumor cells show avoidance to apoptosis, which causes treatment resistance and recurrence, numerous studies have been devoted to alternative cancer cell mortality processes, namely necroptosis, pyroptosis, ferroptosis, and cuproptosis; these RCD modalities have been extensively studied and shown to be crucial to cancer therapy effectiveness. Furthermore, evidence suggests that tumor cells undergoing regulated death may alter the immunogenicity of the tumor microenvironment (TME) to some extent, rendering it more suitable for inhibiting cancer progression and metastasis. In addition, other types of cells and components in the TME undergo the abovementioned forms of death and induce immune attacks on tumor cells, resulting in enhanced antitumor responses. Hence, this review discusses the molecular processes and features of necroptosis, pyroptosis, ferroptosis, and cuproptosis and the effects of these novel RCD modalities on tumor cell proliferation and cancer metastasis. Importantly, it introduces the complex effects of novel forms of tumor cell death on the TME and the regulated death of other cells in the TME that affect tumor biology. It also summarizes the potential agents and nanoparticles that induce or inhibit novel RCD pathways and their therapeutic effects on cancer based on evidence from in vivo and in vitro studies and reports clinical trials in which RCD inducers have been evaluated as treatments for cancer patients. Lastly, we also summarized the impact of modulating the RCD processes on cancer drug resistance and the advantages of adding RCD modulators to cancer treatment over conventional treatments.

Rethinking the chemokine cascade in brain metastasis: Preventive and therapeutic implications

Brain metastasis (BrM) is one of the major causes of death in cancer patients and is associated with an estimated 10-40 % of total cancer cases. The survival rate of brain metastatic patients has not improved due to intratumor heterogeneity, the survival adaptations of brain homing metastatic cells, and the lack of understanding of underlying molecular mechanisms that limit the availability of effective therapies. The heterogeneous population of immune cells and tumor-initiating cells or cancer stem cells in the tumor microenvironment (TME) release various factors, such as chemokines that upon binding to their cognate receptors enhance tumor growth at primary sites and help tumor cells metastasize to the brain. Furthermore, brain metastatic sites have unique heterogeneous microenvironment that fuels cancer cells in establishing BrM. This review explores the crosstalk of chemokines with the heterogeneous TME during the progression of BrM and recognizes potential therapeutic approaches. We also discuss and summarize different targeted, immunotherapeutic, chemotherapeutic, and combinatorial strategies (with chemo-/immune- or targeted-therapies) to attenuate chemokines mediated BrM.

Chemokine clouding and liver cancer heterogeneity: Does it impact clinical outcomes?

Tumor heterogeneity is a predominant feature of hepatocellular carcinoma (HCC) that plays a crucial role in chemoresistance and limits the efficacy of available chemo/immunotherapy regimens. Thus, a better understanding regarding the molecular determinants of tumor heterogeneity will help in developing newer strategies for effective HCC management. Chemokines, a sub-family of cytokines are one of the key molecular determinants of tumor heterogeneity in HCC and are involved in cell survival, growth, migration, and angiogenesis. Herein, we provide a panoramic insight into the role of chemokines in HCC heterogeneity at genetic, epigenetic, metabolic, immune cell composition, and tumor microenvironment levels and its impact on clinical outcomes. Interestingly, our in-silico analysis data showed that expression of chemokine receptors impacts infiltration of various immune cell populations into the liver tumor and leads to heterogeneity. Thus, it is evident that aberrant chemokines clouding impacts HCC tumor heterogeneity and understanding this phenomenon in depth could be harnessed for the development of personalized medicine strategies in future.

Risk model of hepatocellular carcinoma based on cuproptosis-related genes

Background: Owing to the heterogeneity displayed by hepatocellular carcinoma (HCC) and the complexity of tumor microenvironment (TME), it is noted that the long-term effectiveness of the cancer therapy poses a severe clinical challenge. Hence, it is essential to categorize and alter the treatment intervention decisions for these tumors.

Materials and methods: “ConsensusClusterPlus” tool was used for developing a secure molecular classification system that was based on the cuproptosis-linked gene expression. Furthermore, all clinical properties, pathway characteristics, genomic changes, and immune characteristics of different cell types involved in the immune pathways were also assessed. Univariate Cox regression and the least absolute shrinkage and selection operator (Lasso) analyses were used for designing the prognostic risk model associated with cuproptosis.

Results: Three cuproptosis-linked subtypes (clust1, clust2, and clust3) were detected. Out of these, Clust3 showed the worst prognosis, followed by clust2, while Clust1 showed the best prognosis. Three subtypes had significantly different enrichment in pathways related to Tricarboxylic Acid (TCA) cycle, cell cycle, and cell senescence (p < 0.01). The clust3 subtype with poor prognosis had a low “ImmuneScore” and low immune cell infiltration, and the three subtypes had significant differences in the antigen processing and presentation pathway of the macrophages. Clust1 had a low TIDE score and was sensitive to immunotherapy. Then, according to the prognosis-related genes of cuproptosis, a prognosis risk model related to cuproptosis was constructed, containing seven genes (KIF2C, PTTG1, CENPM, CDC20, CYP2C9, SFN, and CFHR3). “High” group had a higher TIDE score compared to the TIDE score value shown by the “Low” group, which benefited less from immunotherapy, whereas the “High” group patients were more sensitive to the conventional drugs. Finally, the prognosis risk model related to cuproptosis was combined with clinical pathological characteristics to further improve the prognostic model and survival prediction.

Conclusion: Three new molecular subgroups based on cuproptosis-linked genes were revealed, and a cuproptosis-related prognostic risk model comprising seven genes was established in this study, which could assist in predicting the prognosis and identifying the patients benefit from immunotherapy.

Molecular Typing Based on Oxidative Stress Genes and Establishment of Prognostic Characteristics of 7 Genes in Lung Adenocarcinoma

Oxidative stress could maintain different biological processes in human cancer. However, the effect of oxidative stress on lung adenocarcinoma (LUAD) should be studied. This study analyzed the expression and clinical importance of oxidative stress in LUAD in detail. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were employed for obtaining LUAD expression profiles. Based on oxidative stress-related genes, molecular subtypes substantially correlated with the LUAD prognosis were discovered with ConsensusClusterPlus. Differentially expressed genes (DEGs) among subtypes were found using the Limma software package. Least absolute shrinkage and selection operator- (Lasso-) Cox analysis was employed to create the polygenic risk model. RiskScore and clinically relevant features were used to create nomograms. By utilizing oxidative stress-related genes and reliable clustering, stable molecular subtypes were first discovered. The prognosis, clinical characteristics, route characteristics, and immunological characteristics of these three molecular subtypes were all different. Subsequently, by using differential expression genes among molecular subtypes and Lasso, 7 main genes linked with the oxidative stress phenotype were discovered. A prognostic risk model was also built on the basis of major genes associated with the oxidative stress phenotype. The model demonstrated a high level of resilience and was unaffected by clinical-pathological features. It played a stable predictive role in independent datasets. Ultimately, to improve the prognosis model and survival prediction, RiskScore (RS) was combined with clinicopathological variables, and a decision tree model was used. The model exhibited a high prediction accuracy as well as the ability to predict survival. This research found that oxidative stress-related genes have a major involvement in the onset and progression of LUAD and that they may influence LUAD susceptibility to immunotherapy and standard chemotherapy. Furthermore, the identified risk models for 7 genes linked with oxidative stress exhibited could assist clinical treatment decisions and prognosis prediction. The classifier could be used as a molecular diagnostic tool for assessing LUAD patients' prognosis risk.

The Expression Pattern of Bcl-2 and Bax in the Tumor and Stromal Cells in Colorectal Carcinoma

Background and objectives: The epithelial and stromal tissues both play a role in the progression of colorectal cancer (CRC). The aim of this study was to assess the expression of anti-apoptotic Bcl-2 and pro-apoptotic Bax in the epithelium as well as the lamina propria of normal colonic controls, low-grade tumor samples and high-grade tumor samples. Materials and Methods: A total of 60 samples consisting of both normal colonic and carcinoma samples was collected from the Department of Pathology, Cytology and Forensic Medicine, University Hospital Center, Split from January 2020 to December 2021. The expression of Bcl-2 and Bax markers was semi-quantitatively and quantitatively evaluated by recording immunofluorescence stain intensity and by counting stained cells in the lamina propria and epithelium. Analysis of positive cells was performed using the Mann-Whitney test. Results: In all samples, Bcl-2 was significantly more expressed in the lamina propria when compared with the epithelium. Bax was significantly more expressed in the epithelium of normal and low-grade cancer samples when compared with their respective laminae propriae. The percentage of Bcl-2-positive cells in lamina propria is about two times lower in high-grade CRC and about three times lower in low-grade CRC in comparison with healthy controls. Contrary to this, the percentage of Bax-positive cells was greater in the epithelium of low-grade CRC in comparison with healthy control and high-grade CRC. Conclusions: Our study provides a new insight into Bcl-2 and Bax expression pattern in CRC. Evaluation of Bcl-2 expression in the lamina propria and Bax expression in the epithelium could provide important information for colorectal cancer prognosis as well as potential treatment strategies.

Proteomics approaches to characterize the immune responses in cancer

Despite the dynamic development of cancer research, annually millions of people die of cancer. The human immune system is the major 'guard' against tumor development. Unfortunately, cancer cells have the ability to evade the immune system and continue to grow. The proper understanding of the intricate immune response in tumorigenesis remains the holy grail of cancer immunology and designing effective immunotherapy. To decode the immune responses in cancer, in recent years, proteomics studies have received considerable attention. Proteomics studies focus on the detection and quantification of proteins, which are the effectors of biological functions, and as such, are proven to reflect the cell state more accurately, in comparison to genomic or transcriptomic studies. In this review, we discuss the proteomics studies applied to characterize the immune responses in cancer and tumor immune microenvironment heterogeneity. Further, we describe emerging single-cell proteomics approaches that have the potential to be applied in cancer immunity studies.

The expression pattern of Immune checkpoints after chemo/radiotherapy in the tumor microenvironment

As a disease with the highest disease-associated burden worldwide, cancer has been the main subject of a considerable proportion of medical research in recent years, intending to find more effective therapeutic approaches with fewer side effects. Combining conventional methods with newer biologically based treatments such as immunotherapy can be a promising approach to treating different tumors. The concept of "cancer immunoediting" that occurs in the field of the tumor microenvironment (TME) is the aspect of cancer therapy that has not been at the center of attention. One group of the role players of the so-called immunoediting process are the immune checkpoint molecules that exert either co-stimulatory or co-inhibitory effects in the anti-tumor immunity of the host. It involves alterations in a wide variety of immunologic pathways. Recent studies have proven that conventional cancer therapies, such as chemotherapy, radiotherapy, or a combination of them, i.e., chemoradiotherapy, alter the "immune compartment" of the TME. The mentioned changes encompass a wide range of variations, including the changes in the density and immunologic type of the tumor-infiltrating lymphocytes (TILs) and the alterations in the expression patterns of the different immune checkpoints. These rearrangements can have either anti-tumor immunity empowering or immune attenuating sequels. Thus, recognizing the consequences of various chemo(radio)therapeutic regimens in the TME seems to be of great significance in the evolution of therapeutic approaches. Therefore, the present review intends to summarize how chemo(radio)therapy affects the TME and specifically some of the most important, well-known immune checkpoints' expressions according to the recent studies in this field.

Pyroptosis-Related Signature as Potential Biomarkers for Predicting Prognosis and Therapy Response in Colorectal Cancer Patients

Background: Abnormal mucosal inflammation is a critical risk factor for pathogenesis and progression of colorectal cancer (CRC). As a type of proinflammatory death, pyroptosis can recast a suitable microenvironment to promote tumor growth. However, the potential role of pyroptosis in CRC remains unclear.

Methods: A total of 38 pyroptosis-related gene (PRG) expression profiles and clinical information were collected from multiple public datasets. Bioinformatics methods were used to analyze the clinical significance, functional status, immune infiltration, genomic alteration, and drug sensitivity in different subgroups. Whole-genome microarray analysis was performed to analyze the regulation of gut microbiota on the expression of PRGs.

Results: Two distinct molecular subtypes were identified and suggested that multilayer PRG alterations were associated with patient clinicopathological features, prognosis, and tumor microenvironment (TME) infiltrating characteristics. Furthermore, we obtained eight PRG signatures by applying differential expression analysis and univariate Cox and Lasso regression analyses. A risk prognosis model was constructed for predicting overall survival (OS) and recurrence-free survival (RFS) based on the PRG signature. There were significant differences in clinical characteristics, 22 immune cells, and immune functions between the high- and low-risk groups. In addition, the PRG signature was significantly associated with the microsatellite instability (MSI), tumor mutation burden (TMB), cancer stem cell (CSC) index, immunotherapeutic characteristics, and chemotherapeutic drug sensitivity. Moreover, the in vitro experiments had shown that Fusobacterium nucleatum (F.n) could affect the CASP6 expression, which was associated with the chemoresistance to 5-fluorouracil (5-Fu) in CRC.

Conclusion: Our findings provided a foundation for future research targeting pyroptosis and a new insight into the prognosis and immune cell infiltration of CRC, and they suggested that F.n might influence CRC progression through pyroptosis.

Clinical significance and immune landscape of KIR2DL4 and the senescence-based signature in cutaneous melanoma

Senescence is an effective barrier to tumor progression. Mutations that inhibit senescence and promote cell division are mandatory for the development of cancer. Therefore, it is particularly important to explore the differences between cutaneous melanoma (CM) patients with severe and mild degrees of senescence. We clustered all the patients with CM in the Cancer Genome Atlas (TCGA) database based on all the genes of the senescence pathway in the cellAge and MSigDB database. The prognosis, immunotherapy effect, tumor microenvironment score, NRAS mutation rate, expression of CD274, CTLA4, and PDCD1, and abundance of CD8+ T and NK cell infiltration in the younger group of patients (YG) were higher than those in the older group (OG). Compared with the American Joint Committee on Cancer (AJCC) stage, the risk scoring system stratified the risk of CM patients and guided immunotherapy more accurately. The nomogram model, which combined the AJCC stage and risk score, greatly improved the ability and accuracy of prognosis prediction. As KIR2DL4 is the core molecule in the risk scoring system (RSS), knocking down the KIR2DL4 of human NK cells in vitro can inhibit the cytotoxicity of NK cells and can also inhibit the secretion of tumor necrosis factor-α and interferon-γ by NK cells. In contrast, upregulation of KIR2DL4 can activate the MEK/ERK signaling pathway, which is the activation pathway of NK cells. OurRSS and nomogram model can accurately stratify the risk of CM patients and effectively predict the effect of immunotherapy and prognosis in CM patients.

Morphological Dependence of Breast Cancer Cell Responses to Doxorubicin on Micropatterned Surfaces

Cell morphology has been widely investigated for its influence on the functions of normal cells. However, the influence of cell morphology on cancer cell resistance to anti-cancer drugs remains unclear. In this study, micropatterned surfaces were prepared and used to control the spreading area and elongation of human breast cancer cell line. The influences of cell adhesion area and elongation on resistance to doxorubicin were investigated. The percentage of apoptotic breast cancer cells decreased with cell spreading area, while did not change with cell elongation. Large breast cancer cells had higher resistance to doxorubicin, better assembled actin filaments, higher DNA synthesis activity and higher expression of P-glycoprotein than small breast cancer cells. The results suggested that the morphology of breast cancer cells could affect their resistance to doxorubicin. The influence was correlated with cytoskeletal organization, DNA synthesis activity and P-glycoprotein expression.

(Dis)similarities between the Decidual and Tumor Microenvironment

Placenta-specific trophoblast and tumor cells exhibit many common characteristics. Trophoblast cells invade maternal tissues while being tolerated by the maternal immune system. Similarly, tumor cells can invade surrounding tissues and escape the immune system. Importantly, both trophoblast and tumor cells are supported by an abetting microenvironment, which influences invasion, angiogenesis, and immune tolerance/evasion, among others. However, in contrast to tumor cells, the metabolic, proliferative, migrative, and invasive states of trophoblast cells are under tight regulatory control. In this review, we provide an overview of similarities and dissimilarities in regulatory processes that drive trophoblast and tumor cell fate, particularly focusing on the role of the abetting microenvironments.

Nucleolin Overexpression Predicts Patient Prognosis While Providing a Framework for Targeted Therapeutic Intervention in Lung Cancer

Simple Summary

Despite the clinical benefit of new anticancer therapies, such as immune checkpoint inhibitors, lung cancer remains the most frequent cause of cancer-related death worldwide, thus supporting the need to develop novel anticancer treatments. Endothelial cells of the tumor-associated vasculature are easily accessible to drugs administered intravenously, besides having greater genetic stability than neoplastic cells and thus lowering the risk of developing drug resistance. In this respect, the identification of alternative targets, and therapeutic strategies, within the tumor vasculature is of high relevance. Accordingly, this work aimed at characterizing nucleolin expression in patient-derived pulmonary carcinomas and further validating nucleolin as a novel target to mediate successful therapeutic interventions against human lung cancers. The highlighted prognostic value of nucleolin points towards the applicability of nucleolin-based targeting strategies against nucleolin^high pulmonary carcinomas, present in every disease stage, in a clinical trial setting.

Abstract

Notwithstanding the advances in the treatment of lung cancer with immune checkpoint inhibitors, the high percentage of non-responders supports the development of novel anticancer treatments. Herein, the expression of the onco-target nucleolin in patient-derived pulmonary carcinomas was characterized, along with the assessment of its potential as a therapeutic target. The clinical prognostic value of nucleolin for human pulmonary carcinomas was evaluated through data mining from the Cancer Genome Atlas project and immunohistochemical detection in human samples. Cell surface expression of nucleolin was evaluated by flow cytometry and subcellular fraction Western blotting in lung cancer cell lines. Nucleolin mRNA overexpression correlated with poor overall survival of lung adenocarcinoma cancer patients and further predicted the disease progression of both lung adenocarcinoma and squamous carcinoma. Furthermore, a third of the cases presented extra-nuclear expression, contrasting with the nucleolar pattern in non-malignant tissues. A two- to twelve-fold improvement in cytotoxicity, subsequent to internalization into the lung cancer cell lines of doxorubicin-loaded liposomes functionalized by the nucleolin-binding F3 peptide, was correlated with the nucleolin cell surface levels and the corresponding extent of cell binding. Overall, the results suggested nucleolin overexpression as a poor prognosis predictor and thus a target for therapeutic intervention in lung cancer.

Intratumoral Platelets: Harmful or Incidental Bystanders of the Tumor Microenvironment?

Simple Summary

The tumor microenvironment (TME) is the complex and heterogenous ecosystem of solid tumors known to influence their growth and their progression. Besides tumor cells, the TME comprises a variety of host-derived cell types, ranging from endothelial cells to fibroblasts and immune cells. Clinical and experimental data are converging to indicate that platelets, originally known for their fundamental hemostatic function, also participate in tumor development and shaping of the TME. Considering the abundance of antiplatelet drugs, understanding if and how platelets contribute to the TME may lead to new therapeutic tools for improved cancer prevention and treatments.

Abstract

The tumor microenvironment (TME) has gained considerable interest because of its decisive impact on cancer progression, response to treatment, and disease recurrence. The TME can favor the proliferation, dissemination, and immune evasion of cancer cells. Likewise, there is accumulating evidence that intratumoral platelets could favor the development and aggressiveness of solid tumors, notably by influencing tumor cell phenotype and shaping the vascular and immune TME components. Yet, in contrast to other tumor-associated cell types like macrophages and fibroblasts, platelets are still often overlooked as components of the TME. This might be due, in part, to a deficit in investigating and reporting the presence of platelets in the TME and its relationships with cancer characteristics. This review summarizes available evidence from clinical and animal studies supporting the notion that tumor-associated platelets are not incidental bystanders but instead integral and active components of the TME. A particular emphasis is given to the description of intratumoral platelets, as well as to the functional consequences and possible mechanisms of intratumoral platelet accumulation.

Cross-Talk Between m6A- and m5C-Related lncRNAs to Construct a Novel Signature and Predict the Immune Landscape of Colorectal Cancer Patients

Background

N6-methyladenosine (m⁶A) and 5-methylcytosine (m⁵C) can modify long non-coding RNAs (lncRNAs), thereby affecting tumorigenesis and tumor progression. However, there is a lack of knowledge regarding the potential roles and cross-talk of m⁶A- and m⁵C-related lncRNAs in the tumor microenvironment (TME) and their effect on prognosis.

Methods

We systematically evaluated the expression patterns of m⁶A- and m⁵C-related lncRNAs in 1358 colorectal cancer (CRC) samples from four datasets. Consensus clustering was conducted to identify molecular subtypes of CRC, and the clinical significance, TME, tumor-infiltrating immune cells (TIICs), and immune checkpoints in the different molecular subtypes were analyzed. Finally, we established a m⁶A- and m⁵C-related lncRNA signature and a prognostic nomogram.

Results

We identified 141 m⁶A- and m⁵C-related lncRNAs by co-expression analysis, among which 23 lncRNAs were significantly associated with the overall survival (OS) of CRC patients. Two distinct molecular subtypes (cluster A and cluster B) were identified, and these two distinct molecular subtypes could predict clinicopathological features, prognosis, TME stromal activity, TIICs, immune checkpoints. Next, a m⁶A- and m⁵C-related lncRNA signature for predicting OS was constructed, and its predictive capability in CRC patients was validated. We then constructed a highly accurate nomogram for improving the clinical applicability of the signature. Analyses of clinicopathological features, prognosis, TIICs, cancer stem cell (CSC), and drug response revealed significant differences between two risk groups. In addition, we found that patients with a low-risk score exhibited enhanced response to anti-PD-1/L1 immunotherapy. Functional enrichment analysis showed that these lncRNAs related to the high-risk group were involved in the development and progression of CRC.

Conclusions

We conducted a comprehensive analysis of m⁶A- and m⁵C-related lncRNAs in CRC and revealed their potential functions in predicting tumor-immune-stromal microenvironment, clinicopathological features, and prognosis, and determined their role in immunotherapy. These findings may improve our understanding of the cross-talk between m⁶A- and m⁵C-related lncRNAs in CRC and pave a new road for prognosis assessment and more effective immunotherapy strategies.

Clinical utility of a regional precision medicine molecular tumor board and challenges to implementation

Purpose

Molecular tumor boards provide precision treatment recommendations based on cancer genomic profile. However, practical barriers limit their benefits. We studied the clinical utility of the precision medicine molecular tumor board (PMMTB) and described challenges with PMMTB implementation.

Methods

An observational cohort study included patients reviewed by the PMMTB between September 2015 to December 2017. Patients who had consented to the registry study were included. The primary endpoint of this study was time on treatment (ToT) ratio. Clinical utility was established if the primary endpoint had least 15% of patients achieving a ToT ratio of ≥1.3.

Results

Overall, 278 patients were presented to the PMMTB and 113 cases were included in the final analysis. The PMMTB identified at least one nonstandard of care (SOC) clinically actionable mutation for 69.0% (78/113) of cases. In patients who received non-SOC treatment, 43.8% (7/16) achieved a ToT ratio of 1.3 or more (p < 0.001). Fifty-nine patients did not receive non-SOC recommendations. Reasons for not pursuing treatment included 35.6% having response to current treatment, 20.3% died prior to starting or considering PMMTB recommendations, 13.6% pursued other treatment options based on clinician discretion, another 10.2% pursued other treatment options because clinical trials recommended were not geographically accessible, 8.5% had rapid decline of performance status, 6.8% lacked of financial support for treatment, and 5.1% were excluded from clinical trials due to abnormal laboratory values.

Conclusion

The regional PMMTB non-SOC recommendations benefitted a majority of patients and additional processes were implemented to assist with non-SOC treatment accessibility.

Dual Effect of Immune Cells within Tumour Microenvironment: Pro- and Anti-Tumour Effects and Their Triggers

Our body is constantly exposed to pathogens or external threats, but with the immune response that our body can develop, we can fight off and defeat possible attacks or infections. Nevertheless, sometimes this threat comes from an internal factor. Situations such as the existence of a tumour also cause our immune system (IS) to be put on alert. Indeed, the link between immunology and cancer is evident these days, with IS being used as one of the important targets for treating cancer. Our IS is able to eliminate those abnormal or damaged cells found in our body, preventing the uncontrolled proliferation of tumour cells that can lead to cancer. However, in several cases, tumour cells can escape from the IS. It has been observed that immune cells, the extracellular matrix, blood vessels, fat cells and various molecules could support tumour growth and development. Thus, the developing tumour receives structural support, irrigation and energy, among other resources, making its survival and progression possible. All these components that accompany and help the tumour to survive and to grow are called the tumour microenvironment (TME). Given the importance of its presence in the tumour development process, this review will focus on one of the components of the TME: immune cells. Immune cells can support anti-tumour immune response protecting us against tumour cells; nevertheless, they can also behave as pro-tumoural cells, thus promoting tumour progression and survival. In this review, the anti-tumour and pro-tumour immunity of several immune cells will be discussed. In addition, the TME influence on this dual effect will be also analysed.

Next-generation cancer organoids

Organotypic models of patient-specific tumours are revolutionizing our understanding of cancer heterogeneity and its implications for personalized medicine. These advancements are, in part, attributed to the ability of organoid models to stably preserve genetic, proteomic, morphological and pharmacotypic features of the parent tumour in vitro, while also offering unprecedented genomic and environmental manipulation. Despite recent innovations in organoid protocols, current techniques for cancer organoid culture are inherently uncontrolled and irreproducible, owing to several non-standardized facets including cancer tissue sources and subsequent processing, medium formulations, and animal-derived three-dimensional matrices. Given the potential for cancer organoids to accurately recapitulate the intra- and intertumoral biological heterogeneity associated with patient-specific cancers, eliminating the undesirable technical variability accompanying cancer organoid culture is necessary to establish reproducible platforms that accelerate translatable insights into patient care. Here we describe the current challenges and recent multidisciplinary advancements and opportunities for standardizing next-generation cancer organoid systems.

Myelin and lymphocyte protein serves as a prognostic biomarker and is closely associated with the tumor microenvironment in the nephroblastoma

Nephroblastoma, also known as Wilms' tumor (WT), is the most common renal tumor that occurs in children. Although the efficacy of treatment has been significantly improved by a series of comprehensive treatments, some patients still have poor prognosis. Myelin and lymphocyte (MAL) protein, a highly hydrophobic integrated membrane‐bound protein, has been implicated in many tumors and is also closely linked to kidney development. However, the relationship between MAL and WT has not yet been elucidated. Therefore, we attempted to evaluate the feasibility of MAL as a promising prognosis factor for WT. The differential expression of MAL was investigated using TARGET database and was verified using the Gene Expression Omnibus database and real‐time quantitative PCR. The prognostic ability of MAL was determined using Kaplan–Meier and Cox regression analyses. Pearson correlation analysis was applied to explore the relationship between MAL expression and methylation sites. The ESTIMATE and CIBERSORT algorithms showed that MAL expression was associated with the WT tumor microenvironment. Gene Set Enrichment Analysis (GSEA) indicated that multiple signaling pathways closely associated with tumorigenesis were differentially enriched between the high‐ and low‐MAL groups. In conclusion, our study comprehensively explored the potential of MAL as a prognosis factor for WT. Meanwhile, we also demonstrated that MAL, as a prognostic factor for WT, may be closely related to the tumor microenvironment.

EMT and Inflammation: Crossroads in HCC

Hepatocellular carcinoma is one of the major causes of cancer-related deaths worldwide and is associated with several inflammatory mediators, since 90% of HCCs occur based on chronic hepatitis B or C, alcoholism or increasingly metabolic syndrome-associated inflammation. EMT is a physiological process, with coordinated changes in epithelial gene signatures and is regulated by multiple factors, including cytokines and growth factors such as TGFβ, EGF, and FGF. Recent reports propose a strong association between EMT and inflammation, which is also correlated with tumor aggressiveness and poor outcomes. Cellular heterogeneity results collectively as an outcome of EMT, inflammation, and the tumor microenvironment, and it plays a fundamental role in the progression, complexity of cancer, and chemoresistance. In this review, we highlight recent developments concerning the association of EMT and inflammation in the context of HCC progression. Identifying potential EMT-related biomarkers and understanding EMT regulatory molecules will likely contribute to promising developments in clinical practice and will be a valuable tool for predicting metastasis in general and specifically in HCC.