Influence of tumour micro-environment heterogeneity on therapeutic response

Advances in nuclear medicine-based molecular imaging in head and neck squamous cell carcinoma

Head and neck squamous cell carcinomas (HNSCCs) are often aggressive, making advanced disease very difficult to treat using contemporary modalities, such as surgery, radiation therapy, and chemotherapy. However, targeted therapy, e.g., cetuximab, an epidermal growth factor receptor inhibitor, has demonstrated survival benefit in HNSCC patients with locoregional failure or distant metastasis. Molecular imaging aims at various biomarkers used in targeted therapy, and nuclear medicine-based molecular imaging is a real-time and non-invasive modality with the potential to identify tumor in an earlier and more treatable stage, before anatomic-based imaging reveals diseases. The objective of this comprehensive review is to summarize recent advances in nuclear medicine-based molecular imaging for HNSCC focusing on several commonly radiolabeled biomarkers. The preclinical and clinical applications of these candidate imaging strategies are divided into three categories: those targeting tumor cells, tumor microenvironment, and tumor angiogenesis. This review endeavors to expand the knowledge of molecular biology of HNSCC and help realizing diagnostic potential of molecular imaging in clinical nuclear medicine.

Predicting chemotherapy response in non-small-cell lung cancer via computed tomography radiomic features: Peritumoral, intratumoral, or combined?

Purpose

This study aims to evaluate the ability of peritumoral, intratumoral, or combined computed tomography (CT) radiomic features to predict chemotherapy response in non-small cell lung cancer (NSCLC).

Methods

After excluding subjects with incomplete data or other types of treatments, 272 (Dataset 1) and 43 (Dataset 2, external validation) NSCLC patients who were only treated with chemotherapy as the first-line treatment were enrolled between 2015 and 2019. All patients were divided into response and nonresponse based on the response evaluation criteria in solid tumors, version 1.1. By using 3D slicer and morphological operations in python, the intra- and peritumoral regions of lung tumors were segmented from pre-treatment CT images (unenhanced) and confirmed by two experienced radiologists. Then radiomic features (the first order, texture, shape, et al.) were extracted from the above regions of interest. The models were trained and tested in Dataset 1 and further validated in Dataset 2. The performance of models was compared using the area under curve (AUC), confusion matrix, accuracy, precision, recall, and F1-score.

Results

The radiomic model using features from the peritumoral region of 0–3 mm outperformed that using features from 3–6, 6–9, 9–12 mm peritumoral region, and intratumoral region (AUC: 0.95 versus 0.87, 0.86, 0.85, and 0.88). By the fusion of features from 0–3 and 3–6 mm peritumoral regions, the logistic regression model achieved the best performance, with an AUC of 0.97. This model achieved an AUC of 0.85 in the external cohort. Moreover, among the 20 selected features, seven features differed significantly between the two groups (p < 0.05).

Conclusions

CT radiomic features from both the peri- and intratumoral regions can predict chemotherapy response in NSCLC using machine learning models. Combined features from two peritumoral regions yielded better predictions.

Current understanding of the human microbiome in glioma

There is mounting evidence that the human microbiome is highly associated with a wide variety of central nervous system diseases. However, the link between the human microbiome and glioma is rarely noticed. The exact mechanism of microbiota to affect glioma remains unclear. Recent studies have demonstrated that the microbiome may affect the development, progress, and therapy of gliomas, including the direct impacts of the intratumoral microbiome and its metabolites, and the indirect effects of the gut microbiome and its metabolites. Glioma-related microbiome (gut microbiome and intratumoral microbiome) is associated with both tumor microenvironment and tumor immune microenvironment, which ultimately influence tumorigenesis, progression, and responses to treatment. In this review, we briefly summarize current knowledge regarding the role of the glioma-related microbiome, focusing on its gut microbiome fraction and a brief description of the intratumoral microbiome, and put forward the prospects in which microbiome can be applied in the future and some challenges still need to be solved.

Nanomedicines Targeting Metabolism in the Tumor Microenvironment

Cancer cells reprogram their metabolism to meet their growing demand for bioenergy and biosynthesis. The metabolic profile of cancer cells usually includes dysregulation of main nutritional metabolic pathways and the production of metabolites, which leads to a tumor microenvironment (TME) having the characteristics of acidity, hypoxic, and/or nutrient depletion. Therapies targeting metabolism have become an active and revolutionary research topic for anti-cancer drug development. The differential metabolic vulnerabilities between tumor cells and other cells within TME provide nanotechnology a therapeutic window of anti-cancer. In this review, we present the metabolic characteristics of intrinsic cancer cells and TME and summarize representative strategies of nanoparticles in metabolism-regulating anti-cancer therapy. Then, we put forward the challenges and opportunities of using nanoparticles in this emerging field.

Epigenetic and breast cancer therapy: Promising diagnostic and therapeutic applications

The global burden of breast cancer (BC) is increasing significantly. This trend is caused by several factors such as late diagnosis, limited treatment options for certain BC subtypes, drug resistance which all lead to poor clinical outcomes. Recent research has reported the role of epigenetic alterations in the mechanism of BC pathogenesis and its hallmarks include drug resistance and stemness features. The understanding of these modifications and their significance in the management of BC carcinogenesis is challenging and requires further attention. Nevertheless, it promises to provide novel insight needed for utilizing these alterations as potential diagnostic, prognostic markers, predict treatment efficacy, as well as therapeutic agents. This highlights the importance of continuing research development to further advance the existing knowledge on epigenetics and BC carcinogenesis to overcome the current challenges. Hence, this review aims to shed light and discuss the current state of epigenetics research in the diagnosis and management of BC.

Deep learning-based transcription factor activity for stratification of breast cancer patients

Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activities can be used to characterize genomic aberrations in cancer cell. In this study, the activity profile of transcription factors inferred by VIPER algorithm. The autoencoder model was applied for compressing the transcription factor activity profile for obtaining more useful transformed features for stratifying patients into two different breast cancer subtypes. The deep learning-based subtypes exhibited superior prognostic value and yielded better risk-stratification than the transcription factor activity-based method. Importantly, according to transformed features, a deep neural network was constructed to predict the subtypes, and achieved the accuracy of 94.98% and area under the ROC curve of 0.9663, respectively. The proposed subtypes were found to be significantly associated with immune infiltration, tumor immunogenicity and so on. Furthermore, the ceRNA network was constructed for the breast cancer subtypes. Besides, 11 master regulators were found to be associated with patients in cluster 1. Given the robustness performance of our deep learning model over multiple breast cancer cohorts, we expected this model may be useful in the area of prognosis prediction and lead some possibility for personalized medicine in breast cancer patients.

The role of the tumor microenvironment in colorectal cancer and the potential therapeutic approaches

BACKGROUND

Colorectal cancer (CRC) with a high prevalence is recognized as the fourth most common cause of cancer-related death globally. Over the past decade, there has been growing interest in the network of tumor cells, stromal cells, immune cells, blood vessel cells, and fibroblasts that comprise the tumor microenvironment (TME) to identify new therapeutic interventions.

METHODS

Databases, such as Google Scholar, PubMed, and Scopus, were searched to provide an overview of the recent research progress related to targeting the TME as a novel therapeutic approach.

RESULTS

Tumor microenvironment as a result of the cross talk between these cells may result in either advantages or disadvantages in tumor development and metastasis, affecting the signals and responses from the surrounding cells. Whilst chemotherapy has led to an improvement in CRC patients' survival, the metastatic aspect of the disease remains difficult to avoid.

CONCLUSIONS

The present review emphasizes the structure and function of the TME, alterations in the TME, its role in the incidence and progression of CRC, the effects on tumor development and metastasis, and also the potential of its alterations as therapeutic targets. It should be noted that providing novel studies in this field of research might help us to achieve practical therapeutic strategies based on their interaction.

The emerging era of personalized medicine in advanced colorectal cancer

Colorectal cancer (CRC) is a genetically heterogeneous disease with its pathogenesis often driven by varying genetic or epigenetic alterations. This has led to a substantial number of patients developing chemoresistance and treatment failure, resulting in a high mortality rate for advanced disease. Deep molecular analysis has allowed for the discovery of key intestinal signaling pathways which impacts colonic epithelial cell fate, and the integral role of the tumor microenvironment on cancer growth and dissemination. Through transitioning pre-clinical knowledge in research into clinical practice, many potential druggable targets within these pathways have been discovered in the hopes of overcoming the roadblocks encountered by conventional therapies. A personalized approach tailoring treatment according to the histopathological and molecular features of individual tumors can hopefully translate to better patient outcomes, and reduce the rate of recurrence in patients with advanced CRC. Herein, the latest understanding on the molecular science behind CRC tumorigenesis, and the potential treatment targets currently at the forefront of research are summarized.

InterCellDB: A User-Defined Database for Inferring Intercellular Networks

Recent advances in single cell RNA sequencing (scRNA-seq) empower insights into cell-cell crosstalk within specific tissues. However, customizable data analysis tools that decipher intercellular communication from gene expression in association with biological functions are lacking. The authors have developed InterCellDB, a platform that allows a user-defined analysis of intercellular communication using scRNA-seq datasets in combination with protein annotation information, including cellular localization and functional classification, and protein interaction properties. The application of InterCellDB in tumor microenvironment research is exemplified using two independent scRNA-seq datasets from human and mouse and it is demonstrated that InterCellDB-inferred cell-cell interactions and ligand-receptor pairs are experimentally valid.

[Characterization of the tumor microenvironment by highly multiplexed microscopy]

Malignant neoplasms are highly complex ecosystems consisting of tumor cells and the tumor microenvironment (TME), which is composed of structural elements (vessels, fibroblasts, extracellular matrix) and a wide variety of infiltrating immune cell types of the innate and adaptive immune systems. The TME is the main site of tumor cell-immune cell interactions and plays a critical role in antitumoral immunity. Immunotherapies can affect the interactions between immune cell types and tumor cells in the TME, boost immune responses, and lead to tumor elimination. Novel highly multiplexed microscopy techniques, which enable the detection of more than 50 simultaneous markers in tissues, facilitate the in-depth characterization of the TME at single-cell resolution in clinically relevant samples. Detailed knowledge about the cellular and spatial composition of the TME, the specific cell types and their functional properties, and cell-cell interactions-as revealed by highly multiplexed microscopy-will improve our understanding of immunotherapies' mechanisms of action and reveal new potential therapeutic targets and predictive biomarkers.

Comprehensive Bioinformatics Analysis of Toll-Like Receptors (TLRs) in Pan-Cancer

Background

To conduct a comprehensive bioinformatics analysis on the transcriptome signatures of Toll-like receptors (TLRs) in pan-cancer. Materials and methods. A total of 11,057 tissues consisting of 33 types of carcinoma in The Cancer Genome Atlas (TCGA) were retrieved, and then we further explored the correlation between TLRs' expression with tumorigenesis, immune infiltration, and drug sensitivity. We conducted a comprehensive bioinformatics analysis on TLR1 to 10 in pan-cancer, including differential expression analysis between normal and tumor tissues, differential immune subtype correlation, survival analysis, tumor immune infiltration estimating, stemness indices correlation, and drug responses correlation.

Results

TLR2 was highly expressed in most types of tumors. TLR9 was hardly expressed compared to other TLR genes, which lead to TLR9 showing less correlation with both immune-estimate scores and stromal-estimate scores. All the TLRs were related with immune subtype of tumor samples that all of them were differentially expressed in differential immune subtype samples. The expression of TLRs was positively related with immune-estimate scores and stromal-estimate scores in almost all types of tumor. The expression of TLRs was negatively correlated with mRNA expression-based stemness scores (RNAss) in nearly almost type of tumors except kidney renal clear cell carcinoma (KIRC) and also negatively correlated with DNA methylation-based stemness scores (DNAss) in many types of tumors except adrenocortical carcinoma (ACC), cholangiocarcinoma (CHOL), KIRC, acute myeloid leukemia (LAML), low-grade glioma (LGG), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), and uveal melanoma (UVM). The expression of TLR9 was significantly positively correlated with the drug sensitivity of fluphenazine, alectinib, carmustine, and 7-hydroxystaurosporine. TLR7 was significantly positively correlated with the drug sensitivity of alectinib.

Conclusions

Our study reveals the significant role of TLRs family in pan-cancer and provides potential therapeutic strategies of cancer.

A Cu-based nanoplatform for near-infrared light amplified multi-mode prostate cancer specific therapy

Chemodynamic therapy (CDT), as a new method for oncotherapy, can convert less reactive hydrogen peroxide (H₂O₂) into highly toxic hydroxyl radicals (˙OH) in the tumor microenvironment (TME) to kill tumor cells and inhibit tumor growth. However, the TME usually presents a low content of endogenous H₂O₂ and weak acidity, which weakens the therapeutic effect of CDT to a certain extent. Here, we developed a multifunctional nanoplatform based on Cu-doped mesoporous carbon nanospheres loaded with free radical generator 2'-azobis[2-(2-imidazolin-2-yl)propane]-dihydrochloride (AIPH) and polyacrylic acid (Cu-MNCS-AIPH@PAA). Cu-MNCS-AIPH@PAA exhibited high photothermal conversion efficiency, and could not only act as a good photothermal agent for photothermal therapy (PTT) but also trigger AIPH to produce alkyl radicals. In response to the specificity of the TME, Cu-MNCS-AIPH@PAA could generate ˙OH through a Fenton-like reaction for CDT and enhance the efficacy of CDT by a photothermal effect. The excellent anticancer efficiency by the synergistic effect of CDT, PTT and free radicals, high biocompatibility and low adverse effects of Cu-MNCS-AIPH@PAA make it an ideal nanoplatform for tumor therapy.

MALDI-MSI: A Powerful Approach to Understand Primary Pancreatic Ductal Adenocarcinoma and Metastases

Cancer-related deaths are very commonly attributed to complications from metastases to neighboring as well as distant organs. Dissociate response in the treatment of pancreatic adenocarcinoma is one of the main causes of low treatment success and low survival rates. This behavior could not be explained by transcriptomics or genomics; however, differences in the composition at the protein level could be observed. We have characterized the proteomic composition of primary pancreatic adenocarcinoma and distant metastasis directly in human tissue samples, utilizing mass spectrometry imaging. The mass spectrometry data was used to train and validate machine learning models that could distinguish both tissue entities with an accuracy above 90%. Model validation on samples from another collection yielded a correct classification of both entities. Tentative identification of the discriminative molecular features showed that collagen fragments (COL1A1, COL1A2, and COL3A1) play a fundamental role in tumor development. From the analysis of the receiver operating characteristic, we could further advance some potential targets, such as histone and histone variations, that could provide a better understanding of tumor development, and consequently, more effective treatments.

m6A Regulator-Based Methylation Modification Patterns Characterized by Distinct Tumor Microenvironment Immune Profiles in Rectal Cancer

Background

Previous studies reported the related role of RNA n6-methyladenosine (m6A) modification in tumorigenesis and development. However, it is not clear whether m6A modification also plays a potential role in the immune regulation of rectal cancer (RC) and the formation of tumor microenvironment.

Methods

In this study, we screened 23 m6A regulatory factors from 369 rectal cancer specimens, further determined the modification patterns of m6A in RC, and systematically linked these modification patterns with the characteristics of TME cell infiltration. The principal component analysis (PCA) algorithm was used to evaluate the m6A modification pattern of a single tumor related to immune response.

Results

Three different m6A modification patterns were found in the measurement results, which are related to different clinical results and biological pathways. TME identification results show that the identified m6A pattern is closely related to immune characteristics. According to the m6Ascore extracted from m6A-related signature genes, RC patients were divided into high and low score subgroups combined with tumor mutation burden. Patients with high tumor mutation burden and higher m6Ascore have a significant survival advantage and enhanced immune infiltration. Further analysis showed that patients with higher m6Ascore had higher PD-L1 expression levels and showed better immune response and lasting clinical benefits.

Conclusions

M6A modification plays a crucial role in the formation of TME diversity and complexity. The evaluation of the m6A modification mode will help us to enhance our understanding of the characteristics of TME infiltration and provide new insights for more effective immunotherapy strategies.

Epigenomic interplay in tumor heterogeneity: Potential of epidrugs as adjunct therapy

"Heterogeneity" in tumor mass has immense importance in cancer progression and therapy. The impact of tumor heterogeneity is an emerging field and not yet fully explored. Tumor heterogeneity is mainly considered as intra-tumor heterogeneity and inter-tumor heterogeneity based on their origin. Intra-tumor heterogeneity refers to the discrepancy within the same cancer mass while inter-tumor heterogeneity refers to the discrepancy between different patients having the same tumor type. Both of these heterogeneity types lead to variation in the histopathological as well as clinical properties of the cancer mass which drives disease resistance towards therapeutic approaches. Cancer stem cells (CSCs) act as pinnacle progenitors for heterogeneity development along with various other genetic and epigenetic parameters that are regulating this process. In recent times epigenetic factors are one of the most studied parameters that drive oxidative stress pathways essential during cancer progression. These epigenetic changes are modulated by various epidrugs and have an impact on tumor heterogeneity. The present review summarizes various aspects of epigenetic regulation in the tumor microenvironment, oxidative stress, and progression towards tumor heterogeneity that creates complications during cancer treatment. This review also explores the possible role of epidrugs in regulating tumor heterogeneity and personalized therapy against drug resistance.

Enhancing Adoptive Cell Therapy by T Cell Loading of SHP2 Inhibitor Nanocrystals before Infusion

Whereas adoptive T cell therapy has been extensively studied for cancer treatment, the response is still limited primarily due to immune dysfunction related to poor cell engraftment, tumor infiltration and engagement, and lack of a target. In addition, the modification of therapeutic T cells often suffers from being complex and expensive. Here, we present a strategy to load T cells with SHP099, an allosteric SHP2 inhibitor, to enhance the therapeutic efficacy of the T cells. Remote-loading of SHP099 into lipid nanoparticles decorated with triarginine motifs resulted in nanocrystal formation of SHP099 inside the lipid vesicles and allowed high loading efficiency and prolonged retention of SHP099 nanocrystals within T cells. Cell-loaded SHP099 enabled sustained inhibition of the PD-1/PD-L1 signaling and increased cytolytic activity of the T cells. We show in a mouse model that tumor-homing T cells can circulate with the cargos, improving their tumor accumulation compared to systemically administered lipid nanoparticles. On an established solid tumor model, adoptively transferred SHP099 loaded T cells induced complete tumor eradication and durable immune memory against tumor rechallenging on all treated mice by effectively inhibiting the PD-1/PD-L1 checkpoint signal. We demonstrate that the combination of T cell therapy with SHP2 inhibition is a promising therapeutic strategy, and the lipid nanocrystal platform could be generalized as a promising approach for T cell loading of immunomodulatory drugs.

Extracellular vesicles in pancreatic cancer immune escape: Emerging roles and mechanisms

Pancreatic cancer (PC) is the most lethal malignancy worldwide due to its delayed diagnosis and limited treatment options. Despite great progress in clinical trials of immunotherapies for various cancers, their effectiveness in PC is very low, indicating that immune evasion is still a major obstacle to immunotherapy in PC. However, the mechanism of immune escape in PC is not fully understood, which substantially restricts the development of immunotherapy. As an important component of intercellular communication networks, extracellular vesicles (EVs) have attracted increasing attention in relation to immune escape. This review aims to provide a better understanding of the roles of EVs in tumor immune escape and the potential to expand their application in cancer immunotherapy. The relationship between PC and the tumor immune microenvironment is briefly introduced. Then, the mechanism by which EVs are involved in immune regulation is summarized, and the latest progress in determining the role of EVs in regulating PC immune escape is highlighted.

The cell-line-derived subcutaneous tumor model in preclinical cancer research

Tumor-bearing experimental animals are essential for preclinical cancer drug development. A broad range of tumor models is available, with the simplest and most widely used involving a tumor of mouse or human origin growing beneath the skin of a mouse: the subcutaneous tumor model. Here, we outline the different types of in vivo tumor model, including some of their advantages and disadvantages and how they fit into the drug-development process. We then describe in more detail the subcutaneous tumor model and key steps needed to establish it in the laboratory, namely: choosing the mouse strain and tumor cells; cell culture, preparation and injection of tumor cells; determining tumor volume; mouse welfare; and an appropriate experimental end point. The protocol leads to subcutaneous tumor growth usually within 1-3 weeks of cell injection and is suitable for those with experience in tissue culture and mouse experimentation.

Identification of ENO1 as a prognostic biomarker and molecular target among ENOs in bladder cancer

Background

Enolase is an essential enzyme in the process of glycolysis and has been implicated in cancer progression. Though dysregulation of ENOs has been reported in multiple cancers, their prognostic value and specific role in bladder cancer (BLCA) remain unclear.

Methods

Multiple databases were employed to examine the expression of ENOs in BLCA. The expression of ENO1 was also validated in BLCA cell lines and tissue samples by western blotting and immunohistochemistry. Kaplan–Meier analysis, ROC curve, univariate and multivariate Cox regression were performed to evaluate the predictive capability of the ENO1. Gene ontology (GO) and Gene Set Enrichment Analyses (GSEA) analysis were employed to perform the biological processes enrichment. Function experiments were performed to explore the biological role of ENO1 in BLCA. The correlation of ENO1 with immune cell infiltration was explored by CIBERSORT.

Results

By analyzing three ENO isoforms in multiple databases, we identified that ENO1 was the only significantly upregulated gene in BLCA. High expression level of ENO1 was further confirmed in BLCA tissue samples. Aberrant ENO1 overexpression was associated with clinicopathological characteristics and unfavorable prognosis. Functional studies demonstrated that ENO1 depletion inhibited cancer cell aggressiveness. Furthermore, the expression level of ENO1 was correlated with the infiltration levels of immune cells and immune-related functions.

Conclusions

Taken together, our results indicated that ENO1 might serve as a promising prognostic biomarker for prognosticating prognosis associated with the tumor immune microenvironment, suggesting that ENO1 could be a potential immune-related target against BLCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03509-1.

Necroptosis-Related Genes Signatures Identified Molecular Subtypes and Underlying Mechanisms in Hepatocellular Carcinoma

Background

Although emerging evidence supports the relationship between necroptosis (NEC) related genes and hepatocellular carcinoma (HCC), the contribution of these necroptosis-related genes to the development, prognosis, and immunotherapy of HCC is unclear.

Methods

The expression of genes and relevant clinical information were downloaded from TCGA-LIHC, LIRI-JP, GSE14520/NCI, GSE36376, GSE76427, GSE20140, GSE27150, and IMvigor210 datasets. Next, we used an unsupervised clustering method to assign the samples into phenotype clusters base on 15 necroptosis-related genes. Subsequently, we constructed a NEC score based on NEC phenotype-related prognostic genes to quantify the necroptosis related subtypes of individual patients.

Results

We divided the samples into the high and low NEC score groups, and the high NEC score showed a poor prognosis. Simultaneously, NEC score is an effective and stable model and had a good performance in predicting the prognosis of HCC patients. A high NEC score was characterized by activation of the stroma and increased levels of immune infiltration. A high NEC score was also related to low expression of immune checkpoint molecules (PD-1/PD-L1). Importantly, the established NEC score would contribute to predicting the response to anti-PD-1/L1 immunotherapy.

Conclusions

Our study provide a comprehensive analysis of necroptosis-related genes in HCC. Stratification based on the NEC score may enable HCC patients to benefit more from immunotherapy and help identify new cancer treatment strategies.

A systematic evaluation of the computational tools for ligand-receptor-based cell-cell interaction inference

Cell-cell interactions (CCIs) are essential for multicellular organisms to coordinate biological processes and functions. One classical type of CCI interaction is between secreted ligands and cell surface receptors, i.e. ligand-receptor (LR) interactions. With the recent development of single-cell technologies, a large amount of single-cell ribonucleic acid (RNA) sequencing (scRNA-Seq) data has become widely available. This data availability motivated the single-cell-resolution study of CCIs, particularly LR-based CCIs. Dozens of computational methods and tools have been developed to predict CCIs by identifying LR-based CCIs. Many of these tools have been theoretically reviewed. However, there is little study on current LR-based CCI prediction tools regarding their performance and running results on public scRNA-Seq datasets. In this work, to fill this gap, we tested and compared nine of the most recent computational tools for LR-based CCI prediction. We used 15 well-studied scRNA-Seq samples that correspond to approximately 100K single cells under different experimental conditions for testing and comparison. Besides briefing the methodology used in these nine tools, we summarized the similarities and differences of these tools in terms of both LR prediction and CCI inference between cell types. We provided insight into using these tools to make meaningful discoveries in understanding cell communications.

MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method

Precision medicine for cancer affords a new way for the most accurate and effective treatment to each individual cancer. Given the high time-evolving intertumor and intratumor heterogeneity features of personal medicine, there are still several obstacles hindering its diagnosis and treatment in clinical practice regardless of extensive exploration on it over the past years. This paper is to investigate radiogenomics methods in the literature for precision medicine for cancer focusing on the heterogeneity analysis of tumors. Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging and molecular expression are conducted. These investigations are intended to (i) fully excavate the multidimensional spatial, temporal, and semantic related information regarding high-dimensional breast magnetic resonance imaging data, with integration of the highly specific structured data of genomics and combination of the diagnosis and cognitive process of doctors, and (ii) establish a radiogenomics data representation model based on multidimensional consistency analysis with multilevel spatial-temporal correlations.

Spatial specific delivery of combinational chemotherapeutics to combat intratumoral heterogeneity

Hypoxia-induced intratumoral heterogeneity poses a major challenge in tumor therapy due to the varying susceptibility to chemotherapy. Moreover, the spatial distribution patterns of hypoxic and normoxic tissues makes conventional combination therapy less effective. In this study, a tumor-acidity and bioorthogonal chemistry mediated in situ size transformable nanocarrier (NP@DOX_DBCO plus iCPPA_N3) was developed to spatially deliver two combinational chemotherapeutic drugs (doxorubicin (DOX) and PR104A) to combat hypoxia-induced intratumoral heterogeneity. DOX is highly toxic to tumor cells in normoxia state but less toxic in hypoxia state due to the hypoxia-induced chemoresistance. Meanwhile, PR104A is a hypoxia-activated prodrug has less toxic in normoxia state. Two nanocarriers, NP@DOX_DBCO and iCPPA_N3, can cross-link near the blood vessel extravasation sites through tumor acidity responsive bioorthogonal click chemistry to enhance the retention of DOX in tumor normoxia. Moreover, PR104A conjugated to the small-sized dendritic polyamidoamine (PAMAM) released under tumor acidity can penetrate deep tumor tissues for hypoxic tumor cell killing. Our study has demonstrated that this site-specific combination chemotherapy is better than the traditional combination chemotherapy. Therefore, spatial specific delivery of combinational therapeutics via in situ size transformable nanocarrier addresses the challenges of hypoxia induced intratumoral heterogeneity and provides insights into the combination therapy.

Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma

Background

Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD).

Methods

A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties.

Results

Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III.

Conclusion

We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

Long-term cultured microvascular networks on chip for tumor vascularization research and drug testing

The vascular structure of the tumor microenvironment (TME) plays an essential role in the process of metastasis. In vitro microvascular structures that can be maintained for a long time will greatly promote metastasis research. In this study, we constructed a mimicking breast cancer invasion model based on a microfluidic chip platform, and the maintenance time of the self-assembled microvascular networks significantly improved by culturing with fibroblasts (up to 13 days). Using this model, we quantified the invasion ability of breast cancer cells and angiogenesis sprouts caused by cancer cells, and the intravasation behavior of cancer cells was also observed in sprouts. We found that cancer cells could significantly cause angiogenesis by promoting sprouting behaviors of the self-assembled human umbilical vein endothelial cells, which, in turn, promoted the invasion behavior of cancer cells. The drug test results showed that the drug resistance of the widely used anti-cancer drugs 5-Fluorouracil (5-FU) and Doxorubicin (DOX) in the 3D model was higher than that in the 2D model. Meanwhile, we also proved that 5-FU and DOX had the effect of destroying tumor blood vessels. The anti-angiogenic drug Apatinib (VEGFR inhibitor) enhanced the drug effect of DOX on MDA-MB-231 cells, further proving the promoting effect of angiogenesis on the invasion ability of cancer cells. These results indicate that our model is of great value in reconstructing TME and drug testing in vitro.

Patient-derived Tumour Organoids: A Bridge between Cancer Biology and Personalised Therapy

Patient-derived tumour organoids (PDOs) have revolutionised our understanding of cancer biology and the applications of personalised therapies. These advancements are principally ascribed to the ability of PDOs to consistently recapitulate and maintain the genomic, proteomic and morphological characteristics of parental tumours. Given these characteristics, PDOs (and their extended biobanks) are a representative preclinical model eminently suited to translate relevant scientific findings into personalized therapies rapidly. Here, we summarise recent advancements in PDOs from the perspective of cancer biology and clinical applications, focusing on the current challenges and opportunities of reconstructing and standardising more sophisticated PDO models. STATEMENT OF SIGNIFICANCE: Patient-derived tumour organoids (PDOs), three-dimensional (3D) self-assembled organotypic structures, have revolutionised our understanding of cancer biology and the applications of personalised therapies. These advancements are principally ascribed to the ability of PDOs to consistently recapitulate and maintain the genomic, proteomic and morphological characteristics of parental tumours. Given these characteristics, PDOs (and their extended biobanks) are a representative preclinical model eminently suited to translate relevant scientific findings into personalized therapies rapidly.

A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions

Drug combinations are a promising strategy to counter antibiotic resistance. However, current experimental and computational approaches do not account for the entire complexity involved in combination therapy design, such as the effect of pathogen metabolic heterogeneity, changes in the growth environment, drug treatment order, and time interval. To address these limitations, we present a comprehensive approach that uses genome-scale metabolic modeling and machine learning to guide combination therapy design. Our mechanistic approach (a) accommodates diverse data types, (b) accounts for time- and order-specific interactions, and (c) accurately predicts drug interactions in various growth conditions and their robustness to pathogen metabolic heterogeneity. Our approach achieved high accuracy (area under the receiver operating curve (AUROC) = 0.83 for synergy, AUROC = 0.98 for antagonism) in predicting drug interactions for Escherichia coli cultured in 57 metabolic conditions based on experimental validation. The entropy in bacterial metabolic response was predictive of combination therapy outcomes across time scales and growth conditions. Simulation of metabolic heterogeneity using population FBA identified two subpopulations of E. coli cells defined by the levels of three proteins (eno, fadB, and fabD) in glycolysis and lipid metabolism that influence cell tolerance to a broad range of antibiotic combinations. Analysis of the vast landscape of condition-specific drug interactions revealed a set of 24 robustly synergistic drug combinations with potential for clinical use.

Design and prediction of novel pyrazole derivatives as potential anti-cancer compounds based on 2D-2D-QSAR study against PC-3, B16F10, K562, MDA-MB-231, A2780, ACHN and NUGC cancer cell lines

Despite the decades of scientific studies for developing promising new therapies, cancer remains a major cause of illness and mortality, worldwide. Several cancer types are the major topic of research in drug discovery programs due to their global incidence cases and growing frequency. In the present study, using two different statistical approaches PCA (principal component analysis) and PLS (partial least squares), six 2D-QSAR (quantitative structure activity relationship) models have been developed for the set of compounds retrieved against seven cancer cell lines vizPC-3, B16F10, K562, MDA-MB-231, A2780, and ACHN. For the creation and validation of 2D-QSAR models, OECD (Organization for Economic Co-operation and Development) requirements have been strictly followed. All of the generated 2D-QSAR models produce a significant and high correlation coefficient value with several other statistical parameters. Moreover, developed 2D-QSAR models have been used for activity predictions of in-house synthesized 63 pyrazole derivatives compounds. Precisely, most statistically significant and accepted2D-QSAR model generated for each cancer cell line has been used to predict the pIC₅₀ value (anti-cancer activity) of all 63 synthesized pyrazole derivatives. Furthermore, designing of novel pyrazole derivatives has been carried out by substituting the essential functional groups based on the best derived 2D-QSAR models for each cancer cell line, more precisely, based on the most significant molecular descriptors with enhanced anti-cancer activity. Finally, the prediction of the new designed molecules reveals higher pIC₅₀ than the standard compounds.

Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.

A Novel Matrisomal-Related LncRNA Signature Associated With Survival Outcome and Immune Evasion in Patients With Gastric Cancer

Background

Different matrisomal patterns are shared across carcinomas. However, little is known about whether there exists a unique tumor matrisome that modulates GC progression and immune regulation.

Methods

We conducted a genome-wide analysis based on matrisomal-related lncRNAs (MRLs) in 375 patients with GC from the Cancer Genome Atlas (TCGA) database. Patients were split into the training set and validation set at a ratio of 1:1 using the R package cart. Pearson correlation analysis (PCA) was performed to identify lncRNAs that correlated with matrisome based on differential expression genes. Subsequently, we performed univariate Cox regression analyses and lasso Cox analysis on these lncRNAs to construct a risk model. Considering the primary effect of GRASLND on the GC prognosis, we chose it for further validation in an experimental setting.

Results

We identified a 15-MRL signature to predict overall survival and immune cell infiltration of patients with GC. The AUC values to predict 5-year outcome in three sets were 0.89, 0.65, and 0.78, respectively. Further analyses suggested that the high-risk group showed more obvious immune cell infiltration, and demonstrated an immunologically “cold” profile. In vitro, knockdown of GRASLND could inhibit the invasion capability of GC cells, and downregulate the protein expression of crucial matrisomal-related gene MMP9.

Conclusions

The 15-MRL gene signature might serve as a relatively good predictive tool to manage patients with GC.

Data-Driven Identification of Biomarkers for In Situ Monitoring of Drug Treatment in Bladder Cancer Organoids

Three-dimensional (3D) organoid culture recapitulating patient-specific histopathological and molecular diversity offers great promise for precision medicine in cancer. In this study, we established label-free imaging procedures, including Raman microspectroscopy (RMS) and fluorescence lifetime imaging microscopy (FLIM), for in situ cellular analysis and metabolic monitoring of drug treatment efficacy. Primary tumor and urine specimens were utilized to generate bladder cancer organoids, which were further treated with various concentrations of pharmaceutical agents relevant for the treatment of bladder cancer (i.e., cisplatin, venetoclax). Direct cellular response upon drug treatment was monitored by RMS. Raman spectra of treated and untreated bladder cancer organoids were compared using multivariate data analysis to monitor the impact of drugs on subcellular structures such as nuclei and mitochondria based on shifts and intensity changes of specific molecular vibrations. The effects of different drugs on cell metabolism were assessed by the local autofluorophore environment of NADH and FAD, determined by multiexponential fitting of lifetime decays. Data-driven neural network and data validation analyses (k-means clustering) were performed to retrieve additional and non-biased biomarkers for the classification of drug-specific responsiveness. Together, FLIM and RMS allowed for non-invasive and molecular-sensitive monitoring of tumor-drug interactions, providing the potential to determine and optimize patient-specific treatment efficacy.

Multidrug Resistance in Cancer: Understanding Molecular Mechanisms, Immunoprevention and Therapeutic Approaches

Cancer is one of the leading causes of death worldwide. Several treatments are available for cancer treatment, but many treatment methods are ineffective against multidrug-resistant cancer. Multidrug resistance (MDR) represents a major obstacle to effective therapeutic interventions against cancer. This review describes the known MDR mechanisms in cancer cells and discusses ongoing laboratory approaches and novel therapeutic strategies that aim to inhibit, circumvent, or reverse MDR development in various cancer types. In this review, we discuss both intrinsic and acquired drug resistance, in addition to highlighting hypoxia- and autophagy-mediated drug resistance mechanisms. Several factors, including individual genetic differences, such as mutations, altered epigenetics, enhanced drug efflux, cell death inhibition, and various other molecular and cellular mechanisms, are responsible for the development of resistance against anticancer agents. Drug resistance can also depend on cellular autophagic and hypoxic status. The expression of drug-resistant genes and the regulatory mechanisms that determine drug resistance are also discussed. Methods to circumvent MDR, including immunoprevention, the use of microparticles and nanomedicine might result in better strategies for fighting cancer.

Clonal Distribution and Intratumor Heterogeneity of the TCR Repertoire in Papillary Thyroid Cancer With or Without Coexistent Hashimoto's Thyroiditis

The intratumor heterogeneity (ITH) of the amount and TCR repertoires of tumor infiltrating lymphocytes (TILs) in PTC with and without coexistent Hashimoto's thyroiditis (HT) are unclear. Here, we investigated the amount of T cells in tumor and corresponding normal tissues by immunohistochemical staining on 80 tumor samples and 40 normal samples from 40 patients. The immune repertoire of T cells was identified on 24 tumor samples and 12 normal samples from 12 patients using TCR high-throughput sequencing. The results demonstrated that the numbers of CD3+, CD4+ and CD8+ T cells in PTC without coexistent HT (PTC-WO) were significantly lower than those in PTC with existing HT (PTC-W). In PTC-W, the density of CD4+ TILs were generally higher when compared with CD8+ TILs. Furthermore, we found that the numbers of CD3+ T cells and their CD4+, CD8+ subtypes in tumor samples were generally higher than those in normal tissue in PTC-WO and moreover, the number of CD3+ T cells was negatively associated with TCR clonality in PTC-WO. In addition, although ITH of the TCR repertoire truly existed in PTC-W and PTC-WO, the TCR repertoires between distinct regions of the non-adjacent tumor foci were presented with a higher degree of similarity than those between tumor and matched normal tissue in PTC-WO, yet the similarity of intratumor repertoires was not significantly higher than those between tumor and corresponding normal samples in PTC-W. This research comprehensively delineated the quantity and TCR repertoire ITH of T cells in PTC-W and PTC-WO, suggesting that TILs might be reactive to tumor antigens in PTC-WO. Moreover, multiregion biopsies should be performed to precisely identify the immune background in PTC-W and PTC-WO.

A bioinformatics analysis of zinc finger protein family reveals potential oncogenic biomarkers in breast cancer

BACKGROUND

Zinc finger protein family is the largest transcription factor family in the human genome. Studies have shown that the aberrant expression of zinc finger protein (ZNF) had a potential role in tumorigenesis. However, due to the high complexity of the ZNF family genes, the role of the ZNF family genes in breast cancer (BRCA) is still lacking in systematic understanding.

AIM

In the study, we aim to understand the expression profile, prognostic value, immune invasion pattern, tumor microenvironment, epigenetic and pathway relationships, and drug sensitivity of ZNFs using multi-omics data from public databases.

RESULTS

We focused on six members of ZNFs, which were upregulated in a variety of cancers. Notably, ZNF750 and ZNF224 were lower expressed in BRCA, and their expressions were significantly associated with BRCA prognosis. We confirmed the observations obtained by analyzing the clinic-pathological data. Otherwise, the expressions of ZNFs were significantly related to stromal and immune scores, and was significantly different among different immune subtypes in BRCA. Here, we found down-regulated methylation of ZNF217 and ZNF750. The relationship between methylation and survival showed the survival was worse for hypo-methylation of ZNF750 in BRCA, which is consistent with the correlation of high expression of ZNF750 in BRCA with worse survival.

CONCLUSIONS

Collectively, our results provide clues for a better understanding of the characterization of ZNF family genes in BRCA from a multi-omics perspective and show their potential for use as new tumor markers and therapeutic targets.

Preclinical models of epithelial ovarian cancer: practical considerations and challenges for a meaningful application

Despite many improvements in ovarian cancer diagnosis and treatment, until now, conventional chemotherapy and new biological drugs have not been shown to cure the disease, and the overall prognosis remains poor. Over 90% of ovarian malignancies are categorized as epithelial ovarian cancers (EOC), a collection of different types of neoplasms with distinctive disease biology, response to chemotherapy, and outcome. Advances in our understanding of the histopathology and molecular features of EOC subtypes, as well as the cellular origins of these cancers, have given a boost to the development of clinically relevant experimental models. The overall goal of this review is to provide a comprehensive description of the available preclinical investigational approaches aimed at better characterizing disease development and progression and at identifying new therapeutic strategies. Systems discussed comprise monolayer (2D) and three-dimensional (3D) cultures of established and primary cancer cell lines, organoids and patient-derived explants, animal models, including carcinogen-induced, syngeneic, genetically engineered mouse, xenografts, patient-derived xenografts (PDX), humanized PDX, and the zebrafish and the laying hen models. Recent advances in tumour-on-a-chip platforms are also detailed. The critical analysis of strengths and weaknesses of each experimental model will aid in identifying opportunities to optimize their translational value.

New dawn for cancer cell death: Emerging role of lipid metabolism

BACKGROUND

Resistance to cell death, a protective mechanism for removing damaged cells, is a "Hallmark of Cancer" that is essential for cancer progression. Increasing attention to cancer lipid metabolism has revealed a number of pathways that induce cancer cell death.

SCOPE OF REVIEW

We summarize emerging concepts regarding lipid metabolic reprogramming in cancer that is mainly involved in lipid uptake and trafficking, de novo synthesis and esterification, fatty acid synthesis and oxidation, lipogenesis, and lipolysis. During carcinogenesis and progression, continuous metabolic adaptations are co-opted by cancer cells, to maximize their fitness to the ever-changing environmental. Lipid metabolism and the epigenetic modifying enzymes interact in a bidirectional manner which involves regulating cancer cell death. Moreover, lipids in the tumor microenvironment play unique roles beyond metabolic requirements that promote cancer progression. Finally, we posit potential therapeutic strategies targeting lipid metabolism to improve treatment efficacy and survival of cancer patient.

MAJOR CONCLUSIONS

The profound comprehension of past findings, current trends, and future research directions on resistance to cancer cell death will facilitate the development of novel therapeutic strategies targeting the lipid metabolism.

Thrombospondin-2 acts as a bridge between tumor extracellular matrix and immune infiltration in pancreatic and stomach adenocarcinomas: an integrative pan-cancer analysis

Background

Thrombospondin-2 (THBS2) is a versatile glycoprotein that regulates numerous biological functions, including the apoptosis-proliferation balance in endothelial cells, and it has been linked to tumor angiogenesis. However, the exact role of THBS2 in human cancer remains unknown. This study aimed to determine THBS2 expression in a pan-cancer analysis and its association with pan-cancer prognosis and to further identify its possible roles in tumor immunity and the extracellular matrix (ECM).

Methods

Data on THBS2 expression in cancers and normal tissues were downloaded from the Genotype-Tissue Expression portal and UCSC Xena visual exploration tool and analyzed using the ONCOMINE database, Perl programming language, and Gene Expression Profiling and Interactive Analyses vision 2 webserver. In addition, survival prognosis was analyzed using the survival, survminer, limma, and forestplot packages in R v. 4.0.3.Immune and matrix components were also analyzed using R v. 4.0.3. Most importantly, we partially validated the role and mechanism of THBS2 in pancreatic and gastric cancers in vitro using PANC1 and BGC-823 cell lines.

Results

THBS2 was significantly overexpressed in 17 of the 33 investigated cancers and linked to a poor prognosis in pan-cancer survival analysis. High THBS2 expression was an independent unfavorable prognostic factor in kidney renal papillary cell, mesothelioma, and stomach and pancreatic adenocarcinomas. Immune infiltration and THBS2 expression were also related. THBS2 expression has been linked to immune and stromal scores and immune checkpoint markers in various cancers. The protein–protein interaction network revealed that THBS2 is associated with multiple ECM and immune proteins. THBS2 knockdown decreased the expression of CD47 and matrix metallopeptidase 2 (MMP-2) as well as the proliferation, migration, and invasion of PANC1 and BGC-823 cells in vitro.

Conclusions

Our findings suggested that THBS2 might promote cancer progression by remodeling the tumor microenvironment, affecting CD47-mediated signaling pathways, activating the pro-tumor functions of a disintegrin and metalloproteinase with thrombospondin motifs, and enhancing MMP-2 expression. Furthermore, it functions as a bridge between the ECM and immune infiltration in cancer and serves as a potential prognostic biomarker for several cancers, especially pancreatic and gastric adenocarcinomas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02622-x.

Patient-Derived Organoid Model in the Prediction of Chemotherapeutic Drug Response in Colorectal Cancer

As an emerging technology in precision medicine, the patient-derived organoid (PDO) technology has been indicated to provide novel modalities to judge the sensitivity of individual tumors to cancer drugs. In this work, an in vitro model of colorectal cancer (CRC) was established using the PDO culture, and it is demonstrated that the PDO samples preserved, to a great extent, the histologic features and marker expression of the original tumor tissues. Subsequently, cancer drugs 5-FU, oxaliplatin, and irinotecan were selected and screened on five CRC PDO samples, while the patient-derived organoid xenograft (PDOX) model was applied for comparison. The receiver operating characteristic (ROC) curve was drawn according to the IC₅₀ data from the PDO model and the relative tumor proliferation rate (T/C%) from PDOX. Interestingly, the area under the ROC curve was 0.84 (95% CI, 0.64-1.04, P value = 0.028), which suggested that the IC₅₀ of cancer drugs from the PDO model was strongly correlated with PDOX responses. In addition, the optimal sensitivity cutoff value for drug screening in CRC PDOs was identified at 10.35 μM, which could act as a reference value for efficacy evaluation of 5-FU, oxaliplatin, and irinotecan in the colorectal cancer drug screening. Since there are no unified criteria to judge the sensitivity of drugs in vitro, our work provides a method for establishing in vitro evaluation criteria via PDO and PDOX model using the patient tissues received from local hospitals, exhibiting potential in clinical cancer therapy and precision medicine.

A role for microfluidic systems in precision medicine

Precision oncology continues to challenge the "one-size-fits-all" dogma. Under the precision oncology banner, cancer patients are screened for molecular tumor alterations that predict treatment response, ideally leading to optimal treatments. Functional assays that directly evaluate treatment efficacy on the patient's cells offer an alternative and complementary tool to improve the accuracy of precision oncology. Unfortunately, traditional Petri dish-based assays overlook much tumor complexity, limiting their potential as predictive functional biomarkers. Here, we review past applications of microfluidic systems for precision medicine and discuss the present and potential future role of functional microfluidic assays as treatment predictors.

Janus-like Bx C/C Quantum Sheets with Z-Scheme Mechanism Strengthen Tumor Photothermal-Immunotherapy in NIR-II Biowindow

Carbon dots (CDs) are one of the most popular photothermal agents (PTAs) as a noninvasive strategy for tumor treatment. However, because of the inherent dominant fluorescent emission, the CDs-based PTAs hardly achieve a single photothermal conversion, which causes low photothermal conversion efficiency and poor photothermal performance. In this regard, finding a new CDs-based material system to greatly restrain its fluorescence to enhance its photothermal conversion efficiency is highly required, however, it is still a grand challenge. Herein, a kind of Z-scheme CDs-based PTAs consisting of 2D ultrathin nonmetallic B_x C/C Janus quantum sheets (B_x C/C JQSs) is reported to greatly enhance the photothermal conversion efficiency. It is demonstrated that the heterogeneous growth of Z-scheme B_x C/C JQSs enables the NIR-driven quick injection of hot electrons from C into the conjugated B_x C, realizing a single conversion of light to heat, and resulting in a high photothermal conversion of 60.0% in NIR-II. Furthermore, these new Z-scheme B_x C/C-polyethylene glycol JQSs display outstanding biocompatibility and show effective tumor elimination outcome both in vitro and in vivo through the synergistic photothermal-immunotherapy in the NIR-II biowindow with undetectable harm to normal tissues.

Role of tumor infiltrating lymphocytes and spatial immune heterogeneity in sensitivity to PD-1 axis blockers in non-small cell lung cancer

BACKGROUND

Tumor infiltrating lymphocytes (TILs) reflect adaptive antitumor immune responses in cancer and are generally associated with favorable prognosis. However, the relationships between TILs subsets and their spatial arrangement with clinical benefit from immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC) remains less explored.

METHODS

We used multiplexed quantitative immunofluorescence panels to determine the association of major TILs subpopulations, CD8+ cytotoxic T cells, CD4+ helper T cells and CD20+ B cells, and T cell exhaustion markers, programmed cell death protein-1 (PD-1),lymphocyte-activation gene 3 (LAG-3) and T cell immunoglobulin mucin-3 (TIM-3) with outcomes in a multi-institutional cohort of baseline tumor samples from 179 patients with NSCLC treated with ICI. The analysis of full-face tumor biopsies including numerous fields of view allowed a detailed spatial analysis and assessment of tumor immune heterogeneity using a multiparametric quadratic entropy metric (Rao's Q Index (RQI)).

RESULTS

TILs were preferentially located in the stromal tissue areas surrounding tumor-cell nests and CD8+ T cells were the most abundant subset. Higher density of stromal CD8+ cytotoxic T cells was significantly associated with longer survival, and this effect was more prominent in programmed death ligand-1 (PD-L1) positive cases. The role of baseline T cell infiltration to stratify PD-L1 expressing cases was confirmed measuring the T cell receptor-burden in an independent NSCLC cohort studied with whole-exome DNA sequencing. High levels of LAG-3 on T cells or elevated RQI heterogeneity index were associated with worse survival in the cohort.

CONCLUSION

Baseline T cell density and T cell exhaustion marker expression can stratify outcomes in PD-L1 positive patients with NSCLC treated with ICI. Spatial immune heterogeneity can be measured using the RQI and is associated with survival in NSCLC.

Size Changeable Nanomedicines Assembled by Noncovalent Interactions of Responsive Small Molecules for Enhancing Tumor Therapy

The size of nanocarriers strongly affects their performance in biological systems, especially the capacity to overcome various barriers before delivering the payloads to destinations. However, the optimum size varies at different delivery stages in cancer therapy due to the complicated tumor microenvironment. Relatively large particles are favored for long-term circulation in vivo, while smaller particles contribute to deep penetration into tumor tissues. This dilemma in the size of particles stimulates the development of stimuli-responsive size-shrinking nanocarriers. Herein, we report a facile strategy to construct a tumor-triggered tannic acid (TA) nanoassembly with improved drug delivery efficiency. Cystamine (CA), a small molecule with a disulfide bond, is thus used to mediate TA assembling via cooperative noncovalent interactions, which endows the nanoassembly with intrinsic pH/GSH dual-responsiveness. The obtained TA nanoassemblies were systematically investigated. DOX encapsulated nanoassembly labeled TCFD NP shows high drug loading efficiency, pH/GSH-responsiveness and significant size shrinkage from 122 to 10 nm with simultaneous drug release. The in vitro and in vivo experimental results demonstrate the excellent biocompatibility, sufficient intracellular delivery, enhanced tumor retention/penetration, and superior anticancer efficacy of the small-molecule-mediated nanoassembly. This noncovalent strategy provides a simple method to fabricate a tumor-triggered size-changeable delivery platform to overcome biological barriers.

Bioinformatic analysis of cancer-associated fibroblast related gene signature as a predictive model in clinical outcomes and immune characteristics of gastric cancer

Background

Gastric cancer (GC) has a high incidence and high mortality rate among Asian countries, and distinguishing predictive prognosis biomarkers for GC are essential. Cancer-associated fibroblasts (CAFs) play a significant role in the progression, immune evasion, and therapeutic resistance of GC. Therefore, CAF-associated genes might have huge potential as prognostic biomarkers for predicting tumor progression and survival rate in GC pateints.

Methods

A sum of 1,134 GC patients from the The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD), GSE62254, and GSE84437 datasets as well as GC cohorts from Xijing hospital were included. Firstly, we performed univariate Cox regression analysis to identify CAF-associated prognostic genes. Subsequently, the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to develop a CAF gene signature (CAFGS) in the TCGA-STAD training cohort. CAFGS’s predictive performance was examined in both the training and validation cohorts, and the relationship between CAFGS and the tumor microenvironment (TME) was investigated by ssGSEA, CIBERSORT, TIMER, and ESTIMATE. Finally, a nomogram of CAFGS was established.

Results

Ten CAF-associated genes (ANGPTL4, CPNE8, CST2, HTR1F, IL1RAP, NR1D1, NTAN1, OLFML2B, TMEM259, and VTN) were identified to develop CAFGS. A high CAFGS score represented a worse outcome for GC patients in four cohorts, and a strong correlation was found between CAFGS and the infiltration of immune cells. We showed that CAFs contribute to immune evasion and unfavorable prognoses of GC patients by promoting the formation of an immunosuppressive microenvironment, and a high level of CAF infiltration may attenuate the efficacy of immunotherapy. The nomogram based on CAFGS showed reasonable predictive ability and may deliver great clinical net benefits.

Conclusions

We established a CAFGS model with 10 CAF-associated genes that had a great predictive value for GC prognosis and survival rate evaluation. This study could provide a novel insight for investigating the role of CAFs in GC.

Vascularized Lung Cancer Model for Evaluating the Promoted Transport of Anticancer Drugs and Immune Cells in an Engineered Tumor Microenvironment

The tumor microenvironment (TME) is the environment around the tumor, including blood vessels, immune cells, fibroblasts, signaling molecules, and the extracellular matrix (ECM). Owing to its component interactions, the TME influences tumor growth and drug delivery in a highly complex manner. Although several vascularized cancer models are developed to mimic the TME in vitro, these models cannot comprehensively reflect blood vessel-tumor spheroid interactions. Here, a method for inducing controlled tumor angiogenesis by engineering the microenvironment is presented. The interstitial flow direction regulates the direction of capillary sprouting, showing that angiogenesis occurs in the opposite direction of flow, while the existence of lung fibroblasts affects the continuity and lumen formation of sprouted capillaries. The vascularized tumor model shows enhanced delivery of anticancer drugs and immune cells to the tumor spheroids because of the perfusable vascular networks. The possibility of capillary embolism using anticancer drug-conjugated liquid metal nanoparticles is investigated using the vascularized tumor model. This vascularized tumor platform can aid in the development of effective anticancer drugs and cancer immunotherapy.

Anti-Cancer Effects of YAP Inhibitor (CA3) in Combination with Sorafenib against Hepatocellular Carcinoma (HCC) in Patient-Derived Multicellular Tumor Spheroid Models (MCTS)

PURPOSE

To assess the expression levels of YAP and TAZ in patient-derived HCC tissue and identify the effects of YAP/TAZ inhibition depending on the baseline YAP/TAZ expression when combined with sorafenib using a patient-derived multicellular tumor spheroid (MCTS) model.

METHODS

Primary HCC cell lines were established from patient-derived tissue. Six patient-derived HCC cell lines were selected according to YAP/TAZ expression on Western blot: high, medium, low. Then, MCTS was generated by mixing patient-derived HCC cells and stroma cells (LX2, WI38, and HUVECs) and YAP/TAZ expression was assessed using Western blot. Cell viability of MCTS upon 48 h of drug treatment (sorafenib, sorafenib with CA3 0.1 µM, and CA3 (novel YAP1 inhibitor)) was analyzed.

RESULTS

Out of six patient-derived HCC cell lines, cell lines with high YAP/TAZ expression at the MCTS level responded more sensitively to the combination therapy (Sorafenib + CA3 0.1 μM) despite the potent cytotoxic effect of CA3 exhibited in all of the patient-derived HCCs.

CONCLUSION

Targeting YAP/TAZ inhibition using the novel YAP1 inhibitor CA3 could be a promising therapeutic strategy to enhance sensitivity to sorafenib especially in HCCs with high YAP/TAZ expression in MCTS.

[Comparative Study of PD-L1 Expression in Different Sites of Non-small Cell Lung Cancer]

BACKGROUND

The expression of programmed cell death ligand 1 (PD-L1) as a biomarker for immunotherapy in non-small cell lung cancer (NSCLC) is routinely detected in clinical pathology department. However, the spatial heterogeneity of PD-L1 expression in intrapulmonary tumors and extrapulmonary metastases is still a challenge for the clinical testing. This study aims to explore the differences of PD-L1 expression in test samples obtaining from different sites of NSCLC. This study may contribute to the detection strategy of PD-L1 in patients with advanced lung cancer.

METHODS

One hundred and thirty-one cases of consecutively detected PD-L1 (22c3 assay, Dako) staining in metastatic NSCLC and 972 cases of non-paired intrapulmonary NSCLC were collected. The discrepancies of tumor proportion score (TPS) of PD-L1 expression in intrapulmonary samples and extrapulmonary metastatic samples of different sites were compared.

RESULTS

The positive expression rate of PD-L1 in extrapulmonary metastatic NSCLC (TPS ≥ 1%) was 61.83%, and the TPS was significantly higher than that in intrapulmonary tumors (P=0.03). The PD-L1 scores of the specimens obtained from different sites were significantly different (P=0.007). The positive rates of PD-L1 in liver and adrenal metastases were 85.71% and 77.78% respectively, and their TPS were significantly higher than that of the intrapulmonary samples (P<0.05). The positive rates of PD-L1 in lymph node, bone, brain, soft tissue, and pleural metastases was 40.00%-66.67%, with no significant differences compared to intrapulmonary tumors. The analysis of histological subtype and sample type showed that the PD-L1 score of extrapulmonary samples of adenocarcinoma subtype or surgical specimen was significantly higher than that of intrapulmonary tumors. The analysis of clinicopathological parameters showed that the PD-L1 positive expression or high expression were significantly correlated with male patients, smoking history, and epidermal growth factor receptor (EGFR) wild type.

CONCLUSIONS

The expression of PD-L1 in metastatic NSCLC is generally higher than that in intrapulmonary tumor, and the positive rate of PD-L1 expression was discrepant in different sites of specimen. The differences of PD-L1 score between extrapulmonary metastatic samples and intrapulmonary samples may be associated with different metastatic sites, histological subtype, and specimen type.