The significance of intertumor and intratumor heterogeneity in liver cancer

Differentiation stage predicts radiosensitivity in mesenchymal-like pancreatic cancer

PURPOSE

To derive a genomic classifier to predict radiosensitivity of pancreatic cancer cell lines and pancreatic cancer patients to allow genomic-guided radiotherapy.

METHODS AND MATERIALS

We collected a comprehensive dataset of full clonogenic cell survival curves of 45 pancreatic cancer cell lines irradiated with clinical photon and proton beams. We derived classifiers based on data from human embryonic and fetal pancreas single-cell RNA-sequencing (scRNA-seq) to distinguish between epithelial and mesenchymal cells and to predict pancreas cell-line differentiation stage. Independent testing was done with an embryonic mouse pancreas scRNA-seq dataset. We then used bulk RNA-seq profiles from the Cancer Cell Line Encyclopedia (CCLE) to classify our pancreatic cancer cell lines using our epithelial-mesenchymal and differentiation stage classifiers. We then correlated the differentiation stage classifier with the radiosensitivity of the pancreatic cancer cell lines as well as with pancreatic cancer patient data from The Cancer Genome Atlas.

RESULTS

We found wide variability in radiosensitivity to both photons and protons among pancreatic cancer cell lines. We showed that the differentiation stage is predictive of radiosensitivity of mesenchymal pancreatic cancer cell lines but not epithelial pancreatic cancer cell lines. We found that chromatin compaction is associated with the differentiation stage and showed that the less differentiated mesenchymal pancreatic cancer cell lines tend to be radioresistant and with more compact chromatin than the radiosensitive differentiated cell lines. Patients with more differentiated tumors exhibit better overall survival.

CONCLUSIONS

We found that mesenchymal-like undifferentiated pancreatic cancer cell lines are more radioresistant than mesenchymal-like differentiated ones and that pancreatic cancer patients with mesenchymal-like undifferentiated tumors treated with radiotherapy tend to have lower overall survival compared to patients with mesenchymal-like differentiated tumors. We show that it is feasibility to use the differentiation stage of mesenchymal pancreatic cancer cells to predict tumor specific radiosensitivity.

Translational Advances in Oncogene and Tumor-Suppressor Gene Research

Cancer, characterized by the uncontrolled proliferation of cells, is one of the leading causes of death globally, with approximately one in five people developing the disease in their lifetime. While many driver genes were identified decades ago, and most cancers can be classified based on morphology and progression, there is still a significant gap in knowledge about genetic aberrations and nuclear DNA damage. The study of two critical groups of genes-tumor suppressors, which inhibit proliferation and promote apoptosis, and oncogenes, which regulate proliferation and survival-can help to understand the genomic causes behind tumorigenesis, leading to more personalized approaches to diagnosis and treatment. Aberration of tumor suppressors, which undergo two-hit and loss-of-function mutations, and oncogenes, activated forms of proto-oncogenes that experience one-hit and gain-of-function mutations, are responsible for the dysregulation of key signaling pathways that regulate cell division, such as p53, Rb, Ras/Raf/ERK/MAPK, PI3K/AKT, and Wnt/β-catenin. Modern breakthroughs in genomics research, like next-generation sequencing, have provided efficient strategies for mapping unique genomic changes that contribute to tumor heterogeneity. Novel therapeutic approaches have enabled personalized medicine, helping address genetic variability in tumor suppressors and oncogenes. This comprehensive review examines the molecular mechanisms behind tumor-suppressor genes and oncogenes, the key signaling pathways they regulate, epigenetic modifications, tumor heterogeneity, and the drug resistance mechanisms that drive carcinogenesis. Moreover, the review explores the clinical application of sequencing techniques, multiomics, diagnostic procedures, pharmacogenomics, and personalized treatment and prevention options, discussing future directions for emerging technologies.

State-of-the-Art Liver Cancer Organoids: Modeling Cancer Stem Cell Heterogeneity for Personalized Treatment

Liver cancer poses a global health challenge with limited therapeutic options. Notably, the limited success of current therapies in patients with primary liver cancers (PLCs) may be attributed to the high heterogeneity of both hepatocellular carcinoma (HCCs) and intrahepatic cholangiocarcinoma (iCCAs). This heterogeneity evolves over time as tumor-initiating stem cells, or cancer stem cells (CSCs), undergo (epi)genetic alterations or encounter microenvironmental changes within the tumor microenvironment. These modifications enable CSCs to exhibit plasticity, differentiating into various resistant tumor cell types. Addressing this challenge requires urgent efforts to develop personalized treatments guided by biomarkers, with a specific focus on targeting CSCs. The lack of effective precision treatments for PLCs is partly due to the scarcity of ex vivo preclinical models that accurately capture the complexity of CSC-related tumors and can predict therapeutic responses. Fortunately, recent advancements in the establishment of patient-derived liver cancer cell lines and organoids have opened new avenues for precision medicine research. Notably, patient-derived organoid (PDO) cultures have demonstrated self-assembly and self-renewal capabilities, retaining essential characteristics of their respective in vivo tissues, including both inter- and intratumoral heterogeneities. The emergence of PDOs derived from PLCs serves as patient avatars, enabling preclinical investigations for patient stratification, screening of anticancer drugs, efficacy testing, and thereby advancing the field of precision medicine. This review offers a comprehensive summary of the advancements in constructing PLC-derived PDO models. Emphasis is placed on the role of CSCs, which not only contribute significantly to the establishment of PDO cultures but also faithfully capture tumor heterogeneity and the ensuing development of therapy resistance. The exploration of PDOs' benefits in personalized medicine research is undertaken, including a discussion of their limitations, particularly in terms of culture conditions, reproducibility, and scalability.

Integrated Bioinformatic Analyses Reveal Thioredoxin as a Putative Marker of Cancer Stem Cells and Prognosis in Prostate Cancer

Objectives

Prostate cancer stem cells (CSCs) play an important role in cancer cell survival, proliferation, metastasis, and recurrence; thus, removing CSCs is important for complete cancer removal. However, the mechanisms underlying CSC functions remain largely unknown, making it difficult to develop new anticancer drugs targeting CSCs. Herein, we aimed to identify novel factors that regulate stemness and predict prognosis.

Methods

We reanalyzed 2 single-cell RNA sequencing data of prostate cancer (PCa) tissues using Seurat. We used gene set enrichment analysis (GSEA) to estimate CSCs and identified common upregulated genes in CSCs between these datasets. To investigate whether its expression levels change over CSC differentiation, we performed a trajectory analysis using monocle 3. In addition, GSEA helped us understand how the identified genes regulate stemness. Finally, to assess their clinical significance, we used the Cancer Genome Atlas database to evaluate their impact on prognosis.

Results

The expression of thioredoxin (TXN), a redox enzyme, was approximately 1.2 times higher in prostate CSCs than in PCa cells (P < 1 × 10-10), and TXN expression decreased over CSC differentiation. In addition, GSEA suggested that intracellular signaling pathways, including MYC, may be involved in stemness regulation by TXN. Furthermore, TXN expression correlated with poor prognosis (P < .05) in PCa patients with high stemness.

Conclusions

Despite the limited sample size in our study and the need for further in vitro and in vivo experiments to demonstrate whether TXN functionally regulates prostate CSCs, our findings suggest that TXN may serve as a novel therapeutic target against CSCs. Moreover, TXN expression in CSCs could be a useful marker for predicting the prognosis of PCa patients.

HepatoPredict Accurately Selects Hepatocellular Carcinoma Patients for Liver Transplantation Regardless of Tumor Heterogeneity

BACKGROUND/OBJECTIVES

Hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths rising worldwide. This is leading to an increased demand for liver transplantation (LT), the most effective treatment for HCC in its initial stages. However, current patient selection criteria are limited in predicting recurrence and raise ethical concerns about equitable access to care. This study aims to enhance patient selection by refining the HepatoPredict (HP) tool, a machine learning-based model that combines molecular and clinical data to forecast LT outcomes.

METHODS

The updated HP algorithm was trained on a two-center dataset and assessed against standard clinical criteria. Its prognostic performance was evaluated through accuracy metrics, with additional analyses considering tumor heterogeneity and potential sampling bias.

RESULTS

HP outperformed all clinical criteria, particularly regarding negative predictive value, addressing critical limitations in existing selection strategies. It also demonstrated improved differentiation of recurrence-free and overall survival outcomes. Importantly, the prognostic accuracy of HP remained largely unaffected by intra-nodule and intra-patient heterogeneity, indicating its robustness even when biopsies were taken from smaller or non-dominant nodules.

CONCLUSIONS

These findings support the usage of HP as a valuable tool for optimizing LT candidate selection, promoting fair organ allocation and enhancing patient outcomes through integrated analysis of molecular and clinical data.

iRGD-Targeted Biosynthetic Nanobubbles for Ultrasound Molecular Imaging of Osteosarcoma

Purpose

Osteosarcoma is the most common primary malignant tumor of the bone. However, there is a lack of effective means for early diagnosis due to the heterogeneity of tumors and the complexity of tumor microenvironment. αvβ3 integrin, a crucial role in the growth and spread of tumors, is not only an effective biomarker for cancer angiogenesis, but also highly expressed in many tumor cells. Here, we selected it as the imaging target and fabricated iRGD-sGVs acoustic probe for the early-stage diagnosis of osteosarcoma.

Materials and Methods

Biological nanoscale gas vesicles (sGVs) were extracted from Serratia 39006. Their morphology was analyzed with phase contrast and transmission electron microscopes. Particle size and zeta potential were measured by a Zetasizer. iRGD-targeted molecular probes (iRGD-sGVs) were prepared by coupling iRGD to sGVs via Mal-PEG2000-NHS. Targeting efficiency of iRGD-sGVs was evaluated using flow cytometry and confocal microscopy on endothelial and K7M2 osteosarcoma cells. In vivo contrast-enhanced ultrasound imaging of iRGD-sGVs was performed in osteosarcoma-bearing mice, and the expression of avβ3 in osteosarcoma was detected through immunofluorescence staining assay. Biocompatibility of sGVs was assessed by hemolysis tests, CCK8 cytotoxicity assays, blood biochemical tests, and HE staining.

Results

sGVs from Serratia.39006 have smaller particle size (about 160 nm). Our in vitro and in vivo experiments showed the specifically binding ability of iRGD-sGVs to both vascular endothelial cells and tumor cells, producing the stronger and longer acoustic signals in tumors in comparison with the control probe. Immunofluorescence staining results indicated iRGD-sGVs were co-localized with highly expressed αvβ3 in tumor vasculature and osteosarcoma cells. Biocompatibility analysis showed no significant cytotoxicity of iRGD-sGVs to mice.

Conclusion

Our study provides a new strategy for early diagnosis of osteosarcoma.

Research trends of cellular immunotherapy for primary liver cancer: A bibliometric analysis

Cellular immunotherapy has shown considerable potential for the treatment of primary liver cancer (PLC), particularly hepatocellular carcinoma (HCC), although it is in the early stages of development. This study used bibliometric methods to delineate the evolution of research on cellular immunotherapy for PLC. Data were sourced from the Web of Science Core Collection (WoSCC) on April 22, 2024. Using the "Bibliometrix" R package, we examined primary bibliometric features, collaboration frequency between countries, and article output of the journals. Furthermore, we employed VOSviewer for coauthorship analysis and visualization and CiteSpace to assess keyword co-occurrence, as well as to spotlight keywords and references with the strongest citation bursts. Our analysis encompassed 492 publications focused on PLC and cellular immunotherapy, and we pinpointed China, Japan, and the USA as the foremost contributing nations and identified "Cancer Immunology Immunotherapy" as the journal with the most contributions in this area. Sun Yat-sen University emerged as the institution with the most significant output, and Li Zonghai authored the greatest number of leading articles. Prominent keywords that displayed a notable citation burst in the later years included "chimeric antigen receptor," "combination therapy", "CAR-T cells," "TCR-T cells," and "liver transplantation." This bibliometric study outlined a foundational knowledge framework, surveyed over three decades of research on cellular immunotherapy for PLC, and revealed the key players and trends, thereby offering a thorough understanding of the field, especially in relation to HCC.

Dynamics of liver cancer cellular taxa revealed through single-cell RNA sequencing: Advances and challenges

Liver cancer is a leading cause of death worldwide, representing a substantial public health challenge. The advent of single-cell RNA sequencing has significantly advanced our understanding of cellular dynamics from the onset of liver cancer to therapeutic intervention. This technology has unveiled profound insights into cancer heterogeneity and the tumor microenvironment (TME), enabling the identification of key molecular drivers and phenotypic landscapes of liver cancer at a single-cell resolution. This review highlights recent advancements in mapping functional cell subsets, phenotypic alterations, and the diversity of the TME. These insights are pivotal for advancing targeted therapies and developing prognostic tools. Moreover, this review covers the ongoing challenges and advances from tumor initiation to progression, offering a detailed perspective on advancing personalized treatment.

Expression of EGFRvIII and its co‑expression with wild‑type EGFR, or putative cancer stem cell biomarkers CD44 or EpCAM are associated with poorer prognosis in patients with hepatocellular carcinoma

The aberrant expression of HER family members and cancer stem cells (CSCs) have been associated with tumour progression and resistance to therapy. At present, several HER inhibitors have been approved for the treatment of patients with a range of cancers but not for the treatment of patients with hepatocellular carcinoma (HCC). The present study investigated the co‑expression and prognostic significance of HER family members, type‑III deletion mutant EGFR (EGFRvIII), and the putative CSC biomarkers CD44 and epithelial cell adhesion molecule (EpCAM) in 43 patients with HCC. The relative expression of these biomarkers was determined using immunohistochemistry. At a cut off value of >5% of tumour cells stained for these biomarkers, 35% [wild‑type (wt)EGFR], 58% (HER‑2), 0% (HER‑3), 19% (HER‑4), 26% (EGFRvIII), 40% (CD44) and 33% (EpCAM) of patients were positive. In 23, 14 and 9% of the patients, wtEGFR expression was accompanied by co‑expression with HER‑2, EGFRvIII and HER‑2/EGFRvIII, respectively. EGFRvIII expression, membranous expression of CD44 and co‑expression of wtEGFR/EGFRvIII were associated with poor overall survival (OS). By contrast, cytoplasmic CD44 expression was associated with a longer OS time. The present study also investigated the effect of several agents targeting one or more members of the HER family, other growth factor receptors and cell signalling proteins on the proliferation of HCC cell lines. Among agents targeting one or more members of the HER family, the pan‑HER family blocker afatinib was the most effective, inhibiting the proliferation of three out of seven human liver cancer cell lines (LCCLs), while the CDK inhibitor dinacicilib was the most effective agent, inhibiting the proliferation of all human LCCLs tested. Taken together, the present results suggested that EGFRvIII expression and its co‑expression with wtEGFR or CD44 was of prognostic significance. These results also support further investigations of the therapeutic potential of drugs targeting EGFRvIII and other members of the HER family in patients with HCC.

Phytochemicals and Their Nanoformulations for Targeting Hepatocellular Carcinoma: Exploring Potential and Targeting Strategies

Hepatocellular carcinoma (HCC) continues to pose a global health concern, necessitating the exploration of innovative therapeutic approaches. In the recent decade, targeting tumor stroma consisting of extracellular matrix (ECM), immune cells, vascular system, hypoxia, and also suppressive mechanisms in HCC has attracted interest in repressing tumor growth and metastasis. Phytochemicals have attained considerable attention because of their manifold biological effects and high capacity for anticancer activities. These chemical agents have shown the capability to modulate different cells and secretions within the stroma of malignancies. In recent years, the development of nanoformulations has further enhanced the therapeutic potential of phytochemicals by improving their solubility, bioavailability, and targeted delivery to tumor tissues. This review aims to provide an encyclopedic overview of the potential of phytochemicals and their nanoformulations as promising therapeutic strategies for targeting HCC. The review initially highlights the broad array of phytochemicals exhibiting potent anticancer properties, including flavonoids, alkaloids, terpenoids, and phenolic compounds, among others. Then, the nanoformulations and modification of these agents will be reviewed. Finally, we will review the latest experiments that have examined the modulation of HCC using adjuvant phytochemicals and their nanoformulations.

Effect of fibroblast heterogeneity on prognosis and drug resistance in high-grade serous ovarian cancer

Tumor heterogeneity is associated with poor prognosis and drug resistance, leading to therapeutic failure. Here, we used tumor evolution analysis to determine the intra- and intertumoral heterogeneity of high-grade serous ovarian cancer (HGSOC) and analyze the correlation between tumor heterogeneity and prognosis, as well as chemotherapy response, through single-cell and spatial transcriptomic analysis. We collected and curated 28 HGSOC patients' single-cell transcriptomic data from five datasets. Then, we developed a novel text-mining-based machine-learning approach to deconstruct the evolutionary patterns of tumor cell functions. We then identified key tumor-related genes within different evolutionary branches, characterized the microenvironmental cell compositions that various functional tumor cells depend on, and analyzed the intra- and intertumoral heterogeneity as well as the tumor microenvironments. These analyses were conducted in relation to the prognosis and chemotherapy response in HGSOC patients. We validated our findings in two spatial and seven bulk transcriptomic datasets (total: 1,030 patients). Using transcriptomic clusters as proxies for functional clonality, we identified a significant increase in tumor cell state heterogeneity that was strongly correlated with patient prognosis and treatment response. Furthermore, increased intra- and intertumoral functional clonality was associated with the characteristics of cancer-associated fibroblasts (CAFs). The spatial proximity between CXCL12-positive CAFs and tumor cells, mediated through the CXCL12/CXCR4 interaction, was highly positively correlated with poor prognosis and chemotherapy resistance in HGSOC. Finally, we constructed a panel of 24 genes through statistical modeling that correlate with CXCL12-positive fibroblasts and can predict both prognosis and the response to chemotherapy in HGSOC patients. Our study offers insights into the collective behavior of tumor cell communities in HGSOC, as well as potential drivers of tumor evolution in response to therapy. There was a strong association between CXCL12-positive fibroblasts and tumor progression, as well as treatment outcomes.

Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma

Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

A Cancer Subpopulation Competition Model Reveals Optimal Levels of Immune Response that Minimize Tumor Size

Breast cancer is a complex disease with significant phenotypic heterogeneity of cells, even within a single breast tumor. Emerging evidence underscores the significance of intratumoral competition, which can serve as a key contributor to cancer drug resistance, imparting substantial clinical implications. Understanding the competitive dynamics is paramount as it can significantly influence disease progression and treatment outcomes. In the present work, a mathematical model was developed using a system of differential equations to describe the dynamic interactions between two cancer subtypes (each further classified into cancer stem cells and tumor cells) and innate immune cells. The purpose of the model is to comprehensively understand the competitive interactions between the heterogeneous subpopulations. The equilibrium points and stability analysis for each equilibrium point were established. Model simulations showed that the competition between two cancer subtypes directly affects the number of both species. When competition between two cancer subtypes is strong, increasing the immune response rate specific to the more competitive species effectively reduces the tumor size. However, if the competition is relatively weak, an optimal immune response rate is required to minimize the total number of tumor cells. Rates below the optimal level fail to reduce the population of the stronger species, whereas rates above the optimal level can lead to the recurrence of the weaker species. Overall, this model provides insights into breast cancer dynamics and guides the development of effective treatment strategies.

Small-molecule GSDMD agonism in tumors stimulates antitumor immunity without toxicity

Gasdermin-mediated inflammatory cell death (pyroptosis) can activate protective immunity in immunologically cold tumors. Here, we performed a high-throughput screen for compounds that could activate gasdermin D (GSDMD), which is expressed widely in tumors. We identified 6,7-dichloro-2-methylsulfonyl-3-N-tert-butylaminoquinoxaline (DMB) as a direct and selective GSDMD agonist that activates GSDMD pore formation and pyroptosis without cleaving GSDMD. In mouse tumor models, pulsed and low-level pyroptosis induced by DMB suppresses tumor growth without harming GSDMD-expressing immune cells. Protection is immune-mediated and abrogated in mice lacking lymphocytes. Vaccination with DMB-treated cancer cells protects mice from secondary tumor challenge, indicating that immunogenic cell death is induced. DMB treatment synergizes with anti-PD-1. DMB treatment does not alter circulating proinflammatory cytokine or leukocyte numbers or cause weight loss. Thus, our studies reveal a strategy that relies on a low level of tumor cell pyroptosis to induce antitumor immunity and raise the possibility of exploiting pyroptosis without causing overt toxicity.

Prognostic and therapeutic implications of tumor-restrictive type III collagen in the breast cancer microenvironment

Collagen plays a critical role in regulating breast cancer progression and therapeutic resistance. An improved understanding of both the features and drivers of tumor-permissive and -restrictive collagen matrices are critical to improve prognostication and develop more effective therapeutic strategies. In this study, using a combination of in vitro, in vivo and bioinformatic experiments, we show that type III collagen (Col3) plays a tumor-restrictive role in human breast cancer. We demonstrate that Col3-deficient, human fibroblasts produce tumor-permissive collagen matrices that drive cell proliferation and suppress apoptosis in non-invasive and invasive breast cancer cell lines. In human triple-negative breast cancer biopsy samples, we demonstrate elevated deposition of Col3 relative to type I collagen (Col1) in non-invasive compared to invasive regions. Similarly, bioinformatics analysis of over 1000 breast cancer patient biopsies from The Cancer Genome Atlas BRCA cohort revealed that patients with higher Col3:Col1 bulk tumor expression had improved overall, disease-free, and progression-free survival relative to those with higher Col1:Col3 expression. Using an established 3D culture model, we show that Col3 increases spheroid formation and induces the formation of lumen-like structures that resemble non-neoplastic mammary acini. Finally, our in vivo study shows co-injection of murine breast cancer cells (4T1) with rhCol3-supplemented hydrogels limits tumor growth and decreases pulmonary metastatic burden compared to controls. Taken together, these data collectively support a tumor-suppressive role for Col3 in human breast cancer and suggest that strategies that increase Col3 may provide a safe and effective therapeutic modality to limit recurrence in breast cancer patients.

Innovative optical imaging strategies for monitoring immunotherapy in the tumor microenvironments

BACKGROUND

The tumor microenvironment (TME) plays a critical role in cancer progression and response to immunotherapy. Immunotherapy targeting the immune system has emerged as a promising treatment modality, but challenges in understanding the TME limit its efficacy. Optical imaging strategies offer noninvasive, real-time insights into the interactions between immune cells and the TME.

OBJECTIVE

This review assesses the progress of optical imaging technologies in monitoring immunotherapy within the TME and explores their potential applications in clinical trials and personalized cancer treatment.

METHODS

This is a comprehensive literature review based on the advances in optical imaging modalities including fluorescence imaging (FLI), bioluminescence imaging (BLI), and photoacoustic imaging (PAI). These modalities were analyzed for their capacity to provide high-resolution, real-time imaging of immune cell dynamics, tumor vasculature, and other critical components of the TME.

RESULTS

Optical imaging techniques have shown significant potential in tracking immune cell infiltration, assessing immune checkpoint inhibitors, and visualizing drug delivery within the TME. Technologies like FLI and BLI are pivotal in tracking immune responses in preclinical models, while PAI provides functional imaging with deeper tissue penetration. The integration of these modalities with immunotherapy holds promise for improving treatment monitoring and outcomes.

CONCLUSION

Optical imaging is a powerful tool for understanding the complexities of the TME and optimizing immunotherapy. Further advancements in imaging technologies, combined with nanomaterial-based approaches, could pave the way for enhanced diagnostic accuracy and therapeutic efficacy in cancer treatment.

Radiomics-based prediction of recurrence for head and neck cancer patients using data imbalanced correction

OBJECTIVES

To propose a radiomics-based prediction model for head and neck squamous cell carcinoma (HSNCC) recurrence after radiation therapy using a novel data imbalance correction method known as Gaussian noise upsampling (GNUS).

MATERIALS AND METHODS

The dataset includes 97 HNSCC patients treated with definitive radiotherapy alone or concurrent chemoradiotherapy at two institutions. We performed radiomics analysis using nine segmentations created on pretreatment positron emission tomography and computed tomography images. Feature selection was performed by the least absolute shrinkage and selection operator analysis via five-fold cross-validation. The proposed GNUS was compared with seven conventional data-imbalance correction methods. Classification models of HNSCC recurrence were constructed on oversampled features using the machine learning algorithms of linear regression. Their predictive performance was evaluated based on accuracy, sensitivity, specificity, and the area under the curve (AUC) of the receiver operating performance characteristic curve via five-fold cross-validation using the same combinations as for feature selection.

RESULT

The prediction model without data imbalance correction shows sensitivity, specificity, accuracy, and AUC values of 83 %, 96 %, 92 %, and 0.96, respectively. The conventional model with the best performance is the random over-sampler model, which shows sensitivity, specificity, accuracy, and AUC values of 93 %, 91 %, 92 %, 0.97, respectively, whereas the GNUS model shows values of 93 %, 94 %, 94 %, 0.98, respectively.

CONCLUSION

Oversampling methods can reduce sensitivity and specificity bias. The proposed GNUS can improve accuracy as well as reduce sensitivity and specificity bias.

Heterogeneity of hepatocellular carcinoma: from mechanisms to clinical implications

Hepatocellular Carcinoma (HCC) is one of the most common types of primary liver cancer. Current treatment options have limited efficacy against this malignancy, primarily owing to difficulties in early detection and the inherent resistance to existing drugs. Tumor heterogeneity is a pivotal factor contributing significantly to treatment resistance and recurrent manifestations of HCC. Intratumoral heterogeneity is an important aspect of the spectrum of complex tumor heterogeneity and contributes to late diagnosis and treatment failure. Therefore, it is crucial to thoroughly understand the molecular mechanisms of how tumor heterogeneity develops. This review aims to summarize the possible molecular dimensions of tumor heterogeneity with an emphasis on intratumoral heterogeneity, evaluate its profound impact on the diagnosis and therapeutic strategies for HCC, and explore the suitability of appropriate pre-clinical models that can be used to best study tumor heterogeneity; thus, opening new avenues for cancer treatment.

Development and validation of a machine learning-based 18F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival

BACKGROUND

Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-node-metastasis (TNM) staging to predict overall survival (OS) in patients with GC.

METHODS

We reviewed the clinical information of a total of 327 patients with pathological confirmation of GC undergoing 18 F-fluorodeoxyglucose (18 F-FDG) PET scans. The patients were randomly classified into training (n = 229) and validation (n = 98) cohorts. We extracted 171 PET radiomics features from the PET images and determined the PET radiomics scores (RS) using the least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF). A radiomics model, including PET RS and clinical TNM staging, was constructed to predict the OS of patients with GC. This model was evaluated for discrimination, calibration, and clinical usefulness.

RESULTS

On multivariate COX regression analysis, the difference between age, carcinoembryonic antigen (CEA), clinical TNM, and PET RS in GC patients was statistically significant (p < 0.05). A radiomics model was developed based on the results of COX regression. The model had the Harrell's concordance index (C-index) of 0.817 in the training cohort and 0.707 in the validation cohort and performed better than a single clinical model and a model with clinical features combined with clinical TNM staging. Further analyses showed higher PET RS in patients who were older (p < 0.001) and those who had elevated CEA (p < 0.001) and higher clinical TNM (p < 0.001). At different clinical TNM stages, a higher PET RS was associated with a worse survival prognosis.

CONCLUSIONS

Radiomics models based on PET RS, clinical TNM, and clinical features may provide new tools for predicting OS in patients with GC.

Predicting Survival Using Whole-Liver MRI Radiomics in Patients with Hepatocellular Carcinoma After TACE Refractoriness

PURPOSE

To develop a model based on whole-liver radiomics features of pre-treatment enhanced MRI for predicting the prognosis of hepatocellular carcinoma (HCC) patients undergoing continued transarterial chemoembolization (TACE) after TACE-resistance.

MATERIALS AND METHODS

Data from 111 TACE-resistant HCC patients between January 2014 and March 2018 were retrospectively collected. At a ratio of 7:3, patients were randomly assigned to developing and validation cohorts. The whole-liver were manually segmented, and the radiomics signature was extracted. The tumor and liver radiomics score (TLrad-score) was calculated. Models were trained by machine learning algorithms and their predictive efficacies were compared.

RESULTS

Tumor stage, tumor burden, body mass index, alpha-fetoprotein, and vascular invasion were revealed as independent risk factors for survival. The model trained by Random Forest algorithms based on tumor burden, whole-liver radiomics signature, and clinical features had the highest predictive efficacy, with c-index values of 0.85 and 0.80 and areas under the ROC curve of 0.96 and 0.83 in the developing cohort and validation cohort, respectively. In the high-rad-score group (TLrad-score > - 0.34), the median overall survival (mOS) was significantly shorter than in the low-rad-score group (17 m vs. 37 m, p < 0.001). A shorter mOS was observed in patients with high tumor burden compared to those with low tumor burden (14 m vs. 29 m, p = 0.007).

CONCLUSION

The combined radiomics model from whole-liver signatures may effectively predict survival for HCC patients continuing TACE after TACE refractoriness. The TLrad-score and tumor burden are potential prognostic markers for TACE therapy following TACE-resistance.

Multiomics profiling of buffy coat and plasma unveils etiology-specific signatures in hepatocellular carcinoma

BACKGROUND/AIMS

Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide. Despite identification of several biomarkers for HCC diagnosis, challenges such as low sensitivity and intratumoral heterogeneity have impeded early detection, highlighting the need for etiology-specific blood biomarkers.

METHODS

We generated whole-transcriptome sequencing (WTS) and targeted proteome data from buffy coat and plasma samples from HCC patients. By integrating etiological information on viral infection, we investigated the etiology-specific gene expression landscape at the blood level. Validation of differentially expressed genes (DEGs) was performed using publicly available RNA-seq datasets and qRT‒PCR with AUC analyses.

RESULTS

Differential expression analyses with multiomics data revealed distinct gene expression profiles between HBV-associated HCC and nonviral HCC, indicating the presence of etiology-specific blood biomarkers. The identified DEGs were validated across multiple independent datasets, underscoring their utility as biomarkers. Additionally, single-cell RNA-seq analysis of HCC confirmed differences in DEG expression across distinct immune cell types.

CONCLUSION

Our buffy coat WTS data and plasma proteome data may serve as reliable sources for identifying etiology-specific blood biomarkers of HCC and might contribute to discovery of therapeutic targets for HCC across different etiologies.

Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors

Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors.

SIGNIFICANCE

A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.

Optimizing cancer therapy: a review of the multifaceted effects of metronomic chemotherapy

Metronomic chemotherapy (MCT), characterized by the continuous administration of chemotherapeutics at a lower dose without prolonged drug-free periods, has garnered significant attention over the last 2 decades. Extensive evidence from both pre-clinical and clinical settings indicates that MCT induces distinct biological effects than the standard Maximum Tolerated Dose (MTD) chemotherapy. The low toxicity profile, reduced likelihood of inducing acquired therapeutic resistance, and low cost of MCT render it an attractive chemotherapeutic regimen option. One of the most prominent aspects of MCT is its anti-angiogenesis effects. It has been shown to stimulate the expression of anti-angiogenic molecules, thereby inhibiting angiogenesis. In addition, MCT has been shown to decrease the regulatory T-cell population and promote anti-tumor immune response through inducing dendritic cell maturation and increasing the number of cytotoxic T-cells. Combination therapies utilizing MCT along with oncolytic virotherapy, radiotherapy or other chemotherapeutic regimens have been studied extensively. This review provides an overview of the current status of MCT research and the established mechanisms of action of MCT treatment and also offers insights into potential avenues of development for MCT in the future.

Signaling, cancer cell plasticity, and intratumor heterogeneity

Cancer's complexity is in part due to the presence of intratumor heterogeneity and the dynamic nature of cancer cell plasticity, which create substantial obstacles in effective cancer management. Variability within a tumor arises from the existence of diverse populations of cancer cells, impacting the progression, spread, and resistance to treatments. At the core of this variability is the concept of cellular plasticity - the intrinsic ability of cancer cells to alter their molecular and cellular identity in reaction to environmental and genetic changes. This adaptability is a cornerstone of cancer's persistence and progression, making it a formidable target for treatments. Emerging studies have emphasized the critical role of such plasticity in fostering tumor diversity, which in turn influences the course of the disease and the effectiveness of therapeutic strategies. The transformative nature of cancer involves a network of signal transduction pathways, notably those that drive the epithelial-to-mesenchymal transition and metabolic remodeling, shaping the evolutionary path of cancer cells. Despite advancements, our understanding of the precise molecular machinations and signaling networks driving these changes is still evolving, underscoring the necessity for further research. This editorial presents a series entitled "Signaling Cancer Cell Plasticity and Intratumor Heterogeneity" in Cell Communication and Signaling, dedicated to unraveling these complex processes and proposing new avenues for therapeutic intervention.

Heterogeneity in Liver Cancer Immune Microenvironment: Emerging Single-Cell and Spatial Perspectives

Primary liver cancer is a solid malignancy with a high mortality rate. The success of immunotherapy has shown great promise in improving patient care and highlights a crucial need to understand the complexity of the liver tumor immune microenvironment (TIME). Recent advances in single-cell and spatial omics technologies, coupled with the development of systems biology approaches, are rapidly transforming the landscape of tumor immunology. Here we review the cellular landscape of liver TIME from single-cell and spatial perspectives. We also discuss the cellular interaction networks within the tumor cell community in regulating immune responses. We further highlight the challenges and opportunities with implications for biomarker discovery, patient stratification, and combination immunotherapies.

Mechanisms of drug resistance in HCC

HCC comprises ∼80% of primary liver cancer. HCC is the only major cancer for which death rates have not improved over the last 10 years. Most patients are diagnosed with advanced disease when surgical and locoregional treatments are not feasible or effective. Sorafenib, a multikinase inhibitor targeting cell growth and angiogenesis, was approved for advanced unresectable HCC in 2007. Since then, other multikinase inhibitors have been approved. Lenvatinib was found to be noninferior to sorafenib as a first-line agent. Regorafenib, cabozantinib, and ramucirumab were shown to prolong survival as second-line agents. Advances in immunotherapy for HCC have also added hope for patients, but their efficacy remains limited. A large proportion of patients with advanced HCC gain no long-term benefit from systemic therapy due to primary and acquired drug resistance, which, combined with its rising incidence, keeps HCC a highly fatal disease. This review summarizes mechanisms of primary and acquired resistance to therapy and includes methods for bypassing resistance. It addresses recent advancements in immunotherapy, provides new perspectives on the linkage between drug resistance and molecular etiology of HCC, and evaluates the role of the microbiome in drug resistance. It also discusses alterations in signaling pathways, dysregulation of apoptosis, modulations in the tumor microenvironment, involvement of cancer stem cells, changes in drug metabolism/transport, tumor hypoxia, DNA repair, and the role of microRNAs in drug resistance. Understanding the interplay among these factors will provide guidance on the development of new therapeutic strategies capable of improving patient outcomes.

PDCL3 is a prognostic biomarker associated with immune infiltration in hepatocellular carcinoma

BACKGROUND

Phosducin-like 3 (PDCL3) is a member of the photoreceptor family, characterized by a thioredoxin-like structural domain and evolutionary conservation. It plays roles in angiogenesis and apoptosis. Despite its significance, research on the biological role of PDCL3 in liver hepatocellular carcinoma (LIHC) remains limited. This study aims to explore the prognostic value and potential mechanisms of PDCL3 in cancer, particularly in LIHC, through bioinformatics analysis.

METHODS

RNA-seq data and corresponding clinical information for pan-cancer and LIHC were extracted from the TCGA database to analyze PDCL3 expression and survival prognosis. Differential expression of PDCL3 was analyzed using the HPA database. GO and KEGG enrichment analysis were performed for PDCL3-associated genes. The relationship between PDCL3 expression and various immune cell types was examined using the TIMER website. Clinical samples were collected, and immunohistochemistry and immunofluorescence experiments were conducted to validate the differential expression of PDCL3 in LIHC and normal tissues. In vitro assays, including CCK-8, wound healing, Transwell, and colony formation experiments, were employed to determine the biological functions of PDCL3 in LIHC cells.

RESULTS

Analysis from TIMER, GEPIA, UALCAN, and HPA databases revealed differential expression of PDCL3 in various tumors. Prognostic analysis from GEPIA and TCGA databases indicated that high PDCL3 expression was associated with poorer clinical staging and prognosis in LIHC. Enrichment analysis of PDCL3-associated genes revealed its involvement in various immune responses. TCGA and TIMER databases showed that high PDCL3 expression in LIHC decreased tumor immune activity by reducing macrophage infiltration. PDCL3 exhibited positive correlations with multiple immune checkpoint genes. Immunohistochemistry (IHC) and immunofluorescence (IF) experiments confirmed elevated PDCL3 expression in LIHC tissues compared to adjacent normal tissues. In vitro experiments demonstrated that PDCL3 promoted LIHC cell proliferation, migration, invasion, and colony-forming ability.

CONCLUSION

PDCL3 is highly expressed in various cancer types. Our study suggests that elevated PDCL3 expression in hepatocellular carcinoma is associated with poorer prognosis and may serve as a potential diagnostic biomarker for LIHC. PDCL3 may regulate the biological functions of LIHC by modulating immune infiltration. However, the precise regulatory mechanisms of PDCL3 in cancer warrant further investigation.

Using imaging modalities to predict nanoparticle distribution and treatment efficacy in solid tumors: The growing role of ultrasound

Nanomedicine in oncology has not had the success in clinical impact that was anticipated in the early stages of the field's development. Ideally, nanomedicines selectively accumulate in tumor tissue and reduce systemic side effects compared to traditional chemotherapeutics. However, this has been more successful in preclinical animal models than in humans. The causes of this failure to translate may be related to the intra- and inter-patient heterogeneity of the tumor microenvironment. Predicting whether a patient will respond positively to treatment prior to its initiation, through evaluation of characteristics like nanoparticle extravasation and retention potential in the tumor, may be a way to improve nanomedicine success rate. While there are many potential strategies to accomplish this, prediction and patient stratification via noninvasive medical imaging may be the most efficient and specific strategy. There have been some preclinical and clinical advances in this area using MRI, CT, PET, and other modalities. An alternative approach that has not been studied as extensively is biomedical ultrasound, including techniques such as multiparametric contrast-enhanced ultrasound (mpCEUS), doppler, elastography, and super-resolution processing. Ultrasound is safe, inexpensive, noninvasive, and capable of imaging the entire tumor with high temporal and spatial resolution. In this work, we summarize the in vivo imaging tools that have been used to predict nanoparticle distribution and treatment efficacy in oncology. We emphasize ultrasound imaging and the recent developments in the field concerning CEUS. The successful implementation of an imaging strategy for prediction of nanoparticle accumulation in tumors could lead to increased clinical translation of nanomedicines, and subsequently, improved patient outcomes. This article is categorized under: Diagnostic Tools In Vivo Nanodiagnostics and Imaging Therapeutic Approaches and Drug Discovery Nanomedicine for Oncologic Disease Therapeutic Approaches and Drug Discovery Emerging Technologies.

Drivers of cancer metastasis - Arise early and remain present

Cancer and its metastases arise from mutations of genes, drivers that promote a tumor's growth. Analyses of driver events provide insights into cancer cell history and may lead to a better understanding of oncogenesis. We reviewed 27 metastatic research studies, including pan-cancer studies, individual cancer studies, and phylogenetic analyses, and summarized our current knowledge of metastatic drivers. All of the analyzed studies had a high level of consistency of driver mutations between primary tumors and metastasis, indicating that most drivers appear early in cancer progression and are maintained in metastatic cells. Additionally, we reviewed data from around 50,000 metastatic cancer patients and compiled a list of genes altered in metastatic lesions. We performed Gene Ontology analysis and confirmed that the most significantly enriched processes in metastatic lesions were the epigenetic regulation of gene expression, signal transduction, cell cycle, programmed cell death, DNA damage, hypoxia and EMT. In this review, we explore the most recent discoveries regarding genetic factors in the advancement of cancer, specifically those that drive metastasis.

A robust primary liver cancer subtype related to prognosis and drug response based on a multiple combined classifying strategy

The recurrence or resistance to treatment of primary liver cancer (PLL) is significantly related to the heterogeneity present within the tumor. In this study, we integrated prognosis risk score, mRNAsi index, and immune characteristics clustering to classify patients. The four subtypes obtained from the combined classification are associated with PLC's prognosis and drug response. In these subtypes, we observed mRNAsiH_ICCA subtype, the intersection between high mRNAsi and immune characteristics clustering A, had the worst prognosis. Specifically, immune characteristics clustering B (ICC_B) had high drug sensitivity in most drugs regardless of the value of mRNAsi. On the other hand, patients with low mRNAsi responded better to ten drugs including KU-55933 and NU7441, while patients with high mRNAsi might benefit from drugs like Leflunomide. By matching the specific characteristics of each combined subtype with the drug-induced cell line expression profile, we identified a group of potential therapeutic drugs that might regulate the expression of disease signature genes. We developed a feasible multiple combined typing strategy, hoping to guide therapeutic selection and promote the development of precision medicine.

Prediction of CAF-related genes in immunotherapy and drug sensitivity in hepatocellular carcinoma: a multi-database analysis

This study aims to identify the cancer-associated fibroblasts (CAF)-related genes that can affect immunotherapy and drug sensitivity in hepatocellular carcinoma (HCC). Expression data and survival data associated with HCC were obtained in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted correlation network analysis (WGCNA) analysis was performed to obtain CAF-related genes. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for regression analysis and risk models. Subsequently, Gene Set Enrichment Analysis (GSEA) analysis, Gene Set Enrichment Analysis (ssGSEA) analysis, Tumor Immune Dysfunction and Exclusion (TIDE) analysis and drug sensitivity analysis were performed on the risk models. Survival analysis of CAF scores showed that the survival rate was lower in samples with high CAF scores than those with low scores. However, this difference was not significant, suggesting CAF may not directly influence the prognosis of HCC patients. Further screening of CAF-related genes yielded 33 CAF-related genes. Seven risk models constructed based on CDR2L, SPRED1, PFKP, ENG, KLF2, FSCN1 and VCAN, showed significant differences in immunotherapy and partial drug sensitivity in HCC. Seven CAF-related genes may have important roles in immunotherapy, drug sensitivity and prognostic survival in HCC patients.

Targeting the mechano-microenvironment and liver cancer stem cells: a promising therapeutic strategy for liver cancer

Over the past 2 decades, cancer stem cells (CSCs) have been identified as the root cause of cancer occurrence, progression, chemoradioresistance, recurrence, and metastasis. Targeting CSCs is a novel therapeutic strategy for cancer management and treatment. Liver cancer (LC) is a malignant disease that can endanger human health. Studies are increasingly suggesting that changes in the liver mechanical microenvironment are a primary driver triggering the occurrence and development of liver cancer. In this review, we summarize current understanding of the roles of the liver mechano-microenvironment and liver cancer stem cells (LCSCs) in liver cancer progression. We also discuss the relationship between the mechanical heterogeneity of liver cancer tissues and LCSC recruitment and metastasis. Finally, we highlight potential mechanosensitive molecules in LCSCs and mechanotherapy in liver cancer. Understanding the roles and regulatory mechanisms of the mechano-microenvironment and LCSCs may provide fundamental insights into liver cancer progression and aid in further development of novel therapeutic strategies.

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles

Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.

A radiomics nomogram based on 18 F-FDG PET/CT and clinical risk factors for the prediction of peritoneal metastasis in gastric cancer

PURPOSE

Peritoneal metastasis (PM) is usually considered an incurable factor of gastric cancer (GC) and not fit for surgery. The aim of this study is to develop and validate an 18 F-FDG PET/CT-derived radiomics model combining with clinical risk factors for predicting PM of GC.

METHOD

In this retrospective study, 410 GC patients (PM - = 281, PM + = 129) who underwent preoperative 18 F-FDG PET/CT images from January 2015 to October 2021 were analyzed. The patients were randomly divided into a training cohort (n = 288) and a validation cohort (n = 122). The maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator method were applied to select feature. Multivariable logistic regression analysis was preformed to develop the predicting model. Discrimination, calibration, and clinical usefulness were used to evaluate the performance of the nomogram.

RESULT

Fourteen radiomics feature parameters were selected to construct radiomics model. The area under the curve (AUC) of the radiomics model were 0.86 [95% confidence interval (CI), 0.81-0.90] in the training cohort and 0.85 (95% CI, 0.78-0.92) in the validation cohort. After multivariable logistic regression, peritoneal effusion, mean standardized uptake value (SUVmean), carbohydrate antigen 125 (CA125) and radiomics signature showed statistically significant differences between different PM status patients( P  < 0.05). They were chosen to construct the comprehensive predicting model which showed a performance with an AUC of 0.92 (95% CI, 0.89-0.95) in the training cohort and 0.92 (95% CI, 0.86-0.98) in the validation cohort, respectively.

CONCLUSION

The nomogram based on 18 F-FDG PET/CT radiomics features and clinical risk factors can be potentially applied in individualized treatment strategy-making for GC patients before the surgery.

Adipose-enriched peri-tumoral stroma, in contrast to myofibroblast-enriched stroma, prognosticates poorer survival in breast cancers

Despite our understanding of the genetic basis of intra-tumoral heterogeneity, the role of stromal heterogeneity arising from an altered tumor microenvironment in affecting tumorigenesis is poorly understood. In particular, extensive study on the peri-tumoral stroma in the morphologically normal tissues surrounding the tumor is lacking. Here, we examine the heterogeneity in tumors and peri-tumoral stroma from 8 ER+/PR+/HER2- invasive breast carcinomas, through multi-region transcriptomic profiling by microarray. We describe the regional heterogeneity observed at the intrinsic molecular subtype, pathway enrichment, and cell type composition levels within each tumor and its peri-tumoral region, up to 7 cm from the tumor margins. Moreover, we identify a pro-inflammatory adipose-enriched peri-tumoral subtype which was significantly associated with poorer overall survival in breast cancer patients, in contrast to an adaptive immune cell- and myofibroblast-enriched subtype. These data together suggest that peri-tumoral heterogeneity may be an important determinant of the evolution and treatment of breast cancers.

A Novel Redox-Sensitive Drug Delivery System Based on Trimethyl-Locked Polycarbonate

Stimuli-responsive polymer nanocarriers, capable of exploiting subtle changes in the tumor microenvironment for controlled drug release, have gained significant attention in cancer therapy. Notably, NAD(P)H: quinone oxidoreductase 1 (NQO1), found to be upregulated in various solid tumors, represents a promising therapeutic target due to its effective capability to enzymatically reduce trimethyl-locked (TML) benzoquinone structures in a physiological condition. In this study, a novel redox-sensitive carbonate monomer, MTC, was synthesized, and its amphiphilic block copolymers were prepared through ring-opening polymerization. By successfully self-assembling poly(ethylene glycol)-b-PMTC micelles, the model drug doxorubicin (DOX) was encapsulated with high efficiency. The micelles exhibited redox-responsive behavior, leading to rapid drug release. In vitro assessments confirmed their excellent biocompatibility and hemocompatibility. Furthermore, the inhibition of the NQO1 enzyme reduced drug release in NQO1-overexpressed cells but not in control cells, resulting in decreased cytotoxicity in the presence of NQO1 enzyme inhibitors. Overall, this study showcases the potential of MTC-based polycarbonate micelles to achieve targeted and specific drug release in the NQO1 enzyme-mediated tumor microenvironment. Therefore, the self-assembly of MTC-based polymers into nanomicelles holds immense promise as intelligent nanocarriers in drug delivery applications.

Multi-site pre-therapeutic biopsies demonstrate genetic heterogeneity in patients with newly diagnosed diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, both regarding clinical presentation, response to treatment and outcome. Recently, subclassification of DLBCL based on mutational profile has been suggested, and next generation sequencing (NGS) analysis may be relevant as part of the diagnostic workflow. This will, however, often be based on analysis of one tumor biopsy. Here, we present a prospective study where multi-site sampling was performed prior to treatment in patients with newly diagnosed DLBCL. Two spatially different biopsies from 16 patients were analyzed using NGS with an in-house 59-gene lymphoma panel. In 8/16 (50%) patients, mutational differences were found between the two biopsy sites, including differences in TP53 mutational status. Our data indicate that a biopsy from the extra-nodal site may represent the most advanced clone, and an extra-nodal biopsy should be preferred for analysis, if safely accessible. This will help ensure a standardized stratification and treatment decision.

Organoids technology for advancing the clinical translation of cancer nanomedicine

The past decades have witnessed the rapid development and widespread application of nanomedicines in cancer treatment; however, the clinical translation of experimental findings has been low, as evidenced by the low percentage of commercialized nanomedicines. Incomplete understanding of nanomedicine-tumor interactions and inappropriate evaluation models are two important challenges limiting the clinical translation of cancer nanomedicines. Currently, nanomedicine-tumor interaction and therapeutic effects are mainly investigated using cell lines or mouse models, which do not recapitulate the complex tumor microenvironment in human patients. Thus, information obtained from cell lines and mouse models cannot provide adequate guidance for the rational redesign of nanomedicine. Compared with other preclinical models, tumor organoids constructed from patient-derived tumor tissues are superior in retaining the key histopathological, genetic, and phenotypic features of the parent tumor. We speculate that organoid technology would help elucidate nanomedicine-tumor interaction in the tumor microenvironment and guide the design of nanomedicine, making it a reliable tool to accurately predict drug responses in patients with cancer. This review highlighted the advantages of drug delivery systems in cancer treatment, challenges limiting the clinical translation of antitumor nanomedicines, and potential application of patient-derived organoids (PDO) in nanomedicine. We propose that combining organoids and nanotechnology would facilitate the development of safe and effective cancer nanomedicines and accelerate their clinical application. This review discussed the potential translational value of integrative research using organoids and cancer nanomedicine. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease.

Advances in single-cell RNA sequencing and its applications in cancer research

Cancers are a group of heterogeneous diseases characterized by the acquisition of functional capabilities during the transition from a normal to a neoplastic state. Powerful experimental and computational tools can be applied to elucidate the mechanisms of occurrence, progression, metastasis, and drug resistance; however, challenges remain. Bulk RNA sequencing techniques only reflect the average gene expression in a sample, making it difficult to understand tumor heterogeneity and the tumor microenvironment. The emergence and development of single-cell RNA sequencing (scRNA-seq) technologies have provided opportunities to understand subtle changes in tumor biology by identifying distinct cell subpopulations, dissecting the tumor microenvironment, and characterizing cellular genomic mutations. Recently, scRNA-seq technology has been increasingly used in cancer studies to explore tumor heterogeneity and the tumor microenvironment, which has increased the understanding of tumorigenesis and evolution. This review summarizes the basic processes and development of scRNA-seq technologies and their increasing applications in cancer research and clinical practice.

Standard deviation of CT radiomic features among malignancies in each individual: prognostic ability in lung cancer patients

PURPOSE

It was reported that individual heterogeneity among malignancies (IHAM) might correlate well to the prognosis of lung cancer; however, seldom radiomic study is on this field. Standard deviation (SD) in statistics could scale average amount of variability of a variable; therefore, we used SD of CT feature (FeatureSD) among primary tumor and malignant lymph nodes (LNs) in an individual to represent IHAM, and its prognostic ability was explored.

METHODS

The enrolled patients who had accepted PET/CT scans were selected from our previous study (ClinicalTrials.gov, NCT03648151). The patients had primary tumor and at least one LN, and standardized uptake value of LN higher than 2.0 and 2.5 were enrolled as the cohort 1 (n = 94) and 2 (n = 88), respectively. FeatureSD from the combined or thin-section CT were calculated among primary tumor and malignant LNs in each patient, and were separately selected by the survival XGBoost method. Finally, their prognostic ability was compared to the significant patient characteristics identified by the Cox regression.

RESULTS

In the univariate and multi-variate Cox analysis, surgery, target therapy, and TNM stage were significantly against OS in the both cohorts. In the survival XGBoost analysis of the thin-section CT dataset, none FeatureSD could be repeatably ranked on the top list of the both cohorts. For the combined CT dataset, only one FeatureSD ranked in the top three of both cohorts, but the three significant factors in the Cox regression were not even on the list. Both in the cohort 1 and 2, C-index of the model composed of the three factors could be improved by integrating the continuous FeatureSD; furthermore, that of each factor was obviously lower than FeatureSD.

CONCLUSION

Standard deviation of CT features among malignant foci within an individual was a powerful prognostic factor in vivo for lung cancer patients.

Pharmaco-proteogenomic characterization of liver cancer organoids for precision oncology

Organoid models have the potential to recapitulate the biological and pharmacotypic features of parental tumors. Nevertheless, integrative pharmaco-proteogenomics analysis for drug response features and biomarker investigation for precision therapy of patients with liver cancer are still lacking. We established a patient-derived liver cancer organoid biobank (LICOB) that comprehensively represents the histological and molecular characteristics of various liver cancer types as determined by multiomics profiling, including genomic, epigenomic, transcriptomic, and proteomic analysis. Proteogenomic profiling of LICOB identified proliferative and metabolic organoid subtypes linked to patient prognosis. High-throughput drug screening revealed distinct response patterns of each subtype that were associated with specific multiomics signatures. Through integrative analyses of LICOB pharmaco-proteogenomics data, we identified the molecular features associated with drug responses and predicted potential drug combinations for personalized patient treatment. The synergistic inhibition effect of mTOR inhibitor temsirolimus and the multitargeted tyrosine kinase inhibitor lenvatinib was validated in organoids and patient-derived xenografts models. We also provide a user-friendly web portal to help serve the biomedical research community. Our study is a rich resource for investigation of liver cancer biology and pharmacological dependencies and may help enable functional precision medicine.

Promising Novel Biomarkers for Hepatocellular Carcinoma: Diagnostic and Prognostic Insights

The systemic therapy landscape for hepatocellular carcinoma is rapidly evolving, as the recent approvals of checkpoint inhibitor-based regimens such as atezolizumab-bevacizumab and durvalumab-tremelimumab in advanced disease have led to an expanding therapeutic armamentarium. The development of biomarkers, however, has not kept up with the approvals of new agents. Nevertheless, biomarker research for hepatocellular carcinoma has recently been growing at a rapid pace. The most active areas of research are biomarkers for early detection and screening, accurate prognostication, and detection of minimal residual disease following curative intent therapies, and, perhaps most importantly, predictive markers to guide selection and sequencing of the individual agents, including tyrosine kinase inhibitors and immunotherapy. In this review, we briefly summarize the recent developments in systemic therapeutics for hepatocellular carcinoma, introduce the key completed and ongoing prospective and retrospective studies evaluating diagnostic, prognostic, and predictive biomarkers with high clinical relevance, highlight several potentially important areas of future research, and share our insights for each biomarker.

Organotypic Models for Functional Drug Testing of Human Cancers

In the era of personalized oncology, there have been accelerated efforts to develop clinically relevant platforms to test drug sensitivities of individual cancers. An ideal assay will serve as a diagnostic companion to inform the oncologist of the various treatments that are sensitive and insensitive, thus improving outcome while minimizing unnecessary toxicities and costs. To date, no such platform exists for clinical use, but promising approaches are on the horizon that take advantage of improved techniques in creating human cancer models that encompass the entire tumor microenvironment, alongside technologies for assessing and analyzing tumor response. This review summarizes a number of current strategies that make use of intact human cancer tissues as organotypic cultures in drug sensitivity testing.

Activation of T Lymphocytes with Anti-PDL1-BiTE in the Presence of Adipose-Derived Mesenchymal Stem Cells (ASCs)

Background

Due to their ability to recruit immune cells to kill tumor cells directly, bispecific T cell engager antibodies (BiTE) hold great potential in T cell redirecting therapies. BiTE is able to activate T cells through CD3 and target them to tumor-expressed antigens. However, there are many components in the tumor microenvironment (TME) such as mesenchymal stem cells (MSCs) that may interfere with BiTE function. Herein, we designed an anti-PDL1-BiTE that targets programmed death ligand 1 (PDL1) and CD3 and investigated its effect on PDL1pos cancer cells in the presence or absence of adipose-derived MSCs (ASCs).

Method

Our anti-PDL1-BiTE comprises of VL and VH chains of anti-CD3 monoclonal antibody (mAb) linked to the VL and VH chains of anti-PDL1 mAb, which simultaneously bind to the CD3ε subunit on T cells and PDL1 on tumor cells. Flow cytometry was employed to assess the strength of binding of anti-PDL1-BiTE to tumor cells and T cells. Cytotoxicity, proliferation, and activation of peripheral blood lymphocyte (PBLs) were evaluated by CFSE assay and flow cytometry after using anti-PDL1-BiTE in the presence or absence of ASCs and their conditioned media (C.M.).

Results

Anti-PDL1-BiTE had the ability to induce selective lysis of PDL1pos U251-MG cancer cells while PDL1neg cells were not affected. Also, anti-PDL1-BiTE significantly stimulated peripheral blood lymphocyte (PBL) proliferation and CD69 expression. ASCs/C.M. did not show a significant effect on the biological activity of anti-PDL1-BiTE.

Conclusion

Overall, anti-PDL1-BiTE selectively depletes PDL1pos cells and represents a new immunotherapeutic approach. It would increase the accumulation of T cells and can improve the prognosis of PDL1pos cancers in spite of the immunomodulatory effects of ASCs and C.M.

Value of [18F]FDG PET/CT radiomic parameters in the context of response to chemotherapy in advanced cervical cancer

The first-order statistical (FOS) and second-order texture analysis on basis of Gray-Level Co-occurence Matrix (GLCM) were obtained to assess metabolic, volumetric, statistical and radiomic parameters of cervical cancer in response to chemotherapy, recurrence and age of patients. The homogeneous group of 83 patients with histologically confirmed IIIC1-IVB stage cervical cancer were analyzed, retrospectively. Before and after chemotherapy, the advancement of the disease and the effectiveness of the therapy, respectively, were established using [¹⁸F] FDG PET/CT imaging. The statistically significant differences between pre- and post-therapy parameters were observed for SUV_max, SUV_mean, TLG, MTV, asphericity (ASP, p = 0.000, Z > 0), entropy (E, p = 0.0000), correlation (COR, p = 0.0007), energy (En, p = 0.000) and homogeneity (H, p = 0.0018). Among the FOS parameters, moderate correlation was observed between pre-treatment coefficient of variation (COV) and patients' recurrence (R = 0.34, p = 0.001). Among the GLCM textural parameters, moderate positive correlation was observed for post-treatment contrast (C) with the age of patients (R = 0.3, p = 0.0038) and strong and moderate correlation was observed in the case of En and H with chemotherapy response (R = 0.54 and R = 0.46, respectively). All correlations were statistically significant. This study indicates the remarkable importance of pre- and post-treatment [¹⁸F] FDG PET statistical and textural GLCM parameters according to prediction of recurrence and chemotherapy response of cervical cancer patients.

Making In Vitro Tumor Models Whole Again

As a reductionist approach, patient-derived in vitro tumor models are inherently still too simplistic for personalized drug testing as they do not capture many characteristics of the tumor microenvironment (TME), such as tumor architecture and stromal heterogeneity. This is especially problematic for assessing stromal-targeting drugs such as immunotherapies in which the density and distribution of immune and other stromal cells determine drug efficacy. On the other end, in vivo models are typically costly, low-throughput, and time-consuming to establish. Ex vivo patient-derived tumor explant (PDE) cultures involve the culture of resected tumor fragments that potentially retain the intact  TME of the original tumor. Although developed decades ago, PDE cultures have not been widely adopted likely because of their low-throughput and poor long-term viability. However, with growing recognition of the importance of patient-specific TME in mediating drug response, especially in the field of immune-oncology, there is an urgent need to resurrect these holistic cultures. In this Review, the key limitations of patient-derived tumor explant cultures are outlined and technologies that have been developed or could be employed to address these limitations are discussed. Engineered holistic tumor explant cultures may truly realize the concept of personalized medicine for cancer patients.

Synchronous Bilateral Breast Cancer With Discordant Receptor Status: Treating One Patient but Two Diseases

The expression of hormone receptors (estrogen and progesterone) and human epidermal growth factor receptor-2 (HER2) has been used for both therapeutic and prognostic purposes in the management of breast cancer. The presence of a discordant receptor status complicates the approach to treatment in patients with synchronous bilateral breast cancer. We describe the case of a 45-year-old female with synchronous bilateral breast cancer with a triple-negative tumor and a contralateral HER2-positive tumor and discussed the impact of this on the approach to therapeutic management.

Integrated proteogenomic characterization reveals an imbalanced hepatocellular carcinoma microenvironment after incomplete radiofrequency ablation

BACKGROUND

Efforts to precisely assess tumor-specific T-cell immune responses still face major challenges, and the potential molecular mechanisms mediating hepatocellular carcinoma (HCC) microenvironment imbalance after incomplete radiofrequency ablation (iRFA) are unclear. This study aimed to provide further insight into the integrated transcriptomic and proteogenomic landscape and identify a new target involved in HCC progression following iRFA.

METHODS

Peripheral blood and matched tissue samples were collected from 10 RFA-treated HCC patients. Multiplex immunostaining and flow cytometry were used to assess local and systemic immune responses. Differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) were explored via transcriptomic and proteogenomic analyses. Proteinase-3 (PRTN3) was identified in these analyses. And then, the ability of PRTN3 to predict overall survival (OS) was assessed in 70 HCC patients with early recurrence after RFA. In vitro CCK-8, wound healing and transwell assays were conducted to observe interactions between Kupffer cells (KCs) and HCC cells induced by PRTN3. The protein levels of multiple oncogenic factors and signaling pathway components were detected by western blotting. A xenograft mouse model was built to observe the tumorigenic effect of PRTN3 overexpression on HCC.

RESULTS

Multiplex immunostaining revealed no immediate significant change in local immune cell counts in periablational tumor tissues after 30 min of iRFA. Flow cytometry showed significantly increased levels of CD4⁺ T cells, CD4⁺CD8⁺ T cells, and CD4⁺CD25⁺CD127^- Tregs and significantly decreased the levels of CD16⁺CD56⁺ natural killer cells on day 5 after cRFA (p < 0.05). Transcriptomics and proteomics revealed 389 DEGs and 20 DEPs. Pathway analysis showed that the DEP-DEGs were mainly enriched in the immunoinflammatory response, cancer progression and metabolic processes. Among the DEP-DEGs, PRTN3 was persistently upregulated and closely associated with the OS of patients with early recurrent HCC following RFA. PRTN3 expressed in KCs may affect the migration and invasion of heat stress-treated HCC cells. PRTN3 promotes tumor growth via multiple oncogenic factors and the PI3K/AKT and P38/ERK signaling pathways.

CONCLUSIONS

This study provides a comprehensive overview of the immune response and transcriptomic and proteogenomic landscapes of the HCC milieu induced by iRFA, revealing that PRTN3 promotes HCC progression after iRFA.

TRIAL REGISTRATION

ChiCTR2200055606, http://www.chictr.org.cn/showproj.aspx?proj=32588 .