Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15:81-94. [PMID: 29115304 DOI: 10.1038/nrclinonc.2017.166]

PCA-based synthetic sensitivity coefficients for chemical reaction network in cancer

Chemical reaction networks are powerful tools for modeling cell signaling and its disruptions in diseases like cancer. Realistic chemical reaction networks involve hundreds of proteins and reactions, resulting in a model depending on a consistently large number of kinetic parameters. Since finely calibrating all the parameters would require an unrealistic amount of data, proper sensitivity analysis is required to identify a subset of parameters for which fine tuning is needed and thus provide a fundamental tool for the qualitative analysis of the network. We present a multidisciplinary approach for computing a set of synthetic sensitivity indices. These indices rank the kinetic parameters, based on the impact that errors in their values would have on the protein concentration profile at equilibrium. Our tests on a chemical reaction network devised for colorectal cells demonstrate the effectiveness of the considered sensitivity indices in different scenarios including in-silico drug dosage and novel therapeutic target discovery. The Matlab code for computing the synthetic sensitivity indices and the data concerning the network for colorectal cells are available at https://github.com/theMIDAgroup/CRN_sensitivity.

Imaging-guided companion diagnostics in radiotherapy by monitoring APE1 activity with afterglow and MRI imaging

Companion diagnostics using biomarkers have gained prominence in guiding radiotherapy. However, biopsy-based techniques fail to account for real-time variations in target response and tumor heterogeneity. Herein, we design an activated afterglow/MRI probe as a companion diagnostics tool for dynamically assessing biomarker apurinic/apyrimidinic endonuclease 1(APE1) during radiotherapy in vivo. We employ ultrabright afterglow nanoparticles and ultrasmall FeMnOx nanoparticles as dual contrast agents, significantly broadening signal change range and enhancing the sensitivity of APE1 imaging (limit of detection: 0.0092 U/mL in afterglow imaging and 0.16 U/mL in MRI). We devise longitudinally and transversely subtraction-enhanced imaging (L&T-SEI) strategy to markedly enhance MRI contrast and signal-to-noise ratio between tumor and normal tissue of living female mice. The combined afterglow and MRI facilitate both anatomical and functional imaging of APE1 activity. This probe enables correlation of afterglow and MRI signals with APE1 expression, radiation dosage, intratumor ROS, and DNA damage, enabling early prediction of radiotherapy outcomes (as early as 3 h), significantly preceding tumor size reduction (6 days). By monitoring APE1 levels, this probe allows for early and sensitive detection of liver organ injury, outperforming histopathological analysis. Furthermore, MRI evaluates APE1 expression in radiation-induced abscopal effects provides insights into underlying mechanisms, and supports the development of treatment protocols.

SCCNAInfer: a robust and accurate tool to infer the absolute copy number on scDNA-seq data

MOTIVATION

Copy number alterations (CNAs) play an important role in disease progression, especially in cancer. Single-cell DNA sequencing (scDNA-seq) facilitates the detection of CNAs of each cell that is sequenced at a shallow and uneven coverage. However, the state-of-the-art CNA detection tools based on scDNA-seq are still subject to genome-wide errors due to the wrong estimation of the ploidy.

RESULTS

We developed SCCNAInfer, a computational tool that utilizes the subclonal signal inside the tumor cells to more accurately infer each cell's ploidy and CNAs. Given the segmentation result of an existing CNA detection method, SCCNAInfer clusters the cells, infers the ploidy of each subclone, refines the read count by bin clustering, and accurately infers the CNAs for each cell. Both simulated and real datasets show that SCCNAInfer consistently improves upon the state-of-the-art CNA detection tools such as Aneufinder, Ginkgo, SCOPE and SeCNV.

AVAILABILITY AND IMPLEMENTATION

SCCNAInfer is freely available at https://github.com/compbio-mallory/SCCNAInfer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Signaling dynamics in coexisting monoclonal cell subpopulations unveil mechanisms of resistance to anti-cancer compounds

BACKGROUND

Tumor heterogeneity is a main contributor of resistance to anti-cancer targeted agents though it has proven difficult to study. Unfortunately, model systems to functionally characterize and mechanistically study dynamic responses to treatment across coexisting subpopulations of cancer cells remain a missing need in oncology.

METHODS

Using single cell cloning and expansion techniques, we established monoclonal cell subpopulations (MCPs) from a commercially available epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer cell line. We then used this model sensitivity to the EGFR inhibitor osimertinib across coexisting cell populations within the same tumor. Pathway-centered signaling dynamics associated with response to treatment and morphological characteristics of the MCPs were assessed using Reverse Phase Protein Microarray. Signaling nodes differentially activated in MCPs less sensitive to treatment were then pharmacologically inhibited to identify target signaling proteins putatively implicated in promoting drug resistance.

RESULTS

MCPs demonstrated highly heterogeneous sensitivities to osimertinib. Cell viability after treatment increased > 20% compared to the parental line in selected MCPs, whereas viability decreased by 75% in other MCPs. Reduced treatment response was detected in MCPs with higher proliferation rates, EGFR L858R expression, activation of EGFR binding partners and downstream signaling molecules, and expression of epithelial-to-mesenchymal transition markers. Levels of activation of EGFR binding partners and MCPs' proliferation rates were also associated with response to c-MET and IGFR inhibitors.

CONCLUSIONS

MCPs represent a suitable model system to characterize heterogeneous biomolecular behaviors in preclinical studies and identify and functionally test biological mechanisms associated with resistance to targeted therapeutics.

Advances in Single-Cell Techniques for Linking Phenotypes to Genotypes

Single-cell analysis has become an essential tool in modern biological research, providing unprecedented insights into cellular behavior and heterogeneity. By examining individual cells, this approach surpasses conventional population-based methods, revealing critical variations in cellular states, responses to environmental cues, and molecular signatures. In the context of cancer, with its diverse cell populations, single-cell analysis is critical for investigating tumor evolution, metastasis, and therapy resistance. Understanding the phenotype-genotype relationship at the single-cell level is crucial for deciphering the molecular mechanisms driving tumor development and progression. This review highlights innovative strategies for selective cell isolation based on desired phenotypes, including robotic aspiration, laser detachment, microraft arrays, optical traps, and droplet-based microfluidic systems. These advanced tools facilitate high-throughput single-cell phenotypic analysis and sorting, enabling the identification and characterization of specific cell subsets, thereby advancing therapeutic innovations in cancer and other diseases.

Targeted Radionuclide Therapy in Glioblastoma

Despite the development of various novel therapies, glioblastoma (GBM) remains a devastating disease, with a median survival of less than 15 months. Recently, targeted radionuclide therapy has shown significant progress in treating solid tumors, with the approval of Lutathera for neuroendocrine tumors and Pluvicto for prostate cancer by the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This achievement has shed light on the potential of targeted radionuclide therapy for other solid tumors, including GBM. This review presents the current status of targeted radionuclide therapy in GBM, highlighting the commonly used therapeutic radionuclides emitting alpha, beta particles, and Auger electrons that could induce potent molecular and cellular damage to treat GBM. We then explore a range of targeting vectors, including small molecules, peptides, and antibodies, which selectively target antigen-expressing tumor cells with minimal or no binding to healthy tissues. Considering that radiopharmaceuticals for GBM are often administered locoregionally to bypass the blood-brain barrier (BBB), we review prominent delivery methods such as convection-enhanced delivery, local implantation, and stereotactic injections. Finally, we address the challenges of this therapeutic approach for GBM and propose potential solutions.

Uncovering Porphyrin Accumulation in the Tumor Microenvironment

Heme, an iron-containing tetrapyrrole, is essential in almost all organisms. Heme biosynthesis needs to be precisely regulated particularly given the potential cytotoxicity of protoporphyrin IX, the intermediate preceding heme formation. Here, we report on the porphyrin intermediate accumulation within the tumor microenvironment (TME), which we propose to result from dysregulation of heme biosynthesis concomitant with an enhanced cancer survival dependence on mid-step genes, a process we recently termed "Porphyrin Overdrive". Specifically, porphyrins build up in both lung cancer cells and stromal cells in the TME. Within the TME's stromal cells, evidence supports cancer-associated fibroblasts (CAFs) actively producing porphyrins through an imbalanced pathway. Conversely, normal tissues exhibit no porphyrin accumulation, and CAFs deprived of tumor cease porphyrin overproduction, indicating that both cancer and tumor-stromal porphyrin overproduction is confined to the cancer-specific tissue niche. The clinical relevance of our findings is implied by establishing a correlation between imbalanced porphyrin production and overall poorer survival in more aggressive cancers. These findings illuminate the anomalous porphyrin dynamics specifically within the tumor microenvironment, suggesting a potential target for therapeutic intervention.

Multicellular immune ecotypes within solid tumors predict real-world therapeutic benefits with immune checkpoint inhibitors

Background

Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME.

Methods

We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset.

Results

Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes.

Conclusion

Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME.

Advancing cancer therapeutics: Integrating scalable 3D cancer models, extracellular vesicles, and omics for enhanced therapy efficacy

Cancer remains as one of the highest challenges to human health. However, anticancer drugs exhibit one of the highest attrition rates compared to other therapeutic interventions. In part, this can be attributed to a prevalent use of in vitro models with limited recapitulative potential of the in vivo settings. Three dimensional (3D) models, such as tumor spheroids and organoids, offer many research opportunities to address the urgent need in developing models capable to more accurately mimic cancer biology and drug resistance profiles. However, their wide adoption in high-throughput pre-clinical studies is dependent on scalable manufacturing to support large-scale therapeutic drug screenings and multi-omic approaches for their comprehensive cellular and molecular characterization. Extracellular vesicles (EVs), which have been emerging as promising drug delivery systems (DDS), stand to significantly benefit from such screenings conducted in realistic cancer models. Furthermore, the integration of these nanomedicines with 3D cancer models and omics profiling holds the potential to deepen our understanding of EV-mediated anticancer effects. In this chapter, we provide an overview of the existing 3D models used in cancer research, namely spheroids and organoids, the innovations in their scalable production and discuss how omics can facilitate the implementation of these models at different stages of drug testing. We also explore how EVs can advance drug delivery in cancer therapies and how the synergy between 3D cancer models and omics approaches can benefit in this process.

Advanced Hydrogels in Breast Cancer Therapy

Breast cancer is the most common malignancy among women and is the second leading cause of cancer-related death for women. Depending on the tumor grade and stage, breast cancer is primarily treated with surgery and antineoplastic therapy. Direct or indirect side effects, emotional trauma, and unpredictable outcomes accompany these traditional therapies, calling for therapies that could improve the overall treatment and recovery experiences of patients. Hydrogels, biomimetic materials with 3D network structures, have shown great promise for augmenting breast cancer therapy. Hydrogel implants can be made with adipogenic and angiogenic properties for tissue integration. 3D organoids of malignant breast tumors grown in hydrogels retain the physical and genetic characteristics of the native tumors, allowing for post-surgery recapitulation of the diseased tissues for precision medicine assessment of the responsiveness of patient-specific cancers to antineoplastic treatment. Hydrogels can also be used as carrier matrices for delivering chemotherapeutics and immunotherapeutics or as post-surgery prosthetic scaffolds. The hydrogel delivery systems could achieve localized and controlled medication release targeting the tumor site, enhancing efficacy and minimizing the adverse effects of therapeutic agents delivered by traditional procedures. This review aims to summarize the most recent advancements in hydrogel utilization for breast cancer post-surgery tissue reconstruction, tumor modeling, and therapy and discuss their limitations in clinical translation.

3D bioprinted CRC model brings to light the replication necessity of an oncolytic vaccinia virus encoding FCU1 gene to exert an efficient anti-tumoral activity

The oncolytic virus represents a promising therapeutic strategy involving the targeted replication of viruses to eliminate cancer cells, while preserving healthy ones. Despite ongoing clinical trials, this approach encounters significant challenges. This study delves into the interaction between an oncolytic virus and extracellular matrix mimics (ECM mimics). A three-dimensional colorectal cancer model, enriched with ECM mimics through bioprinting, was subjected to infection by an oncolytic virus derived from the vaccinia virus (oVV). The investigation revealed prolonged expression and sustained oVV production. However, the absence of a significant antitumor effect suggested that the virus's progression toward non-infected tumoral clusters was hindered by the ECM mimics. Effective elimination of tumoral cells was achieved by introducing an oVV expressing FCU1 (an enzyme converting the prodrug 5-FC into the chemotherapeutic compound 5-FU) alongside 5-FC. Notably, this efficacy was absent when using a non-replicative vaccinia virus expressing FCU1. Our findings underscore then the crucial role of oVV proliferation in a complex ECM mimics. Its proliferation facilitates payload expression and generates a bystander effect to eradicate tumors. Additionally, this study emphasizes the utility of 3D bioprinting for assessing ECM mimics impact on oVV and demonstrates how enhancing oVV capabilities allows overcoming these barriers. This showcases the potential of 3D bioprinting technology in designing purpose-fit models for such investigations.

Neuronal Distribution in Colorectal Cancer: Associations With Clinicopathological Parameters and Survival

Over the past years, insights in the cancer neuroscience field increased rapidly, and a potential role for neurons in colorectal carcinogenesis has been recognized. However, knowledge on the neuronal distribution, subtypes, origin, and associations with clinicopathological characteristics in human studies is sparse. In this study, colorectal tumor tissues from the Netherlands Cohort Study on diet and cancer (n = 490) and an in-cohort validation population (n = 529) were immunohistochemically stained for the pan-neuronal markers neurofilament (NF) and protein gene product 9.5 (PGP9.5) to study the association between neuronal marker expression and clinicopathological characteristics. In addition, tumor and healthy colon tissues were stained for neuronal subtype markers, and their immunoreactivity in colorectal cancer (CRC) stroma was analyzed. NF-positive and PGP9.5-positive nerve fibers were found within the tumor stroma and mostly characterized by the neuronal subtype markers vasoactive intestinal peptide and neuronal nitric oxide synthase, suggesting that inhibitory neurons are the most prominent neuronal subtype in CRC. NF and PGP9.5 protein expression were not consistently associated with tumor stage, sublocation, differentiation grade, and median survival. NF immunoreactivity was associated with a worse CRC-specific survival in the study cohort (P = .025) independent of other prognostic factors (hazard ratio, 2.31; 95% CI, 1.33-4.03; P = .003), but these results were not observed in the in-cohort validation group. PGP9.5, in contrast, was associated with a worse CRC-specific survival in the in-cohort validation (P = .046) but not in the study population. This effect disappeared in multivariate analyses (hazard ratio, 0.81; 95% CI, 0.50-1.32; P = .393), indicating that this effect was dependent on other prognostic factors. This study demonstrates that the tumor stroma of CRC patients mainly harbors inhibitory neurons and that NF as a single marker is significantly associated with a poorer CRC-specific survival in the study cohort but necessitates future validation.

Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity

Radiomics has traditionally focused on individual tumors, often neglecting the integration of metastatic disease, particularly in patients with non-small cell lung cancer. This study sought to examine intra-patient inter-tumor lesion heterogeneity indices using radiomics, exploring their relevance in metastatic lung adenocarcinoma. Consecutive adults newly diagnosed with metastatic lung adenocarcinoma underwent contrast-enhanced CT scans for lesion segmentation and radiomic feature extraction. Three methods were devised to measure distances between tumor lesion profiles within the same patient in radiomic space: centroid to lesion, lesion to lesion, and primitive to lesion, with subsequent calculation of mean, range, and standard deviation of these distances. Associations between HIs, disease control rate, objective response rate to first-line treatment, and overall survival were explored. The study included 167 patients (median age 62.3 years) between 2016 and 2019, divided randomly into experimental (N = 117,546 lesions) and validation (N = 50,232 tumor lesions) cohorts. Patients without disease control/objective response and with poorer survival consistently systematically exhibited values of all heterogeneity indices. Multivariable analyses revealed that the range of primitive-to-lesion distances was associated with disease control in both cohorts and with objective response in the validation cohort. This metrics showed univariable associations with overall survival in the experimental. In conclusion, we proposed original methods to estimate the intra-patient inter-tumor lesion heterogeneity using radiomics that demonstrated correlations with patient outcomes, shedding light on the clinical implications of inter-metastases heterogeneity. This underscores the potential of radiomics in understanding and potentially predicting treatment response and prognosis in metastatic lung adenocarcinoma patients.

Synthesis of a new series of 4-pyrazolylquinolinones with apoptotic antiproliferative effects as dual EGFR/BRAFV600E inhibitors

The current study focuses on developing a single molecule that acts as an antiproliferative agent with dual or multi-targeted action, reducing drug resistance and adverse effects. A new series of 4-pyrazolylquinolin-2-ones (5a-j) with apoptotic antiproliferative effects as dual EGFR/BRAFV600E inhibitors were designed and synthesized. Compounds 5a-j were investigated for their cell viability effect against a normal cell line (MCF-10A). Results showed that none of the compounds were cytotoxic, and all 5a-j demonstrated more than 90% cell viability at 50 μM concentration. Using erlotinib as a reference, the MTT assay investigated the antiproliferative impact of targets 5a-j against four human cancer cell lines. Compounds 5e, 5f, 5h, 5i, and 5j were the most potent antiproliferative agents with GI50 values of 42, 26, 29, 34, and 37 nM, making compounds 5f and 5h more potent than erlotinib (GI50 = 33 nM). Moreover, compounds 5e, 5f, 5h, 5i, and 5j were further investigated as dual EGFR/BRAFV600E inhibitors, and results revealed that compounds 5f, 5h, and 5i are potent antiproliferative agents that act as dual EGFR/BRAFV600E inhibitors. Cell cycle analysis and apoptosis detection revealed that compound 5h displaying cell cycle arrest at the G1 transition could induce apoptosis with a high necrosis percentage. Docking studies revealed that compound 5f exhibited a strong affinity for EGFR and BRAFV600E, with high docking scores of -8.55 kcal mol-1 and -8.22 kcal mol-1, respectively. Furthermore, the ADME analysis of compounds 5a-j highlighted the diversity in their pharmacokinetic properties, emphasizing the importance of experimental validation.

A Glucose Oxidase-Curcumin Composite Nanoreactor for Multimodal Synergistic Cancer Therapy

Glucose oxidase (GOx) selectively oxidizes β-d-glucose into gluconic acid and hydrogen peroxide; thus, it has emerged as a promising anticancer agent by tumor starvation and oxidative therapy. Here, we developed a nanoscale platform or "nanoreactor" that incorporates GOx and the bioactive natural product curcumin (CUR) to achieve a multimodal anticancer nanocomposite. The composite nanoreactor was formed by loading CUR in biodegradable polymeric nanoparticles (NPs) of poly(ethylene glycol)-b-poly(ε-caprolactone) (PEG-PCL). Prime-coating of the NPs with an iron(III)-tannic acid complex enabled facile immobilization of GOx on the NP surface. The NPs were monodisperse with a hydrodynamic diameter of 122 nm and a partially negative surface charge. The NPs were also associated with an excellent CUR loading efficiency and sustained release up to 96 h, which was accelerated by surface-immobilized GOx and followed supercase II transport. Viability assays were conducted on two model cancer cell lines, MCF-7 and MDA-MB-231 cells, as well as human dermal fibroblasts as a representative normal cell line. The assays revealed significantly improved potency of CUR in the composite nanoreactor, with up to 6000- and 1280-fold increase in MCF-7 and MDA-MB-231 cells, respectively, and lower toxicity toward normal cells. The NPs were also able to promote intracellular reactive oxygen species (ROS) generation and dissipation of the mitochondrial membrane potential, providing important clues on the mechanism of action of the nanoreactor. Further investigation of caspase-3 activity revealed that the nanoreactor had no effect or inhibited caspase-3 levels, signifying a caspase-independent mechanism of inducing apoptosis. Our findings present a promising nanocarrier platform that combines therapeutic agents with distinct mechanisms of action acting in synergy for more effective cancer therapy.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on drug response genes to predict prognosis and therapeutic response in ovarian cancer

Ovarian cancer represents a severe gynecological malignancy with a dire prognosis, underscoring the imperative need for dependable biomarkers that can accurately predict drug response and guide therapeutic choices. In this study, we harnessed online single-cell RNA sequencing (scRNAseq) and bulk RNA sequencing (RNAseq) datasets, applying the Scissor algorithm to identify cells responsive to paclitaxel. From these cells, we derived a gene signature, subsequently used to construct a prognostic model that demonstrated high sensitivity and specificity in predicting patient outcomes. Moreover, we conducted pathway and functional enrichment analyses to uncover potential molecular mechanisms driving the prognostic gene signature. This study illustrates the critical role of scRNAseq and bulk RNAseq in developing precise prognostic models for ovarian cancer, potentially transforming clinical decision-making.

Single-cell RNA sequencing in ovarian cancer: revealing new perspectives in the tumor microenvironment

Single-cell sequencing technology has emerged as a pivotal tool for unraveling the complexities of the ovarian tumor microenvironment (TME), which is characterized by its cellular heterogeneity and intricate cell-to-cell interactions. Ovarian cancer (OC), known for its high lethality among gynecologic malignancies, presents significant challenges in treatment and diagnosis, partly due to the complexity of its TME. The application of single-cell sequencing in ovarian cancer research has enabled the detailed characterization of gene expression profiles at the single-cell level, shedding light on the diverse cell populations within the TME, including cancer cells, stromal cells, and immune cells. This high-resolution mapping has been instrumental in understanding the roles of these cells in tumor progression, invasion, metastasis, and drug resistance. By providing insight into the signaling pathways and cell-to-cell communication mechanisms, single-cell sequencing facilitates the identification of novel therapeutic targets and the development of personalized medicine approaches. This review summarizes the advancement and application of single-cell sequencing in studying the stromal components and the broader TME in OC, highlighting its implications for improving diagnosis, treatment strategies, and understanding of the disease's underlying biology.

TRIM59 deficiency promotes M1 macrophage activation and inhibits colorectal cancer through the STAT1 signaling pathway

Tumor-associated macrophages play a crucial role in the tumor microenvironment. Tripartite motif 59 (TRIM59), a member of the tripartite motif (TRIM) family, is known to be associated with immunological diseases and macrophage activation. The functional and molecular mechanisms by which TRIM59 affects the occurrence and development of colorectal cancer (CRC) through macrophages are still not well understood. To address this, we generated macrophage-specific TRIM59 conditional knockout mice and utilized these mice to establish colitis-associated cancer and MC38 transplanted CRC models for further investigation. We found that the deficiency of TRIM59 in macrophages inhibited colorectal tumorigenesis in mice. This tumor-suppressive effect was achieved by promoting the activation of M1 macrophages via STAT1 signaling pathway. Further mechanistic studies revealed that TRIM59 could regulate macrophage polarization by ubiquitinating and degrading STAT1. These findings provide evidence that TRIM59 deficiency promotes M1 macrophage activation and inhibits CRC through the STAT1 signaling pathway, suggesting that the TRIM59/STAT1 signaling pathway may be a promising target for CRC.

Multi-omics based artificial intelligence for cancer research

With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogeneity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic view of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. However, the exponential growth of data generated by multi-omics studies present significant analytical challenges. Processing, analyzing, integrating, and interpreting these multi-omics datasets to extract meaningful insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a powerful solution to these challenges, demonstrating exceptional capabilities in deciphering complex patterns and extracting valuable information from large-scale, intricate omics datasets. This review delves into the synergy of AI and multi-omics, highlighting its revolutionary impact on oncology. We dissect how this confluence is reshaping the landscape of cancer research and clinical practice, particularly in the realms of early detection, diagnosis, prognosis, treatment and pathology. Additionally, we elaborate the latest AI methods for multi-omics integration to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. Finally, we discuss the current challenges of data harmonization, algorithm interpretability, and ethical considerations. Addressing these challenges necessitates a multidisciplinary collaboration, paving the promising way for more precise, personalized, and effective treatments for cancer patients.

BNIP3-mediated mitophagy boosts the competitive growth of Lenvatinib-resistant cells via energy metabolism reprogramming in HCC

An increasing evidence supports that cell competition, a vital selection and quality control mechanism in multicellular organisms, is involved in tumorigenesis and development; however, the mechanistic contributions to the association between cell competition and tumor drug resistance remain ill-defined. In our study, based on a contructed lenvitinib-resistant hepatocellular carcinoma (HCC) cells display obvious competitive growth dominance over sensitive cells through reprogramming energy metabolism. Mechanistically, the hyperactivation of BCL2 interacting protein3 (BNIP3) -mediated mitophagy in lenvatinib-resistant HCC cells promotes glycolytic flux via shifting energy production from mitochondrial oxidative phosphorylation to glycolysis, by regulating AMP-activated protein kinase (AMPK) -enolase 2 (ENO2) signaling, which perpetually maintaining lenvatinib-resistant HCC cells' competitive advantage over sensitive HCC cells. Of note, BNIP3 inhibition significantly sensitized the anti-tumor efficacy of lenvatinib in HCC. Our findings emphasize a vital role for BNIP3-AMPK-ENO2 signaling in maintaining the competitive outcome of lenvitinib-resistant HCC cells via regulating energy metabolism reprogramming; meanwhile, this work recognizes BNIP3 as a promising target to overcome HCC drug resistance.

Insight into Mantle Cell Lymphoma Pathobiology, Diagnosis, and Treatment Using Network-Based and Drug-Repurposing Approaches

Mantle cell lymphoma (MCL) is a rare, incurable, and aggressive B-cell non-Hodgkin lymphoma (NHL). Early MCL diagnosis and treatment is critical and puzzling due to inter/intra-tumoral heterogeneity and limited understanding of the underlying molecular mechanisms. We developed and applied a multifaceted analysis of selected publicly available transcriptomic data of well-defined MCL stages, integrating network-based methods for pathway enrichment analysis, co-expression module alignment, drug repurposing, and prediction of effective drug combinations. We demonstrate the "butterfly effect" emerging from a small set of initially differentially expressed genes, rapidly expanding into numerous deregulated cellular processes, signaling pathways, and core machineries as MCL becomes aggressive. We explore pathogenicity-related signaling circuits by detecting common co-expression modules in MCL stages, pointing out, among others, the role of VEGFA and SPARC proteins in MCL progression and recommend further study of precise drug combinations. Our findings highlight the benefit that can be leveraged by such an approach for better understanding pathobiology and identifying high-priority novel diagnostic and prognostic biomarkers, drug targets, and efficacious combination therapies against MCL that should be further validated for their clinical impact.

Multiparametric MRI subregion radiomics for preoperative assessment of high-risk subregions in microsatellite instability of rectal cancer patients: a multicenter study

BACKGROUND

Microsatellite instability (MSI) is associated with treatment response and prognosis in patients with rectal cancer (RC). However, intratumoral heterogeneity limits MSI testing in patients with RC. The authors developed a subregion radiomics model based on multiparametric MRI to preoperatively assess high-risk subregions with MSI and predict the MSI status of patients with RC.

METHODS

This retrospective study included 475 patients (training cohort, 382; external test cohort, 93) with RC from two participating hospitals between April 2017 and June 2023. In the training cohort, subregion radiomic features were extracted from multiparametric MRI, which included T2-weighted, T1-weighted, diffusion-weighted, and contrast-enhanced T1-weighted imaging. MSI-related subregion radiomic features, classical radiomic features, and clinicoradiological variables were gathered to build five predictive models using logistic regression. Kaplan-Meier survival analysis was conducted to explore the prognostic information.

RESULTS

Among the 475 patients [median age, 64 years (interquartile range, IQR: 55-70 years); 304 men and 171 women], the prevalence of MSI was 11.16% (53/475). The subregion radiomics model outperformed the classical radiomics and clinicoradiological models in both training [area under the curve (AUC)=0.86, 0.72, and 0.59, respectively] and external test cohorts (AUC=0.83, 0.73, and 0.62, respectively). The subregion-clinicoradiological model combining clinicoradiological variables and subregion radiomic features performed the optimal, with AUCs of 0.87 and 0.85 in the training and external test cohorts, respectively. The 3-year disease-free survival rate of MSI groups predicted based on the model was higher than that of the predicted microsatellite stability groups in both patient cohorts (training, P =0.032; external test, P =0.046).

CONCLUSIONS

The authors developed and validated a model based on subregion radiomic features of multiparametric MRI to evaluate high-risk subregions with MSI and predict the MSI status of RC preoperatively, which may assist in individualized treatment decisions and positioning for biopsy.

Antibody-drug conjugates in lung and breast cancer: current evidence and future directions-a position statement from the ETOP IBCSG Partners Foundation

Following the approval of the first antibody-drug conjugates (ADCs) in the early 2000s, development has increased dramatically, with 14 ADCs now approved and >100 in clinical development. In lung cancer, trastuzumab deruxtecan (T-DXd) is approved in human epidermal growth factor receptor 2 (HER2)-mutated, unresectable or metastatic non-small-cell lung cancer, with ADCs targeting HER3 (patritumab deruxtecan), trophoblast cell-surface antigen 2 [datopotamab deruxtecan and sacituzumab govitecan (SG)] and mesenchymal-epithelial transition factor (telisotuzumab vedotin) in late-stage clinical development. In breast cancer, several agents are already approved and widely used, including trastuzumab emtansine, T-DXd and SG, and multiple late-stage trials are ongoing. Thus, in the coming years, we are likely to see significant changes to treatment algorithms. As the number of available ADCs increases, biomarkers (of response and resistance) to better select patients are urgently needed. Biopsy sample collection at the time of treatment selection and incorporation of translational research into clinical trial designs are therefore critical. Biopsy samples taken peri- and post-ADC treatment combined with functional genomics screens could provide insights into response/resistance mechanisms as well as the impact of ADCs on tumour biology and the tumour microenvironment, which could improve understanding of the mechanisms underlying these complex molecules. Many ADCs are undergoing evaluation as combination therapy, but a high bar should be set to progress clinical evaluation of any ADC-based combination, particularly considering the high cost and potential toxicity implications. Efforts to optimise ADC dosing/duration, sequencing and the potential for ADC rechallenge are also important, especially considering sustainability aspects. The ETOP IBCSG Partners Foundation are driving strong collaborations in this field and promoting the generation/sharing of databases, repositories and registries to enable greater access to data. This will allow the most important research questions to be identified and prioritised, which will ultimately accelerate progress and help to improve patient outcomes.

Radiation therapy with phenotypic medicine: towards N-of-1 personalization

In current clinical practice, radiotherapy (RT) is prescribed as a pre-determined total dose divided over daily doses (fractions) given over several weeks. The treatment response is typically assessed months after the end of RT. However, the conventional one-dose-fits-all strategy may not achieve the desired outcome, owing to patient and tumor heterogeneity. Therefore, a treatment strategy that allows for RT dose personalization based on each individual response is preferred. Multiple strategies have been adopted to address this challenge. As an alternative to current known strategies, artificial intelligence (AI)-derived mechanism-independent small data phenotypic medicine (PM) platforms may be utilized for N-of-1 RT personalization. Unlike existing big data approaches, PM does not engage in model refining, training, and validation, and guides treatment by utilizing prospectively collected patient's own small datasets. With PM, clinicians may guide patients' RT dose recommendations using their responses in real-time and potentially avoid over-treatment in good responders and under-treatment in poor responders. In this paper, we discuss the potential of engaging PM to guide clinicians on upfront dose selections and ongoing adaptations during RT, as well as considerations and limitations for implementation. For practicing oncologists, clinical trialists, and researchers, PM can either be implemented as a standalone strategy or in complement with other existing RT personalizations. In addition, PM can either be used for monotherapeutic RT personalization, or in combination with other therapeutics (e.g. chemotherapy, targeted therapy). The potential of N-of-1 RT personalization with drugs will also be presented.

The unfolded protein response machinery in glioblastoma genesis, chemoresistance and as a druggable target

BACKGROUND

The role of the unfolded protein response (UPR) has been progressively unveiled over the last decade and several studies have investigated its implication in glioblastoma (GB) development. The UPR restores cellular homeostasis by triggering the folding and clearance of accumulated misfolded proteins in the ER consecutive to endoplasmic reticulum stress. In case it is overwhelmed, it induces apoptotic cell death. Thus, holding a critical role in cell fate decisions.

METHODS

This article, reviews how the UPR is implicated in cell homeostasis maintenance, then surveils the evidence supporting the UPR involvement in GB genesis, progression, angiogenesis, GB stem cell biology, tumor microenvironment modulation, extracellular matrix remodeling, cell fate decision, invasiveness, and grading. Next, it concurs the evidence showing how the UPR mediates GB chemoresistance-related mechanisms.

RESULTS

The UPR stress sensors IRE1, PERK, and ATF6 with their regulator GRP78 are upregulated in GB compared to lower grade gliomas and normal brain tissue. They are activated in response to oncogenes and are implicated at different stages of GB progression, from its genesis to chemoresistance and relapse. The UPR arms can be effectors of apoptosis as mediators or targets.

CONCLUSION

Recent research has established the role of the UPR in GB pathophysiology and chemoresistance. Targeting its different sensors have shown promising in overcoming GB chomo- and radioresistance and inducing apoptosis.

Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC

The tumor microenvironment (TME) plays a fundamental role in tumorigenesis, tumor progression, and anti-cancer immunity potential of emerging cancer therapeutics. Understanding inter-patient TME heterogeneity, however, remains a challenge to efficient drug development. This article applies recent advances in machine learning (ML) for survival analysis to a retrospective study of NSCLC patients who received definitive surgical resection and immune pathology following surgery. ML methods are compared for their effectiveness in identifying prognostic subtypes. Six survival models, including Cox regression and five survival machine learning methods, were calibrated and applied to predict survival for NSCLC patients based on PD-L1 expression, CD3 expression, and ten baseline patient characteristics. Prognostic subregions of the biomarker space are delineated for each method using synthetic patient data augmentation and compared between models for overall survival concordance. A total of 423 NSCLC patients (46% female; median age [inter quantile range]: 67 [60-73]) treated with definite surgical resection were included in the study. And 219 (52%) patients experienced events during the observation period consisting of a maximum follow-up of 10 years and median follow up 78 months. The random survival forest (RSF) achieved the highest predictive accuracy, with a C-index of 0.84. The resultant biomarker subtypes demonstrate that patients with high PD-L1 expression combined with low CD3 counts experience higher risk of death within five-years of surgical resection.

FPR3 reprograms glycolytic metabolism and stemness in gastric cancer via calcium-NFATc1 pathway

Aerobic glycolysis accelerates tumor proliferation and progression, and inhibitors or drugs targeting abnormal cancer metabolism have been developing. Cancer stem-like cells (CSCs) significantly contribute to tumor initiation, metastasis, therapy resistance, and recurrence. Formyl peptide receptor 3 (FPR3), a member of FPR family, involves in inflammation, tissue repair, and angiogenesis. However, studies in exploring the regulatory mechanisms of aerobic glycolysis and CSCs by FPR3 in gastric cancer (GC) remain unknown. Here, we demonstrated that overexpressed FPR3 suppressed glycolytic capacity and stemness of tumor cells, then inhibited GC cells proliferation. Mechanistically, FPR3 impeded cytoplasmic calcium ion flux and hindered nuclear factor of activated T cells 1 (NFATc1) nuclear translocation, leading to the transcriptional inactivation of NFATc1-binding neurogenic locus notch homolog protein 3 (NOTCH3) promoter, subsequently obstructing NOTCH3 expression and the AKT/mTORC1 signaling pathway, and ultimately downregulating glycolysis. Additionally, NFATc1 directly binds to the sex determining region Y-box 2 (SOX2) promoter and modifies stemness in GC. In conclusion, our work illustrated that FPR3 played a negative role in GC progression by modulating NFATc1-mediated glycolysis and stemness in a calcium-dependent manner, providing potential insights into cancer therapy.

Hybrid Cell Membrane-Based Nanoplatforms for Enhanced Immunotherapy against Cancer and Infectious Diseases

Immunotherapy based on nanoplatforms is a promising approach to treat cancer and infectious diseases, and it has achieved considerable progress in clinical practices. Cell membrane-based nanoplatforms endow nanoparticles with versatile characteristics, such as half-life extension, targeting ability, and immune-system regulation. However, monotypic cell membrane usually fails to provoke strong immune response for immunotherapy while maintaining good biosafety. The integration of different cell-membrane types provides a promising approach to construct multifunctional nanoplatforms for improved immunotherapeutic efficacy by enhancing immunogenicity or targeting function, evading immune clearance, or combining with other therapeutic modalities. In this review, the design principles, preparation strategies, and applications of hybrid cell membrane-based nanoplatforms for cancer and infection immunotherapy are first discussed. Furthermore, the challenges and prospects for the potential clinical translation of hybrid cell membrane-based nanoplatforms are discussed.

Stromal Rigidity Stress Accelerates Pancreatic Intraepithelial Neoplasia Progression and Chromosomal Instability via Nuclear Protein Tyrosine Kinase 2 Localization

Because the mechanotransduction by stromal stiffness stimulates the rupture and repair of the nuclear envelope in pancreatic progenitor cells, accumulated genomic aberrations are under selection in the tumor microenvironment. Analysis of cell growth, micronuclei, and phosphorylated Ser-139 residue of the histone variant H2AX (γH2AX) foci linked to mechanotransduction pressure in vivo during serial orthotopic passages of mouse KrasLSL-G12D/+;Trp53flox/flox;Pdx1-Cre (KPC) cancer cells in the tumor and in migrating through the size-restricted 3-μm micropores. To search for pancreatic cancer cell-of-origin, analysis of single-cell data sets revealed that the extracellular matrix shaped an alternate route of acinar-ductal transdifferentiation of acinar cells into topoisomerase II α (TOP2A)-overexpressing cancer cells and derived subclusters with copy number amplifications in MYC-PTK2 (protein tyrosine kinase 2) locus and PIK3CA. High-PTK2 expression is associated with 171 differentially methylated CpG loci, 319 differentially expressed genes, and poor overall survival in The Cancer Genome Atlas-Pancreatic Adenocarcinoma cohort. Abolished RGD-integrin signaling by disintegrin KG blocked the PTK2 phosphorylation, increased cancer apoptosis, decreased vav guanine nucleotide exchange factor 1 (VAV1) expression, and prolonged overall survival in the KPC mice. Reduction of α-smooth muscle actin deposition in the CD248 knockout KPC mice remodeled the tissue stroma and down-regulated TOP2A expression in the epithelium. In summary, stromal stiffness induced the onset of cancer cells-of-origin by ectopic TOP2A expression, and the genomic amplification of MYC-PTK2 locus via alternative transdifferentiation of pancreatic progenitor cells is the vulnerability useful for disintegrin KG treatment.

Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods

BACKGROUND

Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions.

RESULTS

In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers.

CONCLUSIONS

Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516 , enabling further developments in deconvolution methods.

Deconstructing Intratumoral Heterogeneity through Multiomic and Multiscale Analysis of Serial Sections

Tumors may contain billions of cells, including distinct malignant clones and nonmalignant cell types. Clarifying the evolutionary histories, prevalence, and defining molecular features of these cells is essential for improving clinical outcomes, since intratumoral heterogeneity provides fuel for acquired resistance to targeted therapies. Here we present a statistically motivated strategy for deconstructing intratumoral heterogeneity through multiomic and multiscale analysis of serial tumor sections (MOMA). By combining deep sampling of IDH-mutant astrocytomas with integrative analysis of single-nucleotide variants, copy-number variants, and gene expression, we reconstruct and validate the phylogenies, spatial distributions, and transcriptional profiles of distinct malignant clones. By genotyping nuclei analyzed by single-nucleus RNA-seq for truncal mutations, we further show that commonly used algorithms for identifying cancer cells from single-cell transcriptomes may be inaccurate. We also demonstrate that correlating gene expression with tumor purity in bulk samples can reveal optimal markers of malignant cells and use this approach to identify a core set of genes that are consistently expressed by astrocytoma truncal clones, including AKR1C3, whose expression is associated with poor outcomes in several types of cancer. In summary, MOMA provides a robust and flexible strategy for precisely deconstructing intratumoral heterogeneity and clarifying the core molecular properties of distinct cellular populations in solid tumors.

Targeting initial tumour-osteoclast spatiotemporal interaction to prevent bone metastasis

Bone is the most common site of metastasis, and although low proliferation and immunoediting at the early stage make existing treatment modalities less effective, the microenvironment-inducing behaviour could be a target for early intervention. Here we report on a spatiotemporal coupling interaction between tumour cells and osteoclasts, and named the tumour-associated osteoclast 'tumasteoclast'-a subtype of osteoclasts in bone metastases induced by tumour-migrasome-mediated cytoplasmic transfer. We subsequently propose an in situ decoupling-killing strategy in which tetracycline-modified nanoliposomes encapsulating sodium bicarbonate and sodium hydrogen phosphate are designed to specifically release high concentrations of hydrogen phosphate ions triggered by tumasteoclasts, which depletes calcium ions and forms calcium-phosphorus crystals. This can inhibit the formation of migrasomes for decoupling and disrupt cell membrane for killing, thereby achieving early prevention of bone metastasis. This study provides a research model for exploring tumour cell behaviour in detail and a proof-of-concept for behaviour-targeting strategy.

Manipulating Redox Homeostasis of Cancer Stem Cells Overcome Chemotherapeutic Resistance through Photoactivatable Biomimetic Nanodiscs

Tumor heterogeneity remains a significant obstacle in cancer therapy due to diverse cells with varying treatment responses. Cancer stem-like cells (CSCs) contribute significantly to intratumor heterogeneity, characterized by high tumorigenicity and chemoresistance. CSCs reside in the depth of the tumor, possessing low reactive oxygen species (ROS) levels and robust antioxidant defense systems to maintain self-renewal and stemness. A nanotherapeutic strategy is developed using tumor-penetrating peptide iRGD-modified high-density lipoprotein (HDL)-mimetic nanodiscs (IPCND) that ingeniously loaded with pyropheophorbide-a (Ppa), bis (2-hydroxyethyl) disulfide (S-S), and camptothecin (CPT) by synthesizing two amphiphilic drug-conjugated sphingomyelin derivatives. Photoactivatable Ppa can generate massive ROS which as intracellular signaling molecules effectively shut down self-renewal and trigger differentiation of the CSCs, while S-S is utilized to deplete GSH and sustainably imbalance redox homeostasis by reducing ROS clearance. Simultaneously, the depletion of GSH is accompanied by the release of CPT, which leads to subsequent cell death. This dual strategy successfully disturbed the redox equilibrium of CSCs, prompting their differentiation and boosting the ability of CPT to kill CSCs upon laser irradiation. Additionally, it demonstrated a synergistic anti-cancer effect by concurrently eliminating therapeutically resistant CSCs and bulk tumor cells, effectively suppressing tumor growth in CSC-enriched heterogeneous colon tumor mouse models.

Double-Layered Hollow Mesoporous Cuprous Oxide Nanoparticles for Double Drug Sequential Therapy of Tumors

Cancer stem cells (CSCs) are one of the determinants of tumor heterogeneity and are characterized by self-renewal, high tumorigenicity, invasiveness, and resistance to various therapies. To overcome the resistance of traditional tumor therapies resulting from CSCs, a strategy of double drug sequential therapy (DDST) for CSC-enriched tumors is proposed in this study and is realized utilizing the developed double-layered hollow mesoporous cuprous oxide nanoparticles (DL-HMCONs). The high drug-loading contents of camptothecin (CPT) and all-trans retinoic acid (ATRA) demonstrate that the DL-HMCON can be used as a generic drug delivery system. ATRA and CPT can be sequentially loaded in and released from CPT3@ATRA3@DL-HMCON@HA. The DDST mechanisms of CPT3@ATRA3@DL-HMCON@HA for CSC-containing tumors are demonstrated as follows: 1) the first release of ATRA from the outer layer induces differentiation from CSCs with high drug resistance to non-CSCs with low drug resistance; 2) the second release of CPT from the inner layer causes apoptosis of non-CSCs; and 3) the third release of Cu+ from DL-HMCON itself triggers the Fenton-like reaction and glutathione depletion, resulting in ferroptosis of non-CSCs. This CPT3@ATRA3@DL-HMCON@HA is verified to possess high DDST efficacy for CSC-enriched tumors with high biosafety.

Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research

Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.

Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data

INTRODUCTION

Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs.

AREAS COVERED

Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination.

EXPERT OPINION

Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.

Softness-Aided Mild Hyperthermia Boosts Stiff Nanomedicine by Regulating Tumor Mechanics

Aberrant tumor mechanical microenvironment (TMME), featured with overactivated cancer-associated fibroblasts (CAFs) and excessive extracellular matrix (ECM), severely restricts penetration and accumulation of cancer nanomedicines, while mild-hyperthermia photothermal therapy (mild-PTT) has been developed to modulate TMME. However, photothermal agents also encounter the barriers established by TMME, manifesting in limited penetration and heterogeneous distribution across tumor tissues and ending with attenuated efficiency in TMME regulation. Herein, it is leveraged indocyanine green (ICG)-loaded soft nanogels with outstanding deformability, for efficient tumor penetration and uniform distribution, in combination with mild-PTT to achieve potent TMME regulation by inhibiting CAFs and degrading ECM. As a result, doxorubicin (DOX)-loaded stiff nanogels gain greater benefits in tumor penetration and antitumor efficacy than soft counterparts from softness-mediated mild-PTT. This study reveals the crucial role of nanomedicine mechanical properties in tumor distribution and provides a novel strategy for overcoming the barriers of solid tumors with soft deformable nanogels.

Mimicking Tumor Metastasis Using a Transwell-Integrated Organoids-On-a-Chip Platform

The mortality rate among cancer patients is primarily attributed to tumor metastasis. The evaluation of metastasis potential provides a powerful framework for personalized therapies. However, little work has so far been undertaken to precisely model tumor metastasis in vitro, hindering the development of preventive and therapeutic interventions. In this work, a tumor-metastasis-mimicked Transwell-integrated organoids-on-a-chip platform (TOP) for precisely evaluating tumor metastatic potential is developed. Unlike the conventional Transwell device for detecting cell migration, the engineered device facilitates the assessment of metastasis in patient-derived organoids (PDO). Furthermore, a novel Transwell chamber with a hexagon-shaped structure is developed to mimic the migration of tumor cells into surrounding tissues, allowing for the evaluation of tumor metastasis in a horizontal direction. As a proof-of-concept demonstration, tumor organoids and metastatic clusters are further evaluated at the protein, genetic, and phenotypic levels. In addition, preliminary drug screening is undertaken to highlight the potential for using the device to combat cancers. In summary, the tumor-metastasis-mimicked TOP offers unique capabilities for evaluating the metastasis potential of tumor organoids and contributes to the development of personalized cancer therapies.

Mouse Models to Evaluate the Functional Role of the Tumor Microenvironment in Cancer Progression and Therapy Responses

The tumor microenvironment (TME) is a complex ecosystem of both cellular and noncellular components that functions to impact the evolution of cancer. Various aspects of the TME have been targeted for the control of cancer; however, TME composition is dynamic, with the overall abundance of immune cells, endothelial cells (ECs), fibroblasts, and extracellular matrix (ECM) as well as subsets of TME components changing at different stages of progression and in response to therapy. To effectively treat cancer, an understanding of the functional role of the TME is needed. Genetically engineered mouse models have enabled comprehensive insight into the complex interactions within the TME ecosystem that regulate disease progression. Here, we review recent advances in mouse models that have been employed to understand how the TME regulates cancer initiation, progression, metastasis, and response to therapy.

Global trends of single cell sequence associated in cancer from 2011 to 2024: A bibliometric analysis

Objective

Exploring the different molecular and clinicopathological features of nodal cancer based on single cell sequencing can reveal the intertumoral heterogeneity in cancer, and provide new ideas for early diagnosis, treatment and prognosis analysis of cancer.

Methods

The hotspots, the features of worldwide scientific output, and the frontiers concerning single cell sequence related to cancer from 2011 to 2024 were determined using our bibliometric analysis. Web of Science Core Collection (WOSCC) database was searched for publications on single cell sequence associated with cancer that were published between 2011 and 2024. According to the journals, keywords, number of records, affiliations, citations, and countries, we conducted a bibliometric analysis. With the use of the data gathered from the WOSCC, geographic distribution was visualized, keyword, affiliation, and author cluster analyses were conducted, and co-cited references were reviewed and a descriptive analysis was also performed.

Results

From the analysis, it was concluded that 6189 articles that were published between 2011 and 2024 in total were identified. Frontiers in immunology is the leading journal with the most publications in field of the research. The five clusters that were identified for hotspots included immunotherapy, single-cell RNA sequencing, hepatocellular carcinoma, proliferation, gene expression appeared the most frequently. Journals, nations, organizations, scholars with most contribution and most referenced publications globally were extracted. Studies have mostly concentrated on the spatial transcriptomics, pan-cancer analysis, hepatocellular carcinoma et al.

Conclusion

Single-cell sequencing plays a significant role in tumor diagnosis, treatment and prognosis.

Immunological Signatures for Early Detection of Human Head and Neck Squamous Cell Carcinoma through RNA Transcriptome Analysis of Blood Platelets

Although there has been a reduction in head and neck squamous cell carcinoma occurrence, it continues to be a serious global health concern. The lack of precise early diagnostic biomarkers and postponed diagnosis in the later stages are notable constraints that contribute to poor survival rates and emphasize the need for innovative diagnostic methods. In this study, we employed machine learning alongside weighted gene co-expression network analysis (WGCNA) and network biology to investigate the gene expression patterns of blood platelets, identifying transcriptomic markers for HNSCC diagnosis. Our comprehensive examination of publicly available gene expression datasets revealed nine genes with significantly elevated expression in samples from individuals diagnosed with HNSCC. These potential diagnostic markers were further assessed using TCGA and GTEx datasets, demonstrating high accuracy in distinguishing between HNSCC and non-cancerous samples. The findings indicate that these gene signatures could revolutionize early HNSCC identification. Additionally, the study highlights the significance of tumor-educated platelets (TEPs), which carry RNA signatures indicative of tumor-derived material, offering a non-invasive source for early-detection biomarkers. Despite using platelet and tumor samples from different individuals, our results suggest that TEPs reflect the transcriptomic and epigenetic landscape of tumors. Future research should aim to directly correlate tumor and platelet samples from the same patients to further elucidate this relationship. This study underscores the potential of these biomarkers in transforming early diagnosis and personalized treatment strategies for HNSCC, advocating for further research to validate their predictive and therapeutic potential.

Oncotree2vec - a method for embedding and clustering of tumor mutation trees

MOTIVATION

Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient's tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor subtypes, for more accurate clinical diagnosis and prognosis.

RESULTS

Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees.

AVAILABILITY AND IMPLEMENTATION

https://github.com/cbg-ethz/oncotree2vec.

Epidermal Growth Factor Receptor Targeting in Colorectal Carcinoma: Antibodies and Patient-Derived Organoids as a Smart Model to Study Therapy Resistance

Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Therefore, the need for new therapeutic strategies is still a challenge. Surgery and chemotherapy represent the first-line interventions; nevertheless, the prognosis for metastatic CRC (mCRC) patients remains unacceptable. An important step towards targeted therapy came from the inhibition of the epidermal growth factor receptor (EGFR) pathway, by the anti-EGFR antibody, Cetuximab, or by specific tyrosine kinase inhibitors (TKI). Cetuximab, a mouse-human chimeric monoclonal antibody (mAb), binds to the extracellular domain of EGFR thus impairing EGFR-mediated signaling and reducing cell proliferation. TKI can affect the EGFR biochemical pathway at different steps along the signaling cascade. Apart from Cetuximab, other anti-EGFR mAbs have been developed, such as Panitumumab. Both antibodies have been approved for the treatment of KRAS-NRAS wild type mCRC, alone or in combination with chemotherapy. These antibodies display strong differences in activating the host immune system against CRC, due to their different immunoglobulin isotypes. Although anti-EGFR antibodies are efficient, drug resistance occurs with high frequency. Resistant tumor cell populations can either already be present before therapy or develop later by biochemical adaptations or new genomic mutations in the EGFR pathway. Numerous efforts have been made to improve the efficacy of the anti-EGFR mAbs or to find new agents that are able to block downstream EGFR signaling cascade molecules. Indeed, we examined the importance of analyzing the anti-EGFR antibody-drug conjugates (ADC) developed to overcome resistance and/or stimulate the tumor host's immunity against CRC growth. Also, patient-derived CRC organoid cultures represent a useful and feasible in vitro model to study tumor behavior and therapy response. Organoids can reflect tumor genetic heterogeneity found in the tissue of origin, representing a unique tool for personalized medicine. Thus, CRC-derived organoid cultures are a smart model for studying the tumor microenvironment and for the preclinical assay of anti-EGFR drugs.

Translation of Epigenetics in Cell-Free DNA Liquid Biopsy Technology and Precision Oncology

Technological advancements in cell-free DNA (cfDNA) liquid biopsy have triggered exponential growth in numerous clinical applications. While cfDNA-based liquid biopsy has made significant strides in personalizing cancer treatment, the exploration and translation of epigenetics in liquid biopsy to clinical practice is still nascent. This comprehensive review seeks to provide a broad yet in-depth narrative of the present status of epigenetics in cfDNA liquid biopsy and its associated challenges. It highlights the potential of epigenetics in cfDNA liquid biopsy technologies with the hopes of enhancing its clinical translation. The momentum of cfDNA liquid biopsy technologies in recent years has propelled epigenetics to the forefront of molecular biology. We have only begun to reveal the true potential of epigenetics in both our understanding of disease and leveraging epigenetics in the diagnostic and therapeutic domains. Recent clinical applications of epigenetics-based cfDNA liquid biopsy revolve around DNA methylation in screening and early cancer detection, leading to the development of multi-cancer early detection tests and the capability to pinpoint tissues of origin. The clinical application of epigenetics in cfDNA liquid biopsy in minimal residual disease, monitoring, and surveillance are at their initial stages. A notable advancement in fragmentation patterns analysis has created a new avenue for epigenetic biomarkers. However, the widespread application of cfDNA liquid biopsy has many challenges, including biomarker sensitivity, specificity, logistics including infrastructure and personnel, data processing, handling, results interpretation, accessibility, and cost effectiveness. Exploring and translating epigenetics in cfDNA liquid biopsy technology can transform our understanding and perception of cancer prevention and management. cfDNA liquid biopsy has great potential in precision oncology to revolutionize conventional ways of early cancer detection, monitoring residual disease, treatment response, surveillance, and drug development. Adapting the implementation of liquid biopsy workflow to the local policy worldwide and developing point-of-care testing holds great potential to overcome global cancer disparity and improve cancer outcomes.

Antibody-Drug Conjugates-Evolution and Perspectives

Antineoplastic therapy is one of the main research themes of this century. Modern approaches have been implemented to target and heighten the effect of cytostatic drugs on tumors and diminish their general/unspecific toxicity. In this context, antibody-drug conjugates (ADCs) represent a promising and successful strategy. The aim of this review was to assess different aspects regarding ADCs. They were presented from a chemical and a pharmacological perspective and aspects like structure, conjugation and development particularities alongside effects, clinical trials, safety issues and perspectives and challenges for future use of these drugs were discussed. Representative examples include but are not limited to the following main structural components of ADCs: monoclonal antibodies (trastuzumab, brentuximab), linkers (pH-sensitive, reduction-sensitive, peptide-based, phosphate-based, and others), and payloads (doxorubicin, emtansine, ravtansine, calicheamicin). Regarding pharmacotherapy success, the high effectiveness expectation associated with ADC treatment is supported by the large number of ongoing clinical trials. Major aspects such as development strategies are first discussed, advantages and disadvantages, safety and efficacy, offering a retrospective insight on the subject. The second part of the review is prospective, focusing on various plans to overcome the previously identified difficulties.

Identification and Validation of JAM-A as a Novel Prognostic and Immune Factor in Human Tumors

Junctional adhesion molecule-A (JAM-A), also known as F11 receptor (F11R), is a transmembrane glycoprotein that is involved in various biological processes, including cancer initiation and progression. However, the functional characteristics and significance of JAM-A in pan-cancer remain unexplored. In this study, we used multiple databases to gain a comprehensive understanding of JAM-A in human cancers. JAM-A was widely expressed in various tissues, mainly located on the microtubules and cell junctions. Aberrant expression of JAM-A was detected in multiple cancers at both mRNA and protein levels, which can be correlated with poorer prognosis and may be attributed to genetic alterations and down-regulated DNA methylation. JAM-A expression was also associated with immune infiltration and may affect immunotherapy responses in several cancers. Functional enrichment analysis indicated that JAM-A participated in tight junction and cancer-related pathways. In vitro experiments verified that JAM-A knockdown suppressed the proliferation and migration abilities of breast cancer cells and liver cancer cells. Overall, our study suggests that JAM-A is a pan-cancer regulator and a potential biomarker for predicting prognosis and immune-therapeutic responses for different tumors.

Double-negative T cells utilize a TNFα-JAK1-ICAM-1 cytotoxic axis against acute myeloid leukemia

ABSTRACT

Allogeneic double-negative T cells (DNTs) are a rare T-cell subset that effectively target acute myeloid leukemia (AML) without inducing graft-versus-host disease in an allogeneic setting. A phase 1 clinical trial demonstrated the feasibility, safety, and potential efficacy of allogeneic DNT therapy among patients with relapsed AML. However, the molecular mechanisms of DNT-mediated cytotoxicity against AML remain elusive. Thus, we used a flow cytometry-based high throughput screening to compare the surface molecule expression profile on DNTs during their interaction with DNT-susceptible or -resistant AML cells and identified a tumor necrosis factor α (TNFα)-dependent cytotoxic pathway in DNT-AML interaction. TNFα secreted by DNTs, upon encountering susceptible AML targets, sensitized AML cells to DNT-mediated killing, including those otherwise resistant to DNTs. Mechanistically, TNFα upregulated ICAM-1 on AML cells through a noncanonical JAK1-dependent pathway. DNTs then engaged with AML cells more effectively through an ICAM-1 receptor, lymphocyte function-associated antigen 1, leading to enhanced killing. These results reveal a TNFα-JAK1-ICAM-1 axis in DNT-mediated cytotoxicity against AML to improve therapeutic efficacy.

Dark force rising: Reawakening and targeting of fetal-like stem cells in colorectal cancer

Stem cells play pivotal roles in maintaining intestinal homeostasis, orchestrating regeneration, and in key steps of colorectal cancer (CRC) initiation and progression. Intriguingly, adult stem cells are reduced during many of these processes. On the contrary, primitive fetal programs, commonly detected in development, emerge during tissue repair, CRC metastasis, and therapy resistance. Recent findings indicate a dynamic continuum between adult and fetal stem cell programs. We discuss critical mechanisms facilitating the plasticity between stem cell states and highlight the heterogeneity observed upon the appearance of fetal-like states. We focus on therapeutic opportunities that arise by targeting fetal-like CRC cells and how those concepts can be translated into the clinic.

Silica nanoparticle design for colorectal cancer treatment: Recent progress and clinical potential

Colorectal cancer (CRC) is the third most common cancer worldwide and the second most common cause of cancer death. Nanotherapies are able to selectively target the delivery of cancer therapeutics, thus improving overall antitumor efficiency and reducing conventional chemotherapy side effects. Mesoporous silica nanoparticles (MSNs) have attracted the attention of many researchers due to their remarkable advantages and biosafety. We offer insights into the recent advances of MSNs in CRC treatment and their potential clinical application value.

Current Applications and Future Directions of Circulating Tumor Cells in Colorectal Cancer Recurrence

The ability to predict or detect colorectal cancer (CRC) recurrence early after surgery enables physicians to apply appropriate treatment plans and different follow-up strategies to improve patient survival. Overall, 30-50% of CRC patients experience cancer recurrence after radical surgery, but current surveillance tools have limitations in the precise and early detection of cancer recurrence. Circulating tumor cells (CTCs) are cancer cells that detach from the primary tumor and enter the bloodstream. These can provide real-time information on disease status. CTCs might become novel markers for predicting CRC recurrence and, more importantly, for making decisions about additional adjuvant chemotherapy. In this review, the clinical application of CTCs as a therapeutic marker for stage II CRC is described. It then discusses the utility of CTCs for monitoring cancer recurrence in advanced rectal cancer patients who undergo neoadjuvant chemoradiotherapy. Finally, it discusses the roles of CTC subtypes and CTCs combined with clinicopathological factors in establishing a multimarker model for predicting CRC recurrence.