Tumor heterogeneity: causes and consequences

A comprehensive review on overcoming the multifaceted challenge of cancer multidrug resistance: The emerging role of mesoporous silica nanoparticles

Multidrug resistance (MDR) is a significant challenge in tumor treatment, severely reducing the effectiveness of anticancer drugs and contributing to high mortality rates. This article overviews the various factors involved in the development of MDR, such as changes in drug targets, increased DNA repair mechanisms, and the impact of the tumor microenvironment. It also emphasizes the potential of mesoporous silica nanoparticles (MSNs) as a drug delivery system to combat MDR. With their unique characteristics-such as a high surface area, adjustable pore sizes, and the ability to be functionalized for targeted delivery-MSNs serve as excellent carriers for the simultaneous delivery of chemotherapeutics and siRNAs aimed at reversing resistance pathways. The paper focuses on innovative methods using MSNs for direct intranuclear delivery of their cargos to overcome efflux barrier and improve the effectiveness of combination therapies. This review highlights a promising approach for enhancing cancer treatment outcomes by integrating advanced nanotechnology with traditional therapies, addressing the ongoing challenge of MDR in oncology.

Real-time monitoring of single-cell extracellular pH based on stretchable microelectrode array

The study of cell mechanics was significant for understanding cellular physiological functions, the mechanisms of disease occurrence, and the development of novel therapeutic approaches. However, research on the mechanism of mechanical strain action at the single-cell level was relatively lacking. Herein, we developed a serpentine stretchable sensor array capable of exerting precise mechanical strain on cells and monitoring extracellular pH (pHe) changes at single cell level. The PANI sensor array based on the serpentine structure exhibited good electrochemical stability against mechanical deformation enabling accurate measurement of pHe in single-cell during stretching. The results showed that mechanical stimulation induced cells to undergo deformation, thereby promoting increased cell acid excretion. Both stretching and compressive deformation were able to promote the increase of cell acid excretion. This sensor served as a powerful tool for researching the effect of cellular mechanics on cell metabolism at the single-cell level.

Asymmetric cell division of ALDH1-positive cancer stem cells generates glycolytic metabolically diverse cell populations

Metabolic heterogeneity in various cancer cells within a tumor causes resistance to medical therapies and promotes tumor recurrence and metastasis. However, the mechanisms by which tumors acquire metabolic heterogeneity are poorly understood. Here, we revealed that PKCλ-dependent asymmetric division of ALDH1-positive cancer stem cells (CSCs) led to an uneven distribution of glycolytic capacity, which is crucial for understanding metabolic heterogeneity within a tumor. The rate-limiting enzyme PFKP and the metabolic probe CDG in glycolysis codistributed with the ALDH1A3 protein during the post-cell division phase, highlighting a mechanism for acquiring metabolic diversity. PKCλ deficiency reduced the asymmetric distribution of these proteins in ALDH1high cells with high ALDH1 activity, suggesting a fundamental role for PKCλ in metabolic heterogeneity. We identified 28 distinct distribution patterns combining PFKP and CDG distributions, demonstrating the complexity of glycolytic heterogeneity. Furthermore, validation and prediction of cell distribution patterns via a probabilistic model confirmed that PKCλ deficiency diminished glycolytic diversity in individual cells within a cancer cell colony generated from an ALDH1-positive CSC. These findings suggest that PKCλ-dependent asymmetric cell division of ALDH1-positive CSCs is crucial for glycolytic heterogeneity in cancer cells within a tumor, potentially offering new therapeutic targets against tumor resistance and metastasis.

Tumor heterogeneity in retinoblastoma: a literature review

Tumor heterogeneity, characterized by the presence of diverse cell populations within a tumor, is a key feature of the complex nature of cancer. This diversity arises from the emergence of cells with varying genomic, epigenetic, transcriptomic, and phenotypic profiles over the course of the disease. Host factors and the tumor microenvironment play crucial roles in driving both inter-patient and intra-patient heterogeneity. These diverse cell populations can exhibit different behaviors, such as varying rates of proliferation, responses to treatment, and potential for metastasis. Both inter-patient heterogeneity and intra-patient heterogeneity pose significant challenges to cancer therapeutics and management. In retinoblastoma, while heterogeneity at the clinical presentation level has been recognized for some time, recent attention has shifted towards understanding the underlying cellular heterogeneity. This review primarily focuses on retinoblastoma heterogeneity and its implications for therapeutic strategies and disease management, emphasizing the need for further research and exploration in this complex and challenging area.

Improved acid-driven inhibition of effector T cell function by a pHLIP variant-conjugated PD-L1

Programmed cell death-ligand 1 (PD-L1)/PD-1 axis is crucial for maintenance of immune homeostasis and its impairment partially accounts for the pathogenesis of inflammatory diseases. Hence, augmenting PD-L1/PD-1 signals represents a novel strategy to prevent destructive inflammation and induce immune tolerance. Recently, we developed a new cargo by conjugating the ectodomain of PD-L1 with pHLIP, a low pH-responding and membrane-inserting peptide, and demonstrated its potent immune-suppressive activity under weakly acidic (pH6.1) conditions in vitro. Herein, we further showed that PD-L1-pHLIP (termed as PD-L1-pHLIPwt) responded well to weakly acidic buffer, but not in nearly neutral pH (pH6.8) solutions. To overcome this obstacle, pHLIPwt was replaced by a variant harboring two mutations (Asp14Gla and Asp25Aad) and PD-L1 ectodomain was conjugated to the N-terminus of pHLIP variant via sulfo-SMCC linker (termed as PD-L1-pHLIPva). PD-L1-pHLIPva potently inhibited T effector function including proliferation, activation as well as proinflammatory cytokine release in nearly neutral pH buffer through PD-L1/PD-1 interaction. The inhibitory function of PD-L1-pHLIPva was attributed to more amounts of PD-L1 anchored on the surface of several types of immune cells compared with PD-L1-pHLIPwt. Given that the niche in the lesions of inflammation is weakly acidic even nearly neutral pH, PD-L1-pHLIPva represents a new arsenal to potentially dampen excessive inflammatory reactions.

Targeting the tumour cell surface in advanced prostate cancer

Prostate cancer remains a substantial health challenge, with >375,000 annual deaths amongst men worldwide. Most prostate cancer-related deaths are attributable to the development of resistance to standard-of-care treatments. Characterization of the diverse and complex surfaceome of treatment-resistant prostate cancer, combined with advances in drug development that leverage cell-surface proteins to enhance drug delivery or activate the immune system, have provided novel therapeutic opportunities to target advanced prostate cancer. The prostate cancer surfaceome, including proteins such as prostate-specific membrane antigen (PSMA), B7-H3, six transmembrane epithelial antigen of the prostate 1 (STEAP1), delta-like ligand 3 (DLL3), trophoblastic cell-surface antigen 2 (TROP2), prostate stem cell antigen (PSCA), HER3, CD46 and CD36, can be exploited as therapeutic targets, as regulatory mechanisms might contribute to the heterogeneity of expression of these proteins and subsequently affect treatment response and resistance. Specific treatment strategies targeting the surfaceome are in clinical development, including radionuclides, antibody-drug conjugates, T cell engagers and chimeric antigen receptor (CAR) T cells. Ultimately, biomarker development and clinical implementation of these agents will be informed and refined by further understanding of the biology of various targets; the target specificity and sensitivity of different agents; and off-target and toxic effects associated with these agents. Understanding the dynamic nature of cell-surface targets and non-overlapping expression patterns might also lead to future combinational strategies.

ChatGPT in Oncology Diagnosis and Treatment: Applications, Legal and Ethical Challenges

PURPOSE OF REVIEW

This study aims to systematically review the trajectory of artificial intelligence (AI) development in the medical field, with a particular emphasis on ChatGPT, a cutting-edge tool that is transforming oncology's diagnosis and treatment practices.

RECENT FINDINGS

Recent advancements have demonstrated that ChatGPT can be effectively utilized in various areas, including collecting medical histories, conducting radiological & pathological diagnoses, generating electronic medical record (EMR), providing nutritional support, participating in Multidisciplinary Team (MDT) and formulating personalized, multidisciplinary treatment plans. However, some significant challenges related to data privacy and legal issues that need to be addressed for the safe and effective integration of ChatGPT into clinical practice. ChatGPT, an emerging AI technology, opens up new avenues and viewpoints for oncology diagnosis and treatment. If current technological and legal challenges can be overcome, ChatGPT is expected to play a more significant role in oncology diagnosis and treatment in the future, providing better treatment options and improving the quality of medical services.

Evolutionary measures show that recurrence of DCIS is distinct from progression to breast cancer

BACKGROUND

Progression from pre-cancers like ductal carcinoma in situ (DCIS) to invasive disease (cancer) is driven by somatic evolution and is altered by clinical interventions. We hypothesized that genetic and/or phenotypic intra-tumor heterogeneity would predict clinical outcomes for DCIS since it serves as the substrate for natural selection among cells.

METHODS

We profiled two samples from two geographically distinct foci from each DCIS in both cross-sectional (n = 119) and longitudinal cohorts (n = 224), with whole exome sequencing, low-pass whole genome sequencing, and a panel of immunohistochemical markers.

RESULTS

In the longitudinal cohorts, the only statistically significant associations with time to non-invasive DCIS recurrence were the combination of treatment (lumpectomy only vs mastectomy or lumpectomy with radiation, HR 12.13, p = 0.003, Wald test with FDR correction), ER status (HR 0.16 for ER+ compared to ER-, p = 0.0045), and divergence in SNVs between the two samples (HR 1.33 per 10% divergence, p = 0.018). SNV divergence also distinguished between pure DCIS and DCIS synchronous with invasive disease in the cross-sectional cohort. In contrast, the only statistically significant associations with time to progression to invasive disease were the combination of the width of the surgical margin (HR 0.67 per mm, p = 0.043) and the number of mutations that were detectable at high allele frequencies (HR 1.30 per 10 SNVs, p = 0.02). No predictors were significantly associated with both DCIS recurrence and progression to invasive disease, suggesting that the evolutionary scenarios that lead to these clinical outcomes are markedly different.

CONCLUSIONS

These results imply that recurrence with DCIS is a clinical and biological process different from invasive progression.

APOBEC3A drives ovarian cancer metastasis by altering epithelial-mesenchymal transition

High-grade serous ovarian cancer (HGSOC) is the most prevalent and aggressive histological subtype of ovarian cancer and often presents with metastatic disease. The drivers of metastasis in HGSOC remain enigmatic. APOBEC3A (A3A), an enzyme that generates mutations across various cancers, has been proposed as a mediator of tumor heterogeneity and disease progression. However, the role of A3A in HGSOC has not been explored. We observed an association between high levels of APOBEC3-mediated mutagenesis and poor overall survival in primary HGSOC. We experimentally addressed this correlation by modeling A3A expression in HGSOC, and this resulted in increased metastatic behavior of HGSOC cells in culture and distant metastatic spread in vivo, which was dependent on catalytic activity of A3A. A3A activity in both primary and cultured HGSOC cells yielded consistent alterations in expression of epithelial-mesenchymal transition (EMT) genes resulting in hybrid EMT and mesenchymal signatures, providing a mechanism for their increased metastatic potential. Inhibition of key EMT factors TWIST1 and IL-6 resulted in mitigation of A3A-dependent metastatic phenotypes. Our findings define the prevalence of A3A mutagenesis in HGSOC and implicate A3A as a driver of HGSOC metastasis via EMT, underscoring its clinical relevance as a potential prognostic biomarker. Our study lays the groundwork for the development of targeted therapies aimed at mitigating the deleterious effect of A3A-driven EMT in HGSOC.

Single-cell copy number calling and event history reconstruction

MOTIVATION

Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations.

RESULTS

We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to two breast cancer samples from different sequencing protocols.

AVAILABILITY AND IMPLEMENTATION

SCICoNE is available at https://github.com/cbg-ethz/SCICoNE.

Rad4XCNN: A new agnostic method for post-hoc global explanation of CNN-derived features by means of Radiomics

BACKGROUND AND OBJECTIVE

In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI models poses significant challenges, particularly in medical contexts where reliability is a mandatory aspect. However, it appears that explainability is inversely proportional to accuracy. For this reason, achieving transparency without compromising predictive accuracy remains a key challenge.

METHODS

This paper presents a novel method, namely Rad4XCNN, to enhance the predictive power of CNN-derived features with the inherent interpretability of radiomic features. Rad4XCNN diverges from conventional methods based on saliency maps, by associating intelligible meaning to CNN-derived features by means of Radiomics, offering new perspectives on explanation methods beyond visualization maps.

RESULTS

Using a breast cancer classification task as a case study, we evaluated Rad4XCNN on ultrasound imaging datasets, including an online dataset and two in-house datasets for internal and external validation. Some key results are: (i) CNN-derived features guarantee more robust accuracy when compared against ViT-derived and radiomic features; (ii) conventional visualization map methods for explanation present several pitfalls; (iii) Rad4XCNN does not sacrifice model accuracy for their explainability; (iv) Rad4XCNN provides a global explanation enabling the physician to extract global insights and findings.

CONCLUSIONS

Our method can mitigate some concerns related to the explainability-accuracy trade-off. This study highlighted the importance of proposing new methods for model explanation without affecting their accuracy.

The discordance of biomarkers in primary tumour and synchronous axillary lymph node metastasis of the breast cancer, and its clinical significance in patients undergoing neoadjuvant therapy

BACKGROUND

This study explores the disparity in ER, PR, HER-2, and Ki-67 status between primary breast tumors (PBT) and axillary lymph node metastasis (ALNM) at initial admission in patients undergoing neoadjuvant chemotherapy (NAC).

RESEARCH DESIGN AND METHODS

Demographic-clinicopathological characteristics and histopathological response to NAC in both PBT and ALNM were recorded. Immunohistochemical analysis of ER, PR, HER-2, and Ki67 was performed separately in PBT and ALNM, with discordance rates compared. The disparity in biomarkers was assessed in relation to the histopathological response to NAC in both PBT and ALNM.

RESULTS

In 96 female patients, discordance rates between PBT and ALNM were 16.67% for ER, 16.67% for PR, 20.83% for HER-2, and 15.63% for Ki-67. Statistically significant differences in ER and PR discordance between PBT and ALNM were observed. Additionally, a significant difference in histopathological response to NAC was noted based on ER discordance.

CONCLUSION

Precise assessment of ER and HER-2 status in axillary lymph node biopsy specimens is crucial in breast cancer patients with ALNM, potentially optimizing systemic and surgical treatment selection.

T-cell Receptor Repertoire Analysis in the Context of Transarterial Chemoembolization Synergy with Systemic Therapy for Hepatocellular Carcinoma

T-cell receptor (TCR) sequencing provides a novel platform for insight into and characterization of intricate T-cell profiles, advancing the understanding of tumor immune heterogeneity. Recently, transarterial chemoembolization (TACE) combined with systemic therapy has become the recommended regimen for advanced hepatocellular carcinoma. The regulation of the immune microenvironment after TACE and its impact on tumor progression and recurrence has been a focus of research. By examining and tracking fluctuations in the TCR repertoire following combination treatment, novel perspectives on the modulation of the tumor microenvironment post-TACE and the underlying mechanisms governing tumor progression and recurrence can be gained. Clarifying the distinctive metrics and dynamic alterations of the TCR repertoire within the context of combination therapy is imperative for understanding the mechanisms of anti-tumor immunity, assessing efficacy, exploiting novel treatments, and further advancing precision oncology in the treatment of hepatocellular carcinoma. In this review, we initially summarized the fundamental characteristics of TCR repertoire and depicted immune microenvironment remodeling after TACE. Ultimately, we illustrated the prospective applications of TCR repertoires in TACE combined with systemic therapy.

A Self-Assembled Nano-Molecular Glue (Nano-mGlu) Enables GSH/H2O2-Triggered Targeted Protein Degradation in Cancer Therapy

Molecular glues are promising protein-degrading agents that hold great therapeutic potential but face significant challenges in rational design, effective synthesis, and precise targeting of tumor sites. In this study, we first overcame some of these limitations by introducing a fumarate-based molecular glue handle onto specific ligands of therapeutic kinases (TBK1, FGFR, and Bcr-Abl), resulting in the effective degradation of these important cancer targets. Despite the broad applicability of the strategy, we unexpectedly discovered potent and widespread cytotoxicity across various cell lines, including noncancerous ones, rendering it less effective in cancer therapy. To address this critical issue, we next developed a self-assembled nanoparticle-based molecular glue (nano-mGlu) based on one of the newly discovered Bcr-Abl-degrading molecular glues (H1-mGlu). We showed that the resulting nano-mGlu (named Cle-NP) was able to release H1-mGlu in vitro in the presence of a high concentration of GSH or H2O2 (commonly found in the tumor microenvironment). Subsequent in vivo antitumor studies with a K562-xenografted mouse model indicated that Cle-NP was highly effective in tumor-specific degradation of endogenous Bcr-Abl expressed in K562 cells, leading to eventual tumor regression while maintaining good biosafety profiles. With key advantages of generality in molecular glue design, targeted delivery (e.g., H1-mGlu), potent antitumor activity partially induced by target-specific degradation, and minimized collateral damage to healthy tissues, our self-assembled nano-mGlu strategy thus provides a novel approach that might hold a significant promise for effective and personalized cancer therapy.

Single-Cell Transcriptomics Sheds Light on Tumor Evolution: Perspectives from City of Hope's Clinical Trial Teams

Tumor heterogeneity is a significant factor influencing cancer treatment effectiveness and can arise from genetic, epigenetic, and phenotypic variations among cancer cells. Understanding how tumor heterogeneity impacts tumor evolution and therapy response can lead to more effective treatments and improved patient outcomes. Traditional bulk genomic approaches fail to provide insights into cellular-level events, whereas single-cell RNA sequencing (scRNA-seq) offers transcriptomic analysis at the individual cell level, advancing our understanding of tumor growth, progression, and drug response. However, implementing single-cell approaches in clinical trials involves challenges, such as obtaining high-quality cells, technical variability, and the need for complex computational analysis. Effective implementation of single-cell genomics in clinical trials requires a collaborative "Team Medicine" approach, leveraging shared resources, expertise, and workflows. Here, we describe key technical considerations in implementing the collection of research biopsies and lessons learned from integrating scRNA-seq into City of Hope's clinical trial design, highlighting collaborative efforts between computational and clinical teams across breast, brain, and ovarian cancer studies to understand the composition, phenotypic state, and underlying resistance mechanisms within the tumor microenvironment.

Advances in the delivery of anticancer drugs by nanoparticles and chitosan-based nanoparticles

Cancer is the leading cause of death globally, and conventional treatments have limited efficacy with severe side effects. The use of nanotechnology has the potential to reduce the side effects of drugs by creating efficient and controlled anticancer drug delivery systems. Nanoparticles (NPs) used as drug carriers offer several advantages, including enhanced drug protection, biodistribution, selectivity and, pharmacokinetics. Therefore, this review is devoted to various organic (lipid, polymeric) as well as inorganic nanoparticles based on different building units and providing a wide range of potent anticancer drug delivery systems. Within these nanoparticulate systems, chitosan (CS)-based NPs are discussed with particular emphasis due to the unique properties of CS and its derivatives including non-toxicity, biodegradability, mucoadhesivity, and tunable physico-chemical as well as biological properties allowing their alteration to specifically target cancer cells. In the context of streamlining the nanoparticulate drug delivery systems (DDS), innovative nanoplatform-based cancer therapy pathways involving passive and active targeting as well as stimuli-responsive DDS enhancing overall orthogonality of developed NP-DDS towards the target are included. The most up-to-date information on delivering anti-cancer drugs using modern dosage forms based on various nanoparticulate systems and, specifically, CSNPs, are summarised and evaluated concerning their benefits, limitations, and advanced applications.

Survival prediction and molecular subtyping of squamous cell lung cancer based on network embedding

Squamous cell lung cancer (SQCLC), which is fatal to humans, is heterogeneous with different genetic and histological features. We used SBMOI, a multi-omics data integration method from previous study, to integrate clinical, gene expression, and somatic mutation data of SQCLC to construct new patient features. Next, random survival forest (RSF) model and SimpleMKL model were constructed to predict the survival of SQCLC patients, and K-means model was constructed to perform molecular subtyping. The results of the RSF model showed that when the dimension of the patient features were 11 × 364 and the hard threshold was 0.2, we obtained the best results, and the AUC value of the 1-year time-dependent ROC curve was 0.706. The SimpleMKL model, constructed using the same patient features, performed exceptionally well, with 1-year, 5-year, and 10-year survival prediction AUC values of 0.944, 0.947 and 0.950, respectively. We used K-means analysis to identify three SQCLC molecular subtypes with significant survival differences. The patient features constructed by SBMOI were used to effectively predict the survival and molecular subtyping of SQCLC patients. In addition, our study further confirmed the effectiveness in multi-omics data integration task and broad applicability of SBMOI.

Synthesis and Preclinical Evaluation of a Bispecific PSMA-617/RM2 Heterodimer Targeting Prostate Cancer

Prostate-specific membrane antigen (PSMA) and gastrin-releasing peptide receptor (GRPR) have been used for diagnostic molecular imaging/therapy of prostate cancer (PCa). To address tumor heterogeneity, we synthesized and evaluated a bispecific PSMA/GRPR ligand (3) combining PSMA-617 (1) and the GRPR antagonist RM2 (2) with the radiometal chelator DOTA. 3 was radiolabeled with 68Ga ([68Ga]Ga-3) and 177Lu ([177Lu]Lu-3). [68Ga]Ga-3 was tested in the following PCa cell lines for receptor affinity, time kinetic cell-binding/specificity, and cell-internalization: PC-3 and LNCaP. Compared to the monomers (1 and 2), ligand 3 showed specific cell binding, similar receptor affinities, and higher lipophilicity, while its internalization rates and cell-binding were superior. Docking calculations showed that 3 can have binding interactions of PSMA-617 (1) inside the PSMA receptor funnel and RM2 (2) inside the GRPR. In vivo biodistribution studies for [68Ga]Ga-3 showed dual targeting for PSMA(+) and GRPR(+) tumors and higher tumor uptake, faster pharmacokinetic, and lower kidney uptake compared to 1 and 2.

Advances in mRNA LNP-Based Cancer Vaccines: Mechanisms, Formulation Aspects, Challenges, and Future Directions

After the COVID-19 pandemic, mRNA-based vaccines have emerged as a revolutionary technology in immunization and vaccination. These vaccines have shown remarkable efficacy against the virus and opened up avenues for their possible application in other diseases. This has renewed interest and investment in mRNA vaccine research and development, attracting the scientific community to explore all its other applications beyond infectious diseases. Recently, researchers have focused on the possibility of adapting this vaccination approach to cancer immunotherapy. While there is a huge potential, challenges still remain in the design and optimization of the synthetic mRNA molecules and the lipid nanoparticle delivery system required to ensure the adequate elicitation of the immune response and the successful eradication of tumors. This review points out the basic mechanisms of mRNA-LNP vaccines in cancer immunotherapy and recent approaches in mRNA vaccine design. This review displays the current mRNA modifications and lipid nanoparticle components and how these factors affect vaccine efficacy. Furthermore, this review discusses the future directions and clinical applications of mRNA-LNP vaccines in cancer treatment.

Machine Learning Classification of Integrin-Expression-Based Magnetic Sorted SW 620 Cells by Simultaneous O-PTIR and SERS

Immortalized cell lines are commonly used for in vitro studies such as drug efficacy, toxicology, and life cycle due to their cost effectiveness and accessibility; however, subpopulations within a cell line can arise from random mutations or asynchronous cell cycles which may lead to results that make interpretation difficult. A method that could classify these differences and separate unique subpopulations would increase our understanding of heterogeneous cellular responses. In the present work, we explore spectroscopic signals associated with subpopulations of cells magnetically sorted on the basis of α5β1 integrin binding to cyclic-RGDfC which mimics fibronectin in the extracellular matrix. SW620 colon cancer cells were incubated with cyclic-RGDfC functionalized gold-coated, iron core nanoparticles and magnetically sorted. The subpopulations from the sort were imaged (N = 10 positive and N = 10 negative, number of cells) via simultaneous surface-enhanced Raman scattering (SERS) and optical-photothermal infrared spectroscopy (O-PTIR). Pearson correlations of the standard peptide-protein interaction in the SERS channel allowed for visualization of the cyclic RGDfC-integrin α5β1 interaction. Partial least-squares discriminant analysis of the O-PTIR spectra collected from cell maps successfully classified the positively or negatively sorted cells. These results demonstrate that biochemical changes within a single cell line can be sorted via an integrin-activity-based assay using simultaneous SERS and O-PTIR.

APOBEC3A drives metastasis of high-grade serous ovarian cancer by altering epithelial-to-mesenchymal transition

High-grade serous ovarian cancer (HGSOC) is the most prevalent and aggressive histological subtype of ovarian cancer, and often presents with metastatic disease. The drivers of metastasis in HGSOC remain enigmatic. APOBEC3A (A3A), an enzyme that generates mutations across various cancers, has been proposed as a mediator of tumor heterogeneity and disease progression. However, the role of A3A in HGSOC has not been explored. Through analysis of genome sequencing from primary HGSOC, we observed an association between high levels of APOBEC3 mutagenesis and poor overall survival. We experimentally addressed this correlation by modeling A3A activity in HGSOC cell lines and mouse models which resulted in increased metastatic behavior of HGSOC cells in culture and distant metastatic spread in vivo . A3A activity in both primary and cultured HGSOC cells yielded consistent alterations in expression of epithelial-mesenchymal-transition (EMT) genes resulting in hybrid EMT and mesenchymal signatures, and providing a mechanism for their increased metastatic potential. Our findings define the prevalence of A3A mutagenesis in HGSOC and implicate A3A as a driver of HGSOC metastasis via EMT, underscoring its clinical relevance as a potential prognostic biomarker. Our study lays the groundwork for the development of targeted therapies aimed at mitigating the deleterious impact of A3A-driven EMT in HGSOC.

Single-Cell Proteomic and Transcriptomic Characterization of Drug-Resistant Prostate Cancer Cells Reveals Molecular Signatures Associated with Morphological Changes

This study delves into the proteomic intricacies of drug-resistant cells (DRCs) within prostate cancer, which are known for their pivotal roles in therapeutic resistance, relapse, and metastasis. Utilizing single-cell proteomics (SCP) with an optimized high-throughput Data Independent Acquisition (DIA) approach with the throughput of 60 sample per day, we characterized the proteomic landscape of DRCs in comparison to parental PC3 cells. This optimized DIA method allowed for robust and reproducible protein quantification at the single-cell level, enabling the identification and quantification of over 1,300 proteins per cell on average. Distinct proteomic sub-clusters within the DRC population were identified, closely linked to variations in cell size. The study uncovered novel protein signatures, including the regulation of proteins critical for cell adhesion and metabolic processes, as well as the upregulation of surface proteins and transcription factors pivotal for cancer progression. Furthermore, by integrating SCP and single-cell RNA-seq (scRNA-seq) data, we identified six upregulated and ten downregulated genes consistently altered in drug-treated cells across both SCP and scRNA-seq platforms. These findings underscore the heterogeneity of DRCs and their unique molecular signatures, providing valuable insights into their biological behavior and potential therapeutic targets.

Microenvironmental alkalization promotes the therapeutic effects of MSLN-CAR-T cells

Triple-negative breast cancer (TNBC) is characterized by high invasion, prone metastasis, frequent recurrence and poor prognosis. Unfortunately, the curative effects of current clinical therapies, including surgery, radiotherapy, chemotherapy and immunotherapy, are still limited in patients with TNBC. In this study, we showed that the heterogeneous expression at the protein level and subcellular location of mesothelin (MSLN), a potential target for chimeric antigen receptor-T (CAR-T) cell therapy in TNBC, which is caused by acidification of the tumor microenvironment, may be the main obstacle to therapeutic efficacy. Alkalization culture or sodium bicarbonate administration significantly promoted the membrane expression of MSLN and enhanced the killing efficiency of MSLN-CAR-T cells both in vitro and in vivo, and the same results were also obtained in other cancers with high MSLN expression, such as pancreatic and ovarian cancers. Moreover, mechanistic exploration revealed that the attenuation of autophagy-lysosome function caused by microenvironmental alkalization inhibited the degradation of MSLN. Hence, alkalization of the microenvironment improves the consistency and high expression of the target antigen MSLN and constitutes a routine method for treating diverse solid cancers via MSLN-CAR-T cells.

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

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

Advanced tumor organoid bioprinting strategy for oncology research

Bioprinting is a groundbreaking technology that enables precise distribution of cell-containing bioinks to construct organoid models that accurately reflect the characteristics of tumors in vivo. By incorporating different types of tumor cells into the bioink, the heterogeneity of tumors can be replicated, enabling studies to simulate real-life situations closely. Precise reproduction of the arrangement and interactions of tumor cells using bioprinting methods provides a more realistic representation of the tumor microenvironment. By mimicking the complexity of the tumor microenvironment, the growth patterns and diffusion of tumors can be demonstrated. This approach can also be used to evaluate the response of tumors to drugs, including drug permeability and cytotoxicity, and other characteristics. Therefore, organoid models can provide a more accurate oncology research and treatment simulation platform. This review summarizes the latest advancements in bioprinting to construct tumor organoid models. First, we describe the bioink used for tumor organoid model construction, followed by an introduction to various bioprinting methods for tumor model formation. Subsequently, we provide an overview of existing bioprinted tumor organoid models.

The evolutionary theory of cancer: challenges and potential solutions

The clonal evolution model of cancer was developed in the 1950s-1970s and became central to cancer biology in the twenty-first century, largely through studies of cancer genetics. Although it has proven its worth, its structure has been challenged by observations of phenotypic plasticity, non-genetic forms of inheritance, non-genetic determinants of clone fitness and non-tree-like transmission of genes. There is even confusion about the definition of a clone, which we aim to resolve. The performance and value of the clonal evolution model depends on the empirical extent to which evolutionary processes are involved in cancer, and on its theoretical ability to account for those evolutionary processes. Here, we identify limits in the theoretical performance of the clonal evolution model and provide solutions to overcome those limits. Although we do not claim that clonal evolution can explain everything about cancer, we show how many of the complexities that have been identified in the dynamics of cancer can be integrated into the model to improve our current understanding of cancer.

Development and Validation of Radiomics-Based Models for Predicting the Parametrial Invasion in Stage IB1 to IIA2 Cervical Cancer

Objective

To develop an early warning system that enables accurate parametrial invasion (PMI) risk prediction in cervical cancer patients with early-stage.

Methods

We retrospectively collected 218 early-stage cervical cancer patients who were treated in Jingzhou Central Hospital from January 31, 2015, to January 31, 2023, and diagnosed with early stage cervical cancer by pathology. The prediction model training is achieved by randomly dividing 70% of the training queue population, with the remaining 30% used as the testing queue. Then, a prediction model based on machine learning algorithms (including random forest, generalized linear regression, decision tree, support vector machine, and artificial neural network) is constructed to predict the risk of PMI occurrence. Ultimately, the analysis of receiver operating characteristic curve (ROC) and decision curve analysis (DCA) is used to evaluate the predictive ability of various prediction models.

Results

We finally included radiomics-based candidate variables that can be used for PMI model. Multivariate logistic regression analysis showed that energy, correlation, sum entropy (SUE), entropy, mean sum (MES), variance of differences (DIV), and inverse difference (IND) were independent risk factors for PMI occurrence. The predictive performance AUC of five types of machine learning ranges from 0.747 to 0.895 in the training set and can also reach a high accuracy of 0.905 in the testing set, indicating that the predictive model has ideal robustness.

Conclusion

Our ML-based model incorporating GLCM parameters can predict PMI in cervical cancer patients with stage IB1 to IIA2, particularly the RFM, which could contribute to distinguishing PMI before surgery, especially in assisting decision-making on surgical scope.

Evaluation of Cellpose segmentation with sequential thresholding for instance segmentation of cytoplasms within autofluorescence images

BACKGROUND

Autofluorescence imaging of the coenzyme, reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H), provides a label-free technique to assess cellular metabolism. Because NAD(P)H is localized in the cytosol and mitochondria, instance segmentation of cell cytoplasms from NAD(P)H images allows quantification of metabolism with cellular resolution. However, accurate cytoplasmic segmentation of autofluorescence images is difficult due to irregular cell shapes and cell clusters.

METHOD

Here, a cytoplasm segmentation method is presented and tested. First, autofluorescence images are segmented into cells via either hand-segmentation or Cellpose, a deep learning-based segmentation method. Then, a cytoplasmic post-processing algorithm (CPPA) is applied for cytoplasmic segmentation. CPPA uses a binarized segmentation image to remove non-segmented pixels from the NAD(P)H image and then applies an intensity-based threshold to identify nuclei regions. Errors at cell edges are removed using a distance transform algorithm. The nucleus mask is then subtracted from the cell segmented image to yield the cytoplasm mask image. CPPA was tested on five NAD(P)H images of three different cell samples, quiescent T cells, activated T cells, and MCF7 cells.

RESULTS

Using POSEA, an evaluation method tailored for instance segmentation, the CPPA yielded F-measure values of 0.89, 0.87, and 0.94 for quiescent T cells, activated T cells, and MCF7 cells, respectively, for cytoplasm identification of hand-segmented cells. CPPA achieved F-measure values of 0.84, 0.74, and 0.72 for Cellpose segmented cells.

CONCLUSION

These results exceed the F-measure value of a comparative cell segmentation method (CellProfiler, ∼0.50-0.60) and support the use of artificial intelligence and post-processing techniques for accurate segmentation of autofluorescence images for single-cell metabolic analyses.

RNA splicing factor RBFOX2 is a key factor in the progression of cancer and cardiomyopathy

BACKGROUND

Alternative splicing of pre-mRNA is a fundamental regulatory process in multicellular eukaryotes, significantly contributing to the diversification of the human proteome. RNA-binding fox-1 homologue 2 (RBFOX2), a member of the evolutionarily conserved RBFOX family, has emerged as a critical splicing regulator, playing a pivotal role in the alternative splicing of pre-mRNA. This review provides a comprehensive analysis of RBFOX2, elucidating its splicing activity through direct and indirect binding mechanisms. RBFOX2 exerts substantial influence over the alternative splicing of numerous transcripts, thereby shaping essential cellular processes such as differentiation and development.

MAIN BODY OF THE ABSTRACT

Dysregulation of RBFOX2-mediated alternative splicing has been closely linked to a spectrum of cardiovascular diseases and malignant tumours, underscoring its potential as a therapeutic target. Despite significant progress, current research faces notable challenges. The complete structural characterisation of RBFOX2 remains elusive, limiting in-depth exploration beyond its RNA-recognition motif. Furthermore, the scarcity of studies focusing on RBFOX2-targeting drugs poses a hindrance to translating research findings into clinical applications.

CONCLUSION

This review critically assesses the existing body of knowledge on RBFOX2, highlighting research gaps and limitations. By delineating these areas, this analysis not only serves as a foundational reference for future studies but also provides strategic insights for bridging these gaps. Addressing these challenges will be instrumental in unlocking the full therapeutic potential of RBFOX2, paving the way for innovative and effective treatments in various diseases.

Insights into metabolic heterogeneity of colorectal cancer gained from fluorescence lifetime imaging

Heterogeneity of tumor metabolism is an important, but still poorly understood aspect of tumor biology. Present work is focused on the visualization and quantification of cellular metabolic heterogeneity of colorectal cancer using fluorescence lifetime imaging (FLIM) of redox cofactor NAD(P)H. FLIM-microscopy of NAD(P)H was performed in vitro in four cancer cell lines (HT29, HCT116, CaCo2 and CT26), in vivo in the four types of colorectal tumors in mice and ex vivo in patients' tumor samples. The dispersion and bimodality of the decay parameters were evaluated to quantify the intercellular metabolic heterogeneity. Our results demonstrate that patients' colorectal tumors have significantly higher heterogeneity of energy metabolism compared with cultured cells and tumor xenografts, which was displayed as a wider and frequently bimodal distribution of a contribution of a free (glycolytic) fraction of NAD(P)H within a sample. Among patients' tumors, the dispersion was larger in the high-grade and early stage ones, without, however, any association with bimodality. These results indicate that cell-level metabolic heterogeneity assessed from NAD(P)H FLIM has a potential to become a clinical prognostic factor.

Evolutionary Measures Show that Recurrence of DCIS is Distinct from Progression to Breast Cancer

Progression from pre-cancers like ductal carcinoma in situ (DCIS) to invasive disease (cancer) is driven by somatic evolution and is altered by clinical interventions. We hypothesized that genetic and/or phenotypic intra-tumor heterogeneity would predict clinical outcomes for DCIS since it serves as the substrate for natural selection among cells. We profiled two samples from two geographically distinct foci from each DCIS in both cross-sectional (N = 119) and longitudinal cohorts (N = 224), with whole exome sequencing, low-pass whole genome sequencing, and a panel of immunohistochemical markers. In the longitudinal cohorts, the only statistically significant predictors of time to non-invasive DCIS recurrence were the combination of treatment (lumpectomy only vs mastectomy or lumpectomy with radiation, HR = 12.13, p = 0.003, Wald test with FDR correction), ER status (HR = 0.16 for ER+ compared to ER-, p = 0.0045), and divergence in SNVs between the two samples (HR = 1.33 per 10% divergence, p = 0.018). SNV divergence also distinguished between pure DCIS and DCIS synchronous with invasive disease in the cross-sectional cohort. In contrast, the only statistically significant predictors of time to progression to invasive disease were the combination of the width of the surgical margin (HR = 0.67 per mm, p = 0.043) and the number of mutations that were detectable at high allele frequencies (HR = 1.30 per 10 SNVs, p = 0.02). These results imply that recurrence with DCIS is a clinical and biological process different from invasive progression.

Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

Antibody Drug Conjugates for Cancer Therapy: From Metallodrugs to Nature-Inspired Payloads

This review highlights significant advancements in antibody-drug conjugates (ADCs) equipped with metal-based and nature-inspired payloads, focusing on synthetic strategies for antibody conjugation. Traditional methods such us maleimide and succinimide conjugation and classical condensation reactions are prevalent for metallodrugs and natural compounds. However, emerging non-conventional strategies such as photoconjugation are gaining traction due to their milder conditions and, in an aspect which minimizes side reactions, selective formation of ADC. The review also summarizes the therapeutic and diagnostic properties of these ADCs, highlighting their enhanced selectivity and reduced side effects in cancer treatment compared to non-conjugated payloads. ADCs combine the specificity of monoclonal antibodies with the cytotoxicity of chemotherapy drugs, offering a targeted approach to the elimination of cancer cells while sparing healthy tissues. This targeted mechanism has demonstrated impressive clinical efficacy in various malignancies. Key future advancements include improved linker technology for enhanced stability and controlled release of cytotoxic agents, incorporation of novel, more potent, cytotoxic agents, and the identification of new cancer-specific antigens through genomic and proteomic technologies. ADCs are also expected to play a crucial role in combination therapies with immune checkpoint inhibitors, CAR-T cells, and small molecule inhibitors, leading to more durable and potentially curative outcomes. Ongoing research and clinical trials are expanding their capabilities, paving the way for more effective, safer, and personalized treatments, positioning ADCs as a cornerstone of modern medicine and offering new hope to patients.

Capturing cell heterogeneity in representations of cell populations for image-based profiling using contrastive learning

Image-based cell profiling is a powerful tool that compares perturbed cell populations by measuring thousands of single-cell features and summarizing them into profiles. Typically a sample is represented by averaging across cells, but this fails to capture the heterogeneity within cell populations. We introduce CytoSummaryNet: a Deep Sets-based approach that improves mechanism of action prediction by 30-68% in mean average precision compared to average profiling on a public dataset. CytoSummaryNet uses self-supervised contrastive learning in a multiple-instance learning framework, providing an easier-to-apply method for aggregating single-cell feature data than previously published strategies. Interpretability analysis suggests that the model achieves this improvement by downweighting small mitotic cells or those with debris and prioritizing large uncrowded cells. The approach requires only perturbation labels for training, which are readily available in all cell profiling datasets. CytoSummaryNet offers a straightforward post-processing step for single-cell profiles that can significantly boost retrieval performance on image-based profiling datasets.

Clinicopathological significance of mutation profile detected by next generation sequencing in different metastatic organs of non-small cell lung cancers

BACKGROUND

The primary tumor and it's metastases show heterogeneity in molecular studies for targeted therapies in Non-Small Cell Lung Cancer(NSCLC), the leading cause of cancer-related deaths worldwide. The study aimed to identify somatic mutations in biopsies from NSCLC patients' metastatic organs using Next-Generation Sequencing(NGS) and examine their association with clinicopathological parameters.

MATERIALS AND METHODS

The study included 128 NSCLC patients and, NGS was performed on tumor biopsies from different metastatic organs at Molecular Pathology laboratory of the Department of Medical Pathology in Aydın Adnan Menderes University Faculty of Medicine. The age, gender, histopathological diagnoses, metastatic organs, smoking and mutation status were all recorded, along with the analysis results of 72 genes and 4149 primers in the panel of the NGS system.

RESULTS

53.9 % of the cases had a history of smoking and patients with brain metastases had a higher smoking rate(p=0.000). The most common occurrence(39.8 %) was lymph node metastasis, followed by brain(19.5 %). There was a strong correlation between mutation presence and metastasis in the liver(p=0.012), bone(p=0.002), and pleura(p=0.008). Smokers had a higher frequency of KRAS(p=0.000) and TP53(p=0.001) mutations. Brain metastases showed a statistically significant NF1 mutation(p=0.001), while the liver exhibited a significant BRAF mutation(p=0.000). NF1-TP53, PTEN-TP53 and NF1-PTEN were the most common concomitant mutations and, the brain was the most common metastatic organ in which they occurred.

CONCLUSION

Our results suggest prizing assessing detected mutations, in the prediction, follow-up and management of metastases, especially in patients with lung adenocarcinoma. The assessment also needs to consider the tumor's mutation status in metastatic organs. New therapeutic agents targeting NF1 mutations will be available in the future to treat NSCLC, especially in metastases.

S-allyl-cysteine triggers cytotoxic events in rat glioblastoma RG2 and C6 cells and improves the effect of temozolomide through the regulation of oxidative responses

Glioblastoma (GBM) is an aggressive form of cancer affecting the Central Nervous System (CNS) of thousands of people every year. Redox alterations have been shown to play a key role in the development and progression of these tumors as Reactive Oxygen Species (ROS) formation is involved in the modulation of several signaling pathways, transcription factors, and cytokine formation. The second-generation oral alkylating agent temozolomide (TMZ) is the first-line chemotherapeutic drug used to treat of GBM, though patients often develop primary and secondary resistance, reducing its efficacy. Antioxidants represent promising and potential coadjutant agents as they can reduce excessive ROS formation derived from chemo- and radiotherapy, while decreasing pharmacological resistance. S-allyl-cysteine (SAC) has been shown to inhibit the proliferation of several types of cancer cells, though its precise antiproliferative mechanisms remain poorly investigated. To date, SAC effects have been poorly explored in GBM cells. Here, we investigated the effects of SAC in vitro, either alone or in combination with TMZ, on several toxic and modulatory endpoints-including oxidative stress markers and transcriptional regulation-in two glioblastoma cell lines from rats, RG2 and C6, to elucidate some of the biochemical and cellular mechanisms underlying its antiproliferative properties. SAC (1-750 µM) decreased cell viability in both cell lines in a concentration-dependent manner, although C6 cells were more resistant to SAC at several of the tested concentrations. TMZ also produced a concentration-dependent effect, decreasing cell viability of both cell lines. In combination, SAC (1 µM or 100 µM) and TMZ (500 µM) enhanced the effects of each other. SAC also augmented the lipoperoxidative effect of TMZ and reduced cell antioxidant resistance in both cell lines by decreasing the TMZ-induced increase in the GSH/GSSG ratio. In RG2 and C6 cells, SAC per se had no effect on Nrf2/ARE binding activity, while in RG2 cells TMZ and the combination of SAC + TMZ decreased this activity. Our results demonstrate that SAC, alone or in combination with TMZ, exerts antitumor effects mediated by regulatory mechanisms of redox activity responses. SAC is also a safe drug for testing in other models as it produces non-toxic effects in primary astrocytes. Combined, these effects suggest that SAC affords antioxidant properties and potential antitumor efficacy against GBM.

Organoid Cultures for the Study of Mammary Biology and Breast Cancer: The Promise and Challenges

During the last decade, biomedical research has experienced a resurgence in the use of three-dimensional culture models for studies of normal and cancer biology. This resurgence has been driven by the development of models in which primary cells are grown in tissue-mimicking media and extracellular matrices to create organoid or organotypic cultures that more faithfully replicate the complex architecture and physiology of normal tissues and tumors. In addition, patient-derived tumor organoids preserve the three-dimensional organization and characteristics of the patient tumors ex vivo, becoming excellent preclinical models to supplement studies of tumor xenografts transplanted into immunocompromised mice. In this perspective, we provide an overview of how organoids are being used to investigate normal mammary biology and as preclinical models of breast cancer and discuss improvements that would enhance their utility and relevance to the field.

A modelling framework for cancer ecology and evolution

Cancer incidence increases rapidly with age, typically as a polynomial. The somatic mutation theory explains this increase through the waiting time for enough mutations to build up to generate cells with the full set of traits needed to grow without control. However, lines of evidence ranging from tumour reversion and dormancy to the prevalence of presumed cancer mutations in non-cancerous tissues argue that this is not the whole story, and that cancer is also an ecological process, and that mutations only lead to cancer when the systems of control within and across cells have broken down. Aging thus has two effects: the build-up of mutations and the breakdown of control. This paper presents a mathematical modelling framework to unify these theories with novel approaches to model the mutation and diversification of cell lineages and of the breakdown of the layers of control both within and between cells. These models correctly predict the polynomial increase of cancer with age, show how germline defects in control accelerate cancer initiation, and compute how the positive feedback between cell replication, ecology and layers of control leads to a doubly exponential growth of cell populations.

Mechanism of the Radioresistant Colorectal Cancer Cell Line SW480RR Established after Fractionated X Irradiation

Radioresistant cancer cells are risk factors for recurrence and are occasionally detected in recurrent tumors after radiotherapy. Intratumor heterogeneity is believed to be a potential cause of treatment resistance. Heterogeneity in DNA content has also been reported in human colorectal cancer; however, little is known about how such heterogeneity changes with radiotherapy or how it affects cancer radioresistance. In the present study, we established radioresistant clone SW480RR cells after fractionated X-ray irradiation of human colorectal cancer-derived SW480.hu cells, which are composed of two cell populations with different chromosome numbers, and examined how cellular radioresistance changed with fractionated radiotherapy. Compared with the parental cell population, which mostly comprised cells with higher ploidy, the radioresistant clones showed lower ploidy and less initial DNA damage. The lower ploidy cells in the parental cell population were identified as having radioresistance prior to irradiation; thus, SW480RR cells were considered intrinsically radioresistant cells selected from the parental population through fractionated irradiation. This study presents a practical example of the emergence of radioresistant cells from a cell population with ploidy heterogeneity after irradiation. The most likely mechanism is the selection of an intrinsically radioresistant population after fractionated X-ray irradiation, with a background in which lower ploidy cells exhibit lower initial DNA damage.

Engineering principles for rationally design therapeutic strategies against hepatocellular carcinoma

The search for new therapeutic strategies against cancer has favored the emergence of rationally designed treatments. These treatments have focused on attacking cell plasticity mechanisms to block the transformation of epithelial cells into cancerous cells. The aim of these approaches was to control particularly lethal cancers such as hepatocellular carcinoma. However, they have not been able to control the progression of cancer for unknown reasons. Facing this scenario, emerging areas such as systems biology propose using engineering principles to design and optimize cancer treatments. Beyond the possibilities that this approach might offer, it is necessary to know whether its implementation at a clinical level is viable or not. Therefore, in this paper, we will review the engineering principles that could be applied to rationally design strategies against hepatocellular carcinoma, and discuss whether the necessary elements exist to implement them. In particular, we will emphasize whether these engineering principles could be applied to fight hepatocellular carcinoma.

In silico study of heterogeneous tumour-derived organoid response to CAR T-cell therapy

Chimeric antigen receptor (CAR) T-cell therapy is a promising immunotherapy for treating cancers. This method consists in modifying the patients' T-cells to directly target antigen-presenting cancer cells. One of the barriers to the development of this type of therapies, is target antigen heterogeneity. It is thought that intratumour heterogeneity is one of the leading determinants of therapeutic resistance and treatment failure. While understanding antigen heterogeneity is important for effective therapeutics, a good therapy strategy could enhance the therapy efficiency. In this work we introduce an agent-based model (ABM), built upon a previous ABM, to rationalise the outcomes of different CAR T-cells therapies strategies over heterogeneous tumour-derived organoids. We found that one dose of CAR T-cell therapy should be expected to reduce the tumour size as well as its growth rate, however it may not be enough to completely eliminate it. Moreover, the amount of free CAR T-cells (i.e. CAR T-cells that did not kill any cancer cell) increases as we increase the dosage, and so does the risk of side effects. We tested different strategies to enhance smaller dosages, such as enhancing the CAR T-cells long-term persistence and multiple dosing. For both approaches an appropriate dosimetry strategy is necessary to produce "effective yet safe" therapeutic results. Moreover, an interesting emergent phenomenon results from the simulations, namely the formation of a shield-like structure of cells with low antigen expression. This shield turns out to protect cells with high antigen expression. Finally we tested a multi-antigen recognition therapy to overcome antigen escape and heterogeneity. Our studies suggest that larger dosages can completely eliminate the organoid, however the multi-antigen recognition increases the risk of side effects. Therefore, an appropriate small dosages dosimetry strategy is necessary to improve the outcomes. Based on our results, it is clear that a proper therapeutic strategy could enhance the therapies outcomes. In that direction, our computational approach provides a framework to model treatment combinations in different scenarios and to explore the characteristics of successful and unsuccessful treatments.

In Situ Detection of Nucleic Acids in Extracellular Vesicles via Membrane Fusion

Extracellular vesicles (EVs) carry diverse biomolecules (e. g., nucleic acids, proteins) for intercellular communication, serving as important markers for diseases. Analyzing nucleic acids derived from EVs enables non-invasive disease diagnosis and prognosis evaluation. Membrane fusion, a fundamental cellular process wherein two lipid membranes merge, facilitates cell communication and cargo transport. Building on this natural phenomenon, recent years have witnessed the emergence of membrane fusion-based strategies for the detection of nucleic acids within EVs. These strategies entail the encapsulation of detection probes within either artificial or natural vesicles, followed by the induction of membrane fusion with EVs to deliver probes. This innovative approach not only enables in situ detection of nucleic acids within EVs but also ensures the maintenance of structural integrity of EVs, thus preventing nucleic acid degradation and minimizing the interference from free nucleic acids. This concept categorizes approaches into universal and targeted membrane fusion strategies, and discusses their application potential, and challenges and future prospects.

Advancement in Cancer Vasculogenesis Modeling through 3D Bioprinting Technology

Cancer vasculogenesis is a pivotal focus of cancer research and treatment given its critical role in tumor development, metastasis, and the formation of vasculogenic microenvironments. Traditional approaches to investigating cancer vasculogenesis face significant challenges in accurately modeling intricate microenvironments. Recent advancements in three-dimensional (3D) bioprinting technology present promising solutions to these challenges. This review provides an overview of cancer vasculogenesis and underscores the importance of precise modeling. It juxtaposes traditional techniques with 3D bioprinting technologies, elucidating the advantages of the latter in developing cancer vasculogenesis models. Furthermore, it explores applications in pathological investigations, preclinical medication screening for personalized treatment and cancer diagnostics, and envisages future prospects for 3D bioprinted cancer vasculogenesis models. Despite notable advancements, current 3D bioprinting techniques for cancer vasculogenesis modeling have several limitations. Nonetheless, by overcoming these challenges and with technological advances, 3D bioprinting exhibits immense potential for revolutionizing the understanding of cancer vasculogenesis and augmenting treatment modalities.

Modeling tumors as species-rich ecological communities

Many advanced cancers resist therapeutic intervention. This process is fundamentally related to intra-tumor heterogeneity: multiple cell populations, each with different mutational and phenotypic signatures, coexist within a tumor and its metastatic nodes. Like species in an ecosystem, many cancer cell populations are intertwined in a complex network of ecological interactions. Most mathematical models of tumor ecology, however, cannot account for such phenotypic diversity nor are able to predict its consequences. Here we propose that the Generalized Lotka-Volterra model (GLV), a standard tool to describe complex, species-rich ecological communities, provides a suitable framework to describe the ecology of heterogeneous tumors. We develop a GLV model of tumor growth and discuss how its emerging properties, such as outgrowth and multistability, provide a new understanding of the disease. Additionally, we discuss potential extensions of the model and their application to three active areas of cancer research, namely phenotypic plasticity, the cancer-immune interplay and the resistance of metastatic tumors to treatment. Our work outlines a set of questions and a tentative road map for further research in cancer ecology.

Extrachromosomal circular DNA (eccDNA): from carcinogenesis to drug resistance

Extrachromosomal circular DNA (eccDNA) is a circular form of DNA that exists outside of the chromosome. Although it has only been a few decades since its discovery, in recent years, it has been found to have a close relationship with cancer, which has attracted widespread attention from researchers. Thus far, under the persistent research of researchers from all over the world, eccDNA has been found to play an important role in a variety of tumors, including breast cancer, lung cancer, ovarian cancer, etc. Herein, we review the sources of eccDNA, classifications, and the mechanisms responsible for their biogenesis. In addition, we introduce the relationship between eccDNA and various cancers and the role of eccDNA in the generation and evolution of cancer. Finally, we summarize the research significance and importance of eccDNA in cancer, and highlight new prospects for the application of eccDNA in the future detection and treatment of cancer.

Exploration of identifying individual tumor tissue based on probabilistic model

Variations in the tumor genome can result in allelic changes compared to the reference profile of its homogenous body source on genetic markers. This brings a challenge to source identification of tumor samples, such as clinically collected pathological paraffin-embedded tissue and sections. In this study, a probabilistic model was developed for calculating likelihood ratio (LR) to tackle this issue, which utilizes short tandem repeat (STR) genotyping data. The core of the model is to consider tumor tissue as a mixture of normal and tumor cells and introduce the incidence of STR variants (φ) and the percentage of normal cells (Mxn) as a priori parameters when performing calculations. The relationship between LR values and φ or Mxn was also investigated. Analysis of tumor samples and reference blood samples from 17 colorectal cancer patients showed that all samples had Log 10(LR) values greater than 1014. In the non-contributor test, 99.9% of the quartiles had Log 10(LR) values less than 0. When the defense's hypothesis took into account the possibility that the tumor samples came from the patient's relatives, LR greater than 0 was still obtained. Furthermore, this study revealed that LR values increased with decreasing φ and increasing Mxn. Finally, LR interval value was provided for each tumor sample by considering the confidence interval of Mxn. The probabilistic model proposed in this paper could deal with the possibility of tumor allele variability and offers an evaluation of the strength of evidence for determining tumor origin in clinical practice and forensic identification.

Identifying Optimal Candidates for Primary Tumor Resection Among Metastatic Pancreatic Cancer Patients: A Population-Based Predictive Model

BACKGROUND

There is a controversy about whether surgery should proceed among metastatic pancreatic cancer (mPC) patients. A survival benefit was observed in mPC patients who underwent primary tumor resection; however, determining which patients would benefit from surgery is complex. For this purpose, we created a model to identify mPC patients who may benefit from primary tumor excision.

METHODS

Patients with mPC were extracted from the Surveillance, Epidemiology, and End Results database, and separated into surgery and nonsurgery groups based on whether the primary tumor was resected. Propensity score matching (PSM) was applied to balance confounding factors between the two groups. A nomogram was developed using multivariable logistic regression to estimate surgical benefit. Our model is evaluated using multiple methods.

RESULTS

About 662 of 14,183 mPC patients had primary tumor surgery. Kaplan-Meier analyses showed that the surgery group had a better prognosis. After PSM, a survival benefit was still observed in the surgery group. Among the surgery cohort, 202 patients survived longer than 4 months (surgery-beneficial group). The nomogram discriminated better in training and validation sets under the receiver operating characteristic (ROC) curve (AUC), and calibration curves were consistent. Decision curve analysis (DCA) revealed that it was clinically valuable. This model is better at identifying candidates for primary tumor excision.

CONCLUSION

A helpful prediction model was developed and validated to identify ideal candidates who may benefit from primary tumor resection in mPC.