Quantifying dynamic changes in cell populations is crucial for a comprehensive understanding of biological processes such as cell proliferation, injury repair, and disease progression. However, compared to directly measuring the absolute cell numbers of specific subpopulations, relative proportion data demonstrate greater reproducibility and yield more stable, reliable outcomes. Therefore, inferring absolute cell numbers from relative proportion data may present a novel approach for effectively predicting changes in cell population sizes. To address this, we establish two mathematical mappings between cell proportions and population sizes using moment equations derived from stochastic cell-plasticity models. Notably, our findings indicate that one of these mappings does not require prior knowledge of the initial population size, highlighting the value of incorporating variance information into cell proportion data. We evaluated the robustness of our methods from multiple perspectives and extended their application to various biological mechanisms within the context of cell plasticity models. These methods help mitigate the limitations associated with the direct measurement of absolute cell counts through experimental techniques. Moreover, they provide new insights into leveraging the stochastic dynamics of cell populations to quantify interactions between different biomasses within the system.

Cholangiocarcinoma (CCA), a neoplasm arising from biliary epithelial cells, is particularly widespread in Southeast Asia, with northeastern Thailand exhibiting the greatest prevalence attributed to Opisthorchis viverrini infection. This malignancy exhibits considerable molecular heterogeneity, leading to therapeutic resistance and recurrence. Comprehending its molecular mechanisms is essential for enhancing diagnostic and treatment approaches. Our research utilized multi-region LC-MS/MS proteomic analysis to investigate intratumor heterogeneity (ITH) in CCA. We examined 52 tumor areas and 13 neighboring tissues from 13 patients, concentrating on protein profiling, pathway analysis, differential protein expression, and the identification of shared and unique protein signatures. The findings indicated considerable inter-patient proteome variability, characterized by markedly distinct protein expressions among individuals, aligning with prior cancer research. Intra-tumor heterogeneity was apparent, with merely 18 proteins common to all tumor areas and patients, underscoring the intricacy of CCA. Significantly, the common proteins were associated with metabolic reprogramming and oxidative stress pathways, indicating possible indicators and therapeutic targets. This work highlights the significant proteome variability in CCA at both intra-tumor and inter-patient levels, underscoring the necessity for customized therapeutic approaches to tackle the disease's complexity and improve treatment outcomes.

This study aimed to visually analyze the heterogeneity of vascularity and cellularity across different sub-regions of breast cancer using habitat imaging (HI) to predict human epidermal growth factor receptor 2 (HER2) expression and evaluate the effectiveness of neoadjuvant therapy (NAT) in breast cancer patients.

A retrospective analysis was conducted on 76 patients diagnosed with breast cancer. Diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI (DCE-MRI) sequences were utilized to acquire MR images. Apparent diffusion coefficient (ADC), Ktrans, Kep, and Ve values were measured for each sub-region, and the percentage of each sub-region relative to the total lesion was calculated. Statistical analyses, including t-tests, rank-sum tests, chi-square tests, and Spearman correlation, were performed.

Three distinct sub-regions within breast cancer lesions were identified through HI, characterized physiologically as: low vascularity-high cellularity (LV-HC), low vascularity-low cellularity (LV-LC), and high vascularity-low cellularity (HV-LC). Significant differences were observed in the proportions of these tumor sub-regions between HER2-positive and HER2-negative breast cancers. Additionally, HER2-low and HER2-zero breast cancers demonstrated statistical differences in the second sub-region (LV-LC). Furthermore, the proportion of the first sub-region (LV-HC) was negatively correlated with the efficacy of NAT in breast cancer patients.

Habitat imaging can identify distinct sub-regions within breast cancer lesions, providing a noninvasive imaging biomarker for predicting HER2 expression levels and assessing the efficacy of NAT in breast cancer patients.

Early and appropriate diagnosis of soft-tissue sarcomas (STSs) is hampered by their relatively low prevalence and sometimes unusual clinical appearance. It takes a comprehensive diagnostic work-up to differentiate between different types of soft-tissue sarcomas. Determining tumor margins by preoperative imaging is important, especially in order to preserve the affected limb and improve quality of life. Misjudgment of tumor margins may increase or decrease the stage of soft-tissue sarcoma and thus influence the patient's prognosis. The applicability of conventional MRI alone for determining the tumor margin is limited. Additional information regarding the peritumoral tissue, particularly at the cellular level, can be obtained via diffusion-weighted imaging (DWI). However, there are not many publications on employing DWI to evaluate tumor margin infiltration in soft-tissue sarcoma patients. Because the immune system plays a variety of roles during oncogenesis, it can occasionally be difficult to distinguish between tumor invasion and the presence of a reactive inflammatory infiltrate. Clarifying the predictive importance of lymphocyte infiltration in soft-tissue sarcomas was the goal of this investigation. We examined the correlations between expression of CD4, CD8, and CD34 and tumor margin infiltration observed on a DWI sequence. CD4, CD8, and CD34 marker positivity was linked to soft-tissue sarcomas that were less aggressive and did not invade the tumor margins, indicating a higher survival percentage for these individuals.

Despite the advances in cancer prevention, early detection, and treatments, all of which have led to improved cancer survival, globally, there is an increased incidence in cancer-related deaths. Although each patient and each tumor is wholly unique, the tipping point to incurable disease is common across all patients: the dual capacity for cancers to metastasize and resist systemic treatment. The discovery of genetic mutations and epigenetic variation that emerges during cancer progression highlights that evolutionary and ecology principles can be used to understand how cancer evolves to a lethal phenotype. By applying such an eco-evolutionary framework, the authors reinterpret our understanding of the metastatic process as one of an ecologic invasion and define the eco-evolutionary paths of evolving therapy resistance. With this understanding, the authors draw from successful strategies optimized in evolutionary ecology to define strategic interventions with the goal of altering the evolutionary trajectory of lethal cancer. Ultimately, studying, understanding, and treating cancer using evolutionary ecology principles provides an opportunity to improve the lives of patients with cancer.

Extracellular vesicles (EVs) have emerged as key cell-to-cell communication mediators and play significant roles in both physiological and pathological processes. In EVs, exosomes represent a distinct subpopulation of EVs that have been found to be involved in cancer initiation and therapeutic resistance. Exosomes transfer a diverse spectrum of molecular cargos that have significant effects on the tumor microenvironment (TME), thereby enabling cancer initiation, metastasis, and therapeutic resistance. Exosomes have recently been of interest in cancer therapy due to their role as important mediators of treatment resistance. The exosomal molecular content-proteins, miRNAs, and lncRNAs-allows exosomes to perform functions including drug efflux and detoxification, cell death pathway modulation, induction of epithelial-to-mesenchymal transition (EMT), and suppression of the immune system. In addition to facilitating immune and stromal cell interactions, exosomes cause extracellular matrix remodeling and induce tumor heterogeneity, making it more difficult to respond to therapy. This review covers intricate roles of exosomes in cancer therapy resistance with regard to their biogenesis, molecular content, and functional impact in the TME. Along with this, we also discuss new therapeutic strategies to overcome exosome-mediated resistance including utilizing exosome inhibitors, designed exosome therapy, and combination with conventional therapies. While exosomes hold promise in prediction and diagnosis through their biomarker function, their heterogeneous origins and cryptic functions make it difficult to target interventions. This review emphasizes that research on exosome-mediated pathways is urgently required to develop new therapeutic strategies that can improve cancer treatment outcomes.

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.

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.

Over the past few years, immune checkpoint inhibitors resulted in magnificent and durable successes in treating cancer; however, only a minority of patients respond favorably to the treatment due to a broad-spectrum of tumor-intrinsic and tumor-extrinsic factors. With the recent insights gained into the mechanisms of resistance, combination treatment strategies to overcome the resistance and enhance the therapeutic potential of immune checkpoint inhibitors are emerging and showing promising results in both pre-clinical and clinical settings. This has been derived through multiple interconnected mechanisms such as enhancing tumor immunogenicity, improving neoantigen processing and presentation in addition to augmenting T cell infiltration and cytotoxic potentials. In the clinical settings, several avenues of combination treatments involving immune checkpoint inhibitors were associated with considerable improvement in the therapeutic outcome in terms of patient's survival and tumor growth control. This, in turn, increased the spectrum of cancer patients benefiting from the unprecedented and durable effects of immune checkpoint inhibitors leading to their adoption as a first-line treatment for certain cancers. Moreover, the significance of precision medicine in cancer immunotherapy and the unmet demand to develop more personalized predictive biomarkers and treatment strategies are also highlighted in this review.

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, 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.

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.

Cancer is responsible for nearly one in six global fatalities, making it a major health issue worldwide. Despite advancements in early detection, surgery, and targeted therapies, effective treatment remains challenging due to the complexity and heterogeneity of the disease. A key factor in cancer progression and resistance to treatment is the tumor microenvironment (TME). It is a complex ecosystem comprising cancer cells, stromal cells, immune cells, extracellular matrix (ECM), and soluble factors like cytokines and chemokines. These components interact dynamically to influence tumor growth, metastasis, immune evasion, and treatment resistance. Cancer cells drive the formation of the TME by releasing signaling molecules, while stromal cells, such as fibroblasts and endothelial cells, support tumor metabolism, angiogenesis, and invasion. Immune cells within the TME can either suppress or promote tumor progression, depending on their activation state. Additionally, the TME can promote the growth of immunosuppressive cells that aid cancer cells in evading immune surveillance, such as regulatory T-cells and myeloid-derived suppressor cells. The TME also impedes drug delivery by creating defective blood vessels, contributing to drug resistance. Recent technological advancements have deepened our understanding of the TME, revealing its role in immune modulation, metabolism, and extracellular matrix remodeling. As a result, targeting the TME has become a promising strategy to overcome treatment resistance and improve cancer therapy outcomes.

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.

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 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.

Breast cancer (BC) remains a significant global health challenge due to its complex biology, which complicates both diagnosis and treatment. Immunotherapy and cancer vaccines have emerged as promising alternatives, harnessing the body's immune system to precisely target and eliminate cancer cells. However, several key factors influence the selection and effectiveness of these therapies, including BC subtype, tumor mutational burden (TMB), tumor-infiltrating lymphocytes (TILs), PD-L1 expression, HER2 resistance, and the tumor microenvironment (TME). BC subtypes play a critical role in shaping treatment responses. Triple-negative breast cancer (TNBC) exhibits the highest sensitivity to immunotherapy, while HER2-positive and hormone receptor-positive (HR+) subtypes often require combination strategies for optimal outcomes. High TMB enhances immune responses by generating neoantigens, making tumors more susceptible to immune checkpoint inhibitors (ICIs); whereas, low TMB may indicate resistance. Similarly, elevated TIL levels are associated with better immunotherapy efficacy, while PD-L1 expression serves as a key predictor of checkpoint inhibitor success. Meanwhile, HER2 resistance and an immunosuppressive TME contribute to immune evasion, highlighting the need for multi-faceted treatment approaches. Current breast cancer immunotherapies encompass a range of targeted treatments. HER2-directed therapies, such as trastuzumab and pertuzumab, block HER2 dimerization and enhance antibody-dependent cellular cytotoxicity (ADCC), while small-molecule inhibitors, like lapatinib and tucatinib, suppress HER2 signaling to curb tumor growth. Antibody-drug conjugates (ADCs) improve tumor targeting by coupling monoclonal antibodies with cytotoxic agents, minimizing off-target effects. Meanwhile, ICIs, including pembrolizumab, restore T-cell function, and CAR-macrophage (CAR-M) therapy leverages macrophages to reshape the TME and overcome immunotherapy resistance. While immunotherapy, particularly in TNBC, has demonstrated promise by eliciting durable immune responses, its efficacy varies across subtypes. Challenges such as immune-related adverse events, resistance mechanisms, high costs, and delayed responses remain barriers to widespread success. Breast cancer vaccines-including protein-based, whole-cell, mRNA, dendritic cell, and epitope-based vaccines-aim to stimulate tumor-specific immunity. Though clinical success has been limited, ongoing research is refining vaccine formulations, integrating combination therapies, and identifying biomarkers for improved patient stratification. Future advancements in BC treatment will depend on optimizing immunotherapy through biomarker-driven approaches, addressing tumor heterogeneity, and developing innovative combination therapies to overcome resistance. By leveraging these strategies, researchers aim to enhance treatment efficacy and ultimately improve patient outcomes.

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 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.

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

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.

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.

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.

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

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.

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.

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.

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.

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).

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.

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.

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 (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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.