Single-cell transcriptional changes associated with drug tolerance and response to combination therapies in cancer

Chaperone directed heterobifunctional molecules circumvent KRASG12C inhibitor resistance

While KRASG12C inhibitors have shown promising results in clinical activity, acquired resistance remains a significant barrier to durable responses. Combination therapies have been explored to improve the efficacy of KRASG12C inhibitors; however, their use is often restricted due to toxicity and limitations in clinically amenable dosing schedules. Transcriptomic profiling and functional assays on acquired resistant models to adagrasib identified an enrichment of HSP90 client proteins in resistant phenotypes, suggesting a therapeutic vulnerability. To address the finding, RNK07421, a novel heterobifunctional molecule, was developed to simultaneously target KRASG12C and HSP90-client oncoproteins. Structural and biochemical analyses demonstrated that RNK07421 disrupts KRASG12C interactions by inducing a non-natural interface with HSP90, thereby impairing oncogenic signaling. In vitro, RNK07421 effectively suppressed ERK reactivation and reduced viability in KRASG12C-mutant cell lines exhibiting either intrinsic or acquired resistance. In vivo, RNK07421 significantly reduced tumor burden in xenograft models, outperforming both monotherapies and combination therapies. These findings highlight dual KRASG12C and HSP90 inhibition as a promising strategy to overcome resistance in KRASG12C-driven cancers.

Interpretable Differential Abundance Signature (iDAS)

Single-cell technologies have revolutionized the understanding of cellular dynamics by allowing researchers to investigate individual cell responses under various conditions, such as comparing diseased versus healthy states. Many differential abundance methods have been developed in this field, however, the understanding of the gene signatures obtained from those methods is often incomplete, requiring the integration of cell type information and other biological factors to yield interpretable and meaningful results. To better interpret the gene signatures generated in the differential abundance analysis, iDAS is developed to classify the gene signatures into multiple categories. When applied to melanoma single-cell data with multiple cell states and treatment phenotypes, iDAS identified cell state- and treatment phenotype-specific gene signatures, as well as interaction effect-related gene signatures with meaningful biological interpretations. The iDAS model is further applied to a longitudinal study and spatially resolved omics data to demonstrate its versatility in different analytical contexts. These results demonstrate that the iDAS framework can effectively identify robust, cell-state specific gene signatures and is versatile enough to accommodate various study designs, including multi-factor longitudinal and spatially resolved data.

Epigenomic analysis identifies DTP subpopulation using HOPX to develop targeted therapy resistance in lung adenocarcinoma

Genomic studies have identified oncogenic drivers in lung cancer, enabling effective targeted therapies. However, patients who initially respond inevitably experience regrowth. The drug-tolerant persister (DTP) stage is a key source of non-genetic resistance, yet its epigenetic regulation remains unclear. Using single-cell chromatin accessibility profiling (scATAC-seq), we identified two distinct DTP subpopulations in EGFR- and KRAS-inhibited models. The integrative network and pathway analysis revealed that one subpopulation is associated with cell cycle, while the other is enriched in developmental pathways. HOPX was the most enriched alveolar signature gene in the latter. It was transiently upregulated with cytoplasmic-to-nuclear translocation, and its deletion significantly delayed DTP regrowth. Mechanistically, HOPX regulates NF-κB activation and repressive histone modifications. Combining targeted therapy with NF-κB or histone-methyltransferase inhibitors nearly abolished DTP regrowth. These findings highlight a potential anti-relapse strategy by targeting developmental pathways to modulate key lineage factors during lung regeneration in patients relapsing on targeted therapy.

Single-cell technology for cell-based drug delivery and pharmaceutical research

Leveraging the capacity to precisely manipulate and analyze individual cells, single-cell technology has rapidly become an indispensable tool in the advancement of cell-based drug delivery systems and innovative cell therapies. This technology offers powerful means to address cellular heterogeneity and significantly enhance therapeutic efficacy. Recent breakthroughs in techniques such as single-cell electroporation, mechanical perforation, and encapsulation, particularly when integrated with microfluidics and bioelectronics, have led to remarkable improvements in drug delivery efficiency, reductions in cytotoxicity, and more precise targeting of therapeutic effects. Moreover, single-cell analyses, including advanced sequencing and high-resolution sensing, offer profound insights into complex disease mechanisms, the development of drug resistance, and the intricate processes of stem cell differentiation. This review summarizes the most significant applications of these single-cell technologies, highlighting their impact on the landscape of modern biomedicine. Furthermore, it provides a forward-looking perspective on future research directions aimed at further optimizing drug delivery strategies and enhancing therapeutic outcomes in the treatment of various diseases.

Comprehensive Analysis of GPSM2: From Pan-Cancer Analysis to Experimental Validation

G-protein signalling modulator 2 (GPSM2) plays an important role in maintaining cell polarisation and regulating the cell cycle; however, a systematic and comprehensive analysis of GPSM2 in cancer is still lacking. Using extensive multi-omics data, we explored the pan-cancer expression levels of GPSM2 from multiple perspectives and its association with prognosis, diagnosis, tumour stemness, immune-related genes, immune cell infiltration, genomic instability, and response to immunotherapy. We also elucidated the potential pan-cancer biological functions of GPSM2 using gene set enrichment analysis (GSEA) and searched for targeted drugs that affect GPSM2 expression using connectivity map analysis. To elucidate the effect of GPSM2 on colon cancer, we evaluated its effect on the biological behaviour of two colon cancer cell lines. In this study, GPSM2 was systematically analysed and shown to have satisfactory performance in disease diagnosis and prognostic prediction of various cancers. G-protein signalling modulator 2 plays an important role in the genesis and development of various tumours and is a potential tumour diagnostic and prognostic biomarker as well as an anti-cancer therapeutic target.

How to view the female reproductive tract through single-cell looking glasses

Single-cell technologies have emerged as an unprecedented tool for biologists and clinicians, allowing them to assess organs and tissues at the level of individual cells. In the field of women's reproductive biology, single-cell studies have provided insights into the cellular and molecular processes that regulate reproductive and obstetrical functions in health and disease. The knowledge that these studies generate is helping clinicians to improve the understanding and diagnosis of infertility related issues or pregnancy complications and to find new avenues for their treatment. However, navigating the expansive landscape of this type of transcriptomic data analysis represents a pivotal challenge in current research. Single cell RNA sequencing involves isolating cells into droplets, reverse transcribing RNA to generate complementary DNA, with each droplet content uniquely labeled by a barcode. Upon sequencing the complementary DNAs, the barcodes enable the reassignment of sequencing reads to individual droplets, facilitating the reconstruction of the cellular landscape of the sample obtained from a tissue or organ and beyond. Researchers, equipped with the metaphorical 'single-cell glasses,' must adequately choose from a plethora of strategies to dissect and interpret cellular information. Sophisticated algorithms and the decision-making process are often underestimated, resulting in artefactual or cumbersome interpreted results. Computational biologists apply and innovate computational tools designed to process, model, and interpret expansive datasets. The ramifications of their work extend far beyond the realm of data processing; they give shape to the outcome of analyses, playing a pivotal role in drawing meaningful conclusions from the wealth of information garnered. In this review, we describe the wide variety of approaches and analytical steps available with enough detail to gain a concise picture of what a complete examination of a single-cell dataset would be. We commence with a discussion on key points in experimental design, highlighting crucial questions one should consider. Following this, we delve into the various preprocessing and quality control steps essential for any single-cell dataset. The subsequent section offers a detailed guide on constructing a single-cell atlas, exploring nuances such as differential characteristics in visualization and clustering techniques, as well as strategies for assigning identity to cell populations through gene marker annotations. Moving beyond the creation of an atlas, we explore methods for investigating pathological conditions. This involves conducting cell population comparison tests between conditions and analyzing specific cell-to-cell communications and cellular differentiation trajectories in both health and disease scenarios. This work aims to furnish a newcomer researcher and/or clinician with essential guidelines to embark on a single-cell adventure without succumbing to common pitfalls. By bridging the gap between theory and practice, it facilitates the translation of single-cell technologies into clinically relevant applications. Throughout the manuscript, practical examples of its usage in women's reproductive health studies are provided. Various sections delve into specific clinical scenarios, demonstrating how these guidelines can be instrumental in unraveling the molecular landscapes of diseases and physiological processes related to women's reproduction.

Identification of drug-resistant individual cells within tumors by semi-supervised transfer learning from bulk to single-cell transcriptome

The presence of pre-existing or acquired drug-resistant cells within the tumor often leads to tumor relapse and metastasis. Single-cell RNA sequencing (scRNA-seq) enables elucidation of the subtle differences in drug responsiveness among distinct cell subpopulations within tumors. A few methods have employed scRNA-seq data to predict the drug response of individual cells to date, but their performance is far from satisfactory. In this study, we propose SSDA4Drug, a semi-supervised few-shot transfer learning method for inferring drug-resistant cancer cells. SSDA4Drug extracts pharmacogenomic features from both bulk and single-cell transcriptomic data using semi-supervised adversarial domain adaptation. This allows us to transfer knowledge of drug sensitivity from bulk-level cell lines to single cells. We conduct extensive performance evaluation experiments across multiple independent scRNA-seq datasets, demonstrating SSDA4Drug's superior performance over current state-of-the-art methods. Remarkably, with only one or two labeled target-domain samples, SSDA4Drug significantly boosts the predictive performance of single-cell drug responses. Moreover, SSDA4Drug accurately recapitulates the temporally dynamic changes of drug responses during continuous drug exposure of tumor cells, and successfully identifies reversible drug-responsive states in lung cancer cells, which initially acquire resistance through drug exposure but later restore sensitivity during drug holidays. Also, our predicted drug responses consistently align with the developmental patterns of drug sensitivity observed along the evolutionary trajectory of oral squamous cell carcinoma cells. In addition, our derived SHAP values and integrated gradients effectively pinpoint the key genes involved in drug resistance in prostate cancer cells. These findings highlight the exceptional performance of our method in determining single-cell drug responses. This powerful tool holds the potential for identifying drug-resistant tumor cell subpopulations, paving the way for advancements in precision medicine and novel drug development.

High resolution tissue and cell type identification via single cell transcriptomic profiling

Tissue identification can be instrumental in reconstructing a crime scene but remains a challenging task in forensic investigations. Conventionally, identifying the presence of certain tissue from tissue mixture by predefined cell type markers in bulk fashion is challenging due to limitations in sensitivity and accuracy. In contrast, single-cell RNA sequencing (scRNA-Seq) is a promising technology that has the potential to enhance or even revolutionize tissue and cell type identification. In this study, we developed a high sensitive general purpose single cell annotation pipeline, scTissueID, to accurately evaluate the single cell profile quality and precisely determine the cell and tissue types based on scRNA profiles. By incorporating a crucial and unique reference cell quality differentiation phase of targeting only high confident cells as reference, scTissueID achieved better and consistent performance in determining cell and tissue types compared to 8 state-of-art single cell annotation pipelines and 6 widely adopted machine learning algorithms, as demonstrated through a large-scale and comprehensive comparison study using both forensic-relevant and Human Cell Atlas (HCA) data. We highlighted the significance of cell quality differentiation, a previously undervalued factor. Thus, this study offers a tool capable of accurately and efficiently identifying cell and tissue types, with broad applicability to forensic investigations and other biomedical research endeavors.

Pan-cancer analysis shapes the understanding of cancer biology and medicine

Advances in multi-omics datasets and analytical methods have revolutionized cancer research, offering a comprehensive, pan-cancer perspective. Pan-cancer studies identify shared mechanisms and unique traits across different cancer types, which are reshaping diagnostic and treatment strategies. However, continued innovation is required to refine these approaches and deepen our understanding of cancer biology and medicine. This review summarized key findings from pan-cancer research and  explored their potential to drive future advancements in oncology.

Transforming agents: The power of structural modifications in glioblastoma multiforme therapy

Glioblastoma (GBM) is a very deadly type of brain tumor with a poor prognosis and a short survival rate. Recent advancements in understanding GBM's molecular and genetic characteristics have led to the development of various therapeutic and diagnostic strategies. Key elements such as microRNAs, lncRNAs, exosomes, angiogenesis, and chromatin modifications are highlighted, alongside significant epigenetic alterations that impact therapy and diagnosis. Despite these advancements, molecular classifications have not improved patient outcomes due to intratumoral diversity complicating targeted therapies. In this article, it is tried to emphasize the potential of investigating the epigenetic landscape of GBM, particularly identifying patients with diffuse hypermethylation at gene promoters associated with better outcomes. Integrating epigenetic and genetic data has enhanced the identification of glioma subtypes with high diagnostic precision. The reversibility of epigenetic changes offers promising therapeutic prospects, as recent insights into the "epigenetic orchestra" suggest new avenues for innovative treatment modalities for this challenging cancer. In this review article, we focus on the roles of translational elements and their alterations in the context of GBM diagnosis and therapy.

A multiplex single-cell RNA-Seq pharmacotranscriptomics pipeline for drug discovery

The gene-regulatory dynamics governing drug responses in cancer are yet to be fully understood. Here, we report a pipeline capable of producing high-throughput pharmacotranscriptomic profiling through live-cell barcoding using antibody-oligonucleotide conjugates. This pipeline combines drug screening with 96-plex single-cell RNA sequencing. We show the potential of this approach by exploring the heterogeneous transcriptional landscape of primary high-grade serous ovarian cancer (HGSOC) cells after treatment with 45 drugs, with 13 distinct classes of mechanisms of action. A subset of phosphatidylinositol 3-OH kinase (PI3K), protein kinase B (AKT) and mammalian target of rapamycin (mTOR) inhibitors induced the activation of receptor tyrosine kinases, such as the epithelial growth factor receptor (EGFR), and this was mediated by the upregulation of caveolin 1 (CAV1). This drug resistance feedback loop could be mitigated by the synergistic action of agents targeting PI3K-AKT-mTOR and EGFR for HGSOC with CAV1 and EGFR expression. Using this workflow could enable the personalized testing of patient-derived tumor samples at single-cell resolution.

Molecular mechanisms altering cell identity in cancer

Intrinsic and extrinsic factors influence cancer cell identity throughout its lifespan. During tumor progression and metastasis formation, cancer cells are exposed to different environmental stimuli, resulting in a stepwise cellular reprogramming. Similar stepwise changes of cell identity have been shown as a major consequence of cancer treatment, as cells are exposed to extracellular stress that can result in the establishment of subpopulations exhibiting different epigenetic and transcriptional patterns, indicating a rapid adaptation mechanism of cellular identity by extrinsic stress factors. Both mechanisms, tumor progression-mediated changes and therapy response, rely on signaling pathways affecting the epigenetic and subsequent transcriptional landscape, which equip the cells with mechanisms for survival and tumor progression. These non-genetic alterations are propagated to the daughter cells, indicating a need for successful information propagation and transfer to the daughter generations, thereby allowing for a stepwise adaptation to environmental cues. However, the exact mechanisms how these cell identity changes are occurring, which context-specific mechanisms are behind and how this can be exploited for future therapeutic interventions is not yet fully understood and exploited. In this review, we discuss the current knowledge on cell identity maintenance mechanisms intra- and intergenerational in development and disease and how these mechanisms are altered in cancer. We will as well address how cancer treatment might target these properties.

Single-Cell RNA Sequencing in Unraveling Acquired Resistance to EGFR-TKIs in Non-Small Cell Lung Cancer: New Perspectives

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) have demonstrated remarkable efficacy in treating non-small cell lung cancer (NSCLC), but acquired resistance greatly reduces efficacy and poses a significant challenge to patients. While numerous studies have investigated the mechanisms underlying EGFR-TKI resistance, its complexity and diversity make the existing understanding still incomplete. Traditional approaches frequently struggle to adequately reveal the process of drug resistance development through mean value analysis at the overall cellular level. In recent years, the rapid development of single-cell RNA sequencing technology has introduced a transformative method for analyzing gene expression changes within tumor cells at a single-cell resolution. It not only deepens our understanding of the tumor microenvironment and cellular heterogeneity associated with EGFR-TKI resistance but also identifies potential biomarkers of resistance. In this review, we highlight the critical role of single-cell RNA sequencing in lung cancer research, with a particular focus on its application to exploring the mechanisms of EGFR-TKI-acquired resistance in NSCLC. We emphasize its potential for elucidating the complexity of drug resistance mechanism and its promise in informing more precise and personalized treatment strategies. Ultimately, this approach aims to advance NSCLC treatment toward a new era of precision medicine.

Integrating multiscale mathematical modeling and multidimensional data reveals the effects of epigenetic instability on acquired drug resistance in cancer

Biological and dynamic mechanisms by which Drug-tolerant persister (DTP) cells contribute to the development of acquired drug resistance have not been fully elucidated. Here, by integrating multidimensional data from drug-treated PC9 cells, we developed a novel multiscale mathematical model from an evolutionary perspective that encompasses epigenetic and cellular population dynamics. By coupling stochastic simulation with quantitative analysis, we identified epigenetic instability as the most prominent kinetic feature related to the emergence of DTP cell subpopulations and the effectiveness of intermittent treatment. Moreover, we revealed the optimal schedule for intermittent treatment, including the optimal area for therapeutic time and drug holidays. By leveraging single-cell RNA-seq data characterizing the drug tolerance of lung cancer, we validated the predictions made by our model and further revealed previously unrecognized biological features of DTP cells, such as cell autophagy and migration, as well as new biomarker genes of therapeutic tolerance. Our work not only provides a paradigm for the integration of multiscale mathematical models with newly emerging genomics data but also improves our understanding of the crucial roles of DTP cells and offers guidance for developing new intermittent treatment strategies against acquired drug resistance in cancer.

New advances in the therapeutic strategy of head and neck squamous cell carcinoma: A review of latest therapies and cutting-edge research

Head and neck squamous cell carcinoma (HNSCC) is a common and aggressive malignancy with a poor prognosis, particularly when diagnosed at advanced stages. Despite progress in surgical, chemotherapeutic, and radiotherapeutic interventions, the five-year survival rate remains low due to high rates of recurrence and therapeutic resistance. This review explores recent advances in therapeutic strategies for HNSCC, focusing on targeted therapies, immunotherapy, and innovative drug delivery systems. Targeted therapies, such as EGFR inhibitors and PI3K/AKT/mTOR pathway inhibitors, offer promising options for overcoming HNSCC, though resistance challenges persist. Emerging treatments, including dual-target inhibitors and personalized therapeutic approaches, show potential in addressing these limitations. Immunotherapy, particularly PD-1/PD-L1 blockade, has achieved positive outcomes in a subset of patients, though overall response rates remain modest. Strategies aimed at enhancing immune responses, such as combination therapies and nanotechnology-based drug delivery systems, are actively being investigated to improve efficacy. This review also underscores the critical role of the tumor microenvironment and epithelial-mesenchymal transition (EMT) in HNSCC progression and therapeutic resistance. Novel approaches, including smart drug delivery systems utilizing nanotechnology and immune modulation, are opening new avenues for more personalized and effective treatments. Ongoing interdisciplinary research into molecular targets and advanced drug delivery techniques holds great promise for significantly improving patient outcomes in HNSCC.

Applications and emerging challenges of single-cell RNA sequencing technology in tumor drug discovery

Current therapeutic drugs are inadequate for curing tumors, highlighting the need for novel tumor drugs. The advancement of single-cell RNA sequencing (scRNA-seq) technology offers new opportunities for tumor drug discovery. This technology allows us to explore tumor heterogeneity and developmental mechanisms at the single-cell level. In this review, we outline the application of scRNA-seq in tumor drug discovery stages, including elucidating tumor mechanisms, identifying targets, screening drugs, and understanding drug action and resistance. We also discuss the challenges and future prospects of using scRNA-seq in drug development, providing a scientific foundation for advancing tumor therapies.

Dynamic evolution of TCF3-PBX1 leukemias at the single-cell level under chemotherapy pressure

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. The translocation t(1;19), encoding the TCF3-PBX1 fusion, is associated with intermediate risk and central nervous system (CNS) infiltration at relapse. Using our previously generated TCF3-PBX1 conditional knock-in mice, we established a model to study relapsed clones after in vivo chemotherapy treatment, CNS infiltration, and clonal dynamic evolution of phenotypic diversity at the single cell-level using next-generation sequencing technologies and mass cytometry. Mice transplanted with TCF3-PBX1 + leukemia cells and treated with vehicle succumbed to disease, whereas 40% of treated mice with prednisolone or daunorubicin survived. Bulk and single-cell RNA sequencing of FACS-sorted GFP+ cells from TCF3-PBX1 + leukemias arising after chemotherapy treatment revealed that apoptosis, interleukin-, and TGFβ-signaling pathways were regulated in CNS-infiltrating leukemic cells. Across tissues, upregulation of the MYC signaling pathway was detected in persisting leukemic cells and its downregulation by BRD3/4 inhibition increased sensitivity to chemotherapy. In TCF3-PBX1+ leukemia cells collected after chemotherapy treatment, mass cytometry identified increased phosphorylation of STAT3/5 upon preBCR stimulation, which was susceptible to inhibition by the proteasome inhibitor bortezomib. In summary, we developed a TCF3-PBX1+ ALL mouse model and characterized relapsed disease after in vivo chemotherapy and cell phenotype dependence on microenvironment. Transcriptomics and phospho-proteomics revealed distinct pathways that may underlie chemotherapy resistance and might be suitable for pharmacological interventions in human ALL.

Single-Cell RNA Profiling of Ocular Adnexal Sebaceous Carcinoma Reveals a Complex Tumor Microenvironment and Identifies New Biomarkers

PURPOSE

Ocular adnexal sebaceous carcinoma (OaSC) is an aggressive malignancy that often necessitates orbital exenteration. Its tumor composition and transcriptional profile remain largely unknown, which poses a significant barrier to medical advances. Here, we report the first in-depth transcriptomic analysis of OaSC at the single-cell resolution and discern mechanisms underlying cancer progression for the discovery of potential globe-sparing immunotherapies, targeted therapies, and biomarkers to guide clinical management.

DESIGN

Laboratory investigation with a retrospective observational case series.

METHODS

Single-cell RNA sequencing was performed on six patient specimens: three primary tumors, two tumors with pagetoid spread, and a normal tarsus sample. Cellular components were identified via gene signatures. Molecular pathways underlying tumorigenesis and pagetoid spread were discerned via gene ontology analysis of the differentially expressed genes between specimens. CALML5 immunohistochemistry was performed on an archival cohort of OaSC, squamous cell carcinoma, ocular surface squamous neoplasia (OSSN), and basal cell carcinoma cases.

RESULTS

Analysis of 29,219 cells from OaSC specimens revealed tumor, immune, and stromal cells. Tumor-infiltrating immune cells include a diversity of cell types, including exhausted T-cell populations. In primary OaSC tumors, mitotic nuclear division and oxidative phosphorylation pathways are upregulated, while lipid biosynthesis and metabolism pathways are downregulated. Epithelial tissue migration pathways are upregulated in tumor cells undergoing pagetoid spread. Single-cell RNA sequencing analyses also revealed that CALML5 is upregulated in OaSC tumor cells. Diffuse nuclear and cytoplasmic CALML5 staining was present in 28 of 28 (100%) OaSC cases. Diffuse nuclear and membranous CALML5 staining was present in 5 of 25 (20%) squamous cell carcinoma and OSSN cases, while diffuse nuclear staining was present in 1 of 12 (8%) basal cell carcinoma cases.

CONCLUSIONS

This study reveals a complex OaSC tumor microenvironment and confirms that the CALML5 immunohistochemical stain is a sensitive diagnostic marker.

Identifying Safeguards Disabled by Epstein-Barr Virus Infections in Genomes From Patients With Breast Cancer: Chromosomal Bioinformatics Analysis

Background

The causes of breast cancer are poorly understood. A potential risk factor is Epstein-Barr virus (EBV), a lifelong infection nearly everyone acquires. EBV-transformed human mammary cells accelerate breast cancer when transplanted into immunosuppressed mice, but the virus can disappear as malignant cells reproduce. If this model applies to human breast cancers, then they should have genome damage characteristic of EBV infection.

Objective

This study tests the hypothesis that EBV infection predisposes one to breast cancer by causing permanent genome damage that compromises cancer safeguards.

Methods

Publicly available genome data from approximately 2100 breast cancers and 25 ovarian cancers were compared to cancers with proven associations to EBV, including 70 nasopharyngeal cancers, 90 Burkitt lymphomas, 88 diffuse large B-cell lymphomas, and 34 gastric cancers. Calculation algorithms to make these comparisons were developed.

Results

Chromosome breakpoints in breast and ovarian cancer clustered around breakpoints in EBV-associated cancers. Breakpoint distributions in breast and EBV-associated cancers on some chromosomes were not confidently distinguished (P>.05), but differed from controls unrelated to EBV infection. Viral breakpoint clusters occurred in high-risk, sporadic, and other breast cancer subgroups. Breakpoint clusters disrupted gene functions essential for cancer protection, which remain compromised even if EBV infection disappears. As CRISPR (clustered regularly interspaced short palindromic repeats)-like reminders of past infection during evolution, EBV genome fragments were found regularly interspaced between Piwi-interacting RNA (piRNA) genes on chromosome 6. Both breast and EBV-associated cancers had inactivated genes that guard piRNA defenses and the major histocompatibility complex (MHC) locus. Breast and EBV-associated cancer breakpoints and other variations converged around the highly polymorphic MHC. Not everyone develops cancer because MHC differences produce differing responses to EBV infection. Chromosome shattering and mutation hot spots in breast cancers preferentially occurred at incorporated viral sequences. On chromosome 17, breast cancer breakpoints that clustered around those in EBV-mediated cancers were linked to estrogen effects. Other breast cancer breaks affected sites where EBV inhibits JAK-STAT and SWI-SNF signaling pathways. A characteristic EBV-cancer gene deletion that shifts metabolism to favor tumors was also found in breast cancers. These changes push breast cancer into metastasis and then favor survival of metastatic cells.

Conclusions

EBV infection predisposes one to breast cancer and metastasis, even if the virus disappears. Identifying this pathogenic viral damage may improve screening, treatment, and prevention. Immunizing children against EBV may protect against breast, ovarian, other cancers, and potentially even chronic unexplained diseases.

SOS1 Inhibition Enhances the Efficacy of KRASG12C Inhibitors and Delays Resistance in Lung Adenocarcinoma

The clinical effectiveness of KRASG12C inhibitors (G12Ci) is limited both by intrinsic and acquired resistance, necessitating the development of combination approaches. Here, we identified targeting proximal receptor tyrosine kinase signaling using the SOS1 inhibitor (SOS1i) BI-3406 as a strategy to improve responses to G12Ci treatment. SOS1i enhanced the efficacy of G12Ci and limited rebound receptor tyrosine kinase/ERK signaling to overcome intrinsic/adaptive resistance, but this effect was modulated by SOS2 protein levels. G12Ci drug-tolerant persister (DTP) cells showed up to a 3-fold enrichment of tumor-initiating cells (TIC), suggestive of a sanctuary population of G12Ci-resistant cells. SOS1i resensitized DTPs to G12Ci and inhibited G12C-induced TIC enrichment. Co-mutation of the tumor suppressor KEAP1 limited the clinical effectiveness of G12Ci, and KEAP1 and STK11 deletion increased TIC frequency and accelerated the development of acquired resistance to G12Ci, consistent with clinical G12Ci resistance seen with these co-mutations. Treatment with SOS1i both delayed acquired G12Ci resistance and limited the total number of resistant colonies regardless of KEAP1 and STK11 mutational status. Together, these data suggest that targeting SOS1 could be an effective strategy to both enhance G12Ci efficacy and prevent G12Ci resistance regardless of co-mutations. Significance: The SOS1 inhibitor BI-3406 both inhibits intrinsic/adaptive resistance and targets drug tolerant persister cells to limit the development of acquired resistance to clinical KRASG12C inhibitors in lung adenocarcinoma cells.

Targeting chromosomally unstable tumors with a selective KIF18A inhibitor

Chromosome instability is a prevalent vulnerability of cancer cells that has yet to be fully exploited therapeutically. To identify genes uniquely essential to chromosomally unstable cells, we mined the Cancer Dependency Map for genes essential in tumor cells with high levels of copy number aberrations. We identify and validate KIF18A, a mitotic kinesin, as a vulnerability of chromosomally unstable cancer cells. Knockdown of KIF18A leads to mitotic defects and reduction of tumor growth. Screening of a chemical library for inhibitors of KIF18A enzymatic activity identified a hit that was optimized to yield VLS-1272, which is orally bioavailable, potent, ATP non-competitive, microtubule-dependent, and highly selective for KIF18A versus other kinesins. Inhibition of KIF18A's ATPase activity prevents KIF18A translocation across the mitotic spindle, resulting in chromosome congression defects, mitotic cell accumulation, and cell death. Profiling VLS-1272 across >100 cancer cell lines demonstrates that the specificity towards cancer cells with chromosome instability differentiates KIF18A inhibition from other clinically tested anti-mitotic drugs. Treatment of tumor xenografts with VLS-1272 results in mitotic defects leading to substantial, dose-dependent inhibition of tumor growth. The strong biological rationale, robust preclinical data, and optimized compound properties enable the clinical development of a KIF18A inhibitor in cancers with high chromosomal instability.

scPharm: Identifying Pharmacological Subpopulations of Single Cells for Precision Medicine in Cancers

Intratumour heterogeneity significantly hinders the efficacy of anticancer therapies. Compared with drug perturbation experiments, which yield pharmacological data at the bulk cell level, single-cell RNA sequencing (scRNA-seq) technology provides a means to capture molecular heterogeneity at single-cell resolution. Here, scPharm is introduced, a computational framework that integrates pharmacological profiles with scRNA-seq data to identify pharmacological subpopulations of cells within a tumour and prioritize tailored drugs. scPharm uses the normalized enrichment scores (NESs) determined from gene set enrichment analysis to assess the distribution of cell identity genes in drug response-determined gene lists. Based on the strong correlation between the NES and drug response at single-cell resolution, scPharm successfully identified the sensitive subpopulations in ER-positive and HER2-positive human breast cancer tissues, revealed dynamic changes in the resistant subpopulation of human PC9 cells treated with erlotinib, and expanded its ability to a mouse model. Its superior performance and computational efficiency are confirmed through comparative evaluations with other single-cell prediction tools. Additionally, scPharm predicted combination drug strategies by gauging compensation or booster effects between drugs and evaluated drug toxicity in healthy cells in the tumour microenvironment. Overall, scPharm provides a novel approach for precision medicine in cancers by revealing therapeutic heterogeneity at single-cell resolution.

Targeting PRMT1 Reduces Cancer Persistence and Tumor Relapse in EGFR- and KRAS-Mutant Lung Cancer

SIGNIFICANCE

Eliminating "persisters" before relapse is crucial for achieving durable treatment efficacy. This study provides a rationale for developing PRMT1-selective inhibitors to target cancer persisters and achieve more durable outcomes in oncogene-targeting therapies.

PFKFB3-dependent redox homeostasis and DNA repair support cell survival under EGFR-TKIs in non-small cell lung carcinoma

BACKGROUND

The efficacy of tyrosine kinase inhibitors (TKIs) targeting the EGFR is limited due to the persistence of drug-tolerant cell populations, leading to therapy resistance. Non-genetic mechanisms, such as metabolic rewiring, play a significant role in driving lung cancer cells into the drug-tolerant state, allowing them to persist under continuous drug treatment.

METHODS

Our study employed a comprehensive approach to examine the impact of the glycolytic regulator 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFKFB3) on the adaptivity of lung cancer cells to EGFR TKI therapies. We conducted metabolomics to trace glucose rerouting in response to PFKFB3 inhibition during TKI treatment. Live cell imaging and DCFDA oxidation were used to quantify levels of oxidation stress. Immunocytochemistry and Neutral Comet assay were employed to evaluate DNA integrity in response to therapy-driven oxidative stress.

RESULTS

Our metabolic profiling revealed that PFKFB3 inhibition significantly alters the metabolic profile of TKI-treated cells. It limited glucose utilization in the polyol pathway, glycolysis, and TCA cycle, leading to a depletion of ATP levels. Furthermore, pharmacological inhibition of PFKFB3 overcome TKI-driven redox capacity by diminishing the expression of glutathione peroxidase 4 (GPX4), thereby exacerbating oxidative stress. Our study also unveiled a novel role of PFKFB3 in DNA oxidation and damage by controlling the expression of DNA-glycosylases involved in base excision repair. Consequently, PFKFB3 inhibition improved the cytotoxicity of EGFR-TKIs by facilitating ROS-dependent cell death.

CONCLUSIONS

Our results suggest that PFKFB3 inhibition reduces glucose utilization and DNA damage repair, limiting the adaptivity of the cells to therapy-driven oxidative stress and DNA integrity insults. Inhibiting PFKFB3 can be an effective strategy to eradicate cancer cells surviving under EGFR TKI therapy before they enter the drug-resistant state. These findings may have potential implications in the development of new therapies for drug-resistant cancer treatment.

Statistical inference with a manifold-constrained RNA velocity model uncovers cell cycle speed modulations

Across biological systems, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. While low-dimensional dynamics can be extracted using RNA velocity, these algorithms can be fragile and rely on heuristics lacking statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. To address these challenges, we introduce a Bayesian model of RNA velocity that couples velocity field and manifold estimation in a reformulated, unified framework, identifying the parameters of an explicit dynamical system. Focusing on the cell cycle, we implement VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validate its ability to infer cell cycle periods using live imaging. We also apply VeloCycle to reveal speed differences in regionally defined progenitors and Perturb-seq gene knockdowns. Overall, VeloCycle expands the single-cell RNA sequencing analysis toolkit with a modular and statistically consistent RNA velocity inference framework.

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

scStateDynamics: deciphering the drug-responsive tumor cell state dynamics by modeling single-cell level expression changes

Understanding tumor cell heterogeneity and plasticity is crucial for overcoming drug resistance. Single-cell technologies enable analyzing cell states at a given condition, but catenating static cell snapshots to characterize dynamic drug responses remains challenging. Here, we propose scStateDynamics, an algorithm to infer tumor cell state dynamics and identify common drug effects by modeling single-cell level gene expression changes. Its reliability is validated on both simulated and lineage tracing data. Application to real tumor drug treatment datasets identifies more subtle cell subclusters with different drug responses beyond static transcriptome similarity and disentangles drug action mechanisms from the cell-level expression changes.

Protein kinase A and local signaling in cancer

Protein kinase A (PKA) is a basophilic kinase implicated in the modulation of many cell-signaling and physiological processes. PKA also contributes to cancer-relevant events such as growth factor action, cell cycle control, cell migration and tumor metabolism. Germline and somatic mutations in PKA, gene amplifications, and chromosome rearrangements that encode kinase fusions, are linked to a growing number of malignant neoplasms. Mislocalization of PKA by exclusion from A-Kinase Anchoring Protein (AKAP) signaling islands further underlies cancer progression. This article highlights the influence of AKAP signaling and local kinase action in selected hallmarks of cancer. We also feature the utility of kinase inhibitor drugs as frontline and future anti-cancer therapies.

Multilevel Mechanisms of Cancer Drug Resistance

Cancer drug resistance represents one of the most significant challenges in oncology and manifests through multiple interconnected molecular and cellular mechanisms. Objective: To provide a comprehensive analysis of multilevel processes driving treatment resistance by integrating recent advances in understanding genetic, epigenetic, and microenvironmental factors. This is a systematic review of the recent literature focusing on the mechanisms of cancer drug resistance, including genomic studies, clinical trials, and experimental research. Key findings include the following: (1) Up to 63% of somatic mutations can be heterogeneous within individual tumors, contributing to resistance development; (2) cancer stem cells demonstrate enhanced DNA repair capacity and altered metabolic profiles; (3) the tumor microenvironment, including cancer-associated fibroblasts and immune cell populations, plays a crucial role in promoting resistance; and (4) selective pressure from radiotherapy drives the emergence of radioresistant phenotypes through multiple adaptive mechanisms. Understanding the complex interplay between various resistance mechanisms is essential for developing effective treatment strategies. Future therapeutic approaches should focus on combination strategies that target multiple resistance pathways simultaneously, guided by specific biomarkers.

In Situ and Label-Free Quantification of Membrane Protein-Ligand Interactions Using Optical Imaging Techniques: A Review

Membrane proteins are crucial for various cellular processes and are key targets in pharmacological research. Their interactions with ligands are essential for elucidating cellular mechanisms and advancing drug development. To study these interactions without altering their functional properties in native environments, several advanced optical imaging methods have been developed for in situ and label-free quantification. This review focuses on recent optical imaging techniques such as surface plasmon resonance imaging (SPRi), surface plasmon resonance microscopy (SPRM), edge tracking approaches, and surface light scattering microscopy (SLSM). We explore the operational principles, recent advancements, and the scope of application of these methods. Additionally, we address the current challenges and explore the future potential of these innovative optical imaging strategies in deepening our understanding of biomolecular interactions and facilitating the discovery of new therapeutic agents.

HiDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data

In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, when in reality only a small fraction of them may actually be perturbed. Here, using simulations, we demonstrate that the standard approach to single cell analysis fails to isolate the subset of affected case cells and their markers when either the affected subset is small, or when the strength of the perturbation is mild. To address this fundamental limitation, we introduce HiDDEN, a computational method that refines the case-control labels to accurately reflect the perturbation status of each cell. We show HiDDEN's superior ability to recover biological signals missed by the standard analysis workflow in simulated ground truth datasets of cell type mixtures. When applied to a dataset of human multiple myeloma precursor conditions, HiDDEN recapitulates the expert manual annotation and discovers malignancy in early stage samples missed in the original analysis. When applied to a mouse model of demyelination, HiDDEN identifies an endothelial subpopulation playing a role in early stage blood-brain barrier dysfunction. We anticipate that HiDDEN should find wide usage in contexts that require the detection of subtle transcriptional changes in cell types across conditions.

Research and application of omics and artificial intelligence in cancer

Cancer has a high incidence and lethality rate, which is a significant threat to human health. With the development of high-throughput technologies, different types of cancer genomics data have been accumulated, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. A comprehensive analysis of various omics data is needed to understand the underlying mechanisms of tumor development. However, integrating such a massive amount of data is one of the main challenges today. Artificial intelligence (AI) techniques such as machine learning are now becoming practical tools for analyzing and understanding multi-omics data on diseases. Enabling great optimization of existing research paradigms for cancer screening, diagnosis, and treatment. In addition, intelligent healthcare has received widespread attention with the development of healthcare informatization. As an essential part of innovative healthcare, practical, intelligent prognosis analysis and personalized treatment for cancer patients are also necessary. This paper introduces the advanced multi-omics data analysis technology in recent years, presents the cases and advantages of the combination of both omics data and AI applied to cancer diseases, and finally briefly describes the challenges faced by multi-omics analysis and AI at the current stage, aiming to provide new perspectives for oncology research and the possibility of personalized cancer treatment.

Characterization of Cancer Stem Cells in Laryngeal Squamous Cell Carcinoma by Single-cell RNA Sequencing

Cancer stem cells (CSCs) constitute a pivotal element within the tumor microenvironment (TME), driving the initiation and progression of cancer. However, the identification of CSCs and their underlying molecular mechanisms in laryngeal squamous cell carcinoma (LSCC) remains a formidable challenge. Here, we employed single-cell RNA sequencing of matched primary tumor tissues, paracancerous tissues, and local lymph nodes from three LSCC patients to comprehensively characterize the CSCs in LSCC. Two distinct clusters of stem cells originating from epithelial populations were delineated and verified as CSCs and normal stem cells (NSCs), respectively. CSCs were abundant in the paracancerous tissues compared to those in the tumor tissues. CSCs showed high expression of stem cell marker genes such as PROM1, ALDH1A1, and SOX4, and increased the activity of tumor-related hypoxia, Wnt/β-catenin, and Notch signaling pathways. We then explored the intricate crosstalk between CSCs and the TME cells and identified targets within the TME that related with CSCs. We also found eight marker genes of CSCs that were correlated significantly with the prognosis of LSCC patients. Furthermore, bioinformatics analyses showed that drugs such as erlotinib, OSI-027, and ibrutinib selectively targeted the CSC-specifically expressed genes. In conclusion, our results represent the first comprehensive characterization of CSC properties in LSCC at the single-cell level.

Graph-Based Spatial Proximity of Super-Resolved Protein-Protein Interactions Predicts Cancer Drug Responses in Single Cells

Purpose

Current bulk molecular assays fail to capture spatial signaling activities in cancers, limiting our understanding of drug resistance mechanisms. We developed a graph-based super-resolution protein-protein interaction (GSR-PPI) technique to spatially resolve single-cell signaling networks and evaluate whether higher resolution microscopy enhances the biological study of PPIs using deep learning classification models.

Methods

Single-cell spatial proximity ligation assays (PLA, ≤ 9 PPI pairs) were conducted on EGFR mutant (EGFRm) PC9 and HCC827 cells (>10,000 cells) treated with 100 nM Osimertinib. Multiplexed PPI images were obtained using wide-field and super-resolution microscopy (Zeiss Airyscan, SRRF). Graph-based deep learning models analyzed subcellular protein interactions to classify drug treatment states and test GSR-PPI on clinical tissue samples. GSR-PPI triangulated PPI nodes into 3D relationships, predicting drug treatment labels. Biological discriminative ability (BDA) was evaluated using accuracy, AUC, and F1 scores. The method was also applied to 3D spatial proteomic molecular pixelation (PixelGen) data from T cells.

Results

GSR-PPI outperformed baseline models in predicting drug responses from multiplexed PPI imaging in EGFRm cells. Super-resolution data significantly improved accuracy over localized wide-field imaging. GSR-PPI classified drug treatment states in cancer cells and human lung tissues, with performance improving as imaging resolution increased. It differentiated single and combination drug therapies in HCC827 cells and human tissues. Additionally, GSR-PPI accurately distinguished T-cell stimulation states, identifying key nodes such as CD44, CD45, and CD54.

Conclusion

The GSR-PPI framework provides valuable insights into spatial protein interactions and drug responses, enhancing the study of signaling biology and drug resistance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-024-00822-1.

Cancer drug-tolerant persister cells: from biological questions to clinical opportunities

The emergence of drug resistance is the most substantial challenge to the effectiveness of anticancer therapies. Orthogonal approaches have revealed that a subset of cells, known as drug-tolerant 'persister' (DTP) cells, have a prominent role in drug resistance. Although long recognized in bacterial populations which have acquired resistance to antibiotics, the presence of DTPs in various cancer types has come to light only in the past two decades, yet several aspects of their biology remain enigmatic. Here, we delve into the biological characteristics of DTPs and explore potential strategies for tracking and targeting them. Recent findings suggest that DTPs exhibit remarkable plasticity, being capable of transitioning between different cellular states, resulting in distinct DTP phenotypes within a single tumour. However, defining the biological features of DTPs has been challenging, partly due to the complex interplay between clonal dynamics and tissue-specific factors influencing their phenotype. Moreover, the interactions between DTPs and the tumour microenvironment, including their potential to evade immune surveillance, remain to be discovered. Finally, the mechanisms underlying DTP-derived drug resistance and their correlation with clinical outcomes remain poorly understood. This Roadmap aims to provide a comprehensive overview of the field of DTPs, encompassing past achievements and current endeavours in elucidating their biology. We also discuss the prospect of future advancements in technologies in helping to unveil the features of DTPs and propose novel therapeutic strategies that could lead to their eradication.

DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer

The field of computational drug repurposing aims to uncover novel therapeutic applications for existing drugs through high-throughput data analysis. However, there is a scarcity of drug repurposing methods leveraging the cellular-level information provided by single-cell RNA sequencing data. To address this need, we propose DrugReSC, an innovative approach to drug repurposing utilizing single-cell RNA sequencing data, intending to target specific cell subpopulations critical to disease pathology. DrugReSC constructs a drug-by-cell matrix representing the transcriptional relationships between individual cells and drugs and utilizes permutation-based methods to assess drug contributions to cellular phenotypic changes. We demonstrate DrugReSC's superior performance compared to existing drug repurposing methods based on bulk or single-cell RNA sequencing data across multiple cancer case studies. In summary, DrugReSC offers a novel perspective on the utilization of single-cell sequencing data in drug repurposing methods, contributing to the advancement of precision medicine for cancer.

scDTL: enhancing single-cell RNA-seq imputation through deep transfer learning with bulk cell information

The increasing single-cell RNA sequencing (scRNA-seq) data enable researchers to explore cellular heterogeneity and gene expression profiles, offering a high-resolution view of the transcriptome at the single-cell level. However, the dropout events, which are often present in scRNA-seq data, remaining challenges for downstream analysis. Although a number of studies have been developed to recover single-cell expression profiles, their performance may be hindered due to not fully exploring the inherent relations between genes. To address the issue, we propose scDTL, a deep transfer learning based approach for scRNA-seq data imputation by harnessing the bulk RNA-sequencing information. We firstly employ a denoising autoencoder trained on bulk RNA-seq data as the initial imputation model, and then leverage a domain adaptation framework that transfers the knowledge learned by the bulk imputation model to scRNA-seq learning task. In addition, scDTL employs a parallel operation with a 1D U-Net denoising model to provide gene representations of varying granularity, capturing both coarse and fine features of the scRNA-seq data. Finally, we utilize a cross-channel attention mechanism to fuse the features learned from the transferred bulk imputation model and U-Net model. In the evaluation, we conduct extensive experiments to demonstrate that scDTL could outperform other state-of-the-art methods in the quantitative comparison and downstream analyses.

Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for elucidating cellular heterogeneity and tissue function in various biological contexts. However, the sparsity in scRNA-seq data limits the accuracy of cell type annotation and transcriptomic analysis due to information loss. To address this limitation, we present scRobust, a robust self-supervised learning strategy to tackle the inherent sparsity of scRNA-seq data. Built upon the Transformer architecture, scRobust employs a novel self-supervised learning strategy comprising contrastive learning and gene expression prediction tasks. We demonstrated the effectiveness of scRobust using nine benchmarks, additional dropout scenarios, and combined datasets. scRobust outperformed recent methods in cell-type annotation tasks and generated cell embeddings that capture multi-faceted clustering information (e.g. cell types and HbA1c levels). In addition, cell embeddings of scRobust were useful for detecting specific marker genes related to drug tolerance stages. Furthermore, when we applied scRobust to scATAC-seq data, high-quality cell embedding vectors were generated. These results demonstrate the representational power of scRobust.

Assessing Drug Uptake and Response Differences in 2D and 3D Cellular Environments Using Stimulated Raman Scattering Microscopy

The architecture of cell culture, two-dimensional (2D) versus three-dimensional (3D), significantly impacts various cellular factors, including cell-cell interactions, nutrient and oxygen gradients, metabolic activity, and gene expression profiles. This can result in different cellular responses during cancer drug treatment, with 3D-cultured cells often exhibiting higher resistance to chemotherapeutic drugs. While various genetic and proteomic analyses have been employed to investigate the underlying mechanisms of this increased resistance, complementary techniques that provide experimental evidence of spatial molecular profiling data are limited. Stimulated Raman scattering (SRS) microscopy has demonstrated its capability to measure both intracellular drug uptake and growth inhibition. In this work, we applied three-band (C-D, C-H, and fingerprint regions) SRS imaging to 2D and 3D cell cultures and performed a comparative analysis of drug uptake and response with the goal of understanding whether the difference in drug uptake explains the drug resistance in 3D culture compared to 2D. Our investigations revealed that despite similar intracellular drug levels in 2D and 3D A549 cells during lapatinib treatment, the growth of 3D spheroids was less impacted, supporting an enhanced drug tolerance in the 3D microenvironment. We further elucidated drug penetration patterns and the resulting heterogeneous cellular responses across different spheroid layers. Additionally, we investigated the role of the extracellular matrix in modulating drug delivery and cell response and discovered that limited drug penetration in 3D could also contribute to lower drug response. Our study provides valuable insights into the intricate mechanisms of increased drug resistance in 3D tumor models during cancer drug treatments.

Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs

There is growing interest in designing multidrug therapies that leverage tradeoffs to combat resistance. Tradeoffs are common in evolution and occur when, for example, resistance to one drug results in sensitivity to another. Major questions remain about the extent to which tradeoffs are reliable, specifically, whether the mutants that provide resistance to a given drug all suffer similar tradeoffs. This question is difficult because the drug-resistant mutants observed in the clinic, and even those evolved in controlled laboratory settings, are often biased towards those that provide large fitness benefits. Thus, the mutations (and mechanisms) that provide drug resistance may be more diverse than current data suggests. Here, we perform evolution experiments utilizing lineage-tracking to capture a fuller spectrum of mutations that give yeast cells a fitness advantage in fluconazole, a common antifungal drug. We then quantify fitness tradeoffs for each of 774 evolved mutants across 12 environments, finding these mutants group into classes with characteristically different tradeoffs. Their unique tradeoffs may imply that each group of mutants affects fitness through different underlying mechanisms. Some of the groupings we find are surprising. For example, we find some mutants that resist single drugs do not resist their combination, while others do. And some mutants to the same gene have different tradeoffs than others. These findings, on one hand, demonstrate the difficulty in relying on consistent or intuitive tradeoffs when designing multidrug treatments. On the other hand, by demonstrating that hundreds of adaptive mutations can be reduced to a few groups with characteristic tradeoffs, our findings may yet empower multidrug strategies that leverage tradeoffs to combat resistance. More generally speaking, by grouping mutants that likely affect fitness through similar underlying mechanisms, our work guides efforts to map the phenotypic effects of mutation.

A distinct subset of urothelial cells with enhanced EMT features promotes chemotherapy resistance and cancer recurrence by increasing COL4A1-ITGB1 mediated angiogenesis

Drug resistance and tumor recurrence remain clinical challenges in the treatment of urothelial carcinoma (UC). However, the underlying mechanism is not fully understood. Here, we performed single-cell RNA sequencing and identified a subset of urothelial cells with epithelial-mesenchymal transition (EMT) features (EMT-UC), which is significantly correlated with chemotherapy resistance and cancer recurrence. To validate the clinical significance of EMT-UC, we constructed EMT-UC like cells by introducing overexpression of two markers, Zinc Finger E-Box Binding Homeobox 1 (ZEB1) and Desmin (DES), and examined their histological distribution characteristics and malignant phenotypes. EMT-UC like cells were mainly enriched in UC tissues from patients with adverse prognosis and exhibited significantly elevated EMT, migration and gemcitabine tolerance in vitro. However, EMT-UC was not specifically identified from tumorous tissues, certain proportion of them were also identified in adjacent normal tissues. Tumorous EMT-UC highly expressed genes involved in malignant behaviors and exhibited adverse prognosis. Additionally, tumorous EMT-UC was associated with remodeled tumor microenvironment (TME), which exhibited high angiogenic and immunosuppressive potentials compared with the normal counterparts. Furthermore, a specific interaction of COL4A1 and ITGB1 was identified to be highly enriched in tumorous EMT-UC, and in the endothelial component. Targeting the interaction of COL4A1 and ITGB1 with specific antibodies significantly suppressed tumorous angiogenesis and alleviated gemcitabine resistance of UC. Overall, our findings demonstrated that the driven force of chemotherapy resistance and recurrence of UC was EMT-UC mediated COL4A1-ITGB1 interaction, providing a potential target for future UC treatment.

PrPC controls epithelial-to-mesenchymal transition in EGFR-mutated NSCLC: implications for TKI resistance and patient follow-up

Patients with EGFR-mutated non-small cell lung cancer (NSCLC) benefit from treatment with tyrosine kinase inhibitors (TKI) targeting EGFR. Despite improvements in patient care, especially with the 3rd generation TKI osimertinib, disease relapse is observed in all patients. Among the various processes involved in TKI resistance, epithelial-to-mesenchymal transition (EMT) is far from being fully characterized. We hypothesized that the cellular prion protein PrPC could be involved in EMT and EGFR-TKI resistance in NSCLC. Using 5 independent lung adenocarcinoma datasets, including our own cohort, we document that the expression of the PRNP gene encoding PrPC is associated with EMT. By manipulating the levels of PrPC in different EGFR-mutated NSCLC cell lines, we firmly establish that the expression of PrPC is mandatory for cells to maintain or acquire a mesenchymal phenotype. Mechanistically, we show that PrPC operates through an ILK-RBPJ cascade, which also controls the expression of EGFR. Our data further demonstrate that PrPC levels are elevated in EGFR-mutated versus wild-type tumours or upon EGFR activation in vitro. In addition, we provide evidence that PRNP levels increase with TKI resistance and that reducing PRNP expression sensitizes cells to osimertinib. Finally, we found that plasma PrPC levels are increased in EGFR-mutated NSCLC patients from 2 independent cohorts and that their longitudinal evolution mirrors that of disease. Altogether, these findings define PrPC as a candidate driver of EMT-dependent resistance to EGFR-TKI in NSCLC. They further suggest that monitoring plasma PrPC levels may represent a valuable non-invasive strategy for patient follow-up and warrant considering PrPC-targeted therapies for EGFR-mutated NSCLC patients with TKI failure.

Cell death pathways: molecular mechanisms and therapeutic targets for cancer

Cell death regulation is essential for tissue homeostasis and its dysregulation often underlies cancer development. Understanding the different pathways of cell death can provide novel therapeutic strategies for battling cancer. This review explores several key cell death mechanisms of apoptosis, necroptosis, autophagic cell death, ferroptosis, and pyroptosis. The research gap addressed involves a thorough analysis of how these cell death pathways can be precisely targeted for cancer therapy, considering tumor heterogeneity and adaptation. It delves into genetic and epigenetic factors and signaling cascades like the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) and mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) pathways, which are critical for the regulation of cell death. Additionally, the interaction of the microenvironment with tumor cells, and particularly the influence of hypoxia, nutrient deprivation, and immune cellular interactions, are explored. Emphasizing therapeutic strategies, this review highlights emerging modulators and inducers such as B cell lymphoma 2 (BCL2) homology domain 3 (BH3) mimetics, tumour necrosis factor-related apoptosis-inducing ligand (TRAIL), chloroquine, and innovative approaches to induce ferroptosis and pyroptosis. This review provides insights into cancer therapy's future direction, focusing on multifaceted approaches to influence cell death pathways and circumvent drug resistance. This examination of evolving strategies underlines the considerable clinical potential and the continuous necessity for in-depth exploration within this scientific domain.

Variational inference of single cell time series

Time course single-cell RNA sequencing (scRNA-seq) enables researchers to probe genome-wide expression dynamics at the the single cell scale. However, when gene expression is affected jointly by time and cellular identity, analyzing such data - including conducting cell type annotation and modeling cell type-dependent dynamics - becomes challenging. To address this problem, we propose SNOW (SiNgle cell flOW map), a deep learning algorithm to deconvolve single cell time series data into time-dependent and time-independent contributions. SNOW has a number of advantages. First, it enables cell type annotation based on the time-independent dimensions. Second, it yields a probabilistic model that can be used to discriminate between biological temporal variation and batch effects contaminating individual timepoints, and provides an approach to mitigate batch effects. Finally, it is capable of projecting cells forward and backward in time, yielding time series at the individual cell level. This enables gene expression dynamics to be studied without the need for clustering or pseudobulking, which can be error prone and result in information loss. We describe our probabilistic framework in detail and demonstrate SNOW using data from three distinct time course scRNA-seq studies. Our results show that SNOW is able to construct biologically meaningful latent spaces, remove batch effects, and generate realistic time-series at the single-cell level. By way of example, we illustrate how the latter may be used to enhance the detection of cell type-specific circadian gene expression rhythms, and may be readily extended to other time-series analyses.

Drug tolerant persister cell plasticity in cancer: A revolutionary strategy for more effective anticancer therapies

Non-genetic mechanisms have recently emerged as important drivers of anticancer drug resistance. Among these, the drug tolerant persister (DTP) cell phenotype is attracting more and more attention and giving a predominant non-genetic role in cancer therapy resistance. The DTP phenotype is characterized by a quiescent or slow-cell-cycle reversible state of the cancer cell subpopulation and inert specialization to stimuli, which tolerates anticancer drug exposure to some extent through the interaction of multiple underlying mechanisms and recovering growth and proliferation after drug withdrawal, ultimately leading to treatment resistance and cancer recurrence. Therefore, targeting DTP cells is anticipated to provide new treatment opportunities for cancer patients, although our current knowledge of these DTP cells in treatment resistance remains limited. In this review, we provide a comprehensive overview of the formation characteristics and underlying drug tolerant mechanisms of DTP cells, investigate the potential drugs for DTP (including preclinical drugs, novel use for old drugs, and natural products) based on different medicine models, and discuss the necessity and feasibility of anti-DTP therapy, related application forms, and future issues that will need to be addressed to advance this emerging field towards clinical applications. Nonetheless, understanding the novel functions of DTP cells may enable us to develop new more effective anticancer therapy and improve clinical outcomes for cancer patients.

Know your molecule: pharmacological characterization of drug candidates to enhance efficacy and reduce late-stage attrition

Despite advances in chemical, computational and biological sciences, the rate of attrition of drug candidates in clinical development is still high. A key point in the small-molecule discovery process that could provide opportunities to help address this challenge is the pharmacological characterization of hit and lead compounds, culminating in the selection of a drug candidate. Deeper characterization is increasingly important, because the 'quality' of drug efficacy, at least for G protein-coupled receptors (GPCRs), is now understood to be much more than activation of commonly evaluated pathways such as cAMP signalling, with many more 'efficacies' of ligands that could be harnessed therapeutically. Such characterization is being enabled by novel assays to characterize the complex behaviour of GPCRs, such as biased signalling and allosteric modulation, as well as advances in structural biology, such as cryo-electron microscopy. This article discusses key factors in the assessments of the pharmacology of hit and lead compounds in the context of GPCRs as a target class, highlighting opportunities to identify drug candidates with the potential to address limitations of current therapies and to improve the probability of them succeeding in clinical development.

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

Single-cell spatial multiomics reveals tumor microenvironment vulnerabilities in cancer resistance to immunotherapy

Heterogeneous resistance to immunotherapy remains a major challenge in cancer treatment, often leading to disease progression and death. Using CITE-seq and matched 40-plex PhenoCycler tissue imaging, we performed longitudinal multimodal single-cell analysis of tumors from metastatic melanoma patients with innate resistance, acquired resistance, or response to immunotherapy. We established the multimodal integration toolkit to align transcriptomic features, cellular epitopes, and spatial information to provide deeper insights into the tumors. With longitudinal analysis, we identified an "immune-striving" tumor microenvironment marked by peri-tumor lymphoid aggregates and low infiltration of T cells in the tumor and the emergence of MITF+SPARCL1+ and CENPF+ melanoma subclones after therapy. The enrichment of B cell-associated signatures in the molecular composition of lymphoid aggregates was associated with better survival. These findings provide further insights into the establishment of microenvironmental cell interactions and molecular composition of spatial structures that could inform therapeutic intervention.

The Potential Links between lncRNAs and Drug Tolerance in Lung Adenocarcinoma

Lung cancer patients treated with targeted therapies frequently respond well but invariably relapse due to the development of drug resistance. Drug resistance is in part mediated by a subset of cancer cells termed "drug-tolerant persisters" (DTPs), which enter a dormant, slow-cycling state that enables them to survive drug exposure. DTPs also exhibit stem cell-like characteristics, broad epigenetic reprogramming, altered metabolism, and a mutagenic phenotype mediated by adaptive mutability. While several studies have characterised the transcriptional changes that lead to the altered phenotypes exhibited in DTPs, these studies have focused predominantly on protein coding changes. As long non-coding RNAs (lncRNAs) are also implicated in the phenotypes altered in DTPs, it is likely that they play a role in the biology of drug tolerance. In this review, we outline how lncRNAs may contribute to the key characteristics of DTPs, their potential roles in tolerance to targeted therapies, and the emergence of genetic resistance in lung adenocarcinoma.

Stochastic Modeling of Biophysical Responses to Perturbation

Recent advances in high-throughput, multi-condition experiments allow for genome-wide investigation of how perturbations affect transcription and translation in the cell across multiple biological entities or modalities, from chromatin and mRNA information to protein production and spatial morphology. This presents an unprecedented opportunity to unravel how the processes of DNA and RNA regulation direct cell fate determination and disease response. Most methods designed for analyzing large-scale perturbation data focus on the observational outcomes, e.g., expression; however, many potential transcriptional mechanisms, such as transcriptional bursting or splicing dynamics, can underlie these complex and noisy observations. In this analysis, we demonstrate how a stochastic biophysical modeling approach to interpreting high-throughout perturbation data enables deeper investigation of the 'how' behind such molecular measurements. Our approach takes advantage of modalities already present in data produced with current technologies, such as nascent and mature mRNA measurements, to illuminate transcriptional dynamics induced by perturbation, predict kinetic behaviors in new perturbation settings, and uncover novel populations of cells with distinct kinetic responses to perturbation.