Translational Oncology

BackgroundAltered metabolism is a hallmark of cancer and glycolysis is one of the important factors promoting tumor development. Given that the absence of multi-sample big data research about glycolysis, the molecular mechanisms involved in glycolysis or the relationships between glycolysis and tumor microenvironment are not fully studied. Thus, a more comprehensive approach in a pan-cancer landscape may be needed. MethodsHere, we develop a computational pipeline to study multi-omics molecular features defining glycolysis activity and identify molecular alterations that correlate with glycolysis. We apply a 22-gene expression signature to define the glycolysis activity landscape and verify the robustness using clinically defined glycolysis samples from several previous studies. Based on gene expression signature, we classify about 5552 of 9229 tumor samples into glycolysis score-high and score-low groups across 25 cancer types from The Cancer Genome Atlas (TCGA) and demonstrate their prognostic associations. Moreover, using genomes and transcriptome data, we characterize the association of copy-number aberrations (CNAs), somatic single-nucleotide variants (SNVs) and hypoxia signature with glycolysis activity. FindingsGene set variation analysis (GSVA) score by gene set expression was verified robustly to represent glycolytic activity and highly glycolytic tumors presented a poor overall survival in some cancer types. Then, we identified various types of molecular features promoting tumor cell proliferation were associated with glycolysis activity. Our study showed that TCA cycle and respiration electron transport were active in glycolysis-high tumors, indicating glycolysis was not a symptom of impaired oxidative metabolism. The glycolytic score significantly correlated with hypoxia score across all cancer types. Glycolysis score was also associated with elevated genomic instability. In all tumor types, high glycolysis tumors exhibited characteristic driver genes altered by CNAs identified multiple oncogenes and tumor suppressors. We observed widespread glycolysis-associated dysregulation of mRNA across cancers and screened out HSPA8 and P4HA1 as the potential modulating factor to glycolysis. Besides, the expression of genes encoding glycolytic enzymes positively correlated with genes in cell cycle. InterpretationThis is the first study to identify gene expression signatures that reflect glycolysis activity, which can be easily applied to large numbers of patient samples. Our analysis establishes a computational framework for characterizing glycolysis activity using gene expression data and defines correlation of glycolysis with the hypoxia microenvironment, tumor cell cycle and proliferation at a pan-cancer landscape. The findings suggest that the mechanisms whereby hypoxia influence glycolysis are likely multifactorial. Our finding is significant not just in demonstrating definition value for glycolysis but also in providing a comprehensive molecular-level understanding of glycolysis and suggesting a framework to guide combination therapy that may block the glycolysis pathway to control tumor growth in hypoxia microenvironment.

BackgroundOvarian cancer (OC) is a significant gynecological malignancy characterized by its high mortality rate, poor long-term survival rate, and late-stage diagnosis. OC is the 5th leading cause of cancer death among woman and counts 2.1% of all cancer death. OC survival rates are much lower than other cancers that affect woman. Its 5-year survival rate is less than 50%. Only [~]17% of OC patients are diagnosed within the early stage. The majority are diagnosed at an advanced stage, making early detection and effective treatment critical challenges. Currently, the identified OC predictive genes are still very sparse, resulting in pool prognostic performance. There exists unmet needs to identify novel prognostic gene biomarkers for OC occurrence, survival, and clinical stages to promote the likelihood of survival and to perform optimal treatments or therapeutic strategies at the earliest stage possible. MethodsPrevious RNAseq analysis on OC focused on detecting differentially expressed (DE) genes only. Many genes, although having weak marginal differential effects, may still exude strong predictive effects on disease outcomes though regulating other DE genes. In this work, we employed a new machine learning method, netLDA, to detect such predictive coregulating genes with weak marginal DE effects for predicting OC occurrence, 5-year survival, and clinical stage. The netLDA detects predictive gene networks (PGN) containing strong DE genes as hub genes and detects coregulating weak genes within the PGNs. The network structures of the detected PGNs along with the strong and weak genes therein are then used in outcome prediction on test datasets. ResultsWe identified different sets of signature genes for OC occurrence, survival, and clinical stage. Previously identified prognostic genes, such as EPCAM, UBE2C, CHD1L, TP53,CD24, WFDC2, and FANCI, were confirmed. We also identified novel predictive coregulating weak genes including GIGYF2, GNPAT, RAD54L, and ELL. Many of the detected predictive gene networks and coregulating weak genes therein overlapped with OC-related biological pathways such as KEGG tight junction, ribosome, and cell cycle pathways. The detection and incorporation of the gene networks and weak genes significantly improved the prediction performance. Cellular mapping of selected feature genes using single-cell RNAseq data further revealed the heterogeneous expression distributions of the signature genes on different cell types. ConclusionsWe established a transcriptomic gene network profile for OC prediction. The novel genes detected provide new targets for early diagnostics and new drug development for OC.

BackgroundDormant or slow cycling cells (SSCs) pre-exist in tumor and responsible for chemo-resistant and tumor recurrence. Due to their low differentiation and dormancy characteristics, SCCs are resistant to standard chemotherapy and targeted therapy. Label-retaining is a common method used to identify and isolate live SCCs. However, it remains unclear whether different label-retaining methods yield distinct SCC subpopulations. In this study, we investigated that various label-retaining methods result in overlapping yet heterogeneous subpopulations of SCCs. Additionally, we explored the molecular mechanisms regulating dormancy in triple-negative breast cancer (TNBC). MethodsWe employed multiple label-retaining methods to simultaneously label MDA-MB-231 cells, thereby generating distinct subpopulations of SCCs. We subsequently analyzed these subpopulations for heterogeneity in cell cycle distribution, drug resistance, invasive capacity, and other characteristics using real-time PCR, flow cytometry, and Transwell assays. RNA-seq analysis was performed to characterize the gene expression profiles of the SCCs. Furthermore, we used real-time PCR, Western blotting, immunofluorescence, and luciferase assays to investigate the role of characteristic AP-1 expression in dormancy regulation. Finally, the therapeutic effects of targeting AP-1 in the treatment of TNBC were assessed using a cell-derived xenograft model. ResultsWe labeled and separated three overlapping but non-identical SCCs subpopulations. We found that all three SCCs subgroups are cell cycle arrested. Additionally, Violet enriched SCCs showed stronger drug resistance and more G1 phase arrest, while Claret enriched SCCs demonstrated enhanced migratory and invasive abilities, along with more G2/M phase arrest. Furthermore, we observed upregulation of AP-1 expression in SCCs, and the JunB subunit of AP-1 promoted the expression of CDKN1A and GADD45A, thereby maintaining cell cycle arrest. CC-930 can inhibit AP-1 transcriptional activity by suppressing JNK activity, ultimately improving the therapeutic efficacy and prognosis of TNBC when used in combination with chemotherapy drugs. ConclusionsWe obtained three subpopulations of SCCs with heterogeneous drug resistance. Our findings suggest that AP-1 plays a regulatory role in dormancy regulation in TNBC, and elucidated the molecular function of JunB subunit. Targeting AP-1 with CC-930 has the potential to improve the treatment and prognosis of TNBC. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/566980v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@129ce76org.highwire.dtl.DTLVardef@1b1cfa1org.highwire.dtl.DTLVardef@b68315org.highwire.dtl.DTLVardef@57faf1_HPS_FORMAT_FIGEXP M_FIG C_FIG TNBC harbors both fast-cycling cells (FCCs) and functionally overlapping slow-cycling cell (SCC) subpopulations that manifest differential drug sensitivities and motility (A) but are commonly regulated by the JunB-containing AP1 complex (B). (A). Slow-cycling (quiescent) TNBC cells in culture (a) identified by different label-retaining approaches phenotypically overlap (b), display differential drug sensitivities (c, d) and motility (e) but share common gene expression profiles (f). (B). Schematic depicting regulation of proliferation in FCCs by the c-Jun/c-Fos AP1 complex (left) and regulation of cellular dormancy in SCCs by c-Jun/JunB AP1 complex.

ObjectivePFKP (Phosphofructokinase, Platelet Type isoform), as an essential metabolic enzyme, contributes to the high glycolysis rates seen in cancers, while its role in oncogenic pathways, especially from a non-metabolic aspect, is not fully understood. MethodsHere we performed a comprehensive analysis of published RNA-seq, microarray data, and immunohistochemistry of tissue microarray to evaluate the significance of PFKP expression in non-small cell lung cancer (NSCLC). Functionally, we tested the cell proliferation, colony formation, invasion, and migration upon PFKP knockdown in lung cancer cells. Mechanistically, we performed RNA-seq, DIA-mass spectrum, western blot, and qPCR to probe the change of cell signaling pathways upon PFKP silencing. Co-immunoprecipitation and mass spectrum were used to uncover potential PFKP interacting proteins. ResultsWe found that PFKP was highly expressed in NSCLC and was related to poor patient survival. Knockdown of PFKP significantly inhibited cell proliferation, colony formation, invasion, and migration of NSCLC cells. Mechanistically, we found that PFKP can directly bind with AXL and promote its phosphorylation at Y779, thus activating the AXL signaling pathway and promoting MET phosphorylation. In addition, several glycolysis, glutaminolysis, and TCA cycle proteins were downregulated following PFKP silencing. ConclusionsThese data demonstrate that PFKP, beyond its known role in glycolysis, also has a distinct non-metabolic function in affecting lung cancer progression by directly interacting with the AXL-MET axis, thus indicating a potential therapeutic target for lung cancer.

Palbociclib is an excellent CDK4/6 inhibitor, but its clinical application is mainly limited to the treatment of advanced ER+/HR+ and HER2- breast cancer. Its efficacy in colorectal cancer (CRC) is evaluated in multiple trials and so far remains undetermined. We found that the PIK3CA-mutant CRC cells were insensitive to palbociclib treatment compared with the PIK3CA wild-type counterparts, and they were in an OIS (oncogene-induced senescence) state which was predisposed to a strong senescence-associated secretory phenotype (SASP) upon treatment. These senescent cells excessively secreted various SASP factors LCN2, and ultimately caused palbociclib-resistance via upregulation of EGFR in the non-senescent CRC cells. Importantly, a drug combination screen identified that erlotinib could synergize with palbociclib to overcome the SASP-induced resistance. Overall, we found that PIK3CA mutation-induced senescence compromised the efficacy of palbociclib treatment in CRC but co-targeting EGFR could minimize the OIS-associated side effects while preserving the beneficial effects.

BackgroundAdenocarcinomas are malignancies arising from glandular epithelial cells or secretory tissue, and account for most cancer-related mortalities worldwide, despite advances in treatment. New treatments are often developed in the context of the organ from which the tumor originates. However, there exists shared biology across glandular epithelium at the molecular level regardless of organ system, and there has been a shift towards the molecular classification of tumors. Defining and targeting pan-adenocarcinoma specific molecular features may facilitate the development of treatments applicable across adenocarcinomas, expediting drug discovery efforts and translation to the clinical setting. MethodsWe performed an integrated transcriptomics analysis of adenocarcinomas originating from different organ systems. RNA sequencing expression profiles from lung adenocarcinoma (LUAD), stomach adenocarcinoma (STAD), and colorectal adenocarcinoma (COAD) with matched normal tissue from The Cancer Genome Atlas (TCGA) was used to discover a pan-adenocarcinoma specific transcriptomic module. A standardized DESeq2 based pipeline was used to test for differentially expressed genes (DEGs) between each adenocarcinoma and its matched normal tissues, followed by cross-cancer comparisons to identify a consensus transcriptional module. Enrichment analysis was performed to reveal biological pathways associated with the consensus module, with a particular focus on those involved in oncogenesis. Prognostic significance of the consensus transcriptional module was evaluated using survival modeling, and the module was tested against in vitro transcriptomic drug perturbation signatures using Connectivity Map (cMAP) analysis to identify candidate drugs targeting adenocarcinomas, regardless of organ system of origin. ResultsDespite the diversity of adenocarcinomas tested, there existed a significant and large overlap of the genes dysregulated across tested tumors. A consensus transcriptomic module was defined, and it predicted patient prognosis in each of the three adenocarcinomas. Leveraging the top shared biomarkers through cMAP analysis, we identified 36 FDA-approved drugs that are capable of reversing the shared malignant transcriptional module towards the normal state. Among the FDA-approved drugs were the EGFR and ALK inhibitors gefitinib and crizotinib, both currently used for treating LUAD, providing validation the pipeline could discover efficacious drugs. Taken together, we developed an approach for organ system independent cancer biomarkers and drug discovery, and leveraged it to identify drug candidates for expanded use in adenocarcinomas. The FDA-approved drugs identified through this pipeline serve as candidates to repurpose as pan-adenocarcinoma anti-cancer therapeutics, reducing both time and cost for drug development. ConclusionOur research provided insights into the common molecular mechanisms across multiple adenocarcinomas and unveiled potential drug candidates for future therapeutic testing.

Cancer cells acquire stem cell and mesenchymal properties during epithelial-to-mesenchymal transition (EMT), facilitating metastasis and chemoresistance [1-6]. In this study, we find that mammary epithelial cells quickly develop mesenchymal phenotype in response to EMT-inducing signals; however, acquiring stemness takes several days and always requires a preceding mesenchymal program. In addition, we observe that carcinoma cells, over a period of time, switch their cell division from symmetrical differentiated type to symmetrical self-renewal type. Importantly, epithelial cells can gain mesenchymal properties without undergoing cell division, but cell disivion is vital for these cells to gain stem cell properties during EMT. The EMT-induced stemness signature (SC-sig) is capable of predicting progression-free and overall-survival of breast cancer patients but not the EMT-induced mesenchymal signature (M-sig). Collectively, our findings demonstrate that the use of mesenchymal markers alone is insufficient to identify tumors with metastatic and chemoresistance potential and emphasize that the markers of EMT-induced stem cell program are central for clinical prediction. Most importantly, our data, for the first time, demonstrate that acquisition of stem cell properties during EMT depends on cell division but not the mesenchymal program.

CK2 is considered a constitutively active protein kinase promoting/supporting several neoplastic properties and inducing a so-called non-oncogene addiction in tumor cells. Compared to the extensive body of pre-clinical research, the translational and clinical information on CK2 is still limited. The holoenzyme, composed by a tetrameric array of two catalytic (CSNK2A1 and/or CSNK2A1) and two regulatory (CSNK2B) subunits, remains to be clinically validated. Herein, we interrogated available cancer multiomics databases to unravel CK2 deregulated expression in NSCLC. We focused our analysis on individual CK2 subunits assuming subunit-specific tumor supportive roles across cancers and particularly, within two major NSCLC subtypes. Moreover, we performed meta-analysis to uncover associations between CK2 expression and patient survival, as well as further correlations analysis with components of the tumor-microenvironment. The genomic and transcriptomic data analysis was complemented by IHC evaluation of CSNK2A1, CSNK2A2 and CSNK2B subunit expression, and CK2 enzymatic activity thereof. Overall, our data suggests that epigenetic, transcriptional and post-transcriptional regulatory mechanisms rather than mutational/gene amplification events may account for differential CK2 subunits expression/activity in NSCLC. Of note, CSNK2A1 and CSNK2B mRNA up-regulation consistently determine a worse patient prognosis in LUAD and correlated with increased infiltration of MDSCs/CAFs. Importantly, we corroborated that CK2 protein subunits levels and enzymatic activity are significantly exacerbated in LUAD and LUSC, but only CSNK2A1 positively correlated with tumor size and disease stage in the analyzed patient cohort, thus supporting our transcriptomic-based correlation analysis. Finally, we concluded that CSNK2A1 alone and/or the homo-tetramer thereof may be more instrumental to support NSCLC than CSNK2A2; thus, tailored drugs against these molecular CK2 entities may achieve better therapeutic windows at least for advanced lung cancer treatment.

Renal cell carcinoma (RCC) exhibits significant heterogeneity, making it challenging to predict tumor aggressiveness and therapeutic response. To improve prognostic accuracy and develop tailored treatment strategies, it is crucial to mimic both cancer cells and their microenvironment in vitro. Using a combination of in vitro and in vivo models, we investigated the invasive properties of three RCC cell lines--RCC10, RCC7 and 786-O-- that displayed distinct signaling profiles, combining EMT characteristics and upregulation of key metastatic markers. Our findings revealed that RCC7 and 786-O exhibited greater metastatic potential than RCC10, as demonstrated by increased extravasation in zebrafish embryos and higher lung metastases in the chorioallantoic membrane (CAM) and mice models. Comparative pathway analysis indicated that RCC7 displays partial epithelial-mesenchymal transition (pEMT) characteristics and upregulates key metastatic markers. Furthermore, our 3D spheroid invasion model as well as our patient-derived RCC tumoroid system predicted accurately their metastatic behavior, closely mirroring their aggressiveness in vivo. Thus, these 3D models might be predictive of tumor outcome, underscoring their utility as reliable predictive tools for RCC progression and therapeutic response. Novelty and ImpactNon-uniform distribution of genetic and phenotypic subpopulations within RCC tumors causes many tumors of similar histological grade to have vastly different metastatic potential. We show that 3D spheroids and RCC patient-derived tumoroid models more accurately reflect in vivo invasive behavior than traditional 2D assays, providing powerful predictive tools for RCC aggressiveness and metastatic disease. These findings have significant implications for precision oncology, enabling better preclinical evaluation of the metastatic risk to the patient.

Mitoribosomes play a pivotal role in cellular energy metabolism by synthesizing proteins involved in oxidative phosphorylation (OXPHOS) system. Dysregulation of mitoribosomes has been linked to Cancer, yet there have been no studies demonstrating the genetics and epigenetic landscape of mitoribosomal proteins (MRPs). In this study, we conducted a comprehensive analysis of expression, copy number variations, mutations, data from TCGA NSCLC patients to elucidate the genetic mechanism regulating MRPs in NSCLC. Consequently, we identified MRPL47, a significantly amplified and overexpressed mitoribosomal gene. We also found significant correlation of MRPL47 expression and copy number with patients survival. Functionally, we showed that inhibition of MRPL47 was associated with reduced cell proliferation and migration. Furthermore, silencing MRPL47 impaired the enzymatic activity of electron chain complex I & III leading to a defective OXPHOS system and elevated mitochondrial ROS level. Further, we showed ROS-mediated increase in CDKN1A through the p38 MAPK pathway. The increased CDKN1A level induced G1 cell cycle arrest by inhibiting E2F activity. RNA sequencing analysis further confirmed that MRPL47 hinders cell growth by inhibiting E2F pathway. Additionally, we found that MRPL47 selectively regulates mitochondrial translation of specific OXPHOS proteins rather than influencing all mitochondrial proteins. Altogether, these findings suggest that MARPL47, is amplified and overexpressed in NSCLC and plays a critical role in tumor progression by regulating ROS signaling pathways.

BackgroundIt has been challenging to develop agents directly targeting KRAS driver mutations in colorectal cancer (CRC). Recent efforts have focused on developing inhibitors targeting SOS1 as an attractive approach for KRAS-mutant cancers. Here, we aimed to study the translational role of SOS1 in CRC using patient-derived organoids (PDOs). MethodIn this study, we used CRC PDOs as preclinical models to evaluate their sensitivity to SOS1 inhibitor BI3406 and its cellular effects. We utilized large CRC datasets including GENIE cohort, TCGA PanCancer Atlas, and CPTAC-2 cohort to study the significance of molecular alterations of SOS1 in CRC. To identify potential predictive markers, we performed immunohistochemistry (IHC) on CRC tissue for SOS1/2 protein expression and RNA sequencing to identify discriminative gene sets for sensitivity to SOS1 inhibition. The findings were validated by DepMap data for SOS1 dependency. ResultCRC PDOs instead of cell lines had differential sensitivities to BI3406. There was a significant correlation between SOS1 and SOS2 mRNA expressions (Spearmans {rho} 0.56, p<0.001). SOS1/2 protein expression by IHC were universal with heterogeneous patterns in cancer cells but only minimal to none in surrounding non-malignant cells. SOS1 protein expression was associated with worse overall survival in patients with RAS/RAF mutant CRC (p=0.04). We also found that SOS1/SOS2 protein expression ratio > 1 by IHC (p=0.03) instead of KRAS mutation (p=1) was a better predictive marker to BI3406 sensitivity of CRC PDOs. This was concordant with the significant correlation between SOS1/SOS2 protein expression ratio by mass spectrometry and SOS1 dependency score. RNA-seq and gene set enrichment analysis revealed differentially expressed genes and 7 enriched gene sets involving cholesterol homeostasis, epithelial mesenchymal transition, and TNF/NF{kappa}B signaling in BI3406-resistant CRC PDOs. We further discovered that GTP-bound RAS level underwent rebound at 48 hours upon treatment with BI3406 even in BI3406-sensitive PDOs with no change of KRAS effector genes downstream. Cellular adaptation mechanisms to SOS1 inhibition may involve upregulation of SOS1/2 mRNA and SOS1 protein expressions, which may be overcome by SOS1 knockdown/degradation or synergistic effect of BI3406 with trametinib. ConclusionIn summary, CRC PDOs could serve as better models for translational study of SOS1 in CRC. High SOS1 protein expression was a worse prognostic factor in CRC. High SOS1/SOS2 protein expression ratio predicted sensitivity to SOS1 inhibition and dependency. Our preclinical findings supported further clinical development of SOS1-targeting agents in CRC.

Non-small cell lung cancers demonstrate intrinsic resistance to cell death even in response to chemotherapy. Previous work suggested that defective nuclear translocation of active caspase 3 may play a role in resistance to cell death. Separately, our group has identified that mitogen activated protein kinase activated protein kinase 2 (MK2) is required for nuclear translocation of active caspase 3 in the execution of apoptosis. This study demonstrates a relatively low expression of MK2 in non-small cell lung carcinoma cell lines compared to small cell carcinoma cell lines. Further, overexpression of MK2 in non-small cell lung carcinoma cell lines results in increased caspase 3 activity and caspase 3 mediated cell death. Higher MK2 transcript levels were observed in patients with earlier-stage non-small cell lung cancer. Higher expression of MK2 is associated with better survival in patients with early stage non-small cell lung cancer across two independent clinical datasets. Using data sets spanning multiple cancer types, we observed improved survival with higher MK2 expression was unique to lung adenocarcinoma. Mechanistically, MK2 promotes nuclear translocation of caspase 3 leading to PARP1 cleavage and execution of cell death. While MK2 can directly phosphorylate caspase 3, neither phosphorylation status of caspase 3 nor the kinase activity of MK2 impacts caspase 3 activation, nuclear translocation and execution of cell death. Rather, a non-kinase function of MK2, specifically trafficking via its nuclear localization sequence, is required for caspase 3 mediated cell death. In summary this study highlights the importance of a non-enzymatic function of MK2 in the execution of apoptosis, which may be leveraged in the adjunctive treatment of NSCLC or other conditions where regulation of apoptosis is crucial.

BackgroundStage III colorectal cancer poses a significant threat of metastasis development, as tumour resection and adjuvant chemotherapy do not guarantee prolonged disease-free survival. ObjectiveThe spatial, quantitative, and qualitative characteristics of various cell types within tumour tissues could be key to developing accurate prognostic AI models. DesignTissue microarrays created from primary tumour tissues collected during surgical resection from a cohort of 493 stage III colorectal cancer (CRC) patients were analysed for 61 protein markers at the single-cell level using multiplexed immunofluorescence imaging via the Cell DIVE platform. Subsequent cell-type classification enabled quantitative cell-type analyses, co-localisation neighbourhood assessments, and cell-type-specific protein signature discoveries that distinguish between early and late/non-recurring patient samples. ResultsThis study identifies a stem cell protein profile that drives tumour relapse. A deep neural network (DNN) model, based on a stem cell protein signature composed of BAX, MLKL, FLIP, GLUT1, and CDX2, provided accurate prognosis for stage III CRC patients in both discovery and validation cohorts and in an independent validation cohort. Nodal count-based metric further increased prognosis accuracy. Our study also revealed distinct spatial arrangements of immune, endothelial, and stem cells that were linked to early tumour recurrence. ConclusionOur findings propose a clinically promising prognostic tool based on a five-protein stem cell signature. These markers not only predict chemotherapy resistance in cancer stem cells but also suggest potential therapeutic strategies such as combinatorial treatments incorporating small molecule inhibitors targeting FLIP and GLUT1. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIMore than 20% of stage III colorectal cancer patients will experience early tumour recurrence within the first 3 years post treatment that includes surgery and adjuvant 5-FU based chemotherapy treatment. C_LIO_LISeveral studies pointed towards involvements of number of cell type specific spatial neighbourhoods in tumour progression where some immune tumour microenvironment promoting angiogenesis and intravasation events, some may provide immunosuppression. C_LIO_LICancer stem cells could be responsible for metastatic tumour spread, early recurrence and chemoresistance. C_LI What this study addsO_LISpatial single cell quantitative multiplex profiling of 45 cancer hallmark proteins and 15 cell identity markers in 493 stage III CRC patients tissue samples demonstrated significant differences in cellular proximity neighbourhoods, cell type specific abundance and expression between the early and late recurrence samples. C_LIO_LIWe discover that macrophages show spatial association with the blood vessels in early recurrence samples. Moreover, we observed conglomeration of B cells and macrophages with Tregulatory, Thelper and Tcytotoxic cells in association with early recurrences. C_LIO_LIWe showed that stromal abundance of Tregulatory, Thelper, Tcytotoxic cells and monocytes are significantly in late, and no recurrence samples compared to early recurrence samples. C_LIO_LIThe most differential expression profile that differentiates late and no recurrence samples from the early recurrence samples is related to the stem cell population. Particularly, we found overexpression of GLUT1, FLIP and downregulation of BAX, BAK, MLKL and CDX2 proteins in the cancer stem cell of early recurrence samples. C_LIO_LIWe built a neural network based on the cancer stem cell protein signature (BAX, MLKL, FLIP, GLUT1 and CDX2 proteins) that delivers a high-performance prognostic classifier. C_LI How this study might affect research, practice or policyO_LIOur results propose a clinically promising prognostic tool based on a five-protein stem cell signature that outperforms existing clinical and proposed transcriptomic based signatures for separation between risk groups. C_LIO_LIMoreover, our five-protein signature markers not only predict stem cell chemotherapy resistance and therefore tumour recurrence but also suggest potential therapeutic strategies. For instance, this approach could guide combinatorial treatments at high risk of chemoresistance, such as incorporating small molecule inhibitors targeting FLIP (currently in discovery phase) and GLUT1 (already under preclinical trial evaluation). C_LI

PurposePancreatic ductal adenocarcinoma (PDAC) is characterized by a nutrient-deprived and hypoxic tumor microenvironment (TME) that imposes severe metabolic stress on cancer cells. Under these conditions, tumor cells frequently activate the integrated stress response (ISR) to adapt to TME and develop resistance to therapies. However, how TME components support tumor adaptation to mitochondrial metabolic stress remains incompletely understood. Here, we aimed to identify key metabolite involved in ISR adaptation under oxidative phosphorylation (OXPHOS) inhibition and to elucidate the metabolic symbiosis between cancer-associated fibroblasts (CAFs) and PDAC cells. MethodsWe integrated transcriptomic and metabolomic analyses with functional assays. ISR activation was evaluated by assessing the phosphorylation of eIF2 (p-eIF2) following treatment with carboxyamidotriazole orotate (CTO), an Complex I inhibitor. Metabolomic profiling was used to identify metabolites involved in ISR activation alleviation. Mouse models were used to assess therapeutic responses following depletion of the identified metabolite under CTO treatment. Genetic perturbation of Slc38a4 was performed to assess its functional role in tumor cell adaptation to metabolic stress. ResultsWe identified asparagine (ASN) as a critical metabolite supplied by CAFs to PDAC cells under OXPHOS inhibition. A minimum level of ASN is required for PDAC cells to execute ISR downstream adaptation. ASN depletion significantly enhanced the anti-tumor efficacy of OXPHOS inhibition both in vitro and in vivo. SLC38A4 emerged as a potential mediator of this interaction. SLC38A4 expression was associated with c-Myc, and its loss increased the sensitivity of PDAC cells to CTO-induced metabolic stress. ConclusionOur findings reveal a CAF-tumor metabolic crosstalk in which stromal-derived ASN supports PDAC cell adaptation to mitochondrial metabolic stress. Adaptive outcome of ISR signaling depends on the availability of key metabolic substrates such as ASN. When extracellular ASN supply is limited, the ATF4-dependent adaptive program collapses, converting ISR from a pro-survival response into a therapeutic vulnerability. SLC38A4 may function as a key mediator of this metabolic coupling and represents a potential target for enhancing the efficacy of OXPHOS inhibition in PDAC.

Peritoneal metastases (PM) portend limited survival in patients with Gastric Adenocarcinoma (GCa), and strategies to prevent and/or more effectively treat PM are needed. Existing models are limited in recapitulating key elements of the peritoneal metastatic cascade. To explore the underlying cellular and molecular mechanisms of PM, we have developed an ex vivo human peritoneal explant model. Fresh peritoneal tissue samples were obtained from patients undergoing abdominal surgery and suspended, mesothelial layer down but without direct contact, above a monolayer of red-fluorescent stained AGS human GCa cells for 24hrs, then washed and cultured for a further 3 days. Implantation and invasion of GCa cells within the explant were examined using real-time confocal fluorescence microscopy. Superficial implantation of AGS GCa cells within the mesothelial surface was readily detected, and colonies expanded over 3 days. To investigate the sensitivity of the model to altered GCa cellular implantation, we stably transfected AGS cells with E-Cadherin, restoring the E-Cadherin that they otherwise lack. This markedly suppressed implantation and invasion of AGS cells into the submesothelial mesenchymal layer. Here we show that this ex vivo human peritoneal explant model is responsive to manipulation of genetic factors that regulate peritoneal implantation and invasion by GCa cells, with reproducible results.

BackgroundColorectal cancer (CRC) remains a major global health concern, with significant morbidity and mortality rates. Identifying reliable prognostic indicators is essential for optimizing risk stratification and guiding clinical management. In this study, we aimed to develop a comprehensive blood indicator based on systemic inflammation and nutritional condition to predict the prognosis of resectable CRC patients. MethodsA retrospective cohort of 210 CRC patients who underwent radical resection at the First Affiliated Hospital of Chongqing Medical University, China, between January 2015 and December 2017, was included in the analysis. Baseline characteristics, preoperative blood markers, including neutrophil count, monocyte count, lymphocyte count, platelets, albumin, and CEA were retrospectively reviewed. Various blood indicators, such as NLR, PLR, MLR, SIRI and OPNI were calculated. The least absolute shrinkage and selection operator method (LASSO) was employed to select indicators to establish a novel comprehensive biomarker (named PSI). Kaplan-Meier survival curves and log-rank tests were used to evaluate the prognostic impact of preoperative OPNI, SIRI, and PSI. Univariate and multivariate Cox regression model were conducted to identify independent prognostic factors for CRC. The receiver operating characteristic (ROC) method assessed the predictive ability of PSI, stage, OPNI, and SIRI. ResultsPatients with higher preoperative OPNI and lower SIRI values had significantly better overall survival (OS). PSI was identified as an independent prognostic factor for OS in both univariate and multivariate analysis. Patients with medium (28.3-43.4) and high (>43.4) PSI scores exhibited superior OS compared to those with low ([&le;] 28.3) PSI scores. PSI showed higher predictive ability (AUC: 0.734) than individual indicators alone (OPNI: 0.721, SIRI: 0.645, stage: 0.635). ConclusionThe novel comprehensive indicator, PSI, based on preoperative SIRI and OPNI, demonstrated significant prognostic value for resectable CRC patients. PSI outperformed individual indicators and could serve as a reliable tool for risk stratification and prognostic management in CRC patients.

BackgroundAlthough surgical resection is the standard therapy for stage II/III colorectal cancer (CRC), recurrence rates exceed 30%. Circulating tumor DNA (ctDNA) emerged as a promising recurrence predictor, detecting molecular residual disease (MRD). However, spatial information about the tumor and its microenvironment is not directly measured by ctDNA. Deep Learning (DL) can predict prognosis directly from routine histopathology slides. MethodsWe developed a DL pipeline utilizing vision transformers to predict disease-free survival (DFS) based on histological hematoxylin & eosin (H&E) stained whole slide images (WSIs) from patients with resectable stage II-IV CRC. This model was trained on the DACHS cohort (n=1766) and independently validated on the GALAXY cohort (n=1555). Patients were categorized into high- or low-risk groups based on the DL-prediction scores. In the GALAXY cohort, the DL-scores were combined with the four-weeks post-surgery MRD status measured by ctDNA for prognostic stratification. ResultsIn GALAXY, the DL-model categorized 307 patients as DL high-risk and 1248 patients as DL low-risk (p<0.001; HR 2.60, CI 95% 2.11-3.21). Combining the DL scores with the MRD status significantly stratified both the MRD-positive group into DL high-risk (n=81) and DL low-risk (n=160) (HR 1.58 (CI 95% 1.17-2.11; p=0.002) and the MRD-negative group into DL high-risk (n=226) and DL low-risk (n=1088) (HR 2.37 CI 95% 1.73-3.23; p<0.001). Moreover, MRD-negative patients had significantly longer DFS when predicted as DL high-risk and treated with ACT (HR 0.48, CI 95% 0.27-0.86; p= 0.01), compared to the MRD-negative patients predicted as DL low-risk (HR=1.14, CI 95% 0.8-1.63; p=0.48). ConclusionDL-based spatial assessment of tumor histopathology slides significantly improves the risk stratification provided by MRD alone. Combining histologic information with ctDNA yields the most powerful predictor for disease recurrence to date, with the potential to improve follow-up, withhold adjuvant chemotherapy in low-risk patients and escalate adjuvant chemotherapy in high-risk patients. Highlights- This study combines MRD status measured by ctDNA with a DL-based risk assessment trained on histological image data to enhance recurrence prediction. - DL-based spatial assessment of tumor histopathology slides significantly improves the risk stratification provided by MRD alone. - MRD-negative patients with high DL-based risk had a significantly longer DFS if treated with ACT, compared to MRD-negative and DL low risk patients - The DL model is fully open-source and publicly available.

BackgroundGastric cancer (GC) continues to be among the most commonly identified cancers worldwide. This study integrates glycosylation and inflammation-related gene features for the first time to construct a prognostic model for gastric cancer, providing new theoretical basis for revealing immune escape mechanisms and personalized treatment strategies. MethodsTranscriptomic and clinical data derived from GC samples were meticulously examined, utilizing resources from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Through differential expression analysis, we successfully identified glycosylation and inflammatory-related differentially expressed genes (GANDIRDEGs). To construct a prognostic gene signature, we applied least absolute shrinkage and selection operator (LASSO) analysis in conjunction with Cox regression analysis. Additionally, we performed somatic mutation (SM) along with copy number variation (CNV) analyses, alongside gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, we conducted gene set enrichment analysis (GSEA) along with a comprehensive evaluation of immune infiltration and drug sensitivity. ResultsWe identified and validated a six-gene (INHBA, OLR1, ROS1, EPHA5, TACR1, and IL6) signature, termed GANDIRDEGs, which showed excellent performance in distinguishing overall survival (OS) between high-risk (HR) and low-risk (LR) cohorts. Moreover, we developed a prognostic nomogram utilizing this six-gene signature that provides highly accurate predictions of GC patient outcomes.SM and CNV analyses revealed that MSR1 had the highest mutation rate among the GANDIRDEGs, with a mutation rate of 5%. GO, KEGG, and GSEA revealed significant associations of each pivotal gene with pathways, including cytokine signaling, the inflammatory response, and apoptosis mediated by CDKN1A through TP53, among various biological functions and signal transduction pathways. Our findings offer a novel gene signature, GANDIRDEGs, that correlated with prognosis, immune infiltration, and therapeutic sensitivity in patients with GC. ConclusionThis study establishes a prognostic signature integrating glycosylation and inflammatory pathways in GC, providing valuable insights into the mechanisms of immune evasion and potential personalized treatment approaches.

BackgroundEpithelial-mesenchymal transition (EMT) has emerged as a key mechanism underlying resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) in EGFR-mutant non-small cell lung cancer (NSCLC). However, the intricacies of EMT-mediated resistance, driven by tumor microenvironment (TME) interactions, remain enigmatic. This study aimed to probe EMT-induced resistance in NSCLC using innovative in vitro organoid models. MethodsWe generated organoids by co-culturing EGFR-mutant NSCLC cells (HCC827, H1975), mesenchymal stem cells and endothelial cells. Drug susceptibility was compared between organoids and spheroids (cancer cells only) using EGFR TKIs - Gefitinib, Afatinib, Osimertinib. EMT marker (E-cadherin, ZEB1) expression was analyzed via immunofluorescence and western blotting. The effects of Bevacizumab and miR200c on overcoming resistance were also investigated. ResultsThe study identified a significant link between EMT and EGFR-TKI resistance. Notable findings included the decrease of E-cadherin and an increase in ZEB1, both of which influenced EMT and resistance to treatment. Bevacizumab showed promise in improving drug resistance and mitigating EMT, suggesting an involvement of the VEGF cascade. Transfection with miR200c was associated with improved EMT and drug resistance, further highlighting the role of EMT in TKI resistance. ConclusionsThis study offered vital insights into EMT-driven EGFR TKI resistance, highlighting the utility of organoid models in evaluating resistance modulated by TME interactions. Our findings reveal promising directions for overcoming EMT-mediated resistance involving Bevacizumab and miR200c, warranting further in vivo validation.

The genomic diversity of circulating tumor cells (CTCs) and its clinical implications remain poorly understood. In this study, we characterized the mutational landscape of CTC pools stemming from 29 metastatic colorectal cancer (mCRC) patients and examined its relationship with disease progression. Our analysis revealed substantial variation in mutational burden among patients, with all CTC pools harboring non-silent mutations in key CRC driver genes. Importantly, higher genomic diversity in CTC pools was significantly associated with reduced overall survival. Furthermore, non-silent mutations in BCL9L emerged as a strong predictor of patient survival. Taken together, these findings underscore the potential of CTC genomic profiling as a promising prognostic tool in mCRC and highlight the need for further research into its clinical applications.