Biology subjects

Gyorffy, B.

Publications and source records attributed to Gyorffy, B..

At least 19 recordsLinked to original sources

bioRxiv2026

XPO1 controls VDR localization and activity in triple-negative breast cancer cells

The association of cytoplasmic localization of VDR in TNBC tumors and disease aggressivity prompted investigation of its regulation by XPO1. In TNBC cell lines, the XPO1 inhibitors selinexor and eltanexor increased nuclear VDR localization. Their effects were particularly strong when combined with vitamin D treatment. Similarly, XPO1 knockdown increased nuclear VDR levels, both alone and in combination with vitamin D. These results identify XPO1 as an important regulator of VDR subcellular distribution. XPO1 inhibition also enhanced vitamin D-dependent VDR transcriptional activity. Indeed, selinexor, eltanexor, and XPO1 silencing increased vitamin D-dependent CYP24A1 reporter activity and the expression of genes such as CYP24A1 or CDH1. RNA sequencing showed that XPO1 modulation markedly reshaped the vitamin D transcriptional response. The affected genes were mainly involved in metabolism, cell-cycle regulation, protein localization, and cellular organization. Functional analyses demonstrated that cytoplasmic VDR promoted TNBC cell proliferation. Conversely, nuclear VDR was required for the antiproliferative effects of vitamin D. Accordingly, XPO1 inhibition or depletion potentiated vitamin D-mediated growth suppression. This finding suggested functional cooperation between XPO1-dependent export and VDR activity. Finally, high VDR expression was associated with poor disease-free or overall survival only in tumors with high XPO1 expression. Overall, XPO1 negatively regulates VDR nuclear localization and vitamin D responsiveness in TNBC. Targeting XPO1 may therefore enhance the therapeutic potential of vitamin D-based strategies in TNBC.

cell biology↗

bioRxiv2026

Prognostic stratification by LGR5 expression identifies surface-accessible, structurally ligandable and condensate-forming targets in colorectal cancer

Background: LGR5 marks colorectal cancer stem cells and is associated with poor outcome, but its expression on normal intestinal stem cells has constrained direct therapeutic targeting, and the molecular landscape of LGR5-high tumors remains incompletely defined. A transcriptional signature is not itself a set of drug targets: its constituent genes differ in whether and how they can be engaged pharmacologically, a distinction rarely applied systematically to a tumor-defined gene set. Methods: We stratified 396 colorectal tumors from The Cancer Genome Atlas by LGR5 expression and compared transcriptional, somatic mutation, and copy number profiles between LGR5-high and LGR5-low groups using non-parametric testing with combined significance and effect-size thresholds. Genome-wide CRISPR knockout data were interrogated to test genetic dependency. Each signature gene was then triaged by pharmacological tractability rather than essentiality, along three axes: surface accessibility, from surfaceome annotation and membrane topology; cavity ligandability, from pocket detection on predicted structures using three independent algorithms; and condensate propensity, from saturation concentration prediction and coarse-grained molecular dynamics simulation. Results: LGR5-high tumors displayed a coordinated program spanning Wnt signaling, stemness, and matrix remodeling, arising on an APC-mutant background with co-occurring IGF2 amplification. No constituent gene scored as a selective dependency. The three axes partitioned the signature with minimal overlap and nominated three candidates engaged by orthogonal modalities: ENPP3, a single-pass ectoenzyme presenting an accessible ectodomain and carrying clinical antibody-drug conjugate precedent; PLCB4, combining a well-defined catalytic pocket with additional predicted ligandable sites; and NKD1, accessible by neither route but undergoing RNA-stabilized homotypic phase separation, unlike SATB1 and MEX3A. Simulations further indicated that NKD1 partitions into DVL2-containing condensates and reduces DVL2-Wnt contacts, suggesting a biophysical basis for its negative-feedback role. Conclusions: LGR5 expression defines a colorectal cancer subset that is pharmacologically tractable despite the absence of genetic dependency. Triaging by modality rather than essentiality converts descriptive tumor signatures into stratified, experimentally testable therapeutic hypotheses, including condensate-directed modulation of NKD1 as a route to targets inaccessible by antibody- or pocket-based approaches.

molecular biology↗

bioRxiv2026

Neuroendocrine-like/EMT dedifferentiation mediates resistance to EGFR inhibitors via the NRG1/HER3 axis

Patients with non-small cell lung cancer (NSCLC) carrying activating EGFR mutations typically respond favorably to third-generation EGFR tyrosine kinase inhibitors (TKIs) such as osimertinib. Nevertheless, resistance almost inevitably emerges, ultimately limiting the durability of these treatments. We investigated non-genomic mechanisms enabling drug-tolerant persister cells to survive EGFR inhibition, likely co-opting compensatory HER3 activation, whose underlying mechanisms remain unclear. Using a combination of immortalized and patient-derived cellular models, together with single-cell RNA sequencing, we demonstrate that activation of EGFR/HER3 axis constitutes an early adaptive response to TKI exposure enriched in pulmonary alveolar type I and type II cancer cells. This response is driven by neuregulin-1 (NRG1), produced by stromal cells and by cancer cells undergoing NE-like/EMT dedifferentiation. Importantly, in vivo studies demonstrated that combining EGFR inhibition and NRG1 neutralization by monoclonal antibody successfully eradicated tumors. Together, these findings point to a therapeutic strategy to overcome TKI resistance in NSCLC through targeting HER3 signalling, its interplay with EGFR, and tumor microenvironment-derived cues.

cancer biology↗

bioRxiv2026

Condensate-Driven Transcriptional Reprogramming Defines Core Vulnerabilities in Esophageal and Gastric Cancers

Biomolecular condensates organize key nuclear functions by compartmentalizing biomolecules, yet their contribution to gastrointestinal tumorigenesis remains poorly defined. Integrating multi-omics profiling, functional genomics, and molecular dynamics simulations, we reveal that esophageal and gastric cancers share a condensate-enriched transcriptional program driven by intrinsically disordered proteins involved in transcription, RNA processing, and replication stress. Transcriptomic analyses identify a hyperactive transcriptional state with upregulation of condensate-associated genes, including TOPBP1 and CHERP. Dependency mapping demonstrates that these proteins are essential for tumor cell viability, defining a conserved condensate core across different tumor types. Machine-learned predictions and residue-resolution coarse-grained simulations confirm that TOPBP1 and CHERP undergo phase separation through homotypic interactions mediated by intrinsically disordered regions, with saturation concentrations below 2 {micro}M, consistent with spontaneous condensate formation observed in vitro. Together, these findings establish condensate organization as a fundamental mesoscale principle in upper gastrointestinal cancers and nominate condensate scaffolds as tractable therapeutic vulnerabilities.

bioinformatics↗

bioRxiv2025

Site-dependent Treg cell transcriptional reprograming in a metastatic colorectal cancer model holds prognostic significance

In colorectal cancer (CRC), tumor-infiltrating regulatory T (Treg) cells suppress anti-tumor immunity, promoting immune evasion and tumor progression. Effective therapies require selectively targeting tumor-infiltrating Treg (TI-Treg) cells while preserving systemic Treg cells, necessitating insight into their adaptations within the tumor microenvironment. Here, CRC-organoids were implanted in the liver of Foxp3eGFP mice to investigate location-specific phenotypic differences in TI-Treg cells. Tumor tissue exhibited an increased proportion of Treg cells and a decrease of effector CD4 and CD8 T cells compared to matched healthy tissue. RNA sequencing of Treg cells isolated from the spleen, primary liver tumor transplant, or metastases identified gene expression profiles previously associated with CRC-related Treg cells in patients. Location-specific differences included elevated expression of WNT-pathway genes in peritoneal TI-Treg cells compared to liver counterparts. Higher expression of genes upregulated in liver TI-Treg cells correlated with poor CRC prognosis. Splenic Treg cells from tumor-bearing mice displayed distinct transcriptional profiles from both their healthy counterparts and TI-Treg cells, suggesting they represent a distinct CD4+ population. Taken together, these findings highlight TI-Treg cells heterogeneity across different tumor sites and the distinct nature of splenic Treg cells in tumor-bearing hosts.

immunology↗

bioRxiv2024

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making. O_FIG O_LINKSMALLFIG WIDTH=164 HEIGHT=200 SRC="FIGDIR/small/592032v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@754668org.highwire.dtl.DTLVardef@1616263org.highwire.dtl.DTLVardef@827009org.highwire.dtl.DTLVardef@1899ba7_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Identifying Ferroptosis Inducers, HDAC, and RTK Inhibitor Sensitivity in Melanoma Subtypes through Unbiased Drug Target Prediction

The utilization of PD1 and CTLA4 inhibitors has revolutionized the treatment of malignant melanoma (MM). However, resistance to targeted and immune-checkpoint-based therapies still poses a significant problem. Here we mine large scale MM proteogenomic data integrating it with MM cell line dependency screen, and drug sensitivity data to identify druggable targets and forecast treatment efficacy and resistance. Leveraging protein profiles from established MM subtypes and molecular structures of 82 cancer treatment drugs, we identified nine candidate hub proteins, mTOR, FYN, PIK3CB, EGFR, MAPK3, MAP4K1, MAP2K1, SRC and AKT1, across five distinct MM subtypes. These proteins serve as potential drug targets applicable to one or multiple MM subtypes. By analyzing transcriptomic data from 48 publicly accessible melanoma cell lines sourced from Achilles and CRISPR dependency screens, we forecasted 162 potentially targetable genes. We also identified genetic resistance in 260 genes across at least one melanoma subtype. In addition, we employed publicly available compound sensitivity data (Cancer Therapeutics Response Portal, CTRPv2) on the cell lines to assess the correlation of compound effectiveness within each subtype. We have identified 20 compounds exhibiting potential drug impact in at least one melanoma subtype. Remarkably, employing this unbiased approach, we have uncovered compounds targeting ferroptosis, that demonstrate a striking 30x fold difference in sensitivity among different subtypes. This implies that the proteogenomic classification of melanoma has the potential to predict sensitivity to ferroptosis compounds. Our results suggest innovative and novel therapeutic strategies by stratifying melanoma samples through proteomic profiling, offering a spectrum of novel therapeutic interventions and prospects for combination therapy. Highlights(1) Proteogenomic subtype classification can define the landscape of genetic dependencies in melanoma (2) Nine proteins from molecular subtypes were identified as potential drug targets for specified MM patients (3) 20 compounds identified that show potential effectiveness in at least one melanoma subtype (4) Proteogenomics can predict specific ferroptosis inducers, HDAC, and RTK Inhibitor sensitivity in melanoma subtypes O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=184 SRC="FIGDIR/small/579424v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@73dd77org.highwire.dtl.DTLVardef@d0cb8eorg.highwire.dtl.DTLVardef@1e4117forg.highwire.dtl.DTLVardef@38b727_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

cancer biology↗

bioRxiv2023

CDK19 and CDK8 Mediator kinases drive androgen-independent in vivo growth of castration-resistant prostate cancer

Castration-resistant prostate cancer (CRPC) remains incurable due to its high plasticity. We found that Mediator kinases CDK8 and CDK19, pleiotropic regulators of transcriptional reprogramming, are differentially affected by androgen, which downregulates CDK8 and upregulates CDK19. Accordingly, expression of CDK8 decreases while CDK19 increases during prostate carcinogenesis, but both CDK19 and CDK8 are upregulated in metastatic CRPC. Genetic inactivation of CDK8 and CDK19 suppresses CRPC tumor growth in castrated male mice and renders CRPC responsive to androgen deprivation. Restoration of active CDK19 or CDK8 kinases reverses this phenotype, indicating that CRPC becomes dependent on Mediator kinase activity for in vivo growth under the conditions of androgen deprivation. Selective CDK8/19 inhibitors suppress androgen-independent growth of cell line-based and patient-derived CRPC xenografts, whereas prolonged inhibitor treatment induces tumor regression and even leads to cures. Mediator kinase activity was found to affect tumor and stromal gene expression preferentially in castrated mice, orchestrating castration-induced transcriptional reprogramming. These results warrant the exploration of Mediator kinase inhibitors for CRPC therapy.

cancer biology↗

bioRxiv2022

CovidOutcome2: a tool for SARS-CoV2 mutation identification and for disease severity prediction

Our goal was to develop a platform, CovidOutcome2, capable of predicting disease severity from viral mutation profiles using automated machine learning (autoML) and deep neural networks applied to the available large corpus of sequenced SARS-CoV2 genomes. CovidOutcome2 accepts either user-submitted genomes or user defined mutation combinations as the input. The output is a predicted severity score plus a list of identified, annotated mutations and their functional effects in VCF format. The best model performance is a ROC-AUC 0.899 for the model including patient age and ROC-AUC 0.83 for the model without patient age. AvailabilityCovidOutcome is freely available online under the URL https://www.covidoutcome.bio-ml.com as well as in a standalone version https://github.com/bio-apps/covid-outcome.

bioinformatics↗

bioRxiv2022

Microenvironmental Ammonia Enhances T cell Exhaustion in Colorectal Cancer

Effective therapies are lacking for patients with advanced colorectal cancer (CRC). The CRC tumor microenvironment has elevated metabolic waste products due to altered metabolism and proximity to the microbiota. The role of metabolite waste in tumor development, progression, and treatment resistance is unclear. We generated an autochthonous metastatic mouse model of CRC and unbiased multi-omic analyses in this model reveals a robust accumulation of tumoral ammonia. The high ammonia levels induce T cell metabolic reprogramming, increase exhaustion and decrease proliferation. CRC patients have increased serum ammonia, and our ammonia-related gene signature correlates with altered T cell response, adverse patient outcomes, and lack of response to immune checkpoint blockade. We demonstrate that enhancing ammonia clearance reactivates T cells, decreases tumor growth, and extends survival. Moreover, decreasing tumor-associated ammonia enhances anti-PD-L1 efficacy. Our findings indicate that ammonia detoxification can reactivate T cells, highlighting a new approach to enhance the efficacy of immunotherapies. Statement of SignificanceWe demonstrate that ammonia accumulates in the microenvironment of colorectal cancer. Ammonia alters T-cells redox singling leading to a decrease in T cell proliferation and an increase in T cell exhaustion. Enhancing ammonia clearance reduces tumor size, increases survival, and increases the efficacy to immunotherapies.

cancer biology↗

bioRxiv2021

COVIDOUTCOME - Estimating COVID Severity Based on Mutation Signatures in the SARS-CoV-2 Genome

IntroductionNumerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome. MethodsWe used an automated machine learning approach where 1,594 viral genomes with available clinical follow-up data were used as the training set (797 "severe" and 797 "mild"). The best algorithm, based on random forest classification combined with the LASSO feature selection algorithm was employed to the training set to link mutation signatures and outcome. The performance of the final model was estimated by repeated, stratified, 10-fold cross validation (CV), then adjusted for multiple testing with Bootstrap Bias Corrected CV. ResultsWe identified 26 protein and UTR mutations significantly linked to severe outcome. The best classification algorithm uses a mutation signature of 22 mutations as well as the patients age as the input and shows high classification efficiency with an AUC of 0.94 (CI: [0.912, 0.962]) and a prediction accuracy of 87% (CI: [0.830, 0.903]). Finally, we established an online platform (https://covidoutcome.com/) which is capable to use a viral sequence and the patients age as the input and provides a percentage estimation of disease severity. DiscussionWe demonstrate a statistical association between mutation signatures of SARS-CoV-2 and severe outcome of COVID-19. The established analysis platform enables a real-time analysis of new viral genomes. KEY MESSAGESO_LIA statistical link between SARS-Cov-2 mutation status and severe COVID outcome was established using automated machine learning techniques based on random forest and logistic regression combined with feature selection algorithms. C_LIO_LIA mutation signature based on 3,779 protein coding and 36 UTR mutations capable to identify severe outcome cases was established. C_LIO_LIThe trained model showed high classification performance (AUC=0.94 (CI: [0.912, 0.962]), accuracy=0.87 (CI: [0.830, 0.903])). C_LIO_LIA registration-free web-server for automated classification of new samples was set up and is accessible at http://www.covidoutcome.com. C_LIO_LIThe established pipeline provides a quick assessment of future patients warranting a prospective clinical validation. C_LI

bioinformatics↗

bioRxiv2021

Identification of a Nervous System Gene Expression Signature in Colon Cancer Stem Cells Reveals a Role for Neural Crest Regulators EGR2 and SOX2 in Tumorigenesis

Recent data support a hierarchical model of colon cancer driven by a population of cancer stem cells (CSCs). Greater understanding of the mechanisms that regulate CSCs may therefore lead to more effective treatments. Serial limiting dilution xenotransplantation assays of colon cancer patient-derived tumors demonstrated ALDHPositive cells to be enriched for tumorigenic self-renewing CSCs. In order to identify CSC modulators, we performed RNA-sequencing analysis of ALDHPositive CSCs from a panel of colon cancer patient-derived organoids (PDOs) and xenografts (PDXs). These studies demonstrated CSCs to be enriched for embryonic and neural development gene sets. Functional analyses of genes differentially expressed in both ALDHPositive PDO and PDX CSCs demonstrated the neural crest stem cell (NCSC) regulator and wound response gene EGR2 to be required for CSC tumorigenicity and to control expression of homeobox superfamily embryonic master transcriptional regulator HOX genes and the embryonic and neural stem cell regulator SOX2. In addition, we identify EGR2, HOXA2, HOXA4, HOXA5, HOXA7, HOXB2, HOXB3 and the tumor suppressor ATOH1 as new prognostic biomarkers in colorectal cancer.

cell biology↗

bioRxiv2021

RNA-Sequencing of Long-Term Label-Retaining Colon Cancer Stem Cells Identifies Novel Regulators of Quiescence

Recent data suggests that colon tumors contain a subpopulation of therapy resistant quiescent cancer stem cells (qCSCs) that are the source of relapse following treatment. Here, using colon cancer patient-derived organoids (PDOs) and xenograft (PDX) models, we identify a rare population of long-term label-retaining (PKH26Positive) qCSCs that can re-enter the cell cycle to generate new tumors. RNA-sequencing analyses demonstrated that these cells are enriched for stem cell associated gene sets such as Wnt and hedgehog signaling, epithelial-to-mesenchymal transition (EMT), embryonic development, tissue development and p53 pathway but have downregulated expression of genes associated with cell cycle, transcription, biosynthesis and metabolism. Furthermore, qCSCs are enriched for p53 interacting negative regulators of cell cycle, including AKAP12, CD82, CDKN1A, FHL2, GPX3, KIAA0247, LCN2, TFF2, UNC5B and ZMAT3, that we show are indicators of poor prognosis and may be targeted for qCSC abolition. Interestingly, CD82, KIAA0247 and UNC5B proteins localize to the cell surface and may therefore be potential markers for the prospective isolation of qCSCs. These data support the temporal inhibition of p53 signaling for the elimination of qCSCs and prevention of relapse in colorectal cancer.

cell biology↗

bioRxiv2021

MultipleTesting.com: a tool for life science researchers for multiple hypothesis testing correction

Scientists from nearly all disciplines face the problem of simultaneously evaluating many hypotheses. Conducting multiple comparisons increases the likelihood that a non-negligible proportion of associations will be false positives, clouding real discoveries. Drawing valid conclusions require taking into account the number of performed statistical tests and adjusting the statistical confidence measures. Several strategies exist to overcome the problem of multiple hypothesis testing. We aim to summarize critical statistical concepts and widely used correction approaches while also draw attention to frequently misinterpreted notions of statistical inference. We provide a step-by-step description of each multiple-testing correction method with clear examples and present an easy-to-follow guide for selecting the most suitable correction technique. To facilitate multiple-testing corrections, we developed a fully automated solution not requiring programming skills or the use of a command line. Our registration free online tool is available at www.multipletesting.com and compiles the five most frequently used adjustment tools, including the Bonferroni, the Holm (step-down), the Hochberg (step-up) corrections, allows to calculate False Discovery Rates (FDR) and q-values. The current summary provides a much needed practical synthesis of basic statistical concepts regarding multiple hypothesis testing in a comprehensible language with well-illustrated examples. The web tool will fill the gap for life science researchers by providing a user-friendly substitute for command-line alternatives.

bioinformatics↗

bioRxiv2020

Pancancer survival analysis of cancer hallmark genes

Cancer hallmark genes are responsible for the most essential phenotypic characteristics of malignant transformation and progression. In this study, our aim was to estimate the prognostic effect of the established cancer hallmark genes in multiple distinct cancer types. RNA-seq HTSeq counts and survival data from 26 different tumor types were acquired from the TCGA repository. DESeq was used for normalization. Correlations between gene expression and survival were computed using the Cox proportional hazards regression and by plotting Kaplan-Meier survival plots. The false discovery rate was calculated to correct for multiple hypothesis testing. Signatures based on genes involved in genome instability and invasion reached significance in most individual cancer types. Thyroid and glioblastoma were independent of hallmark genes (61 and 54 genes significant, respectively), while renal clear cell cancer and low grade gliomas harbored the most prognostic changes (403 and 419 genes significant, respectively). The eight genes with the highest significance included BRCA1 (genome instability, HR=4.26, p<1E-16), RUNX1 (sustaining proliferative signaling, HR=2.96, p=3.1E-10) and SERPINE1 (inducing angiogenesis, HR=3.36, p=1.5E-12) in low grade glioma, CDK1 (cell death resistance, HR=5.67, p=2.1E-10) in kidney papillary carcinoma, E2F1 (tumor suppressor, HR=0.38, p=2.4E-05) and EREG (enabling replicative immortality, HR=3.23, p=2.1E-07) in cervical cancer, FBP1 (deregulation of cellular energetics, HR=0.45, p=2.8E-07) in kidney renal clear cell carcinoma and MYC (invasion and metastasis, HR=1.81, p=5.8E-05) in bladder cancer. We observed unexpected heterogeneity and tissue specificity when correlating cancer hallmark genes and survival. These results will help to prioritize future targeted therapy development in different types of solid tumors.

bioinformatics↗

bioRxiv2020

TNMplot.com: a web tool for the comparison of gene expression in normal, tumor and metastatic tissues

Genes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation, and can serve as biomarkers of progression or as therapy targets with minimal off-target effects. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web-platform enabling mining of this database by comparing normal, tumor and metastatic data across all genes in real time. We utilized data generated by either gene arrays or RNA-seq. Gene array data were manually selected from NCBI-GEO. RNA sequencing data was downloaded from the TCGA, TARGET, and GTEx repositories. TCGA and TARGET contain predominantly tumor and metastatic samples from adult and pediatric patients, while GTEx samples are from healthy tissues. Statistical significance was computed using Mann-Whitney or Kruskall-Wallis tests. The entire database contains 56,938 samples including 33,520 samples from 3,180 gene chip-based studies (453 metastatic, 29,376 tumorous and 3,691 normal samples), 11,010 samples from TCGA (394 metastatic, 9,886 tumorous and 730 normal), 1,193 samples from TARGET (1 metastatic, 1,180 tumor, 12 normal) and 11,215 normal samples from GTEx. The most consistently up-regulated genes across multiple tumor types were TOP2A (mean FC=7.8), SPP1 (FC=7.0) and CENPA (FC=6.03) and the most consistently down-regulated gene was ADH1B (mean FC=0.15). Validation of differential expression using equally sized training and test sets confirmed reliability of the database in breast, colon, and lung cancer (p<0.0001). The online analysis platform enables unrestricted mining of the database and is accessible at www.tnmplot.com.

bioinformatics↗

bioRxiv2020

Pharmacological inhibition of the ALK axis elicits therapeutic potential in Consensus Molecular Subtype 1 colon cancer patients

In the last years, several efforts have been made to classify colorectal cancer (CRC) into well-defined molecular subgroups, representing the intrinsic inter-patient heterogeneity, known as Consensus Molecular Subtypes (CMSs). In this work, we performed a meta-analysis of 1700 CRC patients stratified into four CMSs. We identified a negative correlation between a high level of anaplastic lymphoma kinase (ALK) expression and relapse-free survival, exclusively in CMS1 subtype. Stemming from this observation, we tested several CMSs in vitro models with crizotinib (CZB) or alectinib (ALC), potent ALK inhibitors, already approved for clinical use. ALK interception strongly inhibits cell proliferation already at nanomolar doses, specifically in CMS1 cell lines, while no effect was found in CMS2/3/4 groups. Furthermore, in vivo imaging identified a role for ALK in the dynamic formation of 3D spheroids, which was impaired by the pharmacological inhibition of ALK. Consistently, CZB was responsible for the dampened activation of ALK along with the downstream AKT cascade. Mechanistically, we found a specific pro-apoptotic effect of ALK inhibition in CMS1 cell lines, both in 2D and 3D. Confocal analysis suggests that inhibition in CMS1 cells enhances cell-cell adhesion when growing in 3D. In agreement with our findings, an ALK signature encompassing 65 genes statistically associated with worse relapse-free survival in CMS1 subtype. Finally, the efficacy of ALK inhibition treatment was demonstrated in patient-derived organoids. Collectively, our findings suggest that ALK inhibition may represent an attractive therapy for CRC, and CMS classification may provide a useful tool to identify patients who could benefit from this treatment. These findings offer rationale and pharmacological strategies for the treatment of CMS1 CRC.

cancer biology↗

bioRxiv2020

A meta-analysis of the inhibin network reveals prognostic value in multiple solid tumors

ABSTRACTInhibins and activins are dimeric ligands belonging to the TGFβ superfamily with emergent roles in cancer. Inhibins contain an α-subunit (INHA) and a β-subunit (either INHBA or INHBB), while activins are mainly homodimers of either βA (INHBA) or βB (INHBB) subunits. Inhibins are biomarkers in a subset of cancers and utilize the coreceptors betaglycan (TGFBR3) and endoglin (ENG) for physiological or pathological outcomes. Given the array of prior reports on inhibin, activin and the coreceptors in cancer, this study aims to provide a comprehensive analysis, assessing their functional prognostic potential in cancer using a bioinformatics approach. We identify cancer cell lines and cancer types most dependent and impacted, which included p53 mutated breast and ovarian cancers and lung adenocarcinomas. Moreover, INHA itself was dependent on TGFBR3 and ENG in multiple cancer types. INHA, INHBA, TGFBR3, and ENG also predicted patients’ response to anthracycline and taxane therapy in luminal A breast cancers. We also obtained a gene signature model that could accurately classify 96.7% of the cases based on outcomes. Lastly, we cross-compared gene correlations revealing INHA dependency to TGFBR3 or ENG influencing different pathways themselves. These results suggest that inhibins are particularly important in a subset of cancers depending on the coreceptor TGFBR3 and ENG and are of substantial prognostic value, thereby warranting further investigation.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cancer biology↗