Biology subjects

Vidales, E.

Publications and source records attributed to Vidales, E..

3 recordsLinked to original sources

bioRxiv2023

Reinstatement of CDX2 as a differentiation therapy for colorectal cancers

Despite advances in artificial intelligence (AI) within cancer research, its application toward realizing differentiation therapy in solid tumors remains limited. Using colorectal cancer (CRC) as a model, we developed a machine learning (ML) framework, CANDiT (Cancer Associated Nodes for Differentiation Targeting), to selectively induce differentiation and death of cancer stem cells (CSCs)--a key obstacle to durable response. Centering on one node, CDX2, a master differentiation factor lost in high-risk, poorly differentiated CRCs, we built a transcriptomic network to identify therapeutic strategies for CDX2 restoration. Network-based prioritization identified PRKAB1, a stress polarity sensor, as a top target. A clinical-grade PRKAB1 agonist reprogrammed transcriptional networks, induced crypt differentiation, and selectively eliminated CDX2-low CSCs in CRC cell lines, xenografts and patient-derived organoids (PDOs). Multivariate analyses in PDOs revealed a strong therapeutic index, linking efficacy (IC) to the biomarker-defined CDX2-low state. A 50-gene response signature--derived from an integrated analyses of all three models and trained across multiple datasets--revealed that CDX2 restoration therapy may translate into a [~]50% reduction in recurrence and mortality risk. Mechanistically, treatment activated a differentiation-associated stress polarity signaling axis while dismantling Wnt and YAP-driven stemness programs essential to CSC survival. Thus, CANDiT offers a scalable path to CSC-directed therapy in solid tumors by translating transcriptomic vulnerabilities into precision treatments. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=174 SRC="FIGDIR/small/557628v4_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@16eeb1eorg.highwire.dtl.DTLVardef@10e308borg.highwire.dtl.DTLVardef@9512f0org.highwire.dtl.DTLVardef@10e74eb_HPS_FORMAT_FIGEXP M_FIG C_FIG One sentence summaryIn this work, Sinha et al. introduce a machine learning-guided framework to identify and target transcriptomic vulnerabilities in colorectal cancer, demonstrating that differentiation therapy selectively eliminates cancer stem cells and reduces recurrence risk. HighlightsO_LIAn ML framework (CANDiT) identifies target for differentiation therapy for CRCs C_LIO_LITherapy induces crypt differentiation and CSC-specific cytotoxicity C_LIO_LICDX2-low state predicts therapeutic response; restoration improves prognosis C_LIO_LITherapy dismantles stemness via reactivation of stress polarity signaling C_LI

cancer biology↗

bioRxiv2023

A Living Organoid Biobank of Crohn's Disease Patients Reveals Molecular Subtypes for Personalized Therapeutics

ABSTRACT (Structured)Crohns disease (CD) is a complex, clinically heterogeneous disease of multifactorial origin; there is no perfect pre-clinical model, little insight into the basis for such heterogeneity, and still no cure. To address these unmet needs, we sought to explore the translational potential of adult stem cell-derived organoids that not only retain their tissue identity, but also their genetic and epigenetic disease-driving traits. We prospectively created a biobank of CD patient-derived organoid cultures (PDOs) using biopsied tissues from colons of 34 consecutive subjects representing all clinical subtypes (Montreal Classification B1-B3 and perianal disease). PDOs were generated also from healthy subjects. Comparative gene expression analyses enabled benchmarking of PDOs as tools for modeling the colonic epithelium in active disease and revealed that despite the clinical heterogeneity there are two major molecular subtypes: immune-deficient infectious-CD [IDICD] and stress and senescence-induced fibrostenotic-CD [S2FCD]. The transcriptome, genome and phenome show a surprising degree of internal consistency within each molecular subtype. The spectrum of morphometric, phenotypic, and functional changes within the "living biobank" reveals distinct differences between the molecular subtypes. These insights enabled drug screens that reversed subtype-specific phenotypes, e.g., impaired microbial clearance in IDICD was reversed using agonists for nuclear receptors, and senescence in S2FCD was rectified using senotherapeutics, but not vice versa. Phenotyped-genotyped CD-PDOs may fill the gap between basic biology and patient trials by enabling pre-clinical Phase 0 human trials for personalized therapeutics. GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/532245v1_ufig1.gif" ALT="Figure 1"> View larger version (58K): org.highwire.dtl.DTLVardef@1edcea0org.highwire.dtl.DTLVardef@198a3c8org.highwire.dtl.DTLVardef@28d394org.highwire.dtl.DTLVardef@5a9dbb_HPS_FORMAT_FIGEXP M_FIG C_FIG In BriefThis work creates a prospectively biobanked phenotyped-genotyped Crohns disease patient-derived organoids (CD-PDOs) as platforms for molecular subtyping of disease and for ushering personalized therapeutics. HIGHLIGHTSO_LIProspectively biobanked CD-organoids recapitulate the disease epithelium in patients C_LIO_LIThe phenome-transcriptome-genome of CD-organoids converge on two molecular subtypes C_LIO_LIOne subtype shows impaired microbial clearance, another increased cellular senescence C_LIO_LIPhenotyped-genotyped PDOs are then used for integrative and personalized therapeutics C_LI

immunology↗

bioRxiv2021

Artificial Intelligence-rationalized balanced PPARα/γ dual agonism resets the dysregulated macrophage processes in inflammatory bowel disease

A computational platform, the Boolean network explorer (BoNE), has recently been developed to infuse AI-enhanced precision into drug discovery; it enables querying and navigating invariant Boolean Implication Networks of disease maps for prioritizing high-value targets. Here we used BoNE to query an Inflammatory Bowel Disease (IBD)-map and prioritize a therapeutic strategy that involves dual agonism of two nuclear receptors, PPAR/{gamma}. Balanced agonism of PPAR/{gamma} was predicted to modulate macrophage processes, ameliorate colitis in network-prioritized animal models, reset the gene expression network from disease to health, and achieve a favorable therapeutic index that tracked other FDA-approved targets. Predictions were validated using a balanced and potent PPAR/{gamma}-dual agonist (PAR5359) in two pre-clinical murine models, i.e., Citrobacter rodentium-induced infectious colitis and DSS-induced colitis. Using a combination of selective inhibitors and agonists, we show that balanced dual agonism promotes bacterial clearance more efficiently than individual agonists, both in vivo and in vitro. PPARa is required and its agonism is sufficient to induce the pro-inflammatory cytokines and cellular ROS, which are essential for bacterial clearance and immunity, whereas PPARg-agonism blunts these responses, delays microbial clearance and induces the anti-inflammatory cytokine, IL10; balanced dual agonism achieved controlled inflammation while protecting the gut barrier and reversal of the transcriptomic network. Furthermore, dual agonism reversed the defective bacterial clearance observed in PBMCs derived from IBD patients. These findings not only deliver a macrophage modulator for use as barrier-protective therapy in IBD, but also highlight the potential of BoNE to rationalize combination therapy.

bioinformatics↗