Biology subjects

Bandyopadhyay, S.

Publications and source records attributed to Bandyopadhyay, S..

At least 19 recordsLinked to original sources

bioRxiv2026

A transcriptional signature of LKB1 functional loss defines a large, therapeutically addressable patient population across human cancers

Loss of the tumor suppressor LKB1 (STK11) drives metabolic reprogramming and immune evasion, but its clinical footprint is defined almost entirely by somatic mutation and deletion. Because emerging therapies aim to reactivate this pathway in structurally wild-type tumors, defining the full LKB1-deficient population (including tumors silenced by non-genomic mechanisms) is a prerequisite for patient selection. We derived a 30-gene transcriptional signature of LKB1 functional loss, training on genomically-defined STK11 loss in lung adenocarcinoma and validating in an independent squamous cohort (AUROC 0.926). It transferred to four independent non-TCGA LUAD cohorts (AUROC 0.92-0.98), outperforming the published Kaufman 16-gene classifier. Critically, restoring wild-type LKB1 in LKB1-mutant NSCLC lines reversed the signature while a kinase-dead mutant did not, establishing that it reads out LKB1 kinase function, not merely mutation status. Applied pan-cancer, the signature identified functional LKB1 loss in wild-type tumors at 7.2%, expanding total LKB1-loss prevalence 3.7-fold over genomic loss alone (2.7% [->] 9.9%), largest in esophageal, colorectal, endometrial and cutaneous cancers. This population is decoupled from LKB1 mutation and deletion frequency, yet enriched in BRAF-mutant colorectal (25% signature-positive) and HER2-amplified breast (21% signature-positive) cancers: readily-testable subgroups for screening. In lung adenocarcinoma, signature positivity rose across a co-mutation gradient from 6% background to 15% in KRAS-mutant, 35% in NRF2-pathway-mutant (KEAP1 or NFE2L2), to 64% in KRAS/NRF2 co-mutant tumors. Similar to KRAS/LKB1 co-mutant tumors, LKB1 wild-type but signature-high tumors were immune-cold, correlating negatively with antigen-presentation, interferon-{gamma} and T-cell-inflamed programs in multiple independent lung cancer cohorts. These results define a broad set of tumors with LKB1 functional loss that may open up new avenues for precision therapeutics.

cancer biology↗

bioRxiv2026

SRSA-VAE: Self-Attention-Based Feature Learning for Single-Cell Multimodal Clustering

Clustering plays a critical role in the analysis of single-cell omics data for identifying cellular heterogeneity and uncovering biological mechanisms. However, the high dimensionality, sparsity, and multimodal nature of single-cell datasets such as single-cell RNA sequencing (scRNA-seq) and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) pose significant challenges for effective feature learning and representation learning. Traditional dimensionality reduction methods often rely on linear transformations and fail to capture complex nonlinear relationships between gene and protein expression profiles. In this work, we propose SRSA-VAE, a scalable variational autoencoder framework that integrates a residual self-attention encoder for context-aware feature learning and multimodal representation learning. The proposed model dynamically contextualizes gene and protein representations through a self-attention mechanism, enabling the encoder to capture inter-cell relationships and emphasize biologically informative signals. A scalable residual connection further stabilizes training and preserves essential input information during latent representation learning. We evaluate SRSA-VAE on five large-scale publicly available single-cell datasets, including both scRNA-seq and CITE-seq data, and compare its performance with established deep generative models. Experimental results demonstrate that SRSA-VAE consistently outperforms existing methods in Adjusted Rand Index (ARI) across benchmark datasets, with particularly strong gains on complex immune cell populations. Ablation studies further confirm the importance of the self-attention mechanism and residual connection in enhancing model stability and clustering accuracy. The proposed model offers a generalizable, robust, and scalable solution for single-cell clustering tasks. Code Repositoryhttps://github.com/rangan2510/srsa-vae

bioinformatics↗

bioRxiv2026

MYC Overexpression Confers Sensitivity to TACC3 Inhibition for Triple-Negative Breast Cancer

The oncogenic transcription factor MYC is overexpressed across many cancer types, including triple-negative breast cancers (TNBCs), where it drives cell proliferation, chromosomal instability, and immune suppression. As MYC has proven difficult to target directly, identifying vulnerabilities created by MYC-driven mitotic rewiring is of significant therapeutic interest. Using a focused pharmacological screen of mitosis-related targets that are overexpressed in the context of MYC signaling, we identified several previously unrecognized dependencies on mitotic spindle proteins, including KIF18A and TACC3 in MYCHIGH cells. We focused on TACC3 and found the small molecule inhibitor BO-264 induced pronounced micronuclei formation, cell cycle arrest, transcriptional reactivation of inflammatory signaling, and heightened cytotoxicity in MYCHIGH models. TACC3 inhibition activates interferon-stimulated response element (ISRE) mediated inflammatory signaling and can activate the cGAS-STING pathway in a subset of triple-negative breast cancer contexts. Together, these findings identify TACC3 as a MYC-driven mitotic vulnerability. We highlight TACC3 inhibition as a promising strategy to impair tumor cell fitness and activate tumor-intrinsic inflammatory responses in MYC-driven cancers.

cancer biology↗

bioRxiv2026

Altered chromatin accessibility and nucleosome positioning landscape upon HDAC and LSD1 inhibition in cancer cell

Epigenetic enzymes, writers, readers and erasers regulate chromatin landscapes and participate in tumor heterogeneity. While therapeutic targeting of these enzymes has shown clinical promise, the comparative efficacy of mono-versus dual-inhibitor strategies remain unclear. Here, we introduce a multi-modal platform that uses NicE-viewSeq and integrates automated deep learning based spatially resolved chromatin accessibility profiling with high-throughput sequencing following epigenetic inhibitor application. Accessible chromatin landscapes were altered along with nucleosome positioning following inhibition of either LSD1 or HDACs alone, or both together. Coordinated modulation of histone marks and the CoREST complex on chromatin was observed across inhibitory conditions. Transcription factor binding analysis identified three predominant families, ETS, RUNT, and bZIP with enhanced chromatin association upon treatments. Mechanistically, a CoREST-RUNX regulatory axis was uncovered wherein JunB, a member of bZIP family displaces CoREST-RUNX at differentially accessible regions, triggering apoptotic pathways. Therefore, JunB-mediated mechanism reveals a convergent therapeutic vulnerability, offering new avenues for optimizing different combinatorial epigenetic therapy in cancer.

genetics↗

bioRxiv2026

RSTG: Robust Generation of High Quality Spatial Transcriptomics Data using Beta Divergence Based AutoEncoder

One of the key challenges in spatial transcriptomics data analysis is the lack of sufficient data to train models. To address this shortcoming, multiple generative models have been developed to generate synthetic spatial transcriptomics samples in a controlled environment. However, these models often fail in out-of-the-box generation in the presence of noise (such as outliers). To tackle this challenge, we propose RSTG (Robust Spatial Transcriptomic Generator), an autoencoder that incorporates the {beta}-ELBO loss, to generate realistic and high-quality spatial transcriptomic sequences. Our model uncovers data intrinsic structure by approximating its underlying distribution through variational inference, resulting in more interpretable and robust density estimation. We validate the effectiveness of RSTG across multiple tasks, including the recovery of cellular positions in both the 2D spatial and location domains. Our method shows improved performance, both qualitatively and quantitatively, across multiple datasets, including brain and liver samples generated using MERFISH, MERSCOPE, and Visium technologies. We further illustrate the robustness of RSTG to outliers by contaminating a portion of the data with anomalies (such as white noise, batch effects, and dropouts) as well as on a reallife degraded sample. The results show that our proposal maintains high quality and stability even when the training data are contaminated, across a variety of experimental settings and in comparison with existing approaches.

bioinformatics↗

bioRxiv2025

Dual Preconditioned Mesenchymal Stem Cells-Derived Culture Conditioned Media Augment Immunomodulation and Drive Metabolic Reprogramming in Acute Graft-versus-Host Disease

BackgroundThe toxicity associated with conventional conditioning regimens limits treatment outcomes in autoimmune disorders such as acute graft-versus-host disease (aGVHD). Developing effective adjunctive interventions to restore immune balance is therefore essential. This study investigated the immunomodulatory potential of mesenchymal stem cells (MSCs)-derived culture-conditioned media (CCM) from naive and preconditioned MSCs-hypoxic (MSCsHYP), apoptotic (MSCsAPO), and dual preconditioned (MSCsHYP+APO) in aGVHD. MethodsHuman MSCs isolated from bone marrow and Whartons Jelly were preconditioned under hypoxia (1% O2), apoptosis (1 {micro}M staurosporine, 24 h), or both. The immunoregulatory and antioxidant properties of their CCM were evaluated through T-cell proliferation assays, Treg induction, macrophage polarization, mitochondrial function, and T-cell bioenergetics. Comparative proteomic profiling of CCM from WJ-MSCs and WJ-MSCsHYP+APO co-cultured with aGVHD patient-derived activated PBMNCs was performed using LC-MS/MS, alongside in vivo validation in a chemotherapy-induced aGVHD murine model. ResultsWJ-MSCsHYP+APO-CCM exhibited superior immunomodulatory efficacy, suppressing T-cell proliferation, promoting Treg and Th2/Th9 differentiation, and driving M2 macrophage polarization. It reduced mitochondrial ROS, enhanced mitochondrial polarization, and shifted T-cell metabolism from glycolysis toward oxidative phosphorylation. Proteomic analysis revealed modulation of IL-12, IL-17, and JAK-STAT pathways, along with regulation of complement, coagulation, and metabolic cascades. Interaction with immune cells further enhanced its antioxidant and tissue-reparative properties via extracellular matrix remodeling. ConclusionDual preconditioning under hypoxia and apoptosis amplifies the immunomodulatory, antioxidant, and reparative efficacy of WJ-MSC-CCM, offering a potent non-cellular therapeutic strategy for aGVHD management. GRAPHICAL ABSTRACTImmunomodulatory and immune metabolic reprogramming potential of naive and preconditioned MSCs (MSCs, MSCsHYP, MSCsAPO, MSCsHYP+APO) derived CCM in Acute GVHD (created using Biorender.com) O_FIG O_LINKSMALLFIG WIDTH=95 HEIGHT=200 SRC="FIGDIR/small/686182v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@f7ed73org.highwire.dtl.DTLVardef@1063015org.highwire.dtl.DTLVardef@13ed827org.highwire.dtl.DTLVardef@e103c0_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Hypoxia-Mediated Molecular Interactions of Tissue-Specific Mesenchymal Stem Cells Drive Metabolic Reprogramming and Immunomodulation in Acute Graft-versus-Host Disease

BackgroundMesenchymal stem cells (MSCs) mediate immunomodulation through various mechanisms, including apoptosis, efferocytosis, and mitochondrial transfer. Our study investigates the impact of hypoxia preconditioning on the immune metabolic reprogramming and immunomodulatory potential of MSCs in acute graft-versus-host disease (aGVHD). Additionally, we explored the differential immunomodulatory effects of tissue-specific MSCs, specifically bone marrow (BM) and Whartons Jelly (WJ), and elucidated the mechanisms underlying variability in their therapeutic efficacy. MethodsMSCs were isolated from BM and WJ and subjected to hypoxia preconditioning. Their immunometabolic programming potential was assessed by evaluating T-cell proliferation, regulatory T-cell (Treg) induction, effector T-cell differentiation toward Th2, Th9 phenotypes, and macrophage polarization, T-cell bioenergetics in the direct co-culture systems. ResultsWJ-MSCsHYP exhibited superior immunomodulatory properties compared to BM-MSCsHYP, by inhibiting T-cell proliferation, enhancing Treg induction, and promoting anti-inflammatory macrophage polarization. WJ-MSCsHYP demonstrated enhanced mitochondrial transfer to T-cell, improving mitochondrial health, reducing ROS, and promoting oxidative phosphorylation, leading to immune homeostasis. Unlike BM-MSCs, WJ-MSCs exhibited higher rates of apoptosis, which facilitated immune modulation through mechanisms independent of efferocytosis. ConclusionOur findings highlight that WJ-MSCsHYP is a superior candidate for aGVHD by utilizing apoptosis, mitochondrial transfer, and metabolic reprogramming to achieve immune regulation.

immunology↗

bioRxiv2024

BRDKRM: An Explainable Framework for Disease Modifying Drug Identification

Drug classification into disease-modifying (DM) and symptomatic (SYM) categories is crucial for clinical decision-making and therapeutic strategy development. To address the limitations of current methods, which often lack transparency and interpretability, we propose the Boundary Restricted Dynamic Key Route Mapping (BRDKRM) framework. This novel approach leverages the contextual overlap between disease and drug nodes in a heterogeneous graph, aggregating genes from the top K shortest paths to delineate disease neighborhood boundaries. Inspired by the classic Hansel and Gretel folklore, BRDKRM metaphorically marks boundary nodes along metapaths from disease to drug, akin to Hansel s breadcrumbs, which are then used to classify the therapeutic effect of candidate drugs. Our method achieved 86.78% accuracy in categorizing drug-disease treatments and identified 530 genes involved in both disease modification and symptomatic relief. The efficacy of BRDKRM is demonstrated through case studies on multiple sclerosis, offering an explainable approach to drug classification that bypasses extensive clinical trials. By providing biologically sound interpretations of drug classifications, our framework enhances understanding of therapeutic interventions, paving the way for more precise and efficient healthcare solutions while offering a novel approach to mapping disease-drug interactions.

bioinformatics↗

bioRxiv2024

IS-PRM-based peptide targeting informed by long-read sequencing for alternative proteome detection

Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of pre-defined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (LR RNAseq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNAseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This LR RNA seq-informed Tomahto targeted approach, called LRP-IS-PRM, is a new modality for generating protein-level evidence of alternative isoforms - a critical first step in designing functional studies and eventually clinical assays.

molecular biology↗

bioRxiv2024

Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics and Proteomic Imaging

The bone marrow is the organ responsible for blood production. Diverse non-hematopoietic cells contribute essentially to hematopoiesis. However, these cells and their spatial organization remain largely uncharacterized as they have been technically challenging to study in humans. Here, we used fresh femoral head samples and performed single-cell RNA sequencing (scRNA-Seq) to profile 29,325 enriched non-hematopoietic bone marrow cells and discover nine transcriptionally distinct subtypes. We next employed CO-detection by inDEXing (CODEX) multiplexed imaging of 18 individuals, including both healthy and acute myeloid leukemia (AML) samples, to spatially profile over one million single cells with a novel 53-antibody panel. We discovered a relatively hyperoxygenated arterio-endosteal niche for early myelopoiesis, and an adipocytic, but not endosteal or perivascular, niche for early hematopoietic stem and progenitor cells. We used our atlas to predict cell type labels in new bone marrow images and used these predictions to uncover mesenchymal stromal cell (MSC) expansion and leukemic blast/MSC-enriched spatial neighborhoods in AML patient samples. Our work represents the first comprehensive, spatially-resolved multiomic atlas of human bone marrow and will serve as a reference for future investigation of cellular interactions that drive hematopoiesis.

cell biology↗

bioRxiv2024

A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma

Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities to adult glioma, it is increasingly recognized as a molecularly distinct, yet highly heterogeneous disease. In this study, we longitudinally profiled a molecularly diverse cohort of 16 pHGG patients before and after standard therapy through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of the tumor microenvironment during progression following treatment. We found that the canonical neoplastic cell phenotypes of adult glioblastoma are insufficient to capture the range of tumor cell states in a pediatric cohort and observed differential tumor-myeloid interactions between malignant cell states. We identified key transcriptional regulators of pHGG cell states and did not observe the marked proneural to mesenchymal shift characteristic of adult glioblastoma. We showed that essential neuromodulators and the interferon response are upregulated post-therapy along with an increase in non-neoplastic oligodendrocytes. Through in vitro pharmacological perturbation, we demonstrated novel malignant cell-intrinsic targets. This multiomic atlas of longitudinal pHGG captures the key features of therapy response that support distinction from its adult counterpart and suggests therapeutic strategies which are targeted to pediatric gliomas.

cancer biology↗

bioRxiv2023

Sex and age independent predictive call sequence alterations of pups in an autism spectrum disorder mouse model

Social communication deficit is a hallmark of autism spectrum disorders (ASDs). Mouse ultrasonic-vocalizations (USVs), with communicative significance, are extensively used to probe vocalization-based social communication impairment. Despite the predictable nature of mouse USVs, very few studies have taken advantage of the same. The current work explores USV pup-isolation-call (PIC) features and alterations in structural content of predictive PIC sequences of the well-established in-utero valproic-acid (VPA) exposure-based ASDs model. Our study shows that along with call features, even higher-order USV structures undergo alterations in the ASDs model at all developmental ages and sexes. Confirming prior observations, we found reduced call rates and durations, as well as heightened peak frequencies in ASD model pups. Our data also highlights trends in call features, syllable composition, and transitions across sexes and age. The ASD female mice exhibited higher within group heterogeneity in syllable composition and transition over age compared to ASD males or typically developing males and females. Analysis of sequences of USVs emitted by pups using mutual information between syllables at different positions revealed that dependencies between syllables were higher in typically developing mice of both sexes compared to ASD model pups. In brief, we found that PICs call features were altered in VPA mouse models both for male and female pups and their vocalizations lack the complex syllable sequence order emitted by typically developing ones. Our studies will help establish and further investigate ASD mouse models to get a clearer picture of abnormalities related to social communication deficits over sexes and age.

neuroscience↗

bioRxiv2023

Computational drug repositioning for the identification of new agents to sensitize drug-resistant breast tumors across treatments and receptor subtypes

Drug resistance is a major obstacle in cancer treatment and can involve a variety of different factors. Identifying effective therapies for drug resistant tumors is integral for improving patient outcomes. In this study, we applied a computational drug repositioning approach to identify potential agents to sensitize primary drug resistant breast cancers. We extracted drug resistance profiles from the I-SPY 2 TRIAL, a neoadjuvant trial for early stage breast cancer, by comparing gene expression profiles of responder and non-responder patients stratified into treatments within HR/HER2 receptor subtypes, yielding 17 treatment-subtype pairs. We then used a rank-based pattern-matching strategy to identify compounds in the Connectivity Map, a database of cell line derived drug perturbation profiles, that can reverse these signatures in a breast cancer cell line. We hypothesize that reversing these drug resistance signatures will sensitize tumors to treatment and prolong survival. We found that few individual genes are shared among the drug resistance profiles of different agents. At the pathway level, however, we found enrichment of immune pathways in the responders in 8 treatments within the HR+HER2+, HR+HER2-, and HR-HER2-receptor subtypes. We also found enrichment of estrogen response pathways in the non-responders in 10 treatments primarily within the hormone receptor positive subtypes. Although most of our drug predictions are unique to treatment arms and receptor subtypes, our drug repositioning pipeline identified the estrogen receptor antagonist fulvestrant as a compound that can potentially reverse resistance across 13/17 of the treatments and receptor subtypes including HR+ and triple negative. While fulvestrant showed limited efficacy when tested in a panel of 5 paclitaxel-resistant breast cancer cell lines, it did increase drug response in combination with paclitaxel in HCC-1937, a triple negative breast cancer cell line.

cell biology↗

bioRxiv2023

HBV rewires liver cancer signaling by altering PP2A complexes

Infection by hepatitis B virus (HBV) increases risk for liver cancer by inducing inflammation, cellular stress and cell death. To elucidate the molecular pathways by which HBV promotes cancer development and progression, we used affinity purification mass spectrometry to comprehensively map a network of 145 physical interactions between HBV and human host proteins in hepatocellular carcinoma (HCC). We find that viral proteins target host factors that are preferentially mutated in non-HBV-associated HCC, implicating cancer pathways whose interaction with HBV plays a role in HCC. Focusing on proteins that directly interact with the HBV oncoprotein X (HBx), we show that HBx remodels the PP2A phosphatase complex, altering its effect on tumor signaling. HBx excludes striatin-family regulatory subunits from PP2A, causing Hippo kinase activation and unmasking a requirement for mTOR complex 2 to maintain expression of the YAP oncoprotein in HCC. Thus, HBV rewires HCC to expose potentially targetable signaling dependencies. SignificancePrecision medicine has revolutionized cancer treatment but remains elusive for HCC. We used proteomics to define HBV/host interactions and integrated them with HCC mutations. The results implicate modifiers of HCC behavior via remodeling of host complexes and illuminate new biological mechanisms in advanced disease for therapeutic investigation.

cancer biology↗

bioRxiv2021

A copula based topology preserving graph convolution network for clustering of single-cell RNA-seq data

Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. There are various issues in single cell sequencing that effect homogeneous grouping (clustering) of cells, such as small amount of starting RNA, limited per-cell sequenced reads, cell-to-cell variability due to cell-cycle, cellular morphology, and variable reagent concentrations. Moreover, single cell data is susceptible to technical noise, which affects the quality of genes (or features) selected/extracted prior to clustering. Here we introduce sc-CGconv (copula based graph convolution network for single cell clustering), a stepwise robust unsupervised feature extraction and clustering approach that formulates and aggregates cell-cell relationships using copula correlation (Ccor), followed by a graph convolution network based clustering approach. sc-CGconv formulates a cell-cell graph using Ccor that is learned by a graph-based artificial intelligence model, graph convolution network. The learned representation (low dimensional embedding) is utilized for cell clustering. sc-CGconv features the following advantages. a. sc-CGconv works with substantially smaller sample sizes to identify homogeneous clusters. b. sc-CGconv can model the expression co-variability of a large number of genes, thereby outperforming state-of-the-art gene selection/extraction methods for clustering. c. sc-CGconv preserves the cell-to-cell variability within the selected gene set by constructing a cell-cell graph through copula correlation measure. d. sc-CGconv provides a topology-preserving embedding of cells in low dimensional space. The source code and usage information are available at https://github.com/Snehalikalall/CopulaGCN Contact: sumanta.ray@cwi.nl

bioinformatics↗

bioRxiv2021

Social experience dependent plasticity of mouse song selectivity without that of song components

Syllable sequences in male mouse ultrasonic-vocalizations (USVs), "songs", contain structure -quantified through predictability, like birdsong and aspects of speech. Apparent USV innateness and lack of learnability, discount mouse USVs for modelling speech-like social communication and its deficits. Informative contextual natural sequences (SN) were theoretically extracted and they were preferred by female mice. Primary auditory cortex (A1) supragranular neurons show differential selectivity to the same syllables in SN and random sequences (SR). Excitatory neurons (EXNs) in females showed increases in selectivity to whole SNs over SRs based on extent of social exposure with male, but syllable selectivity remained unchanged. Thus mouse A1 single neurons adaptively represent entire order of acoustic units without altering selectivity of individual units, fundamental to speech perception. Additionally, observed plasticity was replicated with silencing of somatostatin positive neurons, which had plastic effects opposite to EXNs, thus pointing out possible pathways involved in perception of sound sequences.

neuroscience↗

bioRxiv2021

A focal adhesion kinase-YAP signaling axis drives drug tolerant persister cells and residual disease in lung cancer

Targeted therapy is effective in many tumor types including lung cancer, the leading cause of cancer mortality. Paradigm defining examples are targeted therapies directed against non-small cell lung cancer (NSCLC) subtypes with oncogenic alterations in EGFR, ALK and KRAS. The success of targeted therapy is limited by drug-tolerant tumor cells which withstand and adapt to treatment and comprise the residual disease state that is typical during treatment with clinical targeted therapies. Here, we integrate studies in patient-derived and immunocompetent lung cancer models and clinical specimens obtained from patients on targeted therapy to uncover a focal adhesion kinase (FAK)-YAP signaling axis that promotes residual disease during oncogenic EGFR-, ALK-, and KRAS-targeted therapies. FAK-YAP signaling inhibition combined with the primary targeted therapy suppressed residual drug-tolerant cells and enhanced tumor responses. This study unveils a FAK-YAP signaling module that promotes residual disease in lung cancer and mechanism-based therapeutic strategies to improve tumor response.

cancer biology↗

bioRxiv2021

Targeting the Cx26/NANOG/Focal Adhesion Kinase Complex via Cell Penetrating Peptides in Triple Negative Breast Cancers

Triple-negative breast cancer (TNBC) represents the most lethal and treatment-resistant breast cancer subtype with limited treatment options. We previously identified a protein complex unique to TNBC cancer stem cells composed of the gap junction protein connexin 26 (Cx26), the pluripotency transcription factor NANOG, and focal adhesion kinase (FAK). We sought to determine whether a peptide mimetic of Cx26 designed to target the complex attenuated tumor growth in pre-clinical models. Histological assessment was employed to verify expression of complex members. We designed peptides based on Cx26 juxtamembrane domains and performed binding experiments with NANOG and FAK using surface plasmon resonance. Peptides with high affinity were engineered with a cell-penetrating sequence and assessed in functional assays including cell proliferation, self-renewal, and in vivo tumor growth, and downstream signaling changes were measured. Binding studies revealed that the Cx26 C-terminal tail and intracellular loop bound to NANOG and FAK with submicromolar-to-micromolar affinity and that a 5-amino acid sequence in the C-terminal tail of Cx26 (RYCSG) was sufficient for binding. The Cx26 C-terminal tail was tagged with an antennapedia cell-penetrating peptide sequence and intracellular localization was confirmed. The cell-penetrating Cx26 peptide (aCx26-pep) disrupted self-renewal as assessed by tumorsphere formation assay while reducing nuclear FAK and NANOG and inhibiting NANOG target gene expression in TNBC cells but not luminal mammary epithelial cells. In vivo, aCx26-pep reduced tumor growth and proliferation and induced cell death. We provide proof-of-concept that a Cx26 peptide-based strategy inhibits growth and alters NANOG activity in TNBC.

cancer biology↗