Biology subjects

Chatterjee, N.

Publications and source records attributed to Chatterjee, N..

At least 19 recordsLinked to original sources

bioRxiv2025

REV1 inhibition enhances trinucleotide repeat mutagenesis

Trinucleotide repeat (TNR) instability has been implicated in the pathogenesis of numerous neurodegenerative disorders. Because TNR instability causes mutagenesis of the underlying gene, we refer to the repeat instability phenomenon as TNR mutagenesis in this study. While germline expansions destabilize TNR to cause disease anticipation, somatic cell TNR instability drives earlier onset of symptoms and further disease progression. However, the drivers behind these repeat length changes remain unclear. Current models suggest that DNA replication slippage events and the action of genome instability pathways, such as DNA repair, cause TNR mutagenesis. Whether mutagenic polymerases from the translesion synthesis (TLS) pathway result in TNR instability is unclear. TLS polymerases are best at bypassing difficult-to-replicate DNA regions due to bulky lesions or gaps in DNA. While some effects of TLS polymerases on TNR instability have been explored in lower organisms, evidence in human cells is lacking. Using a quantitative GFP reporter with expanded CAG repeats, we show that inhibition of the TLS polymerase REV1 by its inhibitor, JH-RE-06, or siRNA knockdown increases TNR instability and the underlying mutability. These results suggest that REV1 protects Trinucleotide repeat length mutagenesis through potential continuous DNA synthesis when replicative polymerases stall ahead of repeat secondary structures. Collectively, we present evidence of the role of the TLS pathway in TNR instability, with potential implications for understanding mutability mechanisms, disease biology, and therapeutic targeting.

genetics↗

bioRxiv2025

Of Shared Homes and Pathways: Free-Ranging Dog Movement and Habitat Use in a Human-Wildlife Landscape in India

BackgroundFree-ranging dogs are widely considered to have negative impacts on wildlife in shared landscapes. Understanding their use of these landscapes is therefore essential to develop effective management strategies in wildlife-adjacent regions. In this study, we investigated the movement patterns of free-ranging dogs in the protected area matrix of Mudumalai Tiger Reserve (MTR) in Southern India. MethodsFifteen owned free-ranging dogs (FRD) were collared from two adjacent villages with different levels of human habitation. We hypothesized that increased settlement cover would provide dogs with a greater human shield effect, allowing them more freedom of movement. We estimated dog activity ranges and movement patterns using autocorrelated kernel density estimates (AKDE). Large-scale movement was characterized through multiple metrics (intensity of space use and mean distance from home) and compared across villages to understand underlying drivers. Lastly, we quantified fine-scale movement and habitat selection using integrated step selection analysis (iSSA). ResultsWe found that FRD in MTR had directionally dependent home-ranging behavior and small activity ranges, with the mean AKDE activity range being 9.88 {+/-} 7.69 ha (median = 6.09 ha, range 3.28-26.16 ha), and primarily utilized the area within 500 m of their homes. None of the movement metrics varied significantly between villages, except for intensity of use, which was higher in one village than the other, suggesting that dogs in the less populated village perceived a greater threat from surrounding wildlife and were more driven to seek refuge inside their activity ranges. iSSA revealed that dogs selected strongly for human settled or human- modified habitats, and moved significantly faster in forest land than any other habitat. ConclusionsOur findings indicate that owned FRD pose a relatively lower threat to wildlife in MTR than feral dogs may. They also support the human shield hypothesis, show that FRD display behavioral plasticity at fine scales of <5km, and highlight the role of human land use intensity in shaping the movement of domestic dogs. As humans can thus mediate potential effects of dogs on wildlife, anthropogenic factors should be taken in consideration when designing management strategies that aim to curb dog movement.

ecology↗

bioRxiv2024

Harnessing Environmental DNA (eDNA) to Explore Frugivorous Interactions: A Case Study in Papaya (Carica papaya) and Pineapple (Ananas comosus)

Plant-animal interactions (PAIs) are critical in ecosystem function, mediating energy flow and species interactions. Traditional methods of tracking PAIs, such as morphological identification and camera trapping, are limited in speed and scalability, posing challenges for comprehensive biodiversity monitoring. Recently, environmental DNA (eDNA) metabarcoding has emerged as a promising technique for detecting species interactions non-destructively. This pilot study explores the application of eDNA metabarcoding to investigate interactions involving partially consumed and intact fruits of Carica papaya and Ananas comosus. eDNA metabarcoding were performed from of 36 partially consumed and 6 intact fruit samples. Metabarcoding of mitochondrial COI gene fragments revealed a diverse range of taxa, with Arthropoda, particularly insects, being the most abundant. Results indicated significant differences in taxonomic composition between pineapple and papaya samples, where both the fruit hold some unique as well as shared taxa. Furthermore, the diversity also differed between consumed and intact fruits, suggesting that partially consumed fruits serve as rich eDNA sources, capturing interactions with various frugivores and decomposers. Signal from various organisms detected through eDNA metabarcoding from consumed and damaged fruits allowed us to capture a wide array of taxa, revealing insights into species composition and ecological relationships. The unique ASVs associated with each fruit type suggest that certain taxa may showing preferences based on fruit characteristics such as sugar content, texture, or chemical profile. Present work highlighted the importance of eDNA based methods in unraveling the taxonomic composition of fruit-associated plant-animal interactions. This method needs limited taxonomic expertise, less labors, fast and effective, which can be implemented in monitoring ecological and economical species interactions.

ecology↗

bioRxiv2024

Antagonistic activity of AEA on TAT treated Human Astrocytes identify Inflammaging pathways

Physiologically, the endocannabinoids are known to reduce inflammation by decreasing production of inflammatory factors in immune and glial cells. Astrocytes secrete soluble inflammatory mediators and prolonged activation of astrocytes is associated with accelerated aging in Central Nervous System. Many reports show miRNAs as critical gene regulators in inflammation and astrogliosis in astrocytes. The aim of this study is to investigate the microRNA changes affected by Anandamide on HIV1 TAT (Trans-activator of transcription protein) stimulated normal human astrocytes. We performed global microRNA profile in TAT activated astrocytes and analysed changes on exposure to AEA. To delineate the mechanism of action we assessed with bioinformatic tools miRWalk, KEGG and Cytoscape the global microarray for significantly impacted miRNAs, and their gene targets. TAT activation in astrocytes upregulated 122 miRNAs significantly (p < 0.05). Addition of AEA in activated astrocytes downregulate the expression of 57 miRNAs significantly. Out of 122 upregulated miRNAs on TAT treatment, 37 miRNAs which were common in TAT and TAT+AEA cells showed reversal suggesting clues to critical miRNAs for the AEA-induced mitigation of neuroinflammation. Reversal in expression of selected group of miRNAs identify antagonistic pathways which are promoting anti-inflammatory environment. Pathway analysis of key 37 miRNAs show gene targets that regulate inflammation and senescence.

immunology↗

bioRxiv2024

Perceived and observed biases within scientific communities: a case study in movement ecology

Who conducts biological research, where, and how the results are disseminated varies among geographies and identities. Identifying and documenting these forms of bias by research communities is a critical first step towards addressing them. We documented perceived and observed biases in movement ecology. Movement ecology is a rapidly expanding sub-discipline of biology, which is strongly underpinned by fieldwork and technology use. First, we surveyed attendees of an international conference, and discussed the results at the conference (comparing uninformed vs informed perceived bias). Although most researchers identified as bias-aware, only a subset of biases were discussed in conversation. Next, by considering author affiliations from publications in the journal Movement Ecology, we found among-country discrepancies between the country of the authors affiliation and study site location related to national economics. At the within-country scale, we found that race-gender identities of postgraduate biology researchers in the USA differed from national demographics. We discuss the role of potential specific causes for the emergence of bias in the sub-discipline, e.g. parachute-science or accessibility to fieldwork. Undertaking data-driven analysis of bias within research sub-disciplines can help identify specific barriers and first steps towards the inclusion of a greater diversity of participants in the scientific process.

ecology↗

bioRxiv2023

Modeling individual variability in habitat selection and movement using integrated step-selection analyses

1. Integrated step-selection analyses (ISSAs) are frequently used to study habitat selection using animal movement data. Methods for incorporating random effects in ISSAs have been developed, making it possible to quantify variability among animals in their space-use patterns. Although it is possible to model variability in both habitat selection and movement parameters, applications to date have focused on the former despite the widely acknowledged and important role that movement plays in determining ecological processes from the individual to ecosystem level. One potential explanation for this omission is the absence of readily-available software or examples demonstrating methods for estimating movement parameters in ISSAs with random effects. 2. We demonstrated methods for characterizing among-individual variability in both movement and habitat-selection parameters using a simulated data set and by fitting two models to an acoustic telemetry data set containing locations of 35 red snapper (Lutjanus campechanus). Movement kernels were assumed to depend on either the type of benthic reef habitat in which the fish was located (model 1) or the distance between the fishs current location and nearest edge habitat (model 2). In both models, we also quantified habitat selection for different benthic habitat classes and distance to edge habitat, and we allowed for individual variability in movement and habitat-selection parameters using random effects. 3. The simulation example highlights the benefits of a mixed effects specification, namely we can increase precision when estimating individual-specific movement parameters by borrowing information across like individuals. In our applied example, we found substantial among-individual variability in both habitat selection and movement parameters. Nonetheless, most red snapper selected for hardbottom habitat and for locations nearer to edge habitat. They also moved less when in hardbottom habitat. Turn angles were frequently near {+/-}{pi}, but were more dispersed when fish were far away from edge habitat. 4. We provide code templates and functions for quantifying variability in movement and habitat-selection parameters when implementing ISSAs with random effects. In doing so, we hope to encourage ecologists conducting ISSAs to take full advantage of their ability to model among-individual variability in both habitat-selection and movement patterns.

ecology↗

bioRxiv2023

ME-Bayes SL: Enhanced Bayesian Polygenic Risk Prediction Leveraging Information across Multiple Ancestry Groups

Polygenic risk scores (PRS) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across different populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in the summary statistics from genome-wide association studies (GWAS) across multiple ancestry groups. MUSSEL conducts Bayesian hierarchical modeling under a MUltivariate Spike-and-Slab model for effect-size distribution and incorporates an Ensemble Learning step using super learner to combine information across different tuning parameter settings and ancestry groups. In our simulation studies and data analyses of 16 traits across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. The method, for example, has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African Ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, underlying trait architecture, and the choice of reference samples for LD estimation, and thus ultimately, a combination of methods may be needed to generate the most robust PRS across diverse populations.

genetics↗

bioRxiv2023

An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of [L]1 (lasso) and [L]2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

genetics↗

bioRxiv2023

Joint Modeling of Gene-Environment Correlations and Interactions using Polygenic Risk Scores in Case-Control Studies

Polygenic risk scores (PRS) are rapidly emerging as aggregated measures of disease-risk associated with many genetic variants. Understanding the interplay of PRS with environmental factors is critical for interpreting and applying PRS in a wide variety of settings. We develop an efficient method for simultaneously modeling gene-environment correlations and interactions using PRS in case-control studies. We use a logistic-normal regression modeling framework to specify the disease risk and PRS distribution in the underlying population and propose joint inference across the two models using the retrospective likelihood of the case-control data. Extensive simulation studies demonstrate the flexibility of the method in trading-off bias and efficiency for the estimation of various model parameters compared to the standard logistic regression or a case-only analysis for gene-environment interactions, or a control-only analysis for gene-environment correlations. Finally, using simulated case-control datasets within the UK Biobank study, we demonstrate the power of the proposed method for its ability to recover results from the full prospective cohort for the detection of an interaction between long-term oral contraceptive use and PRS on the risk of breast cancer. This method is computationally efficient and implemented in a user-friendly R package.

genetics↗

bioRxiv2023

Incorporating Radiopacity into Implantable Polymeric Biomedical Devices for Clinical Radiological Monitoring

Longitudinal radiological monitoring of biomedical devices is increasingly important, driven by risk of device failure following implantation. Polymeric devices are poorly visualized with clinical imaging, hampering efforts to use diagnostic imaging to predict failure and enable intervention. Introducing nanoparticle contrast agents into polymers is a potential method for creating radiopaque materials that can be monitored via computed tomography. However, properties of composites may be altered with nanoparticle addition, jeopardizing device functionality. This, we investigated material and biomechanical response of model nanoparticle-doped biomedical devices (phantoms), created from 0-40wt% TaOx nanoparticles in polycaprolactone, poly(lactide-co-glycolide) 85:15 and 50:50, representing non-, slow and fast degrading systems, respectively. Phantoms degraded over 20 weeks in vitro, in simulated physiological environments: healthy tissue (pH 7.4), inflammation (pH 6.5), and lysosomal conditions (pH 5.5), while radiopacity, structural stability, mechanical strength and mass loss were monitored. The polymer matrix determined overall degradation kinetics, which increased with lower pH and higher TaOx content. Importantly, all radiopaque phantoms could be monitored for a full 20-weeks. Phantoms implanted in vivo and serially imaged, demonstrated similar results. An optimal range of 5-20wt% TaOx nanoparticles balanced radiopacity requirements with implant properties, facilitating next-generation biomedical devices.

bioengineering↗

bioRxiv2022

Genome-Wide Large-Scale Multi-Trait Analysis Characterizes Global Patterns of Pleiotropy and Unique Trait-Specific Variants

Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, an extension of the method ASSET, to allow computationally efficient detection of variant-level pleiotropic association across a large number of traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the NIH GRASP repository and a number of other large GWAS consortia. We identify a total of 2,293 independent loci at the genome-wide significance level and found that the lead variants in nearly all of these loci ([~]99%) to be associated with to two or more (median = 6) traits. Further, the estimated degree of pleiotropy for the detected variants strongly predicted their degree of pleiotropy across a much larger number of traits (K=4,114) in the UK Biobank Study. Follow-up analyses of 21 unique trait-specific variants suggest that they are often linked to the expression in trait-related tissues for a small number of genes, some of which are well known to be involved in relevant biological processes. Our findings provide deeper insight into the nature of complex trait pleiotropy and leads to, for the first time, identification of highly unique trait-specific susceptibility variants.

genetics↗

bioRxiv2022

Novel Methods for Multi-ancestry Polygenic Prediction and their Evaluations in 3.7 Million Individuals of Diverse Ancestry

Polygenic risk scores (PRS) increasingly predict complex traits, however, suboptimal performance in non-European populations raise concerns about clinical applications and health inequities. We developed CT-SLEB, a powerful and scalable method to calculate PRS using ancestry-specific GWAS summary statistics from multi-ancestry training samples, integrating clumping and thresholding, empirical Bayes and super learning. We evaluate CT-SLEB and nine-alternatives methods with large-scale simulated GWAS ([~]19 million common variants) and datasets from 23andMe Inc., the Global Lipids Genetics Consortium, All of Us and UK Biobank involving 5.1 million individuals of diverse ancestry, with 1.18 million individuals from four non-European populations across thirteen complex traits. Results demonstrate that CT-SLEB significantly improves PRS performance in non-European populations compared to simple alternatives, with comparable or superior performance to a recent, computationally intensive method. Moreover, our simulation studies offer insights into sample size requirements and SNP density effects on multi-ancestry risk prediction.

genetics↗

bioRxiv2022

Antagonistic regulation of Drosophila mitochondrial uncoupling protein UCP4b by cold and BMP signaling

Regulation of energy metabolism and response to cold are intimately linked in mammals. Central to these two processes are the mitochondrial uncoupling proteins (UCPs), which by promoting proton leakage across the inner mitochondrial membrane lead to the generation of heat instead of ATP synthesis. In addition to heat generation, UCPs also influence energy storage and can protect against obesity and diabetes. Cold-blooded animals like flies also contain UCPs that protect from cold, however their regulations are poorly understood. We find that Drosophila UCP4b is induced by cold in a cell-intrinsic manner and protects against cold and obesity in fly models. Mechanistically, cold regulates UCP4b expression through calcium signaling and Spargel (Srl), the Drosophila ortholog of mammalian PGC1. To the opposite, MAD, acting downstream of the BMP branch of the TGF{beta} signaling pathway, represses UCP4b expression independently of cold. Interestingly, the two mechanisms of UCP4b regulation are integrated as MAD binding to the UCP4b promoter is displaced by cold in a Srl-dependent manner. We discuss the similarities between the regulation of mammalian and Drosophila UCPs. SignificanceMitochondrial uncoupling proteins (UCPs) that uncouple the mitochondrial respiration from ATP synthesis regulate energy metabolism, non-shivering thermogenesis, and redox balance in vertebrates and invertebrates. However, their regulation in Drosophila is poorly understood. We found that Drosophila uncoupling protein UCP4b is induced by cold in a cell-autonomous fashion. Conversely, MAD, acting downstream of BMP signaling, inhibits UCP4b expression. MAD is displaced from the upstream regions of the UCP4b gene by cold. UCP4b protects Drosophila against cold and diet-induced obesity. The regulation of UCP4b by cold and BMP signaling is reminiscent of the regulation of mammalian uncoupling protein UCP1. Altogether, we discovered an important regulator of Drosophila energy metabolism which is controlled by regulatory processes that are similar between Drosophila and mammals.

genetics↗

bioRxiv2021

The miR-124-AMPAR pathway connects polygenic risks with behavioral changes shared between schizophrenia and bipolar disorder

Schizophrenia (SZ) and bipolar disorder (BP) are highly heritable major psychiatric disorders that share a substantial portion of genetic risk as well as their clinical manifestations. This raises a fundamental question of whether, and how, common neurobiological pathways translate their shared polygenic risks into shared clinical manifestations. The present study shows the miR-124-AMPAR pathway as a key common neurobiological mediator that connects polygenic risks with behavioral changes shared between these two psychotic disorders. We discovered upregulation of miR-124 in biopsied neuronal cells and postmortem prefrontal cortex from both SZ and BP patients, implying its role not only as a biomarker, but also as a pathophysiological mediator. Intriguingly, the upregulation is associated with the polygenic risks shared between these two disorders. Seeking mechanistic dissection, we generated a mouse model that upregulates miR-124 in the medial prefrontal cortex, which includes brain regions homologous to sub-regions of the human prefrontal cortex. We demonstrated that upregulation of miR-124 increases GRIA2-lacking calcium permeable-AMPARs and perturbs AMPAR-mediated excitatory synaptic transmission, leading to deficits in the behavioral dimensions shared between SZ and BP.

neuroscience↗

bioRxiv2021

SARS-CoV-2 triggers DNA damage response in Vero E6 cells

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus responsible for the current COVID-19 pandemic and has now infected more than 200 million people with more than 4 million deaths globally. Recent data suggest that symptoms and general malaise may continue long after the infection has ended in recovered patients, suggesting that SARS-CoV-2 infection has profound consequences in the host cells. Here we report that SARS-CoV-2 infection can trigger a DNA damage response (DDR) in African green monkey kidney cells (Vero E6). We observed a transcriptional upregulation of the Ataxia telangiectasia and Rad3 related protein (ATR) in infected cells. In addition, we observed enhanced phosphorylation of CHK1, a downstream effector of the ATR DNA damage response, as well as H2AX. Strikingly, SARS-CoV-2 infection lowered the expression of TRF2 shelterin-protein complex, and reduced telomere lengths in infected Vero E6 cells. Thus, our observations suggest SARS-CoV-2 may have pathological consequences to host cells beyond evoking an immunopathogenic immune response.

cancer biology↗

bioRxiv2021

Large Bi-Ethnic Study of Plasma Proteome Leads to Comprehensive Mapping of cis-pQTL and Models for Proteome-wide Association Studies

Improved understanding of genetic regulation of proteome can facilitate the identification of causal mechanisms for complex traits. We analyzed data on 4,657 plasma proteins from 7,213 European American (EA) and 1,871 African American (AA) individuals from the ARIC study, and further replicated findings on 467 AA individuals from the AASK study. Here we identified 2,004 proteins in EA and 1,618 in AA, with majority overlapping, which showed associations with common variants in cis-regions. Availability of AA samples led to smaller credible sets and significant number of population-specific cis-pQTLs. Elastic-net produced powerful models for protein prediction in both populations. An application of proteome-wide association studies (PWAS) to serum urate and gout, implicated several proteins, including IL1RN, revealing the promise of the drug anakinra to treat acute gout flares. Our study demonstrates the value of large and diverse ancestry study for genetic mechanisms of molecular phenotypes and their relationship with complex traits.

genetics↗

bioRxiv2021

Mendelian Randomization Analysis Using Multiple Biomarkers of an Underlying Common Exposure

SO_SCPLOWUMMARYC_SCPLOWMendelian Randomization (MR) analysis is increasingly popular for testing the causal effect of exposures on disease outcomes using data from genome-wide association studies. In some settings, the underlying exposure, such as systematic inflammation, may not be directly observable, but measurements can be available on multiple biomarkers or other types of traits that are coregulated by the exposure. We propose a method for MR analysis on latent exposures (MRLE), which tests the significance for, and the direction of, the effect of a latent exposure by leveraging information from multiple related traits. The method is developed by constructing a set of estimating functions based on the second-order moments of GWAS summary association statistics for the observable traits, under a structural equation model where genetic variants are assumed to have indirect effects through the latent exposure and potentially direct effects on the traits. Simulation studies show that MRLE has well-controlled type I error rates and enhanced power compared to single-trait MR tests under various types of pleiotropy. Applications of MRLE using genetic association statistics across five inflammatory biomarkers (CRP, IL-6, IL-8, TNF-, and MCP-1) provide evidence for potential causal effects of inflammation on increasing the risk of coronary artery disease, colorectal cancer, and rheumatoid arthritis, while standard MR analysis for individual biomarkers fails to detect consistent evidence for such effects.

genetics↗

bioRxiv2020

Deficiency of the splicing factor RBM10 limits EGFR inhibitor response in EGFR mutant lung cancer

Molecularly targeted cancer therapy has improved outcomes for cancer patients with targetable oncoproteins, such as mutant epidermal growth factor receptor (EGFR) in lung cancer. Yet, long-term patient survival remains limited because treatment responses are typically incomplete. One potential explanation for the lack of complete and durable responses is that oncogene-driven cancers with activating mutations in the EGFR often harbor additional co-occurring genetic alterations. This hypothesis remains untested for most genetic alterations that co-occur with mutant EGFR. Here, we report the functional impact of inactivating genetic alteration of the mRNA splicing factor RBM10 that co-occur with mutant EGFR. RBM10 deficiency decreased EGFR inhibitor efficacy in patient-derived EGFR mutant tumor models. RBM10 modulated mRNA alternative splicing of the mitochondrial apoptotic regulator Bcl-x to regulate tumor cell apoptosis during treatment. Genetic inactivation of RBM10 diminished EGFR inhibitor-mediated apoptosis by decreasing the ratio of Bcl-xS-(pro-apoptotic)-to-Bcl-xL(anti-apoptotic) Bcl-x isoforms. RBM10 deficiency was a biomarker of poor response to EGFR inhibitor treatment in clinical samples. Co-inhibition of Bcl-xL and mutant EGFR overcame resistance induced by RBM10 deficiency. This study sheds light on the role of co-occurring genetic alterations, and on the impact of splicing factor deficiency in the modulation of sensitivity to targeted kinase inhibitor cancer therapy.

cancer biology↗