Biology subjects

Lei, J. T.

Publications and source records attributed to Lei, J. T..

7 recordsLinked to original sources

bioRxiv2026

Mitochondrial rewiring supports survival of triple negative breast cancer cells after ionizing radiation

Triple negative breast cancer (TNBC) is an aggressive disease with limited therapeutic options. Conventional treatments include neoadjuvant chemo-immunotherapy followed by surgical resection and may include further adjuvant immunotherapy and/or radiotherapy of the tumor bed and lymph nodes. Nonetheless, TNBC patients with residual disease have rapid metastatic recurrence. While the roles of metabolic and mitochondrial adaptations in chemotherapeutic resistance have been the subject of many studies, their importance in the context of ionizing radiation (IR) therapy remains poorly understood. We established longitudinal in vitro models of post-IR human TNBC, characterized by cellular regression to a residual phenotypic state, then eventual cell repopulation. This was accompanied by plastic adoption of unique metabolic, proteomic, and morphologic features that largely reverted when cells regrew. Following IR, residual cells exhibited extensive mitochondrial rewiring, including elevated mitochondrial content, oxidative phosphorylation (oxphos) rates, cristae structures, and metabolite levels. Concomitantly, levels of the short protein isoform of the mitochondrial inner membrane protein optic atrophy 1 (OPA1) were significantly elevated in residual cells, and OPA1 knockout ablated mitochondrial adaptations induced by IR. OPA1 genetic or pharmacologic perturbations led to improved cellular responses to IR. Metabolomic and proteomic analyses of radio-residual cells uncovered a coordinated program of antioxidant and redox capacity elevation with mitochondrial metabolism, which was corroborated by analyses of external datasets. Together, these findings provide evidence that TNBC cells surviving radiotherapy adopt an OPA1-dependent program of mitochondrial reorganization that supports their survival and regrowth, thereby positioning OPA1 as a therapeutic dependency that could improve radiotherapy efficacy in TNBC.

cancer biology↗

bioRxiv2026

Mitochondrial protein translation is a heightened dependence and therapeutic vulnerability of chemo-refractory triple negative breast cancer

Triple negative breast cancer (TNBC) patients harboring residual cancer burden following completion of conventional neoadjuvant chemo-immunotherapy regimens have poor relapse-free and overall survival rates and limited therapeutic options. We and others have demonstrated that mitochondrial function is required for the survival of chemo-refractory TNBC. Here, we define the mitochondrial translation machinery as a critical and targetable dependency underlying chemo-refractory TNBC. Analyses of human and orthotopic patient-derived xenograft (PDX) mass spectrometry proteomics datasets revealed that mitochondrial protein translation-related signatures were among the top associated with chemoresistance. These signatures included core mitoribosome components and the mitoribosome-associated factor Oxidase (Cytochrome C) Assembly 1-Like (OXA1L), which was consistently enriched in chemoresistant versus chemosensitive TNBC across datasets. OXA1L, a key mediator of mitochondrial translation and electron transport chain (ETC) assembly, has not been functionally characterized in cancer. We therefore tested whether OXA1L-dependent mitochondrial translation sustains mitochondrial function and chemoresistance in TNBC. Knockdown (KD) of OXA1L in human TNBC cells reduced ETC protein levels, mitochondrial respirasome supercomplex levels, ATP production, and oxidative phosphorylation (oxphos), establishing a requirement for OXA1L in maintaining mitochondrial bioenergetics in TNBC. OXA1L was required for the characteristic oxphos elevation induced by carboplatin (CRB), and KD significantly enhanced CRB sensitivity, demonstrating that mitochondrial translation supports adaptive metabolic responses to chemotherapy. To explore the translational potential of targeting the mitoribosome in TNBC, we leveraged the bacterial ancestry of mitochondria to repurpose the FDA-approved antibiotic tigecycline (TIG) as a mitochondrial translation inhibitor. Direct measurement of mitochondrial nascent peptide levels revealed that, while CRB elevated mitochondrial translation, TIG potently suppressed mitochondrial translation as monotherapy and in combination with CRB or docetaxel (DTX). TIG abolished CRB-induced oxphos, decreased oxphos in combination with DTX, and significantly improved chemotherapy sensitivity in human TNBC cell lines, PDX-derived spheroids, and in vivo. TIG sensitivity associated with mitochondrial translation-related proteomic signatures, concordant with PDX and patient-derived signatures associated with chemoresistance. Together, these data identify OXA1L-dependent mitochondrial translation as a targetable dependency that sustains mitochondrial function and chemoresistance in TNBC, demonstrate that its inhibition enhances chemotherapeutic response, and nominate a mitochondrial translation-related protein signature as a candidate predictive biomarker of TIG sensitivity and chemoresistance. These findings support mitochondrial translation inhibition as a potential therapeutic strategy in chemo-refractory disease. DISCLOSURESGVE is co-founder, Chief Scientific Officer, and an equity stakeholder of Nemea Therapeutics, Inc. G.V.E. formerly received sponsored research funding from Chimerix Inc. G.V.E. receives experimental compounds from the Lead Discovery Center of Germany and from Jazz Pharmaceuticals. MLB is a co-inventor at Nemea Therapeutics. MTL is a founder and limited partner in StemMed Ltd. and a manager in StemMed Holdings, its general partner. MTL is a founder and equity stakeholder in Tvardi Therapeutics Inc. The BCM PDX models, are exclusively licensed to StemMed Ltd., resulting in royalty income to MTL when used for commercial purposes. LED is a compensated employee of StemMed Ltd; however, the PDX models resulting in royalty income to LED were not included in this study. All other authors have nothing to disclose.

cancer biology↗

bioRxiv2024

Patient-Derived Xenografts of Triple-Negative Breast Cancer Enable Deconvolution and Prediction of Chemotherapy Responses

Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significanceIntegration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.

cancer biology↗

bioRxiv2024

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

Kinases play a central role in regulating cellular processes, making their study essential for understanding cellular function and disease mechanisms. To investigate the regulatory state of a kinase, numerous methods have been, and continue to be, developed to infer kinase activities from phosphoproteomics data. These methods usually rely on a set of kinase targets collected from various kinase-substrate libraries. However, only a small percentage of measured phosphorylation sites can usually be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. In addition, the inferred activities from different methods can vary making it crucial to evaluate them for accurate interpretation. Here, we present a comprehensive evaluation of kinase activity inference methods using multiple kinase-substrate libraries combined with different inference algorithms. Additionally, we try to overcome the coverage limitations for measured targets in kinase substrate libraries by adding predicted kinase-substrate interactions for activity inference. For the evaluation, in addition to classical cell-based perturbation experiments, we introduce a tumor-based benchmarking approach that utilizes multi-omics data to identify highly active or inactive kinases per tumor type. We show that while most computational algorithms perform comparably regardless of their complexity, the choice of kinase-substrate library can highly impact the inferred kinase activities. Hereby, manually curated libraries, particularly PhosphoSitePlus, demonstrate superior performance in recapitulating kinase activities from phosphoproteomics data. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance, while normalizing sites to host protein levels reduces kinase activity inference performance. We then showcase how kinase activity inference can help in characterizing the response to kinase inhibitors in different cell lines. Overall, the selection of reliable kinase activity inference methods is important in identifying deregulated kinases and novel drug targets. Finally, to facilitate the evaluation of novel methods in the future, we provide both benchmarking approaches in the R package benchmarKIN. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/601117v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@44de1org.highwire.dtl.DTLVardef@9510a3org.highwire.dtl.DTLVardef@775225org.highwire.dtl.DTLVardef@1b2256f_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Deep learning prediction boosts phosphoproteomics-based discoveries through improved phosphopeptide identification

Shotgun phosphoproteomics enables high-throughput analysis of phosphopeptides in biological samples, but low phosphopeptide identification rate in data analysis limits the potential of this technology. Here we present DeepRescore2, a computational workflow that leverages deep learning-based retention time and fragment ion intensity predictions to improve phosphopeptide identification and phosphosite localization. Using a state-of-the-art computational workflow as a benchmark, DeepRescore2 increases the number of correctly identified peptide-spectrum matches by 17% in a synthetic dataset and identifies 19%-46% more phosphopeptides in biological datasets. In a liver cancer dataset, 30% of the significantly altered phosphosites between tumor and normal tissues and 60% of the prognosis-associated phosphosites identified from DeepRescore2-processed data could not be identified based on the state-of-the-art workflow. Notably, DeepRescore2-processed data uniquely identifies EGFR hyperactivation as a new target in poor-prognosis liver cancer, which is validated experimentally. Integration of deep learning prediction in DeepRescore2 improves phosphopeptide identification and facilitates biological discoveries.

bioinformatics↗

bioRxiv2021

A Network Approach to Identify Biomarkers of Differential Chemotherapy Response Using Patient-Derived Xenografts of Triple-Negative Breast Cancer

Triple negative breast cancer (TNBC) is a highly heterogeneous set of diseases that has, until recently, lacked any FDA-approved, molecularly targeted therapeutics. Thus, systemic chemotherapy regimens remain the standard of care for many. Unfortunately, even combination chemotherapy is ineffective for many TNBC patients, and side-effects can be severe or lethal. Identification of predictive biomarkers for chemotherapy response would allow for the prospective selection of responsive patients, thereby maximizing efficacy and minimizing unwanted toxicities. Here, we leverage a cohort of TNBC PDX models with responses to single-agent docetaxel or carboplatin to identify biomarkers predictive for differential response to these two drugs. To demonstrate their ability to function as a preclinical cohort, PDX were molecularly characterized using whole-exome DNA sequencing, RNAseq transcriptomics, and mass spectrometry-based total proteomics to show proteogenomic consistency with TCGA and CPTAC clinical samples. Focusing first on the transcriptome, we describe a network-based computational approach to identify candidate epithelial and stromal biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (ITGA7, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). Biomarker panels are predictive in PDX expression datasets (RNAseq and Affymetrix) for both taxane (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating both cross expression platform and cross drug class robustness. Biomarker panels were also predictive in clinical datasets with response to cisplatin or paclitaxel, thus demonstrating translational potential of PDX-based preclinical trials. This network-based approach is highly adaptable and can be used to evaluate biomarkers of response to other agents.

cancer biology↗

bioRxiv2021

Epithelial Immunomodulation by Aerosolized Toll-like Receptor Agonists Attenuates Allergic Responsiveness in Mice

Allergic asthma is a chronic inflammatory respiratory disease associated with eosinophilic infiltration, increased mucus production, airway hyperresponsiveness (AHR), and airway remodeling. Epidemiologic data has revealed that the prevalence of allergic sensitization and associated diseases has increased in the twentieth century. This has been hypothesized to be partly due to reduced contact with microbial organisms (the hygiene hypothesis) in industrialized society. Airway epithelial cells, once considered a static physical barrier between the body and the external world, are now widely recognized as immunologically active cells that can initiate, maintain, and restrain inflammatory responses, such as those that mediate allergic disease. Airway epithelial cells can sense allergens via myriad expression of Toll-like receptors (TLRs) and other pattern-recognition receptors (PRRs). We sought to determine whether the innate immune response stimulated by a combination of Pam2CSK4 ("Pam2", TLR2/6 ligand) and a class C oligodeoxynucleotide ODN362 ("ODN", TLR9 ligand) when delivered together by aerosol ("Pam2ODN"), can modulate the allergic immune response to allergens. Treatment with Pam2ODN 7 days before sensitization to House Dust Mite (HDM) extract resulted in a strong reduction in eosinophilic and lymphocytic inflammation. This Pam2ODN immunomodulatory effect was also seen using Ovalbumin (OVA) and A. oryzae (Ao) mouse models. The immunomodulatory effect was observed as much as 30 days before sensitization to HDM, but ineffective just 2 days after sensitization, suggesting that Pam2ODN immunomodulation lowers the allergic responsiveness of airway epithelial cells. Furthermore, Pam2 and ODN cooperated synergistically suggesting that this treatment is superior to any single agonist in the setting of allergen immunotherapy. One Sentence SummaryA synergistic combination of Toll-like Receptor agonists, delivered directly into the lung mucosa, can attenuate allergic responsiveness of airway epithelial cells and prevent host sensitization to aeroallergens. What is already knownO_LIAllergic sensitization has increased in the 20th century due to reduced contact with microbial organisms in industrialized society (ie. hygiene hypothesis) C_LIO_LIWe have previously identified a pharmacological means to stimulate innate immunity of lung epithelial cells. C_LI What this study addsO_LIActivation of innate immunity in lung epithelial cells attenuates the allergic responsiveness of mice. C_LIO_LISynergistic cooperation of pattern recognition receptors induces stronger immunomodulatory responses C_LI What is the clinical significanceO_LIAerosolized Toll-like Receptor agonists have been demonstrated as safe in human clinical trials C_LIO_LIThis study provides proof-of-principle that aerosolized toll-like receptor agonists could have clinical efficacy in the setting of the allergen immunotherapy C_LI

immunology↗