Biology subjects

Ha, M. J.

Publications and source records attributed to Ha, M. J..

7 recordsLinked to original sources

bioRxiv2024

Therapeutic modulation of ROCK overcomes metabolic adaptation of cancer cells to OXPHOS inhibition and drives synergistic anti-tumor activity

Genomic studies have identified frequent mutations in subunits of the SWI/SNF chromatin remodeling complex including SMARCA4 and ARID1A in non-small cell lung cancer. Previously, we and others have identified that SMARCA4-mutant lung cancers are highly dependent on oxidative phosphorylation (OXPHOS). Despite initial excitements, therapeutics targeting metabolic pathways such as OXPHOS have largely been disappointing due to rapid adaptation of cancer cells to inhibition of single metabolic enzymes or pathways, suggesting novel combination strategies to overcome adaptive responses are urgently needed. Here, we performed a functional genomics screen using CRISPR-Cas9 library targeting genes with available FDA approved therapeutics and identified ROCK1/2 as a top hit that sensitizes cancer cells to OXPHOS inhibition. We validate these results by orthogonal genetic and pharmacologic approaches by demonstrating that KD025 (Belumosudil), an FDA approved ROCK inhibitor, has highly synergistic anti-cancer activity in vitro and in vivo in combination with OXPHOS inhibition. Mechanistically, we showed that this combination induced a rapid, profound energetic stress and cell cycle arrest that was in part due to ROCK inhibition-mediated suppression of the adaptive increase in glycolysis normally seen by OXPHOS inhibition. Furthermore, we applied global phosphoproteomics and kinase-motif enrichment analysis to uncover a dynamic regulatory kinome upon combination of OXPHOS and ROCK inhibition. Importantly, we found converging phosphorylation-dependent regulatory cross-talk by AMPK and ROCK kinases on key RHO GTPase signaling/ROCK-dependent substrates such as PPP1R12A, NUMA1 and PKMYT1 that are known regulators of cell cycle progression. Taken together, our study identified ROCK kinases as critical mediators of metabolic adaptation of cancer cells to OXPHOS inhibition and provides a strong rationale for pursuing ROCK inhibitors as novel combination partners to OXPHOS inhibitors in cancer treatment.

cancer biology↗

bioRxiv2021

3-Phosphoinositide-dependent kinase 1 drives acquired resistance to osimertinib

Osimertinib sensitive and resistant NSCLC NCI-H1975 clones were used to model osimertinib acquired resistance in humanized mice and delineate potential resistance mechanisms. No new EGFR mutations or loss of the EGFR T790M mutation were found in resistant clones. Resistant tumors in humanized mice were initially partially responsive to osimertinib, then aggressive tumor regrowth occurred accompanied by an immunosuppressive tumor microenvironment. 3-phosphoinositide-dependent kinase 1 (PDK1) was identified as a potential driver of osimertinib acquired resistance, and its selective inhibition by BX795 and CRISPR gene knock out, sensitized resistant clones and a patient derived xenograft (PDX) with acquired resistance to osimertinib. PDK1 knock-out dysregulated PI3K/Akt/mTOR signaling, promoted cell cycle arrest at the G1 phase, and inhibited nuclear translocation of yes-associated protein (YAP). Higher expression of PDK1 was found in patients with progressive disease following osimertinib treatment. PDK1 is a central upstream regulator of two critical drug resistance pathways: PI3K/AKT/mTOR and YAP.

cancer biology↗

bioRxiv2021

CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts

MotivationAnticancer combination therapy has been developed to increase efficacy by enhancing synergy. Patient-derived xenografts (PDXs) have emerged as reliable preclinical models to develop effective treatments in translational cancer research. However, in most PDX combination experiments, PDXs are tested on single dose levels and dose-response surface methods are not applicable for testing synergism. ResultsWe propose a comprehensive statistical framework to assess joint action of drug combinations from PDX tumor growth curve data. We provide various metrics and robust statistical inference procedures that locally (at a fixed time) and globally (across time) access combination effects under classical drug interaction models. Integrating genomic and pharmacological profiles in non-small-cell lung cancer (NSCLC), we have shown the utilities of combPDX in discovering effective therapeutic combinations and relevant biological mechanisms. AvailabilityWe provide an interactive web server, combPDX (https://licaih.shinyapps.io/CombPDX/), to analyze PDX tumor growth curve data and perform power analyses. ContactMJHa@mdanderson.org Supplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗

bioRxiv2021

A Bayesian Precision Medicine Framework for Calibrating Individualized Therapeutic Indices in Cancer

The development and clinical implementation of evidence-based precision medicine strategies has become a realistic possibility, primarily due to the rapid accumulation of large-scale genomics and pharmacological data from diverse model systems: patients, cell-lines and drug perturbation studies. We introduce a novel Bayesian modeling framework called the individualized theRapeutic index (iRx) model to integrate high-throughput pharmacogenomic data across model systems. Our iRx model achieves three main goals: first, it exploits the conserved biology between patients and cell-lines to calibrate therapeutic response of drugs in patients; second, it finds optimal cell line avatars as proxies for patient(s); and finally, it identifies key genomic drivers explaining cell line-patient similarities. This is achieved through a semi-supervised learning approach, that conflates (unsupervised) sparse latent factor models with (supervised) penalized regression techniques. We propose a unified and tractable Bayesian model for estimation, and inference is conducted via efficient posterior sampling schemes. We illustrate and validate our approach using two existing clinical trial datasets in multiple myeloma and breast cancer studies. We show that our iRx model improves prediction accuracy compared to naive alternative approaches, and it consistently outperforms existing methods in literature in both in multiple simulation scenarios as well as real clinical examples.

genomics↗

bioRxiv2019

Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome

AO_SCPCAPBSTRACTC_SCPCAPO_ST_ABSPurposeC_ST_ABSPersonalized network inference on diverse clinical and in vitro model systems across cancer types can be used to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer.\n\nDatasets and methodsWe developed TransPRECISE, a multi-scale Bayesian network modeling framework, to analyze the pan-cancer patient and cell line interactome to identify differential and conserved intra-pathway activities, globally assess cell lines as representative models for patients and develop drug sensitivity prediction models. We assessed pan-cancer pathway activities for a large cohort of patient samples (>7700) from The Cancer Proteome Atlas across [≥]30 tumor types and a set of 640 cancer cell lines from the M.D. Anderson Cell Lines Project spanning16 lineages, and [≥]250 cell lines response to >400 drugs.\n\nResultsTransPRECISE captured differential and conserved proteomic network topologies and pathway circuitry between multiple patient and cell line lineages: ovarian and kidney cancers shared high levels of connectivity in the hormone receptor and receptor tyrosine kinase pathways, respectively, between the two model systems. Our tumor stratification approach found distinct clinical subtypes of the patients represented by different sets of cell lines: head and neck patient tumors were classified into two different subtypes that are represented by head and neck and esophagus cell lines, and had different prognostic patterns (456 vs. 654 days of median overall survival; P=0.02). The TransPRECISE-based sample-specific pathway scores achieved high predictive accuracy for drug sensitivities in cell lines across multiple drugs (median AUC >0.8).\n\nConclusionOur study provides a generalizable analytical framework to assess the translational potential of preclinical model systems and guide pathway-based personalized medical decision-making, integrating genomic and molecular data across model systems.

bioinformatics↗

bioRxiv2019

Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis

Patient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.

cancer biology↗

bioRxiv2019

Network-Based Matching of Patients and Targeted Therapies for Precision Oncology

The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible - allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the \"missing link\" between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies.\n\nData and code availabilityhttps://github.com/bayesrx/bayesrx.github.io/tree/master/authors/liu-q./ Shiny app (data and results visualization tool): https://qingzliu.shinyapps.io/psb-app/

bioinformatics↗