Biology subjects

Tsao, M.-S.

Publications and source records attributed to Tsao, M.-S..

10 recordsLinked to original sources

bioRxiv2026

mSWI/SNF complex inhibition sensitizes KRAS-mutant lung cancers to targeted therapies via epithelial-mesenchymal subversion

Targeted therapies for KRAS-mutant non-small lung cancer (NSCLC) have shown promising clinical results, however, incomplete tumoral responses and the inevitable emergence of therapeutic resistance remain critical challenges. Here we identify mSWI/SNF chromatin remodeling complexes as critical determinants of (EMT)-mediated KRAS inhibitor inefficacy and resistance in KRAS G12C lung cancers. Treatment with the clinical-grade SMARCA4/2 inhibitor, FHD-286, dampens EMT-mediated acquired resistance in drug-responsive models and similarly resensitizes drug-refractory models by rewiring mSWI/SNF chromatin localization and activities that modulate epithelial transcriptional programs and cell state. Further, synergistic mSWI/SNF and KRAS inhibitor combination treatment sensitizes non-G12C KRAS-mutant NSCLC cells to pan-RAS and G12D-specific inhibitors. Finally, FHD-286 and sotorasib combination treatment results in potent anti-tumor efficacy in both G12Ci-resistant and - sensitive organoid models and in vivo patient-derived xenograft (PDX) systems. These data nominate mSWI/SNF inhibition as a combination strategy to improve KRAS inhibitor efficacy, response duration, and to mitigate emergence of resistance.

cancer biology↗

bioRxiv2024

Pan-cancer N-glycoproteomic atlas of patient derived xenografts uncovers FAT2 as a therapeutic target for head and neck cancers

Cell surface proteins offer significant cancer therapeutic potential attributable to their accessible membrane localization and central role in cellular signaling. Despite this, their promise remains largely untapped due to the technical challenges inherent to profiling cell surface proteins. Here, we employed N-glycoproteomics to analyze 85 patient-derived xenografts (PDX), constructing Glyco PDXplorer - an in vivo pan-cancer atlas of cancer-derived cell surface proteins. We developed a target discovery pipeline to prioritize proteins with favorable expression profiles for immunotherapeutic targeting and validated FAT2 as a head and neck squamous cancer (HNSC) enriched surface protein with limited expression in normal tissue. Functional studies revealed that FAT2 is essential for HNSC growth and adhesion through regulation of surface architecture and integrin-PI3K signaling. Chimeric antigen receptor (CAR) T cells targeting FAT2 demonstrated potent anti-tumor activity in HNSC models. This work lays the foundation for developing FAT2-targeted therapies and represents a pivotal resource to inform therapeutic target discovery for multiple cancers. HIGHLIGHTSO_LIPan-cancer landscape of cancer-derived cell surface proteins detected in vivo C_LIO_LIDevelopment of a multi-omic discovery pipeline to prioritize proteins with optimal expression profiles as immunotherapy targets C_LIO_LIIdentification and validation of FAT2 as a head and neck squamous cancer enriched surface protein with minimal expression in normal tissues C_LIO_LIFAT2 coordinates cell surface organization, adhesion, growth and survival through the integrin-PI3K-AKT pathway C_LIO_LIFAT2 CAR T cells demonstrate anti-tumour activity in pre-clinical models C_LI

cancer biology↗

bioRxiv2024

Identification of KIFC1 as a putative vulnerability in lung cancers with centrosome amplification

Centrosome amplification (CA), an abnormal increase in the number of centrosomes in the cell, is a recurrent phenomenon in lung and other malignancies. Although CA contributes to tumor development and progression by promoting genomic instability (GIN), it also induces mitotic stress that jeopardizes cellular integrity. The presence of extra centrosomes leads to the formation of multipolar mitotic spindles prone to causing lethal chromosome segregation errors during cell division. To sustain the benefits of CA, malignant cells are dependent on adaptive mechanisms to mitigate its detrimental consequences, and these mechanisms represent therapeutic vulnerabilities. We aimed to discover genetic dependencies associated with CA in lung cancer. Combining a CRISPR/Cas9 functional genomics screen with analyses of tumor genomic data, we identified the motor protein KIFC1 as a putative vulnerability specifically in lung cancers with CA. KIFC1 expression was positively correlated with CA in lung adenocarcinoma (LUAD) cell lines and with a gene expression signature predictive of CA in LUAD tumor tissues. High KIFC1 expression was associated with worse patient outcomes, smoking history, and indicators of GIN. KIFC1 loss-of-function sensitized LUAD cells to potentiation of CA and sensitization was associated with a diminished ability of KIFC1-depleted cells to cluster extra centrosomes into pseudo-bipolar mitotic spindles. Our work suggests that KIFC1 inhibition represents a novel approach for potentiating GIN to lethal levels in LC with CA by forcing cells to divide with multipolar spindles, rationalizing the clinical development of KIFC1 inhibitors and further studies to investigate its therapeutic potential.

cancer biology↗

bioRxiv2023

lower case Greek beta-catenin mediates growth defects induced my centrosome loss in APC mutant colorectal cancer independently of p53

Colorectal cancer is the third most common cancer and the second leading cause of cancer-related deaths worldwide. The centrosome is the main microtubule-organizing center in animal cells and centrosome amplification is a hallmark of cancer cells. To investigate the importance of centrosomes in colorectal cancer, we induced centrosome loss in normal and cancer human-derived colorectal organoids using centrinone B, a Polo-like kinase 4 (Plk4) inhibitor. We show that centrosome loss represses human normal colorectal organoid growth in a p53-dependent manner in accordance with previous studies in cell models [1]. However, cancer colorectal organoid lines exhibited different sensitivities to centrosome loss independently of p53. Centrinone-induced cancer organoid growth defect/death positively correlated with a loss of function mutation in the APC gene, suggesting a causal role of the hyperactive WNT pathway. Consistent with this notion, {beta}-catenin inhibition using XAV-939 or ICG-001 partially prevented centrinone-induced death and rescued the growth of APC-mutant organoid lines. Our study reveals a novel role for canonical WNT signaling in regulating centrosome loss-induced growth defect/death in APC-mutant colorectal cancer independently of the classical p53 pathway.

cancer biology↗

bioRxiv2023

Detection of circular RNAs and their potential as biomarkers predictive of drug response

The introduction of high-throughput sequencing technologies has allowed for comprehensive RNA species detection, both coding and non-coding, which opened new avenues for the discovery of predictive and prognostic biomarkers. However the consistency of the detection of different RNA species depends on the RNA selection protocol used for RNA-sequencing. While preliminary reports indicated that non-coding RNAs, in particular circular RNAs, constitute a rich source of biomarkers predictive of drug response, the reproducibility of this novel class of biomarkers has not been rigorously investigated. To address this issue, we assessed the inter- lab consistency of circular RNA expression in cell lines profiled in large pharmacogenomic datasets. We found that circular RNA expression quantified from rRNA-depleted RNA-seq data is stable and yields robust prognostic markers in cancer. On the other hand, quantification of the expression of circular RNA from poly(A)-selected RNA-seq data yields highly inconsistent results, calling into question results from previous studies reporting their potential as predictive biomarkers in cancer. We have also identified median expression of transcripts and transcript length as potential factors influencing the consistency of RNA detection. Our study provides a framework to quantitatively assess the stability of coding and non-coding RNA expression through the analysis of biological replicates within and across independent studies.

bioinformatics↗

bioRxiv2022

Unsupervised determination of lung tumor margin with widefield polarimetric second-harmonic generation microscopy

The extracellular matrix (ECM) is amongst many tissue components affected by cancer, however, morphological changes of the ECM are not well-understood and thus, often omitted from diagnostic considerations. Polarimetric second-harmonic generation (P-SHG) microscopy allows for visualization and characterization of collagen ultrastructure in the ECM, aiding in better understanding of the changes induced by cancer throughout the tissue. In this paper, a large region of hematoxylin and eosin-stained human lung section, encompassing a tumor margin, connecting a significant tumor portion to normal tissue was imaged with P-SHG microscopy. The resulting polarimetric parameters were utilized in principal components analysis and unsupervised k-means clustering to separate normal- and tumor-like tissue. Consequently, a pseudo-color map of the clustered tissue regions is generated to highlight the irregularity of the ECM collagen structure throughout the region of interest and to identify the tumor margin.

pathology↗

bioRxiv2020

KuLGaP: A Selective Measure for Assessing Therapy Response in Patient-Derived Xenografts

Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, and yet remains a challenging task. A preferred measure to quantify this response should take into account as much of the experimental data as possible, i.e. both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. KuLGaP is more selective than currently existing measures, reduces the risk of false positive calls and improves translation of the lab results to clinical practice.

bioinformatics↗

bioRxiv2018

Integrative Pharmacogenomics Analysis of Patient Derived Xenografts

One of the key challenges in cancer precision medicine is finding robust biomarkers of drug response. Patient-derived tumor xenografts (PDXs) have emerged as reliable preclinical models since they better recapitulate tumor response to chemo- and targeted therapies. However, the lack of standard tools poses a challenge in the analysis of PDXs with molecular and pharmacological profiles. Efficient storage, access and analysis is key to the realization of the full potential of PDX pharmacogenomic data. We have developed Xeva (XEnograft Visualization & Analysis), an open-source software package for processing, visualization and integrative analysis of a compendium of in vivo pharmacogenomic datasets. The Xeva package follows the PDX minimum information (PDX-MI) standards and can handle both replicate-based and 1x1x1 experimental designs. We used Xeva to characterize the variability of gene expression and pathway activity across passages. We found that only a few genes and pathways have passage specific alterations (median intraclass correlation of 0.53 for genes and positive enrichment score for 92.5% pathways). For example, activity of the mRNA 3-end processing and elongation arrest and recovery pathways were strongly affected by model passaging (gene set enrichment analysis false discovery rate [FDR] <5%). We then leveraged our platform to link the drug response and the pathways whose activity is consistent across passages by mining the Novartis PDX Encyclopedia (PDXE) data containing 1,075 PDXs spanning 5 tissue types and 62 anticancer drugs. We identified 87 pathways significantly associated with response to 51 drugs (FDR < 5%), including associations such as erlotinib response and signaling by EGFR in cancer pathways and MAP kinase activation in TLR cascade and binimetinib response. Among the significant pathway-drug associations, we found novel biomarkers based on gene expressions, Copy Number Aberrations (CNAs) and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, a major step toward precision oncology.

bioinformatics↗

bioRxiv2018

Non-driver somatic alteration burden confers good prognosis in non-small cell lung cancer

BackgroundGenomic profiling of patient tumors has linked somatic driver mutations to survival outcomes of non-small cell lung cancer (NSCLC) patients, especially for those receiving targeted therapies. However, it remains unclear whether specific non-driver mutations have any prognostic utility.\n\nMethodsWhole exomes and transcriptomes were measured from NSCLC xenograft models of patients with diverse clinical outcomes. Penalised regression analysis was performed to identify a set of 865 genes associated with patient survival. The number of somatic copy number aberrations, point mutations and associated expression changes within the 865 genes were used to stratify independent NSCLC patient populations, filtered for chemotherapy naive and early-stage. In-depth genomic analysis and functional testing was conducted on the genomic alterations to understand their effect on improving survival.\n\nResultsHigh burden of somatic alterations are associated with longer disease-free survival (HR=0.153, P=1.48x10-4) in NSCLC patients. When somatic alterations burden was integrated with gene expression, we were able to predict prognosis in three independent patient datasets. Patients with high alteration burden could be further stratified based on the presence of immunogenic mutations, revealing another subgroup of patients with even better prognosis (85% with >5 years survival), and associated with cytotoxic T-cell expression. In addition, 95% of these 865 genes lack documented activity relevant to cancer, but are in pathways regulating cell proliferation, motility and immune response were implicated.\n\nConclusionOur results demonstrate that non-driver somatic alterations may influence the outcome of cancer patients by increasing beneficial immune response and inhibiting processes associated to tumorigenesis.

cancer biology↗

bioRxiv2017

Whole Genomes Define Concordance of Matched Primary, Xenograft, and Organoid Models of Pancreas Cancer

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among solid malignancies and improved therapeutic strategies are needed to improve outcomes. Patient-derived xenografts (PDX) and patient-derived organoids (PDO) serve as promising tools to identify new drugs with therapeutic potential in PDAC. For these preclinical disease models to be effective, they should both recapitulate the molecular heterogeneity of PDAC and validate patient-specific therapeutic sensitivities. To date however, deep characterization of PDAC PDX and PDO models and comparison with matched human tumour remains largely unaddressed at the whole genome level. We conducted a comprehensive assessment of the genetic landscape of 16 whole-genome pairs of tumours and matched PDX, from primary PDAC and liver metastasis, including a unique cohort of 5 trios of matched primary tumour, PDX, and PDO. We developed a new pipeline to score concordance between PDAC models and their paired human tumours for genomic events, including mutations, structural variations, and copy number variations. Comparison of genomic events in the tumours and matched disease models displayed single-gene concordance across major PDAC driver genes, and genome-wide similarities of copy number changes. Genome-wide and chromosome-centric analysis of structural variation (SV) events revealed high variability across tumours and disease models, but also highlighted previously unrecognized concordance across chromosomes that demonstrate clustered SV events. Our approach and results demonstrate that PDX and PDO recapitulate PDAC tumourigenesis with respect to simple somatic mutations and copy number changes, and capture major SV events that are found in both resected and metastatic tumours.

bioinformatics↗