Biology subjects

Murray, C. W.

Publications and source records attributed to Murray, C. W..

6 recordsLinked to original sources

bioRxiv2024

Modeling Compound Lipid Homeostasis Using Stable Isotope Tracing

Lipids represent the most diverse pool of metabolites found in cells, facilitating compartmentation, signaling, and other functions. Dysregulation of lipid metabolism is linked to disease states such as cancer and neurodegeneration. However, limited tools are available for quantifying metabolic fluxes across the lipidome. To directly measure reaction fluxes encompassing compound lipid homeostasis, we applied stable isotope tracing, liquid chromatography-high-resolution mass spectrometry, and network-based isotopologue modeling to non-small cell lung cancer (NSCLC) models. Compound lipid metabolic flux analysis (CL-MFA) enables the concurrent quantitation of fatty acid synthesis, elongation, headgroup assembly, and salvage reactions within virtually any biological system. Here, we resolve liver kinase B1 (LKB1)-mediated regulation of sphingolipid recycling in NSCLC cells and precision-cut lung slice cultures. We also demonstrate that widely used tissue culture conditions drive cells to upregulate fatty acid synthase flux to supraphysiological levels. Finally, we identify previously uncharacterized isozyme specificity of ceramide synthase inhibitors. These results highlight the ability of CL-MFA to quantify lipid cycling in biological systems to discover biological function and elucidate molecular mechanisms in membrane lipid metabolism.

cell biology↗

bioRxiv2024

EML4-ALK variant-specific genetic interactions shape lung tumorigenesis

Oncogenic fusions of EML4 and ALK occur in [~]5% of lung adenocarcinomas. More than 15 EML4-ALK variants with distinct breakpoints within EML4 have been identified, but the functional differences between these variants remain poorly understood. Here we use CRISPR/Cas9 somatic genome editing to generate autochthonous mouse models of the two most common EML4-ALK variants, V1 and V3, and show that V3 is more oncogenic than V1. By integrating these models with multiplexed genome editing, we quantify the effects of 29 putative tumor suppressor genes on V1- and V3-driven lung cancer growth in vivo and show that many tumor suppressor genes have dramatically variant-specific effects on tumorigenesis. Analysis of a novel dataset representing the largest human EML4-ALK lung cancer cohort to date identified alterations in the genomic landscape depending on the EML4-ALK variant. These findings demonstrate functional heterogeneity among EML4-ALK variants, suggesting that EML4-ALK variants behave more like distinct oncogenes than a uniform entity. More broadly, these findings highlight the dramatic impact of oncogenic fusions partner proteins on tumor biology.

cancer biology↗

bioRxiv2022

A multiplexed in vivo approach to identify driver genes in small cell lung cancer

Small cell lung cancer (SCLC) is a highly lethal form of lung cancer. The high mutation burden in SCLC cells makes it challenging to predict key drivers of SCLC from genome sequencing data, thereby hindering the identification of possible therapeutic targets. Here we develop a quantitative multiplexed approach based on lentiviral barcoding with somatic CRISPR/Cas9-mediated genome editing to functionally investigate candidate regulators of tumor initiation and growth in genetically engineered mouse models of SCLC. Lentiviral vector-mediated SCLC initiation was greatly enhanced by naphthalene pre-treatment, enabling high multiplicity of tumor clones for analysis through high-throughput sequencing methods. Based on a meta-analysis across multiple human SCLC genomic datasets, we quantified the impact of inactivating 39 genes across many candidate pathways and captured both positive and detrimental effects on SCLC initiation and progression upon gene inactivation. This analysis and subsequent validation in human SCLC cells identified TSC1 in the PI3K-AKT-mTOR pathway as a robust tumor suppressor in SCLC. This new approach should illuminate novel drivers of SCLC, facilitate the development of precision therapies for defined SCLC genotypes, and identify new therapeutic targets.

cancer biology↗

bioRxiv2021

Combinatorial tumor suppressor inactivation efficiently initiates lung adenocarcinoma with therapeutic vulnerabilities

Lung cancer is the leading cause of cancer death worldwide, with lung adenocarcinoma being the most common subtype. Many oncogenes and tumor suppressor genes are altered in this cancer type and the discovery of oncogene mutations has led to the development of targeted therapies that have improved clinical outcomes. However, a large fraction of lung adenocarcinomas lacks mutations in known oncogenes, and the genesis and treatment of these oncogene-negative tumors remain enigmatic. Here, we perform iterative in vivo functional screens using quantitative autochthonous mouse model systems to uncover the genetic and biochemical changes that enable efficient lung tumor initiation in the absence of oncogene alterations. Through the generation of hundreds of diverse combinations of tumor suppressor alterations, we demonstrate that the inactivation of suppressors of the RAS and PI3K pathways drive the development of oncogene-negative lung adenocarcinoma. Human genomic data and histology identified RAS/MAPK and PI3K pathway activation as a common event in oncogene- negative human lung adenocarcinomas. We demonstrate that these Onc-negativeRAS/PI3K tumors and related cell lines are vulnerable to pharmacological inhibition of these signaling axes. These results transform our understanding of this prevalent yet understudied subtype of lung adenocarcinoma.

cancer biology↗

bioRxiv2021

Multiplexed identification of RAS paralog imbalance as a driver of lung cancer growth

Oncogenic KRAS mutations occur in approximately 30% of lung adenocarcinoma. Despite several decades of effort, oncogenic KRAS-driven lung cancer remains difficult to treat, and our understanding of the positive and negative regulators of RAS signaling is incomplete. To uncover the functional impact of diverse KRAS-interacting proteins on lung cancer growth in vivo, we used multiplexed somatic CRISPR/Cas9-based genome editing in genetically engineered mouse models with tumor barcoding and high-throughput barcode sequencing. Through a series of CRISPR/Cas9 screens in autochthonous lung tumors, we identified HRAS and NRAS as key suppressors of KRASG12D-driven tumor growth in vivo and confirmed these effects in oncogenic KRAS-driven human lung cancer cell lines. Mechanistically, RAS paralogs interact with oncogenic KRAS, suppress KRAS-KRAS interactions, and reduce downstream ERK signaling. HRAS mutations identified in KRAS-driven human tumors partially abolished this effect. Comparison of the tumor-suppressive effects of HRAS and NRAS in KRAS- and BRAF-driven lung cancer models confirmed that RAS paralogs are specific suppressors of oncogenic KRAS-driven lung cancer in vivo. Our study outlines a technological avenue to uncover positive and negative regulators of oncogenic KRAS-driven cancer in a multiplexed manner in vivo and highlights the role of RAS paralog imbalance in oncogenic KRAS-driven lung cancer.

cancer biology↗

bioRxiv2020

G-baToN: a versatile cell-cell interaction reporter system.

Cell-cell interactions influence all aspects of development, homeostasis, and disease. In cancer, interactions between cancer cells and stromal cells play a major role in nearly every step of carcinogenesis. Thus, the ability to record cell-cell interactions would facilitate mechanistic delineation of the role of cancer microenvironment. Here, we describe GFP-based Touching Nexus (G-baToN) which relies upon nanobody-directed fluorescent protein transfer to enable sensitive and specific labeling of cells after cell-cell interactions. G-baToN is a generalizable system that enables physical contact-based labeling between various cell types, including diverse cancer-stromal cell pairs. A suite of orthogonal baToN tools enables reciprocal cell-cell labeling, interaction-dependent cargo transfer, and the identification of higher-order cell-cell interactions across a wide range of cell types. The ability to track physically interacting cells with these simple and sensitive systems will greatly accelerate our understanding of the outputs of cell-cell interactions in cancer as well as across many biological processes.

bioengineering↗