Biology subjects

Minna, J.

Publications and source records attributed to Minna, J..

10 recordsLinked to original sources

bioRxiv2024

Cancer Cachexia in STK11/LKB1-mutated NSCLC is Dependent on Tumor-secreted GDF15

Cachexia is a wasting syndrome comprised of adipose, muscle, and weight loss observed in cancer patients. Tumor loss-of-function mutations in STK11/LKB1, a regulator of the energy sensor AMP-activated protein kinase, induce cancer cachexia (CC) in preclinical models and are associated with cancer-related weight loss in NSCLC patients. Here we characterized the relevance of the NSCLC-associated cachexia factor growth differentiation factor 15 (GDF15) in several patient-derived and genetically engineered STK11/LKB1-mutant NSCLC cachexia lines. Both tumor mRNA expression and serum concentrations of tumor-derived GDF15 were significantly elevated in multiple mice transplanted with patient-derived STK11/LKB1-mutated NSCLC lines. GDF15 neutralizing antibody administered to mice transplanted with patient- or mouse-derived STK11/LKB1-mutated NSCLC lines suppressed cachexia-associated adipose loss, muscle atrophy, and changes in body weight. The silencing of GDF15 in multiple human NSCLC lines was also sufficient to eliminate in vivo circulating GDF15 levels and abrogate cachexia induction, suggesting that tumor and not host tissues represent a key source of GDF15 production in these cancer models. Finally, reconstitution of wild-type STK11/LKB1 in a human STK11/LKB1 loss-of-function NSCLC line that normally induces cachexia in vivo correlated with the absence of tumor-secreted GDF15 and rescue from the cachexia phenotype. The current data provide evidence for tumor-secreted GDF15 as a conduit and a therapeutic target through which NSCLCs with STK11/LKB1 loss-of-function mutations promote cachexia-associated wasting.

cancer biology↗

bioRxiv2022

A comparative study of neuroendocrine heterogeneity in SCLC and NBL

Lineage plasticity has long been documented in both small cell lung cancer (SCLC) and neuroblastoma (NBL), two clinically distinct neuroendocrine (NE) cancers. In this study, we quantified the NE features of cancer as NE scores and performed a systematic comparison of SCLC and NBL. We found NBL and SCLC cell lines have highly similar molecular profiles and shared therapeutic sensitivity. In addition, NE heterogeneity was observed at both the inter- and intra-cell line levels. Surprisingly, we did not find a significant association between NE scores and overall survival in SCLC or NBL. We described many shared and unique NE score-associated features between SCLC and NBL, including dysregulation of Myc oncogenes, alterations in protein expression, metabolism, drug resistance, and selective gene dependencies. Our work establishes a reference for molecular changes and vulnerabilities associated with NE to non-NE transdifferentiation through mutual validation of SCLC and NBL samples.

bioinformatics↗

bioRxiv2022

Brief report: Reclassifying SCLC-Y as SMARCA4 deficient malignancies - resolving the controversy

IntroductionThe classification of small cell lung cancer (SCLC) into distinct molecular subtypes defined by ASCL1, NEUROD1, POU2F3 or YAP1 expression, paves the way for the development of targeted therapeutics. However, the existence of a distinct YAP1-expressing SCLC subtype remains controversial. Here we have undertaken a detailed molecular and histological characterisation of YAP1 expressing SCLC-Y to understand the biology of this proposed subtype. MethodsThe mutational landscape of human SCLC cell lines was interrogated to identify pathogenic genomic alterations unique to SCLC-Y. Xenograft tumours generated from cell lines representing the molecular subtypes of SCLC (SCLC-A, -N, -P and -Y) were evaluated by a panel of pathologists. Diagnoses were validated by transcriptomic analysis of primary tumour and human cell line datasets. ResultsUnexpectedly, pathogenic mutations in SMARCA4 were identified in six of eight SCLC-Y cell lines and correlated with reduced SMARCA4 mRNA and protein expression. Pathologist evaluations revealed that SMARCA4-deficient SCLC-Y tumours exhibited features consistent with thoracic SMARCA4-deficient undifferentiated tumours (SMARCA4-UT). Similarly, the transcriptional profile SMARCA4-mutant SCLC-Y lines more closely resembled primary SMARCA4-UT, or SMARCA4-deficient non-small cell carcinoma, than SCLC. Combining clinical, pathological, transcriptomic, and genetic data we found little evidence to support a diagnosis of SCLC for any of the YAP1-expressing cell lines originally used to define the SCLC-Y subtype. ConclusionsSCLC-Y cell lines harbour inactivating SMARCA4 mutations and exhibit characteristics consistent with SMARCA4-deficient malignancies rather than SCLC. Our findings suggest that, unlike ASCL1, NEUROD1 and POU2F3, YAP1 is not a subtype defining transcription factor in SCLC.

cancer biology↗

bioRxiv2021

Phosphatidylserine Receptors Enhance SARS-CoV-2 Infection: AXL as a Therapeutic Target for COVID-19

AUTHOR SUMMARYPhosphatidylserine (PS) receptors are PS binding proteins that mediate uptake of apoptotic bodies. Many enveloped viruses utilize this PS/PS receptor mechanism to adhere to and internalize into the endosomal compartment of cells and this is termed apoptotic mimicry. For viruses that have a mechanism(s) of endosomal escape, apoptotic mimicry is a productive route of virus entry. We evaluated if PS receptors serve as cell surface receptors for SARS-CoV-2 and found that the PS receptors, AXL, TIM-1 and TIM-4, facilitated virus infection when low concentrations of the SARS-CoV-2 cognate receptor, ACE2, was present. Consistent with the established mechanism of PS receptor utilization by other viruses, PS liposomes competed with SARS-CoV-2 for binding and entry. We demonstrated that this PS receptor enhances SARS-CoV-2 binding to and infection of an array of human lung cell lines and is an under-appreciated but potentially important host factor facilitating SARS-CoV-2 entry.

microbiology↗

bioRxiv2021

Targeting de novo lipogenesis and the Lands cycle induces ferroptosis in KRAS-mutant lung cancer

Mutant KRAS (KM) is the most common oncogene in lung cancer (LC). KM regulates several metabolic networks, but their role in tumorigenesis is still not sufficiently characterized to be exploited in cancer therapy. To identify metabolic networks specifically deregulated in KMLC, we characterized the lipidome of genetically engineered LC mice, cell lines, patient derived xenografts and primary human samples. We also determined that KMLC, but not EGFR-mutant (EGFR-MUT) LC, is enriched in triacylglycerides (TAG) and phosphatidylcholines (PC). We also found that KM upregulates fatty acid synthase (FASN), a rate-limiting enzyme in fatty acid (FA) synthesis promoting the synthesis of palmitate and PC. We determined that FASN is specifically required for the viability of KMLC, but not of LC harboring EGFR-MUT or wild type KRAS. Functional experiments revealed that FASN inhibition leads to ferroptosis, a reactive oxygen species (ROS)-and iron-dependent cell death. Consistently, lipidomic analysis demonstrated that FASN inhibition in KMLC leads to accumulation of PC with polyunsaturated FA (PUFA) chains, which are the substrate of ferroptosis. Integrating lipidomic, transcriptome and functional analyses, we demonstrated that FASN provides saturated (SFA) and monounsaturated FA (MUFA) that feed the Lands cycle, the main process remodeling oxidized phospholipids (PL), such as PC. Accordingly, either inhibition of FASN or suppression of the Lands cycle enzymes PLA2 and LPCAT3, promotes the intracellular accumulation of lipid peroxides and ferroptosis in KMLC both in vitro and in vivo. Our work supports a model whereby the high oxidative stress caused by KM dictates a dependency on newly synthesized FA to repair oxidated phospholipids, establishing a targetable vulnerability. These results connect KM oncogenic signaling, FASN induction and ferroptosis, indicating that FASN inhibitors already in clinical trial in KMLC patients (NCT03808558) may be rapidly deployed as therapy for KMLC.

cancer biology↗

bioRxiv2020

The hexosamine biosynthesis pathway is a targetable liability in lung cancers with concurrent KRAS and LKB1 mutations.

In non-small cell lung cancer (NSCLC), concurrent mutations in the oncogene KRAS and the tumor suppressor STK11 encoding the kinase LKB1 result in aggressive tumors prone to metastasis but with liabilities arising from reprogrammed metabolism. We previously demonstrated perturbed nitrogen metabolism and addiction to an unconventional pathway of pyrimidine synthesis in KRAS/LKB1 co-mutant (KL) cancer cells. To gain broader insight into metabolic reprogramming in NSCLC, we analyzed tumor metabolomes in a series of genetically engineered mouse models with oncogenic KRAS combined with mutations in LKB1 or p53. Metabolomics and gene expression profiling pointed towards an activation of the hexosamine biosynthesis pathway (HBP), another nitrogen-related metabolic pathway, in both mouse and human KL mutant tumors. KL cells contain high levels of HBP metabolites, higher flux through the HBP pathway and elevated dependence on the HBP enzyme Glutamine-Fructose-6-Phosphate Transaminase 2 (GFPT2). GFPT2 inhibition selectively reduced KL cell growth in culture and xenografts. Our results define a new metabolic vulnerability in KL tumors and provide a rationale for targeting GFPT2 in this aggressive NSCLC subtype.

cancer biology↗

bioRxiv2019

Computational staining of pathology images to study tumor microenvironment in lung cancer

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). In order to facilitate the study of cellular spatial organization and interactions, we developed a comprehensive nuclei segmentation and classification tool to characterize the TME from standard Hematoxylin and Eosin (H&E)-stained pathology images. This tool can computationally \"stain\" different types of cell nuclei in H&E pathology images to facilitate pathologists in analyzing the TME.\n\nA Mask Regional-Convolutional Neural Network (Mask-RCNN) model was developed to segment the nuclei of tumor, stromal, lymphocyte, macrophage, karyorrhexis and red blood cells in lung adenocarcinoma (ADC). Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas (TCGA) lung ADC dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (pv= 0.001), with a hazard ratio of 2.23 [1.37-3.65] after adjusting for clinical variables. Furthermore, the image-derived TME features were significantly correlated with the gene expression of biological pathways. For example, transcription activation of both the T-cell receptor (TCR) and Programmed cell death protein 1 (PD1) pathways was positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway was positively correlated with the density of stromal cells.\n\nThis study developed a deep learning-based analysis tool to dissect the TME from tumor tissue images. Using this tool, we demonstrated that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. Although developed from the pathology images of lung ADC, this model can be adapted into other types of cancers.

bioinformatics↗

bioRxiv2019

METABOLIC DIVERSITY IN HUMAN NON-SMALL CELL LUNG CANCER CELLS

Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes including signal transduction and gene expression patterns arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here we quantified over 100 metabolic features, mostly from 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.

cancer biology↗

bioRxiv2018

Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis

Signaling pathway models are largely based on the compilation of literature data from heterogeneous cellular contexts. Indeed, de novo reconstruction of signaling interactions from large-scale molecular profiling is still lagging, compared to similar efforts in transcriptional and protein-protein interaction networks. To address this challenge, we introduce a novel algorithm for the systematic inference of protein kinase pathways, and applied it to published mass spectrometry-based phosphotyrosine profile data from 250 lung adenocarcinoma (LUAD) samples. The resulting network includes 43 TKs and 415 inferred, LUAD-specific substrates, which were validated at >60% accuracy by SILAC assays, including \"novel substrates of the EGFR and c-MET TKs, which play a critical oncogenic role in lung cancer. This systematic, data-driven model supported drug response prediction on an individual sample basis, including accurate prediction and validation of synergistic EGFR and c-MET inhibitor activity in cells lacking mutations in either gene, thus contributing to current precision oncology efforts.

systems biology↗

bioRxiv2018

Lung Cancer Explorer (LCE): an open web portal to explore gene expression and clinical associations in lung cancer

We constructed a lung cancer-specific database housing expression data and clinical data from over 6,700 patients in 56 studies. Expression data from 23 \"whole-genome\" based platforms were carefully processed and quality controlled, whereas clinical data were standardized and rigorously curated. Empowered by this lung cancer database, we created an open access web resource - the Lung Cancer Explorer (LCE), which enables researchers and clinicians to explore these data and perform analyses. Users can perform meta-analyses on LCE to gain a quick overview of the results on tumor vs normal differential gene expression and expression-survival association. Individual dataset-based survival analysis, comparative analysis, and correlation analysis are also provided with flexible options to allow for customized analyses from the user.

cancer biology↗