Biology subjects

Wistuba, I. I.

Publications and source records attributed to Wistuba, I. I..

7 recordsLinked to original sources

bioRxiv2022

An atlas of epithelial cell states and plasticity in lung adenocarcinoma

Understanding cellular processes underlying early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation.

cancer biology↗

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↗

bioRxiv2021

The histologic phenotype of lung cancers may be driven by transcriptomic features rather than genomic characteristics

Histology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conducted whole-exome sequencing(WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology. A median of 68.9% of point mutations and 83% of copy number aberrations were shared between different histologic components within the same tumors. Furthermore, different histologic components within the tumors demonstrated similar subclonal architecture. On the other hand, transcriptomic profiling revealed shared pathways between the same histologic subtypes from different patients, which was supported by the analyses of the transcriptomic data from 141 cell lines and 343 lung cancers of different histologic subtypes. These data suggest that histology of lung cancers may be determined at the transcriptomic level rather than the genomic level.

genomics↗

bioRxiv2020

Resolving the spatial and cellular architecture of lung adenocarcinoma by multi-region single-cell sequencing

Little is known of the geospatial architecture of individual cell populations in lung adenocarcinoma (LUAD) evolution. Here, we perform single-cell RNA sequencing of 186,916 cells from 5 early-stage LUADs and 14 multi-region normal lung tissues of defined spatial proximities from the tumors. We show that cellular lineages, states, and transcriptomic features geospatially evolve across normal regions to LUADs. LUADs also exhibit pronounced intratumor cell heterogeneity within single sites and transcriptional lineage-plasticity programs. T regulatory cell phenotypes are increased in normal tissues with proximity to LUAD, in contrast to diminished signatures and fractions of cytotoxic CD8+ T cells, antigen-presenting macrophages and inflammatory dendritic cells. We further find that the LUAD ligand-receptor interactome harbors increased expression of epithelial CD24 which mediates pro-tumor phenotypes. These data provide a spatial atlas of LUAD evolution, and a resource for identification of targets for its treatment. Statement of significanceThe geospatial ecosystem of the peripheral lung and early-stage LUAD is not known. Our multi-region single-cell sequencing analyses unravel cell populations, states, and phenotypes in the spatial and ecological evolution of LUAD from the lung that comprise high-potential targets for early interception.

cancer biology↗

bioRxiv2020

Relationship Between Neuroendocrine and Immune Gene Expression in Small Cell Lung Cancer

Small cell lung cancer (SCLC) is classified as a high-grade neuroendocrine (NE) tumor, but a subset of SCLC has been termed "variant" due to the loss of NE characteristics. In this study, we computed NE scores for patient-derived SCLC cell lines and xenografts, as well as human tumors. We aligned NE properties with transcription factor-defined molecular subtypes. Then we investigated the different immune phenotypes associated with high and low NE scores. We found repression of immune response genes as a shared feature between classic SCLC and pulmonary neuroendocrine cells of the healthy lung. With loss of NE fate, variant SCLC tumors regain cell-autonomous immune gene expression and exhibit higher tumor-immune interactions. Pan-cancer analysis revealed this NE lineage-specific immune phenotype in other cancers. Additionally, we observed MHC I re-expression in SCLC upon development of chemoresistance. These findings provide a new framework to guide design of treatment regimens in SCLC.

cancer biology↗

bioRxiv2020

Single-cell analysis of human lung epithelia reveals concomitant expression of the SARS-CoV-2 receptor ACE2 with multiple virus receptors and scavengers in alveolar type II cells

The novel coronavirus SARS-CoV-2 was identified as the causative agent of the ongoing pandemic COVID 19. COVID-19-associated deaths are mainly attributed to severe pneumonia and respiratory failure. Recent work demonstrated that SARS-CoV-2 binds to angiotensin converting enzyme 2 (ACE2) in the lung. To better understand ACE2 abundance and expression patterns in the lung we interrogated our in-house single-cell RNA-sequencing dataset containing 70,085 EPCAM+ lung epithelial cells from paired normal and lung adenocarcinoma tissues. Transcriptomic analysis revealed a diverse repertoire of airway lineages that included alveolar type I and II, bronchioalveolar, club/secretory, quiescent and proliferating basal, ciliated and malignant cells as well as rare populations such as ionocytes. While the fraction of lung epithelial cells expressing ACE2 was low (1.7% overall), alveolar type II (AT2, 2.2% ACE2+) cells exhibited highest levels of ACE2 expression among all cell subsets. Further analysis of the AT2 compartment (n = 27,235 cells) revealed a number of genes co-expressed with ACE2 that are important for lung pathobiology including those associated with chronic obstructive pulmonary disease (COPD; HHIP), pneumonia and infection (FGG and C4BPA) as well as malarial/bacterial (CD36) and viral (DMBT1) scavenging which, for the most part, were increased in smoker versus light or non-smoker cells. Notably, DMBT1 was highly expressed in AT2 cells relative to other lung epithelial subsets and its expression positively correlated with ACE2. We describe a population of ACE2-positive AT2 cells that co-express pathogen (including viral) receptors (e.g. DMBT1) with crucial roles in host defense thus comprising plausible phenotypic targets for treatment of COVID-19.

genomics↗

bioRxiv2017

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Transcriptomic deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step towards linking tumor transcriptomic data with clinical outcomes. An R package, scripts and data are available: https://github.com/wwylab/DeMixT/.

bioinformatics↗