Jennifer M. Lee’s research while affiliated with Concordia University Ann Arbor and other places

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Publications (6)


Figure 1. Inducible and reversible L-iKRAS G12D mouse model of lung adenocarcinoma. (A) Schematic depicting L-iKRAS G12D mouse model and its inducibility and reversibility of KRAS G12D expression in club cells of the lung with dox and activation of mutant Trp53 expression by ad-Cre. (B) Kaplan-Meier survival analysis comparing control mice (n = 20, Ccsp-rtTa or TetO-Kras G12D with or without Trp53 -/+ allele and with and without dox or ad-Cre), Kras G12D (n = 11, Ccsp-rtTa; TetO-Kras G12D ; Trp53 -/+ on dox, but no ad-Cre or Ccsp-rtTa; TetO-Kras G12D on dox, with or without ad-Cre), and Kras G12D /Trp53 R172H/+ (n = 23, Ccsp-rtTa; TetO-Kras G12D ; Trp53 LSL-R172H/+ on dox plus ad-Cre). Log-rank (Mantel-Cox) test with statistically significant P value of 0.0033. Median survival for all groups is indicated in inset. One-way ANOVA with Tukey's post hoc test showed the median survival of the Kras G12D /Trp53 R172H/+ group was significantly lower than
Figure 2. Oncogenic KRAS changes epithelial gene expression in the tumor microenvironment. (A) UMAP visualization of scRNA-seq data showing unsupervised clustering of cells from L-iKRAS lung samples (KRAS ON = 21 weeks ON dox; KRAS OFF = 20 weeks ON dox + 1 week OFF). Each color represents a distinct cell cluster. (B) UMAP visualization of scRNA-seq data showing overlap of KRAS ON and KRAS OFF groups. (C) Bar graph comparing cell cluster breakdown per sample (red blood cells were removed). (D) UMAP visualization of defined lung epithelial clusters from KRAS ON and KRAS OFF samples. (E) Violin plots of Cxcl2, Wnt4, and Ccl4 comparing expression levels between total epithelial cells from KRAS ON and KRAS OFF samples. Adjusted P value given above each violin plot. Median expression is indicated with a horizontal line. (F) Heatmap showing averaged scRNA-seq expression data (relative to the highest expressor) for genes in epithelial cells curated from the differential gene expression list. Genes from E are marked with an asterisk. (G) GSEA plot of KRAS ON versus KRAS OFF lung epithelia showing the running enrichment score for the "HALLMARK_KRAS_SIGNALING_DN" gene set. Normalized enrichment score (NES) = -1.430058344. Adjusted P value = 0.043658317.
Figure 3. Oncogenic KRAS changes fibroblast gene expression in the tumor microenvironment. (A) Representative images of αSMA/PDGFR/DAPI. Scale bars: 25 mM. (B) Quantification of percentage αSMA + area of total PDGFR + area. (C) Percentage PDGFR + area of total area. (D) UMAP visualization of scRNA-seq data showing unsupervised clustering of subsetted fibroblasts from KRAS ON and KRAS OFF samples. (E) Violin plots of Ccl4, Gas6, Cxcl2, and Timp3 comparing expression levels between total epithelial cells from KRAS ON and KRAS OFF samples. Adjusted P value given above each violin plot. Median expression is indicated with a horizontal line. (F) Heatmap showing averaged scRNA-seq expression data (relative to the highest expressor) for genes in fibroblasts (pericytes removed) curated from the differential gene expression list. Genes from E are marked with an asterisk.
Figure 4. Oncogenic KRAS regulates myeloid compartment of the lung adenocarcinoma microenvironment. (A) Representative images of MPO/F4/80/ECAD/ DAPI. Scale bars: 25 mM. (B) Quantification of percentage F4/80 + area of total area. (C) UMAP visualization of scRNA-seq data showing unsupervised clustering of subsetted myeloid cells from KRAS ON and KRAS OFF samples. (D) Violin plots of Apoe, Mrc1, Csf1r, and C1qa comparing expression levels between macrophages from KRAS ON and KRAS OFF samples. Median expression is indicated with a horizontal line. (E) Heatmap showing averaged scRNA-seq expression data (relative to the highest expressor) for genes in macrophages (combined macrophages, interstitial macrophages, and alveolar macrophages) curated from the differential gene expression list. Genes from D are marked with an asterisk. (F) Violin plots of Ccl3, Cd274, and Icam1 comparing expression levels between neutrophils from KRAS ON and KRAS OFF samples. Median expression is indicated with a horizontal line. (G) Heatmap showing averaged scRNA-seq expression data (relative to the highest expressor) for genes in neutrophils from differential gene expression list. Genes from F are marked with an asterisk. (H) Violin plots showing expression of Egfr, Tgfbr1, and Cxcr2 across all identified cell populations in KRAS ON and KRAS OFF samples combined.
Figure 5. Identification of KRAS G12D -dependent immunosuppressive secretome. (A) Schematic depicting generation of LC3-547, an L-iKRAS cancer cell line from a murine lung tumor. (B) Representative Western blot depicting RAS G12D and p53 protein expression in LC3-547 cells, which were cultured in dox-containing media for 24 hours prior to withdrawal of dox, and in A549 cells as controls. (C) Representative Western blot of RAS G12D expression, p-ERK1/2, and vinculin in LC3-547 cells treated with 1 μM MRTX or 1 μM sotorasib (Soto) for 6 hours. (D) Depiction of the experimental outline for the collection of RNA and tumor-conditioned media (TCM) from LC3-547 cells treated with DMSO, MRTX, or Soto for subsequent analyses. All experiments were repeated at least 3 times, each time with 3 technical replicas. (E) qRT-PCR for Tgfa and Cxcl5 of LC3-547 cells treated with DMSO or 500 nM MRTX for 48 hours. Data are represented as mean ± SEM, and statistical significance was determined with a 2-tailed Student's t test for unpaired samples. (F) Heatmap with z score of Luminex data of TCM from LC3-547 cells treated with 500 nM MRTX, 500 nM Soto, or equimolar concentration DMSO for 48 hours depicting cytokine and growth factor secretion. (G) Quantification (pg/mL) of indicated cytokines in TCM from treated LC3-547 cells. Data are represented as mean ± SEM. Statistical significance was determined using 1-way ANOVA with Tukey's post hoc test. (H) Representative images of p-EGFR/ECAD/DAPI. Scale bars: 25 mM. (I) Quantification of percentage p-EGFR + area of total ECAD + area. (J) Representative Western blot of KRAS G12D expression and p-ERK1/2 in human KRAS G12D lung adenocarcinoma cell line, A427, upon 3 hours treatment with 1 μM MRTX or 1 μM Soto or equimolar DMSO. (K) Experimental design: RNA and TCM were harvested from human KRAS G12D cancer cells cultured with DMSO, 100 nM MRTX, or 500 nM Soto. All experiments were repeated at least 3 times, each time with 3 technical replicas. (L) Quantification of CXCL1, CCL5, and TGF-α cytokines in A427 TCM from cells treated with DMSO, MRTX, or Soto. Data are represented as mean ± SEM and differences were evaluated by 1-way ANOVA with post hoc Tukey's HSD test.

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KRASG12D drives immunosuppression in lung adenocarcinoma through paracrine signaling
  • Article
  • Full-text available

January 2025

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37 Reads

JCI Insight

Emily L. Lasse-Opsahl

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Elyse McLintock

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[...]

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Lung cancer is the leading cause of cancer deaths in the United States. New targeted therapies against the once-deemed undruggable oncogenic KRAS are changing current therapeutic paradigms. However, resistance to targeted KRAS inhibitors almost inevitably occurs; resistance can be driven by tumor cell-intrinsic changes or by changes in the microenvironment. Here, we utilized a genetically engineered mouse model of KRASG12D-driven lung cancer that allows for inducible and reversible expression of the oncogene: activation of oncogenic KRASG12D induces tumor growth; conversely, inactivation of KRASG12D causes tumor regression. We showed that in addition to regulating cancer cell growth and survival, oncogenic KRAS regulated the transcriptional status of cancer-associated fibroblasts and macrophages in this model. Utilizing ex vivo approaches, we showed that secreted factors from cancer cells induced the expression of multiple cytokines in lung fibroblasts, and in turn drove expression of immunosuppressive factors, such as arginase 1, in macrophages. In summary, fibroblasts emerged as a key source of immune regulatory signals, and a potential therapeutic target for improving the efficacy of KRAS inhibitors in lung cancer.

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Abstract 6841: Mutation specific mechanisms of resistance to oncogenic KRAS inhibition

March 2024

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11 Reads

Cancer Research

Mechanisms of resistance to inhibitors against mutant KRAS are linked to the remodeling of the tumor microenvironment (TME). Understanding this remodeling process during intervention and tumor recurrence will likely guide the development of future therapeutic treatment paradigms with long-term responses. 56% of never-smokers with non-small cell lung cancer have KRASG12D mutant cancer and respond little to immune checkpoint therapy (ICI) compared to smokers, who often have mutations in KRASG12C. Our analysis of the TME in a unique KrasG12D inducible and reversible mouse model of NSCLC using single cell RNA sequencing data shows KrasG12D-dependent control of the TME, particularly of the expression of PDL1 in a subset of myeloid cells. PDL1 expression was found to be KrasG12D dependent as it was reduced significantly when KrasG12D was turned ON in mouse lungs. Interestingly, when KrasG12D was turned OFF, the expression of PDL1 increased, indicating mechanisms of resistance. Furthermore, the cytokine expression and secretion of IL6, a known regulator of PDL1 expression, increased in lung tissue and lung cancer cells derived from this model upon genetic and chemical (MRTX1133) Kras inhibition. Moreover, the mechanisms of resistance to oncogenic Kras inhibition appear to be mutant specific. Transcriptome and secretome analyses in human and murine KrasG12D or KrasG12C mutant lung cancer cells show distinct cytokine regulation upon KRAS inhibition. Identification of mechanisms of resistance like the induction of PDL1 expression upon KrasG12D inhibition may provide a strong rationale for co-treatment of KRAS inhibitors with ICI for KrasG12D never-smokers with NSCLC. Citation Format: Ivana Barravecchia, Emily Lasse-Opsahl, Sophia Cavanaugh, Lily Rober, Rachael Baliira, Marzia Robotti, Rachael Hinshaw, Jennifer M. Lee, Yaqing Zhang, Marina Pasca di Magliano, Stefanie Galbán. Mutation specific mechanisms of resistance to oncogenic KRAS inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6841.


Figure 1. Inducible and reversible L-iKras mouse model of lung adenocarcinoma: (A) Genetic makeup of the LiKras* mouse model: CCSP-rtTa; TetO-Kras G12D ; Trp53 LSL-R172H/+ . (B) Kaplan-Meier survival curves for L-iKras mice. Control (n=8), CCSP-rtTa; TetO-Kras G12D ; Trp53 -/+ (n=9), CCSP-rtTa; TetO-Kras G12D ; Trp53 LSL-R172H/+ (n=9). Log-rank (Mantel-Cox) test, p = 0.0097. (C) Experimental design: ad-Cre and doxy were administered to induce Trp53 R172H and Kras G12D expression in lung tissue. Doxy was withdrawn 17 weeks to reverse Kras G12D expression. Mice were sacrificed and lungs were harvested at 17, 18, 19, 21 weeks after study start. Controls include mice lacking TetO-Kras G12D or CCSP-rtTa cassettes, and mice with wildtype background. (D) Representative axial slices from CT scans of L-iKras mice at indicated time points. (E) Representative images of H&E stains of control and L-iKras lungs for indicated time points. Scale bar = 200 µM. (F) Western blot of Kras G12D protein expression in L-iKras lung tissue harvested at indicated timepoints. (G) Representative images of H&E staining of control and L-iKras lungs at the indicated time points. Scale bar = 100 µM. (H) Quantification of lesions in H&E stained lung sections from L-iKras and control mice with and without doxy withdrawal. Data represent mean ±SEM. Statistical analyses determined by one-way ANOVA with post-hoc Tukey HSD test. (I) Representative IF staining for phospho-ERK (green), E-cadherin (red), and DAPI (blue) in L-iKras lung tissue at indicated timepoints. Scale bar = 100 µM. (J) Quantification of % area of p-ERK+ cells per tumor area. Data represent mean ±SEM. Statistical analysis determined by unpaired T test.
Figure 2. Oncogenic Kras changes epithelial and fibroblast gene expression in the tumor microenvironment: (A) UMAP visualization of scRNAseq data showing unsupervised clustering of cells from L-iKras lung samples (Kras ON -21 weeks ON DOX, Kras OFF -20 weeks ON + 1 week OFF). Each color represents a distinct cell cluster. (B) UMAP visualization of scRNAseq data showing overlap of Kras ON and Kras OFF groups. (C) Bar graph comparing cell cluster breakdown per sample (red blood cells were removed). (D) UMAP visualization of defined lung epithelial clusters from Kras ON and Kras OFF samples. (E) Violin plots of Cxcl2, Wnt4, and Ccl4 comparing expression levels between total epithelial cells from Kras ON and Kras OFF samples. Adjusted p-value given above each violin plot. Median expression is marked. (F) Heatmap showing averaged scRNAseq expression data (relative to the highest expressor) for genes in epithelial cells curated from the differential gene expression list. (G) GSEA plot of Kras ON vs Kras OFF lung epithelia showing the running enrichment score for the 'HALLMARK_KRAS_SIGNALING_DN' gene set. NES = -1.430058344. Adjusted p-value = 0.043658317. (H) UMAP visualization of scRNAseq data showing unsupervised clustering of subsetted fibroblasts from Kras ON and Kras OFF samples. (I) Violin plots of Ccl4, Gas6, Cxcl2, and Timp3 comparing expression levels between total epithelial cells from Kras ON and Kras OFF samples. Adjusted p-value given above each violin plot. Median expression is marked. (J) Heatmap showing averaged scRNAseq expression data (relative to the highest expressor) for genes in fibroblasts (pericytes removed) curated from the differential gene expression list.
Figure 3. Oncogenic Kras regulates myeloid compartment of the lung adenocarcinoma microenvironment: (A) UMAP visualization of scRNAseq data showing unsupervised clustering of subsetted myeloid cells from Kras ON and Kras OFF samples. (B-C) Violin plots of C1qa, C1qb, C1qc, Apoe, Mrc1, and Csf1r comparing expression levels between macrophages from Kras ON and Kras OFF samples. Median expression is marked. (D) Heatmap showing averaged scRNAseq expression data (relative to the highest expressor) for genes in macrophages (combined macrophages, interstitial macrophages, and alveolar macrophages) curated from the differential gene expression list. (E) Violin plots of Ccl3, Cd274, and Icam1 comparing expression levels between neutrophils from Kras ON and Kras OFF samples. Median expression is marked. (F) Heatmap showing averaged scRNAseq expression data (relative to the highest expressor) for genes in neutrophils from differential gene expression list. (G) Violin plots showing expression Egfr, Tgfbr1 and Cxcr2 across all identified cell populations in both Kras ON and Kras OFF samples combined.
Figure 6. scRNAseq analysis of human lung adenocarcinoma recapitulates gene expression of distinct immunosuppressive markers from L-iKras epithelial and fibroblast secretomes: (A) UMAP visualization of scRNAseq data showing unsupervised clustering of cells from lung cancer samples. KRAS status either Kras G12D or KRAS wild-type [1]. Each color represents a distinct cell cluster. (B) Interactome showing potential ligand/receptor pair interactions that are significantly (adjusted p-value <0.05) upregulated in the Kras G12D driven lung cancer samples. (C) Heatmap showing averaged scRNAseq expression data (relative to the highest expressor) for genes in epithelial cells curated from the differential gene expression list. (D) Violin plots of CXCL1, CXCL2, and HDGF comparing expression levels between total epithelial cells from Kras G12D vs KRAS-wt samples. Adjusted p-values given above each violin plot. Median expression is marked. (E) Heatmap showing averaged scRNAseq expression data (relative to the highest expressor) for genes in human macrophages [1] curated from the differential gene expression list. (F) Violin plots of C1QB and C1QC comparing expression levels between total epithelial cells from Kras G12D vs KRAS-wt samples. Adjusted p-value given above each violin plot. Median expression is marked.
Oncogenic KRAS G12D extrinsically induces an immunosuppressive microenvironment in lung adenocarcinoma

January 2024

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129 Reads

Withdrawal Statement This manuscript has been withdrawn by the authors due to a dispute over co-first authorship that is currently being arbitrated by the medical school at our institution. Therefore, the authors do not wish this work to be cited as reference for the project. Upon completion of the arbitration process, we will take steps to revert the current withdrawn status. If you have any questions, please contact the corresponding author.


Modeling Molecular Pathogenesis of Idiopathic Pulmonary Fibrosis-Associated Lung Cancer in Mice

November 2023

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35 Reads

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3 Citations

Molecular Cancer Research

Idiopathic pulmonary fibrosis (IPF) is characterized by progressive, often fatal loss of lung function due to overactive collagen production and tissue scarring. Patients with IPF have a sevenfold-increased risk of developing lung cancer. The COVID-19 pandemic has increased the number of patients with lung diseases, and infection can worsen prognoses for those with chronic lung diseases and disease-associated cancer. Understanding the molecular pathogenesis of IPF-associated lung cancer is imperative for identifying diagnostic biomarkers and targeted therapies that will facilitate prevention of IPF and progression to lung cancer. To understand how IPF-associated fibroblast activation, matrix remodeling, epithelial-to-mesenchymal transition (EMT), and immune modulation influences lung cancer predisposition, we developed a mouse model to recapitulate the molecular pathogenesis of pulmonary fibrosis–associated lung cancer using the bleomycin and Lewis lung carcinoma models. We demonstrate that development of pulmonary fibrosis–associated lung cancer is likely linked to increased abundance of tumor-associated macrophages and a unique gene signature that supports an immune-suppressive microenvironment through secreted factors. Not surprisingly, preexisting fibrosis provides a pre-metastatic niche and results in augmented tumor growth, and tumors associated with bleomycin-induced fibrosis are characterized by a dramatic loss of cytokeratin expression, indicative of EMT. Implications This characterization of tumors associated with lung diseases provides new therapeutic targets that may aid in the development of treatment paradigms for lung cancer patients with preexisting pulmonary diseases.


Figure 1: Modeling Pathogenesis of Idiopathic Pulmonary Fibrosis-Associated Lung Cancer in a Murine Model. (A) Schematic indicating experimental outline: C57BL/6-albino mice were randomized into four experimental cohorts for all comparative studies: control, IPF, LC and IPF-LC. Mice in the control group received appropriate vehicles (control), mice in the IPF group received 0.5 mg/kg and 1 mg/kg of bleomycin on day 0 and day 4, respectively, by oropharyngeal aspiration (OA). Mice in the LC group received LLC-1 Luc cells (1x10^6 cells/100µl) by intravenous injection into the tail vein (IV) on day 14 and mice in the IPF-LC group received 0.5 mg/kg and 1 mg/kg of bleomycin (OA) on day 0 and day 4, respectively, and LLC-1 Luc cells (IV) on day 14 . Timepoints for micro CT and bioluminescence imaging (BLI) or tissue collection are denoted by a yellow star, a purple circle and a red arrow respectively. (B) Representative Micro CT images from all groups at study endpoint. Tumor lesions in LC group and IPF-LC group are indicated by red arrows and circled in pink. (C) Representative BLI images from mice in LC and IPF-LC groups at week 1 and 3 (day 21 and day 28) post LLC-1 Luc injection. (D) Quantification of bioluminescence at indicated timepoints of mice in the LC (n=4), and IPF-LC (n=4) groups. Data is presented +/-SEM and statistics were performed using oneway ANOVA with post-hoc Tukey (Honestly Significant Difference (HSD) test with statistical significance denoted as **p≤0.01. (E) Representative images of gross morphology, H&E and trichome staining of lung images and lung sections acquired at study endpoint at 5 weeks post first bleomycin injection. Black arrows indicate tumors on H&E stained lung sections of mice in LC and IPF-LC groups. (F) Quantification of the number of tumors in the LC and IPF-LC groups counted on 1x H&E-stained lung sections presented +/-SEM. Statistical test was performed with one-way ANOVA with post-hoc Tukey HSD test and significance indicated as *p≤0.05. (G) Quantification of the average tumor size in the lung in LC and IPF-LC groups measured on 1x H&E-stained lung sections. Data is represented +/-SEM and statistical significance determined using one-way ANOVA with post-hoc Tukey HSD test and significance of *p≤0.05. (H) Quantification of collagen in trichrome staining of lung sections. No statistical difference was determined between groups.
Figure 3: Increased Tumor associated Macrophages (TAMs) and CD274 expression in IPF-LC. (A) CyTOF of at 3 lungs per experimental group was performed at study endpoint (5 weeks post initial bleomycin injection). Representative tSNE plots of CyTOF analysis with specific immune cell clusters depicted and circled in corresponding colors (B)-(D) Quantification of abundance of cell specific expression markers for macrophages (CD45+, CD11b+, CD274+), TAMs (CD45+, CD206low, CD11c+) and MDSCs (CD45+, CD3+, Ly6Chigh), using FlowSOM-viSNE. Data +/-SEM with statistical analysis using One-way ANOVA with post-hoc Fisher's Least Significant Difference (LSD) Test (*p≤0.05, ns for no statistical difference). (E)-(F) Flow cytometry of lung tissue obtained from 2 mice per group at endpoint stained for CD274 marker expression using anti-CD274-APC. Statistical significance was determined using One-way ANOVA with post-hoc Fisher's Least Significant Difference (LSD) Test (*p≤0.05).
Figure 5: Pathway analysis identifies secreted factors uniquely regulated in IPF-LC. (A) Enrichment of mSigDB Hallmark gene sets (FDR < 0.05) for all IPF-LC-regulated genes (blue: upregulated; red: downregulated). (B) Selected genes, which appear in three indicated Hallmark gene sets depicted in (A) differentially expressed in IPF-LC vs control. Heatmap of log10 normalized counts (z score) for these selected genes in all groups: control, IPF, LC, IPF-LC. Symbols in legend indicate in which Hallmark gene sets they appeared (interferon gamma response, interferon alpha response, inflammatory response). (C) qRT-PCR of selected genes downregulated in the IPF-LC group, normalized to cyclophilin as housekeeping gene and fold change calculated compared to control. Significance was determined using unpaired student T-test of RNA obtained from lung tissue of three mice per control and IPF-LC group. * Denotes statistical significance of p<0.05, *** and p<0.005.
Figure 6: Pronounced EMT in IPF associated lung cancer. RNA sequencing data as described in Figure 4 was used to query genes important in EMT. (A) Heatmap of log10 normalized counts (z score) for selected mesenchymal and epithelial marker genes in all groups: control, IPF, LC, IPF-LC. (B) Representative images (4x, (40x inset)) of lung section in each group stained with H&E (top) and Cytokeratin 7 (bottom).
Modeling Molecular Pathogenesis of Idiopathic Pulmonary Fibrosis-Associated Lung Cancer in Mice

June 2023

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158 Reads

Idiopathic Pulmonary Fibrosis (IPF) is characterized by progressive, often fatal loss of lung function due to overactive collagen production and tissue scarring. IPF patients have a sevenfold-increased risk of developing lung cancer. The COVID-19 pandemic has increased the number of patients with lung diseases, and infection can worsen prognoses for those with chronic lung diseases and disease-associated cancer. Understanding the molecular pathogenesis of IPF associated lung cancer is imperative for identifying diagnostic biomarkers and targeted therapies that will facilitate prevention of IPF and progression to lung cancer. To understand how IPF-associated fibroblast activation, matrix remodeling, epithelial-mesenchymal transition, and immune modulation influences lung cancer predisposition, we developed a mouse model to recapitulate the molecular pathogenesis of pulmonary fibrosis-associated lung cancer using the bleomycin and the Lewis Lung Carcinoma models. Models of pulmonary fibrosis, particularly bleomycin-induced fibrosis, do not recapitulate all aspects of human disease; however, to simplify nomenclature, we refer to our bleomycin-induced fibrosis model as IPF. We demonstrate that development of pulmonary fibrosis-associated lung cancer is linked to increased recruitment or reprogramming of tumor-associated macrophages and a unique gene signature that supports an immune-suppressive microenvironment through secreted factors. Not surprisingly, pre-existing fibrosis provides a pre-metastatic niche and results in augmented tumor growth. Tumors associated with bleomycin-induced fibrosis are characterized by an epithelial-to-mesenchymal transition characterized by dramatic loss of cytokeratin expression. Implications: We provide new therapeutic targets that may aid the characterization of tumors associated with lung diseases and development of treatment paradigms for lung cancer patients with pre-existing pulmonary diseases.


Abstract 3855: Oncogenic Kras-mediated regulation of the tumor microenvironment in lung cancer

August 2020

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31 Reads

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1 Citation

Cancer Research

Lung cancer remains the leading cause of cancer-related deaths worldwide, with an estimated 1.6 million deaths each year. Non-small cell lung cancer (NSCLC) is with 85% by far the most common subtype of lung cancer, comprising adenocarcinomas and lung squamous cell carcinoma. Mutations in Kirsten rat sarcoma viral oncogene homolog (KRAS), epidermal growth factor receptor (EGFR) and anaplastic lymphoma receptor tyrosine kinase (ALK) genes are common with the worst overall survival for KRAS mutant adenocarcinoma patients. We have established a murine model of lung cancer, wherein expression of oncogenic can be controlled genetically, allowing activation of oncogenic G12D (*) to initiate tumor growth, tumor eradication upon * depletion and re-activation as a means to model relapse. Oncogenic depletion (deactivation) has previously been reported to result in tumor cell apoptosis even in the presence of tumor suppressor loss. However, the mechanisms of apoptosis, the role of the immune system on these changes, and the mechanisms allowing some tumor cells to escape apoptosis, which typically results in tumor relapse, are unknown. Here, we interrogated the immune response in mediating tumor regression and relapse using this genetically engineered models. Multiplex immunohistochemistry as well as CyTOF provided insight into the changes in immune contexture upon * depletion in mice haploinsufficient for tumor suppressor p53 or mutant for p53 (R172H). Interestingly, total number of T cells including cytotoxic T cells (CTLs) was elevated in lung tumors from p53 mutant mice supporting findings of heightened immune activation and overall response to immune therapy with an increased mutational burden. * inactivation and thus inhibition of MAPK signaling resulted in an overall decrease in abundance of CTLs and antigen presenting cells (APC) as well as engagement of CTL with tumor cells and APCs indicating a decrease in immune presence likely due to proceeding tumor cell kill and immune recruitment. However, intracellular distance of CTL with tumor cells indicated active tumor cell kill of the CTLs to eradicate remaining tumor cells. In summary, these findings support recent observation of increased immune activation in tumors with higher mutational load as well as changes mediated by inhibition of MAPK signaling which both maybe harnessed for enhancing future immunotherapies. Citation Format: Nina Steele, Kristena Y. Abdelmalak, Sarah F. Ferris, Jennifer M. Lee, Carlos Espinoza, Yaqing Zhang, Sundaresh Ram, Craig Galban, Nithya Ramnath, Timothy L. Frankel, Marina Pasca di Magliano, Stefanie Galbán. Oncogenic -mediated regulation of the tumor microenvironment in lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3855.

Citations (1)


... Lung lesion counting. To quantify the number of lesions per section of the lungs in all experimental groups, we scanned H&E-stained sections at ×1 magnification with the Nikon Supercool Scan 5000, as previously described (40). Three different readers counted lesions on at least 5 sections per experimental group. ...

Reference:

KRASG12D drives immunosuppression in lung adenocarcinoma through paracrine signaling
Modeling Molecular Pathogenesis of Idiopathic Pulmonary Fibrosis-Associated Lung Cancer in Mice

Molecular Cancer Research