Additional file 1 of Extracellular matrix profiles determine risk and prognosis of the squamous cell carcinoma subtype of non-small cell lung carcinoma
Creators: name: Parker, Amelia L.
name: Bowman, Elise
name: Zingone, Adriana
name: Ryan, Brid M.
name: Cooper, Wendy A.
name: Kohonen-Corish, Maija
name: Harris, Curtis C.
name: Cox, Thomas R.
Publisher: name: figshare
Year: 2022
Subjects: subject: Biochemistry
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Physiology
subject: Biotechnology
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: Developmental Biology
subject: Cancer
subject: 110309 Infectious Diseases
subject: FOS: Health sciences

description: Additional file 1: Supplementary Materials and Methods. Figure S1. The extracellular matrix is significantly dysregulated in tumor compared with non-tumor tissue in SqCC. Figure S2. ECM changes associated with increased lung cancer risk and premalignant progression. Figure S3. A) tSNE plot visualization of the expression scores for matrix risk signature genes with positive (B) and negative (C) odds ratios for different cell types (A) in SqCC scRNAseq data presented in Fig. 2D. D) ROC analysis of the minimum matrix risk signature distinguishing progressive from regressive premalignant lesions. E) Matrix risk score at age at diagnosis in the TCGA cohort. Blue line shows linear regression with standard error indicated by grey shading. p=0.00073, r=-0.23, Spearman’s correlation. Figure S4. The ECM-High matreotype is associated with poor prognosis. A-B) Relative Cluster Stability Index (A) and p-values (B) for different cluster numbers confirm the presence of three major matreotypes in SqCC. C) The correlation plot for samples corresponding to the heatmap in Fig. 3A). D) Heatmap of marker genes for the ECM-High and ECM-Low matreotypes in the TCGA cohort. E-F) Matreotype association with survival in early stage (E, Stage I and II) patients (Ci) and late stage patients (F, Stage III and IV) patients in the TCGA LUSC cohort. G) Recurrence-free survival for ECM-High and ECM-Low matreotypes in the UHN cohort of early-stage tumors (log-rank p=0.19). H) Representative H&amp;E and picrosirius red-stained tissue microarray cores corresponding to high (upper panel) and low (lower panel) picrosirius-red-stained tumors. Scale bar = 500 μm. I-J) Overall survival of patients according to picosirius red staining for tumors across all stages (I, univariate cox-proportional hazards model HR = 1.76 [1.04-2.98], p=0.035; multivariate coxproportional hazards model HR = 1.87 [1.09-3.2], p=0.023) and stages I and IIA only (J, univariate cox-proportional hazards model HR = 2.4 [1.3-4.4], p=0.0051; multivariate cox-proportional hazards model HR = 2.4 [1.3-4.4], p=0.0051)). Multivariate cox-proportional hazards models include stage as a clinical covariate. Figure S5. A) Mutational frequency plot showing no enrichment of driver mutations or FGFR amplification in each matreotype. B) Mutational frequency plot showing the top mutated genes differentially enriched in the ECM-High vs ECMLow matreotype tumors. C) Disease-specific survival of adenocarcinoma samples assigned to SqCC matreotypes shows no significant association of SqCC matreotypes with prognosis in the adenocarcinoma subtype (log-rank p=0.74). D-E) The hazard ratios for Disease-specific survival (D) and Progression-Free Survival (E) of ECM-High pan-cancer tumors compared with ECM-Low matreotypes in multiple cancer types. Red dots indicate tumor types with significant hazard ratios for the ECM-High matreotype compared with the ECM-Low matreotype. Hazard ratios and confidence intervals are in Additional File 1: Table S8. Figure S6. SqCC Matreotypes have distinct immunological ecosystems. Figure S7. Extracellular Matrix components contribute to signaling pathways associated with prognosis. Figure S8. ECM-driven integrin signaling is associated with EMT and fibroblast activation in the ECM-High matreotype. Figure S9. The poor prognosis matreotype overlaps with ECM remodeling in Idiopathic Pulmonary Fibrosis. Table S1. Matrix Risk Score. Table S2. Pan-Cancer Risk Score Results. Table S3. Optimized Minimum Matrisomal Linear Model for Premalignant Progression. Table S4. Clinicodemographic Features of the TCGA Matreotypes. Table S5. Clinicodemographic Features of the NCI-MD cohort. Table S6. Clinicodemographic Features of the Tissue Microarray Cohort. Table S7. Cox Proportional Hazards Model Analysis of Picrosirius Red stained TMAs. Table S8. Pan-Cancer Prognostic Matreotype Results.