Additional file 1 of Arachidonic acid drives adaptive responses to chemotherapy-induced stress in malignant mesothelioma

Figure S1. PUFAs were increased in MPM supernatant after pem treatment. Histograms showing the levels of arachidonate (20:4n6) (upper panel), docosahexaenoate (DHA; 22:6n3) (middle panel), eicosenoate (20:1n9 or 11) (lower panel) in the ctrl- or pem-treated MPM cell lines indicated. Statistics: * p0.05). (PPTX 62 kb

Additional file 3 of Arachidonic acid drives adaptive responses to chemotherapy-induced stress in malignant mesothelioma

Figure S3. Arachidonic acid and not its downstream metabolites , mediates the early increase of ALDHbright cells and the NFkB activation. Representative graphs showing the percentage of HP1 ALDHbright cells detected by FACS analysis after addition of ctrl (saline), AA, in absence or presence of pre-treatment with indomethacin (10 microMol/L) and A-64077 (Zileuton) (5 microMol/L) (90 minutes before addition of AA). Left panel; percentage of ALDHbright cells detected 24 hrs after addition of the indicated drugs. Right panel: percentage of ALDHbright cells detected 72 hrs after addition of the indicated drugs. Statistics: * p0.05). (PPTX 233 kb

Additional file 4 of Arachidonic acid drives adaptive responses to chemotherapy-induced stress in malignant mesothelioma

Table S1.. Heat map of the biochemicals profiled in this study. Mean values expressed as fold of change among ctrl- and pemetrexed treated- samples after scaling and background subtraction were expressed with different colors, as statistically significant (green, red) or as approaching significance (pink, light green), respectively. The ratio between spent media and cell free media for ctrl- and pem-treated cells is reported as well to show metabolite variations independent from the presence of cultured cells

Additional file 2 of Arachidonic acid drives adaptive responses to chemotherapy-induced stress in malignant mesothelioma

Figure S2. No significant apoptosis followed pem treatment at 24hrs. Viability assay. Histograms showing the percentage of Sytox Blue negative cells at 0 and up to 96hrs after pem treatment. Statistics: * p0.05). (PPTX 632 kb

Table_1_A novel panel of clinically relevant miRNAs signature accurately differentiates oral cancer from normal mucosa.xlsx

IntroductionAlthough a considerable body of knowledge has been accumulated regarding the early diagnosis and treatment of oral squamous cell carcinoma (OSCC), its survival rates have not improved over the last decades. Thus, deciphering the molecular mechanisms governing oral cancer will support the development of even better diagnostic and therapeutic strategies. Previous studies have linked aberrantly expressed microRNAs (miRNAs) with the development of OSCC.MethodsWe combined bioinformatical and molecular methods to identify miRNAs with possible clinical significance as biomarkers in OSCC. A set of 10 miRNAs were selected via an in silico approach by analysing the 3’untranslated regions (3’UTRs) of cancer-related mRNAs such as FLRT2, NTRK3, and SLC8A1, TFCP2L1 and etc. RT-qPCR was used to compare the expression of in silico identified miRNAs in OSCC and normal tissues (n=32).ResultsAmong the screened miRNAs, miR-21-5p (p DiscussionOur results demonstrate that a novel panel consisting of miR-21-5p, miR-93-5p, miR-133b, miR-146b-5p, miR-155-5p and miR-182-5p could be used as OSCC-specific molecular signature with diagnostic and prognostic significance related to OS and DFS.</p

Table_2_A novel panel of clinically relevant miRNAs signature accurately differentiates oral cancer from normal mucosa.xls

IntroductionAlthough a considerable body of knowledge has been accumulated regarding the early diagnosis and treatment of oral squamous cell carcinoma (OSCC), its survival rates have not improved over the last decades. Thus, deciphering the molecular mechanisms governing oral cancer will support the development of even better diagnostic and therapeutic strategies. Previous studies have linked aberrantly expressed microRNAs (miRNAs) with the development of OSCC.MethodsWe combined bioinformatical and molecular methods to identify miRNAs with possible clinical significance as biomarkers in OSCC. A set of 10 miRNAs were selected via an in silico approach by analysing the 3’untranslated regions (3’UTRs) of cancer-related mRNAs such as FLRT2, NTRK3, and SLC8A1, TFCP2L1 and etc. RT-qPCR was used to compare the expression of in silico identified miRNAs in OSCC and normal tissues (n=32).ResultsAmong the screened miRNAs, miR-21-5p (p DiscussionOur results demonstrate that a novel panel consisting of miR-21-5p, miR-93-5p, miR-133b, miR-146b-5p, miR-155-5p and miR-182-5p could be used as OSCC-specific molecular signature with diagnostic and prognostic significance related to OS and DFS.</p

Table_3_A novel panel of clinically relevant miRNAs signature accurately differentiates oral cancer from normal mucosa.xlsx

IntroductionAlthough a considerable body of knowledge has been accumulated regarding the early diagnosis and treatment of oral squamous cell carcinoma (OSCC), its survival rates have not improved over the last decades. Thus, deciphering the molecular mechanisms governing oral cancer will support the development of even better diagnostic and therapeutic strategies. Previous studies have linked aberrantly expressed microRNAs (miRNAs) with the development of OSCC.MethodsWe combined bioinformatical and molecular methods to identify miRNAs with possible clinical significance as biomarkers in OSCC. A set of 10 miRNAs were selected via an in silico approach by analysing the 3’untranslated regions (3’UTRs) of cancer-related mRNAs such as FLRT2, NTRK3, and SLC8A1, TFCP2L1 and etc. RT-qPCR was used to compare the expression of in silico identified miRNAs in OSCC and normal tissues (n=32).ResultsAmong the screened miRNAs, miR-21-5p (p DiscussionOur results demonstrate that a novel panel consisting of miR-21-5p, miR-93-5p, miR-133b, miR-146b-5p, miR-155-5p and miR-182-5p could be used as OSCC-specific molecular signature with diagnostic and prognostic significance related to OS and DFS.</p

Additional file 1 of Immunosignatures associated with TP53 status and co-mutations classify prognostically head and neck cancer patients

Additional file 1: Fig. S1. A-D) Forest plot representing Odds ratio with 95% CI of clinical predictors of several immune cell types and functional gene sets by using regression models in HNSCC dataset from TCGA. Red line highlights the behaviour of PD-L1 for comparison with other gene sets. All lines that don’t cross the 1 value are statistically significant. Each variable was dichotomized in the models to compare subgroup of patients by HPV status (A), tumor mutational burden (TMB) (B), and TP53 mutational status in concomitance or not with other mutations among FAT1, CDKN2A, PIK3CA (mutX) (b and c, respectively). Fig. S2. A-F) Forest plot representing Odds ratio with 95% CI of clinical predictors of 26 immune cell types and functional gene sets by using regression models in HNSCC dataset from TCGA. Red line highlights the behaviour of PD-L1 for comparison with other gene sets. All lines that don’t cross the 1 value are statistically significant. Each variable was dichotomized in the models to compare subgroup of patients by gender (A), smoking history (B), tumor size (C), lympho-node status (D) and stage (E). Fig. S3. A) Overall Survival (left panel) and Disease Free Survival (right panel) in a TCGA cohort of HNSCC patients who did not receive neoadjuvant therapy. pM1 samples were excluded. Patients were divided based on high and low levels of the Immune Signature, defined as positive and negative z-scores of the average expression of immune gene sets, respectively. The Cox hazard regression model was adjusted for gender, TP53 mutation, HPV status, and smoking history. Differences between curves were evaluated by log-rank test. B) Overall Survival in a TCGA cohort of HPV-negative HNSCC patients, divided based on high and low levels of the Immune Score. Multivariate Cox regression was adjusted for gender, TP53 mutation, HPV status, and smoking history. Differences between curves were evaluated by log-rank test. Fig. S4. We assessed the proportions of mutation types within the evaluated genes using the TCGA HNSCC cohort, with data sourced from the CbioPortal. The final row displays the total number of mutations considered for the four genes and their respective distribution among mutation subtypes. Fig. S5. A) The average expression distributions of the Immune Signature were analyzed based on the mutational status and copy number alterations of three frequently mutated genes in TCGA HNSCC (Alt_X), namely CDKN2A, PIK3CA, and FAT1. Among the patients, genomic alterations were observed in CDKN2A, PIK3CA, and FAT1 genes in 32%, 21%, and 8% of cases, respectively. To evaluate the statistical significance, the Kruskal-Wallis test and Wilcoxon test were employed. B-C) Distributions of the average expression of the Immune Signature (panel B) and PDL1 (panel C) based on the mutational status of 108 HNSCC patients from the Huang et al. cohort. Statistical significance was assessed by the Wilcoxon test. D) Distributions of the average expression of the MYC signature from Ganci et al., based on the mutational status of 108 HNSCC patients in the Huang et al. cohort. Statistical significance was assessed by the Wilcoxon test. E) Spearman's correlation of the 22-gene MYC signature (Ganci et al.) and PDL1 expression. Fig. S6. A-C) box-plot of IS (A), PDL1 (B) and CTLA4 (C) expression in patients with high and low level expression of a 22 genes signature MYC dependent (Ganci et al.) in HNSCC datasets from TCGA. Statistical significance between distributions was assessed by Wilcoxon rank-sum test. Multivariate regression models were built to adjust the differences of the genes between patients with high and low MYC signature. The models include T status, TP53 mutation, gender, smoking status and, HPV status. High and low expression of the MYC signature were evaluated by positive and negative z-scores of the mean gene expression, respectively. D) box-plot of the mean expression of 26 immune gene sets in 28 pre-treated HNSCC patients with high and low level expression of a 22 genes signature MYC dependent in GSE195832 dataset from GEO. Statistical significance between distributions was assessed by Wilcoxon rank-sum test. E) Distributions of the c-Myc protein among different mutational status subgroups from a set of 339 HNSCC patients evaluated by reverse phase protein array (RPPA) in the TCGA cohort. P-values were evaluated by KruskalWallis test. Fig. S7. A) Heatmap of PDL1 and CTLA4 expression from 33 HNSCC cell lines harbouring TP53 mutation and 3 WT cell lines obtained from Iorio et al (array express, E-MTAB-3610) dataset (left panel), and the relative box-plots of the distributions (right panel). Differences were evaluated by Wilcoxon test. B) qRT-PCR analysis of PD-L1 in Cal27 and Detroit 562 cell lines depleted or not of p53 and YAP. Statistics (t-test): * p < 0.01, ** p<0.005. C) qRT-PCR analysis of PD-L1 in Cal27 and Detroit 562 cell lines treated with 5nM of Byl-719. Statistics (t-test): * p < 0.01, ** p<0.005. D) qRT-PCR analysis of PD-L1 in Cal27 treated with JQ-1. Bars indicate the average of at least three independent experiments. Statistics (t-test): * p < 0.01, ** p<0.005. E) qRT-PCR analysis of CTLA4 in Cal27 treated with JQ-1. Bars indicate the average of at least three independent experiments. Statistics (t-test): * p < 0.01, ** p<0.005. Fig. S8. A) Spearman's correlation of CTLA4 with aneuploidy scores in TCGA HNSCC patients. B) Spearman's correlation of the 22-gene MYC signature (Ganci et al.) and the average expression of the genes included in the HALLMARK PI3K_AKT_MTOR_SIGNALING pathway, which are specifically modulated between co-mutated patients and TP53 mutated patients. Fig. S9. A) Cell types enrichment in TCGA HNSCC of TP53 mutated patients and TP53 mutated patients who harboured other mutations. Scores were obtained from Xcell software. P-values were evaluated by Wilcoxon ranksum test. Fig. S10. A) Average expression of the 26 immune gene sets distribution in 102 patients treated with PDL1 inhibitors (GSE159067). The immune gene sets expression was evaluated splitting the patients according to the phenotype classification (“COLD” and “HOT” patients) obtained from Foy JP and colleagues. Differences were evaluated by Wilcoxon test. B) Distribution of the 27-gene signature used by Foy et al. to define the HOT score. The gene set was evaluated in the TCGA HNSCC cohort among WT patients, co-mutated patients, and TP53 only mutated patients. Statistical significance among groups was assessed by the Kruskal-Wallis test.C) Significantly modulated cell type marker genes in 102 patients from GEO database (GSE159067) among the 7 cell types previously identified in the deconvolution analysis of TCGA HNSCC data. Statistical significance between patients with high and low Immune Score was evaluated by Wilcoxon test. Immune Score was defined as z-score of the average expression of the 26 immune gene sets

Table S4: Gene Ontology analysis of LUAD tumors from A Division of Labor between YAP and TAZ in Non–Small Cell Lung Cancer

Gene Ontology results of either the YAP-correlated or TAZ-correlated genes in LUAD TCGA, determined by Metascape.</p

Supplementary Figures file from A Division of Labor between YAP and TAZ in Non–Small Cell Lung Cancer

Supplementary Figures</p