Compiled in mid-2022, this dataset contains the raw data file, randomized ranked lists of R1 and R2 research institutions, and files created to support data visualization for Elizabeth Szkirpan's 2022 study regarding availability of data services and research data information via university libraries for online users. Files are available in Microsoft Excel formats.

Results of the Hierarchical Regression Analysis (Beta-Weights and R2).

Listing of the Genes included in the signatures used from MSigDB or published literatures. Listing of the detailed genes included in the aforementioned signatures, all of which used for gene set enrichment analysis (GSEA) or single sample GSEA (ssGSEA) analysis were extracted either from MSigDB ( http://software.broadinstitute.org/gsea/msigdb/index.jsp ) or published literatures. Table S2. Clinicopathological characteristics of our enrolled ovarian cancer patients analysed by immunohistochemistry dichotomised by SNAI2 protein expression levels in carcinoma cells or tumor stromal fibroblasts. The associations between SNAI2 expression level and variant clinicopathological characteristics of ovarian cancer patients were analysed with χ2 test (Fisher’s exact test). Table S3. Univariate and multivariate Cox regression analysis of SNAI2 expression level and overall survival of ovarian cancer patients. The prognostic significance of SNAI2 expression and other clinicopathological variables was first assessed by univariate Cox, followed by multivariate Cox regression analysis regarding to the overall survival time in a cohort of 160 ovarian cancer patients. Table S4. Genes significantly positively correlated with SNAI2 expression in the ‘mesenchymal’ subtype of ovarian TCGA dataset. List of the 111 genes that were calculated significantly positively related with SNAI2 expression in the ‘mesenchymal’ subtype of ovarian TCGA profile, using the R2 Genomic Analysis and Visualization Platform ( http://r2.amc.nl ). Genes with a Pearson correlation greater than or equal to 0.35 and a p value less than 1% were selected from each cohort. Table S5. Genes significantly positively correlated with Snai2 expression in the ‘C1’ subtype of Tothill’s dataset (GSE9891). List of the 288 genes that were calculated strongly positively correlated with SNAI2 expression in the ‘C1’ subtype of Tothill’s dataset (GSE9891), using the R2 Genomic Analysis and Visualization Platform ( http://r2.amc.nl ). Genes with a Pearson correlation greater than or equal to 0.4 and a p value less than 1% were selected from each cohort. Table S6. Listing of the 77 Genes included in the calculated ‘Snai2 mesenchymal signature’ of ovarian cancer. Designation of our defined ‘Snai2 mesenchymal signature’ and listing of the 77 genes included in the signature, through Overlapping analysis of the gene set that were calculated positively correlated with SNAI2 expression in the ‘mesenchymal’ subtype of ovarian TCGA profile and the ‘C1’ subtype of Tothill’s dataset (GSE9891). (XLSX 120 kb)

Figure S1. Genetic profiles of surgical tissue and the NGT41 cell line. BRAF V600E and TERT promotor (C250T) mutation was confirmed by Sanger sequencing (A), and heterozygous loss of CDKN2A/2B was identified by the multiplex ligation-dependent probe amplification method (B). Figure S2. Evaluation of BRAF V600E using ddPCR. Tumor DNA was extracted from the area of vivid tumor cells in the FFPE tissue by laser microdissection (A). Fractional abundance (FA) of mutated BRAF V600E was calculated as copies of mutated DNA/(copies of mutated DNA + wildtype DNA) (B). Scale bar A: 200 μm. Figure S3. Calculation of growth rate value in NGT41 and U87MG after combination treatment. Dose response curves on relative cell count showed marked response to BRAF and/or MEK inhibitor treatment in NGT41 (A), but minimal reduction in U87MG (B). Figure S4. BRAF and MEK inhibitor induced greater apoptosis and G0/G1 arrest in the NGT41 cell line. In BRAF V600E-mutant cell lines, each treatment significantly increased the number of apoptotic cells (n = 3, *p < 0.05, **p < 0.01; Two-way ANOVA) (A). G0/G1 arrest was induced by each treatment in BRAF V600E mutant-cell lines, whereas no response was observed in BRAF-wildtype cell lines (n = 3) (B). Figure S5. Effect of BRAF and MEK inhibitor in the intracranial model. pMEK and pERK were markedly suppressed in the treatment group (A). Serial body weight calculations in the treatment group were virtually the same as in the control group (B). Histological appearance of intracranial tumor in the treatment group at endpoint was similar to that of the control group (C). Scale bar C: 50 μm. Figure S6. Analysis of the TCGA database included in R2: Genomics Analysis and Visualization Platform showed that BRAF mutations were significantly correlated with CDKN2A alterations (p = 0.025) (A) and TERT promoter mutations (p = 7.03e-03) (B). (ZIP 8066 kb)

There is an equivalent x1.7 length distortion level introduced into all data sets. *To generate a valid cross-comparison, only 130 of the available 7000+ Enolase squiggles were included in this analysis.

Three technical replicates of FACS-sorted T cells (CD45+CD3+) and one replicate of FACS-sorted tumor cells (MCSP+) were loaded to a targeted 10,000 cells per lane on the 10X Genomics Chromium Controler with the single cell 5’ Immune Repertoire and Gene Expression profiling kit. In total, we loaded ~30,000 individual tumor infiltrating lymphocytes (TILs) and ~10,000 melanoma cells on the 10X platform (10X Genomics, CA, USA). Reverse transcription, TCR enrichment, and library preparations were performed according to the 10X Genomics 5’ V(D)J protocol revision C. Transcriptome libraries were pooled and sequenced on the Illumina NovaSeq 6000 S2 flow cell with 26 R1, 8 i7, and 91 R2 cycles respectively. The TCR libraries were pooled and sequenced on the Illumina MiSeq V2 150 cycles paired-end. Single cell transcriptomic and TCR data was processed with the 10X Genomics Cell Ranger Pipeline version 2.2.0 with the software-provided GRCh38 reference transcriptomes. After quality control, there was RNAseq profile data available from 6267 immune and 4303 melanoma cells. Downstream processing and visualization was encompassed through Seurat and tSNE plots.

Hydraulic resistances (in cmH2O*60 s/ml) measured from the slope (m) of the linear regression (R-squared values, R2, are also reported) of renal pelvic pressure versus flow rate values (example in Figure 5A) for each combination of viscosity and severity of obstruction (OB%).

Additional file 2: Table S1. Mock communities data actual and metapop derived abundances. Table S2. Biological virome dataset population abundances. Table S3. Full codon bias usage results for all genes in Staphylococcus aureus ECT-R2.