Omics explorer v 3

The quantitative dataset with peptide precursor intensities was analyzed using Normalyzer v1.1.1 [51 (link)], and after comparing 12 normalization methods the Loess-G was used for normalization [52 ]. The log₂-transformed normalized data were used for visualization and statistical tests for identification of differentially abundant proteins; missing values were treated as described in [12 (link)]. Qlucore Omics Explorer v3.2 software (http://www.qlucore.se/) (accessed on 1 November 2021) was used to generate principal components analysis (PCA) plots and perform comparative analysis. Unsupervised PCA plots were generated to show similarities or differences between the samples. To identify peptide and proteins with differential abundance, comparative analyses were performed two group comparison (t-test) in Qlucore with the Benjamini−Hochberg false discovery ratio (FDR) procedure (q ≤ 0.001). Heat maps sorted with hierarchical clustering were generated and the list of peptide and proteins differentially regulated among the samples was exported for bioinformatics analysis. If the protein was represented by two or more peptides, a median abundance value was calculated.

Quantitation of 31 cytokines in mouse serum was determined using a Bio-Plex Pro™ Mouse Chemokine Panel 31-Plex (Bio-Rad, Hercules, CA, USA) as described [26 (link)]. The assay was performed using mouse serum diluted at 1:5 in sample diluent, data acquisition was performed using the Bio-Plex 200 reader (Bio-Rad, CA, USA) and was analysed using a two-group comparison of fold-change on the Qlucore Omics Explorer v3.5 software (Qlucore AB, Lund, Sweden).

HERV proviruses and other repeat regions were annotated as previously described^{58 (link)}. In brief, hidden Markov models (HMMs) representing known human repeat families (Dfam 2.0 library v.150923) were used to annotate GRCh38 using RepeatMasker, configured with nhmmer. RepeatMasker annotates long terminal repeats (LTRs) and internal regions separately; thus, tabular outputs were parsed to merge adjacent annotations for the same element. A list of HERV proviruses with functional env ORFs was compiled (Supplementary Table 1), and RNA-seq reads from TCGA, GTEx and TRACERx were mapped and counted using a custom transcriptome assembled on a subset of the RNA-seq data from TCGA, as previously described^{58 (link)}. In brief, TPM values were calculated for all transcripts in the transcript assembly with a custom Bash pipeline using GNU parallel and Salmon (v.0.12.0)^{59 (link)}. TPM values were then imported into Qlucore Omics Explorer v.3.3 (Qlucore) for downstream differential expression analysis and visualization. In the case of multiple transcripts transcribed from a given HERV provirus, data were collapsed by summing expression of any of the multiple transcripts overlapping the env ORF of that provirus. Patient-level mean values were calculated across multiple primary tumour regions, as applicable.

Statistical analysis was performed with GraphPad Prism v5.0. The unpaired Student 2-tailed t-test with Welsh-correction was applied for comparisons of differences between the means of two groups. Non-parametric Mann–Whitney test was applied for comparing the NAS-Score. The trapezoidal rule was used to determine the area under the curve (AUC). p-values <0.05 were considered as significant. All results are presented as mean with standard error of the mean (SEM). Graphs were created using the GraphPad Prism v5.0 program and Power Point v2010.
For analysis of the RT-PCR, the Fluidigm Real-Time PCR Analysis Software V.3.0.2 was used. Data were normalized by the mean values of the housekeeping genes glyceraldehyde-3-phosphate dehydrogenase (Gapdh) and actin β (actb) from the genes of interest. Heat map and principal component analysis (PCA) of the –delta Ct values were performed via the using Qlucore Omics Explorer v3.3 (Qlucore, Lund, Sweden). For analysis p-values were set to ≤0.049 for two group comparisons (t-test) and for multigroup comparisons (F-test) (ANOVA) (p < 0.05 and q < 0.2). *, difference significant with p < 0.05. **, difference significant with p < 0.01 ***, difference highly significant with p < 0.001. A p-value > 0.05 was considered to be not significant (ns).