Journal: Nature Cancer

Article Title: A local human Vδ1 T cell population is associated with survival in nonsmall-cell lung cancer

doi: 10.1038/s43018-022-00376-z

Figure Lengend Snippet: a , Overview of study design. Paired tumor regions (red) and NT lung tissues (blue) collected under the TRACERx Study were enzymatically digested to extract tissue/TILs. TILs were cryopreserved and thawed at a later date for flow cytometry ± RNA-seq. In parallel, gDNA was extracted from undigested matched tumor regions and NT lung tissues and sent for subsequent quantitative TCR-seq. In addition, PBMCs were isolated from contemporaneous blood draws and cryopreserved before subsequent thaw for flow cytometry. b , Percentage of CD3 + T cells staining for TCRγδ (left) and percentage of TCRγδ T cells staining for Vδ1 (middle) and Vδ2 (right) in PBMCs (blood), NT lung tissues (tissue) and tumors (tumor). Not all patients had paired samples. The bar represents the median. The Kruskal–Wallis test with post-hoc Dunn’s test corrected for multiple testing was used. c , Absolute counts of total T cells, αβ T cells (TRA), γδ T cells (TRD) and Vδ1 ( TRDV1 ) and Vδ2 ( TRDV2 ) T cells per microgram of DNA determined by TCR-seq. Absolute counts of CD4 + αβ T cells (CD4) and CD8 + αβ T cells (CD8) were determined by mapping the proportion of CD3 + /TCRγδ − T cells staining for CD4 or CD8 in flow cytometry analysis of paired TILs. No significant differences were observed within demarcated T cell subsets between NT tissues and tumors. Samples with <1 cell μg −1 of DNA were not plotted for the purposes of visualization. The bar represents the median. A two-tailed Mann–Whitney U -test was used within demarcated T cell subsets. Significant P values are shown. NS, not significant. The n numbers and datapoints represent independent patients.

Article Snippet: Briefly, region-matched tissues were homogenized using a TissueRuptor II (QIAGEN) and lysates passed through a QIAshredder column (QIAGEN) before DNA extraction using the Allprep DNA/RNA Mini kit (QIAGEN).

Techniques: Flow Cytometry, RNA Sequencing Assay, Isolation, Staining, Two Tailed Test, MANN-WHITNEY

Journal: bioRxiv

Article Title: Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification correlating with T-cell infiltration

doi: 10.1101/2022.09.17.508207

Figure Lengend Snippet: A, Distribution of the total numbers of variants identified from DNA (upper panel) and RNA data (lower panel) identified per tumor sample grouped by tumor entity. Mutations were called by MuTect2 (v4.1.0.0) from whole exome (WES)/whole genome sequencing (WGS) data and by Strelka2 (v2.9.10) from RNA sequencing (RNA-seq) data. SNP-filtering was performed using the dbSNP-all data base. No RNA data was available for patients IN-11-T1, IN-14, IN-16, IN-20, IN-25, IN-31, IN-34. B, Pie chart depicting the proportion of variants only identified from RNA-seq data of all tumor samples combined where the respective wild type (WT) sequence was identified at the DNA level with a coverage of ≥ 3 reads (green) or the respective region was not covered at the DNA level (grey, < 3 reads). C, Distribution of the nucleotide exchange pattern over all single nucleotide variants only identified from RNA-seq data of all tumor samples combined. Variants previously identified in the REDIportal database as RNA editing events are highlighted in green. D, Pie charts depicting the distribution of each mutation type for variants called from all DNA (left) and RNA (right) variants. E, F, Pie charts showing the proportions of unique and shared DNA variants ( E ) and RNA variants ( F ) between different patients. The right bar graph shows the number of variants shared by 4 to 14 patients for DNA variants ( E ) and shared by 10 to 26 patients for RNA variants ( F ) in more detail. A-E, n = 39 tumor samples from n = 32 patients for WES/WGS data; n = 32 tumor samples from n = 26 patients for RNA-seq data (see Suppl. Table S1A). T, tumor; WT, wild type.

Article Snippet: For formalin-fixed and paraffin-embedded (FFPE) samples, the AllPrep DNA/RNA FFPE Kit (Qiagen) was used.

Techniques: Sequencing, RNA Sequencing Assay, Mutagenesis

Journal: bioRxiv

Article Title: Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification correlating with T-cell infiltration

doi: 10.1101/2022.09.17.508207

Figure Lengend Snippet: A, Venn diagram showing the overlap between all variants identified from whole exome (WES)/whole genome sequencing (WGS) data (DNA variants) and from RNA sequencing (RNA-seq) data (RNA variants). B, Distribution of each mutation type for all identified genetic variants regardless of the sequencing origin (WES/WGS and RNA-seq combined). C, Correlation of DNA variants with RNA variants identified from tumor samples where matching WES/WGS and RNA-seq data was available (n = 32 tumor samples). Symbols depict individual tumor samples; Spearman’s rank correlation analysis, ρ = 0.1578; line depicts linear regression, R²=0.008. D, Bar graph showing the number of variants found in each genetic biotype and the originating dataset. E, Forest plot showing the hazard ratio (dot) and 95% confidence intervals (lines) calculated by log rank test and Cox’s proportional hazards model of several genetic parameters for the survival of patients since tumor resection (DNA variants n = 32 patients, RNA variants n = 26 patients). Significant results (p ≤ 0.05) are highlighted in blue. For statistical analysis only one representative tumor sample per patient was used (see core cohort Suppl. Table S1A). F, Upset plot showing the overlap of at least two RNA variants between at least ten tumor samples. The bar graph shows the number of unique variants present in a shared subset of tumors defined as intersection size, dots indicate the tumor samples where the subset is present, and lines connect tumor samples within the same subset. The different genes harbouring the defined genetic variants are coloured in the intersection bar graph. A, B, D, F, n = 32 tumor samples from n = 26 patients for WES/WGS data and for RNA-seq data (see Suppl. Table S1A). Mel, melanoma; Mb, mega base; O, other; OS, overall survival; Proc., processed; T, tumor; TEC, to be experimentally confirmed.

Article Snippet: For formalin-fixed and paraffin-embedded (FFPE) samples, the AllPrep DNA/RNA FFPE Kit (Qiagen) was used.

Techniques: Sequencing, RNA Sequencing Assay, Mutagenesis

Journal: bioRxiv

Article Title: Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification correlating with T-cell infiltration

doi: 10.1101/2022.09.17.508207

Figure Lengend Snippet: A, B, Number of identified neoantigen candidates based on the bioinformatics tool that they were identified with ( A ) and per tumor sample and grouped by tumor entity ( B ). pFIND (v3.1.5) was used at 5% FDR on spectral level for the identification of non-wild type (WT) 8-15mer neoantigen candidates. The machine learning tool Prosit was additionally integrated to rescore the peptide spectra matching to the patient-specific ORF database using unfiltered pFIND data as input. n = 39 tumor samples from n = 32 patients were analysed in total; n = 27 tumor samples from n = 24 patients harboured n = 91 neoantigen candidates. C, Bar graph showing the length distribution of all identified neoantigen candidates in amino acids (aa). D, Genetic origin (DNA or RNA data) of the variants that the identified neoantigen candidates were derived from. E, Pie chart depicting the proportion of neoantigen candidates identified only from RNA sequencing (RNA-seq) data where the respective WT sequence was identified at the DNA level with a coverage of ≥ 3 reads (green) or the respective region was not covered at the DNA level (grey, < 3 reads). F, Distribution of the nucleotide exchange pattern of all variants that yield neoantigen candidates identified only from RNA-seq data. Variants previously identified in the REDIportal database as RNA editing events are highlighted in green. G, Distribution of each mutation type (left) and biotype (right) of all variants that yield neoantigen candidates. A-G, n = 39 tumor samples from n = 32 patients were analysed in total; n = 27 tumor samples from n = 24 patients harboured n = 91 neoantigen candidates; n = 3 neoantigen candidates from DNA variants; n = 8 neoantigen candidates from DNA and RNA variants; n = 80 neoantigen candidates from RNA variants. aa, amino acids; MS, mass spectrometry; Proc., processed; T, tumor; TEC, to be experimentally confirmed; WT, wild type.

Article Snippet: For formalin-fixed and paraffin-embedded (FFPE) samples, the AllPrep DNA/RNA FFPE Kit (Qiagen) was used.

Techniques: Derivative Assay, RNA Sequencing Assay, Sequencing, Mutagenesis, Mass Spectrometry

Journal: bioRxiv

Article Title: Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification correlating with T-cell infiltration

doi: 10.1101/2022.09.17.508207

Figure Lengend Snippet: A, Schematic overview of the immunogenicity assessment by modified accelerated co-cultured dendritic cell (acDC) assay using non-enriched or CD137 + -enriched T cells (PBMCs or TILs) for subsequent IFN-γ ELISpot readout. B, Pie chart depicting the proportion of immunogenic neoantigens identified only from RNA sequencing (RNA-seq) data where the respective wild type (WT) sequence was identified at the DNA level with a coverage of ≥ 3 reads (green) or the respective region was not covered at the DNA level (grey, < 3 reads). C, Distribution of the nucleotide exchange pattern of all single nucleotide variants that yield immunogenic neoantigen candidates identified only from RNA-seq data (n=22). Variants previously identified in the REDIportal database as RNA editing events are highlighted in green. D, For those patients with immunogenic neoantigens, the total number of tested neoantigen candidates is depicted including immunogenic and non-immunogenic ones. B-D, n = 79 neoantigen candidates from n = 24 patients were analysed in total; n = 8 patients harboured n = 24 immunogenic neoantigens; n = 1 immunogenic neoantigens from DNA and RNA variants; n = 23 neoantigen candidates from RNA variants. ca., carcinoma; endom., endometrium; GM-CSF, granulocyte macrophage-colony stimulating factor; IL, interleukin; OKT-3, Muromonab-CD3; Panc., pancreas; TNF-α, tumor necrosis factor-α.

Article Snippet: For formalin-fixed and paraffin-embedded (FFPE) samples, the AllPrep DNA/RNA FFPE Kit (Qiagen) was used.

Techniques: Modification, Cell Culture, Enzyme-linked Immunospot, RNA Sequencing Assay, Sequencing

Journal: bioRxiv

Article Title: Proteogenomic analysis reveals RNA as an important source for tumor-agnostic neoantigen identification correlating with T-cell infiltration

doi: 10.1101/2022.09.17.508207

Figure Lengend Snippet: Summary of immunogenicity assessment data from all performed modified accelerated co- cultured dendritic cell (acDC) assays for neoantigen candidates by ELIspot analysis using patient derived PBMC (non-enriched – left plot, CD137 + enriched – middle plot) or TILs (enriched and non- enriched combined – right plot) ( A ) and allogenic-matched healthy donor PBMCs (non-enriched) ( B ). Mean IFN-γ spot forming units (SFU) for T cells tested against the mutated peptide (test condition) and tested against a control peptide (control condition) were calculated and the ratio as well as the difference of the mean SFU have been determined. Values are shown for every peptide and PBMC or TIL aliquot tested. Highlighted are peptides that elicit an immune response where the ratio of SFU is > 2 and the difference of SFU is > 50. Autologous LCLs or allogenic HLA-matched cells (LCLs or HLA- transduced cell lines) were used as target cells. Negative values (when controls show more spots than the test condition) were set to 0 for better readability. C, Representative IFN-γ ELIspot data showing spots per well for autologous and allogenic-matched PBMCs tested against a control peptide (top) and the indicated neoantigen candidate (bottom). D, Genetic origin (DNA or RNA data) of the variants that the identified immunogenic neoantigens were derived from. E, Distribution of each mutation type (left) and biotype (right) of all variants that yield immunogenic neoantigens. F, Correlation matrix summarizing significant (p ≤ 0.05) Spearman correlations for multiple phenotypic parameters and the size of the immunopeptidome with the number of identified MS-based neoantigens overall and immunogenic ones. Spearman correlation coefficient Rho is labeled in color and size. For statistical analysis only one representative tumor sample per patient was used. A, D-F, n = 79 neoantigen candidates from n = 24 patients were analysed in total; n = 8 patients harboured n = 23 immunogenic neoantigens; n = 22 immunogenic neoantigen candidates from autologous PBMC cultures; n = 3 immunogenic neoantigen candidates from TIL cultures; n = 23 tumor samples from n = 17 patients for immunophenotyping data. B, n = 10 neoantigen candidates from n = 4 patients were analysed in total; n = 5 immunogenic neoantigen candidates from allogenic-matched PBMC cultures. MS, mass spectrometry; PBMCs, peripheral blood mononuclear cells; SFU, spot forming units; TIL, tumor- infiltration lymphocytes.

Article Snippet: For formalin-fixed and paraffin-embedded (FFPE) samples, the AllPrep DNA/RNA FFPE Kit (Qiagen) was used.

Techniques: Modification, Cell Culture, Enzyme-linked Immunospot, Derivative Assay, Mutagenesis, Labeling, Mass Spectrometry

Journal: Data in Brief

Article Title: Data on somatic mutations obtained by whole exome sequencing of FFPE tissue samples from Russian patients with prostate cancer

doi: 10.1016/j.dib.2019.104022

Figure Lengend Snippet: Sample information.

Article Snippet: Experimental features , DNA was isolated from FFPE tissue using AllPrep DNA/RNA FFPE and GeneRead DNA FFPE kits (Qiagen). Whole exome libraries were constructed with Ion AmpliSeq Exome RDY Kit (Thermo Fisher Scientific). Targeted DNA enrichment was performed by GeneRead DNAseq Targeted Human Prostate Cancer Panel..

Techniques: DNA Extraction

Journal: Data in Brief

Article Title: Data on somatic mutations obtained by whole exome sequencing of FFPE tissue samples from Russian patients with prostate cancer

doi: 10.1016/j.dib.2019.104022

Figure Lengend Snippet: Specifications Table

Article Snippet: Experimental features , DNA was isolated from FFPE tissue using AllPrep DNA/RNA FFPE and GeneRead DNA FFPE kits (Qiagen). Whole exome libraries were constructed with Ion AmpliSeq Exome RDY Kit (Thermo Fisher Scientific). Targeted DNA enrichment was performed by GeneRead DNAseq Targeted Human Prostate Cancer Panel..

Techniques: Sequencing, Next-Generation Sequencing, Isolation, Construct