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Additional file 1. Additional material including supplemetary figures and tables.

Additional file 1. Examples of output results obtained by CpGmotifs analysis.

Additional file 1. Supplementary Information, Tables and Figures.

Molecular mimicry is one of the leading mechanisms by which infectious agents can induce autoimmunity. Whether a similar mechanism triggers an antitumor immune response is unexplored, and the role of antiviral T cells infiltrating the tumor has remained anecdotal. To address these questions, we first developed a bioinformatic tool to identify tumor peptides with high similarity to viral epitopes. Using peptides identified by this tool, we demonstrated that, in mice, preexisting immunity toward specific viral epitopes enhanced the efficacy of cancer immunotherapy via molecular mimicry in different settings. To understand whether this mechanism could partly explain immunotherapy responsiveness in humans, we analyzed a cohort of patients with melanoma undergoing anti-PD1 treatment who had a high IgG titer for cytomegalovirus (CMV). In this cohort of patients, we showed that high levels of CMV-specific antibodies were associated with prolonged progression-free survival and found that, in some cases, peripheral blood mononuclear cells (PBMC) could cross-react with both melanoma and CMV homologous peptides. Finally, T-cell receptor sequencing revealed expansion of the same CD8þ T-cell clones when PBMCs were expanded with tumor or homologous viral peptides. In conclusion, we have demonstrated that preexisting immunity and molecular mimicry could influence the response to immunotherapies. In addition, we have developed a free online tool that can identify tumor antigens and neoantigens highly similar to pathogen antigens to exploit molecular mimicry and cross-reactive T cells in cancer vaccine development. publishedVersion

The COVID-19 disease led to an unprecedented health emergency, still ongoing worldwide. Given the lack of a vaccine or a clear therapeutic strategy to counteract the infection as well as its secondary effects, there is currently a pressing need to generate new insights into the SARS-CoV-2 induced host response. Biomedical data can help to investigate new aspects of the COVID-19 pathogenesis, but source heterogeneity represents a major drawback and limitation. In this work, we apply data integration methods to develop a Unified Knowledge Space (UKS) and use it to identify a new set of genes associated with SARS-CoV-2 host response, both in vitro and in vivo. Functional analysis of these genes reveals possible long term systemic effects of the infection, such as vascular remodelling and fibrosis. Finally, we identified a set of potentially interesting drugs targeting proteins involved in multiple steps of the host response to the virus., This project was funded by the Academy of Finland [grant number 322761]

Additional file 3: Unsupervised clustering of array-comparative genomic hybridization (aCGH) and gene-expression (GEX) data. Left, aCGH samples do not cluster according to any of the covariates. Instead, the clustering is based on informative genomic regions and tumors' overall copy-number profiles. Right, unsupervised hierarchical clustering of gene-expression data suggests that estrogen receptor (ER) status and family history of breast cancer have an impact on the tumor's gene-expression profile. The effect of the other variables is likely to be smaller. Replicates that were used as a quality control cluster together as expected. Positions of CHEK2 (checkpoint kinase 2), 1100delC-mutation carrier and control (contr) tumors are indicated by the uppermost color block. NQO1- and NQO1+ stand for samples with germline rs1800566 genotypes: homozygotes of the more common allele (CC) and heterozygotes or homozygotes of the rarer allele (CT or TT), respectively. Frequency of copy-number changes along the genome is depicted with a color scale from blue to red. Blue, few; red, frequent copy-number aberrations. (PDF 115 KB)

Additional file 5: Sample-selection criteria and survival-analysis end points in Uppsala, Stockholm, and Rotterdam cohorts. (PDF 4 KB)

Chemoinformatics has developed efficient ways of representing chemical structures for small molecules as simple text strings, simplified molecular-input line-entry system (SMILES) and the IUPAC International Chemical Identifier (InChI), which are machine-readable. In particular, InChIs have been extended to encode formalized representations of mixtures and reactions, and work is ongoing to represent polymers and other macromolecules in this way. The next frontier is encoding the multi-component structures of nanomaterials (NMs) in a machine-readable format to enable linking of datasets for nanoinformatics and regulatory applications. A workshop organized by the H2020 research infrastructure NanoCommons and the nanoinformatics project NanoSolveIT analysed issues involved in developing an InChI for NMs (NInChI). The layers needed to captureNMstructures include but are not limited to: core composition (possibly multi-layered); surface topography; surface coatings or functionalization; doping with other chemicals; and representation of impurities. NM distributions (size, shape, composition, surface properties, etc.), types of chemical linkages connecting surface functionalization and coating molecules to the core, and various crystallographic forms exhibited by NMs also need to be considered. Six case studies were conducted to elucidate requirements for unambiguous description of NMs. The suggested NInChI layers are intended to stimulate further analysis that will lead to the first version of a “nano” extension to the InChI standard.

Additional file 6: Hierarchical clustering of samples according to 188 differentially expressed genes. All but two CHEK2 (checkpoint kinase 2) 1100delC-mutation carriers cluster together in two branches. Estrogen receptor (ER) status of each sample, either positive or negative, is indicated by the second color block. (PDF 28 KB)

eUTOPIA user manual with sample analysis. (DOCX 15056 kb)

Level 1,2,3 Reactome and Gene Ontology (BP, CC, MF) maps for the proposed case study. Reactome maps have been produced by providing “Additional file 1” as input and choosing “Reactome” enrichment, annotation was performed using “Bonferroni” as multiple testing correction method with “0.001” as significance threshold. Three classes of Gene Ontology maps have been produced by providing “Additional file 1” as input and choosing “GO” and alternatively “BP”, “CC” or “MF” enrichment, annotation was performed using “Bonferroni” as multiple testing correction method with “0.001” as significance threshold. In both cases, for the plotting “median” was chosen as summary statistics and map colors were associated to the summarized each term modification direction by choosing the sign option. (PDF 3044 kb)

Case study KEGG enrichment maps. KEGG enrichment maps showing modification direction after clustering analysis with 11 clusters. Panel A (top) shows enrichment results summarized at KEGG Level 1, panel B (middle) shows enrichment results summarized at KEGG Level 2, panel C (bottom) shows enrichment results summarized at KEGG Level 3 (pathways level). (PPTX 6869 kb)

Additional file 7. Fig. S4. Boxplot of the predicted logKHSA values of the 799 external compounds coming from CMap dataset grouped by ATC codes level 1.

Additional file 2: Figure S1. Lack of PIM target sites does not affect subcellular localization of NFATC1 A. Figure S2. Effects of PIM-dependent phosphorylation on NFAT activity. Figure S3. Lack of PIM1 target sites reduces the ability of NFATC1 to promote cancer cell motility. Figure S4. Microarray analysis reveals phosphorylation-dependent differences in the expression of PIM/NFATC1 target genes in PC-3 cells. Figure S5. Integrin signaling pathway is enriched in PIM1 and NFATC1 expressing cells. Figure S6. ITGA5 mRNA expression levels correlates with those of PIM1 and NFATC1 in clinical prostate cancer samples.

Additional file 5. Fig. S2. Comparison of the model validation curves.

Additional file 8. Fig. S5. Boxplot of the predicted logKHSA values of the 799 external compounds coming from CMap dataset grouped by ATC codes level 2.

FunMappOne user manual. User manual for the FunMappOne tool. (DOCX 1940 kb)

Additional file 6. Fig. S3. Scatterplot of experimental vs. predicted logKHSA values of the 59 drugs, coloured by ATC codes level 1 and 2.

Additional file 4. Fig. S1. Estimation of the optimal Îť; value with the RSVA algorithm.

It contains the gene symbols and description for all shared genes between the tree breast cancer datasets highlighted by the analysis. (DOCX 14 kb)

It contains a section for each step of the methodology in which the tables and figures with the results for each dataset are reported. (PDF 1495 kb)

Genome-wide association studies (GWAS) of breast cancer defined by hormone receptor status have revealed loci contributing to susceptibility of estrogen receptor (ER)-negative subtypes. To identify additional genetic variants for ER-negative breast cancer, we conducted the largest meta-analysis of ER-negative disease to date, comprising 4754 ER-negative cases and 31 663 controls from three GWAS: NCI Breast and Prostate Cancer Cohort Consortium (BPC3) (2188 ER-negative cases; 25 519 controls of European ancestry), Triple Negative Breast Cancer Consortium (TNBCC) (1562 triple negative cases; 3399 controls of European ancestry) and African American Breast Cancer Consortium (AABC) (1004 ER-negative cases; 2745 controls). We performed in silico replication of 86 SNPs at P ≤ 1 × 10(-5) in an additional 11 209 breast cancer cases (946 with ER-negative disease) and 16 057 controls of Japanese, Latino and European ancestry. We identified two novel loci for breast cancer at 20q11 and 6q14. SNP rs2284378 at 20q11 was associated with ER-negative breast cancer (combined two-stage OR = 1.16; P = 1.1 × 10(-8)) but showed a weaker association with overall breast cancer (OR = 1.08, P = 1.3 × 10(-6)) based on 17 869 cases and 43 745 controls and no association with ER-positive disease (OR = 1.01, P = 0.67) based on 9965 cases and 22 902 controls. Similarly, rs17530068 at 6q14 was associated with breast cancer (OR = 1.12; P = 1.1 × 10(-9)), and with both ER-positive (OR = 1.09; P = 1.5 × 10(-5)) and ER-negative (OR = 1.16, P = 2.5 × 10(-7)) disease. We also confirmed three known loci associated with ER-negative (19p13) and both ER-negative and ER-positive breast cancer (6q25 and 12p11). Our results highlight the value of large-scale collaborative studies to identify novel breast cancer risk loci.

Additional file of Breast tumors from CHEK2 1100delC- mutation carriers: genomic landscape and clinical implications

Enrichment plots of the high ranking gene sets from the gene set enrichment analyses (GSEA). (PDF 364 kb)

Authors’ original file for figure 2

Authors’ original file for figure 2

Additional file 2: Supplementary methods. [82–94]. (PDF 19 KB)

The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms’ responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.

Additional file 2: Supplementary methods. [82–94]. (PDF 19 KB)

Additional file of Breast tumors from CHEK2 1100delC- mutation carriers: genomic landscape and clinical implications

Additional file 9. Fig. S6. Boxplot of the expression values of the three selected genes, grouped by immunosuppressant and their less similar drugs.

Additional file 2. This file contains the formal descriptions of the methodology and the RSVA algorithm

Additional file 1: Tumor characteristics of study samples. Numbers of samples from estrogen-negative and -positive tumors, patients with or without a family history of breast cancer, patients with different rs1800566 genotypes, as well as tumors of different histologic and molecular subtypes in groups of CHEK2 (checkpoint kinase 2)-mutation carrier and other tumors for both gene-expression (GEX) and array-comparative genomic hybridization (aCGH) datasets. Molecular subtypes are defined on the basis of Immunohistochemistry results: Luminal A: ER+/PR+, HER2-; Luminal B: ER+/PR+, HER2+; HER2 positive: ER-/PR-, HER2+; Basal: ER-/PR-, HER2-, EGFR+; Other triple negative: ER-/PR-, HER2-, EGFR-. All data were not available for every sample. EGFR, epidermal growth factor receptor; ER, estrogen receptor; Her2, human epidermal growth factor receptor 2; PR, progesterone receptor. (PDF 5 KB)

Authors’ original file for figure 1

Additional file of Breast tumors from CHEK2 1100delC- mutation carriers: genomic landscape and clinical implications

Authors’ original file for figure 1

Additional file 4: Pair-wise correlation plot of aCGH (array-comparative genomic hybridization) quality control (QC) hybridizations. Heatmap of pair-wise correlation between the hybridization from fresh frozen tissue sample (fresh) and the dye-swap hybridizations from formalin-fixed paraffin-embedded (paraf) tissue. (PDF 27 KB)

Additional file 1: Table S1. Primers for site-directed mutagenesis in NFATC1. Table S2. Primers for qRT-PCR. Table S3. Novel NFATC1 phosphorylation sites. Table S4. Phosphorylation-dependent differences in the expression of PIM/NFATC1 target genes in PC-3 cells.

Additional file 2. This file contains the formal descriptions of the methodology and the RSVA algorithm

Additional file of Breast tumors from CHEK2 1100delC- mutation carriers: genomic landscape and clinical implications

Additional file of Breast tumors from CHEK2 1100delC- mutation carriers: genomic landscape and clinical implications

Additional file 9. Fig. S6. Boxplot of the expression values of the three selected genes, grouped by immunosuppressant and their less similar drugs.

The mechanisms of inflammation in acne are currently subject of intense investigation. This study focused on the activation of adaptive and innate immunity in clinically early visible inflamed acne lesions and was performed in two independent patient populations. Biopsies were collected from lesional and non-lesional skin of acne patients. Using Affymetrix Genechips, we observed significant elevation of the signature cytokines of the Th17 lineage in acne lesions compared to non-lesional skin. The increased expression of IL-17 was confirmed at the RNA and also protein level with real-time PCR (RT- PCR) and Luminex technology. Cytokines involved in Th17 lineage differentiation (IL-1β, IL-6, TGF-β, IL23p19) were remarkably induced at the RNA level. In addition, proinflammatory cytokines and chemokines (TNF-α, IL-8, CSF2 and CCL20), Th1 markers (IL12p40, CXCR3, T-bet, IFN-γ), T regulatory cell markers (Foxp3, IL-10, TGF-β) and IL-17 related antimicrobial peptides (S100A7, S100A9, lipocalin, hBD2, hBD3, hCAP18) were induced. Importantly, immunohistochemistry revealed significantly increased numbers of IL-17A positive T cells and CD83 dendritic cells in the acne lesions. In summary our results demonstrate the presence of IL-17A positive T cells and the activation of Th17-related cytokines in acne lesions, indicating that the Th17 pathway is activated and may play a pivotal role in the disease process, possibly offering new targets of therapy.

Additional file of Breast tumors from CHEK2 1100delC- mutation carriers: genomic landscape and clinical implications

Additional file of Breast tumors from CHEK2 1100delC- mutation carriers: genomic landscape and clinical implications

Description, genotyping methods and references for individual studies as reported by the studies. Table S2. Genotype and follow-up data availability in individual studies. Table S3. Sources and scoring of TP53 immunohistochemistry data used in this study. Table S4. Pathological characteristics of 183 breast tumors used in the gene expression analysis. Table S5. Functional annotations enriched in the 21 differentially expressed genes. Table S6. Enriched gene sets at the high and low edges of a list of 1852 genes ranked according to differences between p.I157T and non-carrier breast tumors. (DOCX 72 kb)

Enrichment plots of the high ranking gene sets from the gene set enrichment analyses (GSEA). (PDF 364 kb)

Additional file 1: Table S1. Primers for site-directed mutagenesis in NFATC1. Table S2. Primers for qRT-PCR. Table S3. Novel NFATC1 phosphorylation sites. Table S4. Phosphorylation-dependent differences in the expression of PIM/NFATC1 target genes in PC-3 cells.

Additional file 1: Tumor characteristics of study samples. Numbers of samples from estrogen-negative and -positive tumors, patients with or without a family history of breast cancer, patients with different rs1800566 genotypes, as well as tumors of different histologic and molecular subtypes in groups of CHEK2 (checkpoint kinase 2)-mutation carrier and other tumors for both gene-expression (GEX) and array-comparative genomic hybridization (aCGH) datasets. Molecular subtypes are defined on the basis of Immunohistochemistry results: Luminal A: ER+/PR+, HER2-; Luminal B: ER+/PR+, HER2+; HER2 positive: ER-/PR-, HER2+; Basal: ER-/PR-, HER2-, EGFR+; Other triple negative: ER-/PR-, HER2-, EGFR-. All data were not available for every sample. EGFR, epidermal growth factor receptor; ER, estrogen receptor; Her2, human epidermal growth factor receptor 2; PR, progesterone receptor. (PDF 5 KB)

Additional file 4: Pair-wise correlation plot of aCGH (array-comparative genomic hybridization) quality control (QC) hybridizations. Heatmap of pair-wise correlation between the hybridization from fresh frozen tissue sample (fresh) and the dye-swap hybridizations from formalin-fixed paraffin-embedded (paraf) tissue. (PDF 27 KB)