dataesr/scientific-paragraphs-categorization huggingface dataset:
Importantly, the observational findings of this study need to be extended with analyses of healthy controls. Our study has several existing limitations. Firstly, as BMI was retrospectively collected for most of the cohorts in this study, it was not available for a significant part of the original series. Secondly, there existed differences in clinical and pathological characteristics of patients in different cohorts, making comparison and validation of analysis results across data sets not straightforward. Thirdly, interactions of BMI with other clinicopathological features could not be completely assessed, especially for small cohorts such as Biokey. Nevertheless, we adjusted, where possible, for important features with prominent impact on the tumor molecular biology such as age, menopausal status and tumor grade in our analyses.
Finally, although BMI is a conveniently accessible metric, it may not always be an accurate indicator of metabolic health related to adiposity  71. We intend to address these limitations and further extend the preliminary findings of this study in a prospective study where we will be investigating the TME according to adiposity at the single-cell resolution in a larger series and exploring other anthropometric and histopathological measures of adiposity in addition to BMI (https://clinicaltrials. gov/ct2/show/NCT04200768). In-depth characterization of all cell populations present in the BC tissue, including adipocytes, more scarce immune cell types such as mast cells and dendritic cells, as well as their phenotypes, will be performed in this study. Tumor-adjacent normal tissues will also be available and analyzed within the scope of this study.
Identification of gene-level CNAs and oncogenic CNAs Data of gene-level somatic CNAs for the METABRIC cohort were available for download in the cBioPortal repository (08 April 2019). Somatic CNA events in this dataset were distinguished between four categories, homozygous deletion, hemizygous deletion, lowlevel gain, and high-level amplification. Copy number segmentation calls of the ICGC cohort were classified as homozygous deletions and amplifications using the definition described by Nik-Zainal et al. (homozygous deletion: copy number = 0; amplifications: copy number ≥5 with ploidy <2. 7n, or copy number ≥5 with ploidy >2. 7n). The remaining copy number losses and copy gains were considered hemizygous deletions and low-level gains, respectively. Gene-level CNA of a coding gene was identified by an overlap of at least 50% of the transcript length with a copy number segmentation call.
Genelevel CNA events of the ELBC cohort were available for retrieval from the original publication. Events in this dataset were, however, only distinguished between copy gains and copy losses. Hence, we adopted this existing classification and considered both copy gains and copy losses oncogenic events in downstream analyses of CNA data for this particular cohort. Gene-level CNAs included in downstream analyses were limited to those involving genes that were previously reported to harbor driver CNAs in primary BC by Nik-Zainal et al.
Co-occurrence and mutual exclusivity analyses of somatic genomic alterations A Poisson-Binomial distribution-based analysis implemented in the R package 'Rediscover' (v0. 2. 0) was performed to identify cooccurring or mutually exclusive pairs of somatic oncogenic alterations  73. Owing to the fact that homozygous deletions were very rare, we concentrated on the analyses of gene mutations, gene-level amplifications, and gene-level hemizygous deletions. For each of these three types of alterations and each of the three patient subgroups, a matrix containing expected probabilities per gene per sample was estimated. These subgroup-specific probability matrices and binary matrices indicating the presence or absence of alterations in tumors from patients of each BMI category of the respective patient subgroup were used as the input for pairwise estimation of p values.
1. 0)  75, 76  and the unsupervised single sample-based method Gene Set Variation Analysis (R package 'GSVA'-v1. 40. 1)  77. The former method was executed using the complete list of genes pre-ranked by the logFC of the prior differential gene expression analysis. Hallmark gene sets available in the H collection of MSigDB (v7. 5. 1) were used as references.
The non-linear model was considered if selected by AIC (AIC non-linear < AIC linear ), and additionally evident in a likelihood-ratio test (p value < 0. 05) which is often expected to be more conservative. In case of non-linearity, the log-odds ratio of the event was fitted against continuous BMI, considering a BMI of 20 as the baseline. METABRIC can be accessed through cBioPortal [https://www. cbioportal. org/study/summary?id=brca_metabric] and through published data (Curtis et  ). The download of the read count data per individual patient from BioKey is publicly available at https://lambrechtslab. sites. vib. be/en/single-cell. Raw sequencing reads of the scRNA-seq experiments have been deposited in the European Genome-phenome Archive (EGA) under study no. EGAS00001004809 (with a summary of the BioKey study and patient characteristics) and with data accession no.
Acknowledgements The study was financially supported by the  Luxembourg Cancer Foundation  (grant  FC/2018/07 ), the  Consolidator Grant  approved by the  European Research Council  ( ERC ,  FAT-BC 101003153 ), and the  Internal Funds KU Leuven  ( 3M180676 ). K. V. B. and M. D. S. are funded by the  KU Leuven Fund Nadine de Beauffort. F. R. and T. G. are funded by  FWO  through a research fellowship. G. F. is the recipient of a post-doctoral mandate from the  Klinsche Onderzoek en OpleidingsRaad (KOOR)  of the  University Hospitals Leuven. The  METABRIC  project was funded by  Cancer Research UK , the  British Columbia Cancer Foundation , and  Canadian Breast Cancer Foundation BC/Yukon. The project also received support from the  University of Cambridge ,  Hutchinson Whampoa , the  NIHR Cambridge Biomedical Research Centre , the  Cambridge Experimental Cancer Medicine Centre , the  Centre for Translational Genomics (CTAG) Vancouver , and the  BCCA Breast Cancer Outcomes Unit.
The  ICGC-BRCA  project has been funded through the  ICGC Breast Cancer Working Group  by the  Breast Cancer Somatic Genetics Study (BASIS ) , a European research project funded by the  European Community 's  Seventh Framework Programme  ( FP7/2010-2014 ) under the grant agreement number  242006 ; the  Triple Negative  project funded by the  Wellcome Trust  (grant reference  077012/Z/05/Z ) and the  HER2+  project funded by  Institut National du Cancer (INCa) in France  (Grants  N°226-2009 ,  02-2011 ,  41-2012 ,  144-2008 ,  06-2012 ). The  ICGC Asian Breast Cancer Project  was funded through a grant from the  Korean Health Technology R&D Project ,  Ministry of Health & Welfare, Republic of Korea  ( A111218-SC01 ). The  MINDACT  trial has received grants from the  European Commission   Framework Programme VI  ( FP6-LSHC-CT-2004-503426 ), the  Breast Cancer Research Foundation ,  Novartis ,  F.
L. This  VIB Grand Challenges  project also received support from the  Flemish Government  under Management Agreement  2017-2021  ( VR 2016 2312 doc. 1521/4 ), from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement no. 847912  (RESCUER) and from  KU Leuven  grant ( C14/18/ 092 ) Symbiosys3. We are grateful to all women who participated and donated tissue in all studies used in this project and their families; all the investigators, surgeons, pathologists, and research nurses; and finally our close collaborators for their help in the data collection process and the collaboration on the scientific work.
Code availability The R code for data analyses is available in a CodeOcean capsule [https://doi. org/10. 24433/CO. 8331460. v1]. Results generated from the publicly available data cohorts, namely ICGC, METABRIC, and ELBC, can be fully reproduced within the code capsule. For analyses of data cohorts with restricted access, namely MINDACT and Biokey, complete code is shared but partially not executable due to the unavailability of primary data in the code capsule. Instead, supplementary tables containing secondary data, if applicable, were used for reproducing the displayed figures.
al. Nature 2012, Mukherjee et al. NPJ Breast Cancer 2018). Data from ELBC can be accessed through published data (Desmedt et al. JCO 2016) and Gene Expression Omnibus (accession number GSE88770). BMI data for the ICGC, METABRIC, and ELBC cohorts were additionally collected and are accessible via the CodeOcean capsule (see Code availability). Data from MINDACT can be accessed through the EORTC ([https://www. eortc. org/datasharing/].
We used data from the International Lymphoma Epidemiology Consortium (InterLymph) for discovery and from the Germanspeaking Myeloma Multicenter Group (GMMG), the International Multiple Myeloma rESEarch (IMMEnSE) consortium, as well as summary statistics from the FinnGen study for replication, for a total of 5982 MM cases and 266,173 controls. Detailed characteristics of the study populations are shown in the supplementary methods and supplementary table  1. Candidate SNPs to be replicated were selected based on their association with MM risk and their functional role. First, we obtained summary results including odds ratios (OR), 95% confidence intervals (95%-CI), and p-values of the top SNPs of the InterLymph GWAS. Subsequently, all SNPs in the MM data set from InterLymph with p < 5 × 10 -4 (N = 4396) were looked up in the first replication dataset, the GMMG GWAS.
We did not consider SNPs from 15 loci that were reported to be associated at genomewide significance level in previous GWASs. All SNPs with significant p-values (p < 0. 05) in the GMMG GWAS and ORs going in the same direction in both datasets were selected. The next step was annotating the selected SNPs (N = 136) for their predicted function, using several suitable bioinformatic tools and databases. Specifically, we looked at expression and splicing quantitative trait loci (eQTLs and sQTLs), SNPs located in transcription factor binding sites (TFBS), long non-coding RNA (lncSNPs), SNPs that are located within enhancers, and polymorphisms located in gene coding regions (missense, stop-gain, stop-loss, synonymous SNPs). Supplementary Table  2  shows the details of the 136 SNPs and their predicted functional characterization.
19, 95% C. I. = 0. 72-0. 97, p = 0. 018) and FinnGen (1. 17, 95% C. I. = 1. 05-1. 31, p = 0. 014). The G allele of this SNP resulted to be significantly associated with increased MM risk at a genome-wide level in the meta-analysis of the four datasets (OR = 1. 18, 95% C. I. = 1. 11-1. 23, p = 3. 18 × 10 -10 ) with no heterogeneity among the studies (I 2 = 0) (Table  1 , Fig. 1 ). This polymorphism was selected for being predicted to affect the binding site of three transcription factors: IRF1, STAT2_STAT1 and FOXP1. The strongest effect of the SNP was calculated for IRF1 (interferon regulatory factor 1), a protein member of the IRF family which was first recognized for its role as activator of genes involved in both innate and acquired immune responses. IRF-1 activates a set of target genes associated with regulation of cell cycle, apoptosis and the immune response  [9, 10].
According to the SNP2TFBS database, rs28199 is predicted to modify a binding site of IRF1 leading to a stronger bond, which could in turn result in oncogenesis considering the set of genes that IRF1 regulates. The minor allele of rs28199 is located within a regulatory region which according to the variant effect predictor tool (https:// www. ensembl. org/info/docs/tools/vep/index. html) and HaploReg (https://pubs. broadinstitute. org/mammals/haploreg/haploreg. php) binds the CTCF protein, a highly conserved zinc finger with various cellular regulatory role. CTCF binding perturbations cause different types of 3D genome reorganization and may cause the activation of the neighboring oncogenes  [11]. Among the genes that CTCF regulates there is STK10, which encodes for a serine/threonineprotein kinase, highly expressed in hematopoietic tissue  [12].
In various lymphoid cells rs28199-G is associated with an increased expression of STK10. Overexpression of STK10 has been reported in several cancer types, including acute myeloid leukemia (AML), another blood malignancy  [13, 14]. We used data from the 500 Functional Genomics cohort from the Human Functional Genomics Project (HFGP; http:// www. humanfunctionalgenomics. org/site/) to explore the possible role in modulating immune response of the four SNPs selected for the final replication steps. Namely, we tested if any of the SNPs of interest were cytokine expression quantitative trait loci (cQTL) using data from in vitro stimulation experiments, as well as absolute numbers of 91 blood-derived cell populations and 103 serum or plasmatic inflammatory proteins. The cQTL analyses showed that rs28199-G is also associated with an increased blood level of Interleukin-6 (IL-6) (beta=0.
DATA AVAILABILITY The primary data for this work will be made available to researchers who submit a reasonable request to the corresponding author, conditional to approval by the Steering Committees of the respective studies (InterLymph, GMMG, IMMEnSE) and Ethics Commission of the Medical Faculty of the University of Heidelberg, Germany. Data will be stripped from all information allowing identification of study participants. The summary statistics of the FinnGen GWAS are publicly available at https://www. finngen. fi. Functional data of the Human Functional Genomics Project used in this project have been catalogued and archived in the BBMRI-NL data infrastructure (https://hfgp. bbmri. nl/) using the MOLGENIS open-source platform for scientific data, which allows flexible data querying and download, including sufficiently rich metadata and interfaces for machine processing (R statistics, REST API) and using FAIR principles to optimize Findability, Accessibility, Interoperability and Reusability.
A randomized phase 3 trial is ongoing comparing TACE with TACE plus SBRT in primary and secondary liver carcinoma (NCT03895359). Daily target visualization is a key element to avoid geographical miss and local control impairment. Improvement from planar to tomographic pre-treatment image-guided radiotherapy (IGRT) was the first steps. Fiducial placement remained usually necessary due to low contrast on pre-treatment CBCT. It can be avoided when performing TACE-hRT since TACE can include a radio-opaque agent, thus limiting medical and economic burden  [25]. All our patients beneficiated from daily pre-treatment CBCT. Nevertheless, treatment guidance long remained a critical issue in liver irradiation because of tumor intrafraction motion. Recent development of magnetic resonance-guided radiotherapy (MRgRT) allowed both daily adaptive planning and surveillance of intrafraction tumor motion  [26].
Methods This retrospective observational study was conducted with cases received from members of the International Dermoscopy Society. Inclusion criteria were clinical and/or histopathologic diagnosis of Spitz naevus with starburst appearance in children <12 years old, availability of a dermoscopic image at baseline and after follow-up of at least 1 year, availability of patient' demographics, lesion location, follow-up duration. Cases lacking adequate clinical information were excluded. The centers were asked to provide, with images, a database containing the following information: location of the  With regards to the clinical presentation at baseline, 36 SN were flat/raised (94. 7% -95% CI: 80. 9% to 99. 1%, p<0. 01) and only 2 were nodular (5. 3% -95% CI: 0. 9% to 19%, p<0. 01). Almost all the lesions remained flat at the last follow-up visit (97.
Despite previous efforts to study non-canonical immunopeptidomes, the requirements of such multi-level experimental data (Ribo-seq and/or RNA-Seq) or computational struggles when dealing with large MS databases have hindered their large-scale profiling in a harmonized manner across multiple cancer types from hundreds of samples. With these considerations in mind, we developed COD-dipp (Closed Open De novo -deep immunopeptidomics pipeline), a pipeline based on deep learning de novo MS to enable the discovery of ncMAPs. Owing to the potential involvement of post-translational modifications (PTMs) in this process  (1) , we added an open search component for their discovery. We applied COD-dipp to a large-scale dataset using immunopeptidome profiles of over 772 samples from 26  (1, 13, 14,  published studies and 11 cancer types.
Dataset selection Twenty-four studies were selected based on a list of keywords related to immunopeptidomics (Supplementary Method S1). Low-resolution analyses were eliminated, and MHC class I-related datasets conducted with at least one of the following instruments were kept: Q Exactive, Q Exactive plus/HF/HFX, LTQ Orbitrap Velos, LTQ Orbitrap Elite, Orbitrap Fusion, and Orbitrap Fusion Lumos (Supplementary Table  S1 ). An additional study was considered from the MassIVE (RRID:SCR_013665) database, as it incorporates 95 HLA-A, -B, -C, and -G mono-allelic cell lines  (28, 40). An auxiliary immunopeptidomic dataset (39) covering 30 healthy tissues from 21 healthy individuals was also used to partly assess cancer selectivity.
De novo analysis DeepNovoV2 (  45 ) is a neural-network-based de novo peptide sequencing model that integrates convolutional neural networks (CNNs) and long short-term memory (LSTM) architectures. This deep-learning design extracts features from both the spectrum and the language of the presented peptides. DeepNovo has demonstrated improved performance compared to the state-of-the-art de novo sequencing algorithms by large margins  (45). The model can be tuned on a restricted peptide space to improve its performance. The training, testing, and validation sets were derived from MS-GF+ (v2019. 04. 18, RRID:SCR_015646) database search results for each sample. The search used the ENSEMBL v94 protein database and 8 to 25 amino acid peptides with unspecific cleavage, no fixed/variable PTMs and an FDR of 1% applied by Scavager  (46).
De novo peptide annotation De novo peptides from canonical human proteins were identified using BLAT  (47)  (RRID:SCR_011919) alignment against the target-decoy protein database. Sequences perfectly matching any protein sequence were considered exonic (one mismatch allowed for the isobaric amino acids leucine and isoleucine). All remaining sequences unexplained by proteins were considered potential non-canonical peptides and were aligned against the pre-mRNA 3FT database. Stringently, peptides perfectly matching a 3FT sequence without any mismatch were required to have at least three mismatches with any known protein sequence before being considered non-canonical. Since peptide-spectrum matches (PSMs) can be assigned without complete sequencing accuracy, requiring a 3 amino acid difference alongside the 90% accuracy cutoff above increases the confidence that the peptides assigned fall far outside the standard human proteome.
Second-round search A second-round search was performed using the FragPipe  (41, 43)  headless pipeline, which includes MSFragger v3. 4, MSBooster (bioRxiv 2022. 10. 19. 512904), and Philosopher  (41). Non-canonical peptides from all samples were concatenated with the ENSEMBL v94 protein into a custom database. Only four of the most abundant PTMs were considered to avoid a large search space complexity, inflated FDR, and decreased sensitivity. The following variable PTMs were included: methionine oxidation, N-terminal acetylation, cysteinylation, and cysteine carbamidomethylation (for samples treated with iodoacetamide). Unspecific cleavage generating peptides 7 to 15 amino acids long was considered. The ion, PSM, and peptide-level FDR were maintained at 1%.
Frameshift mutation analysis The COSMIC (42) v95 coding mutations (RRID:SCR_002260) VCF file was downloaded and converted into a protein FASTA file using a VCF-to-Proteogenomics toolkit (https://github. com/immuno-informatics/VCFtoProteogenomics) ncMAPs were then aligned to the resulting 16 GB FASTA using BLAT v35  (47). Only hits with exact matches to sequences from frameshift mutations were considered. Comparison of the identified non-canonical MHC class Iassociated peptides between studies ncMAPs from 4 different studies  (6, 13, 16, 50)  were collected. First, sequences were aligned to the human proteome (ENSEMBL v94) using BLAT v35  (47). Sequences found in human proteins were discarded, and the remaining sequences were aligned to the 3FT database with one mismatch allowance for the isobaric amino acids leucine and isoleucine, as allowed for COD-dipp ncMAPs.
Dimensionality reduction of the HLA-binding motif space Binding affinity prediction was employed to identify similarities and differences in HLAbinding motifs among the 65 healthy and 51 tumor-only HLA alleles. NetMHCpan-4. 1 was utilized to evaluate the binding of 1,000,000 random peptides to each allele, which resulted in a binding matrix (BM) of 116 alleles and 1,000,000 peptides. A value of 1 was assigned to strong binders (EL rank ≤ 0. 5%) in the BM; otherwise, a value of 0 was assigned. A pairwise cosine distance matrix (DM) was then calculated to assess the similarity of binding between alleles. The DM was then reduced using t-SNE to visualize the data in 2D with a perplexity of 20 and 500 iterations.
Step 2: Parental gene expression levels in healthy tissue The gene expression levels of the identified ncMAPs were retrieved from the GTEx v8  (52)  dataset, consisting of 29 tissues from 948 healthy donors and 17,382 overall samples. Considering all individuals, the 90th percentile value of normalized expression was assigned to each gene per tissue as a strict step to guarantee the upper-end gene expression in healthy tissues. A stringent cutoff for cancer selectivity was used to shortlist ncMAPs whose parent genes fell below a 1 TPM expression cutoff (excluding the testis tissue given its immune-privileged status). It is worth noting that this stringent threshold removes 92% of protein-coding genes that show expression above 1 TPM in any tissue within the GTEx v8. Step.
3: Protein expression levels in healthy tissue The protein expression levels of ncMAPs passing the 1 TPM cutoff were retrieved from the Human Protein Atlas V22. 0 database  (53). ncMAPs without parent protein expression in healthy tissues were labeled as cancer-selective (excluding the testis tissue given its immune-privileged status). 1a  and Supplementary Method S1). We selected publicly available studies with data generated using high-resolution MS instruments (LTQ Orbitrap, Q Exactive Plus/HF/HFX, and Fusion Lumos) to minimize the bias associated with older tandem MS instrumentation (Fig. 1b ). Within our dataset, the most commonly used monoclonal antibody for HLA class I immunoprecipitation (IP) was W6/32 in comparison to the other antibodies (BB7. 2 and G46-2. 6) (Fig. 1c , see Supplementary Table  S1 ).
Closed Open De novo -deep immunopeptidomics pipeline (COD-dipp) We present COD-dipp, an open-source high-throughput pipeline with novel post-processing steps, to deeply interrogate immunopeptidomic datasets (Fig. 2 ). We used this pipeline to screen for ncMAPs in datasets utilizing DDA due to its widespread use. fragments. All high-quality de novo peptides (90% accuracy) were sequentially mapped  (47)  to (I) the human reference proteome to reveal the de novo-based canonical MHC class Iassociated peptides, and (II) to a 3FT database to reveal the de novo-based ncMAPs. Finally, an orthogonal validation step was performed by a second-round search to control a 1% FDR for the de novo identified ncMAPs while considering the most abundant PTMs ptmMAPs The open search analysis reported 4. 03% of the MS spectra showing post-translational modifications (Fig.