dataesr/scientific-paragraphs-categorization huggingface dataset:
Limits of CloneSig. It is important to explicitly state some of CloneSig's limits, a number of them largely shared by most ITH methods. CloneSig is currently limited to SNVs, and does not account for indels or structural variants. Regarding the copy number profile of the tumor, CloneSig only considers clonal segments, but provides a complete framework to estimate a mutation's multiplicity, which is an improvement over many existing methods. Regarding the range of application of CloneSig, the results are slightly below some pre-existing methods when fewer than 100 mutations are observed, as more stability is needed to fully benefit for robust estimates of signature activity, but exhibits very good performances for a number of mutations ranging from 100 to 10,000, as in the DREAM, SimClone1000, and PhylogicSim500 datasets, which makes it the most flexible method considered in this study.
Besides the tumor purity p, we assume that the matrix of mutational processes μ 2 ðΣ 96 Þ L is known, as provided by databases like COSMIC and discussed below in Section 4. 7. We note that we could consider μ unknown and use CloneSig to infer a new set mutational signatures from large cohorts of sequenced tumors, but prefer to build on existing work on mutational processes in order to be able to compare the results of CloneSig to the existing literature. Besides p and μ, the free parameters or CloneSig are J, the number of clones, and θ = (ξ, ϕ, π, ρ) which define the distributions of all random variables. On each tumor, we optimize θ separately for J = 1 to J max = 8 clones to maximize the likelihood of the observed SNV data in the tumor. The optimization is achieved approximately by an expectation-maximization (EM) algorithm  45  detailed in Supplementary Section 1.
Simulations for comparison with other ITH and signature methods. To calibrate the custom stopping criterion and for further evaluation of CloneSig, we simulated a dataset comprising 7, 812 samples using the previously described setting, with a few adjustments: we set the minimal proportion of each clone to 0. 1, the minimal difference between 2 successive clone CCFs to 0. 1, and we chose the active signatures among the active signatures for each of the 31 cancer types from the TCGA, and curated using literature knowledge, as described later in Methods, in section 4. 7. We draw the number of active signatures as the minimum of a Pois(7) + 1 distribution and the number of active signatures for this cancer type. We required a cosine distance of at least 0. 05 between the mutational profiles of two successive clones.
In total, for each of the 31 cancer types, we generated a simulated sample for each combination of a number of mutations from the set {20, 50, 100, 300, 600, 1000, 5000} covering the range observed in WES and WGS data, a percentage of the genome that is diploid from the set {0%, 20%, 40%, 60%, 80%, 100%} to assess the impact of copy number variations, and finally, between 1 and 6 clones. Simulations without signature change between clones. We generated a set of simulations similar in all points to the one for comparison with other ITH and signature methods, except that there is a unique signature mixture common to all clones. We used this dataset in two contexts: (i) to evaluate CloneSig in comparison to other methods in the absence of signature change, and (ii) to design a statistical test to assess the significance of a change in mutational signatures.
For the latter, the dataset was limited to the first eleven cancer types to avoid unnecessary computations (2,772 samples). Simulations to assess the separating power of CloneSig. To assess the separating power of CloneSig, we generated a dataset of 12,000 simulated tumor samples with two clones, where each clone represents 50% of the observed SNVs. Our objective was to explore the set of the distance between two clones, in terms of CCF distance, and of cosine distance between the two mutational profiles. For that purpose we first drew ten possible CCF distances evenly on a log scale between 0 and 1, and set to 1 the largest clone CCF. We also generated 50 matrices π with cosine distances covering regularly the possible cosine distances; to obtain them, we first generated 10,000 such π matrices to estimate an empirical distance distribution, and we implemented a rejection sampling strategy to obtain 50 samples from a uniform distribution.
For each pair of CCF distance and π matrix, several samples were generated with the number of mutations varying among {30, 100, 300, 1000}, the diploid proportion of the genome among {0. 1, 0. 5, 0. 9}, and the sequencing depth among {100, 500}. Simulations to assess the sensitivity of the statistical test. To measure the sensitivity of the statistical test to detect a significant signature change along tumor evolution, we generated a dataset of 3,600 simulated tumor samples with 2-6 clones. We used again a rejection sampling strategy to explore the space of the maximal distance between the profiles between any 2 clones, but the target distribution is here a beta distribution of parameters 1. 5 and 8 as a target distribution, as the objective was to sample more thoroughly the small cosine distances.
5. measures for each mutation the cosine distance between the clonal mutation type distribution that generated the mutation and the reconstituted one. We consider a unique global distribution for deconstructSigs. This allows us to measure the relevance of the reconstruction even if the wrong signatures are selected, as several signatures have very similar profiles. The result is a distribution of distances over all mutations, and we report the median of this distribution. We also report in Supplementary Note 2 more results with the minimum, the maximum, and the standard deviation of this distribution (max_diff_distrib_mut, median_diff_distrib_mut), as well as the proportions of mutations with a distance below 0. 05 or 0. 1 (perc_dist_5 and perc_dist_10). Implementation. CloneSig is implemented in Python, and is available as a Python package at https://github.
For the sake of completeness, we therefore applied CloneSig to both sets. Variant calling is known to be a challenging problem, and it is common practice to filter variant callers output, as ITH methods are deemed to be highly sensitive to false positive SNVs. We therefore filtered out indels from the public dataset, and considered the union of the 4 variant callers output SNVs. For the protected data, we also removed indels, and then filtered SNVs on the FILTER columns output by the variant caller ("PASS" only VarScan2, SomaticSniper, "PASS" or "panel_of_normals" for Mutect2, and "Tier1" to "Tier5" for MuSe). In addition, for all variant callers, we removed SNVs with a frequency in 1000 genomes or Exac greater than 0. 01, except if the SNV was reported in COSMIC. We added a coverage filter, and we kept SNVs with at least 6 reads at the position in the normal sample, of which 1 maximum reports the alternative nucleotide (or with a variant allele frequency (VAF) < 0.
01), and for the tumor sample, at least 8 reads covering the position, of which at least 3 reporting the variant, or a VAF > 0. 2. The relative amount of excluded SNVs from protected to public SNV sets varied significantly between the 3 cancer types (see Table  S3 ). All annotations are the ones downloaded from the TCGA, using VEP v84, and GENCODE v. 22, sift v. 5. 2. 2, ESP v. 20141103, polyphen v. 2. 2. 2, dbSNP v. 146, Ensembl genebuild v. 2014-07, Ensembl regbuild v. 13. 0, HGMD public v. 20154, ClinVar v. 201601. We further denote the filtered raw mutation set as "Protected SNVs" and the other one, which is publicly available, as "Public SNVs". For CNA, we collected data from the ASCAT complete results on TCGA data partly reported on the COSMIC database  44, 59. We then converted ASCAT results on hg19 to GRCh38 coordinates using the segmen-t_liftover Python package  60.
3  of the associated publication  12. DREAM simulated cohort. We obtained the preprocessed files and associated truth for the DREAM dataset  20, 21  from Synapse storage, accession ID syn2813581. Processed data includes mutation calling by Mutect2  70  , and the Battenberg algorithm  74  for CNV deciphering, and purity and ploidy estimation. We performed no further filtering, except neglecting the subclonal copy number events, as none of the evaluated method considered them (in that case, we considered the event with the largest cellular prevalence as clonal), and excluding indels, and SNVs without CNV information. To run signature methods in "cancertype" mode, that is with considering only a subset of all available 47 signatures, we attributed the tumors to a cancer type that contained all true signatures based on  12  , and when several were possible, choosing a frequent one.
T2 was considered of the type "Liver-HCC", T3 "Lung-AdenoCA", T4 and T5 "Breast", and T6 "Colorect-AdenoCA". All methods were run on this dataset, on a single CPU node per sample, with a time limit of 48 h, and a memory limit of 10GB. PhylogicSim500 and SimClone1000. We obtained the 500 samples in the Phylo-gicSim500 dataset, and the 972 samples in the SimClone1000 dataset as described in  2, 22. The data comprises the mutations called and CNV information, with subclonal CNV events in the PhylogicSim500 data but not in the SimClone1000. We ignored subclonal CNV events, and for each segment, we kept only the one with largest CCF in the input provided to all methods, as none of them deals with those events. As signature activity was not considered in the simulation, we used the ground truth to generate two versions of each sample, a "constant" setting with a signature activity distribution common to all clones (corresponding to the unfavorable scenario for CloneSig), and a "varying" setting with a different signature activity distribution for each clone (corresponding to the favorable scenario for CloneSig).
In each sample, we drew active signatures according to the PCAWG cancer type specified for samples of the PhylogicSim500 dataset, and a random PCAWG cancer type, as it was not set for the SimClone1000 dataset. For the PhylogicSim500 dataset, each method was run on a single CPU node per sample, with a time limit of 48 h, and a memory limit of 5GB, and for the SimClone1000, each method was run on a single CPU node per sample, with a time limit of 48 h, and a memory limit of 15 GB, only for samples with fewer than 10,000 SNVs to avoid unnecessary computations; this limit in SNV number was set based on observations on the DREAM dataset. ["TCGA"] and files. data_type in ["Aggregated Somatic Mutation", "Masked Somatic Mutation"] and files. experimental_strategy in ["WXS"] to obtain the list of files to download from GDC.
data_type in ["Allele-specific Copy Number Segment"]. For the PCAWG cohort, the mutations are available from the ICGC data portal at https://dcc. icgc. org/releases/PCAWG/consensus_snv_indel, and the CNV data are available at https://dcc. icgc. org/releases/PCAWG/consensus_cnv. A comprehensive list of the files used are presented in Table  1 , as well as their accession IDs on the Synapse platform (https://www. synapse. org), where the data is mirrored. To access information that could potentially identify participants, researchers need to apply to the ICGC Data Access Compliance Office (DACO; http://icgc. org/daco) before downloading the PCAWG data. The signature matrices used in this work are provided in the supplementary Source Data file, and in the folder external_data of the companion github repository  75.
Intratumoral hypoxia is a widely established element of the pancreatic tumor microenvironment (TME) promoting immune escape, tumor invasion, and progression, while contributing to treatment resistance and poor survival. Despite this critical role, hypoxia is underrepresented in molecular signatures of pancreatic ductal adenocarcinoma (PDA) and concurrent investigations into the hypoxia-immune status are lacking. In this work a literature-based approach was applied to derive an eight-gene hypoxia signature that was validated in fourteen cancer cell lines and in a cohort of PDA. The eight-gene hypoxia signature was significantly associated with overall survival in two distinct PDA datasets and showed independent prognostic value in multivariate analysis. Comparative analysis of tumors according to their hypoxia score (high versus low) determined that tumors with high hypoxia were significantly less enriched in cytotoxic T-cells, and cytolytic activity.
In an independent study a hypoxia signature was associated with worse overall survival (OS) and progression free survival (PFS) in patients with primary tumor; however, associations were lost in the presence of other clinical covariates  (9). Recently, a thirty gene hypoxia signature was similarly found to be associated with worse OS and PFS in PDA  (38). Despite these findings, no hypoxia signature to date has been tested in pancreatic cancer cell lines and validated in multiple patient datasets. The availability of a hypoxia metric acting as a surrogate to tumor hypoxia would allow the investigation of other key players in the TME, namely immune cell fractions and immune activation status. Hypoxia promotes an immunosuppressive phenotype in the TME of PDA  (4, 16, 17, 39) ; therefore, identifying the collective hypoxic and immune nature of a tumor could provide a gateway for reprogramming the TME from a non-immunogenic, immune "cold", to an immunogenic, immune "hot", through the reversal of its hypoxic state.
In addition, they have moderate neoantigen load  (44) , low T-cell infiltration and PD-L1  (45, 46) , all of which are ordinarily used as markers of response to ICI  (47). Clearly, other TME markers are required to enhance the response outcome of ICI in this cancer type. Recently, a four-chemokine signature that could inform on a functional effector T-cell antitumor reactivity in the microenvironment of primary and metastatic PDA was suggested as an alternate indicator of response for immunotherapeutic strategies  (48). Nonetheless, no prognostic associations were made, and hypoxia, a key element of the TME, was not investigated in the context of this signature. In this study, we derived an eight-gene in vitro hypoxia gene signature and validated its application as a prognostic marker in two independent PDA datasets.
Patient Datasets Two independent datasets of PDA were included in this study and referred to as PAAD Data-1 and PAAD Data-2 (Table  1 ). PAAD Data-1 consists of 164 patients from the TCGA (The Cancer   (50) , which were downloaded from the E-MTAB-6134 accession on ArrayExpress (https://www. ebi. ac. uk/arrayexpress/). For PAAD Data-1, the cytolytic index was additionally obtained from a previously published work  (45) , which reported it as the geometric mean of expression of GZMA and PRF1, coding for granzyme A and perforin-1, produced by activated cytolytic CD8+ T cells  (45). For PAAD Data-2, the cytolytic index was manually derived by directly calculating the geometric mean of the two genes.
Scoring Patients' Tumors Each dataset was scored separately to prevent bias related to the sequencing platform used. For the hypoxia score (HS), this was done by first considering the difference in expression of the eight genes from their respective median expression in the dataset. If gene expression was less than the median, it was given a gene score of -1, else it scored as 1. The HS of a patient then totals as the sum of the gene scores. Patients were categorized as HS high only if their HS was greater than zero, otherwise they were HS low. The same approach was followed for determining the immune score (IS), only with the eighteen genes of the tumor inflammation signature (52) (Supplementary Table  S3 ). Tumors were grouped as IS high or IS low based on their variation from zero. With respect to the four-chemokine signature, the scoring was done following the approach used in the publication  (48) ; where briefly, patients were grouped into four-chemokine -low, -intermediate or -high, based on variation from the median of all genes.
Statistical and Survival Analysis Two tailed t-test or ordinary one-way ANOVA were applied based on the number of groups being compared and relevant graphs were generated using GraphPad Prism 9. 0. 0 (GraphPad Software, La Jolla, USA). Survival analysis, heatmaps with hierarchical clustering and Spearman correlation analysis were conducted using RStudio V1. 3 (RStudio, PBC, USA). For survival analysis, the following packages were acquired: survival, survminer, and survivalROC. Kaplan-Meier survival plots with log-rank test were used to compare the various groups based on the probability of overall survival (OS), disease specific survival (DSS) and progression free survival (PFS) for PAAD Data-1, and probability of OS and disease-free survival (DFS) for PAAD Data-2. Prognostic factors in the datasets were identified by applying univariate COX proportional hazard (PH) analysis to survival endpoints; while the independent prognostic power of the signature was determined by applying multivariate COX PH analysis, including the hypoxia score and prognostic clinical/ molecular covariates.
To determine the transferability of the signature, tumors of the TCGA pancreatic adenocarcinoma dataset, PAAD Data-1, were given a hypoxia score (HS) and the expression of PDA hypoxia-relevant genes involved in immune evasion, glycolysis, pH regulation and EMT were determined  (6, (22) (23) (24) (25). A significant increase in these genes could be confirmed in HS high tumors compared to HS low tumors, which was especially evident for the glycolysis genes, ENO1 (p<0. 001) and HILPDA (p<0. 0001), as well as the immune evasion gene ADAM10 (p<0. 0001) (Supplementary Figure  S2A ). These genes were additionally significantly correlated with HS, and ENO1 and HILPDA showed positive correlations with all the genes in the signature (Supplementary Figure  S2B ). Taken together, these results indicate that the score itself is acting as a metric for the hypoxic phenotype in patient tumors.
Prognostic and Predictive Value of the Eight-Gene Hypoxia Signature in Two Independent PDA Datasets To determine the prognostic impact of the signature in PDA, two independent datasets were given hypoxia scores, PAAD Data-1   2A, B ), subsequently survival parameters in the HS high and low tumors were compared. Significant association between the signature and survival was found in the two datasets; wherein patients with HS high tumors fared worse than those with HS low tumors with respect to their probability of OS (Figure  2C ), as well as DSS, PFS and DFS (Supplementary Figure  S2C ). The strongest associations were for OS and DFS in PAAD Data-2 (p<0. 0001). The predictive impact was assessed by carrying out ROC curve analysis, which showed that the signature achieves an AUC of 0. 7 for predicting year-1 OS (Figure  2D ) in both datasets, as well as DSS in PAAD Data-1 (Supplementary Figure  S2D ).
For the multivariate PH Cox analysis, significant risk was associated with OS in HS high patients in both PDA datasets, even in the presence of other covariates (Figure  2E ). HR in HS high tumors was 1. 7 (p=0. 016) in PAAD Data-1 and 2. 19 (p<0. 001) in PAAD Data-2. Similar results were achieved for DFS in PAAD Data-2, while borderline significance was noted for DSS in PAAD Data-1 (Supplementary Figure  S2E ). These findings indicate that the signature is not only associated with prognosis, but also acts as an independent prognostic factor, especially for OS, in pancreatic cancer. Moreover, when the eight-gene signature was compared to a recently published PDA hypoxia signature in PAAD Data-1 (38), the prognostic power of that signature was lost, while the eight-gene signature retained association with DSS and OS (Supplementary Figure  S3A ).
Hypoxic Stress in PDA Is Associated With an Immune "Cold" TME The TME of PDA has been captured to some extent by molecular subtypes that integrate transcriptomic, genomic and molecular features. One such classification was used to stratify PAAD Data-2 into five subtypes of ImmuneClassical, PureClassical, Desmoplastic, StromaActivated and PureBasal-like  (50). Comparing the hypoxia scores across these subtypes revealed significant differences in the distribution of hypoxia (Figure  3A ). The biggest difference was between the ImmuneClassical and PureBasal-like subtypes (p<0. 0001), with the highest scores concentrating in the basal-like subtype, while the lowest in the immune subtype. To investigate the seemingly mutual exclusivity between hypoxia, as presented by our signature, and the immune TME, as indicated by the molecular classification, an independent immune score (IS) was given to the same tumors in both datasets using a previously published tissue agnostic eighteen-gene tumor inflammation signature  (45)  (Supplementary Table  S3 ).
The degree of immune reactivity based on IS was investigated in HS high and low tumors and significant differences were found in PAAD Data-1 (p=0. 013) and PAAD Data-2 (p<0. 0001); wherein HS low tumors showed higher immune scores (Figure  3B ). Spearman correlation analysis revealed significant inverse correlations between HS and IS (Figures  3C, D ). In addition, the hypoxia score in both datasets showed negative correlations with ten genes in the tumor inflammation signature reflecting antigen presenting cell abundance (HLA-DRB1 and CMKLR1), T-cell/NK-cell abundance (NKG7, CD8A), IFN activity (CCL5, CXCL9, CD27, CXCR6) and T-cell expressed exhaustion markers (TIGIT, LAG3). On the other hand, a positive correlation was obtained between HS and the inhibitory checkpoint CD276 (B7-H3) (Figures  3C, D ).
These results support the impact of hypoxia on reshaping the tumor immune microenvironment. Immune cell fractions were subsequently determined using CIBERSORTx analysis, which reports on 22 immune cell types (Figure  4A ). HS low tumors in PAAD Data-1 had significantly higher fractions of M2 macrophages, while in PAAD Data-2 they had significantly higher fractions of regulatory T cells, monocytes and resting mast, than the HS high tumors. Importantly, in both datasets HS low PDA showed significantly higher fractions of CD8+ T-cells than HS high tumors, which in turn exhibited significantly increased fractions of M0 macrophages. In addition, the cytolytic index, a measure of intratumoral cytolytic T-cell activity (20), was significantly higher in tumors with low hypoxia and in those with high IS in both PAAD Data-1 (p=0.
A Combined Hypoxia-Immune Metric Enhances Survival Predictions in PDA To determine the prognostic efficiency of a combined hypoxia and immune classification, patients from each dataset were divided based on their immune and hypoxia scores into four distinct groups (Supplementary Table  S8 ) and survival analysis was conducted. Significant difference in OS was detected in PAAD Data-1 (p=0. 003) and PAAD Data-2 (p<0. 0001) (Figure  5A ), as well as differences in DSS (p=0. 0028), PFS (p=0. 0096) and DFS (p<0. 0002) (Supplementary Figure  S4A ). Looking into how the groups varied, the worse probability for all survival endpoints was almost equally experienced by the groups having Hypoxia High, regardless of whether they were Immune Low or Immune High (Figure  5A , Supplementary Figure  S4A ).
This could be due to the IS alone not being associated with survival (Supplementary Figure  S3B ). Nonetheless, the most protective effect was imparted by the Hypoxia Low/Immune Low microenvironment and a difference in survival was observed between Hypoxia High/Immune Low and Hypoxia Low/ Immune Low subgroups in both datasets (Figures  5A, B ; Supplementary Figure  S4A, B ). Further investigation into the impact of the hypoxia score on survival in these two groups showed a significant difference in OS in PAAD Data-1 (p=0. 0013), and PAAD Data-2 (p<0. 0001) (Figure  5B ); as well as differences in DSS (p=00062), PFS (p=0015) in PAAD Data-1 and DFS (p=0. 00075) in PAAD Data-2 (Supplementary Figure  S4B ). In addition, based on ROC curve analysis there was an increase in the AUC of prediction of survival by the HS in these two groups, reaching 0.