dataesr/scientific-paragraphs-categorization huggingface dataset:
Omics data specific to cancer cell lines are shared on specific databases driven and maintained by global consortium such as The Cancer Genome Atlas Program 14 (over 2. 5 petabytes of genomic, epigenomic, transcriptomic, and proteomic data) or the International Cancer Genomics Consortium (Omics data proved to be a priceless resource in pandemic response. The virus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) causing the covid 19 disease quickly spread around the world, causing more than 6 million deaths (as of Mars 2022) and a global health crisis. Its RNA sequence was obtained in January 2020 and allowed the development of detection kits and later RNA based vaccines. Since the beginning of the pandemic the genomic evolution of the virus is followed almost in real time, as new variants (with mutations affecting mostly the spike protein of the virus envelope) are sequenced.
Genetic landscape of WT with focal and diffuse anaplasia Overall, 168 non-silent somatic single nucleotide variants (median 13, range: 6-27) were identified in the 12 anaplastic WTs assessed by WES (Fig. 1a ; Supplementary Data 2). The mean nonsynonymous mutation rate was 0. 27 mutations per Mb, with little variation (0. 12-0. 52). Of the mutations identified, 125 (74. 6%) were not previously reported in the Catalogue of Somatic Mutations in Cancer (COSMIC) database, while 43 (25. 4%) had been previously reported. The majority of the mutations were nonsynonymous single nucleotide substitutions (66. 8%), followed by frameshift insertions and deletions (23. 2%), stop gain (3. 6%), splicing (2. 8%), and non-frameshift deletions and insertions (2. 8%). No difference was observed in terms of total mutation burden (TMB) between DAWT and FAWT (P = 0.
Distinct transcriptome and immunity landscape among kidney cancers We then asked whether WTs display different expression profiles compared to other kidney tumors, such as clear-cell, papillary, and chromophobe RCC. To this end, we performed unsupervised consensus clustering on a dataset of 95 kidney tumors (Supplementary Data 3). As expected, each histopathological subtype clustered separately from the others. In particular, WTs split into two clusters, one of which contained all the FAWT (P = 0. 046), and DAWT or favorable histology WTs (FHWTs) were scattered across both clusters (Fig. 1d ). Subsequent unsupervised clustering using 24 microenvironment cells led to the identification of two distinct subtypes: immune-rich and immune-cold. The immune-rich subtype had significantly higher infiltration of immune and stromal cells (both, P < 0.
002; Fig. 4a ). Infiltrated-like models exhibited activation of p53 (P = 0. 004), IFN-γ (P = 0. 004), and EMT (P = 0. 004) pathways, while desert-like models displayed activation of chromatin remodeling (P = 0. 048), DNA repair (P = 0. 079), and HDAC (P = 0. 048) pathways. Although no statistical difference was observed in EZH2 expression (P = 0. 57), desert-like samples showed significantly higher enrichment of EZH2 partners signature (P = 0. 008; Fig. 4b ). We then questioned if cell lines capture sufficient heterogeneity in tumors from the transcriptome aspect. To answer this, we performed subclass mapping and revealed that iWT (P = 0. 001, FDR = 0. 004) in the SFCE-WT cohort showed a significant overlap in transcriptome program with non-anaplastic models from the WT cells/PDX/primary-culture dataset, while dWT (P = 0.
012, FDR = 0. 048) showed significant similarity with those anaplastic models (Fig. 4c ). In the GEO-WT dataset, we discovered 2368 upregulated and 1631 downregulated genes when comparing dWT to iWT. Furthermore, there was a common subset of 1016 upregulated genes (RF = 3. 1, P < 0. 0001) and 510 downregulated genes (RF = 2. 8, P < 0. 0001) shared with the SFCE-WT cohort (Fig. 4d ). These findings suggested that including non-anaplastic WTs as controls allowed us to capture tumor heterogeneity to the maximum in anaplastic WT subtype. In addition, these data highly supported the conservation of the oncogenic pathway deregulation between patients' samples and cancer cells and suggested that cancer cells could be used to test the sensitivity of therapeutic agents targeting components of a specific pathway.
A combination of WEE1 and HDAC inhibitors has been previously shown to be effective in human acute myeloid leukemia cells with various genetic mutations, regardless of TP53 mutations, and this might be worth exploring as a treatment option in this population  45. We acknowledge the limitations of our in vitro work, which is based on a single cell line and does not fully capture the complexity of the tumor microenvironment. However, only one anaplastic cell (17. 94) was commercially available, regarding the rarity of the disease. In addition, due to the unavailability of suitable murine cell lines or animal models for this specific tumor, conducting in vivo studies was not feasible to directly address the specific hypotheses of our study. To mitigate these limitations, we have leveraged multiple external datasets, including various models, to validate and strengthen our findings.
Patients and tumors selection Clinicopathological data of WTs treated in France through the SIOP-2001 protocol were collected from children registered in 31 centers of the Société Française de Cancérologie de l'Enfant (SFCE); available frozen sample or formol-fixed-paraffin-embedded (FFPE) tissue blocks for tumor and normal kidney were included in the study. All tumors were evaluated by the local pathologist and reviewed at the national level by A. L. C. and L. D. DNA and RNA extractions were performed according to a standard protocol using the phenol-chloroform technique. Informed consent was obtained from all participating families to conduct the study. The study has been approved by the ethical committee of the Pitié-Salpêtrière Hospital (IDF-6, Ile de France) and conducted in accordance with the Helsinki Declaration.
Quality control (QC) of extracted nucleic acids was performed using an Agilent 2100 Bioanalyzer. Twelve samples with matched germline and tumor DNA passed the QC and were subjected to WES. The remaining nine samples have either low quantity or quality allowing to perform WES, and were therefore used for targeted sequencing. RNA has been successfully extracted from 15 samples but only 10 samples passed Agilent QC (RNA integrity number > 7) and were therefore used for subsequent RNA-seq profiling. Clinicopathological details of those cases are reported in Supplementary Data 1. Eleven cases that have FFPE blocks are available for immune markers analysis including CD8 and CD3 counts. Followup and clinical data were collected in the SIOP 2001 database. The median follow-up time for the study cohort was 70 months (interquartile range 34-87 months).
RNA sequencing The total RNA for 10 cases (Supplementary Data 1) was converted into a library of template molecules for sequencing on the Illumina HiSeq 2000 according to the NuGen Ovation RNA-seq System V2 protocol. In brief, first, single-stranded cDNA was synthesized from 100 ng of DNase1-treated total RNA using a mix of DNA/RNA chimeric primers that hybridize to both the 50 portions of the poly (A) sequence and randomly across the transcript. Second, strand synthesis produced double-stranded cDNA, which was amplified using single-primer isothermal strand-displacement amplification. The resultant cDNA was fragmented to 200 bp (mean fragment size) with the S220 Focused-ultrasonicator (Covaris) and used to make barcoded sequencing libraries on the SPRI-TE Nucleic Acid Extractor (Beckman-Coulter).
Libraries were quantitated by qPCR (KAPA Systems), multiplexed and sequenced-four samples per lane-on the HiSeq2000 using 75 bp paired-end sequencing. The resulting data were analyzed with the current Illumina bcl2fastq2 pipeline (v2. 20) to generate raw FASTQ files. The raw, paired-end reads were aligned to the human reference genome, GRCh38/hg38, using MOSAIK (v1. 1. 0021) alignment software. MOSAIK works with paired-end reads from Illumina HiSeq 2000 and uses both a hashing scheme and the Smith-Waterman algorithm to produce gapped optimal alignments and to map exon junction-spanning reads with a local alignment option for RNA-seq. The resulting alignments were then saved as a standard bam file. We then counted the mRNA-mapped reads annotated in GEN-CODE25 to generate the raw counts for each gene using the HTSeqcount script distributed with the HTSeq package.
As a comparator, randomly selected 15 DAWTs and 15 FHWTs derived from previously published RNA-seq data  46  , with available expression profiles (Supplementary Data 3). As an additional control, The Cancer Genome Atlas level-1 raw data was authorized to assess and a dataset comprised of ccRCC (n = 15 cases)  47  , papillary renal cell carcinoma (PRCC; n = 15 cases)  48  , and ChRCC (n = 15 cases)  49  listed in Supplementary Data 3 was also randomly selected. Another 10 ccRCC samples were collected from Pitié-Salpêtrière Hospital (PSL), resulting in four kidney cancer types with a total of 95 samples. To ensure comparability, we analyzed the data using the same framework for sequencing alignment and quantification with the same genome annotation. For all samples with raw count data, the number of fragments per kilobase of non-overlapped exon per million fragments mapped was computed first and transferred into transcripts per kilobase million (TPM) values, which are more comparable between samples  50.
All TPM values went through log 2 transformation and were combined as a single cohort. To mitigate potential batch effects introduced by different cohorts, we employed the ComBat algorithm, a function included in the R package sva (v3. 46. 0), using default parameters. ComBat is an empirical Bayes framework that estimates and removes batch-specific variability, thus allowing us to effectively correct for any potential batch effects introduced by different cohorts. We included the cohort as a covariate in our statistical models to further account for any variability associated with the different cohorts. Principal component analysis was further performed to confirm the removal of cohort-specific effects. Whole exome-sequencing and somatic mutation detection WES was performed on genomic DNA derived from paired tumornormal samples related to those 12 cases using Agilent human V5 (51 Mb) capture and the HiSeq2000 sequencing platform (Supplementary Data 1).
Average coverage was ~100x for cancer samples and 50x for matched normal. After raw paired-end reads from WES were aligned/mapped to the human genome reference (hg38) and PCR duplicate reads were removed by MOSAIK aligner, we then analyzed the resulting alignments using the Bayesian model-based software GigaBayes/FreeBayes that enables the efficient analysis of billions of aligned short-read sequences. The program evaluates each aligned base and base quality value at each position to indicate putative singlenucleotide variations (SNVs) and short insertions/deletions (indels), and their corresponding SNV probability value (PSNV). Base quality values are converted to base probabilities corresponding to each of the four possible nucleotides. Using a Bayesian formulation, a PSNV (or indel probability value, as appropriate) is calculated as the likelihood that multiple different alleles are present between the reference genome sequence and the reads aligned at that position.
If the probability value exceeds a prespecified threshold, the SNV or indel candidate is reported in the output. In this study, we used a PSNV cutoff value (0. 9) to define a high-confidence SNV or short indel candidate  51, 52. We filtered out all known SNVs/indels in the University of California, Santa Cruz (UCSC) dbSNP 135 and 1000 Human Genome Project single nucleotide polymorphisms (SNP) databases and kept any mutations, which are in the COSMIC database, curated by the Wellcome Trust Sanger Institute. The variation classification for each mutation was annotated by ANNOVAR (v2020Jun08) (Supplementary Data 2)  53. Furthermore, we evaluated TP53 mutations status using Sanger sequencing in the additional 9 DAWT and FAWT for which WES was not performed  54. All TP53 somatic mutations detected by WES were also confirmed by Sanger sequencing.
External WT cohorts for validation For in-silico validation, two external WT cohorts were used. The first validation cohort was retrieved from the TARGET initiative (accession number phs000218/DS-PEDCR), containing 114 WT cases with poor outcomes and for which transcriptome expression profiles and clinical outcomes were available. The TARGET-WT cohort included 42 DAWTs and 72 FHWTs that relapsed. The transcriptome raw count data were converted to TPM values accordingly. Genetic alteration of the TARGET-WT cohort was downloaded from the cBioPortal (https://www. cbioportal. org/). The second validation cohort was downloaded from the GEO (accession number GSE156065) according to the literature  55. The GEO-WT dataset encompasses a total of 12 models; four out of these 12 WT models presented with anaplastic features, while the remaining eight models had no anaplastic features.
Calculation of microenvironment cell abundance We modified two gene signatures, LM22  57  and MCPcounter (v1. 2. 0)  58  , to construct our compendium  59. As LM22 does not contain signatures related to fibroblasts and endothelial cells, extra 40 genes were added to account for these cells (32 genes for endothelial cells and 8 genes for fibroblasts) from MCPcounter to our compendium, which consisted of 364 genes representing 24 microenvironment cell types (Supplementary Data 5). We used gene set variation analysis on these gene sets to generate enrichment scores for each cell by using the R package GSVA (v1. 46. 0). The signature of TLS was retrieved from the literature  60  , and the corresponding enrichment score for TLS was presented as a geometric mean value. To describe the constituent pattern (i.
Differential expression and function enrichment analyses Differential expression analysis based on raw count data was conducted by R package edgeR (v3. 40. 0) where the Benjamini-Hochberg method was implemented to adjust nominal P values (FDR) for multiple tests. For GSEA based on transcriptome expression data, we prepared a pre-ranked gene list according to the descending ordered log 2 FoldChange value derived from differential expression analysis; we then leveraged the R package clusterProfiler (v4. 6. 0) to determine functional enrichment based on the Molecular Signature Database (MSigDB)  67, 68. Additionally, EZH2 partners retrieved from the term KAMMINGA_EZH2_TARGETS in MSigDB were identified from a stem cell gene network analysis  69  , where coregulated and positively correlated gene sets were identified as EZH2 targets or partners.
Regulon analysis We used the R package RTN (v2. 22. 0) to reconstruct transcriptional regulatory networks (regulons) including a total of 71 candidate regulators that were relevant to cancerous chromatin remodeling  72, 73. Specifically, mutual information analysis and Spearman rank-order correlation computed the possible associations between a regulator and all potential targets from the transcriptome expression profile, and permutation analysis was used to erase associations. The FDR threshold was set as 0. 01 for the SFCE-WT cohort and 0. 00001 for the TARGET-WT cohort because of different sample sizes. A bootstrapping strategy removed unstable associations through 1000 times of resampling with a consensus bootstrap greater than 95%. Data processing inequality filtering eliminated the weakest associations in the triangles of two regulators and common targets.
Drug sensitivity prediction Expression profile and drug sensitivity data of human cancer cell lines (CCLs) were downloaded from the largest publicly available pharmacogenomics database: Genomics of Drug Sensitivity in Cancer (GDSC) (https://www. cancerrxgene. org/). The half maximal inhibitory concentration (IC 50 ) was provided as a measure of drug sensitivity, and lower IC 50 values indicate increased sensitivity to treatment. A total of 727 CCLs with both expression and drug sensitivity records were identified. K-nearest neighbor imputation was applied to impute the missing IC 50 values. We then employed the R package pRRophetic (v0. 5) to predict the chemotherapeutic sensitivity for each WT case with default settings  74  ; the estimated IC 50 of each sample treated with a specific chemotherapy drug was obtained by ridge regression, and prediction accuracy was measured through tenfold cross-validation with the GDSC training set.
Clonogenic survival assessment WT 17. 94 cells were seeded at 5000 cell density in six-well plates and kept at 37 °C supplemented with 5% CO 2. After 48 h of incubation, the cells were treated with various inhibitors according to the IC 50 obtained for each inhibitor. The cells were incubated for 48 h with the various inhibitors and later media was replaced with the fresh one. The cells were kept in the incubator for an additional 20 days by adding 1 ml of media after three days. At the end of the assay, the media from cells were removed, washed with cold PBS (1x), and fixed with 3. 4% formaldehyde. The colonies were stained with crystal violet. Later pictures were taken and then the colonies were counted using the ImageJ (1. 53n). Each biological experiment was performed in triplicate. The data is presented as mean ± standard deviation.
Acknowledgements This work was supported in part by grants from  Fondation AVEC  (G. M. ),  MSD-AVENIR  (G. M. ),  National Natural Science Foundation of China  [No. 81973145 , No. 82273735 ] (F. Y. ),  Key R&D Program of Jiangsu Province (Social Development)  ( BE2020694 ) (F. Y. ),  CCSG  ( CORE-001 )  Sequence and Microarray Facility  ( P30 CA016672 ) (X. S. ), and  UPR/MDACC: Partnership for Excellence in Research  ( U54 CA096300 ) (X. S. ).
Data availability The raw RNA-seq data of the French SIOP-2001 WT cohort have been deposited in the GEO database under accession code GSE224266. The variant call data of French SIOP-2001 WT study are provided in Supplementary Data 2; other data required to verify the published results are provided as Source Data. The underlying raw WES data are not available for this manuscript because the patients' family did not consent to the release, sharing or distribution of the data. The RNA-seq data for TARGET-WT cohort under accession code phs000218/DS-PEDCR are available under controlled access [https://gdc. cancer. gov]  46. The genetic alteration data for TARGET-WT cohort is publicly available in the cBioPortal database under project Pediatric Wilms' Tumor (TARGET, 2018) [https://www. cbioportal.
org/]  46. The remaining publicly available data used in this study are available in the GEO database under accession code GSE156065  55  , in the ArrayExpress database under accession code E-MTAB-3610  79  , and in the GDSC database for drug screening data [https://www. cancerrxgene. org/]  80. The remaining data are available within the Article, Supplementary Information or Source Data file. Source data are provided with this paper.
We thus focus our attention here on the repeatability of evolution among multiple populations. We also resolve details of the genetic architecture of crypsis, details which could affect the repeatability of evolution and that allow for fuller understanding of the mechanisms of evolution across time scales. We report results from 30 years of data collection on morph frequencies in T. cristinae, representing 692 year-by-host-by-locality estimates of morph frequency derived from 48,349 individuals (Figure  1 , Database S1). Most critically, these data represent 10 localities with at least a decade of the data required to test for NFDS (see Table  S1 ; mean = 14 years, maximum = 22 years). Because of little to no gene flow among them, these 10 populations undergo independent yearly changes in morph frequency at each locality.
Long-term field studies in Timema cristinae We compiled data on morph frequencies in T. cristinae using samples collected in the spring using sweep nets between 1990 and 2023. This builds on a previously published data set that spanned the years 1990-2017 (1). All individuals were scored as 'striped', 'green' (a green morph lacking a stripe), or 'melanistic'. These classifications have been found to be highly repeatable in past work (2,3). Samples from 1990 to 1999 were collected and scored by Cristina Detailed information on the collection localities (i. e. , GPS coordinates and elevations), morph frequencies, sample sizes, etc. is provided in Database S1. We used this data set to estimate the average frequency of striped morphs on Adenostoma and Ceanothus host plants each year of the time series.
In this analysis, we analyzed the 32,867 stick insect observations from Santa Ynez mountain along highway 154 (i. e. , we excluded a second mountain, Refugio mountain, from analysis but included these data in the data archive for completeness). This includes counts of stick insects from 213 population by year combinations on Adenostoma and 206 population by year combinations on Ceanothus. Here, we define a population as the stick insects collected from a locality on a specific host plant species. Treating the stick insects from different host plant species at the same locality as different populations is necessary to estimate morph frequencies by host. However, sympatric and parapatric host-associated populations, that is populations on different hosts but in the same or nearly the same spatial location, exhibit very low levels of genetic differentiation and differences in morph frequency (1, 2, 4).
Purified DNA was treated to remove biotin that was not internal to ligated fragments. The DNA was then sheared to ∼350 bp mean fragment size and sequencing libraries were generated using NEBNext Ultra enzymes and Illumina-compatible adapters. Biotin-containing fragments were isolated using streptavidin beads before PCR enrichment of each library. The libraries were sequenced on an Illumina HiSeq X to a target depth of 30x coverage. The PacBio de novo assembly, Chicago library reads, and Dovetail Hi-C library reads were used as input data for HiRise, a software pipeline designed specifically for using proximity ligation data to scaffold genome assemblies (19). An iterative analysis was conducted. First, Chicago library sequences were aligned to the draft input assembly using a modified SNAP We then aligned this new T.
Reference genome assembly for Timema podura and comparative alignment with T. cristinae We created a de novo reference genome for T. podura. This was done using a combination of  36) was then run to generate a primary assembly. Next, blobtools v1. 1. 1 (37)     was used to identify potential contamination in the assembly based on blast (v2. 9) results of the assembly against the NT database. A fraction of the scaffolds was identified as contaminant and were removed from the assembly. The filtered assembly was then used as an input to purge dups v1. 1. 2 (38), and potential haplotypic duplications were removed from the assembly, resulting in the final assembly. For the Dovetail Omni-C library, chromatin was fixed in place with formaldehyde in the nucleus. Fixed chromatin was digested with DNaseI and then extracted, chromatin ends were repaired and ligated to a biotinylated bridge adapter followed by proximity ligation of adapter containing ends.