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5,287 results for “Colorectal cancer”
ColoPola: A dataset of colorectal cancer polarimetric images (Mueller matrix elements) for colorectal cancer detection
<p><strong>ColoPola</strong> dataset is <strong>Colo</strong>rectal cancer <strong>Pola</strong>rimetric images dataset</p> <p>The dataset consists of 572 slices (specimens) with 20,592 images, 284 slices of which were designated as cancer samples and 288 as normal samples.</p> <p>Each sample has 36 polarimetric images (i.e., HH, HV, HP, HM, HR, HL, VH, VV, VP, VM, VR, VL, PH, PV, PP, PM, PR, PL, MH, MV, MP, MM, MR, ML, RH, RV, RP, RM, RR, RL, LH, LV, LP, LM, LR, and LL).</p> <p>Each folder in the <strong>ColoPola</strong> dataset consists of 36 polarimetric images. Each image is 1280x1024 pixels in size and was created in the TIF file format (HH.tif, HV.tif, ..., LL.tif). </p>
A dataset of colorectal cancer histopathological images
<p>The dataset contains the histopathological images of the ColoPola dataset (https://doi.org/10.5281/zenodo.10068018).</p> <p>CLCXYYZZNN_Hx</p> <p>CLC: colorectal (cancer) tissue</p> <p>NLC: normal tissue</p> <p>X - Times<br>YY - Sample number<br>ZZ - Serial number<br>NN - Image number<br>H - Magnification</p>
Contribution of allelic imbalance to colorectal cancer
<p><strong>Point mutations in cancer have been extensively studied but chromosomal gains and losses have been more challenging to interpret due to their unspecific nature. Here we examine high-resolution allelic imbalance (AI) landscape in 1699 colorectal cancers, 256 of which have been whole genome sequenced (WGSed). The imbalances pinpoint 38 genes as plausible AI targets based on previous knowledge, and unbiased CRISPR-Cas9 knockout and activation screens identified altogether 79 genes within AI peaks regulating cell growth. Genetic and functional data implicates loss of TP53 as a sufficient driver of AI. The WGS highlights an influence of copy number aberrations on the rate of detected somatic point mutations. Importantly, the data reveal several associations between AI target genes, suggesting a role for a network of lineage-determining transcription factors in colorectal tumorigenesis. Overall, the results unravel the contribution of AI in colorectal cancer and provide a plausible explanation why so few genes are commonly affected by point mutations in cancers.</strong></p>
Supplementary material for "Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer"
<p>Supplementary files for "Towards a metagenomics machine learning interpretable model for understanding the transition from adenoma to colorectal cancer".</p>
"The pathway of hyaluronic acid (HA) and its receptors (CD44, RHAMM) in the regulation of Rho GTPases and their effectors in an in vitro colorectal cancer model" ("Szlak kwasu hialuronowego (HA) i jego receptorów (CD44, RHAMM) w regulacji GTPaz Rho i ich efektorów w modelu raka jelita grubego in vitro"); NCN Miniatura 2022/06/X/NZ3/00848
<p>Results from Screening for "The pathway of hyaluronic acid (HA) and its receptors (CD44, RHAMM) in the regulation of Rho GTPases and their effectors in an in vitro colorectal cancer model" the project <strong>Miniatura</strong> (<strong>2022/06/X/NZ3/00848</strong>) funded by Polish <strong>National Science Centre (NCN)</strong></p> <p>Wyniki skriningu w projekcie "Szlak kwasu hialuronowego (HA) i jego receptorów (CD44, RHAMM) w regulacji GTPaz Rho i ich efektorów w modelu raka jelita grubego in vitro", <strong>Miniatura</strong> (<strong>2022/06/X/NZ3/00848</strong>) finansowanym przez <strong>Narodowe Centrum Nauki (NCN)</strong></p>
Data for the publication "Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer"
<p>This dataset encompasses all data needed to reproduce the analyses presented in <a href="https://www.nature.com/articles/s41591-019-0406-6">Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer</a></p> <p>You can also check the <a href="https://github.com/zellerlab/crc_meta">GitHub repository</a></p>
Conventional therapy induces tumor immunoediting and modulates the immune contexture in colorectal cancer
<p>Cancer immunotherapies for patients with colorectal cancer (CRC) continue to lag behind other solid cancer types with the exception of 4% of patients with microsatellite-instable tumors. Thus, there is an urgent need to broaden the clinical benefit of checkpoint blockers to CRC by combining conventional therapies to sensitize tumors to immunotherapy. However, the impact of conventional drugs on immunoediting and hence, imposing positive selection towards less immunogenic variants, and on the tumor immune contexture in CRC remains elusive.</p> <p>In this study, we performed comprehensive multimodal profiling using longitudinal samples from metastatic CRC patients undergoing neoadjuvant therapy with mFOLFOX6 and Bevacizumab. Exome-sequencing, RNA-sequencing and multiplexed immunofluorescence imaging was carried out on tumor samples obtained before and after therapy and the data was analyzed using established methods. The results of the analysis were extrapolated to publicly available datasets (TCGA and CPTAC). In order to identify a surrogate marker, an explainable artificial intelligence method was developed using a transformer-based analytical pipeline for the identification of features in H&E images associated with specific biological processes, followed by manual evaluation of highly informative tiles by a pathologist.</p> <p>We expect that the results of this project will provide a deeper understanding of the tumor-immune interactions and will allow the development of more robust combinatorial therapeutic strategies for MSS CRC.</p>
TMEM206 contributes to cancer hallmark functions in colorectal cancer cells and is regulated by p53 in a p21-dependent manner
<p><span>Acid-induced ion flux plays a role in pathologies where tissue acidification is prevalent, including cancer. In 2019, TMEM206 was identified as the molecular component of acid-induced chloride flux. Localizing to the plasma membrane, TMEM206 contributes to cellular processes like acid-induced cell death. Since over 50% of human cancers carry loss of function mutations in the p53 gene, we aimed to analyze how TMEM206 is regulated by p53 and its role in cancer hallmark function and acid-induced cell death in HCT116 colorectal cancer (CRC) cells. We generated p53-deficient HCT116 cells and assessed TMEM206-mediated Cl<sup>-</sup> currents and transcriptional regulation using the patch-clamp and a dual-luciferase reporter assay, respectively. To investigate the contribution of TMEM206 to cancer hallmark functions we performed migration and metabolic activity assays. The role of TMEM206 in p53-mediated acid-induced cell death has been assessed with cell death assays. TMEM206 mRNA level is significantly elevated in human primary CRC tumors. TMEM206 knockout increased acid-induced cell death and reduced proliferation and migration, indicating a role for TMEM206 in these cancer hallmark functions. Furthermore, we observed increased TMEM206 mRNA levels and currents in HCT116 p53 knockout cells. This phenotype can be rescued by transient overexpression of p53, but not by overexpression of dysfunctional p53. In addition, our data suggests that TMEM206 may mediate cancer hallmark functions within p53-associated pathways. TMEM206 promoter activity is not altered by p53 overexpression. Conversely, knockout of p21, a major target gene of p53, increased TMEM206-mediated currents suggesting expression control of TMEM206 by p21 downstream signaling. Our results show that in colorectal cancer cells, TMEM206 expression is elevated, contributes to cancer hallmark functions and its regulation is dependent on p53 through a p21-dependent mechanism.</span></p>
Vectra Polatis image of human colorectal cancer (CRC1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.
<p>Two 4 µm thick serial sections were cut from CRC1 FFPE block using a microtome. The first slide was dewaxed and rehydrated before carrying out HIER with Antigen Retrieval Reagent-Basic (R&D Systems). The tissue was then blocked and incubated with the anti-CD3 antibody (Dako, Supplementary Table 2) followed by horseradish peroxidase (HRP) conjugated anti-rabbit antibody (Dako) and stained with 3,3' diaminobenzidine (DAB) substrate (Abcam) and haematoxylin. Areas with CD3<sup>+</sup> infiltration in the proximity of the tumour invasive margin were identified by a clinical pathologist (M. R-J.)</p> <p>The second slide was stained with a panel of six antibodies (CD8, PD1, Ki67, PDL1, CD68, GzB, Supplementary Table 2), Opal fluorophores and 4’,6-diamidino-2-phenylindole (DAPI) on a Ventana Discovery Ultra automated staining platform (Roche). Expected expression and cellular localisation of each marker as well as fluorophore brightness were used to minimise fluorescence spillage upon antibody-Opal pairing. Following a one-hour incubation at a 60°C, the slide was subjected to an automated staining protocol on an autostainer. The protocol involved deparaffinisation (EZ-Prep solution, Roche), HIER (DISC. CC1 solution, Roche) and seven sequential rounds of: one hour incubation with the primary antibody, 12 minutes incubation with the HRP-conjugated secondary antibody (DISC. Omnimap anti-Ms HRP RUO or DISC. Omnimap anti-Rb HRP RUO, Roche) and 16 minute incubation with the Opal reactive fluorophore (Akoya Biosciences). For the last round of staining, the slide was incubated with Opal TSA-DIG reagent (Akoya Biosciences) for 12 minutes followed by Opal 780 reactive fluorophore for our hour (Akoya Biosciences). A denaturation step (100°C for 8 minutes) was introduced between each staining round in order to remove the primary and secondary antibodies from the previous cycle without disrupting the fluorescent signal. The slide was counterstained with DAPI (Akoya Biosciences) and coverslipped using ProLong Gold antifade mounting media (Thermo Fisher Scientific). The Vectra Polaris automated quantitative pathology imaging system (Akoya Biosciences) was used to scan the labelled slide. Six fields of view, within the area selected by the pathologist, were scanned at 20x and 40x magnification using appropriate exposure times and loaded into inForm{Kramer, 2018 #23} for spectral unmixing and autofluorescence isolation using the spectral libraries. After spectral unmixing and merging of six 20x fields of view for a total of >5mm<sup>2</sup> ROI (Table 2), one single-tiff image was extracted for each marker and its intensity was rescaled from 0 to 1 with custom R scripts.</p>
Pathobionts in the tumour microbiota predict survival following resection for colorectal cancer - pre-processed data
<p>A multicentre, prospective observational study was conducted of colorectal cancer (CRC) patients undergoing primary surgical resection in the United Kingdom and Czech Republic. Analysis was performed using metataxonomics (microbiome) and ultra-performance liquid chromatography mass spectrometry (UPLC-MS, metabolomics). Both datasets were pre-processed as described in the methods section of the main article. The data here were used as the input to the data analysis workflows available from <a href="https://github.com/jmp111/CRC">Github</a>.</p>
Representative sequences for colorectal cancer additive log ratios
<p>Representative sequences and corresponding feature rank tables calculated in Debelius et al, The local tumor microbiome is associated with survival in late-stage colorectal cancer patients (doi: https://doi.org/10.1101/2022.09.16.22279353)</p> <p> </p> <p>file_s1_tissue_alr_sequences.fasta goes with tissue_only_ranks.tsv and corresponds to Figure 1 in the manuscript.</p> <p>file_s2_interaction_dr_sequences.fasta corresponds to figure 2 and links to interaction_models_interaction_ranks.tsv and interaction_models_tissue_ranks.tsv</p> <p> </p> <p>file_s3_tumor_rPCA_alr_sequences.fasta corresponds to Figure 3 and links to tumor_survival_index_ranks_file3.tsv.</p> <p> </p> <p>The rank files provide the rank and rank group; the fasta files provide the reference sequences for the data. </p>
A Gas Chromatography – Ion Mobility Spectrometry dataset for colorectal cancer diagnostic of 56 urine samples corresponding to 29 subjects.
<p><strong>Contents of the dataset</strong></p> <p>The dataset includes the set of urine samples in .mea format, which can be<br> read using the GCIMS R package.</p> <p>It also contains analytical standards in the same format, used for quality<br> control of the equipment and as a retention time alignment reference.</p> <p>If you want to preview the data, you do not need to download the full Urines.zip<br> and AnalyticalStandards.zip files, but rather use the smaller UrinesDemo.zip and<br> AnalyticalStandardsDemo.zip, with a subset of just three samples of the whole<br> dataset.</p> <p>Besides the actual measurements, you will find the annotations.csv and<br> reference_peaks.csv files, with sample annotations and some reference peaks<br> identified in the samples.</p> <p>See further details below.</p> <p><br> <strong>Sample collection</strong></p> <p>Urine samples from 29 subjects were collected at Hospital de Reus. 15 subjects<br> were diagnosed with colorectal cancer, 14 subjects were controls. The study<br> protocol was approved by the Ethics Committee of Hospital de Reus (study<br> approval no. 074/2018).</p> <p>Samples were aliquoted and frozen at -80ºC for storage.</p> <p><strong>Sample preparation</strong><br> </p> <p>Sample preparation improves urine preservation by blocking bacterial growth in<br> the urine, and favours volatile extraction. It also adds an internal standard<br> for verification of instrument variability.</p> <p><em>Stock solution preparation</em></p> <p>Dissolve 11.69 g of NaCl in about 35 mL deionized water and add 6.5 mg sodium<br> azide (NaN3). Once dissolved, add 5.50 mL 5M HCl and mark up to volume with<br> deionized water until the final volume is 50mL. The HCl 5M is used to obtain<br> an acid pH. The pH is controlled with a pH test paper. The final pH level must<br> be 2 or below. The NaCl favors the volatile extraction, and the NaN3 omits<br> the bacterial growth in the urine.</p> <p><em>Internal standard solution preparation</em><br> </p> <p>The 4-flurobenzaldehyde is located in retention time around 200 seconds and<br> can be used as an internal standard.</p> <p>Prepare a methanol stock solution using 100 ml of methanol grade for<br> preparative chromatography and 200 ml of distilled water.</p> <p>Mix 5 mL of 4-fluorobenzaldehyde with 100 mL of the methanol stock solution.</p> <p>Dilute the previous mixture in 400 mL of mili-Q water.</p> <p><br> <em>Sample preparation</em><br> </p> <p>Aliquotes were thawed before analysis. Once thawed, 300uL of the stock solution<br> were added to the urine sample, and 1.5 ml of the acidified urine sample were<br> transferred into a 20ml vial, ensuring only the supernatant of the sample<br> is transferred.</p> <p>Finally, 20 mL of the internal standard solution is added to the sample.</p> <p><strong>GC-IMS Analysis</strong></p> <p>Samples were analyzed with a GC-IMS FlavourSpec® instrument from<br> G.A.S. Dortmund (Dortmund, Germany). Samples were incubated for 15 minutes<br> at 60ºC, the flow rate of the drift gas was set at 200 ml/min, and the carrier<br> gas was set 11 ml/min. Both the drift and carrier gas were Nitrogen 5.0. The GC<br> and IMS temperature were set at 60ºC and the measurement time lasted 33 minutes.</p> <p>Besides the urines, a set of measurements of a ketone mixture was also analyzed<br> at least once per day as an analytical standard control of the equipment. The mixture<br> included 6 ketones (2-butanone, 2-pentanone, 2-hexanone, 2-heptanone,<br> 2-ocatanone and 2-nonanone). This mixture is measured in the same conditions as<br> the urine samples.</p> <p>Samples are provided in the native instrument format (.mea format), that can be<br> read with the GCIMS R package or with the instrument software.</p> <p><strong>Sample annotations</strong></p> <p>The dataset includes a CSV file with sample annotations.</p> <p>The annotations include the following information:</p> <ul> <li>Diagnostic: Either ColorectalCancer or Control</li> <li>Sex: Either Male or Female</li> <li>Sample volume (in ml)</li> <li>Fasting: Whether the sample was collected with the patient in fasting conditions</li> <li>Age in years</li> <li>Weight_kg</li> <li>Height_cm</li> <li>BMI</li> <li>Smoker: TRUE/FALSE, whether the patient smoked</li> <li>Diseases: Whether the patient suffered from ArterialHypertension, CardiacFailure, Cholesterol, Dyslipidemia, Fibromyalgia or Tuberculosis</li> <li>AnalysisDateTime: Date and time of the GC-IMS analysis of the sample</li> </ul> <p><br> <strong>Reference peaks</strong></p> <p>Some peaks were manually annotated to ease the alignment of the samples and explore<br> alignment solutions. While manual peak labelling is not generally required, we<br> attach those reference peaks as well and their locations, in case they are of<br> interest.</p> <p>These reference peaks are found at reference_peaks.csv.</p> <p> </p>
CRISPR/dCas9-mediated DNA demethylation screen identifies driver epigenetic determinants of colorectal cancer (Processed data)
<p><strong>Background:</strong> Promoter hypermethylation of tumour suppressor genes is frequently observed during the malignant transformation of colorectal cancer (CRC). However, whether this epigenetic mechanism is an actual driver of cancer or is a mere consequence of the carcinogenic process remains to be elucidated.</p> <p><strong>Results: </strong>In this work we performed an integrative multi -omic approach to identify gene candidates with strong correlations between DNA methylation and gene expression in human CRC samples and a set of 8 colon cancer cell lines. As a proof of concept, we combined recent CRISPR-Cas9 epigenome editing tools (dCas9-TET1, dCas9-TET-IM) with a custom arrayed gRNA library to modulate the DNA methylation status of 56 promoters previously linked with strong epigenetic repression in CRC, and we monitored the potential functional consequences of such DNA methylation loss by means of a high-content cell proliferation screen. Overall, the epigenetic modulation of most of these DNA methylated regions had a mild impact in the reactivation of gene expression and in the viability of cancer cells. Interestingly, we found that epigenetic reactivation of RSPO2 in the tumour context was associated with a significant impairment in cell proliferation in p53-/- cancer cell lines and further validation with human samples demonstrated that the epigenetic silencing of RSPO2 is a mid-late event in the adenoma to carcinoma sequence.</p> <p><strong>Conclusions: </strong>These results highlight the potential role of DNA methylation as a driver mechanism of CRC and open up the venue for the identification of novel therapeutic windows based on the epigenetic reactivation of certain tumour suppressor genes.</p>
Dataset related to article "NKp46-expressing human gut-resident intraepithelial Vδ1 T cell subpopulation exhibits high antitumor activity against colorectal cancer"
<p>γδ T cells account for a large fraction of human intestinal intraepithelial lymphocytes (IELs) endowed with potent antitumor activities. However, little is known about their origin, phenotype, and clinical relevance in colorectal cancer (CRC). To determine γδ IEL gut specificity, homing, and functions, γδ T cells were purified from human healthy blood, lymph nodes, liver, skin, and intestine, either disease-free, affected by CRC, or generated from thymic precursors. The constitutive expression of NKp46 specifically identifies a subset of cytotoxic Vδ1 T cells representing the largest fraction of gut-resident IELs. The ontogeny and gut-tropism of NKp46+/Vδ1 IELs depends both on distinctive features of Vδ1 thymic precursors and gut-environmental factors. Either the constitutive presence of NKp46 on tissue-resident Vδ1 intestinal IELs or its induced expression on IL-2/IL-15-activated Vδ1 thymocytes are associated with antitumor functions. Higher frequencies of NKp46+/Vδ1 IELs in tumor-free specimens from CRC patients correlate with a lower risk of developing metastatic III/IV disease stages. Additionally, our in vitro settings reproducing CRC tumor microenvironment inhibited the expansion of NKp46+/Vδ1 cells from activated thymic precursors. These results parallel the very low frequencies of NKp46+/Vδ1 IELs able to infiltrate CRC, thus providing insights to either follow-up cancer progression or to develop adoptive cellular therapies.</p> <p> </p> <p>This dataset is created with fcs files form, in order to guarantee the access we attach a pdf information about</p>
Validation data set for automatic blood vessel segmentation in colorectal cancer histology (IHC)
<p><strong>Content</strong></p> <p>This data set contains 100 histological image patches of 1000 * 1000 px size. The samples were immunostained for CD34 (3,3'-Diaminobenzidine, DAB [brown]) with hematoxylin (blue) counterstain.</p> <p>Furthermore, the data set contains a table of blood vessel counts in each image by three blinded observers as well as an automatic count with a method based on the following paper:</p> <p>Kather, Jakob Nikolas et al. "Continuous Representation Of Tumor Microvessel Density And Detection Of Angiogenic Hotspots In Histological Whole-Slide Images". <em>Oncotarget</em> 6.22 (2015): 19163-19176. http://dx.doi.org/10.18632/oncotarget.4383</p> <p><strong>Image format</strong></p> <p>All images are RGB, 0.50 µm per pixel, digitized with an Aperio ScanScope (Aperio/Leica biosystems), magnification 20x. Histological samples are fully anonymized images of formalin-fixed paraffin-embedded human colorectal adenocarcinomas (primary tumors and liver metastases) from our pathology archive (Institute of Pathology, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany).</p> <p><strong>Ethics statement</strong></p> <p>All experiments were approved by the institutional ethics board (medical ethics board II, University Medical Center Mannheim, Heidelberg University, Germany; approval 2015-868R-MA). The institutional ethics board waived the need for informed consent for this retrospective analysis of anonymized samples. All experiments were carried out in accordance with the Declaration of Helsinki.</p> <p><strong>Contact</strong></p> <p>For questions, please contact:<br> Dr. Jakob Nikolas Kather<br> http://orcid.org/0000-0002-3730-5348<br> ResearcherID: D-4279-2015</p>
Availability of results of interventional trials assessing colorectal cancer over the past seven years
<p>Dataset used for our work "Availability of results of interventional trials assessing colorectal cancer over the past seven years."</p> <p>Part of the dataset was extracted from the AACT database (Clinical Trials Transformation Initiative) and part of the dataset was extracted by the authors.</p> <p>The list of trials are ordered by NTC number (from ClinicalTrials.gov).</p> <p> </p>
Colorectal cancer and adenoma metagenomes
<p>Normalized counts of species identified by shotgun sequencing in 156 stool samples of 51 colorectal cancer patients, 54 patients with adenoma, and 51 controls.</p> <p> </p>
Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and East Asian ancestries
<p><strong>Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions, and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Cross-tissue analyses indicated that over a third of effector genes most likely act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis, and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.</strong></p> <p><strong>The data submitted here are expression and methylation models with LD reference data for the transcriptome-wide (TWAS), methylome-wide (MWAS) and transcript isoform-wide association study (TIsWAS) as described in the manuscript "Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and East Asian ancestries". Details of the methods are presented in the method section and supplementary information file. </strong></p> <p><strong>TWAS analysis </strong></p> <p>Gene expression models for the six in-house expression datasets were generated using the PredictDB v7 pipeline for a total of 1,077 participants. Elastic net model building with 10-fold cross-validation was performed independently for each dataset. The elastic net models for GTEx v8 Colon Transverse were obtained from the PredictDB data repository (<a href="http://predictdb.org/">http://predictdb.org/</a>) and had been generated using the same pipeline. Models were computed using HapMap2 SNPs ±1Mb from each gene, together with covariate factors estimated using PEER32, clinical covariates when appropriate (age, sex and, where appropriate, case-control status, type of polyp and anatomic location in the colorectum), and three PCs from the individual dataset’s SNP genotype data.</p> <p>Transcript-based TWAS analyses (TIsWAS) were likewise performed by using transcript-level data from the SOCCS, BarcUVa-Seq and GTEx Colon Transverse datasets.</p> <p><strong>MWAS analysis </strong></p> <p>Methylation beta values were calculated based on the manufacturer’s standard, ranging from 0 to 1. Quality control and data normalization were performed in R using the ChAMP software pipeline for the EPIC and 450K arrays. Briefly, we filtered out failed probes with detection P > 0.02 in >5% of samples, probes with <3 reads in >5% of samples per probe and all non-CpG probes. Samples with failed probes >0.1 were also excluded from downstream analyses. We discarded all probes with SNPs within 10bp of the interrogated CpG (from 1,000 Genomes Project, CEU population)34, and probes that ambiguously mapped to multiple locations in the human genome with up to two mismatches33. We only considered probes mapping to autosomes and those overlapping between the EPIC and the 450K arrays. Normalization was achieved using the Beta MIxture Quantile (BMIQ) method. Per probe methylation models were created using the PredictDB pipeline on the normalized methylation matrix and the genotypes as per TWAS eQTL analysis. To optimize power, we restricted our analysis to 263,341-238,443 (for the 450K array) and 377,678 (for the EPIC array) probes annotated to Islands, Shores and Shelves, and discarded “Open Sea” regions. </p>
Functional precision profiling reveals non-mutational rewiring of kinase signaling networks in colorectal cancer
<p>Multi-omics profiling of colorectal cancer (CRC) patients and associated patient-derived organoids. Tumor organoids were characterized in steady-state and perturbed using kinase inhibitors.</p>
FOLFOXIRI resistance induction and characterization in human colorectal cancer cells
<p>Supplementary dataset to "FOLFOXIRI resistance induction and characterization in human colorectal cancer cells"</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.