Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

Example of KRAS oncogene mutational regression and HLA haplotype characterization in oligo-metastatic colorectal cancer patient (ID: PAT2)

KRAS oncogene mutational regression from primary tumour to lung metastasis (evaluated through TSO500 panel, Illumina Novaseq 6000 platform) and HLA haplotype characterization (through PCR) in a representative oligo-metastatic colorectal cancer patient (ID: PAT2).

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Data for: Molecular and clinicopathological differences between depressed and protruded T2 colorectal cancer

Background Colorectal cancer (CRC) can be classified into four consensus molecular subtypes (CMS) according to genomic aberrations and gene expression profiles. CMS is expected to be useful in predicting prognosis and selecting chemotherapy regimens. However, there are still no reports on the relationship between the morphology and CMS. Methods This retrospective study included 55 subjects with T2 CRC undergoing surgical resection, of whom 30 had the depressed type and 25 the protruded type. In the classification of the CMS, we first defined cases with deficient mismatch repair as CMS1. And then, CMS2/3 and CMS4 were classified using an online classifier developed by Trinh et al. The staining intensity of CDX2, HTR2B, FRMD6, ZEB1, and KER and the percentage contents of CDX2, FRMD6, and KER are input into the classifier to obtain automatic output classifying the specimen as CMS2/3 or CMS4. Results According to the results yielded by the online classifier, of the 30 depressed-type cases, 15 (50%) were classified as CMS2/3 and 15 (50%) as CMS4. Of the 25 protruded-type cases, 3 (12%) were classified as CMS1 and 22 (88%) as CMS2/3. All of the T2 CRCs classified as CMS4 were depressed CRCs. More malignant pathological findings such as lymphatic invasion were associated with the depressed rather than protruded T2 CRC cases.

opencc-zeroAug 2022View details →

opencc-by-4.0Sep 2022View details →

Colorectal Cancer Histology Image Tiles for Tissue Multi-class Classification

Content The present dataset is linked to a research aimed at discovering the best normalization pipeline and classification model for colorectal cancer multi-class tissue classification. The 15,856 histological image tiles are completely anomized and are extracted from 10 formalin-fized paraffine-embedded samples of patients affected by colorectal cancer. The materials are inside the following zip file: “CRC_Tiles_IRCCS_ISTITUTO_TUMORI_BARI.zip”: a zipped folder containing tiles (n=15,856) annotated by a pathologist, grouped in 6 subdirectories, each of them representing a class. Tiles are of size 224 x 224 px, taken at a resolution of 0.5 μm/px. Ethical Statement The study has been funded by “Tecnopolo per la Medicina di Precisione (CUP B84I18000540002)”. The institutional Ethic Committee approved the study (Prot n. 780/CE). Related Datasets and Works   For further details concerning the aforementioned dataset, refer to the papers below.  Please cite the following articles if you need this dataset for your research. Altini N. et al. (2021) Multi-class Tissue Classification in Colorectal Cancer with Handcrafted and Deep Features. In: Huang DS., Jo KH., Li J., Gribova V., Bevilacqua V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science, vol 12836. Springer, Cham.  https://doi.org/10.1007/978-3-030-84522-3_42 Altini, N., Marvulli, T. M., Zito, F. A., Caputo, M., Tommasi, S., Azzariti, A., ... & Bevilacqua, V. (2023). The Role of Unpaired Image-to-Image Translation for Stain Color Normalization in Colorectal Cancer Histology Classification. Computer Methods and Programs in Biomedicine, 107511.  <a href="https://doi.org/10.1016/j.cmpb.2023.107511">https://doi.org/10.1016/j.cmpb.2023.107511</a> Please also consider the dataset offered in our previous work: Altini N. et al. (2021). Pathologist's Annotated Image Tiles for Multi-Class Tissue Classification in Colorectal Cancer (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4785131

opencc-by-4.0May 2024View details →

Mouse RNASeq data for "The tumor microbiome reacts to hypoxia and can influence response to radiation treatment in colorectal cancer", part 1

Open the record for dataset details and reuse information.

opencc-by-4.0May 2024View details →

Mouse RNASeq data for "The tumor microbiome reacts to hypoxia and can influence response to radiation treatment in colorectal cancer", part 2

Open the record for dataset details and reuse information.

opencc-by-4.0Jun 2024View details →

Comparison between 16S rRNA and shotgun sequencing in colorectal cancer, advanced colorectal lesions, and healthy human gut microbiota

<div> Background: Gut dysbiosis has been associated with colorectal cancer (CRC), the third most prevalent cancer in the world. This study compares microbiota taxonomic and abundance results obtained by 16S rRNA gene sequencing (16S) and whole shotgun metagenomic sequencing to investigate their reliability for bacteria profiling. The experimental design included 156 human stool samples from healthy controls, advanced (high-risk) colorectal lesion patients (HRL), and CRC cases, with each sample sequenced using both 16S and shotgun methods. We thoroughly compared both sequencing technologies at the species, genus, and family annotation levels, the abundance differences in these taxa, sparsity, alpha and beta diversities, ability to train prediction models, and the similarity of the microbial signature derived from these models.  </div> <div> Results: As expected, the results showed that 16S detects only part of the gut microbiota community revealed by shotgun, although some genera were only profiled by 16S. The 16S abundance data was sparser and exhibited lower alpha diversity. In lower taxonomic ranks, shotgun and 16S highly differed, partially due to a disagreement in reference databases. When considering only shared taxa, the abundance was positively correlated between the two strategies. We also found a moderate correlation between the shotgun and 16S alpha-diversity measures, as well as their PCoAs. Regarding the machine learning models, only some of the shotgun models showed some degree of predictive power in an independent test set, but we could not demonstrate a clear superiority of one technology over the other. Microbial signatures from both sequencing techniques revealed taxa previously associated with CRC development, e.g., Parvimonas micra.  </div> <div> Conclusions: Shotgun and 16S sequencing provide two different lenses to examine microbial communities. While we have demonstrated that they can unravel common patterns (including microbial signatures), shotgun often gives a more detailed snapshot than 16S, both in depth and breadth. Instead, 16S will tend to show only part of the picture, giving greater weight to dominant bacteria in a sample. Therefore, we recommend choosing one or another sequencing technique before launching a study. Specifically, shotgun sequencing is preferred for stool microbiome samples and in-depth analyses, while 16S is more suitable for tissue samples and studies with targeted aims.  </div>

opencc-by-4.0Jul 2024View details →

Transcriptome profiling associated with CARD11 overexpres-sion in Colorectal Cancer implicates a potential role for Tumour Immune Microenvironment and Cancer pathways modulation via NF-κB

tables for CARD11

opencc-by-4.0May 2019View details →

Intermediate-onset colorectal cancer: a clinical and familial boundary between both early and late-onset colorectal cancer

Comparative studies of colorectal cancer (CRC) according to the age of onset have found differences between early-onset CRC (EOCRC) and late-onset CRC (LOCRC). Using this as a starting point, we wished to determine whether intermediateonset CRC (IOCRC) might also be considered as an independent group within CRC. We performed a retrospective comparative study of the clinicopathological and familial features, as well as of the symptoms and their duration, of a total of 272 subjects diagnosed with CRC classified into three groups according to the age-of-onset (98 EOCRC, 83 IOCRC and 91 LOCRC). The results show that from a clinicopathological point of view, IOCRC shared certain features with EOCRC (gender, prognosis), and with LOCRC (multiple primary CRCs), whereas it also had characteristics that were specific for IOCRC (mean number of associated polyps). A gradual progression was observed from EOCRC to LOCRC from a greater family aggregation to sporadic cases, in parallel with a change of Lynch Syndrome cases to the sporadic microsatellite instability pathway, with the IOCRC being a boundary group that is more related to EOCRC. With respect to symptoms, duration and correlation with stages, IOCRC appeared more similar to EOCRC. Clinically, IOCRC behaves as a transitional group between EOCRC and LOCRC, with features in common with both groups, but also with IOCRC-specific features. Excluding cases with familial cancer history, the awareness for EOCRC diagnosis should be extended to IOCRC.

opencc-by-4.0Jul 2024View details →

Transcriptome profiling associated with CARD11 overexpres-sion in Colorectal Cancer implicates a potential role for Tumour Immune Microenvironment and Cancer pathways modulation via NF-κB

Images for CARD11 study in IJMS

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Data from: Incidence of cancer-associated thromboembolism in Japanese gastric and colorectal cancer patients receiving chemotherapy: a single-institutional retrospective cohort analysis (Sapporo CAT study)

Objective: Few data regarding the incidence of cancer-associated thromboembolism (TE) are available for Asian populations. We investigated the incidence of TE (TEi) and its risk factors among gastric and colorectal cancer (GCC) patients who received chemotherapy in a daily practice setting. Design: A retrospective cohort study. Setting: A single institutional study that used data from Sapporo City General Hospital, Japan, on patients treated between January 2008 and May 2015. Participants: Five hundred Japanese GCC patients who started chemotherapy from January 2008 to May 2015. Primary and secondary outcome measures: TE was diagnosed by reviewing all the reports of contrast-enhanced computed tomography (CT) performed during the follow-up period. All types of thrombosis detected by CT or additional imaging tests, such as venous TE, arterial TE, and cerebral infarction, were defined as TE. Medical records of all identified patients were reviewed and potential risk factors for TE including clinicopathological backgrounds were collected. We defined the following patients as 'active cancer'; patients with unresectable advanced GCC, cancer recurrence during or after completing adjuvant (Adj) chemotherapy, and/or presence of other malignant tumours. Results: Of the 500 patients, 70 patients (14.0%) developed TE during the follow-up period. TEi was 9.2% and 17.3% in gastric and colorectal cancer patients, 18.1% and 3.5% in active and non-active cancer patients, and 24.0% and 12.9% in multiple and single primary, respectively. Multivariate logistic regression analysis showed that colorectal cancer (odds ratio [OR], 2.371; 95% confidence interval [CI], 1.328 to 4.233), active cancer (OR 7.593; 95% CI 2.950 to 19.543), and multiple primary (OR 2.527; 95% CI 1.189 to 5.370) were independently associated with TEi. Conclusion: TEi was 14.0% among Japanese GCC patients received chemotherapy, and was significantly higher among patients with colorectal cancer, active cancer, and multiple primary than among those with gastric cancer, non-active cancer, and single primary, respectively.

opencc-zeroJul 2019View details →

opencc-by-4.0Nov 2020View details →

Intravoxel incoherent motion model of diffusion weighted imaging and diffusion kurtosis imaging in differentiating of local colorectal cancer recurrence from scar/fibrosis tissue by multivariate logistic regression analysis

We uploaded mean of diffusion coefficient (MD) and mean of diffusional Kurtosis values of 56 patients related to the manuscript: Fusco, Roberta, Vincenza Granata, Mario Sansone, Robert Grimm, Paolo Delrio, Daniela Rega, Fabiana Tatangelo, Antonio Avallone, Nicola Raiano, Giuseppe Totaro, Vincenzo Cerciello, Biagio Pecori, and Antonella Petrillo. 2020. "Intravoxel Incoherent Motion Model of Diffusion Weighted Imaging and Diffusion Kurtosis Imaging in Differentiating of Local Colorectal Cancer Recurrence from Scar/Fibrosis Tissue by Multivariate Logistic Regression Analysis" Applied Sciences 10, no. 23: 8609. https://doi.org/10.3390/app10238609

opencc-by-4.0Aug 2021View details →

Hydroxymethylation profile of cell free DNA is a biomarker for early colorectal cancer

The files in this data release represent processed data from the FORESEE study conducted by Cambridge Epigenetix Ltd, and reported in the preprint manuscript:  "Hydroxymethylation profile of cell free DNA is a biomarker for early colorectal cancer" (<a href="https://www.researchsquare.com/article/rs-667874/v1">Walker et al. 2021</a>).    As described in the manuscript, classifiers were trained and validated on genomic features extracted from sequencing datasets across cases and controls.  Several classes of genomic features were constructed for training and validation data sets which are described below:   CRC_enhancer_znorm_training_matrix_v1.csv CRC_enhancer_znorm_validation_matrix_v1.csv Columns contain sample names, rows contain genomic features.  Description of feature generation process.  To calculate 5hmC levels at gene enhancers, we first calculated read counts using Bam readcounts v0.01. RPKM were calculated over candidate gene-enhancers downloaded from GeneCards v4.4. 5hmC enrichment was computed as the log2 ratio between the hydroxymethylome library RPKM and the input library RPKM after the inclusion of pseudocounts. Feature scaled (z-score normalization) 5hmC levels of enhancers quantile-normalized over samples. CRC_cegxdelfi_znorm_training_matrix_v1.tsv CRC_cegxdelfi_znorm_validation_matrix_v1.tsv Columns contain sample names, rows contain genomic features.    Description of feature generation process.  We divided the genome into 100KB bins and quantified cfDNA fragment sizes per bin. We removed blacklisted regions, genomic gaps (UCSC table) and non-standard chromosomes a priori. We excluded outlier bins in fragment size, only retaining fragments between 100nt to 220nt length. Finally, we split the genome into 100KB bins (in total 26170 non-overlapping genomic regions)  and calculated the following characteristics of fragment size distribution per genomic bin: number of short fragments (100-150nt), number of long fragments (151-220nt), ratio between short and  long fragments and the total number of fragments. This approach generates 26170 features per metric and per sample. The last step is the averaging of the 100 KB bins into larger non-overlapping  genomic regions of 5 MB (in total 512 bins). CRC_cegxnps_znorm_training_matrix_v1.tsv CRC_cegxnps_validation_matrix_v1.tsv   Columns contain sample names, rows contain genomic features.   Description of feature generation process.      Further detail in the manuscript: <a href="http://www.researchsquare.com/article/rs-667874/v1">Walker et al. 2021</a> FORESEE_sample_description.tsv This file holds sample data for colorectal cancer and control samples described in <a href="http://www.researchsquare.com/article/rs-667874/v1">Walker et al. 2021</a> <ul> <li>The sample_name column links to the column names in the *_matrix.tsv files</li> <li>The columns denoted raw_file1 and raw_file2 link the sample metadata with the enhancer readcount files contained in the gh_readcount_training.tar and gh_readcount_validation.tar.</li> </ul> The columns in the table are briefly described below: sample_name: Sample identifier Title: Composed of the the disease name, gender and sample_name Source_name: Tissue source Organism: Contains the term: “Homo sapiens” Characteristics_indication: Disease indication  Characteristics_stage: Cancer stage where appropriate. Indicated by roman numerals (I,II,III,IV) Characteristics_gender: Described as “Female” or “Male” Characteristics_ethnicity: Ethnicity description Characteristics_age_at_collection: Age value in years Molecule: Contains the value “cell free DNA” Description: Contains value: “Training sample” or “Validation sample” Processed_data_file: Contains the term: “CRC_enhancer_training_matrix” or “CRC_enhancer_validation_matrix”.   raw_file1: Refers to the readcount file from the 5hmC capture library raw_file2: Refers to the readcount file from the Input control (shallow sequenced) library   gh_readcount_training.tar gh_readcount_validation.tar These tar files include the raw read counts computed across enhancer regions for case and control data and are referenced in the FORESEE_sample_description.tsv file.   Manuscript Abstract Our classifier discriminated CRC samples from controls with an area under the receiver operating characteristic curve (AUC) of 90% (sensitivity was 55% at 95% specificity). Performance was similar for early stage 1 (AUC 89%) and late stage 4 CRC (AUC 94%). Performance was independent of the proportion of tumor-DNA in the cell free DNA.   We expanded the classifier to include information about cell free DNA fragment size and abundance across the genome. Overall performance was similar (AUC 91%), with gains in sensitivity (63% at 95% specificity).  The 5-hydroxymethylcytosine signal allows detection of CRC, even in cell free DNA samples with undetectable tumor DNA. Including 5-hydroxymethylcytosine in multi-analyte screening, will improve sensitivity for early-stage cancer.

opencc-by-4.0Nov 2022View details →

NGS results of metastatic colorectal cancer patients.

Anonymized examples of complete NGS results of 6 metastatic colorectal cancer patients included in a prognostic analysis focused on p53 mutational status.

opencc-by-4.0Dec 2022View details →

Follow-up representative source data of metastatic colorectal cancer patients treated with TAS-102 and regorafenib

Follow-up representative source data of metastatic colorectal cancer patients treated with TAS-102 and regorafenib (date of vital status and tomographic scans).

Bortezomib in Treating Patients With Metastatic or Recurrent Colorectal Cancer

ClinicalTrials.gov study NCT00052507. IPD Sharing: Not stated. Countries: 1. Publications: 1.

Study of Intravenous Aflibercept in Combination With FOLFIRI in Japanese Patients With Metastatic Colorectal Cancer

ClinicalTrials.gov study NCT00921661. IPD Sharing: Not stated. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

Efficacy and Safety Evaluation of Traditional Chinese Medicine in the Treatment of Advanced Colorectal Cancer

ClinicalTrials.gov study NCT02923622. IPD Sharing: NO. Countries: 1. Publications: 1.

Comparison of Diagnostic Sensitivity Between ctDNA Methylation and CEA in Colorectal Cancer

ClinicalTrials.gov study NCT05558436. IPD Sharing: UNDECIDED. Countries: 1. Publications: 12.

Vaccine Therapy and Chemotherapy With or Without Tetanus Toxoid Compared With Chemotherapy Alone in Treating Patients With Metastatic Colorectal Cancer

ClinicalTrials.gov study NCT00027833. IPD Sharing: Not stated. Countries: 2. Publications: 1.

A Study of On-treatment ctDNA Changes in Chemo-refractory Colorectal Cancer Patients

ClinicalTrials.gov study NCT05487248. IPD Sharing: Not stated. Countries: 2. Publications: 1.