Find research datasets worth reusing

opencc-by-4.0Nov 2022View details →

Oral Squamous Cell Carcinoma - Mass Spectrometry Imaging

The dataset was first featured in <a href="https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/abs/10.1002/pmic.201500458">Widlak, Piotr, et al. "Detection of molecular signatures of oral squamous cell carcinoma and normal epithelium–application of a novel methodology for unsupervised segmentation of imaging mass spectrometry data." Proteomics 16.11-12 (2016): 1613-1621</a>. For the tissue sample's biochemical preparation details, please refer to the original publication. The biological material was collected from five patients who underwent surgery due to Oral Squamous Cell Carcinoma (OSCC). Tissue samples contained both tumor and surrounding healthy tissue. Each specimen was cut into 10 µm sections in a cryostat. During the sample preparation for the MS imaging, a high-resolution optical scan of each section was captured. Tissue sections were subjected to peptide imaging with the use of a MALDI ToF mass spectrometer. Spectra were recorded within m/z range of 800-4,000. A raster width of 100 µm was applied, and 400 shots were collected from each ablation point. The obtained dataset consisted of 45,738 raw spectra with 109,568 mass channels. An experienced pathologist analyzed the optical scan obtained during the data acquisition process, and tissue regions were annotated. For the highest confidence of the results obtained in this work, we will focus on the two tissue samples out of the entire dataset (8,005 and 11,869 spectra), which have the highest confidence labels, as explained by the pathologist. The preprocessing of the spectra was conducted in MATLAB. Standard preprocessing steps were applied to the spectra. Spectra were resampled to unify the m/z axis across the dataset. The baseline was removed with MATLAB procedure msbackadj() from the Bioinformatics Toolbox. Peaks were aligned using Fast Fourier Transform-based spectral alignment. The TIC normalization ensured a similar intensity level for all spectra. Finally, a GMM approach was used to model the spectra. GMM locates the peak but also estimates the peak area instead of a raw magnitude provided by most methods. Note that the peaks in MSI spectra are right-skewed, so the neighboring GMM components resulting from that phenomenon were identified and merged to better correspond to actual chemical compounds. The resulting dataset is characterized by 3,714 GMM components corresponding to MSI spectrum peaks.

opencc-by-4.0Jul 2023View details →

TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Gene Expression

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about HNSC, a type of cancer that originates in the squamous cells lining the mucosal surfaces of the head and neck region, including the oral cavity, throat, and larynx.  The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample. Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script. Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 U-BRITE last update: 07/13/2023

opencc-by-4.0Jul 2023View details →

TCGA Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) Gene Expression

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about CESC, a type of cancer that affects the cells lining the cervix and can have squamous cell or adenocarcinoma histological subtypes. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample. Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script. Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 U-BRITE last update: 07/13/2023

opencc-by-4.0Jul 2023View details →

TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Clinical Data

Abstract: The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about HNSC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset.  Inspiration: This dataset was uploaded to UBRITE for GTKB project.  Instruction: The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed.  Acknowledgments: Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8 Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a> The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764 U-BRITE last update: 07/13/2023

zenodo44/100

Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers

Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers

opencc-by-4.0Jan 2021View details →

zenodo44/100

Case report: Papillary squamous cell carcinoma of the penis

Case history 62-year-old male with a verruciform tumor located in distal penis and involving glans.   Histologic findings The microphotographs show an exophytic verruciform tumor mass characterized by papillomatosis, slight to moderate acanthosis, and hyperkeratosis. Papillae are complex, some with blunt and others with spiky tips. Fibrovascular are present in most but not all papillae and are irregularly shaped. Tumor base is jagged and there is a prominent stromal reaction. Neoplastic cells in papillae and infiltrative tumor nests are well to moderately differentiated (grades 1-2) and no koilocytic atypia is observed.   Discussion Verruciform penile tumors comprise about one-quarter of all penile squamous cell carcinomas (SCC) and include papillary, verrucous, and warty carcinomas, as well as giant condylomas and carcinoma cuniculatum. As a group, verruciform carcinomas are characterized by the presence of papillomatosis, acanthosis, and hyperkeratosis. However, there are distinctive morphological features for each one of these tumors. For papillary SCC, the most distinguishing feature is the presence of complex papillae with irregularly shaped fibrovascular cores. Papillary carcinomas tend to be polymorphic with some areas exhibiting condylomatous papillae and others with a more verrucous-like aspect. Fibrovascular cores are readily found in the former and are scant or even absent in the latter. Another important clue in the differential diagnosis with other verruciform tumors is the presence of a jagged tumor-stroma interface. In verrucous and cuniculatum carcinomas the tumor front is broad-based. The absence of koilocytosis allows the distinction from warty carcinomas and giant condylomas, tumors in which koilocytes are conspicuous. HPV status may be helpful in problematic cases, since in papillary carcinomas the HPV detection rate is very low or even null. Immunohistochemistry for p16INK4a is also useful since the vast majority of papillary carcinomas do not overexpress this protein. In order for a penile tumor to be considered as p16INK4a positive all neoplastic cells should be stained. Cases like this one in which some cells stain and others do not should be regarded as negative for p16INK4a overexpression. The inguinal metastatic rate of penile papillary carcinomas is very low and the prognosis is good. Less than one-fifth of all patients present inguinal involvement and even in these cases the mortality rate is low. Even when tumors invade penile erectile tissues prognosis is good as long as no high-grade areas (observed in a minority of the patients) are identified. Clinically patients should be managed using risk-group stratification systems and taking into account histological grade, anatomical level of maximum tumor infiltration, and the presence of vascular and perineural invasion.    References <a href="https://www.ncbi.nlm.nih.gov/pubmed/20061934">Chaux et al. Am J Surg Pathol. 2010 34(2): 223-30</a> <a href="https://www.ncbi.nlm.nih.gov/pubmed/22641955">Chaux & Cubilla. Semin Diagn Pathol. 2012 29(2): 72-82</a> <a href="https://www.ncbi.nlm.nih.gov/pubmed/22641955">Chaux & Cubilla. Semin Diagn Pathol. 2012 29(2): 67-71</a>

opencc-by-4.0Feb 2019View details →

zenodo44/100

Data from: External validation of prognostic and predictive gene signatures in 1097 European head and neck squamous cell carcinoma patients

Anonymized data containing survival endpoints and gene signature scores for head and neck cancer patients. File data_os_gs.csv : data linking overall survival and gene signature scores File data_dfs_gs.csv : data linking disease-free survival and gene signature scores Variables: <ul> <li>supertreat_id: patient ID</li> <li>GS_score_172GS: gene signature score for the 172-GS signature. The score is Z-score normalized with a mean of 0 and SD of 1. </li> <li>GS_score_3clustersHPV: gene signature score for the 3 clusters HPV signature. The score is Z-score normalized with a mean of 0 and SD of 1. </li> <li>GS_score_RSI: gene signature score for the radiosenstivity index (RSI) signature. The score is Z-score normalized with a mean of 0 and SD of 1. </li> <li>GS_score_pancancerCisplatin: gene signature score for the pancancer-cisplatin signature. The score is Z-score normalized with a mean of 0 and SD of 1. </li> <li>GS_score_cl3Hypoxia: gene signature score for the Cl3-hypoxia signature. The score is Z-score normalized with a mean of 0 and SD of 1. </li> <li>Variables only available in data_os_gs.csv:  <ul> <li>overall_survival_days_2years: Overall survival censored at 2 years since diagnosis. Number of days from diagnosis to death or censoring.</li> <li>overall_survival_days_5years: Overall survival censored at 5 years since diagnosis. Number of days from diagnosis to death or censoring.</li> <li>overall_survival_status_2years: Overall survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if dead. </li> <li>overall_survival_status_5years:  Overall survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if dead. </li> </ul> </li> </ul> <ul> <li>Variables only available in data_dfs_gs.csv: <ul> <li>disease_free_survival_days_2years: Disease-free survival censored at 2 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</li> <li>disease_free_survival_days_5years: Disease-free survival censored at 5 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</li> <li>disease_free_survival_status_2years: Disease-free survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </li> <li>disease_free_survival_status_5years:  Disease-free survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </li> </ul> </li> </ul>

opencc-by-4.0Oct 2024View details →

opencc-by-4.0Feb 2022View details →

Molecular Signatures of Tumour and its Microenvironment for Precise Quantitative Diagnosis of Oral Squamous Cell Carcinoma: An Interna-tional Multi-cohort Diagnostic Validation Study

Supplementary Materials: The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, Table ST1 – qMIDSV2 Gene panel primer sequences; Figure S1 – qMIDSV1 vs qMIDSV2 384-well assay format and protocols; Figure S2. Individual target gene expression pattern in 1761 samples; Figure S3. Various statistical methods used for gene selection analysis on 1761 clinical samples; Figure S4. Diagnostic performance comparison between qMIDSV2 vs qMIDSV2* (with 4 less effective genes removed from the panel of 14 target genes of qMIDSV2); Figure S5. Effect of removing individual genes from the 14-target gene panel qMIDSV2 (qV2) on diagnostic test performance based on the UK patient cohort data.

opencc-by-4.0Jan 2022View details →

Mutations in the telomerase reverse transcriptase promoter and PIK3CA gene are common events in penile squamous cell carcinoma of Italian and Ugandan patients

Somatic mutations in the promoter region of TERT (TERTp) gene are highly frequent in penile carcinoma in Italian and Ugandan patients, predominantly in human papillomavirus (HPV) negative cancers (67.6%). TERTp and PIK3CA hotspot changes coexist in 15.8% of cases. The higher mutant allele frequencies (MAFs) of TERT -124A/-146A compared to PIK3CA E545K MAFs suggest an asynchronous mutation timing. The coexistence of TERTp and PIK3CA mutations may represent a novel co-actionable therapeutic target in penile carcinoma patients.

opencc-by-4.0Jul 2024View details →

High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma

This deposition contains only training dataset of ORCHID database. The validation and test dataset related to the same study can be found at DOI: 10.5281/zenodo.12646943.

dryad40/100

Supplementary data for: Transposon mutagenesis identifies cooperating genetic drivers during keratinocyte transformation and cutaneous squamous cell carcinoma progression

Supplementary Note 1: <ul> <li>S1 Text: Oncogenomic comparisons between SB candidate Trunk driver genes and their direct orthologs in human Cancer Gene Census; Pyrosequencing analysis of SB-driven keratinocyte cancer models; References.</li> </ul> Supplementary Figures 1-11: <ul> <li>S1 Fig: Overview of genetic crosses to generate SB|Trp53|Onc3 mouse model.</li> <li>S2 Fig: SB insertion patterns in activated and inactivated drivers.</li> <li>S3 Fig: Evaluating the reproducibility of SBCapSeq results from bulk cuSCC and normal skin specimens.</li> <li>S4 Fig. Hierarchical two-dimensional clustering of recurrent events in cuKA and cuSCC.</li> <li>S5 Fig. Curated biological pathways and processes enriched within SB-induced cuSCC.</li> <li>S7 Fig: ZMIZ1 metagene within the TCGA Head & Neck Squamous Cell Carcinoma (hnSCC) RNA-seq dataset.</li> <li>S8 Fig: Clonally selected SB insertions affect trunk driver proto-oncogene expression in SB-cuSCC genomes.</li> <li>S9 Fig: Clonally selected SB insertions affect trunk driver genes by inactivating expression in SB-cuSCC genomes.</li> <li>S10 Fig: CREBBP knockdown does not alter proliferation rate in cuSCC cell lines.</li> <li>S11 Fig: Gross photographs of cuSCC xenograft masses collected at necropsy showing robust TurboGFP expression.</li> <li>S12 Fig: SB T2/Onc3 TG.12740 allele donor position mapping and exclusion for SB Driver Analysis.</li> </ul> Supplementary Tables 1-20: <ul> <li>S1 Table: Tumor incidence and subgroup classifications by cohort.</li> <li>S2 Table: Specimen metafile data for projects sequenced using SBCapSeq protocol with Ion Torrent Proton sequencer.</li> <li>S3 Table: Discovery and progression SB Driver Analysis for cuSCC60_SBC.</li> <li>S4 Table: Trunk SB Driver Analysis for cuSCC60_SBC.</li> <li>S5 Table: Discovery and progression SB Driver Analysis for cuKA11_SBC.</li> <li>S6 Table: Trunk SB Driver Analysis for cuKA11_SBC.</li> <li>S7 Table: Discovery and progression SB Driver Analysis for cuSK32_SBC.</li> <li>S8 Table: SBCapSeq read depth and analysis for 4 cuSCC genomes selected for multi-region resequencing because they had intermixing of cuSCC and cuKA histologies.</li> <li>S9 Table: Enrichr gene set pathway enrichment analysis of cuSCC drivers.</li> <li>S10 Table: Summary of 7 cuSCC transcriptomes selected for whole transcriptome RNAseq analysis.</li> <li>S11 Table: BED file of SBfusion insertions in 7 cuSCC genomes by whole transcriptome RNAseq analysis.</li> <li>S12 Table: Venn diagram for overlap of genes with SBfusion reads detected by whole transcriptome RNAseq analysis and cuSCC60_SBC discovery driver.</li> <li>S13 Table: Venn diagram for overlap of genes with SBfusion reads detected by whole transcriptome RNAseq analysis and all cuSCC drivers.</li> <li>S14 Table: Transcripts per million (TPM) normalized whole transcriptome RNAseq values per gene from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S15 Table: Fragments Per Kilobase of Transcripts per Million (FPKM) normalized whole transcriptome RNAseq values per gene transcript from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S16 Table: Normalized microarray values per gene from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S17 Table: Normalized microarray values per probe from RNA isolated from cuSCC genomes with and without Zmiz1 insertions.</li> <li>S18 Table: All 289 genes with differential expression analysis from microarray data from RNA isolated from cuSCC genomes with and without Zmiz1 insertions with P<0.0001 and q<0.05.</li> <li>S19 Table: Lentiviral vectors containing shRNAs used in this study.</li> <li>S20 Table: TaqMan probes used in this study.</li> </ul> Supplementary Datasets 1-5: <ul> <li>S1 Data: BED file of SB insertions for cuSCC60_SBC.</li> <li>S2 Data: BED file of SB insertions for cuKA11_SBC.</li> <li>S3 Data: BED file of SB insertions for cuSK32_SBC</li> <li>S4 Data: BED file of SB insertions for 4 cuSCC genomes selected for multi-region resequencing because they had intermixing of cuSCC and cuKA histologies.</li> <li>S5 Data: Numerical data for graphs pertaining to Figure Panels Fig1A; Fig5A–E; Fig6A–B,D; Fig7C–G; Fig8A–B,D–F; Fig9A–I in the paper on the publicly availble PLOS Genetics Web site.</li> </ul>

opencc-zeroAug 2021View details →

opencc-by-4.0Apr 2023View details →

"PROGNOSTIC ROLE OF TUMOR BUDDING IN ORAL SQUAMOUS CELL CARCINOMA"

Master Data Sheet

opencc-by-4.0May 2023View details →

Basaloid Squamous Cell Carcinoma: A Case Report

Background: The upper aerodigestive tract is where BSCC (basaloid squamous cell carcinoma), a rare variation of conventional SCC, is most frequently found. The hypopharynx, tonsil, supraglottic larynx, tongue (base), and head-neck regions are particularly susceptible to BSCC. Clinically, the presentation of BSCC is similar to that of conventional SCC, but it has a poor prognosis than traditional SCC. BSCC is distinguished histopathologically by a dimorphic-pattern, a distinctive basal cell component paired with a squamous component, and a squamous component. Compared to traditional SCC, the prognosis for BSCC is worse. However, clinically it shows similar features like conventional SCC which makes it difficult to diagnose. Therefore, histopathology and immunohistochemistry have a crucial role in diagnosing such tumors. We here present a case of a seventy-year male diagnosed with BSCC involving the tongue. Keywords: Basaloid squamous cell carcinoma, dimorphic pattern, basaloid cells, comedo necrosis.

Patritumab With Cetuximab and a Platinum Agent for Squamous Cell Carcinoma (Cancer) of the Head and Neck (SCCHN )

ClinicalTrials.gov study NCT02633800. IPD Sharing: YES. Countries: 7. Publications: 2.

A Study to Investigate Tislelizumab (BGB-A317) Versus Placebo in Combination With Concurrent Chemoradiotherapy in Participants With Localized Esophageal Squamous Cell Carcinoma

ClinicalTrials.gov study NCT03957590. IPD Sharing: YES. Countries: 1. Publications: 1.

A Study of Tislelizumab (BGB-A317) in Combination With Chemotherapy as First Line Treatment in Participants With Advanced Esophageal Squamous Cell Carcinoma

ClinicalTrials.gov study NCT03783442. IPD Sharing: YES. Countries: 16. Publications: 2.

Study of Cemiplimab in Patients With Type of Skin Cancer Stage II to IV Cutaneous Squamous Cell Carcinoma

ClinicalTrials.gov study NCT04154943. IPD Sharing: YES. Countries: 3. Publications: 2.

Carboplatin-paclitaxel With Retifanlimab or Placebo in Participants With Locally Advanced or Metastatic Squamous Cell Anal Carcinoma (POD1UM-303/InterAACT 2).

ClinicalTrials.gov study NCT04472429. IPD Sharing: YES. Countries: 13. Publications: 2.