Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

Drug-drug interactions of irinotecan, 5-fluorouracil, folinic acid and oxaliplatin for improved colorectal carcinoma treatment

Research records and experimental data of the study "Drug-drug interactions of irinotecan, 5-fluorouracil, folinic acid and oxaliplatin for improved colorectal carcinoma treatment"

opencc-by-4.0Nov 2021View details →

The evolution of genomic, transcriptomic, and single-cell protein markers of metastatic upper tract urothelial carcinoma

The molecular characteristics of metastatic upper tract urothelial carcinoma (UTUC) are unknown. The genomic and transcriptomic differences between primary and metastatic UTUC is not well described either. We combined whole-exome sequencing, RNA-sequencing, and Imaging Mass CytometryTM (IMCTM) of 44 tumor samples from 28 patients with high-grade primary and metastatic UTUC. IMC enables spatially resolved single-cell analyses to examine the evolution of cancer cell, immune cell, and stromal cell markers using mass cytometry with lanthanide metal-conjugated antibodies. We discovered that actionable genomic alterations are frequently discordant between primary and metastatic UTUC tumors in the same patient. In contrast, molecular subtype membership and immune depletion signature were stable across primary and matched metastatic UTUC. Molecular and immune subtypes were consistent between bulk RNA-sequencing and mass cytometry of protein markers from 340,798 single-cells. Molecular subtyping at the single cell level was highly conserved between primary and metastatic UTUC tumors within the same patient.

opencc-by-4.0Dec 2015View details →

Raman spectra of the Adenoma-Carcinoma-Sequence in a mice model

In the following, a short desciption for each csv files: <ol> <li>Meta data: includes information about mice ID, scans collected from each mouse, location of extracted scans, activity of P53 gene, mouce gender, tissue type.</li> <li>MSpectra: contains mean spectra of tissue types with respect to each extracted scan.</li> <li>TissueLabels: describes different divisions of tissue types;e.g. normal vs abnormal, normal vs HB vs Karzinom, normal vs HB vs adenoma vs carcinoma</li> <li>Wavenumbers: includes Raman spectra wavenumbers. </li> </ol>

opencc-by-4.0Mar 2025View details →

Multi-omics analysis reveals the link between Treg distribution and therapy efficacy in Hepatocellular Carcinoma patients treated with tremelimumab plus durvalumab

Introduction Hepatocellular carcinoma (HCC) remains a significant contributor to cancer-related deaths. Immunotherapy, either alone or in combination, has emerged as the standard treatment for advanced HCC. Notably, the combination of durvalumab (dur) and tremelimumab (trem) has received FDA approval based on findings from the HIMALAYA trial. However, comprehensive studies elucidating immune responses are lacking. We conducted a thorough analysis utilizing clinical samples from tumor biopsies to understand the mechanism of response. Methods Multiplexed immunofluorescence microscopy was used to analyze immune cell infiltration in primary human liver cancer samples. We developed and validated a comprehensive 37-plex antibody panel for immunofluorescence imaging of human FFPE samples. We applied highly multiplexed co-detection by indexing (CODEX) technology to simultaneously profile in situ expression of 37 proteins at sub-cellular resolution in 20 HCC patient samples using whole slide scanning. We established an image analysis pipeline to quantify all major cell populations in the human liver using supervised manual gating and unsupervised clustering algorithms using the exported matrix of the marker expression and spatial information. Clinical metadata including sex, gender, ethnicity, pretreatment, and histopathological reports are available for all patient samples. Results Using high-dimensional spatially resolved quantitative analysis of multiplexed immunofluorescence microscopy images, we generated a unique dataset and profiled the single-cell pathology landscape for human HCC treated with immunotherapy. In situ phenotyping of 400,000 single cells (including 130,000 CD45+ immune cells) allowed for the quantification of cell phenotype clusters, differential analysis of activation markers, and spatial features of each individual cell. This analysis revealed the comprehensive profile of the cell composition and spatial interactions of different cells in the TiME of patients treated with immunotherapy. Further details on the study can be obtained in our paper once it’s published. Conclusion We developed the CODEX panel for FFPE biopsy samples of HCC patients.

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.

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

TCGA Kidney Renal Clear Cell Carcinoma (KIRC) 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 KIRC, the most common and aggressive subtype of kidney cancer, originating from the cells lining the tubules of the kidney and characterized by its clear appearance under the microscope. 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

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

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

TCGA Bladder Urothelial Carcinoma (BLCA) 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 BLCA. 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.  Acknowledgments: 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> 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-4.0Dec 2019View details →

Tumor growth kinetics of human LM2-4LUC+ triple negative breast carcinoma cells

Cell culture and data set Tumor growth data used in this study were obtained from experiments involving the use of a LM2-4LUC+ cells (or LM2-4), a metastatic variant of the  human triple-negative breast carcinoma MDA-MB-231 cells. Animal studies were performed as described previously under Roswell Park Comprehensive Cancer Center (RPCCC) Institutional Animal Care and Use Committee (IACUC) protocol number 1227M [1-7]. Tumor growth data were pooled from eight separate experiments conducted with a total of 581 observations, and represent control (vehicle-treated) animals from published studies [1-7]. Vehicle formulation was carboxymethylcellulose sodium (USP, 0.5% w/v), NaCl (USP, 1.8% w/v), Tween-80 (NF, 0.4% w/v), benzyl alcohol (NF, 0.9% w/v), and reverse osmosis deionized water (added to final volume) and adjusted to pH 6 (see [3]) and was given at 10ml/kg/day for 7-14 days prior after tumor implantation and before tumor resection [1-7]. <ul> </ul> Tumor injections LM2-4LUC+ cells were orthotopically implanted (106 cells per injection) into the right inguinal mammary fat pads of 6- to 8-week-old female severe combined immunodeficient (SCID) mice. Tumor measurements Tumor size was measured regularly with calipers to a maximum volume of  2 cm3, calculated by the formula  \(V = \frac{\pi}{6} w^2 L\) (ellipsoid) where L is the largest and w is the smallest tumor diameter. Please cite: Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a>   In the file, the columns correspond to: <ul> <li>ID: identifier of the animal</li> <li>Time: day of the tumor measurement after implantation</li> <li>Observation: tumor measurement (in mm3)</li> </ul>   References [1] Benzekry, S., Lamont, C., Beheshti, A., Tracz, A., Ebos, J. M. L., Hlatky, L., & Hahnfeldt, P. (2014). Classical mathematical models for description and prediction of experimental tumor growth. PLoS Comput Biol, 10(8), e1003800. http://doi.org/10.1371/journal.pcbi.1003800 [2] Benzekry S, Tracz A, Mastri M, Corbelli R, Barbolosi D, Ebos JML. (2016) Modeling Spontaneous Metastasis Following Surgery: An In Vivo-In Silico Approach. Cancer Res.;76(3):535–547. doi:10.1158/0008-5472.CAN-15-1389. [3] Ebos JML, Lee CR, Bogdanovic E, Alami J, Van Slyke P, Francia G, et al. (2008) Vascular Endothelial Growth Factor-Mediated Decrease in Plasma Soluble Vascular Endothelial Growth Factor Receptor-2 Levels as a Surrogate Biomarker for Tumor Growth. Cancer Res.;68(2):521–529. doi:10.1158/0008-5472.CAN-07-3217. [4] Ebos JML, Mastri M, Lee CR, Tracz A, Hudson JM, Attwood K, et al. (2014) Neoadjuvant antiangiogenic therapy reveals contrasts in primary and metastatic tumor efficacy. EMBO Mol Med;6:1561–76. https://doi.org/10.15252/emmm.201403989 [5] Ebos JML, Lee CR, Cruz-Munoz W, Bjarnason GA, Christensen JG, Kerbel RS. (2009) Accelerated metastasis after short-term treatment with a potent inhibitor of tumor angiogenesis. Cancer Cell;15:232–9. https://doi.org/10.1016/j.ccr.2009.01.021 [6] Mastri M, Tracz A, Lee CR, Dolan M, Attwood K, Christensen JG, et al. (2018) A Transient Pseudosenescent Secretome Promotes Tumor Growth after Antiangiogenic Therapy Withdrawal. Cell Rep.; 25 (13):3706–20 e8. Epub 2018/12/28. https://doi.org/10.1016/j.celrep.2018.12.017 [7] Vaghi C, Rodallec A, Fanciullino R, Ciccolini J, Mochel JP, et al. (2020) Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors, PLoS Comput Biol, 16, p. e1007178. <a href="https://doi.org/10.1371/journal.pcbi.1007178">https://doi.org/10.1371/journal.pcbi.1007178</a>

opencc-by-4.0Oct 2020View details →

RNA sequencing dataset for prediction of liver hepatocellular carcinoma using SIMON analysis

The LIHC dataset was used for data mining and for the generation of machine learning model for the detection of liver hepatocellular carcinoma cells (LIHC) using the SIMON platform as described in the "SIMON: open-source knowledge discovery platform" publication (<a href="https://doi.org/10.1101/2020.08.16.252767">https://doi.org/10.1101/2020.08.16.252767</a>). The LIHC dataset was obtained from the GSEABenchmarkeR package ( <a href="https://doi.org/10.1093/bib/bbz158">https://doi.org/10.1093/bib/bbz158</a>) and it contains RNA expression data from 374 liver hepatocellular carcinoma (LIHC) cells and 50 adjacent normal cells.

opencc-by-4.0Jan 2021View details →

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-4.0Nov 2023View details →

Advanced Non-Clear Cell Renal Cell Carcinoma Treatments and Survival: A Real-World Single-Centre Experience

Dataset of the paper "Advanced Non-Clear Cell Renal Cell Carcinoma Treatments and Survival: A Real-World Single-Centre Experience"

opencc-by-4.0Jul 2024View details →

WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data

The WAW-TACE dataset contains multiphase abdominal CT images from N=233 treatment-naive patients with HCC treated with TACE in monotherapy, annotated with N=377 hand-crafted liver tumor masks, automated segmentations of multiple internal organs, extracted radiomics features, and corresponding extensive clinical data.

opencc-by-4.0Feb 2019View details →

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-4.0Oct 2024View details →

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>