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Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer
<p>Data supporting the findings of "<a href="https://doi.org/10.1158/2326-6066.CIR-23-0873" target="_blank" rel="noopener">Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer</a>" publication. Files include patient and tissue region metadata (in metadata folder) and output of multiplex immunohistochemistry computational image processing workflow for each tissue region (in mIHC_files folder). The code used to produce the results of this study is available at: <a href="https://github.com/kblise/PDAC_mIHC_paper">https://github.com/kblise/PDAC_mIHC_paper</a>.</p>
Tumor and Blood B Cell Abundance Outperforms Established ICB Response Prediction Signatures in Head and Neck Cancer
<div> <div> <div> <div> <p>This dataset contains processed flow cytometry data and clinical information for deidentified patients from Cohort 11, as well as deconvoluted cell abundances and clinical data for deidentified patients from Cohort 10, associated with the study titled <em>"Tumor and Blood B Cell Abundance Outperforms Established Immune Checkpoint Blockade Response Prediction Signatures in Head and Neck Cancer"</em> published in <strong>Annals of Oncology (2024)</strong>. <a href="https://doi.org/10.1016/j.annonc.2024.11.008" target="_new" rel="noopener">DOI: https://doi.org/10.1016/j.annonc.2024.11.008</a>.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Datasets for: Integrative Mapping of Human CD8+ T Cell in Inflammation and Cancer
<p>Files for the integrated pan-disease CD8+ T cell atlas: </p> <ul> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.all_genes.h5ad.gz?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.all_genes.h5ad.gz</a> : 19957 genes raw matrix </li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.h5ad?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.h5ad</a> : 4000 highly variable genes raw matrix</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.X_gex.npy?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.X_gex.npy</a> : scatlasavae model embedding</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.clone_subtype.csv?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.clone_subtype.csv</a> : clone type definition</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.supervised.model?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.supervised.model</a>. scatlasavae model checkpoint</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.h5ad?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.h5ad</a> : Tex subset of 4000 highly variable genes raw matrix</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.Tex.supervised.model?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.Tex.supervised.model</a>. scatlasavae model checkpoint for the Tex subset</li> </ul> <p>Files for the TILs CD8+ T cell atlas</p> <ul> <li><a href="https://zenodo.org/records/13382785/files/adata_cd8_chu.h5ad?download=1" target="_blank" rel="noopener">adata_cd8_chu.h5ad</a>. the Chu <em>et al.</em>, 2023 Dataset</li> <li><a href="https://zenodo.org/records/13382785/files/adata_cd8_zheng.h5ad?download=1" target="_blank" rel="noopener">adata_cd8_zheng.h5ad.</a> The Zheng <em>et al.</em>, 2021 Dataset</li> </ul> <p>Files for the transfer (query) datasets:</p> <ul> <li><a href="https://zenodo.org/records/13382785/files/Zhang_LC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Zhang_LC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Luoma_HNSCC_TIL.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Luoma_HNSCC_TIL.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Luoma_HNSCC_PBMC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Luoma_HNSCC_PBMC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Watson_MELA.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Watson_MELA.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Bassez_BC.cohort1.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Bassez_BC.cohort1.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Bi_RCC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Bi_RCC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Caushi_NSCLC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Caushi_NSCLC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Liu_TNBC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Liu_TNBC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Borra%CC%80s_2024_Colorectal_cancer.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Borràs_2024_Colorectal_cancer.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Garner_2023_MAIT.h5ad" target="_blank" rel="noopener">Garner_2023.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Vorkas_2022_MAIT.h5ad?download=1" target="_blank" rel="noopener">Vorkas_2022_MAIT.h5ad</a></li> </ul>
Associated code and data for "Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization (doi: 10.3389/fcell.2021.74635)"
<p>This deposit contains the data, code, and analysis to reproduce the results in the manuscript - Lai X, Keller C, Santos-Rosales G, Schaft N, Dörrie J, Vera J. Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization. Frontiers in Cell and Developmental Biolology. 2022; 9:746359; <a href="https://www.researchgate.net/publication/358461035_Multi-Level_Computational_Modeling_of_Anti-Cancer_Dendritic_Cell_Vaccination_Utilized_to_Select_Molecular_Targets_for_Therapy_Optimization">doi:10.3389/fcell.2021.746359</a>.</p> <p>If you have used the code for your research, please cite the original publication. Thank you very much.</p> <p> </p>
Bevacizumab plus erlotinib versus erlotinib alone as first line treatment of patients with EGFR-mutated advanced nonsquamous non-small cell lung cancer. BEVacizumab plus ERLotinib studY (BEVERLY): an academic, multicenter, randomised phase III trial.
<p>Background. Adding bevacizumab to erlotinib prolonged PFS of patients with EGFR-mutated advanced NSCLC in the Japanese NEJ026 trial, but limited data were available in non-Asian patients. BEVERLY is an Italian, multicenter, randomized phase III trial of bevacizumab plus erlotinib versus erlotinib alone as first-line treatment of advanced EGFR-mutated NSCLC.</p> <p>Methods. Eligible patients were randomized 1:1 to erlotinib (150mg daily) plus bevacizumab (15mg/kg iv q3w) or erlotinib alone, until disease progression or unacceptable toxicity. Center, ECOG PS and type of mutation (ex19 deletion vs ex21 L858R vs others) were stratification variables. Investigator-assessed PFS (IA-PFS) and blinded-independent centrally-reviewed PFS (BICR-PFS) were co-primary endpoints. With 80% power in detecting a 0·60 HR and 2–sided α error 0·05, 126 events out of 160 patients were needed. The trial was registered as NCT02633189 and EudraCT 2015-002235-17.</p> <p>Findings. From Apr 11, 2016 to Feb 27, 2019, 160 pts were randomized to erlotinib pus bevacizumab (80) or erlotinib alone (80). Baseline characteristics were balanced between arms; 34 (42·5%) patients in erlotinib plus bevacizumab arm and 43 (53·8%) in erlotinib arm were former or current smokers. At a median follow-up of 36·3 months, 140 PFS events (87·5%) were reported, 68 with erlotinib plus bevacizumab and 72 with erlotinib. Median IA-PFS was 15·4 months (95% CI 12·2–18·6) with erlotinib plus bevacizumab and 9·6 months (95% CI 8·2–10·6) with erlotinib (HR 0·66; 95%CI: 0·47–0·92). BICR-PFS analysis confirmed this result. A significant interaction with treatment effect was found for smoking habit (P=0·0323): former or current smokers receiving erlotinib plus bevacizumab had a longer PFS (16·9 months [95% CI 10·2–21·8] versus 8·8 months [95% CI 5·6–9·6]) than those receiving erlotinib alone.</p> <p>Hypertension (grade≥3: 24% vs 5%), skin rash (grade≥3: 31% vs 14%), thromboembolic events (any grade: 11% vs 4%), and proteinuria (any grade: 23% vs 6%) were more frequent with the combination treatment.</p> <p>Interpretation. The addition of bevacizumab to first-line erlotinib significantly prolonged PFS in Italian patients with EGFR-mutated NSCLC, without unexpected safety issues.</p>
Proteomics data of mitochondrial fraction of CRL-2097 cancer cell line model
<p>The cancer cell line model developed using human dermal fibroblasts CRL-2097 was used in these experiments:</p> <p>Sample 1 - CRL2097 + hTERT</p> <p>Sample 2 - CRL2097 + hTERT + LT</p> <p>Sample 2 - CRL2097 + hTERT + LT + Ras</p> <p>The mitochondrial fraction was prepared from each of these cell lines and analysed via mass spec for their proteomics. The experiment was done in duplicates. </p>
Lipogenesis mediated by OGR1 regulates metabolic adaptation to acid stress in cancer cells via autophagy
<p>Malignant tumors exhibit altered metabolism resulting in a highly acidic extracellular microenvironment. Here we show that cytoplasmic lipid droplet (LD) accumulation, indicative of a lipogenic phenotype is a cellular adaption to extracellular acidity. LD marker PLIN2, is strongly associated with poor overall survival in breast cancer patients. Acid-induced LD accumulation is triggered by activation of the acid-sensing GPCR, OGR1 expressed highly in breast tumors. OGR1 depletion inhibited acid induced lipid accumulation while activation by synthetic agonist triggered LD formation. Inhibition of OGR1 downstream signaling abrogated the lipogenic phenotype which could be rescued with OGR1 ectopic expression. OGR1 depleted cells showed growth inhibition under acidic growth conditions in vitro and tumor formation in vivo. Isotope tracing showed that the source of lipid precursors is primarily autophagy-derived ketogenic amino acids. OGR1 depleted cells were defective in endoplasmic reticulum stress response and autophagy, hence failed to accumulate LDs affecting survival under acidic stress.</p>
Data of FigS2, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS2, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS2.PNG). The corresponding raw data and subsequent data analysis obtained from proteomics analysis are provided as nine files in CSV format (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1-3_M1-3.csv). All further experiment related information provided as one meta-data-file (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1_M .txt) in txt format.</p>
Data of FigS4, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS4, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS4.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_6_M1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_6_M.txt) and three files in CSV-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_6_1-3.csv).</p>
Data of Fig7, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig7, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig7.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M.txt), and all further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M1.pdf)</p> <p> </p>
Data of Fig8, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig8, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig8.PNG). The Corresponding raw data and subsequent data analysis obtained for immunohistochemistry contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1_M .txt) and one file in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv), and all further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1_M1 .pdf)</p> <p> </p>
Data of Fig4, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig4, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig4.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_3_M_1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_3_M.txt). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_4_M.txt), all further experiment related information provided as one meta-data-file in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_4_M_1.pdf).</p>
Data of Fig2, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig2, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig2.PNG). The corresponding raw data and subsequent data analysis obtained from proteomics analysis are provided as three files in CSV format (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1-3.csv). All further experiment related information provided as one meta-data-file (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1_M .txt) in txt format. Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_1_M_1 .pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_1_M.txt). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_2_M.txt), all further experiment related information provided as one meta-data-file in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_2_M_1.pdf).</p>
Data of Fig5, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig5, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig5.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_4_M_1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_4_M.txt) and three files in CSV-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_4_3.csv). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_5_M .txt) and three files in CSV format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_5-1-3.csv), all further experiment related information provided as three meta-data-files in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_5_M_1-3.pdf).</p>
Data of Fig6, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig6, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig6.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_5_M1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_5_M.txt) and three files in CSV-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_5_1-3.csv).</p> <p> </p>
Data of Fig3, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig3, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig3.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_2_M_1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_2_M.txt). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_3_M.txt), all further experiment related information provided as one meta-data-file in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_3_M_1.pdf).</p>
Data of Fig1, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig1, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (doi: 10.3390/cancers14133074) contains the original figures as PNG-format (10.3390-cancers14133074_Fig1.PNG). Raw data and related Meta data are provides as one file in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_1_M.txt) and one file in PDF-Format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_1_M_1.pdf).</p>
Data of Fig9, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig9, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig9.PNG). The Corresponding raw data and subsequent data analysis obtained for immunohistochemistry contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1_M.pdf) and one file in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv), and all further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M .txt)</p> <p> </p>
Data of FigS3, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS3, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS3.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3_M .txt), six files in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3-1-6 .csv) and one file in sps-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_3-4 .sps). All further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_2_M 1.pdf).</p>
Data of FigS5, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS5, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS5.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_7_M1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_7_M.txt).</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.