DICOM converted Slide Microscopy images for the HTAN-OHSU collection

<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=htan_ohsu">HTAN-OHSU</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> <p>The Human Tumor Atlas Network (HTAN) [2], part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types.&nbsp;</p> <p>The overall goal of the HTAN OMS Atlas Center is to elucidate mechanisms by which metastatic breast cancers become resistant to current generation pathway- and immune checkpoint-targeted treatments. The OMS Atlas is motivated by the appreciation that these treatments are often effective in primary tumors but only transiently effective in the metastatic setting. Possible resistance mechanisms include tumor-intrinsic genomic instability and epigenomic plasticity, as well as events extrinsic to the cancer cells, including chemical and mechanical signals from the microenvironments, production of mechanical extracellular matrix barriers and/or changes in vasculature that reduce drug and/or immune cell access, nanoscale cancer cell-microenvironment interactions that reduce drug efficacy, and a plethora of immune resistance mechanisms, such as loss of HLA expression and antigen presentation, and immune exhaustion. These mechanisms likely vary between patients and within individual patients and change with time as tumors respond to therapeutic attack. The OMS Atlas will focus on elucidating resistance mechanisms in two specific current generation clinical trial scenarios: (a) hormone receptor-positive breast cancer (HRBC) undergoing treatment with a CDK4/6 inhibitor in combination with endocrine therapy and (b) triple negative breast cancer (TNBC) undergoing treatment with a PARP inhibitor and an immunomodulatory agent.</p> <p>Please see the <a href="https://humantumoratlas.org/hta9/"> HTAN-OHSU <em> </em> </a> information page to learn more about the images and to obtain any supporting metadata for this collection.</p> <p>Citation guidelines can be found on the <a href="https://docs.google.com/document/d/1seSp9yo8mqMK5sFXd4LEf_t_4SEofZi-1p-M75R1lhk/edit#heading=h.ybiv33wzts40"> HTAN Publication Policy <em> </em> </a> information page.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>htan_ohsu-idc_v10-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>htan_ohsu-idc_v10-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>htan_ohsu-idc_v10-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Collection of the images that were converted by IDC was supported through the Human Tumor Atlas Network, grants 1U2CCA233280-01 "Omic and Multidimensional Spatial Atlas of Metastatic Breast and Prostate Cancers" and 1U24CA233243-01 "Human Tumor Atlas Network: Data Coordinating Center" from National Cancer Institute.</p> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. &amp; Kikinis, R. <em>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</em>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p> <p>[2] Rozenblatt-Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J. E., Ashenberg, O., Cerami, E., Coffey, R. J., Demir, E., Ding, L., Esplin, E. D., Ford, J. M., Goecks, J., Ghosh, S., Gray, J. W., Guinney, J., Hanlon, S. E., Hughes, S. K., Hwang, E. S., Iacobuzio-Donahue, C. A., Jan&eacute;-Valbuena, J., Johnson, B. E., Lau, K. S., Lively, T., Mazzilli, S. A., Pe&rsquo;er, D., Santagata, S., Shalek, A. K., Schapiro, D., Snyder, M. P., Sorger, P. K., Spira, A. E., Srivastava, S., Tan, K., West, R. B., Williams, E. H. &amp; Human Tumor Atlas Network. <em>The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution</em>. Cell<strong> 181,</strong> 236&ndash;249 (2020). <a href="http://dx.doi.org/10.1016/j.cell.2020.03.053">http://dx.doi.org/10.1016/j.cell.2020.03.053</a></p>