CAR T-cells, which are immune cells reprogrammed to fight cancer, are promising novel cancer therapies to treat certain types of tumors. But, the underlying biological pathways that lead to remission or toxicity are not fully understood. In a study published in Science Signaling, CPTAC investigator (part of the Proteogenomic Translational Research Centers) Dr. Amanda Paulovich, her team, and fellow researchers at Fred Hutchinson Cancer Research Center used proteomics to profile signaling events in two different, clinically relevant CARs using cell and mouse models.

The NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge sub-challenge 2 closes on December 19 at 07:59:59 UTC. Just two weeks left to submit your compuational algorithms. Remember, if you did not participate in sub-challenge 1, you can still participate in sub-challenge 2.

The second data release for the NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge has launched!  If you did not participate in sub-challenge 1 (submission 1), you can still participate in sub-challenge 2 (submission 2).

Several ongoing Phase I clinical trials are assessing the safety and tolerability of ATM (ataxia-telangiectasia mutated) and ATR (ataxia-telangiectasia and Rad3-Related) inhibitors when administered alone or in combination with existing therapies. ATM and ATR are attractive therapeutic targets in oncology due to defective DNA damage response (DDR) mechanisms found in many human cancers and are thought to resolve DNA damage in cancer cells and promote tumor regression.

The security of precisionFDA users’ personal information and data is of critical importance. To strengthen the safeguards already in place, precisionFDA will be introducing several changes to achieve compliance with the FedRAMP and FISMA Moderate security standards. These changes will take effect during a service outage on the site between 5PM October 31 and 5PM EDT November 1.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) is releasing the latest proteomic discovery datasets for Uterine Corpus Endometrial Carcinoma (UCEC) and Clear Cell Renal Cell Carcinoma (ccRCC). CPTAC has previously demonstrated how proteogenomics reveals new insights into cancer biology. Because of the scientific progress already made by the program, CPTAC is leveraging its investment in proteogenomics by characterizing UCEC and ccRCC and providing publicly available proteomic data for these latest tumor types.

In addition, CPTAC has also released data on the NCI-7 cell line panel and the use of SEER (surveillance, epidemiology and end results) repository tissues for cancer proteomic studies.

Fanconi anemia (FA) is a rare inherited disorder characterized by progressive bone marrow failure and an increased risk of developing certain types of cancer. The FA pathway consists of a network of 21 proteins that is specialized for repairing DNA inter-strand cross-links. Identification of monoubiquitylation (or similar) defects in this network provides an opportunity for therapeutic targeting with a high-throughput, quantitative mechanism. CPTAC investigator Dr. Amanda Paulovich and her research team at the Fred Hutchinson Cancer Research Center demonstrated the utility of an immuno-multiple reaction monitoring (iMRM) assay to quantify the unmodified and monoubiquitinated protein isoforms and peptides unique to FANCD2.

The Office of Cancer Clinical Proteomics Research of the National Cancer Institute (NCI), part of the National Institutes of Health, announces the release of a video titled, “International Cancer Proteogenome Consortium (ICPC)”. Launched during the 2018 Human Proteome Organization World Congress, the video is a call-to-action for both current and potential consortium members to share data and develop a cancer atlas representative of the diversity of people with cancer around the world.

The National Cancer Institute in partnership with the Food and Drug Administration is pleased to announce the launch of the precisionFDA NCI-CPTAC Multi-Omics Challenge. The aim of this crowd-sourced, cloud-based data challenge is to incentivize the development and evaluation of novel computational algorithms that can accurately detect and correct mislabeled samples using rich multi-omics data. The challenge runs on the precisionFDA platform, with the ultimate goal of producing an open-source product that can be incorporated into a quality-management system to reduce mislabeling errors. An immediate outcome of the challenge is a flagship manuscript that highlights the challenge data, questions, design, and outcomes.

Multidimensional multi-omics datasets from The Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and similar initiatives are becoming a powerful approach to understanding the molecular basis of disease and accelerating the translation of new discoveries to patient care. While there is value in multi-omics technologies and datasets to help reach a deeper understanding of a disease and ultimately help a physician and patient determine the most appropriate treatment option, sample mislabeling presents as a roadblock that can occur in data production and analysis pipelines involving data-rich, large-scale omics studies.