The National Cancer Institute (NCI) and Lund University in Sweden are pleased to announce the signing of an extension to their memorandum of understanding (MOU) for proteogenomics cancer research. The MOU, involving NCI’s International Cancer Proteogenome Consortium (ICPC) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), extends the existing partnership that began in January 2017 and will continue to foster international cooperation, investments, and public dissemination of proteogenomics data.

Intrahepatic cholangiocarcinomas (iCC) pose a diagnostic and treatment challenge due to their rarity and inter/intrapatient heterogeny.

Proteogenomic characterization of patient tissues has enriched our understanding of liver cancer, uncovering potential therapeutic targets, new subtypes, and offering hope for more effective treatments. However, there remains a gap in translating these findings into clinical practice.

The Cancer Imaging Archive (TCIA), managed by the National Cancer Institute's Cancer Imaging Program (CIP), collects, curates, and hosts digital histopathology and standard-of-care radiology imaging from CPTAC-enrolled patients to provide data for imaging-omics research.    Algorithmic analysis of the radiology imaging often requires pre-identification and segmentation of the tumors in the images.  To facilitate this research, CIP has funded and released on TCIA comprehensive annotations to four CPTAC imaging collections (Pancreatic Ductal Adenocarcinoma, Clear Cell Renal Cell Carcinoma, Ut

Recent strides in the field of genomics, exemplified by initiatives like The Cancer Genome Atlas (TCGA), have illuminated the genetic landscape of endometrial carcinoma (EC). The identification of four distinct EC subtypes – POLE ultramutated, MSI-H, CNV-L, and CNV-H – based on their genetic patterns, offers a novel framework for understanding the disease biology.

Researchers from the National Cancer Institute’s (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) have produced a resource of global proteomic and post-translational modifications, whole genome and whole exome sequencing, miRNA and totalRNA sequencing, DNA methylation, imaging, and clinical information for more than 1,000 cancer patients across 10 tumor types. A description of the effort to harmonize and disseminate this resource, as well as four subsequent studies that utilize it, have just been published online by Cell Press.

In an effort recently published in Cell, CPTAC researchers aimed to identify patients with high-grade serous ovarian cancer (HGSOC) who may not respond to standard therapies. At present, there is no way to distinguish refractory from sensitive HGSOCs prior to therapy. As a result, patients with treatment-refractory disease at diagnosis (10-20%) often undergo standard-of-care platinum chemotherapy without benefit.

Recently published in Nature Communications, PepQuery2 is a powerful tool that facilitates fast and targeted identification of both new and existing peptides in proteomics datasets obtained from mass spectrometry experiments. It offers a peptide-centric approach, allowing users to search for specific peptide or protein sequences of interest within massive public datasets of MS/MS spectra.

The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Switzerland International Cancer Proteogenome Consortium (ICPC) team at ETH Zürich are pleased to announce the formalization of an extension to their memorandum of understanding (MOU) for collaborative proteogenomics cancer research. This MOU serves to further strengthen the existing partnership that was established in September 2016 and will actively promote international cooperation, investments, and the transparent sharing of data in the field of cancer proteogenomics.

The limited availability of patient tissue samples poses a constant challenge for omics researchers, particularly in determining which analyses are feasible based on sample input requirements. Existing parallel workflows for multi-omic analyses have yielded valuable insights but are often restricted to the analysis of one or two post-translational modifications. The MONTE workflow, described by the Carr lab in a recent study published in Nature Communications, addresses these limitations.