The CPTAC research group led by Dr. David Fenyö at NYU Langone Medical Center has demonstrated the feasibility of a machine learning image processing tool designed to assist pathologists classifying endometrial cancer. Their customized multi-resolution deep convolutional neural network (CNN) model was able to provide information about patients’ histological subtypes, molecular subtypes, and mutation status rapidly and reliably from digitized H&E-stained pathological images.

Despite advances in the efficacy and specificity of cancer treatments over the last decade, pancreatic cancer remains one of the leading causes of death worldwide. The most common type of pancreatic tumor, Pancreatic Ductal Adenocarcinoma (PDAC), has distinctly poor patient outcomes due to its aggressive progression and late symptom presentation.

Lung cancer remains the leading cause of cancer-associated death in the United States and worldwide. Patients with a subtype called lung adenocarcinoma (LUAD) have benefited from the development of new targeted medicines, but the search for effective new therapies for another subtype called lung squamous cell carcinoma (LSCC) has largely come up short.

In the era of precision medicine, human epidermal growth factor receptor 2 (HER2) is one of the important predictive and prognostic biomarkers in breast cancer. At present, conventional HER2-targeting therapies improve outcomes for patients with HER2-positive breast cancer, defined as tumors having HER2 protein overexpression or gene amplification. On the horizon, emerging HER2-targeting compounds are beginning to show benefit in some patients with neither HER2 protein overexpression nor ERBB2 gene amplification.

Kidney cancer is the eighth most diagnosed cancer for both men and women in the United States, and about 76,000 new cases and nearly 14,000 deaths are expected in 2021. It is a category that includes many different kinds of cancer and is dominated by renal cell carcinoma.

Adult acute myeloid leukemia (AML) is a cancer of the blood and bone marrow. It is characterized by an aberrant proliferation of immature myeloblasts that infiltrate the bone marrow and impair normal hematopoiesis. AML is the most common type of acute leukemia in adults and usually worsens quickly if left untreated. An estimated 20,240 people in the United States will be diagnosed with AML and 11,400 will die from this disease in 2021.

In the age of personalized cancer therapy, genetic sequencing technologies allow clinicians to rapidly pinpoint mutations likely involved in driving patient-specific tumorigenesis and disease progression. These oncogenic mutations lead to the aberrant activation of signaling pathways involved in cellular growth and division. Targeted therapeutics that inhibit specific components of these pathways often yield dramatic responses in patients whose cancers rely on them for sustained growth and survival. Unfortunately, tumor relapse is a common eventuality in these settings.

Advancements in science and health care are made possible through widespread access to results from cutting-edge research, enabling scientists to use and build on this knowledge. A critically important aspect of the success of the Human Genome Project was its approach to immediately release pre-publication primary sequence data.

UniProt, the leading online protein reference library, has expanded its collaboration with the National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC). In addition to searching for a protein-of-interest in UniProt, which includes cross-reference links to the NCI CPTAC Assay Portal for fit-for-purpose targeted assays, users now have cross-reference links to the NCI CPTAC Antibody Portal for cancer-associated renewable antibodies.

In biomedical research, sample mislabeling or incorrect annotation has been a long-standing issue contributing to irreproducible results and invalid conclusions. These issues are particularly prevalent in large scale multi-omics studies, in which multiple different omics experiments are carried out at different time periods and/or in different labs and human errors can arise during sample transferring, sample tracking, large-scale data generation, and data sharing/management.