The purpose of this study was to evaluate nationwide trends in pathologic complete response (pCR) and its racial variations for breast cancer. The National Cancer Database was queried for women from 2010 to 2017 with non-metastatic breast cancer who underwent neoadjuvant chemotherapy. The primary endpoints, pCR and overall survival, were evaluated using Cochran-Armitage test, logistic, and Cox regression multivariable analyses. A total of 104,161 women were analyzed. Overall, pCR improved from 2010 to 2017 (15.1% to 27.2%, trend p < 0.001). Compared to non-Hispanic White (NHW) women, Hispanic White (HW) women were more likely to have pCR for hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-positive tumors (adjusted odds ratio (aOR) 1.29, 95% confidence interval (CI) 1.08–1.53, p = 0.005). Black women were less likely to have pCR for HR-HER2+ tumors (aOR 0.81, 95% CI 0.73–0.89, p < 0.001) and triple negative (aOR 0.82, 95% CI 0.77–0.87, p < 0.001) tumors, but more likely for HR+HER2- tumors (aOR 1.13, 95% CI 1.03–1.24, p = 0.009). Among patients who achieved pCR, Asian or Pacific Islander (API) women were associated with better survival (adjusted hazards ratio (aHR) 0.52, 95% CI 0.33–0.82, p = 0.005) than NHW women. Despite positive trends in pCR rates, the likelihood of pCR and survival outcomes may be intricately dependent on racial/ethnic groups and tumor receptor subtypes.

Within the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell types found in hematologic malignancies to provide an ontological representation of them to aid in data annotation and analysis for patient data. We have developed the Cancer Cell Ontology according to OBO Foundry principles as an extension of the Cell Ontology. We define classes in Cancer Cell Ontology by using a genus-differentia approach using logical axioms capturing the expression of cellular surface markers in order to represent types of hematologic malignancies. By adopting conventions used in the Cell Ontology, we have created human and computer-readable definitions for 300 classes of blood cancers, based on the EGIL classification system for leukemias, and relying upon additional classification approaches for multiple myelomas and other hematologic malignancies. We have demonstrated a proof of concept for leveraging the built-in logical axioms of the ontology in order to classify patient surface marker data into appropriate diagnostic categories. We plan to integrate our ontology into existing tools for flow cytometry data analysis to facilitate the automated diagnosis of hematologic malignancies.

Stem cells and stem cell lines are widely used in biomedical research. The Cell Ontology (CL) and Cell Line Ontology (CLO) are two community-based OBO Foundry ontologies in the domains of in vivo cells and in vitro cell line cells, respectively. To support standardized stem cell investigations, we have developed an Ontology for Stem Cell Investigations (OSCI). OSCI imports stem cell and cell line terms from CL and CLO, and investigation-related terms from existing ontologies. A novel focus of OSCI is its application in representing metadata types associated with various stem cell investigations. We also applied OSCI to systematically categorize experimental variables in an induced pluripotent stem cell line cell study related to bipolar disorder. In addition, we used a semi-automated literature mining approach to identify over 200 stem cell gene markers. The relations between these genes and stem cells are modeled and represented in OSCI. OSCI standardizes stem cells found in vivo and in vitro and in various stem cell investigation processes and entities. The presented use cases demonstrate the utility of OSCI in iPSC studies and literature mining related to bipolar disorder.