Recently, molecular classifications of gastric cancer (GC) have been proposed that include TP53 mutations and their functional activity. We aimed to demonstrate the correlation between p53 immunohistochemistry (IHC) and TP53 mutations as well as their clinicopathological significance in GC. Deep targeted sequencing was performed using surgical or biopsy specimens from 120 patients with GC. IHC for p53 was performed and interpreted as strong, weak, or negative expression. In 18 cases (15.0%) with discrepant TP53 mutation and p53 IHC results, p53 IHC was repeated. Strong expression of p53 was associated with TP53 missense mutations, negative expression with other types of mutations, and weak expression with wild-type TP53 (p<.001). The sensitivity for each category was 90.9%, 79.0%, and 80.9%, and the specificity was 95.4%, 88.1%, and 92.3%, respectively. The TNM stage at initial diagnosis exhibited a significant correlation with both TP53 mutation type (p=.004) and p53 expression status (p=.029). The Kaplan-Meier survival analysis for 109 stage II and III GC cases showed that patients with TP53 missense mutations had worse overall survival than those in the wild-type and other mutation groups (p=.028). Strong expression of p53 was also associated with worse overall survival in comparison to negative and weak expression (p=.035). Results of IHC of the p53 protein may be used as a simple surrogate marker of TP53 mutations. However, negative expression of p53 and other types of mutations of TP53 should be carefully interpreted because of its lower sensitivity and different prognostic implications.

Background: Common data models (CDMs) help standardize electronic health record data and facilitate outcome analysis for observational and longitudinal research. An analysis of pathology reports is required to establish fundamental information infrastructure for data-driven colon cancer research. The Observational Medical Outcomes Partnership (OMOP) CDM is used in distributed research networks for clinical data; however, it requires conversion of free text–based pathology reports into the CDM’s format. There are few use cases of representing cancer data in CDM. Objective: In this study, we aimed to construct a CDM database of colon cancer–related pathology with natural language processing (NLP) for a research platform that can utilize both clinical and omics data. The essential text entities from the pathology reports are extracted, standardized, and converted to the OMOP CDM format in order to utilize the pathology data in cancer research. Methods: We extracted clinical text entities, mapped them to the standard concepts in the Observational Health Data Sciences and Informatics vocabularies, and built databases and defined relations for the CDM tables. Major clinical entities were extracted through NLP on pathology reports of surgical specimens, immunohistochemical studies, and molecular studies of colon cancer patients at a tertiary general hospital in South Korea. Items were extracted from each report using regular expressions in Python. Unstructured data, such as text that does not have a pattern, were handled with expert advice by adding regular expression rules. Our own dictionary was used for normalization and standardization to deal with biomarker and gene names and other ungrammatical expressions. The extracted clinical and genetic information was mapped to the Logical Observation Identifiers Names and Codes databases and the Systematized Nomenclature of Medicine (SNOMED) standard terminologies recommended by the OMOP CDM. The database-table relationships were newly defined through SNOMED standard terminology concepts. The standardized data were inserted into the CDM tables. For evaluation, 100 reports were randomly selected and independently annotated by a medical informatics expert and a nurse. Results: We examined and standardized 1848 immunohistochemical study reports, 3890 molecular study reports, and 12,352 pathology reports of surgical specimens (from 2017 to 2018). The constructed and updated database contained the following extracted colorectal entities: (1) NOTE_NLP, (2) MEASUREMENT, (3) CONDITION_OCCURRENCE, (4) SPECIMEN, and (5) FACT_RELATIONSHIP of specimen with condition and measurement. Conclusions: This study aimed to prepare CDM data for a research platform to take advantage of all omics clinical and patient data at Seoul National University Bundang Hospital for colon cancer pathology. A more sophisticated preparation of the pathology data is needed for further research on cancer genomics, and various types of text narratives are the next target for additional research on the use of data in the CDM.

Suh KJ, et al. J Breast Cancer. 2022 Jun;25:e30. https://doi.org/10.4048/jbc.2022.25.e30

Despite recent progress in comprehensive genetic analysis, little is known about the molecular pathogenesis of sebaceous carcinoma. On the basis of the ontogenic proximity of sebaceous and mammary glands, we designed an intrinsic classification for sebaceous carcinoma adapted from that of breast cancer and evaluated its clinical significance. We investigated 42 cases of sebaceous carcinoma, including 32 ocular and 10 extraocular cases. Immunohistochemical analyses for estrogen receptor (ER), progesterone receptor (PR), androgen receptor (AR), HER2, Ki67, and CK5/6 and fluorescence in situ hybridization for the HER2 gene were performed. The immunohistochemistry for ER, PR, and AR showed positivity in 18 (42.9%), 11 (26.2%), and 34 (81.0%) cases, respectively. Expression of the HER2 protein was found in 10 (33.8%) cases, whereas extra copies were found in 3 (7.1%). According to our system, there were 16 (38.1%) cases of the luminal 1 subtype, 4 (9.5%) of the luminal 2 subtype, and 7 (16.7%) of the HER2 subtype, respectively. Fifteen cases (35.7%) belonged to the triple-negative group. In univariable analysis, loss of AR was significantly associated with shorter disease-free survival (P=0.020), whereas the expression of HER2 was associated with a better outcome with borderline significance (P=0.060). The luminal 2 subtype showed the best survival, and the all-negative subtype showed the worst (P=0.001). In multivariable analysis, negativity of PR or AR, low CK5/6, and female sex were independent poor prognostic factors (all P<0.05). This is the first study to categorize sebaceous carcinoma on the basis of the possible link between its molecular pathogenesis and future therapeutic applications.