Over the past decades, consistent studies have shown that race/ethnicity have a great impact on cancer incidence, survival, drug response, molecular pathways and epigenetics. Despite the influence of race/ethnicity in cancer outcomes and its impact in health care quality, a comprehensive understanding of racial/ethnic inclusion in oncological research has never been addressed. We therefore explored the racial/ethnic composition of samples/individuals included in fundamental (patient-derived oncological models, biobanks and genomics) and applied cancer research studies (clinical trials). Regarding patient-derived oncological models (n = 794), 48.3% have no records on their donor’s race/ethnicity, the rest were isolated from White (37.5%), Asian (10%), African American (3.8%) and Hispanic (0.4%) donors. Biobanks (n = 8,293) hold specimens from unknown (24.56%), White (59.03%), African American (11.05%), Asian (4.12%) and other individuals (1.24%). Genomic projects (n = 6,765,447) include samples from unknown (0.6%), White (91.1%), Asian (5.6%), African American (1.7%), Hispanic (0.5%) and other populations (0.5%). Concerning clinical trials (n = 89,212), no racial/ethnic registries were found in 66.95% of participants, and records were mainly obtained from Whites (25.94%), Asians (4.97%), African Americans (1.08%), Hispanics (0.16%) and other minorities (0.9%). Thus, two tendencies were observed across oncological studies: lack of racial/ethnic information and overrepresentation of Caucasian/White samples/individuals. These results clearly indicate a need to diversify oncological studies to other populations along with novel strategies to enhanced race/ethnicity data recording and reporting.

PURPOSE: Ethnic diversity in cancer research is crucial as race/ethnicity influences cancer incidence, survival, drug response, molecular pathways, and epigenetic phenomena. In 2018, we began a project to examine racial/ethnic diversity in cancer research, with a commitment to review these disparities every 4 years. This report is our second assessment, detailing the present state of racial/ethnic diversity in cancer genomics and clinical trials. METHODS: To study racial/ethnic inclusion in cancer genomics, we extracted ethnic records from all data sets available at cBioPortal (n = 125,128 patients) and cancer-related genome-wide association studies (n = 28,011,282 patients) between 2018 and 2022. Concerning clinical trials, we selected studies related to breast cancer (n = 125,518 patients, 181 studies), lung cancer (n = 34,329 patients, 119 studies), and colorectal cancer (n = 40,808 patients, 105 studies). RESULTS: In cancer genomics (N = 28,136,410), 3% of individuals lack racial/ethnic registries; tumor samples were collected predominantly from White patients (89.14%), followed by Asian (7%), African American (0.55%), and Hispanic (0.21%) patients and other populations (0.1%). In clinical trials (N = 200,655), data on race/ethnicity are missing for 60.14% of the participants; for individuals whose race/ethnicity was recorded, most were characterized as White (28.33%), followed by Asian (7.64%), African (1.79), other ethnicities (1.37), and Hispanic (0.73). Racial/ethnic representation significantly deviates from global ethnic proportions ( P ≤ .001) across all data sets, with White patients outnumbering other ethnic groups by a factor of approximately 4-6. CONCLUSION: Our second update on racial/ethnic representation in cancer research highlights the persistent overrepresentation of White populations in cancer genomics and a notable absence of racial/ethnic information across clinical trials. To ensure more equitable and effective precision oncology, future efforts should address the reasons behind the insufficient representation of ethnically diverse populations in cancer research.

Pancreatic ductal adenocarcinoma is an aggressive form of pancreatic cancer and the fourth leading cause of cancer-related death. When possible, curative approaches are based on surgical resection, though not every patient is a candidate for surgery. There are clinical guidelines for the management of these patients that offer different treatment options depending on the clinical and pathologic characteristics. However, the survival rates seen in this kind of patients are still low. The CDSE1 gene is located upstream of NRAS and encodes an RNA-binding protein termed UNR. The aim of this study was to analyze UNR expression and its correlation with outcome in patients with resectable pancreatic ductal adenocarcinoma (PDAC). For this, samples from resectable PDAC patients who underwent duodenopancreatectomy were used to evaluate UNR protein expression by immunohistochemistry using a tissue microarray. Here, we observed that low UNR expression was significantly associated with shorter progression-free survival after surgery (P = 0.010). Moreover, this prognostic marker remained significant after Cox proportional hazards model (P = 0.036). We further studied the role of CDSE1 expression in patient’s prognosis using data from public repositories (GEO and TGCA), confirming our results. Interestingly, CDSE1 expression correlated with that of genes characteristic of an immunogenic molecular subtype of pancreatic cancer. Based on these findings, UNR may be considered a potential prognostic biomarker for resectable PDAC and may serve to guide subsequent adjuvant treatment decisions.

Scientific data recording and reporting systems are of a great interest for endorsing reproducibility and transparency practices among the scientific community. Current research generates large datasets that can no longer be documented using paper lab notebooks (PLNs). In this regard, electronic laboratory notebooks (ELNs) could be a promising solution to replace PLNs and promote scientific reproducibility and transparency. We previously analyzed five ELNs and performed two survey-based studies to implement an ELN in a biomedical research institute. Among the ELNs tested, we found that Microsoft OneNote presents numerous features related to ELN best functionalities. In addition, both surveyed groups preferred OneNote over a scientifically designed ELN (PerkinElmer Elements). However, OneNote remains a general note-taking application and has not been designed for scientific purposes. We therefore provide a quick guide to adapt OneNote to an ELN workflow that can also be adjusted to other nonscientific ELNs.