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Background: The KRAS exon 2 p. G12C mutation in patients with lung adenocarcinoma has been increasing in relevance due to the development and effectiveness of new treatment medications. Studies around different populations indicate that regional variability between ethnic groups and ancestries could play an essential role in developing this molecular alteration within lung cancer. Methods: In a prospective and retrospective cohort study on samples from lung adenocarcinoma from 1000 patients from different administrative regions in Colombia were tested for the KRAS p.G12C mutation. An analysis of STR populations markers was conducted to identify substructure contributions to mutation prevalence. Results: Included were 979 patients with a national mean frequency for the KRAS exon 2 p.G12C mutation of 7.97% (95%CI 6.27–9.66%). Variation between regions was also identified with Antioquia reaching a positivity value of 12.7% (95%CI 9.1–16.3%) in contrast to other regions such as Bogota DC (Capital region) with 5.4% (2.7–8.2%) and Bolivar with 2.4% (95%CI 0–7.2%) (p-value = 0.00262). Furthermore, Short tandem repeat population substructures were found for eight markers that strongly yielded association with KRAS exon 2 p.G12C frequency reaching an adjusted R2 of 0.945 and a p-value of < 0.0001. Conclusions: Widespread identification of KRAS exon 2 p.G12C mutations, especially in cases where NGS is not easily achieved is feasible at a population based level that can characterize regional and national patterns of mutation status. Furthermore, this type of mutation prevalence follows a population substructure pattern that can be easily determined by population and ancestral markers such as STR.

Background: In an attempt to provide quantitative, reproducible, and standardized analyses in cases of eosinophilic esophagitis (EoE), we have developed an artificial intelligence (AI) digital pathology model for the evaluation of histologic features in the EoE/esophageal eosinophilia spectrum. Here, we describe the development and technical validation of this novel AI tool. Methods: A total of 10 726 objects and 56.2 mm2 of semantic segmentation areas were annotated on whole-slide images, utilizing a cloud-based, deep learning artificial intelligence platform (Aiforia Technologies, Helsinki, Finland). Our training set consisted of 40 carefully selected digitized esophageal biopsy slides which contained the full spectrum of changes typically seen in the setting of esophageal eosinophilia, ranging from normal mucosa to severe abnormalities with regard to each specific features included in our model. A subset of cases was reserved as independent “test sets” in order to assess the validity of the AI model outside the training set. Five specialized experienced gastrointestinal pathologists scored each feature blindly and independently of each other and of AI model results. Results: The performance of the AI model for all cell type features was similar/non-inferior to that of our group of GI pathologists (F1-scores: 94.5–94.8 for AI vs human and 92.6–96.0 for human vs human). Segmentation area features were rated for accuracy using the following scale: 1. “perfect or nearly perfect” (95%–100%, no significant errors), 2. “very good” (80%–95%, only minor errors), 3. “good” (70%–80%, significant errors but still captures the feature well), 4. “insufficient” (less than 70%, significant errors compromising feature recognition). Rating scores for tissue (1.01), spongiosis (1.15), basal layer (1.05), surface layer (1.04), lamina propria (1.15), and collagen (1.11) were in the “very good” to “perfect or nearly perfect” range, while degranulation (2.23) was rated between “good” and “very good”. Conclusion: Our newly developed AI-based tool showed an excellent performance (non-inferior to a group of experienced GI pathologists) for the recognition of various histologic features in the EoE/esophageal mucosal eosinophilia spectrum. This tool represents an important step in creating an accurate and reproducible method for semi-automated quantitative analysis to be used in the evaluation of esophageal biopsies in this clinical context.

Effective networking and mentoring are critical determinants of professional satisfaction and success in oncology. There are multiple benefits associated with established mentoring programs. However, these are scarce in Latin America (LATAM). The AAZPIRE project meeting was held to encourage the discussion of mentorship strategies in our region, to create new learning frameworks, and improve cancer care. A group of 30 young oncologists and investigators, together with seven members of LACOG and CLICaP experts of 8 LATAM countries, were reunited to share views and define opportunities, barriers, and possible solutions to implement mentorship programs in LATAM. For each of the mentioned topics, key points were obtained by consensus, and a literature review was conducted to support group conclusions. This article analyses mentoring in LATAM countries and its role on promoting leadership. It will address conceptual frameworks, limitations, and opportunities from the perspectives of both mentor and mentee. The creation of regional and international group stimulation programs and joint projects that impact health policies are attractive, starting points to implement mentorship scenarios.