Liquid Biopsy for Lung Cancer: Up-to-Date and Perspectives for Screening Programs.

Lung cancer is the deadliest cancer worldwide. Tissue biopsy is currently employed for the diagnosis and molecular stratification of lung cancer. Liquid biopsy is a minimally invasive approach to determine biomarkers from body fluids, such as blood, urine, sputum, and saliva. Tumor cells release cfDNA, ctDNA, exosomes, miRNAs, circRNAs, CTCs, and DNA methylated fragments, among others, which can be successfully used as biomarkers for diagnosis, prognosis, and prediction of treatment response. Predictive biomarkers are well-established for managing lung cancer, and liquid biopsy options have emerged in the last few years. Currently, detecting EGFR p.(Tyr790Met) mutation in plasma samples from lung cancer patients has been used for predicting response and monitoring tyrosine kinase inhibitors (TKi)-treated patients with lung cancer. In addition, many efforts continue to bring more sensitive technologies to improve the detection of clinically relevant biomarkers for lung cancer. Moreover, liquid biopsy can dramatically decrease the turnaround time for laboratory reports, accelerating the beginning of treatment and improving the overall survival of lung cancer patients. Herein, we summarized all available and emerging approaches of liquid biopsy-techniques, molecules, and sample type-for lung cancer.

EGFR Mutations and PD-L1 Expression in Early-Stage Non-Small Cell Lung Cancer: A Real-World Data From a Single Center in Brazil.

BACKGROUND: Targeted and immunotherapies are currently moving toward early-stage settings for patients with non-small cell lung cancer (NSCLC). Predictive biomarkers data are scarce in this scenario. We aimed to describe the frequency of EGFR mutations and PD-L1 expression levels in early-stage non-squamous patients with NSCLC from a large, single Brazilian oncology center. METHODS: We retrospectively evaluated patients with NSCLC diagnosed at an early-stage (IB to IIIA-AJCC seventh edition) at Barretos Cancer Hospital (n = 302). EGFR mutational status was assessed in FFPE tumor tissues using distinct methodologies (NGS, Cobas, or Sanger sequencing). PD-L1 expression was evaluated by immunohistochemistry (clone 22C3) and reported as Tumor Proportion Score (TPS), categorized as <1%, 1-49%, and ≥50%. We evaluated the association between EGFR mutational status and PD-L1 expression with sociodemographic and clinicopathological parameters by Fisher's test, qui-square test, and logistic regression. Survival analysis was assessed by the Kaplan-Meier method and Cox regression model. RESULTS: EGFR mutations were detected in 17.3% (n = 48) of cases and were associated with female sex, never smokers, and longer overall and event-free survival. PD-L1 positivity was observed in 36.7% (n = 69) of cases [TPS 1-49% n = 44(23.4%); TPS ≥50% n = 25(13.3%)]. PD-L1 positivity was associated with smoking, weight loss, and higher disease stages (IIB/IIIA). CONCLUSION: The frequencies of EGFR mutations and PD-L1 positivity were described for early-stage non-squamous patients with NSCLC. These results will be essential for guiding treatment strategies with the recent approvals of osimertinib and immunotherapy in the adjuvant setting.

The experience of teaching introductory programming skills to bioscientists in Brazil.

Computational biology has gained traction as an independent scientific discipline over the last years in South America. However, there is still a growing need for bioscientists, from different backgrounds, with different levels, to acquire programming skills, which could reduce the time from data to insights and bridge communication between life scientists and computer scientists. Python is a programming language extensively used in bioinformatics and data science, which is particularly suitable for beginners. Here, we describe the conception, organization, and implementation of the Brazilian Python Workshop for Biological Data. This workshop has been organized by graduate and undergraduate students and supported, mostly in administrative matters, by experienced faculty members since 2017. The workshop was conceived for teaching bioscientists, mainly students in Brazil, on how to program in a biological context. The goal of this article was to share our experience with the 2020 edition of the workshop in its virtual format due to the Coronavirus Disease 2019 (COVID-19) pandemic and to compare and contrast this year's experience with the previous in-person editions. We described a hands-on and live coding workshop model for teaching introductory Python programming. We also highlighted the adaptations made from in-person to online format in 2020, the participants' assessment of learning progression, and general workshop management. Lastly, we provided a summary and reflections from our personal experiences from the workshops of the last 4 years. Our takeaways included the benefits of the learning from learners' feedback (LLF) that allowed us to improve the workshop in real time, in the short, and likely in the long term. We concluded that the Brazilian Python Workshop for Biological Data is a highly effective workshop model for teaching a programming language that allows bioscientists to go beyond an initial exploration of programming skills for data analysis in the medium to long term.