Molecular profile of signaling pathways of non-small cell lung carcinomas by Next Generation Sequencing (NGS) with Emphasis on Mesenchymal Epithelium Transition (EMT)

O câncer de pulmão (CP) é um dos tumores malignos mais comuns e uma das principais causas de morte relacionada ao câncer no mundo. O CP é classificado histologicamente em dois grupos: Carcinoma Pulmonar de Pequenas Células (CPPC) e Carcinoma Pulmonar de Células não Pequenas (CPCNP), sendo este o responsável por 85% dos carcinomas primários de pulmão. Um dos principais desafios do CP é o seu diagnóstico tardio, no qual a doença se encontra em estágio avançado impedindo o tratamento curativo. Uma melhor compreensão da patogênese da doença é necessária para auxiliar o diagnóstico, a seleção do melhor tratamento e o desenvolvimento de novas modalidades terapêuticas, a fim de melhorar os resultados do paciente. Com o advento da tecnologia do Sequenciamento de Nova Geração (NGS) uma compreensão abrangente da patogênese molecular da doença foi alcançada e novos biomarcadores emergiram para auxiliar no manejo da doença, oferecendo terapias eficazes e menos tóxicas. O objetivo geral deste trabalho foi avaliar o perfil de alteração genética em CPCNP utilizando a técnica de NGS, principalmente, a alteração de genes envolvidos no processo biológico conhecido como Transição Epitélio Mesenquimal (EMT). Os objetivos específicos foram: Traçar o perfil imunológico do CPCNP utilizando a imunofluorescência multiplex e correlacionar os achados com o perfil de alteração genética relacionado à EMT; Investigar a influência de variantes não-codificantes do gene PD-L1 em amostras de CPCNP e correlacionar com expressão proteica e as características clinicopatológicas; Estimar a frequência de mutação no gene EGFR, com destaque para as mutações de significado clínico incerto no CPCNP. A extração de DNA total de 70 amostras de CPCNP foi realizada a partir do kit QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). A qualidade das amostras foi avaliada utilizando Qubit® 3.0 Fluorometer (Invitrogen, Life Technologies, CA, USA). O sequenciamento foi realizado através da plataforma de NGS Illumina MiSeq V2 (2x 150, paired-end) e as bibliotecas genômicas foram construídas a partir do TruSeq Custom Amplicon v1.5 kit (TSCAP, Illumina, SanDiego, CA). Variantes genéticas com frequência populacional maior que 1% (popfreq_all >0,01) foram consideradas variantes germinativas e menor que 1% foram classificadas como variantes somáticas. As variantes encontradas foram comparadas com resultados depositados em bancos de dados públicos. Os resultados obtidos para cada objetivo específico foram compilados em três artigos. Artigo 1) Foram encontradas frequências semelhantes de mutações nos genes codificadores de proteínas de checkpoint imunológico e genes relacionados à EMT em nossa coorte e na coorte do TCGA. Os genes mais frequentemente mutados em nossa coorte foram ZEB2 (n = 10; 14%), MMP2 (n = 4; 6%), ZEB1 (n = 2; 3%), CDH1 (n = 2; 3%) e CD44 (n = 2; 3%), todos envolvidos em EMT. Além disso, a variante CTLA4 rs231775 com genótipo AA foi associada a menor sobrevida global (SG), quando comparado ao alelo G (HR = 2,091, IC de 95% = 0,233-2,219, P = 0,05). Artigo 2) As variantes de PD-L1 estudadas não interferiram na expressão da proteína, no entanto, as variantes rs4742098A>G, rs4143815G>C e rs7041009G>A foram associadas à recidiva da doença (P=0,01; P=0,05; P=0,02, respectivamente). No modelo de regressão de Cox, o genótipo GG da variante rs7041009 influenciou a SG (P0.01) were considered germline variants, and less than 1% were classified as somatic variants. The variants found were compared with results deposited in public databases. The results obtained for each specific objective were compiled in three articles. Article 1) Similar frequencies of mutations were found in genes encoding immunological checkpoint proteins and EMT-related genes in our and TCGA cohorts. The most frequently mutated genes in our cohort were ZEB2 (n = 10; 14%), MMP2 (n = 4; 6%), ZEB1 (n = 2; 3%), CDH1 (n = 2; 3%) and CD44 (n = 2; 3%), all involved in TMS. In addition, the CTLA4 rs231775 variant with the AA genotype was associated with lower overall survival (OS) compared to the G allele (HR = 2.091, 95% CI = 0.233-2.219, P = 0.05). Article 2) The PD-L1 variants studied did not interfere with protein expression; however, the rs4742098A>G, rs4143815G>C, and rs7041009G>A variants were associated with disease recurrence (P=0.01; P=0. 05; P=0.02, respectively). In the Cox regression model, the GG genotype of the rs7041009 variant influenced OS (P<0.01), acting as a codependent factor associated with radiotherapy and relapse in NSCLC patients. Article 3) The overall mutation rate was 32.9%, of which 20.0% of the cases harbored classic mutations involving exons 18-21 of the EGFR. Mutations spanning other exons were identified in 12.9% of cases. Eleven variants were classified as of unknown clinical significance. Five were predicted to be pathogenic according to in silico analyses. It is concluded that this work contributes to understanding lung cancer\'s pathogenesis and highlights the importance of using NGS and computational tools in the clinical management of patients with NSCL

Abnormal Long Non-Coding RNAs Expression Patterns Have the Potential Ability for Predicting Survival and Treatment Response in Breast Cancer

Abnormal long non-coding RNAs (lncRNAs) expression has been documented to have oncogene or tumor suppressor functions in the development and progression of cancer, emerging as promising independent biomarkers for molecular cancer stratification and patients’ prognosis. Examining the relationship between lncRNAs and the survival rates in malignancies creates new scenarios for precision medicine and targeted therapy. Breast cancer (BRCA) is a heterogeneous malignancy. Despite advances in its molecular classification, there are still gaps to explain in its multifaceted presentations and a substantial lack of biomarkers that can better predict patients’ prognosis in response to different therapeutic strategies. Here, we performed a re-analysis of gene expression data generated using cDNA microarrays in a previous study of our group, aiming to identify differentially expressed lncRNAs (DELncRNAs) with a potential predictive value for response to treatment with taxanes in breast cancer patients. Results revealed 157 DELncRNAs (90 up- and 67 down-regulated). We validated these new biomarkers as having prognostic and predictive value for breast cancer using in silico analysis in public databases. Data from TCGA showed that compared to normal tissue, MIAT was up-regulated, while KCNQ1OT1, LOC100270804, and FLJ10038 were down-regulated in breast tumor tissues. KCNQ1OT1, LOC100270804, and FLJ10038 median levels were found to be significantly higher in the luminal subtype. The ROC plotter platform results showed that reduced expression of these three DElncRNAs was associated with breast cancer patients who did not respond to taxane treatment. Kaplan–Meier survival analysis revealed that a lower expression of the selected lncRNAs was significantly associated with worse relapse-free survival (RFS) in breast cancer patients. Further validation of the expression of these DELncRNAs might be helpful to better tailor breast cancer prognosis and treatment

Proposing Specific Neuronal Epithelial-to-Mesenchymal Transition Genes as an Ancillary Tool for Differential Diagnosis among Pulmonary Neuroendocrine Neoplasms

Pulmonary neuroendocrine neoplasms (PNENs) are currently classified into four major histotypes, including typical carcinoid (TC), atypical carcinoid (AC), large cell neuroendocrine carcinoma (LCNEC), and small cell lung carcinoma (SCLC). This classification was designed to be applied to surgical specimens mostly anchored in morphological parameters, resulting in considerable overlapping among PNENs, which may result in important challenges for clinicians&rsquo; decisions in the case of small biopsies. Since PNENs originate from the neuroectodermic cells, epithelial-to-mesenchymal transition (EMT) gene expression shows promise as biomarkers involved in the genotypic transformation of neuroectodermic cells, including mutation burden with the involvement of chromatin remodeling genes, apoptosis, and mitosis rate, leading to modification in final cellular phenotype. In this situation, additional markers also applicable to biopsy specimens, which correlate PNENs subtypes with systemic treatment response, are much needed, and current potential candidates are neurogenic EMT genes. This study investigated EMT genes expression and its association with PNENs histotypes in tumor tissues from 24 patients with PNENs. PCR Array System for 84 EMT-related genes selected 15 differentially expressed genes among the PNENs, allowing to discriminate TC from AC, LCNEC from AC, and SCLC from AC. Functional enrichment analysis of the EMT genes differentially expressed among PNENs subtypes showed that they are involved in cellular proliferation, extracellular matrix degradation, regulation of cell apoptosis, oncogenesis, and tumor cell invasion. Interestingly, four EMT genes (MAP1B, SNAI2, MMP2, WNT5A) are also involved in neurological diseases, in brain metastasis, and interact with platinum-based chemotherapy and tyrosine&ndash;kinase inhibitors. Collectively, these findings emerge as an important ancillary tool to improve the strategies of histologic diagnosis in PNENs and unveil the four EMT genes that can play an important role in driving chemical response in PNENs

Natural Language Processing to Extract Information from Portuguese-Language Medical Records

Studies that use medical records are often impeded due to the information presented in narrative fields. However, recent studies have used artificial intelligence to extract and process secondary health data from electronic medical records. The aim of this study was to develop a neural network that uses data from unstructured medical records to capture information regarding symptoms, diagnoses, medications, conditions, exams, and treatment. Data from 30,000 medical records of patients hospitalized in the Clinical Hospital of the Botucatu Medical School (HCFMB), São Paulo, Brazil, were obtained, creating a corpus with 1200 clinical texts. A natural language algorithm for text extraction and convolutional neural networks for pattern recognition were used to evaluate the model with goodness-of-fit indices. The results showed good accuracy, considering the complexity of the model, with an F-score of 63.9% and a precision of 72.7%. The patient condition class reached a precision of 90.3% and the medication class reached 87.5%. The proposed neural network will facilitate the detection of relationships between diseases and symptoms and prevalence and incidence, in addition to detecting the identification of clinical conditions, disease evolution, and the effects of prescribed medications

Impact of the COVID-19 Pandemic on Solid Organ Transplant and Rejection Episodes in Brazil&rsquo;s Unified Healthcare System

Background: Brazil has the world&rsquo;s largest public organ transplant program, which was severely affected by the COVID-19 pandemic. The primary aim of the study was to evaluate differences in solid organ transplants and rejection episodes during the COVID-19 pandemic compared to the five years before the pandemic in the country. Methods: A seven-year database was built by downloading data from the DATASUS server. The pandemic period was defined as March 2020 to December 2021. The pre-pandemic period was from January 2015 to March 2020. Results: During the pandemic, the number of solid organ transplants decreased by 19.3% in 2020 and 22.6% in 2021 compared to 2019. We found a decrease for each evaluated organ, which was more pronounced for lung, pancreas, and kidney transplants. The seasonal plot of rejection data indicated a high rejection rate between 2018 and 2021. There was also an 18% (IRR 1.18 (95% CI 1.01 to 1.37), p = 0.04) increase in the rejection rate during the COVID-19 pandemic. Conclusions: The total number of organ transplants performed in 2021 represents a setback of six years. Transplant procedures were concentrated in the Southeast region of the country, and a higher proportion of rejections occurred during the pandemic. Together, these findings could have an impact on transplant procedures and outcomes in Brazil

Clinical characteristics and outcomes of hospital-manifested COVID-19 among Brazilians

ABSTRACT: Objectives: To analyze the clinical characteristics and outcomes of admitted patients with the hospital- versus community-manifested COVID-19 and to evaluate the risk factors related to mortality in the first population. Methods: This retrospective cohort included consecutive adult patients with COVID-19, hospitalized between March and September 2020. The demographic data, clinical characteristics, and outcomes were extracted from medical records. Patients with hospital-manifested COVID-19 (study group) and those with community-manifested COVID-19 (control group) were matched by the propensity score model. Logistic regression models were used to verify the risk factors for mortality in the study group. Results: Among 7,710 hospitalized patients who had COVID-19, 7.2% developed symptoms while admitted for other reasons. Patients with hospital-manifested COVID-19 had a higher prevalence of cancer (19.2% vs 10.8%) and alcoholism (8.8% vs 2.8%) than patients with community-manifested COVID-19 and also had a higher rate of intensive care unit requirement (45.1% vs 35.2%), sepsis (23.8% vs 14.5%), and death (35.8% vs 22.5%) (P <0.05 for all). The factors independently associated with increased mortality in the study group were increasing age, male sex, number of comorbidities, and cancer. Conclusion: Hospital-manifested COVID-19 was associated with increased mortality. Increasing age, male sex, number of comorbidities, and cancer were independent predictors of mortality among those with hospital-manifested COVID-19 disease

Development and validation of the MMCD score to predict kidney replacement therapy in COVID-19 patients

Abstract Background Acute kidney injury (AKI) is frequently associated with COVID-19, and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalised COVID-19 patients, and to assess the incidence of AKI and KRT requirement. Methods This study is part of a multicentre cohort, the Brazilian COVID-19 Registry. A total of 5212 adult COVID-19 patients were included between March/2020 and September/2020. Variable selection was performed using generalised additive models (GAM), and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). Results The median age of the model-derivation cohort was 59 (IQR 47–70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalisation. The temporal validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. The geographic validation cohort had similar age and sex; however, this cohort had higher rates of ICU admission, AKI, need for KRT and in-hospital mortality. Four predictors of the need for KRT were identified using GAM: need for mechanical ventilation, male sex, higher creatinine at hospital presentation and diabetes. The MMCD score had excellent discrimination in derivation (AUROC 0.929, 95% CI 0.918–0.939) and validation (temporal AUROC 0.927, 95% CI 0.911–0.941; geographic AUROC 0.819, 95% CI 0.792–0.845) cohorts and good overall performance (Brier score: 0.057, 0.056 and 0.122, respectively). The score is implemented in a freely available online risk calculator ( https://www.mmcdscore.com/ ). Conclusions The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalised COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation