High-Resolution Genotyping of Formalin-Fixed Tissue Accurately Estimates Polygenic Risk Scores in Human Diseases.

Formalin-fixed paraffin-embedded (FFPE) tissues stored in biobanks and pathology archives are a vast but underutilized source for molecular studies on different diseases. Beyond being the "gold standard" for preservation of diagnostic human tissues, FFPE samples retain similar genetic information as matching blood samples, which could make FFPE samples an ideal resource for genomic analysis. However, research on this resource has been hindered by the perception that DNA extracted from FFPE samples is of poor quality. Here, we show that germline disease-predisposing variants and polygenic risk scores (PRS) can be identified from FFPE normal tissue (FFPE-NT) DNA with high accuracy. We optimized the performance of FFPE-NT DNA on a genome-wide array containing 657,675 variants. Via a series of testing and validation phases, we established a protocol for FFPE-NT genotyping with results comparable with blood genotyping. The median call rate of FFPE-NT samples in the validation phase was 99.85% (range 98.26%-99.94%) and median concordance with matching blood samples was 99.79% (range 98.85%-99.9%). We also demonstrated that a rare pathogenic PALB2 genetic variant predisposing to cancer can be correctly identified in FFPE-NT samples. We further imputed the FFPE-NT genotype data and calculated the FFPE-NT genome-wide PRS in 3 diseases and 4 disease risk variables. In all cases, FFPE-NT and matching blood PRS were highly concordant (all Pearson's r > 0.95). The ability to precisely genotype FFPE-NT on a genome-wide array enables translational genomics applications of archived FFPE-NT samples with the possibility to link to corresponding phenotypes and longitudinal health data.

Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma.

Image-based deep learning models are used to extract new information from standard hematoxylin and eosin pathology slides; however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous carcinoma of the ovary (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also exhibits striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods. Here, we applied spatially resolved transcriptomics to further profile the AI-identified tumor regions in 16 patients (8 per outcome group) and identify molecular features related to disease outcome in patients who underwent primary debulking surgery and platinum-based chemotherapy. We examined formalin-fixed paraffin-embedded tissue from (1) regions identified by the AI model as highly associated with short or extended chemotherapy response, and (2) background tumor regions (not identified by the AI model as highly associated with outcome status) from the same tumors. We show that the transcriptomic profiles of AI-identified regions are more distinct than background regions from the same tumors, are superior in predicting outcome, and differ in several pathways including those associated with chemoresistance in HGSC. Further, we find that poor outcome and good outcome regions are enriched by different tumor subpopulations, suggesting distinctive interaction patterns. In summary, our work presents proof of concept that AI-guided spatial transcriptomic analysis improves recognition of biologic features relevant to patient outcomes.

The frequency of NRAS mutation in stool samples of Iranian colorectal cancers compared to Finnish patients.

Background: Stools from colorectal cancer patients are noninvasive samples that could be used to compare the frequency of hotspot mutations between two different ethnic cohorts. Materials and Methods: We collected stool samples from the Iranian cohort (52 patients and 49 controls) and the Finnish cohort (40 patients and 14 controls). Following stool DNA extraction, we used the AmpliSeq Colon and Lung Cancer panel to prepare DNA libraries before sequencing. Results: The Iranian cohort exhibited 35 hotspot mutations in the BRAF, ERBB4, FBXW7, FGFR1, FGFR3, KRAS, MAP2K, MET, NRAS, PIK3C, SMAD4, and TP53 genes. In the Finnish cohort, 13 hotspot mutations were found in the AKT1, APC, KIT, KRAS, SMO, STK11, and TP53 genes. Mutations in NRAS and FGFR3 were observed only in the Iranian cohort, while APC mutations were exclusive for the Finnish cohort. Conclusion: Genes involved in MAPK and PI3K-MAPK pathways showed a higher frequency of mutations in Iranian patients which may have therapeutic implications.

Does evaluation of tumour budding in diagnostic biopsies have a clinical relevance? A systematic review.

Tumour budding has emerged as a promising prognostic marker in many cancers. We systematically reviewed all studies that evaluated tumour budding in diagnostic biopsies. We conducted a systematic review of PubMed, MEDLINE, Scopus, Web of Science and Cochrane library for all articles that have assessed tumour budding in diagnostic (i.e. pretreatment or pre-operative) biopsies of any tumour type. Two independent researchers screened the retrieved studies, removed duplicates, excluded irrelevant studies and extracted data from the eligible studies. A total of 13 reports comprising 11 cohorts were found to have studied tumour budding in diagnostic biopsies. All these reports showed that evaluation of tumour budding in diagnostic biopsies was easily applicable. A strong association was observed between tumour budding score in diagnostic biopsies and corresponding surgical samples. Evaluation of tumour budding in diagnostic biopsies had a significant prognostic value for lymph node metastasis and patient survival. In all studies, tumour budding was a valuable marker of tumour aggressiveness and can be evaluated in technically satisfactory diagnostic biopsies. Thus, the assessment of tumour budding seems to identify the behaviour of cancer, and therefore to facilitate treatment planning.

Stool Microbiota Composition Differs in Patients with Stomach, Colon, and Rectal Neoplasms.

BACKGROUND: Microbial ecosystems that inhabit the human gut form central component of our physiology and metabolism, regulating and modulating both health and disease. Changes or disturbances in the composition and activity of this gut microbiota can result in altered immunity, inflammation, and even cancer. AIM: To compare the composition and diversity of gut microbiota in stool samples from patient groups based on the site of neoplasm in the gastrointestinal tract (GIT) and to assess the possible contribution of the bacterial composition to tumorigenesis. METHODS: We studied gut microbiota by16S RNA gene sequencing from stool DNA of 83 patients, who were diagnosed with different GIT neoplasms, and 13 healthy individuals. RESULTS: As compared to healthy individuals, stools of patients with stomach neoplasms had elevated levels of Enterobacteriaceae, and those with rectal neoplasms had lower levels of Bifidobacteriaceae. Lower abundance of Lactobacillaceae was seen in patients with colon neoplasms. Abundance of Lactobacillaceae was higher in stools of GIT patients sampled after cancer treatment compared to samples collected before start of any treatment. In addition to site-specific differences, higher abundances of Ruminococcus, Subdoligranulum and lower abundances of Lachnoclostridium and Oscillibacter were observed in overall GIT neoplasms as compared to healthy controls CONCLUSION: Our study demonstrates that the alterations in gut microbiota vary according to the site of GIT neoplasm. The observed lower abundance of two common families, Lactobacillaceae and Bifidobacteriaceae, and the increased abundance of Enterobacteriaceae could provide indicators of compromised gut health and potentially facilitate GIT disease monitoring.

Expression of Plasma miRNA-221 in Colorectal Carcinoma Patients and its Diagnostic Significance in Comparison with p53 Expression.

BACKGROUND: Colorectal carcinoma development progresses through a sequence of normal mucosa-polyp-carcinoma. Early detection of premalignancy is crucial for improved outcomes. We evaluated the diagnostic performance of plasma miRNA-221 and its feasibility in discriminating premalignant from malignant neoplasms and correlating it with immunohistochemical p53 expression. METHODS: A total of 109 plasma samples were collected (76 carcinoma, 14 premalignant, and 19 controls). MiRNA221 was quantified by qPCR for calculation of ∆Ct using RNU6B as endogenous control. p53 immunohistochemical staining was performed on corresponding tissue. RESULTS: Plasma miRNA-221 and p53 in tissues were significantly overexpressed in the malignant group when compared with the premalignant and control groups. Plasma miRNA-221 was increased in late-stage tumors with nodal or distant metastasis. ROC curve construction for distinguishing between malignant and premalignant tumors revealed a cutoff value of 2.97 with 74% sensitivity, 79% specificity, 73.7% positive predictive value and 78.6% negative predictive value (AUC = 0.824; p = 0.001). Plasma miRNA-221 significantly correlated with p53 in cancer samples (r = 0.507). CONCLUSIONS: MiRNA-221 could have a diagnostic role in differentiating malignant from premalignant neoplasms and could also serve as a predictive marker indicating tumor progression.

Exhaled breath condensate as a source of biomarkers for lung carcinomas. A focus on genetic and epigenetic markers-A mini-review.

Lung carcinoma is one of the most common causes of cancer-related mortality worldwide. It is an aggressive tumor, often diagnosed at an advanced stage when treatment options are limited. Currently, the importance of detection and assessment of various genetic alterations in cancer is recognized as they can serve as very helpful markers in early diagnosis and follow-up of treatment regimens. Recently, several therapeutically important genetic markers have been identified. One major problem is that tumor tissue specimens used to assay these genetic biomarkers are not always available, especially in the early stages of the disease. Therefore, exhaled breath condensates (EBC) could represent a good non-invasive source to allow the evaluation of these important genetic markers; these could help in the diagnosis, follow-up of the disease and/or assessment of treatment efficacy. The key aims of this review are first to describe the origin and constituents of EBC, as well as the different methodological procedures used in studying EBC biomarkers, and second, to document genetic and epigenetic markers that have been analyzed in EBC from lung cancer patients and to estimate their diagnostic and prognostic value. © 2016 Wiley Periodicals, Inc.

A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk.

Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.

Sleep quality, stress level and COVID-19 in university students; the forgotten dimension.

Introduction: COVID-19 is a novel pandemic that has had a profound impact on global physical and psychological health. We aimed to investigate the impact of COVID-19 on stress, sleep quality, and insomnia among South Valley University students in Egypt during the quarantine period. Material and Methods: A questionnaire, including the Pittsburgh sleep quality index, the insomnia severity index, the perceived stress scale and COVID-19 fear index was distributed to the undergraduate students through the online platforms of South Valley University during the period of 1st to 15th June 2020. Results: Of a total respondent sample of 2,474 students, 24.5% had high-perceived stress levels, 31.3% had clinical insomnia, and about 80% were identified as generally poor sleepers by the PSQI. Being female, having a chronic disease, having a sleep disorder before the quarantine, or consuming caffeine were the main factors associated with high stress levels, clinical insomnia, and poor sleep quality. Also, levels of fear of COVID-19 were higher among people with high stress levels, clinical insomnia, and poor sleep quality. Conclusion: Considerable levels of stress and poor sleep quality were identified among undergraduate university students during the pandemic/home isolation period. The effect was more obvious among certain demographic groups and among the students who scored higher in the fear of COVID-19 scale.

Managing Cachexia in Head and Neck Cancer: a Systematic Scoping Review.

INTRODUCTION: Patients with head and neck cancer (HNC) are usually confronted with functional changes due to the malignancy itself or its treatment. These factors typically affect important structures involved in speech, breathing, chewing, swallowing, and saliva production. Consequently, the intake of food will be limited, which further contributes to loss of body weight and muscle mass, anorexia, malnutrition, fatigue, and anemia. This multifactorial condition can ultimately lead to cancer cachexia syndrome. This study aims to examine the treatment of cachexia in HNC patients. METHODS: We systematically searched OvidMedline, PubMed, Scopus, and Web of Science for articles examining the treatment of cachexia in HNC. RESULTS: A total of nine studies were found, and these suggested interventions including nutritional, pharmacologic, therapeutic exercise, and multimodal approaches. The nutritional intervention includes essential components such as dietary counseling, oral nutritional supplements, and medical nutritional support. Individualized nutritional interventions include oral, enteral (feeding tubes i.e., percutaneous endoscopic gastrostomy [PEG], nasogastric tube [NGT]) and parenteral nutrition. The pharmacologic interventions aim at increasing the appetite and weight of cachectic patients. Therapeutic exercise and increased physical activity can help to enhance the synthesis of muscle protein, reducing inflammation and the catabolic effects of cachexia syndrome. CONCLUSION: Owing to the multifactorial nature of this syndrome, it is expected that the management approach should be multi-interventional. Early implementation of these interventions may help to improve survival and quality of health and life of cachectic HNC patients.

Mitigating Burnout in an Oncological Unit: A Scoping Review.

Objectives: The purpose of this study was to provide a scoping review on how to address and mitigate burnout in the profession of clinical oncology. Also, it examines how artificial intelligence (AI) can mitigate burnout in oncology. Methods: We searched Ovid Medline, PubMed, Scopus, and Web of Science, for articles that examine how to address burnout in oncology. Results: A total of 17 studies were found to examine how burnout in oncology can be mitigated. These interventions were either targeted at individuals (oncologists) or organizations where the oncologists work. The organizational interventions include educational (psychosocial and mindfulness-based course), art therapies and entertainment, team-based training, group meetings, motivational package and reward, effective leadership and policy change, and staff support. The individual interventions include equipping the oncologists with adequate training that include-communication skills, well-being and stress management, burnout education, financial independence, relaxation, self-efficacy, resilience, hobby adoption, and work-life balance for the oncologists. Similarly, AI is thought to be poised to offer the potential to mitigate burnout in oncology by enhancing the productivity and performance of the oncologists, reduce the workload and provide job satisfaction, and foster teamwork between the caregivers of patients with cancer. Discussion: Burnout is common among oncologists and can be elicited from different types of situations encountered in the process of caring for patients with cancer. Therefore, for these interventions to achieve the touted benefits, combinatorial strategies that combine other interventions may be viable for mitigating burnout in oncology. With the potential of AI to mitigate burnout, it is important for healthcare providers to facilitate its use in daily clinical practices. Conclusion: These combinatorial interventions can ensure job satisfaction, a supportive working environment, job retention for oncologists, and improved patient care. These interventions could be integrated systematically into routine cancer care for a positive impact on quality care, patient satisfaction, the overall success of the oncological ward, and the health organizations at large.

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review.

The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases-PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.

Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review.

BACKGROUND: Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care. OBJECTIVES: This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice. DATA SOURCES: We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC. ELIGIBILITY CRITERIA: Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered. DATA EXTRACTION: Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies. RESULTS: A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations. CONCLUSION: Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.

Nonmalignant Formalin-Fixed Paraffin-Embedded Tissues as a Source to Study Germline Variants and Cancer Predisposition: A Systematic Review.

Background: Archived formalin-fixed paraffin-embedded (FFPE) specimens from nonmalignant tissues derived from cancer patients are a vast and potentially valuable resource for high-quality genotyping analyses and could have a role in establishing inherited cancer risk. Methods: We systematically searched PubMed, Ovid MEDLINE, and Scopus databases for all articles that compared genotyping performance of DNA from nonmalignant FFPE tissue with blood DNA derived from cancer patients irrespective of tumor type. Two independent researchers screened the retrieved studies, removed duplicates, excluded irrelevant studies, and extracted genotyping data from the eligible studies. These studies included, but were not limited to, genotyping technique, reported call rate, and concordance. Results: Thirteen studies were reviewed, in which DNA from nonmalignant FFPE tissues derived from cancer patients was successfully purified and genotyped. All these studies used different approaches for genotyping of DNA from nonmalignant FFPE tissues to amplify single nucleotide polymorphisms (SNPs) and to estimate of loss of heterozygosity. The concordance between genotypes from nonmalignant FFPE tissues and blood derived from cancer patients was observed to be high, whereas the call rate of the tested SNPs was not reported in all included studies. Conclusion: This review illustrates that DNA from nonmalignant FFPE tissues derived from cancer patients can serve as an alternative and reliable source for assessment of germline DNA for various purposes, including assessment of cancer predisposition.

Risk stratification in oral squamous cell carcinoma using staging of the eighth American Joint Committee on Cancer: Systematic review and meta-analysis.

The eighth edition of the American Joint Committee on Cancer (AJCC8) staging manual has major changes in oral squamous cell carcinoma (OSCC). We searched PubMed, OvidMedline, Scopus, and Web of Science for studies that examined the performance of AJCC8 in OSCC. A total of 40 808 patients were included in the studies of our meta-analysis. A hazard ratio (HR) of 1.87 (95%CI 1.78-1.96) was seen for stage II, 2.65 (95%CI 2.51-2.80) for stage III, 3.46 (95%CI 3.31-3.61) for stage IVa, and 7.09 (95%CI 4.85-10.36) for stage IVb. A similar gradual increase in risk was noted for the N classification. For the T classification, however, there was a less clear variation in risk between T3 and T4. AJCC8 provides a good risk stratification for OSCC. Future research should examine the proposals introduced in the published studies to further improve the performance of AJCC8.

Exhaled breath analysis in the diagnosis of head and neck cancer.

Head and neck cancer (HNC) comprises a heterogeneous group of upper aerodigestive tract malignant neoplasms, the most frequent of which is squamous cell carcinoma. HNC forms the eighth most common cancer type and the incidence is increasing. However, survival has improved only moderately during the past decades. Currently, early diagnosis remains the mainstay for improving treatment outcomes in this patient population. Unfortunately, screening methods to allow early detection of HNC are not yet established. Therefore, many cases are still diagnosed at advanced stage, compromising outcomes. Exhaled breath analysis (EBA) is a diagnostic tool that has been recently introduced for many cancers. Breath analysis is non-invasive, cost-effective, time-saving, and can potentially be applied for cancer screening. Here, we provide a summary of the accumulated evidence on the feasibility of EBA in the diagnosis of HNC.

Gut Microbiota and Host Gene Mutations in Colorectal Cancer Patients and Controls of Iranian and Finnish Origin.

BACKGROUND/AIM: Gut microbiota plays an important role in colorectal cancer (CRC) and its composition in CRC patients can be influenced by ethnicity and tumour genomics. Herein, the aim was to study the possible associations of ethnicity and gene mutations with the gut microbiota in CRC patients. MATERIALS AND METHODS: Bacterial composition in stool samples of 83 CRC patients and 60 controls from Iran and Finland was studied by 16S rRNA gene sequencing. The association of gut microbiota composition with CRC, host mutations in KRAS, NRAS and TP53, and ethnicity analysed. RESULTS: Beta diversity analysis indicated significant differences between the Iranian and Finnish gut microbiota composition, in both controls and patients' groups. The Iranian controls had higher abundance of Prevotella and lower abundance of Bacteroides compared to the Finnish controls, while the Finnish patients had higher abundance of Clostridium compared to Iranian patients. Abundance of Ruminococcus was higher in patients compared to the controls. Higher abundances of Herbaspirillum, Catenibacterium and lower abundances of Barnesiella were associated with mutations in NRAS, TP53, and RAS respectively. CONCLUSION: A possible link of host gene mutations with gut bacterial composition is suggested.

Oncologic and Cosmetic Outcomes of Oncoplastic Breast Surgery in Locally Advanced Breast Cancer After Neoadjuvant Chemotherapy, Experience from a Developing Country.

Oncoplastic surgery (OPS) has emerged as a new approach for extending breast conserving surgery (BCS) possibilities, reducing both mastectomy and re-excision rates, while avoiding breast deformities. OPS is based upon the integration of plastic surgery techniques for immediate reshaping after wide excision for breast cancer. This is a prospective feasibility cohort study of oncoplastic breast surgery after neoadjuvant chemotherapy that was carried at the National Cancer Institute, Cairo University and included 70 patients. The primary outcome was the local recurrence rate. Secondary outcomes included survival and margins obtained as well as cosmetic outcomes. Survival analysis was performed. Oncoplastic breast surgery did not compromise oncologic safety in the patients included in the study. It even allowed wider margins of resection which could be associated with better oncologic outcomes. At the same time, it gave a better cosmetic outcome and therefore higher patient satisfaction. Oncoplastic breast surgery includes a wide spectrum of surgical techniques, ranging from the basic level I techniques in breast conserving surgery to the more complex procedures of level II which are broadly classified into volume replacement (therapeutic mammoplasty) and volume displacement procedures. We suggest that oncoplastic breast surgery techniques should be the standard of care in breast surgery. They are the basis for breast conserving surgery techniques in early breast cancer. In our experience, oncoplastic surgery is feasible in locally advanced tumours after downstaging with neoadjuvant chemotherapy without compromising the oncologic safety.

Hotspot Mutations Detectable by Next-generation Sequencing in Exhaled Breath Condensates from Patients with Lung Cancer.

BACKGROUND: Genetic alterations occurring in lung cancer are the basis for defining molecular subtypes and essential for targeted therapies. Exhaled breath condensate (EBC) is a form of non-invasive sample that, amongst components, contains DNA from pulmonary tissue. Next-generation sequencing (NGS) was herein used to analyze mutations in EBC from patients with lung cancer. MATERIALS AND METHODS: EBC was collected from 26 patients with cancer and 20 healthy controls. Amplicon-based sequencing using Ion Ampliseq Colon and Lung Cancer gene panel v2 was applied. RESULTS: The sequencing was successful in 17 patients and 20 controls. EBC from patients revealed 39 hotspot mutations occurring in: adenomatous polyposis coli (APC), v-raf murine sarcoma viral oncogene homolog B (BRAF), discoidin domain receptor tyrosine kinase 2 (DDR2), epidermal growth factor receptor (EGFR), erb-b2 receptor tyrosine kinase 4 (ERBB4), F-box and WD repeat domain containing 7 (FBXW7), fibroblast growth factor receptor 1 (FGFR1), FGFR3 (fibroblast growth factor receptor 3), Kirsten rat sarcoma viral oncogene homolog (KRAS), mitogen-activated protein kinase kinase 1 (MAP2K1), met proto-oncogene (MET), neuroblastoma RAS viral (v-ras) oncogene homolog (NRAS), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), phosphatase and tensin homolog (PTEN), ret proto-oncogene (RET), SMAD family member 4 (SMAD4), serine/threonine kinase 11 (STK11), and tumor protein p53 (TP53) genes. EBC from controls revealed 35 hotspot mutations. The average mutant allele fraction was higher in patients than controls. CONCLUSION: NGS can identify mutations in EBCs from patients with lung cancer. This could provide a promising non-invasive method for the assessment of gene mutations in lung cancer.

An ecological model of adaptation to displacement: individual, cultural and community factors affecting psychosocial adjustment among Syrian refugees in Jordan.

BACKGROUND: There is a need for ecological approaches to guide global mental health programmes that can appropriately address the personal, family, social and cultural needs of displaced populations. A transactional ecological model of adaptation to displacement was developed and applied to the case of Syrian refugees living in Jordan. METHODS: Syrian and Jordanian psychosocial workers (n = 29) supporting the Syrian refugee community in Jordan were interviewed in three waves (2013-2016). A grounded-theory approach was used to develop a model of key local concepts of distress. Emergent themes were compared with the ecological model, including the five ADAPT pillars identified by Silove (2013). RESULTS: The application of the ecological concept of niche construction demonstrated how the adaptive functions of a culturally significant concept of dignity (karama) are moderated by gender and displacement. This transactional concept brought to light the adaptive capacities of many Syrian women while highlighting the ways that stigma may restrict culturally sanctioned opportunities for others, in particular men. By examining responses to potentially traumatic events at the levels of individual, family/peers, society and culture, adaptive responses to environmental change can be included in the formulation of distress. The five ADAPT pillars showed congruence with the psychosocial needs reported in the community. CONCLUSIONS: The transactional concepts in this model can help clinicians working with displaced people to consider and formulate a broader range of causal factors than is commonly included in individualistic therapy approaches. Researchers may use this model to develop testable hypotheses.