Breast Cancer Cell Subtypes Display Different Metabolic Phenotypes That Correlate with Their Clinical Classification

Metabolic reprogramming of cancer cells represents an orchestrated network of evolving molecular and functional adaptations during oncogenic progression. In particular, how metabolic reprogramming is orchestrated in breast cancer and its decisive role in the oncogenic process and tumor evolving adaptations are well consolidated at the molecular level. Nevertheless, potential correlations between functional metabolic features and breast cancer clinical classification still represent issues that have not been fully studied to date. Accordingly, we aimed to investigate whether breast cancer cell models representative of each clinical subtype might display different metabolic phenotypes that correlate with current clinical classifications. In the present work, functional metabolic profiling was performed for breast cancer cell models representative of each clinical subtype based on the combination of enzyme inhibitors for key metabolic pathways, and isotope-labeled tracing dynamic analysis. The results indicated the main metabolic phenotypes, so-called ‘metabophenotypes’, in terms of their dependency on glycolytic metabolism or their reliance on mitochondrial oxidative metabolism. The results showed that breast cancer cell subtypes display different metabophenotypes. Importantly, these metabophenotypes are clearly correlated with the current clinical classifications.

Management of burnout among the staff of primary care centres in Spain during the pandemic caused by the SARS-CoV-2

Background The provision of healthcare during the pandemic caused by the SARS-CoV-2 virus represented a challenge for the management of the resources in the primary care centres. We proposed assessing burnout among the staff of those centres and identifying factors that contributed to its appearance and those that limited it. Methods An observational study which, by means of anonymous questionnaires, collected information about: (i) demographic variables; (ii) the characteristics of each position; (iii) the measures implemented by the medical decision-makers in order to provide care during the pandemic; and (iv) the Burnout Clinical Subtype Questionnaire (BCSQ-36). We performed a descriptive analysis of the burnout mentioned by the staff, and, by means of a multivariate analysis, we identified the factors which influenced it. Using logit models, we analysed whether receiving specific training in COVID-19, feeling involved in decision-making processes, and/or working within different healthcare systems had effects on the development of burnout. Results We analysed the replies of 252 employees of primary care centres in Spain with an average age of 45 (SD = 15.7) and 22 (SD = 11.4) years of experience. 68% of the participants (n = 173) indicated burnout of the frenetic subtype. 79% (n = 200) of the employees had high scores in at least one burnout subtype, and 62% (n = 156) in at least two. Women older than 45 had a lower probability of suffering burnout. Receiving specific training (OR = 0.28; CI95%: 0.11–0.73) and feeling involved in decision-making (OR = 0.32; CI95%:0.15–0.70) each reduced the probability of developing burnout. Working in a different department increased the likelihood of developing burnout of at least one clinical subtype (OR = 2.85; CI95%: 1.38–5.86). Conclusions The staff in primary care centres have developed high levels of burnout. Participation in decision-making and receiving specific training are revealed as factors that protect against the development of burnout. The measures taken to contain the adverse effects of a heavy workload appear to be insufficient. Certain factors that were not observed, but which are related to decisions taken by the healthcare management, appear to have had an effect on the development of some burnout subtypes.

Occult Nodal Metastasis Defined by PET-CT Identifies a Unique Clinical Subtype of Lung Cancer: A Retrospective Multicenter Study

PurposeTo investigate the surgical prognosis and efficacy of adjuvant therapy in non-small cell lung cancer (NSCLC) with occult lymph node metastasis (ONM) defined by positron emission tomography-computed tomography (PET-CT).MethodsA total of 3537 NSCLC patients receiving surgical resection were included in this study. The prognosis between patients with ONM and evident nodal metastasis, ONM patients with and without adjuvant therapy were compared, respectively.ResultsONM was associated with significantly better prognosis than evident nodal metastasis whether for patients with N1 (5-year OS: 56.8% versus 52.3%, adjusted p value=0.267; 5-year RFS: 44.7% versus 33.2%, adjusted p value=0.031) or N2 metastasis (5-year OS: 42.8% versus 32.3%, adjusted p value=0.010; 5-year RFS: 31.3% versus 21.6%, adjusted p value=0.025). In ONM population, patients receiving adjuvant therapy yielded better prognosis comparing to those without adjuvant therapy (5-year OS: 50.1% versus 33.5%, adjusted p value<0.001; 5-year RFS: 38.4% versus 22.1%, adjusted p value<0.001). ConclusionsONM defined by PET-CT identifies a unique clinical subtype of lung cancer, ONM is a favorable prognostic factor whether for pathological N1 or N2 NSCLC and adjuvant therapy could provide additional survival benefits for ONM patients.

Survival following locoregional recurrence in breast cancer by clinical subtype.

543 Background: We sought to explore the impact of locoregional recurrence (LRR) on survival in breast cancer (BC) patients in British Columbia treated in the modern era. Methods: A retrospective cohort study design identified patients diagnosed with stage I-III BC from 04/2005-12/2013 treated with surgery and who had a subsequent LRR. Exclusions were death or distant metastasis within 120 days of LRR, bilateral previous/synchronous BC, and other invasive cancers. After LRR, overall survival (OS) and factors associated with OS, including clinical subtype and adjuvant therapy (AdTx), were examined. We defined clinical subtypes as: Luminal (Lum) A-estrogen receptor (ER) and progesterone receptor (PR) positive, HER2 negative, and grade 1 or 2; Lum B-as Lum A but grade 3, or as Lum A but only one of ER or PR positive; triple negative (TNBC)-ER and PR and HER2 negative; and HER2 positive (with any ER, PR). In the absence of earlier LRR, we defined adequate AdTx as: (a) TNBC: >=50% of planned chemotherapy (Chx), (b) HER2 positive: >=50% of planned Chx and >=8 cycles of anti-HER2 therapy, (c) Lum A, B: >=4 years of endocrine therapy and (d) after partial mastectomy or positive final margins: >=50% of radiation therapy dosage. Results: The final cohort had 492 patients with a median follow-up of 7.2 years from LRR and 11.8 years from diagnosis. LRR was local in 69.3% (n=341) and regional +/- local in 30.7% (n=151). Compared with local only, regional recurrences were associated with higher T and N stage, grade, and Lum status (p<=0.01). Biomarkers were re-evaluated at LRR in 82% and changed from initial diagnosis in 32% of those tested: ER expression 3.8% gain, 6.1% loss; PR expression 9.1% gain, 15.1% loss; HER2 overexpression 3.7% gain and 4.8% loss. Over half of patients (n=255, 52%) did not receive adequate AdTx, either by choice or recommendation. A similar proportion with local vs. regional recurrence had inadequate AdTx. Time to death from 1st LRR did not vary significantly between local vs. regional recurrences (median 2.7 years). OS after LRR was lowest in TNBC (median 3.1 years, 24.2% 10-year OS) and longest in Lum A (median not reached, 64.7% 10-year OS) (Table). Conclusions: Our data provide rates of OS after LRR in the era of modern adjuvant therapy. OS after LRR varied by clinical subtype, with TNBC faring the worst, and Lum A the best. Over half had not received adequate AdTx. Despite similar treatment options, OS after LRR was significantly longer for Lum A than B subtypes, underscoring the need for therapy tailored to biology. OS was low in all other subtypes, emphasizing the importance of avoiding LRR.[Table: see text]

Multiple sclerosis: prevalence of the ‘central vein’ sign in white matter lesions on gadolinium-enhanced susceptibility-weighted images

Aims To evaluate prospectively whether an intravenous gadolinium injection could improve the detection of the central vein sign on susceptibility-weighted imaging sequences obtained with a 1.5 T magnetic resonance scanner in patients with multiple sclerosis compared to unenhanced susceptibility-weighted images. Materials and methods This prospective, institution review board-approved study included 19 patients affected by multiple sclerosis (six men; 13 women; mean age 40.8 years, range 20–74 years). Patients had the relapsing–remitting clinical subtype in 95% of cases, and only one (5%) patient had the primary progressive clinical subtype of multiple sclerosis. T2-weighted images, fluid-attenuated inversion recovery images, unenhanced and contrast-enhanced susceptibility-weighted images were evaluated in consensus by two neuroradiologists for the presence of the central vein sign. The readers were blinded to magnetic resonance imaging reports, clinical information, the presence and the localisation of focal hyperintense white matter lesions. Any discordance between readers was resolved through a joint review of the recorded images with an additional neuroradiologist. Results A total of 317 multiple sclerosis lesions were analysed. The central vein sign had a higher prevalence detection rate on gadolinium-enhanced susceptibility-weighted images (272 of 317 lesions, 86%) compared to unenhanced susceptibility-weighted images (172 of 317 lesions, 54%). Conclusion Gadolinium-enhanced susceptibility-weighted imaging improves the detection rate of the central vein sign in multiple sclerosis lesions.

Identification and prediction of ALS subgroups using machine learning

Background The disease entity known as amyotrophic lateral sclerosis (ALS) is now known to represent a collection of overlapping syndromes. A better understanding of this heterogeneity and the ability to distinguish ALS subtypes would improve the clinical care of patients and enhance our understanding of the disease. Subtype profiles could be incorporated into the clinical trial design to improve our ability to detect a therapeutic effect. A variety of classification systems have been proposed over the years based on empirical observations, but it is unclear to what extent they genuinely reflect ALS population substructure. Methods We applied machine learning algorithms to a prospective, population-based cohort consisting of 2,858 Italian patients diagnosed with ALS for whom detailed clinical phenotype data were available. We replicated our findings in an independent population-based cohort of 1,097 Italian ALS patients. Findings We found that semi-supervised machine learning based on UMAP applied to the output of a multi-layered perceptron neural network produced the optimum clustering of the ALS patients in the discovery cohort. These clusters roughly corresponded to the six clinical subtypes defined by the Chiò classification system (bulbar ALS, respiratory ALS, flail arm ALS, classical ALS, pyramidal ALS, and flail leg ALS). The same clusters were identified in the replication cohort. A supervised learning approach based on ensemble learning identified twelve clinical parameters that predicted ALS clinical subtype with high accuracy (area under the curve = 0.94). Interpretation Our data-driven study provides insight into the ALS population's substructure and demonstrates that the Chiò classification system robustly identifies ALS subtypes. We provide an interactive website (https://share.streamlit.io/anant-dadu/machinelearningforals/main) so that clinical researchers can predict the clinical subtype of an ALS patient based on a small number of clinical parameters. Funding National Institute on Aging and the Italian Ministry of Health.