Mean first passage time analysis reveals rate-limiting steps, parallel pathways and dead ends in a simple model of protein folding

We have analyzed dynamics on the complex free energy landscape of protein folding in the FOLD-X model, by calculating for each state of the system the mean first passage time to the folded state. The resulting kinetic map of the folding process shows that it proceeds in jumps between well-defined, local free energy minima. Closer analysis of the different local minima allows us to reveal secondary, parallel pathways as well as dead ends.Comment: 7 page

Prediction of metastatic pheochromocytoma and paraganglioma:a machine learning modelling study using data from a cross-sectional cohort

BACKGROUND: Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field.METHODS: In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets.FINDINGS: Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p&lt;0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%.INTERPRETATION: Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up.FUNDING: Deutsche Forschungsgemeinschaft.</p

Prediction of metastatic pheochromocytoma and paraganglioma:a machine learning modelling study using data from a cross-sectional cohort

BACKGROUND: Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field.METHODS: In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets.FINDINGS: Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p&lt;0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%.INTERPRETATION: Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up.FUNDING: Deutsche Forschungsgemeinschaft.</p

Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross-sectional cohort

BACKGROUND Pheochromocytomas and paragangliomas have up to a 20% rate of metastatic disease that cannot be reliably predicted. This study prospectively assessed whether the dopamine metabolite, methoxytyramine, might predict metastatic disease, whether predictions might be improved using machine learning models that incorporate other features, and how machine learning-based predictions compare with predictions made by specialists in the field. METHODS In this machine learning modelling study, we used cross-sectional cohort data from the PMT trial, based in Germany, Poland, and the Netherlands, to prospectively examine the utility of methoxytyramine to predict metastatic disease in 267 patients with pheochromocytoma or paraganglioma and positive biochemical test results at initial screening. Another retrospective dataset of 493 patients with these tumors enrolled under clinical protocols at National Institutes of Health (00-CH-0093) and the Netherlands (PRESCRIPT trial) was used to train and validate machine learning models according to selections of additional features. The best performing machine learning models were then externally validated using data for all patients in the PMT trial. For comparison, 12 specialists provided predictions of metastatic disease using data from the training and external validation datasets. FINDINGS Prospective predictions indicated that plasma methoxytyramine could identify metastatic disease at sensitivities of 52% and specificities of 85%. The best performing machine learning model was based on an ensemble tree classifier algorithm that used nine features: plasma methoxytyramine, metanephrine, normetanephrine, age, sex, previous history of pheochromocytoma or paraganglioma, location and size of primary tumours, and presence of multifocal disease. This model had an area under the receiver operating characteristic curve of 0·942 (95% CI 0·894-0·969) that was larger (p<0·0001) than that of the best performing specialist before (0·815, 0·778-0·853) and after (0·812, 0·781-0·854) provision of SDHB variant data. Sensitivity for prediction of metastatic disease in the external validation cohort reached 83% at a specificity of 92%. INTERPRETATION Although methoxytyramine has some utility for prediction of metastatic pheochromocytomas and paragangliomas, sensitivity is limited. Predictive value is considerably enhanced with machine learning models that incorporate our nine recommended features. Our final model provides a preoperative approach to predict metastases in patients with pheochromocytomas and paragangliomas, and thereby guide individualised patient management and follow-up. FUNDING Deutsche Forschungsgemeinschaft

Adjuvant mitotane versus surveillance in low-grade, localised adrenocortical carcinoma (ADIUVO): an international, multicentre, open-label, randomised, phase 3 trial and observational study

BACKGROUND: Adjuvant treatment with mitotane is commonly used after resection of adrenocortical carcinoma; however, treatment remains controversial, particularly if risk of recurrence is not high. We aimed to assess the efficacy and safety of adjuvant mitotane compared with surveillance alone following complete tumour resection in patients with adrenocortical carcinoma considered to be at low to intermediate risk of recurrence. METHODS: ADIUVO was a multicentre, open-label, parallel, randomised, phase 3 trial done in 23 centres across seven countries. Patients aged 18 years or older with adrenocortical carcinoma and low to intermediate risk of recurrence (R0, stage I-III, and Ki67 ≤10%) were randomly assigned to adjuvant oral mitotane two or three times daily (the dose was adjusted by the local investigator with the target of reaching and maintaining plasma mitotane concentrations of 14-20 mg/L) for 2 years or surveillance alone. All consecutive patients at 14 study centres fulfilling the eligibility criteria of the ADIUVO trial who refused randomisation and agreed on data collection via the European Network for the Study of Adrenal Tumors adrenocortical carcinoma registry were included prospectively in the ADIUVO Observational study. The primary endpoint was recurrence-free survival, defined as the time from randomisation to the first radiological evidence of recurrence or death from any cause (whichever occurred first), assessed in all randomly assigned patients by intention to treat. Overall survival, defined as time from the date of randomisation to the date of death from any cause, was a secondary endpoint analysed by intention to treat in all randomly assigned patients. Safety was assessed in all patients who adhered to the assigned regimen, which was defined by taking at least one tablet of mitotane in the mitotane group and no mitotane at all in the surveillance group. The ADIUVO trial is registered with ClinicalTrials.gov, NCT00777244, and is now complete. FINDINGS: Between Oct 23, 2008, and Dec 27, 2018, 45 patients were randomly assigned to mitotane and 46 to surveillance alone. Because the study was discontinued prematurely, 5-year recurrence-free and overall survival are reported instead of recurrence-free and overall survival as defined in the protocol. 5-year recurrence-free survival was 79% (95% CI 67-94) in the mitotane group and 75% (63-90) in the surveillance group (hazard ratio 0·74 [95% CI 0·30-1·85]). Two people in the mitotane group and five people in the surveillance group died, and 5-year overall survival was not significantly different (95% [95% CI 89-100] in the mitotane group and 86% [74-100] in the surveillance group). All 42 patients who received mitotane had adverse events, and eight (19%) discontinued treatment. There were no grade 4 adverse events or treatment-related deaths. INTERPRETATION: Adjuvant mitotane might not be indicated in patients with low-grade, localised adrenocortical carcinoma considering the relatively good prognosis of these patients, and no significant improvement in recurrence-free survival and treatment-associated toxicity in the mitotane group. However, the study was discontinued prematurely due to slow recruitment and cannot rule out an efficacy of treatment. FUNDING: AIFA, ENSAT Cancer Health F2-2010-259735 programme, Deutsche Forschungsgemeinschaft, Cancer Research UK, and the French Ministry of Health

Clinical Consensus Guideline on the Management of Phaeochromocytoma and Paraganglioma in Patients Harbouring Germline SDHD Pathogenic Variants

Patients with germline SDHD pathogenic variants (encoding succinate dehydrogenase subunit D; ie, paraganglioma 1 syndrome) are predominantly affected by head and neck paragangliomas, which, in almost 20% of patients, might coexist with paragangliomas arising from other locations (eg, adrenal medulla, para-aortic, cardiac or thoracic, and pelvic). Given the higher risk of tumour multifocality and bilaterality for phaeochromocytomas and paragangliomas (PPGLs) because of SDHD pathogenic variants than for their sporadic and other genotypic counterparts, the management of patients with SDHD PPGLs is clinically complex in terms of imaging, treatment, and management options. Furthermore, locally aggressive disease can be discovered at a young age or late in the disease course, which presents challenges in balancing surgical intervention with various medical and radiotherapeutic approaches. The axiom-first, do no harm-should always be considered and an initial period of observation (ie, watchful waiting) is often appropriate to characterise tumour behaviour in patients with these pathogenic variants. These patients should be referred to specialised high-volume medical centres. This consensus guideline aims to help physicians with the clinical decision-making process when caring for patients with SDHD PPGLs