Dynamic clonal progression in xenografts of acute lymphoblastic leukemia with intrachromosomal amplification of chromosome 21

Intrachromosomal amplification of chromosome 21 is a heterogeneous chromosomal rearrangement occurring in 2% of childhood precursor B-cell acute lymphoblastic leukemia. There are no cell lines with iAMP21 and these abnormalities are too complex to faithfully engineer in animal models. As a resource for future functional and pre-clinical studies, we have created xenografts from intrachromosomal amplification of chromosome 21 leukemia patient blasts and characterised them by in-vivo and ex-vivo luminescent imaging, FLOW immunophenotyping, and histological and ultrastructural analysis of bone marrow and the central nervous system. Investigation of up to three generations of xenografts revealed phenotypic evolution, branching genomic architecture and, compared with other B-cell acute lymphoblastic leukemia genetic subtypes, greater clonal diversity of leukemia initiating cells. In support of intrachromosomal amplification of chromosome 21 as a primary genetic abnormality, it was always retained through generations of xenografts, although we also observed the first example of structural evolution of this rearrangement. Clonal segregation in xenografts revealed convergent evolution of different secondary genomic abnormalities implicating several known tumour suppressor genes and a region, containing the B-cell adaptor, PIK3AP1, and nuclear receptor co-repressor, LCOR, in the progression of B-ALL. Tracking of mutations in patients and derived xenografts provided evidence for co-operation between abnormalities activating the RAS pathway in B-ALL and for their aggressive clonal expansion in the xeno-environment. Bi-allelic loss of the CDKN2A/B locus was recurrently maintained or emergent in xenografts and also strongly selected as RNA sequencing demonstrated a complete absence of reads for genes associated with the deletions

Road traffic crash circumstances and consequences among young unlicensed drivers: A Swedish cohort study on socioeconomic disparities

<p>Abstract</p> <p>Background</p> <p>Young car drivers run a higher risk of road traffic crash and injury not only because of their lack of experience but also because of their young age and their greater propensity for adopting unsafe driving practices. Also, low family socioeconomic position increases the risk of crash and of severe crash in particular. Whether this holds true for young unlicensed drivers as well is not known. Increasing attention is being drawn to the prevalence and practice of unlicensed driving among young people as an important contributor to road traffic fatalities.</p> <p>Methods</p> <p>This is a population-based cohort study linking Swedish national register data for a cohort of 1 616 621 individuals born between 1977 and 1991. Crash circumstances for first-time road traffic crash (RTC) were compared considering licensed and unlicensed drivers. The socioeconomic distribution of injury was assessed considering household socioeconomic position, social welfare benefits, and level of urbanicity of the living area. The main outcome measure is relative risk of RTC.</p> <p>Results</p> <p>RTCs involving unlicensed drivers were over-represented among male drivers, suspected impaired drivers, severe injuries, crashes occurring in higher speed limit areas, and in fair road conditions. Unlicensed drivers from families in a lower socioeconomic position showed increased relative risks for RTC in the range of 1.75 to 3.25. Those living in rural areas had an increased relative risk for a severe RTC of 3.29 (95% CI 2.47 - 4.39) compared to those living in metropolitan areas.</p> <p>Conclusions</p> <p>At the time of the crash, young unlicensed drivers display more risky driving practices than their licensed counterparts. Just as licensed drivers, unlicensed young people from low socioeconomic positions are over-represented in the most severe injury crashes. Whether the mechanisms lying behind those similarities compare between these groups remains to be determined.</p

Determinants of disease-specific survival in patients with and without metastatic pheochromocytoma and paraganglioma

BACKGROUND: Pheochromocytomas and paragangliomas (PPGLs) have a heterogeneous prognosis, the basis of which remains unclear. We, therefore, assessed disease-specific survival (DSS) and potential predictors of progressive disease in patients with PPGLs and head/neck paragangliomas (HNPGLs) according to the presence or absence of metastases. METHODS: This retrospective study included 582 patients with PPGLs and 57 with HNPGLs. DSS was assessed according to age, location and size of tumours, recurrent/metastatic disease, genetics, plasma metanephrines and methoxytyramine. RESULTS: Among all patients with PPGLs, multivariable analysis indicated that apart from older age (HR = 5.4, CI = 2.93-10.29, P < 0.0001) and presence of metastases (HR = 4.8, CI = 2.41-9.94, P < 0.0001), shorter DSS was also associated with extra-adrenal tumour location (HR = 2.6, CI = 1.32-5.23, P = 0.0007) and higher plasma methoxytyramine (HR = 1.8, CI = 1.11-2.85, P = 0.0170) and normetanephrine (HR = 1.8, CI = 1.12-2.91, P = 0.0160). Among patients with HNPGLs, those with metastases presented with longer DSS compared to patients with metastatic PPGLs (33.4 versus 20.2 years, P < 0.0001) and only plasma methoxytyramine (HR = 13, CI = 1.35-148, P = 0.0380) was an independent predictor of DSS. For patients with metastatic PPGLs, multivariable analysis revealed that apart from older age (HR = 6.2, CI = 3.20-12.20, P < 0.0001), shorter DSS was associated with the presence of synchronous metastases (HR = 4.9, CI = 2.78-8.80, P < 0.0001), higher plasma methoxytyramine (HR = 2.4, CI = 1.44-4.14, P = 0.0010) and extensive metastatic burden (HR = 2.1, CI = 1.07-3.79, P = 0.0290). CONCLUSIONS: DSS among patients with PPGLs/HNPGLs relates to several presentations of the disease that may provide prognostic markers. In particular, the independent associations of higher methoxytyramine with shorter DSS in patients with HNPGLs and metastatic PPGLs suggest the utility of this biomarker to guide individualized management and follow-up strategies in affected patients

Responses to systemic therapy in metastatic pheochromocytoma/paraganglioma: a retrospective multicenter cohort study

OBJECTIVE The therapeutic options for metastatic pheochromocytomas/paragangliomas (mPPGLs) include chemotherapy with cyclophosphamide/vincristine/dacarbazine (CVD), temozolomide monotherapy, radionuclide therapies, and tyrosine kinase inhibitors such as sunitinib. The objective of this multicenter retrospective study was to evaluate and compare the responses of mPPGLs including those with pathogenic variants in succinate dehydrogenase subunit B (SDHB), to different systemic treatments. DESIGN This is a retrospective analysis of treatment responses of mPPGL patients (n = 74) to systemic therapies. METHODS Patients with mPPGLs treated at 6 specialized national centers were selected based on participation in the ENSAT registry. Survival until detected progression (SDP) and disease-control rates (DCRs) at 3 months were evaluated based on imaging reports. RESULTS For the group of patients with progressive disease at baseline (83.8% of 74 patients), the DCR with first-line CVD chemotherapy was 75.0% (n = 4, SDP 11 months; SDHB [n = 1]: DCR 100%, SDP 30 months), with somatostatin peptide receptor-based radionuclide therapy (PPRT) 85.7% (n = 21, SDP 17 months; SDHB [n = 10]: DCR 100%, SDP 14 months), with 131I-meta-iodobenzylguanidine (131I-MIBG) 82.6% (n = 23, SDP 43 months; SDHB [n = 4]: DCR 100%, SDP 24 months), with sunitinib 100% (n = 7, SDP 18 months; SDHB [n = 3]: DCR 100%, SDP 18 months), and with somatostatin analogs 100% (n = 4, SDP not reached). The DCR with temozolomide as second-line therapy was 60.0% (n = 5, SDP 10 months; SDHB [n = 4]: DCR 75%, SDP 10 months). CONCLUSIONS We demonstrate in a real-life clinical setting that all current therapies show reasonable efficacy in preventing disease progression, and this is equally true for patients with germline SDHB mutations

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

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