Rapid Phenotype-Driven Gene Sequencing with the NeoSeq Panel: A Diagnostic Tool for Critically Ill Newborns with Suspected Genetic Disease

New genomic sequencing techniques have shown considerable promise in the field of neonatology, increasing the diagnostic rate and reducing time to diagnosis. However, several obstacles have hindered the incorporation of this technology into routine clinical practice. We prospectively evaluated the diagnostic rate and diagnostic turnaround time achieved in newborns with suspected genetic diseases using a rapid phenotype-driven gene panel (NeoSeq) containing 1870 genes implicated in congenital malformations and neurological and metabolic disorders of early onset (<2 months of age). Of the 33 newborns recruited, a genomic diagnosis was established for 13 (39.4%) patients (median diagnostic turnaround time, 7.5 days), resulting in clinical management changes in 10 (76.9%) patients. An analysis of 12 previous prospective massive sequencing studies (whole genome (WGS), whole exome (WES), and clinical exome (CES) sequencing) in newborns admitted to neonatal intensive care units (NICUs) with suspected genetic disorders revealed a comparable median diagnostic rate (37.2%), but a higher median diagnostic turnaround time (22.3 days) than that obtained with NeoSeq. Our phenotype-driven gene panel, which is specific for genetic diseases in critically ill newborns is an affordable alternative to WGS and WES that offers comparable diagnostic efficacy, supporting its implementation as a first-tier genetic test in NICUs

Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling

BACKGROUND: Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data. METHODS: Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set. RESULTS: Two hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942). CONCLUSION: Our results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness

Oncogenic driver mutations predict outcome in a cohort of head and neck squamous cell carcinoma (HNSCC) patients within a clinical trial

234 diagnostic formalin-fixed paraffin-embedded (FFPE) blocks from homogeneously treated patients with locally advanced head and neck squamous cell carcinoma (HNSCC) within a multicentre phase III clinical trial were characterised. The mutational spectrum was examined by next generation sequencing in the 26 most frequent oncogenic drivers in cancer and correlated with treatment response and survival. Human papillomavirus (HPV) status was measured by p16INK4a immunohistochemistry in oropharyngeal tumours. Clinicopathological features and response to treatment were measured and compared with the sequencing results. The results indicated TP53 as the most mutated gene in locally advanced HNSCC. HPV-positive oropharyngeal tumours were less mutated than HPV-negative tumours in TP53 (p < 0.01). Mutational and HPV status influences patient survival, being mutated or HPV-negative tumours associated with poor overall survival (p < 0.05). No association was found between mutations and clinicopathological features. This study confirmed and expanded previously published genomic characterization data in HNSCC. Survival analysis showed that non-mutated HNSCC tumours associated with better prognosis and lack of mutations can be identified as an important biomarker in HNSCC. Frequent alterations in PI3K pathway in HPV-positive HNSCC could define a promising pathway for pharmacological intervention in this group of tumours

Programa Gallego de Atenci?n al Infarto Agudo de Miocardio. Protocolo de actuaci?n para pacientes con s?ndrome coronario agudo con elevaci?n del segmento ST en Galicia

A enfermidade coronaria sup?n un importante problema de sa?de p?blica debido ? s?a incidencia crecente e a que constit?e a principal causa de morte no mundo. no ?mbito da Comunidade Aut?noma de Galicia, p?xose en marcha en maio de 2005 o Programa Galego de Atenci?n ao Infarto Agudo de Miocardio (PROGALIAM). Este programa foi un dos primeiros en implantarse en Espa?a (s? por detr?s dos de Murcia e Navarra). Debido ao tempo transcorrido, viuse necesario adaptar o Progaliam do ano 2005 ?s novas e actuais evidencias, e ?s actuais recomendaci?ns das Gu?as de Pr?ctica Cl?nica. ? por iso, que na Direcci?n Xeral de Asistencia Sanitaria, constitu?use un grupo de traballo formado por cardi?logos intervencionistas das 7 ?reas sanitarias, as? como profesionais m?dicos de Atenci?n Primaria e Urxencias.La enfermedad coronaria supone un importante problema de salud p?blica debido a su incidente creciente y la que constituye la principal causa de muerte en el mundo. en el ?mbito de la Comunidad Aut?noma de Galicia, se puso en marcha en mayo de 2005 el Programa Gallego de Atenci?n al Infarto Agudo de Miocardio (PROGALIAM). Este programa fue uno de los primeros en implantarse en Espa?a (solo por detr?s de los de Murcia y Navarra). Debido al tiempo transcurrido, se vio necesario adaptar el Progaliam del a?o 2005 a las noticias y actuales evidencias, y a las actuales recomendaciones de las Gu?as de Pr?ctica Cl?nica. Es por eso, que en la Direcci?n General de Asistencia Sanitaria, se constituy? un grupo de trabajo formado por cardi?logos intervencionistas de las 7 ?reas sanitarias, as? como profesionales m?dicos de Atenci?n Primaria y Urgencias

LipoDDx: a mobile application for identification of rare lipodystrophy syndromes

BACKGROUND: Lipodystrophy syndromes are a group of disorders characterized by a loss of adipose tissue once other situations of nutritional deprivation or exacerbated catabolism have been ruled out. With the exception of the HIV-associated lipodystrophy, they have a very low prevalence, which together with their large phenotypic heterogeneity makes their identification difficult, even for endocrinologists and pediatricians. This leads to significant delays in diagnosis or even to misdiagnosis. Our group has developed an algorithm that identifies the more than 40 rare lipodystrophy subtypes described to date. This algorithm has been implemented in a free mobile application, LipoDDx(R). Our aim was to establish the effectiveness of LipoDDx(R). Forty clinical records of patients with a diagnosis of certainty of most lipodystrophy subtypes were analyzed, including subjects without lipodystrophy. The medical records, blinded for diagnosis, were evaluated by 13 physicians, 1 biochemist and 1 dentist. Each evaluator first gave his/her results based on his/her own criteria. Then, a second diagnosis was given using LipoDDx(R). The results were analysed based on a score table according to the complexity of each case and the prevalence of the disease. RESULTS: LipoDDx(R) provides a user-friendly environment, based on usually dichotomous questions or choice of clinical signs from drop-down menus. The final result provided by this app for a particular case can be a low/high probability of suffering a particular lipodystrophy subtype. Without using LipoDDx(R) the success rate was 17 +/- 20%, while with LipoDDx(R) the success rate was 79 +/- 20% (p < 0.01). CONCLUSIONS: LipoDDx(R) is a free app that enables the identification of subtypes of rare lipodystrophies, which in this small cohort has around 80% effectiveness, which will be of help to doctors who are not experts in this field. However, it will be necessary to analyze more cases in order to obtain a more accurate efficiency value

Genome-wide association analysis of dementia and its clinical endophenotypes reveal novel loci associated with Alzheimer's disease and three causality networks: The GR@ACE project

INTRODUCTION: Large variability among Alzheimer's disease (AD) cases might impact genetic discoveries and complicate dissection of underlying biological pathways. METHODS: Genome Research at Fundacio ACE (GR@ACE) is a genome-wide study of dementia and its clinical endophenotypes, defined based on AD's clinical certainty and vascular burden. We assessed the impact of known AD loci across endophenotypes to generate loci categories. We incorporated gene coexpression data and conducted pathway analysis per category. Finally, to evaluate the effect of heterogeneity in genetic studies, GR@ACE series were meta-analyzed with additional genome-wide association study data sets. RESULTS: We classified known AD loci into three categories, which might reflect the disease clinical heterogeneity. Vascular processes were only detected as a causal mechanism in probable AD. The meta-analysis strategy revealed the ANKRD31-rs4704171 and NDUFAF6-rs10098778 and confirmed SCIMP-rs7225151 and CD33-rs3865444. DISCUSSION: The regulation of vasculature is a prominent causal component of probable AD. GR@ACE meta-analysis revealed novel AD genetic signals, strongly driven by the presence of clinical heterogeneity in the AD series