[] [Science]

Phenotype risk scores identify patients with unrecognized Mendelian disease patterns

“We describe an approach that aggregates phenotypes on the basis of patterns described by Mendelian diseases. We mapped the clinical features of 1204 Mendelian diseases into phenotypes captured from the electronic health record (EHR) and summarized this evidence as phenotype risk scores (PheRSs). In an initial validation, PheRS distinguished cases and controls of five Mendelian diseases.”


Childhood Cancer Genomes

[PMID: 29489754] [Nature]

The landscape of genomic alterations across childhood cancers

“a pan-cancer cohort including 961 tumours from children, adolescents, and young adults, comprising 24 distinct molecular types of cancer.”

[PMID: 29489755] [Nature]

Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours

“a pan-cancer study of somatic alterations, including single nucleotide variants, small insertions or deletions, structural variations, copy number alterations, gene fusions and internal tandem duplications in 1,699 paediatric leukaemias and solid tumours across six histotypes, with whole-genome, whole-exome and transcriptome sequencing data processed under a uniform analytical framework. We report 142 driver genes in paediatric cancers, of which only 45% match those found in adult pan-cancer studies; copy number alterations and structural variants constituted the majority (62%) of events.”

Human Noncoding Genome

[PMID: 29483654] [Nature Genetics]

The human noncoding genome defined by genetic diversity

“11,257 whole-genome sequences and 16,384 heptamers (7-nt motifs) to build a map of sequence constraint for the human species. ” “defined the context-dependent tolerance score (CDTS) as the absolute difference of the observed variation from the expected variation. “


Dearly Drivers of Breast Cancer Mets

[PMID: 29480819] [Journal of Clinical Investigation]

Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer

“matched primary and metastatic breast cancers from 16 individuals and performed RNA-seq and DNA whole-exome sequencing on the primary tumor, 67 matched metastases (2–7 per patient), and a matched normal tissue comparator for each patient.” “predicted metastatic drivers by integrating known protein-protein networks with gene expression and DNA-seq data and the clonal evolution of metastasis within each patient.” “most genetic drivers were DNA copy number changes, the TP53 mutation was a recurrent founding mutation regardless of subtype, and that multiclonal seeding of metastases was frequent and occurred in multiple subtypes. Genetic drivers unique to metastasis were identified as somatic mutations in the estrogen and androgen receptor genes… most metastatic drivers are established in the primary tumor, despite the substantial heterogeneity seen in the metastases.” Work from Charles Perou.


Integrative Omics

[PMID: 29479082] [Nature Reviews Genetics]

Integrative omics for health and disease

Review from Michael Snyder.



[PMID: 29481549] [Nature Methods]

Bias, robustness and scalability in single-cell differential expression analysis

“conquer, a repository of consistently processed, analysis-ready public scRNA-seq data sets that is aimed at simplifying method evaluation and reanalysis of published results.” “Currently, conquer contains 36 data sets: 31 generated with full-length protocols and 5 with 3′-end sequencing (UMI) protocols.”


Deep Learning for Medical Diagnoses

[PMID: 29474911] [Cell]

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

“demonstrate the general applicability of our AI system for diagnosis of pediatric pneumonia using chest X-ray images. “