HER2 expression identifies dynamic functional states within circulating breast cancer cells
“Here we analyse circulating tumour cells from 19 women with ER+/HER2−primary tumours, 84% of whom had acquired circulating tumour cells expressing HER2.” “HER2+ and HER2− circulating tumour cells interconvert spontaneously, with cells of one phenotype producing daughters of the opposite within four cell doublings. Although HER2+ and HER2− circulating tumour cells have comparable tumour initiating potential, differential proliferation favours the HER2+state, while oxidative stress or cytotoxic chemotherapy enhances transition to the HER2−phenotype.” “we have used primary and cultured CTCs from patients with ER+/HER2− breast cancer who developed metastatic multidrug-resistant disease to show that coexisting distinct HER2+ and HER2− tumour cell subpopulations may interconvert, with striking consequences for disease progression and drug response. The comparable tumour initiating potential and similar expression of stem cell marker ALDH1 in HER2+ and HER2− CTCs suggest underlying tumour cell plasticity in these advanced patient-derived breast CTC lines, rather than a hierarchical cancer stem-cell model as described in drug-resistant subpopulations within established breast cancer cell lines.” “we propose a dynamic model, in which the equilibrium between HER2+ and HER2− cells within a heterogeneous tumour population is driven by spontaneous interconversion between these phenotypes, with the more rapidly proliferating HER2+ cells prevalent under baseline conditions, and environmental or therapy-induced stress enhancing conversion to the more resistant HER2− phenotype.”
[PMID:27557938] [Genome Biology]
MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data
“Mutation calling using a Markov Substitution model for Evolution, a novel approach for modeling the evolution of the allelic composition of the tumor and normal tissue at each reference base. MuSE adopts a sample-specific error model that reflects the underlying tumor heterogeneity to greatly improve the overall accuracy. We demonstrate the accuracy of MuSE in calling subclonal mutations in the context of large-scale tumor sequencing projects using whole exome and whole genome sequencing.” David Wheeler is the co-author.
[PMID:27527408] [Nature Communications]
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
“we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors”. Work from Daniel Rubin & Michael Snyder.
Analysis of protein-coding genetic variation in 60,706 humans
“the aggregation and analysis of high-quality exome (protein-coding region) DNA sequence data for 60,706 individuals of diverse ancestries generated as part of the Exome Aggregation Consortium (ExAC).” “identifying 3,230 genes with near-complete depletion of predicted protein-truncating variants, with 72% of these genes having no currently established human disease phenotype.”
[PMID:27560171] [Nature Protocols]
Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown
“This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts.” Work from Steven Salzberg.
[PMID:27526321] [Nature Genetics]
Punctuated copy number evolution and clonal stasis in triple-negative breast cancer
“We sequenced 1,000 single cells from tumors in 12 patients and identified 1–3 major clonal subpopulations in each tumor that shared a common evolutionary lineage. For each tumor, we also identified a minor subpopulation of non-clonal cells that were classified as metastable, pseudodiploid or chromazemic. Phylogenetic analysis and mathematical modeling suggest that these data are unlikely to be explained by the gradual accumulation of copy number events over time. In contrast, our data challenge the paradigm of gradual evolution, showing that the majority of copy number aberrations are acquired at the earliest stages of tumor evolution, in short punctuated bursts, followed by stable clonal expansions that form the tumor mass.” Work from Nicholas Navin.
EnhancerAtlas: a resource for enhancer annotation and analysis in 105 human cell/tissue types
“an atlas of 2,534,123 enhancers for 105 cell/tissue types. A consensus enhancer annotation was obtained for each cell by summation of independent experimental datasets with the relative weights derived from a cross-validation approach.”