Summary Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Here, we describe the genomic landscape of 496 PTCs. We observed a low frequency of somatic alterations (relative to other carcinomas) and extended the set of known PTC driver alterations to include EIF1AX, PPM1D and CHEK2 and diverse gene fusions. These discoveries reduced the fraction of PTC cases with unknown oncogenic driver from 25% to 3.5%. Combined analyses of genomic variants, gene expression, and methylation demonstrated that different driver groups lead to different pathologies with distinct signaling and differentiation characteristics. Similarly, we identified distinct molecular subgroups of BRAF-mutant tumors and multidimensional analyses highlighted a potential involvement of oncomiRs in less-differentiated subgroups. Our results propose a reclassification of thyroid cancers into molecular subtypes that better reflect their underlying signaling and differentiation properties, which has the potential to improve their pathological classification and better inform the management of the disease.

Highlights d 51 cell subsets in colon mucosa of 18 ulcerative colitis and 12 healthy individuals d M-like cells, inflammatory monocytes and fibroblasts, and CD8 + IL-17 + T cells expand in disease d Oncostatin M circuit in inflammatory monocytes and fibroblasts may affect drug response d Co-expression of genes within cells allows inference of causal genes across risk loci

Single nucleus RNA-seq (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but does not provide the throughput required to analyse many cells from complex tissues. Here, we develop DroNc-seq, massively parallel sNuc-Seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient and unbiased classification of cell types, paving the way for systematic charting of cell atlases.

Highlights d A single-cell bone marrow stromal cell atlas at steady-state and emergent leukemia d A portal for comparative cell and molecular analyses of bone marrow stromal cells d Distinction among putative niche cells and types of osteolineage differentiation d Leukemia remodeling of stroma to the disadvantage of normal hematopoietic cells

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genomes provide a rich new source of data for uncovering these subtypes but have proven difficult to compare as two tumors rarely share the same mutations. Here, we introduce a method called Network Based Stratification (NBS) which integrates somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies clear subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature which provides similar information in the absence of DNA sequence.