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.

The pan-cancer analysis of whole genomes The expansion of whole-genome sequencing studies from individual ICGC and TCGA working groups presented the opportunity to undertake a meta-analysis of genomic features across tumour types. To achieve this, the PCAWG Consortium was established. A Technical Working Group implemented the informatics analyses by aggregating the raw sequencing data from different working groups that studied individual tumour types, aligning the sequences to the human genome and delivering a set of high-quality somatic mutation calls for downstream analysis (Extended Data Fig. 1). Given the recent meta-analysis

Transcription of long noncoding RNAs (lncRNAs) within gene regulatory elements can modulate gene activity in response to external stimuli, but the scope and functions of such activity are not known. Here we use an ultra-high density array that tiles the promoters of 56 cell cycle genes to interrogate 108 samples representing diverse perturbations. We identify 216 transcribed regions that encode putative lncRNAs--many with RT-PCR-validated periodic expression during the cell cycle, show altered expression in human cancers, and are regulated in expression by specific oncogenic stimuli, stem cell differentiation, or DNA damage. DNA damage induces five lncRNAs from the CDKN1A promoter, and one such lncRNA, named PANDA, is induced in a p53- dependent manner. PANDA interacts with the transcription factor NF-YA to limit expression of pro-apoptotic genes; PANDA depletion markedly sensitized human fibroblasts to apoptosis by doxorubicin. These findings suggest potentially widespread roles for promoter lncRNAs in cell growth control.

In patients with PTC, BRAF mutation is associated with poorer clinicopathological outcomes and independently predicts recurrence. Therefore, BRAF mutation may be a useful molecular marker to assist in risk stratification for patients with PTC.

Expression of 14-3-3 () is induced in response to DNA damage, and causes cells to arrest in G2. By SAGE (serial analysis of gene expression) analysis, we identified as a gene whose expression is 7-fold lower in breast carcinoma cells than in normal breast epithelium. We verified this finding by Northern blot analysis. Remarkably, mRNA was undetectable in 45 of 48 primary breast carcinomas. Genetic alterations at such as loss of heterozygosity were rare (1͞20 informative cases), and no mutations were detected (0͞34). On the other hand, hypermethylation of CpG islands in the gene was detected in 91% (75͞82) of breast tumors and was associated with lack of gene expression. Hypermethylation of is functionally important, because treatment of -non-expressing breast cancer cell lines with the drug 5-aza-2-deoxycytidine resulted in demethylation of the gene and synthesis of mRNA. Breast cancer cells lacking expression showed increased number of chromosomal breaks and gaps when exposed to ␥-irradiation. Therefore, it is possible that loss of expression contributes to malignant transformation by impairing the G 2 cell cycle checkpoint function, thus allowing an accumulation of genetic defects. Hypermethylation and loss of expression are the most consistent molecular alterations in breast cancer identified so far.A lthough many studies have identified critical genetic and epigenetic changes that mark the transformation of cells in tissues such as colon, pancreas, and lung, similar studies in breast cancer have met with limited success. In this paper we report the identification of a gene, 14-3-3 (), whose expression is undetectable in 94% (45͞48) of breast tumors.was originally identified as an epithelial-specific marker, HME1, which was down-regulated in a few breast cancer cell lines but not in cancer cell lines derived from other tissue types (1). Later studies showed that protein (also called stratifin) was abundant in differentiated squamous epithelial cells, but decreased by 95% in simian virus 40-transformed epithelial cells and in primary bladder tumors (2-4).We investigated the molecular mechanism underlying the low expression of in breast cancers. We find that genetic alterations such as loss of heterozygosity (LOH) and intragenic mutations are not major contributing mechanisms for lack of expression. Instead, we show that hypermethylation of the CpG-rich region in the gene is associated with its transcriptional silencing in the majority of breast tumors. Treatment of breast cancer cell lines that do not express with the DNA methyltransferase inhibitor, 5-aza-2Ј-deoxycytidine (5-aza-dC), leads to partial demethylation of this CpG island and synthesis of mRNA. Thus, hypermethylation appears to be responsible for silencing of gene expression.Recent studies have shed light on the function of . It was originally identified as a p53-inducible gene that is responsive to DNA damaging agents (5). apparently sequesters the mitotic initiation complex, cdc2-cyclin B1, in the cytoplasm after DNA damage (6). This prevents cdc...

BackgroundFirst-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets.ResultsWe used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples.ConclusionsThese results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

The molecular foundations of Hürthle cell carcinoma (HCC) are poorly understood. Here we describe a comprehensive genomic characterization of 56 primary HCC tumors that span the spectrum of tumor behavior. We elucidate the mutational profile and driver mutations and show that these tumors exhibit a wide range of recurrent mutations. Notably, we report a high number of disruptive mutations to both protein-coding and tRNA-encoding regions of the mitochondrial genome. We reveal unique chromosomal landscapes that involve whole-chromosomal duplications of chromosomes 5 and 7 and widespread loss of heterozygosity arising from haploidization and copy-number-neutral uniparental disomy. We also identify fusion genes and disrupted signaling pathways that may drive disease pathogenesis.

We have identi®ed 14-3-3 s (s) as a gene whose expression is lost in breast carcinomas, primarily by methylation-mediated silencing. In this report, we investigated the timing of loss of s gene expression during breast tumorigenesis in vivo. We analysed the methylation status of s in breast cancer precursor lesions using microdissection for selective tissue sampling. We found hypermethylation of s in 24 of 25 carcinomas (96%), 15 of 18 (83%) of ductal carcinoma in situ, and three of eight (38%) of atypical hyperplasias. None of the ®ve hyperplasias without atypia showed s-hypermethylation. Unexpectedly, patients with breast cancer showed s hypermethylation in adjacent histologically normal breast epithelium, while this was never observed in individuals without evidence of breast cancer. Also, samples of periductal stromal breast tissue were consistently hypermethylated, underscoring the importance of selective tissue sampling for accurate assessment of 14-3-3-s methylation in breast epithelium. These results suggest that hypermethylation of 14-3-3-s occurs at an early stage in the progression to invasive breast cancer, and may occur in apparently normal epithelium adjacent to breast cancer. These results provide evidence that loss of expression of s is an early event in neoplastic transformation. Oncogene (2001) 20, 3348 ± 3353.