BackgroundThyroid carcinomas are known to harbor oncogenic driver mutations and advances in sequencing technology now allow the detection of these in fine needle aspiration biopsies (FNA). Recent work by The Cancer Genome Atlas (TCGA) Research Network has expanded the number of genetic alterations detected in papillary thyroid carcinomas (PTC). We sought to investigate the prevalence of these and other genetic alterations in diverse subtypes of thyroid nodules beyond PTC, including a variety of samples with benign histopathology. This is the first clinical evaluation of a large panel of TCGA-reported genomic alterations in thyroid FNAs.ResultsIn FNAs, genetic alterations were detected in 19/44 malignant samples (43 % sensitivity) and in 7/44 histopathology benign samples (84 % specificity). Overall, after adding a cohort of tissue samples, 38/76 (50 %) of histopathology malignant samples were found to harbor a genetic alteration, while 15/75 (20 %) of benign samples were also mutated. The most frequently mutated malignant subtypes were medullary thyroid carcinoma (9/12, 75 %) and PTC (14/30, 47 %). Additionally, follicular adenoma, a benign subtype of thyroid neoplasm, was also found to harbor mutations (12/29, 41 %). Frequently mutated genes in malignant samples included BRAF (20/76, 26 %) and RAS (9/76, 12 %). Of the TSHR variants detected, (6/7, 86 %) were in benign nodules. In a direct comparison of the same FNA also tested by an RNA-based gene expression classifier (GEC), the sensitivity of genetic alterations alone was 42 %, compared to the 91 % sensitivity achieved by the GEC. The specificity based only on genetic alterations was 84 %, compared to 77 % specificity with the GEC.ConclusionsWhile the genomic landscape of all thyroid neoplasm subtypes will inevitably be elucidated, caution should be used in the early adoption of published mutations as the sole predictor of malignancy in thyroid. The largest set of such mutations known to date detects only a portion of thyroid carcinomas in preoperative FNAs in our cohort and thus is not sufficient to rule out cancer. Due to the finding that variants are also found in benign nodules, testing only GEC suspicious nodules may be helpful in avoiding false positives and altering the extent of treatment when selected mutations are found.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0849-9) contains supplementary material, which is available to authorized users.

This paper introduces a new generative semisupervised (transductive) mixture model with a more fine-grained class label generation mechanism than that of previous works. Our approach effectively combines the advantages of standard semisupervised mixtures, which achieve label extrapolation over a mixture component when there are few labeled samples, and nearest-neighbor (NN) classification, which achieves accurate classification in the local vicinity of labeled samples. Toward this end, we propose a two-stage stochastic data generation mechanism, with the unlabeled samples first produced and then the labeled samples generated conditioned on both the unlabeled data and on their components of origin. This nested data generation entails a more complicated (albeit still closed-form) E-step evaluation than that for standard mixtures. Our model is advantageous, compared with previous semisupervised mixtures, when mixture components model data from more than one class and when within-component class proportions are not constant over the feature space region "owned" by a component. Experiments demonstrate gains in classification accuracy over both the previous semisupervised mixture of experts model and over K -NN classification on data sets from the DC Irvine Repository.

Most colorectal (CRC) tumors are dependent on EGFR/KRAS/BRAF/MAPK signaling activation. ARID1A is an epigenetic regulator mutated in approximately 5% of non-hypermutated CRC tumors. Here we show that anti-EGFR but not anti-VEGF treatment enriches for emerging ARID1A mutations in CRC patients. In addition, we find that patients with ARID1A mutations, at baseline, are associated with worse outcome when treated with cetuximab- but not bevacizumab-containing therapies; thus, this suggests that ARID1A mutations may provide both an acquired and intrinsic mechanism of resistance to anti-EGFR therapies. We find that, ARID1A and EGFR-pathway genetic alterations are mutually exclusive across lung and colorectal cancers, further supporting a functional connection between these pathways. Our results not only suggest that ARID1A could be potentially used as a predictive biomarker for cetuximab treatment decisions but also provide a rationale for exploring therapeutic MAPK inhibition in an unexpected but genetically defined segment of CRC patients.

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This paper proposes a new algorithm for adaptive deep image compression (DIC) that can compress images for different purposes or contexts at different rates. The algorithm can compress images with semantic awareness, which means classification-related semantic features are better protected in lossy image compression. It builds on the existing conditional encoder-based DIC method and adds two features: a model-based rate-distortion-classification-perception (RDCP) framework to control the trade-off between rate and performance for different contexts, and a mechanism to generate coding conditions based on image complexity and semantic importance. The algorithm outperforms the QMAP2021 benchmark on the ImageNet dataset. Over the tested rate range, it improves the classification accuracy by 11% and the perceptual quality by 12.4%, 32%, and 1.3% on average for NIQE, LPIPS, and FSIM metrics, respectively.