The mutation profile of differentiated thyroid cancer coexisting with undifferentiated anaplastic cancer resembles that of anaplastic thyroid cancer but not that of archetypal differentiated thyroid cancer.

Differentiated thyroid cancer (DTC) has one of the lowest cancer mutational burdens, while anaplastic thyroid cancer (ATC) has a much higher mutation frequency. A fraction of ATC has an associated differentiated component, which suggests the coevolution of both cancers. Here, we aimed to compare mutation frequency in coexisting ATC and DTC diagnosed concurrently in the same thyroid gland (3 cases) as well as in archetypal DTC and ATC alone (5 cases each). Single-nucleotide variations (SNV) and copy number variations (CNV) were analyzed in each case based on the next-generation sequencing data. We found a similar extent of mutational events, both SNV and CNV, in undifferentiated and differentiated components of thyroid cancers coexisting in one patient. The magnitude of these mutations was comparable to the level of mutations observed in ATC alone; yet, it was much higher than in archetypal DTC. This suggested that, despite histopathological features of differentiated tumors, molecular characteristics of such cancers coexisting with ATC and archetypal DTC could be significantly different. Pairwise comparison of mutational profiles of coexisting cancers enabled assumption on the possible evolution of both components, which appeared distinct in 3 analyzed cases. This included independent development of ATC and DTC diagnosed concurrently in two lobes of the same thyroid, as well as the development of anaplastic and differentiated cancer from the common ancestor that putatively gained a key driver mutation (BRAFV600E or KRASQ61R), which was followed either by early or late molecular separation of both cancers.

MicroRNA Profile of Exosomes and Parental Cells is Differently Affected by Ionizing Radiation.

Exosomes are key mediators of cell-to-cell communication involved in different aspects of the response to ionizing radiation. The functional role of exosomes depends on their molecular cargo, including protein and miRNA content. In this work, we compared the miRNA profile of cells exposed to a high-dose of radiation and the exosomes released by those cells. FaDu cells (derived from human head and neck cancer) were exposed to 2 and 8 Gy doses, exosomes were purified from culture media at 36 h postirradiation using a combination of differential centrifugation, ultrafiltration and precipitation, then microRNA was analyzed using the RNA-seq approach. There were 439 miRNA species quantified, and significant differences in their relative abundance were observed between the cells and exosomes; several low-abundance miRNAs were over-represented while high-abundance miRNA were under-represented in exosomes. There were a few miRNA species markedly affected in irradiated cells and in exosomes released by these cells. However, markedly different radiation-induced effects were observed in both miRNA sets, which could be exemplified by miR-3168 significantly downregulated in cells and upregulated in exosomes. On the other hand, both 2 and 8 Gy radiation doses induced similar effects. Radiation-affected miRNA species present in exosomes are linked to genes involved in the DNA damage and cytokine-mediated response, which may suggest their hypothetical role in the exosome-mediated radiation-induced bystander effect reported elsewhere.

Comprehensive Analysis of MILE Gene Expression Data Set Advances Discovery of Leukaemia Type and Subtype Biomarkers.

Large collections of data in studies on cancer such as leukaemia provoke the necessity of applying tailored analysis algorithms to ensure supreme information extraction. In this work, a custom-fit pipeline is demonstrated for thorough investigation of the voluminous MILE gene expression data set. Three analyses are accomplished, each for gaining a deeper understanding of the processes underlying leukaemia types and subtypes. First, the main disease groups are tested for differential expression against the healthy control as in a standard case-control study. Here, the basic knowledge on molecular mechanisms is confirmed quantitatively and by literature references. Second, pairwise comparison testing is performed for juxtaposing the main leukaemia types among each other. In this case by means of the Dice coefficient similarity measure the general relations are pointed out. Moreover, lists of candidate main leukaemia group biomarkers are proposed. Finally, with this approach being successful, the third analysis provides insight into all of the studied subtypes, followed by the emergence of four leukaemia subtype biomarkers. In addition, the class enhanced DEG signature obtained on the basis of novel pipeline processing leads to significantly better classification power of multi-class data classifiers. The developed methodology consisting of batch effect adjustment, adaptive noise and feature filtration coupled with adequate statistical testing and biomarker definition proves to be an effective approach towards knowledge discovery in high-throughput molecular biology experiments.