Chromosomal aberrations in thyroid follicular-cell neoplasia: in the search of novel oncogenes and tumour suppressor genes

Saliva microbiome changes in thyroid cancer and thyroid nodules patients

Objective

Thyroid disease has been reported to associate with gut microbiota, but the effects of thyroid cancer and thyroid nodules on the oral microbiota are still largely unknown. This study aimed to identify the variation in salivary microbiota and their potential association with thyroid cancer and thyroid nodules.

Methods

We used 16S rRNA high-throughput sequencing to examine the salivary microbiota of thyroid cancer patients (n = 14), thyroid nodules patients (n = 9), and healthy controls (n = 15).

Results

The alpha-diversity indices Chao1 and ACE were found to be relatively higher in patients with thyroid cancer and thyroid nodules compared to healthy controls. The beta diversity in both the thyroid cancer and thyroid nodules groups was divergent from the healthy control group. The genera Alloprevotella, Anaeroglobus, Acinetobacter, unclassified Bacteroidales, and unclassified Cyanobacteriales were significantly enriched in the thyroid cancer group compared with the healthy control group. In contrast, the microbiome of the healthy controls was mainly composed of the genera Haemophilus, Lautropia, Allorhizobium Neorhizobium Pararhizobium Rhizobium, Escherichia Shigella, and unclassified Rhodobacteraceae. The thyroid nodules group was dominated by genre uncultured Candidatus Saccharibacteria bacterium, unclassified Clostridiales bacterium feline oral taxon 148, Treponema, unclassified Prevotellaceae, Mobiluncus, and Acholeplasma. In contrast, the genera unclassified Rhodobacteraceae and Aggregatibacter dominated the healthy control group. The study also found that clinical indicators were correlated with the saliva microbiome.

Conclusion

The salivary microbiota variation may be connected with thyroid cancer and thyroid nodules.

Tumour size can have an impact on the outcomes of epidemiological studies on second cancers after radiotherapy

Obtaining a correct dose-response relationship for radiation-induced cancer after radiotherapy presents a major challenge for epidemiological studies. The purpose of this paper is to gain a better understanding of the associated uncertainties. To accomplish this goal, some aspects of an epidemiological study on breast cancer following radiotherapy of Hodgkin's disease were simulated with Monte Carlo methods. It is demonstrated that although the doses to the breast volume are calculated by one treatment plan, the locations and sizes of the induced secondary breast tumours can be simulated and, based on these simulated locations and sizes, the absorbed doses at the site of tumour incidence can also be simulated. For the simulations of point dose at tumour site, linear and non-linear mechanistic models which predict risk of cancer induction as a function of dose were applied randomly to the treatment plan. These simulations provided for each second tumour and each simulated tumour size the predicted dose. The predicted-dose-response-characteristic from the analysis of the simulated epidemiological study was analysed. If a linear dose-response relationship for cancer induction was applied to calculate the theoretical doses at the simulated tumour sites, all Monte-Carlo realizations of the epidemiological study yielded strong evidence for a resulting linear risk to predicted-dose-response. However, if a non-linear dose-response of cancer induction was applied to calculate the theoretical doses, the Monte Carlo simulated epidemiological study resulted in a non-linear risk to predicted-dose-response relationship only if the tumour size was small (< 1.5 cm). If the diagnosed breast tumours exceeded an average diameter of 1.5 cm, an applied non-linear theoretical-dose-response relationship for second cancer falsely resulted in strong evidence for a linear predicted-dose relationship from the epidemiological study realizations. For a typical distribution of breast cancer sizes, the model selection probability for a resulting predicted-dose linear model was 61% although a non-linear theoretical-dose-response relationship for cancer induction had been applied. The results of this study, therefore, provide evidence that the shapes of epidemiologically obtained dose-response relationships for cancer induction can be biased by the finite size of the diagnosed second tumour, even though the epidemiological study was done correctly.

Risk of secondary cancers: Bridging epidemiology and modeling

Epidemiological studies of long term radiotherapy survivors provide useful insights into dose-response relationships for secondary cancer induction risk at high doses. There are uncertainties involved in estimating the dose to the location of the second malignancy, because the dose distributions in radiotherapy patients can be spatially highly heterogeneous and the size of the diagnosed tumor is on the order of a few cm. Therefor it is nearly impossible to obtain the exact dose corresponding to the exact tumor induction location and so organ specific dose-response relationships have large errors not only in the reported risk, but also in the estimated dose. In this work two alternative methods are proposed for future applications involving investigations into dose response relationships for second cancer induction risk, the method of organ sub-division and the method of risk equivalent dose. The method of organ sub-division takes the inevitable inhomogeneous dose distribution into account by applying epidemiological methods to organ sub-divisions which have a nearly homogenous dose. The method of risk equivalent dose combines risk modeling and epidemiological data analysis. Risk models can be optimized by using an iterative procedure assuming a variation of organ specific dose-responses. The advantage of the alternative methods is that the inhomogeneity of the dose in the organs at risk is taken into account. The second method has the additional advantage that the dose to the location of the tumor site must not be known and that epidemiologically obtained risks that were not stratified by organ specific risk can be used.

Somatic amplifications and deletions in genome of papillary thyroid carcinomas

Somatic gene copy number variation contributes to tumor progression. Using comparative genomic hybridization (CGH) array, the presence of genomic imbalances was evaluated in a series of 27 papillary thyroid carcinomas (PTCs). To detect only somatic imbalances, for each sample, the reference DNA was from normal thyroid tissue of the same patient. The presence of the BRAF V600E mutation was also evaluated. Both amplifications and deletions showed an uneven distribution along the entire PTC cohort; amplifications were more frequent than deletions (mean values of 17.5 and 7.2, respectively). Number of aberration events was not even among samples, the majority of them occurring only in a small fraction of PTCs. Most frequent amplifications were detected at regions 2q35, 4q26, and 4q34.1, containing FN1, PDE5A, and GALNTL6 genes, respectively. Most frequent deletions occurred at regions 6q25.2, containing OPMR1 and IPCEF1 genes and 7q14.2, containing AOAH and ELMO1 genes. Amplification of FN1 and PDE5A genomic regions was confirmed by quantitative PCR. Frequency of amplifications and deletions was in relationship with clinical features and BRAF mutation status of tumor. In fact, according to the American Joint Committee on Cancer stage and American Thyroid Association (ATA) risk classification, amplifications are more frequent in higher risk samples, while deletions tend to prevail in the lower risk tumors. Analysis of single aberrations according to the ATA risk grouping shows that amplifications containing PDE5A, GALNTL6, DHRS3, and DOCK9 genes are significantly more frequent in the intermediate/high risk group than in the low risk group. Thus, our data would indicate that analysis of somatic genome aberrations by CGH array can be useful to identify additional prognostic variables.

Delineating chromosomal breakpoints in radiation-induced papillary thyroid cancer

Recurrent translocations are well known hallmarks of many human solid tumors and hematological disorders, where patient- and breakpoint-specific information may facilitate prognostication and individualized therapy. In thyroid carcinomas, the proto-oncogenes RET and NTRK1 are often found to be activated through chromosomal rearrangements. However, many sporadic tumors and papillary thyroid carcinomas (PTCs) arising in patients with a history of exposure to elevated levels of ionizing irradiation do not carry these known abnormalities. We developed a rapid scheme to screen tumor cell metaphase spreads and identify candidate genes of tumorigenesis and neoplastic progression for subsequent functional studies. Using a series of overnight fluorescence in situ hybridization (FISH) experiments with pools comprised of bacterial artificial chromosome (BAC) clones, it now becomes possible to rapidly refine breakpoint maps and, within one week, progress from the low resolution Spectral Karyotyping (SKY) maps or Giemsa-banding (G-banding) karyotypes to fully integrated, high resolution physical maps including a list of candiate genes in the critical regions.