Common genetic variations of deiodinase genes and prognosis of brain tumor patients

Genetic predictors of neurocognitive outcomes in survivors of pediatric brain tumors

BACKGROUND

Neurocognitive deficits are common in pediatric brain tumor survivors. The use of single nucleotide polymorphism (SNP) analysis in DNA repair genes may identify children treated with radiation therapy for brain tumors at increased risk for treatment toxicity and adverse neurocognitive outcomes.

MATERIALS

The Human 660W-Quad v1.0 DNA BeadChip analysis (Illumina) was used to evaluate 1048 SNPs from 59 DNA repair genes in 46 subjects. IQ testing was measured by the Wechsler Intelligence Scale for Children. Linear regression was used to identify the 10 SNPs with the strongest association with IQ scores while adjusting for radiation type.

RESULTS

The low vs high IQ patient cohorts were well matched for time from first treatment to most recent IQ, first treatment age, sex, and treatments received. 5 SNPs on 3 different genes (CYP29, XRCC1, and BRCA1) and on 3 different chromosomes (10, 19, and 17) had the strongest association with most recent IQ score that was not modified by radiation type. Furthermore, 5 SNPs on 4 different genes (WRN, NR3C1, ERCC4, RAD51L1) on 4 different chromosomes (8, 5, 16, 14) had the strongest association with change in IQ independent of radiation type, first IQ, and years between IQ measures.

CONCLUSIONS

SNPs offer the potential to predict adverse neurocognitive outcomes in pediatric brain tumor survivors. Our results require validation in a larger patient cohort. Improving the ability to identify children at risk of treatment related neurocognitive deficits could allow for better treatment stratification and early cognitive interventions.

Selenium in Prostate Cancer: Prevention, Progression, and Treatment

Selenium, a trace mineral with various biological functions, has become a focal point in prostate cancer research. This review aims to present a comprehensive overview of selenium's involvement in prostate cancer, covering its impact on prevention, development, treatment, and underlying mechanisms. Observational studies have revealed a link between selenium levels and selenoproteins with prostate cancer progression. However, randomized controlled studies have shown that selenium supplementation does not prevent prostate cancer (HR: 0.95; 95% CI 0.80-1.13). This discrepancy might be attributed to selenoprotein single nucleotide polymorphisms. In the context of combinatorial therapy, selenium has demonstrated promising synergistic potential in the treatment of prostate cancer. Emerging evidence highlights the significant role of selenium and selenoproteins in prostate cancer, encompassing AR signaling, antioxidative properties, cell death, cell cycle regulation, angiogenesis, epigenetic regulation, immunoregulation, epithelial-mesenchymal transformation, and redox signal. In conclusion, selenium's diverse properties make it a promising trace mineral in prostate cancer prevention, development, and treatment and as a platform for exploring novel agents.

The Physiological Functions and Polymorphisms of Type II Deiodinase

Type II deiodinase (DIO2) is thought to provide triiodothyronine (T3) to the nucleus to meet intracellular needs by deiodinating the prohormone thyroxine. DIO2 is expressed widely in many tissues and plays an important role in a variety of physiological processes, such as controlling T3 content in developing tissues (e.g., bone, muscles, and skin) and the adult brain, and regulating adaptive thermogenesis in brown adipose tissue (BAT). However, the identification and cloning of DIO2 have been challenging. In recent years, several clinical investigations have focused on the Thr92Ala polymorphism, which is closely correlated with clinical syndromes such as type 2 diabetes, obesity, hypertension, and osteoarthritis. Thr92Ala-DIO2 was also found to be related to bone and neurodegenerative diseases and tumors. However, relatively few reviews have synthesized research on individual deiodinases, especially DIO2, in the past 5 years. This review summarizes current knowledge regarding the physiological functions of DIO2 in thyroid hormone signaling and adaptive thermogenesis in BAT and the brain, as well as the associations between Thr92Ala-DIO2 and bone and neurodegenerative diseases and tumors. This discussion is expected to provide insights into the physiological functions of DIO2 and the clinical syndromes associated with Thr92Ala-DIO2.

Identification of influential observations in high-dimensional survival data through robust penalized Cox regression based on trimming

Penalized Cox regression can efficiently be used for the determination of biomarkers in high-dimensional genomic data related to disease prognosis. However, results of Penalized Cox regression is influenced by the heterogeneity of the samples who have different dependent structure between survival time and covariates from most individuals. These observations are called influential observations or outliers. A robust penalized Cox model (Reweighted Elastic Net-type maximum trimmed partial likelihood estimator, Rwt MTPL-EN) is proposed to improve the prediction accuracy and identify influential observations. A new algorithm AR-Cstep to solve Rwt MTPL-EN model is also proposed. This method has been validated by simulation study and application to glioma microarray expression data. When there were no outliers, the results of Rwt MTPL-EN were close to the Elastic Net (EN). When outliers existed, the results of EN were impacted by outliers. And whenever the censored rate was large or low, the robust Rwt MTPL-EN performed better than EN. and could resist the outliers in both predictors and response. In terms of outliers detection accuracy, Rwt MTPL-EN was much higher than EN. The outliers who "lived too long" made EN perform worse, but were accurately detected by Rwt MTPL-EN. Through the analysis of glioma gene expression data, most of the outliers identified by EN were those "failed too early", but most of them were not obvious outliers according to risk estimated from omics data or clinical variables. Most of the outliers identified by Rwt MTPL-EN were those who "lived too long", and most of them were obvious outliers according to risk estimated from omics data or clinical variables. Rwt MTPL-EN can be adopted to detect influential observations in high-dimensional survival data.

TERT Alterations Predict Tumor Progression in De Novo High-Grade Meningiomas Following Adjuvant Radiotherapy

Background

Adjuvant radiotherapy (RT) is one of the most commonly used treatments for de novo high-grade meningiomas (HGMs) after surgery, but genetic determinants of clinical benefit are poorly characterized.

Objective

We describe efforts to integrate clinical genomics to discover predictive biomarkers that would inform adjuvant treatment decisions in de novo HGMs.

Methods

We undertook a retrospective analysis of 37 patients with de novo HGMs following RT. Clinical hybrid capture-based sequencing assay covering 184 genes was performed in all cases. Associations between tumor clinical/genomic characteristics and RT response were assessed. Overall survival (OS) and progression-free survival (PFS) curves were plotted using the Kaplan–Meier method.

Results

Among the 172 HGMs from a single institution, 42 cases (37 WHO grade 2 meningiomas and five WHO grade 3 meningiomas) were identified as de novo HGMs following RT. Only TERT mutations [62.5% C228T; 25% C250T; 12.5% copy number amplification (CN amp.)] were significantly associated with tumor progression after postoperative RT (adjusted p = 0.003). Potential different somatic interactions between TERT and other tested genes were not identified. Furthermore, TERT alterations (TERT-alt) were the predictor of tumor progression (Fisher’s exact tests, p = 0.003) and were associated with decreased PFS (log-rank test, p = 0.0114) in de novo HGMs after RT.

Conclusion

Our findings suggest that TERT-alt is associated with tumor progression and poor outcome of newly diagnosed HGM patients after postoperative RT.

Deiodinases and Cancer

Hormones are key drivers of cancer development, and alteration of the intratumoral concentration of thyroid hormone (TH) is a common feature of many human neoplasias. Besides the systemic control of TH levels, the expression and activity of deiodinases constitute a major mechanism for the cell-autonomous, prereceptoral control of TH action. The action of deiodinases ensures tight control of TH availability at intracellular level in a time- and tissue-specific manner, and alterations in deiodinase expression are frequent in tumors. Research over the past decades has shown that in cancer cells, a complex and dynamic expression of deiodinases is orchestrated by a network of growth factors, oncogenic proteins, and miRNA. It has become increasingly evident that this fine regulation exposes cancer cells to a dynamic concentration of TH that is functional to stimulate or inhibit various cellular functions. This review summarizes recent advances in the identification of the complex interplay between deiodinases and cancer and how this family of enzymes is relevant in cancer progression. We also discuss whether deiodinase expression could represent a diagnostic tool with which to define tumor staging in cancer treatment or even a therapeutic tool against cancer.

Targeting the right population for T3 + T4 combined therapy: where are we now and where to next?

The universal applicability of levothyroxine (LT4) monotherapy for the treatment of hypothyroidism has been questioned in recent years. Indeed, it is now clear that about 10-15% of LT4-treated hypothyroid patients are dissatisfied with their treatment. It is plausible that this subset of hypothyroid patients may need T3 + T4 combined therapy to restore peripheral euthyroidism. To address this issue, many clinical trials have investigated the effect of T3 + T4 combinations versus standard LT4-based therapy. However, to date, results have been inconclusive, mainly due to the lack of markers that identify candidates for combination therapy. A breakthrough in this field came with the recent finding that several single-nucleotide polymorphisms in the deiodinase genes are associated with the persistence of hypothyroid symptoms in biochemically euthyroid LT4-treated patients, and are thus markers of candidates for combination therapy. In addition, whole-genome association studies are expanding our knowledge of other genes of the thyroid hormone (TH) pathway that affect serum TH levels. To target the right population for the T3 + T4 combined therapy, the next step is to translate these new findings into prospective trials. Hopefully, this will pave the way to personalized therapy for each hypothyroid patient.