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Clinical data in outcome models
Published in Issam El Naqa, A Guide to Outcome Modeling in Radiotherapy and Oncology, 2018
Nicholas J. DeNunzio, Sarah L. Kerns, Michael T. Milano
A seminal GWAS examined the genetic basis of erectile dysfunction (ED) after radiotherapy in 79 patients with prostate cancer [52] in whom approximately 500,000 SNPs were analyzed. Unexpectedly, a SNP in the follicle stimulating hormone receptor gene was significantly associated with development of ED post-treatment. Four additional SNPs showed a trend towards association with ED. The risk allele for each of these top SNPs is common in populations of African ancestry but quite rare in those of European ancestry, underscoring the importance of clearly identifying the study population in GWAS. While this initial study was carried out in a single patient set of very small sample size and requires validation, several other radiogenomic GWAS have been published in larger patient populations following either a multi-cohort staged design or individual patient data meta-analysis approach where results in a single patient set are validated in additional patient sets. These studies have successfully identified risk SNPs in TANC1 [53], KDM3B, and DNAH5 [54]. Importantly, a radiogenomics GWAS of 3,588 patients with breast or prostate cancer provided evidence that many more common SNPs are associated with radiotherapy toxicity and remain to be discovered through additional, larger studies [55].
Identification of the Integrated Prognostic Signature Associated with Immuno-relevant Genes and Long Non-coding RNAs in Acute Myeloid Leukemia
Published in Cancer Investigation, 2022
Chunxia Zhao, Yulu Wang, Amit Sharma, Zifeng Wang, Chafeng Zheng, Ying Wei, Yun Wu, Pingping Liu, Jiachen Liu, Xulong Zhan, Ingo Schmidt-Wolf, Famei Tu
A total of 134 AML patients exhibited genetic mutations across 2274 genes were found (Supplementary File 2). Among them, the 22 most frequently mutated genes (NPM1, DNMT3A, TP53, FLT3, RUNX1, IDH2, WT1, KIT, NRAS, TTN, IDH1, KRAS, TET2, MUC16, ASXL1, SPEN, GATA2, PTPN11, CEBPA, ARHGAP35, TACC2, and KDM3B) were present in at least four patients. Since these mutations were not present in the majority of patients, we did not include them as part of our signature development. However, we compared our obtained signature by classifying these mutations into high- and low-risk groups (Supplementary Figure 6). Notably, we found that 73.33% of AML patients had an association with these 22 gene mutations (Supplemental Figure 6A). In addition, NMP1, TP53, KIT, and RUNX1 mutations were more prominent in the high-risk group, while IDH1, IDH2, WT1, PTPN11, CEBPA, RHGAP35 and KDM3B were more prominent in the low-risk group (supplemental Figure 6B, 6C).
Potential screening assays for individual radiation sensitivity and susceptibility and their current validation state
Published in International Journal of Radiation Biology, 2020
Maria Gomolka, Benjamin Blyth, Michel Bourguignon, Christophe Badie, Annette Schmitz, Christopher Talbot, Christoph Hoeschen, Sisko Salomaa
Radiation genomics (hereafter, radiogenomics) aims to identify genetic markers of radiation responsiveness, with significant progress coming through collaborations organized by the Radiogenomics Consortium (West et al. 2010). Three genetic polymorphisms found by candidate gene studies have replicated evidence for association with adverse reactions, near or in the ATM, TNF and XRCC1 genes (Talbot et al. 2012; Seibold et al. 2015; Andreassen et al. 2016b). These studies were conducted with breast and prostate cancer patients, with each with group sizes of several thousand patients. Additional associations have been found by genome-wide association studies (GWAS) including in the TANC1, SATB2 and CCRN4L genes (Barnett et al. 2014; Fachal et al. 2014). Even when the whole genome is studied, when particular tissue toxicities are investigated by correlation to variant genotypes, identified variants may be limited in their effects to that single toxicity by a tissue-specific mechanism, or may be informative for radiosensitivity more generally. For example, variants in the KDM3B and DNAH5 loci were found to be associated with increased urinary frequency and decreased urine stream, respectively, after radiotherapy for prostate cancer (Kerns et al. 2016). The mechanistic relationship proposed suggests bladder- and kidney-specific roles for these proteins which might be unlikely to translate to normal tissue toxicity risk in other sites (reviewed in Benafif et al. 2018). The genetic associations found to date explain only a small proportion of the heritability of radiosensitivity phenotypes, with the data suggesting the remaining genetic contribution is comprised of hundreds of variants which are common in the population but with small effect size, although the presence of additional rare variants with large effect sizes cannot be ruled out.