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Cancer Informatics
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
Figure 10.19 shows the summaries obtained, note the colours that the plots would normally use, have had to be put into greyscale. At the top left there is a barchart of the numbers of mutations, the missense mutations dominating. The top middle plot is redundant here. The top right plot is a barchart of the particular changes in base pairs, with C>T being the most common. The bottom left plot shows the number of mutations in each sample, the median being 36. The bottom middle plot shows box and whisker plots of the data in the top left plot. Lastly, the bottom right plot shows the 23 genes that had the most mutations, the numbers being split by type of mutation. The scale of the plot has only allowed half of the gene names to be written, the full list is: VHL, PBRM1, TTN, SETD2, MTOR, BAP1, MUC16, DNAH9, LRP2, SPEN, HMCN1, CSMD3, KMT2C, DST, FBN2, RYR3, ANK3, DNAH2, SMARCA4, AKAP9, ATM, ERBB4 and BRCA2. The VHL gene has the most mutations, PBRM1 has the most nonsense mutations.
Ocular Tumors
Published in Ching-Yu Cheng, Tien Yin Wong, Ophthalmic Epidemiology, 2022
Vishal Raval, Alexander Melendez, Hansell Soto, Alléxya Affonso, Rubens Belfort Neto, Arun D. Singh
UV light has been associated as a risk factor for development of cutaneous melanoma.67 On the contrary, the role of UV light in the formation of uveal melanoma has been inconclusive and unclear. Multiple studies finding a link between UV light and uveal melanoma have shown no correlation.45,68,69 However, recent studies have determined that UV light exposure may be a possible risk factor.70 Mutations that were previously implicated only in cutaneous melanoma and attributed to UV light exposure (BRAF, NRAS, CDKN2A, PTEN, TP53, TERT, ARID2, and KMT2C) have now been identified in a minority of cases of uveal melanoma.71
Urothelial and Urethral Cancer
Published in Karl H. Pang, Nadir I. Osman, James W.F. Catto, Christopher R. Chapple, Basic Urological Sciences, 2021
Ibrahim Jubber, Karl H. Pang, James W.F. Catto
Common gene mutations includep53 (48% in TCGA survey):Tumour suppressor gene − role in cell cycle regulation.Epigenetic regulators:KMT2D (28%), KDM6A (26%), ARID1A (25%), and KMT2C (18%).Mutations affect the modelling of chromosomes (via histone modification) to allow up/downregulation of key cellular proteins (and subsequent carcinogenic traits).Cell kinases:PIK3CA (22%) and ERBB2 (12%).Lead to gain of function changes and enhanced signalling (leading to dysregulated pathway activation).Retinoblastoma gene (RB1: 17%):Mutation leads to loss of a cell cycle checkpoint and uncontrolled cell proliferation.DNA repair: ERCC2 (9%).mRNA editing: APOBEC 3A/B (~80% in the TCGA have a mutation signature).Growth factors: FGFR3 (14%), HER2, EGFR family.
LSD1: a viable therapeutic target in cutaneous squamous cell carcinoma?
Published in Expert Opinion on Therapeutic Targets, 2020
Indeed, how LSD1 functions in normal epithelial tissues or other squamous cell carcinomas (SCCs) remains a major gap in knowledge. This is particularly surprising given that LSD1 is commonly overexpressed and known to associate with poor prognosis in SCCs of the esophagus [27,28], oral mucosa [26,29], and lung [25] (Figure 2). Further, the opposing H3K4 histone methyltransferases, KMT2C (MLL3) and KMT2D (MLL4), display exceptionally high rates of loss-of-function mutations in SCCs with the highest rate of mutation occurring in cSCC (>50%) [20,21]. Importantly, cSCC shares common mutational and transcriptional underpinnings with all other forms of SCC which together form a ‘pan-squamous’ group due to their unified histological and genetic features [71,72]. These genetic and clinical data support the idea that LSD1 may be a promising therapeutic target in cSCC and warrant further study of how KMT2C/D-LSD1 epigenetic dysregulation promotes cSCC. Given the incredible accessibility of the skin, this knowledge may also provide critical insight into these other, often difficult to study SCCs.
Ultra-Deep Sequencing of Plasma-Circulating DNA for the Detection of Tumor- Derived Mutations in Patients with Nonmetastatic Colorectal Cancer
Published in Cancer Investigation, 2022
Huu-Thinh Nguyen, Bac An Luong, Duc-Huy Tran, Trong-Hieu Nguyen, Quoc Dat Ngo, Linh Gia Hoang Le, Quoc Chuong Ho, Hue-Hanh Thi Nguyen, Cao Minh Nguyen, Vu Uyen Tran, Truong Vinh Ngoc Pham, Minh Triet Le, Ngoc An Trinh Le, Trung Kien Le, Thanh Luan Nguyen, Hong-Anh Thi Pham, Hong Thuy Le, Hong Diep Thi Duong, Anh Vu Hoang, Hoang Bac Nguyen, Kiet Truong Dinh, Minh-Duy Phan, Hoai-Nghia Nguyen, Thanh-Thuy Thi Do, Hoa Giang, Le Son Tran, Diep Tuan Tran
Specifically, we found that most mutations in the LB samples greatly overlapped with those from matched WBCs, across the 50 patients (median 75.9%; range: 33.3%–91.2%, Figure 1(A), Table 2), and the abundance of those mutations in plasma highly correlated with their abundance in WBCs (r = 0.95, 95% CI 0.94–0.97, p < 0.0001, Figure 1(B)). This finding further confirmed that WBC-derived mutations (WDMs) are the major constituents of CRC patient LBs and are unlikely to be sequencing errors or artifacts. WDMs were detected in 18 of the 20 selected genes, with KMT2C being the most frequently mutated gene (22.8% of all WDMs, Figure S2). After excluding WDMs, the remaining non-overlapping mutations, denoted as LB-unique mutations (LBMs), accounted for lower proportions of total LB-derived mutations, but being potentially of tumor origin (median: 24.1%, range: 8.8%–66.7%, Figure 1(A), Table 2). While LBMs were mostly present as low occurrence mutations and variant allele frequencies (VAFs) <0.1 (Figure 1(C)), WDMs had varying occurrences (range: 1–39 patients, Figure 1(D)) and VAFs (range: 0.001–0.67, Figure 1(D)). Among detected WDMs, the majority were detected at VAFs >0.2, indicating that they were mostly derived from germline mutations (12), while the remainders had VAFs <0.1, comparable to the VAFs of LBMs (VAF < 0.1), which could be CHIP related mutations (Figure 1(C)). We next cross-compared the profiles of WDMs and LBMs, among 50 patients, to examine if they could distinctly classify one from another. Of the total 92 detected WDMs, 9 overlapped with LBMs detected across individual patients, indicating that the spectrums of WDMs and LBMs were not distinct, or that a WDM in a particular patient could be a TDM in another (Figure 1(E)). Together, these data showed that paired sequencing of WBC gDNA and plasma cfDNA may be required to distinguish WDMs from LBMs, in liquid biopsy samples.
A look into the use of Raman spectroscopy for brain and breast cancer diagnostics: linear and non-linear optics in cancer research as a gateway to tumor cell identity
Published in Expert Review of Molecular Diagnostics, 2020
Halina Abramczyk, Beata Brozek-Pluska, Arkadiusz Jarota, Jakub Surmacki, Anna Imiela, Monika Kopec
For example, in medulloblastoma, a series of rare focal copy-number alterations affecting histone-modifying genes, such as EHMT1, SMYD4, L3MBTL3, KDM4C (also known as JMJD2C) and KAT6A (also known as MYST3)37, were reported initially. Similarly, recurrent mutations in KMT2D (also known as MLL2), KMT2C (also known as MLL3), SMARCA4 and other genes were identified in MB tumors by gene re-sequencing [93].