Detection of rare mutations, copy number alterations, and methylation in the same template DNA molecules.

The analysis of cell-free DNA (cfDNA) from plasma offers great promise for the earlier detection of cancer. At present, changes in DNA sequence, methylation, or copy number are the most sensitive ways to detect the presence of cancer. To further increase the sensitivity of such assays with limited amounts of sample, it would be useful to be able to evaluate the same template molecules for all these changes. Here, we report an approach, called MethylSaferSeqS, that achieves this goal, and can be applied to any standard library preparation method suitable for massively parallel sequencing. The innovative step was to copy both strands of each DNA-barcoded molecule with a primer that allows the subsequent separation of the original strands (retaining their 5-methylcytosine residues) from the copied strands (in which the 5-methylcytosine residues are replaced with unmodified cytosine residues). The epigenetic and genetic alterations present in the DNA molecules can then be obtained from the original and copied strands, respectively. We applied this approach to plasma from 265 individuals, including 198 with cancers of the pancreas, ovary, lung, and colon, and found the expected patterns of mutations, copy number alterations, and methylation. Furthermore, we could determine which original template DNA molecules were methylated and/or mutated. MethylSaferSeqS should be useful for addressing a variety of questions relating genetics and epigenetics.

Trends in incidence and histological pattern of thyroid cancer in Ho Chi Minh City, Vietnam (1996-2015): a population-based study.

BACKGROUND: The burden and trend of thyroid cancer in Vietnam have not been well documented. This study aimed to investigate the trends in incidence and histological pattern of thyroid cancer in Ho Chi Minh City from 1996 to 2015. METHODS: A population-based study retrieved data from the Ho Chi Minh City Cancer Registry during 1996-2015. Trends in the incidence of thyroid cancer were investigated based on age, gender, and histology for each 5-year period. Annual percentage change (APC) in incidence rates was estimated using Joinpoint regression analysis. RESULTS: In the study period, there were 5953 thyroid cancer cases (men-to-women ratio 1:4.5) newly diagnosed in Ho Chi Minh City with the mean age of 42.9 years (±14.9 years). The age-standardized incidence rate of thyroid cancer increased from 2.4 per 100,000 during 1996-2000 (95% confidence interval [95% CI]: 2.2-2.6) to 7.5 per 100,000 during 2011-2015 (95% CI: 7.3-7.9), corresponded to an overall APC of 8.7 (95% CI 7.6-9.9). The APC in men and women was 6.2 (95% CI: 4.2-8.2) and 9.2 (95% CI: 8.0-10.4), respectively. The incidence rate in the < 45 years age group was the highest diagnosed overall and increased significantly in both men (APC 11.0) and women (APC 10.1). Both genders shared similar distribution of subtype incidences, with papillary thyroid cancer constituted the most diagnosed (73.3% in men and 85.2% in women). The papillary thyroid cancer observed a markedly increase overall (APC of 10.7 (95% CI 9.3-12.0)). CONCLUSIONS: There were appreciable increases in the age-standardized incidence rate of thyroid cancer in both genders, mainly contributed by the papillary subtype. The age of patients at diagnosis decreased gradually. The widespread utilization of advanced diagnostic techniques and healthcare accessibility improvement might play a potential role in these trends. Further investigations are needed to comprehend the risk factors and trends fully.

Machine learning to detect the SINEs of cancer.

We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer. Samples from patients with cancer and controls were prespecified into four cohorts used for model training, analyte integration, and threshold determination, validation, and reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the validation cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and eight common protein biomarkers detected 51% of the cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature-the global reduction of AluS subfamily elements in the circulating DNA of patients with solid cancer. We confirmed this reduction through the analysis of another independent dataset obtained with a different approach (whole-genome sequencing). The evaluation of Alu elements may therefore have the potential to enhance the performance of several methods designed for the earlier detection of cancer.

Trends in colorectal cancer incidence in Ho Chi Minh City, Vietnam (1996-2015): Joinpoint regression and age-period-cohort analyses.

BACKGROUND: Little is known about the trends in colorectal cancer (CRC) in Vietnam. We aimed to investigate the trends in epidemiology and anatomical subsites of CRC in Ho Chi Minh City, Vietnam. METHODS: Based on the Ho Chi Minh City Cancer Registry data during 1996-2015, we calculated the average annual percent changes (AAPCs) of the age-standardized incidence rates (ASRs) by sex, age groups, and anatomical subsites, using joinpoint regressions analysis. We further performed age-period-cohort (APC) analysis using the United States National Cancer Institute's web-based statistical tool to explore the underlying reason for the incidence trend. RESULTS: Over 20 years the overall ASR of CRC increased from 10.5 to 17.9 per 100,000, a 1.7-fold increase. CRC incidence elevated more rapidly in men (AAPC 4.7, 95%CI 2.2-7.3) than in women (AAPC 2.6, 95%CI 0.6-4.8). The highest and lowest increasing rates of ASRs were observed in the 50-64-year-old age group (AAPC 5.3, 95%CI 2.8-7.9) and < 50-year-old age group (AAPC 1.1, 95%CI -0.7 to 2.9), respectively. Regarding subsites, rectal cancer had the highest rate of increase (AAPC 3.3, 95%CI 1.0-5.7). Furthermore, the APC analysis indicated significant increases in CRC incidence in birth cohorts after 1975 in both genders. CONCLUSIONS: The CRC incidence in Ho Chi Minh City increased, with the more prominent rates being among men and older populations, in rectal subsites, and in people born after 1975. The upward trend of CRC incidence in Ho Chi Minh City may be due to the adoption of a westernized lifestyle.

Enhancement of absolute fracture risk prognosis with genetic marker: the collagen I alpha 1 gene.

An important objective of genetic research in osteoporosis is to translate genotype data into the prognosis of fracture. The present study sought to develop a prognostic model for predicting osteoporotic fracture by using information from a genetic marker and clinical risk factors. It was designed as a prospective epidemiological study which involved 894 women of Caucasian background aged 60+ years who had been followed for a median of 9 years (from 1989 and 2008, range 0.2-18 years). During the follow-up period, fragility fracture was ascertained by X-ray reports for all women. Bone mineral density (BMD) at the femoral neck was measured by dual-energy X-ray absorptiometry. Genotypes of the Sp1 binding site in the first intron of the collagen I alpha 1 (COLIA1) gene polymorphism were determined by polymerase chain reaction, digestion with BalI restriction enzyme, and agarose gel electrophoresis. The relationship between COL1A1 genotype and fracture was assessed by the Cox proportional hazards model, from which nomograms were developed for individualizing the risk of fracture. The distribution of COL1A1 genotypes was consistent with the Hardy-Weinberg equilibrium law: GG (63.8%), GT (32.6%), and TT (3.6%). During the follow-up period, there were 322 fractures, including 77 hip and 127 vertebral fractures. There was an overrepresentation of the TT genotype in the fracture group (6.2%) compared with the nonfracture group (2.3%). Compared with carriers of GT and GG, women carrying the TT genotype had increased risk of any fracture (relative risk [RR] = 1.91, 95% CI 1.21-3.00), hip fracture (RR = 3.67, 95% CI 1.69-8.00), and vertebral fracture (RR = 3.36, 95% CI 1.81-6.24). The incorporation of COL1A1 genotypes improved the risk reclassification by 2% for any fragility fracture, 4% for hip fracture, and 5% for vertebral fracture, beyond age, BMD, prior fracture, and fall. Three nomograms were constructed for predicting fracture risk in an individual woman based on age, BMD, and COLIA1 genotypes. These data suggest that the COLIA1 Sp1 polymorphism is associated with the risk of fragility fracture in Caucasian women and that the polymorphism could enhance the predictive accuracy of fracture prognosis. The nonograms presented here can be useful for individualizing the short- and intermediate-term prognosis of fracture risk and help identify high-risk individuals for intervention for appropriate management of osteoporosis.

Association between LRP5 polymorphism and bone mineral density: a Bayesian meta-analysis.

BACKGROUND: The low-density lipoprotein receptor-related protein 5 gene (LRP5) was identified to be linked to the variation in BMD in high bone mass pedigrees. Subsequent population-based studies of the association between the LRP5 gene and BMD have yielded conflicting results. The present study was aimed at examining the association between LRP5 gene and BMD by using meta-analysis. METHODS: A systematic electronic search of literature was conducted to identify all published studies in English on the association between LRP5 gene and osteoporosis-related phenotypes, including bone mineral density and fracture. BMD data were summarized from individual studies by LRP5 genotype, and a synthesis of data was performed with random-effects meta-analyses. After excluding studies on animal and review papers, there were 19 studies for the synthesis. Among these studies, 10 studies used the rs3736228 (A1330V) polymorphism and reported BMD values. RESULTS: The 10 eligible studies comprised 16,705 individuals, with the majority being women (n = 8444), aged between 18 - 81 years. The overall distribution of genotype frequencies was: AA, 68%, AV and VV, 32%. However, the genotype frequency varied significantly within as well as between ethnic populations. On random-effects meta-analysis, lumbar spine BMD among individuals with the AA genotype was on average 0.018 (95% confidence interval [CI]: 0.012 to 0.023) g/cm2 higher than those with either AV or VV genotype. Similarly, femoral neck BMD among carriers of the AA genotype was 0.011 (95%CI: 0.004 to 0.017) g/cm2 higher than those without the genotype. While there was no significant heterogeneity in the association between the A1330V polymorphism and lumbar spine BMD (p = 0.55), the association was heterogeneous for femoral neck BMD (p = 0.05). The probability that the difference is greater than one standard deviation was 0.34 for femoral neck BMD and 0.54 for lumbar spine BMD. CONCLUSION: These results suggest that there is a modest effect of the A1330V polymorphism on BMD in the general population, and that the modest association may limit its clinical use.

Genetic profiling and individualized prognosis of fracture.

Fragility fracture is a serious public health problem in the world. The risk of fracture is determined by genetic and nongenetic clinical risk factors. This study sought to quantify the contribution of genetic profiling to fracture prognosis. The study was built on the ongoing Dubbo Osteoporosis Epidemiology Study, in which fracture and risk factors of 858 men and 1358 women had been monitored continuously from 1989 and 2008. Fragility fracture was ascertained by radiologic reports. Bone mineral density at the femoral neck was measured by dual-energy X-ray absorptiometry (DXA). Fifty independent genes with allele frequencies ranging from 0.01 to 0.60 and relative risks (RRs) ranging from 1.01 to 3.0 were simulated. Three predictive models were fitted to the data in which fracture was a function of (1) clinical risk factors only, (2) genes only, and (3) clinical risk factors and 50 genes. The area under the curve (AUC) for model 1 was 0.77, which was lower than that of model II (AUC = 0.82). Adding genes into the clinical risk factors model (model 3) increased the AUC to 0.88 and improved the accuracy of fracture classification by 45%, with most (41%) improvement in specificity. In the presence of clinical risk factors, the number of genes required to achieve an AUC of 0.85 was around 25. These results suggest that genetic profiling could enhance the predictive accuracy of fracture prognosis and help to identify high-risk individuals for appropriate management of osteoporosis or intervention.