Genetic counseling is now routinely offered to individuals at high risk of carrying a BRCA1 or BRCA2 mutation. Risk prediction provided by the counselor requires reliable estimates of the mutation ...penetrance. Such penetrance has been investigated by studies worldwide. The reported estimates vary. To facilitate clinical management and counseling of the at-risk population, we address this issue through a meta-analysis.
We conducted a literature search on PubMed and selected studies that had nonoverlapping patient data, contained genotyping information, used statistical methods that account for the ascertainment, and reported risks in a useable format. We subsequently combined the published estimates using the DerSimonian and Laird random effects modeling approach.
Ten studies were eligible under the selection criteria. Between-study heterogeneity was observed. Study population, mutation type, design, and estimation methods did not seem to be systematic sources of heterogeneity. Meta-analytic mean cumulative cancer risks for mutation carriers at age 70 years were as follows: breast cancer risk of 57% (95% CI, 47% to 66%) for BRCA1 and 49% (95% CI, 40% to 57%) for BRCA2 mutation carriers; and ovarian cancer risk of 40% (95% CI, 35% to 46%) for BRCA1 and 18% (95% CI, 13% to 23%) for BRCA2 mutation carriers. We also report the prospective risks of developing cancer for currently asymptomatic carriers.
This article provides a set of risk estimates for BRCA1 and BRCA2 mutation carriers that can be used by counselors and clinicians who are interested in advising patients based on a comprehensive set of studies rather than one specific study.
Abstract
The benefit of integrating batches of genomic data to increase statistical power is often hindered by batch effects, or unwanted variation in data caused by differences in technical factors ...across batches. It is therefore critical to effectively address batch effects in genomic data to overcome these challenges. Many existing methods for batch effects adjustment assume the data follow a continuous, bell-shaped Gaussian distribution. However in RNA-seq studies the data are typically skewed, over-dispersed counts, so this assumption is not appropriate and may lead to erroneous results. Negative binomial regression models have been used previously to better capture the properties of counts. We developed a batch correction method, ComBat-seq, using a negative binomial regression model that retains the integer nature of count data in RNA-seq studies, making the batch adjusted data compatible with common differential expression software packages that require integer counts. We show in realistic simulations that the ComBat-seq adjusted data results in better statistical power and control of false positives in differential expression compared to data adjusted by the other available methods. We further demonstrated in a real data example that ComBat-seq successfully removes batch effects and recovers the biological signal in the data.
Although it has been hypothesized that some of the somatic mutations found in tumors may occur before tumor initiation, there is little experimental or conceptual data on this topic. To gain insights ...into this fundamental issue, we formulated a mathematical model for the evolution of somatic mutations in which all relevant phases of a tissue’s history are considered. The model makes the prediction, validated by our empirical findings, that the number of somatic mutations in tumors of self-renewing tissues is positively correlated with the age of the patient at diagnosis. Importantly, our analysis indicates that half or more of the somatic mutations in certain tumors of self-renewing tissues occur before the onset of neoplasia. The model also provides a unique way to estimate the in vivo tissue-specific somatic mutation rates in normal tissues directly from the sequencing data of tumors. Our results have substantial implications for the interpretation of the large number of genome-wide cancer studies now being undertaken.
Significance The number of driver events required for human tumorigenesis has remained one of the fundamental issues in cancer research since the seminal studies of Armitage and Doll. This question ...has become even more important with the recent genome-wide sequencing studies of cancer, whose major goal is the identification of the driver genes responsible for tumor initiation and progression. By using a novel approach that combines conventional epidemiologic studies with genome-wide sequencing data, we show that only three sequential mutations are required to develop lung and colon adenocarcinomas, a number that is lower than what is typically thought to be required for the formation of cancers of these and other organs. This finding has important implications for the design of future cancer genome-sequencing efforts.
Cancer arises through the sequential accumulation of mutations in oncogenes and tumor suppressor genes. However, how many such mutations are required for a normal human cell to progress to an advanced cancer? The best estimates for this number have been provided by mathematical models based on the relation between age and incidence. For example, the classic studies of Nordling Nordling CO (1953) Br J Cancer 7(1):68–72 and Armitage and Doll Armitage P, Doll R (1954) Br J Cancer 8(1):1–12 suggest that six or seven sequential mutations are required. Here, we describe a different approach to derive this estimate that combines conventional epidemiologic studies with genome-wide sequencing data: incidence data for different groups of patients with the same cancer type were compared with respect to their somatic mutation rates. In two well-documented cancer types (lung and colon adenocarcinomas), we find that only three sequential mutations are required to develop cancer. This conclusion deepens our understanding of the process of carcinogenesis and has important implications for the design of future cancer genome-sequencing efforts.
While various studies have highlighted the prognostic significance of pathologic complete response (pCR) after neoadjuvant chemotherapy (NAT), the impact of additional adjuvant therapy after pCR is ...not known.
PubMed was searched for studies with NAT for breast cancer and individual patient-level data was extracted for analysis using plot digitizer software. HRs, with 95% probability intervals (PI), measuring the association between pCR and overall survival (OS) or event-free survival (EFS), were estimated using Bayesian piece-wise exponential proportional hazards hierarchical models including pCR as predictor.
Overall, 52 of 3,209 publications met inclusion criteria, totaling 27,895 patients. Patients with a pCR after NAT had significantly better EFS (HR = 0.31; 95% PI, 0.24-0.39), particularly for triple-negative (HR = 0.18; 95% PI, 0.10-0.31) and HER2
(HR = 0.32; 95% PI, 0.21-0.47) disease. Similarly, pCR after NAT was also associated with improved survival (HR = 0.22; 95% PI, 0.15-0.30). The association of pCR with improved EFS was similar among patients who received subsequent adjuvant chemotherapy (HR = 0.36; 95% PI, 0.19-0.67) and those without adjuvant chemotherapy (HR = 0.36; 95% PI, 0.27-0.54), with no significant difference between the two groups (
= 0.60).
Achieving pCR following NAT is associated with significantly better EFS and OS, particularly for triple-negative and HER2
breast cancer. The similar outcomes with or without adjuvant chemotherapy in patients who attain pCR likely reflects tumor biology and systemic clearance of micrometastatic disease, highlighting the potential of escalation/deescalation strategies in the adjuvant setting based on neoadjuvant response.
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Estimates of familial cancer risk from population-based studies are essential components of cancer risk prediction.
To estimate familial risk and heritability of cancer types in a large twin cohort.
...Prospective study of 80,309 monozygotic and 123,382 same-sex dizygotic twin individuals (N = 203,691) within the population-based registers of Denmark, Finland, Norway, and Sweden. Twins were followed up a median of 32 years between 1943 and 2010. There were 50,990 individuals who died of any cause, and 3804 who emigrated and were lost to follow-up.
Shared environmental and heritable risk factors among pairs of twins.
The main outcome was incident cancer. Time-to-event analyses were used to estimate familial risk (risk of cancer in an individual given a twin's development of cancer) and heritability (proportion of variance in cancer risk due to interindividual genetic differences) with follow-up via cancer registries. Statistical models adjusted for age and follow-up time, and accounted for censoring and competing risk of death.
A total of 27,156 incident cancers were diagnosed in 23,980 individuals, translating to a cumulative incidence of 32%. Cancer was diagnosed in both twins among 1383 monozygotic (2766 individuals) and 1933 dizygotic (2866 individuals) pairs. Of these, 38% of monozygotic and 26% of dizygotic pairs were diagnosed with the same cancer type. There was an excess cancer risk in twins whose co-twin was diagnosed with cancer, with estimated cumulative risks that were an absolute 5% (95% CI, 4%-6%) higher in dizygotic (37%; 95% CI, 36%-38%) and an absolute 14% (95% CI, 12%-16%) higher in monozygotic twins (46%; 95% CI, 44%-48%) whose twin also developed cancer compared with the cumulative risk in the overall cohort (32%). For most cancer types, there were significant familial risks and the cumulative risks were higher in monozygotic than dizygotic twins. Heritability of cancer overall was 33% (95% CI, 30%-37%). Significant heritability was observed for the cancer types of skin melanoma (58%; 95% CI, 43%-73%), prostate (57%; 95% CI, 51%-63%), nonmelanoma skin (43%; 95% CI, 26%-59%), ovary (39%; 95% CI, 23%-55%), kidney (38%; 95% CI, 21%-55%), breast (31%; 95% CI, 11%-51%), and corpus uteri (27%; 95% CI, 11%-43%).
In this long-term follow-up study among Nordic twins, there was significant excess familial risk for cancer overall and for specific types of cancer, including prostate, melanoma, breast, ovary, and uterus. This information about hereditary risks of cancers may be helpful in patient education and cancer risk counseling.
The mechanisms contributing to transcription-associated genomic instability are both complex and incompletely understood. Although R-loops are normal transcriptional intermediates, they are also ...associated with genomic instability. Here, we show that BRCA1 is recruited to R-loops that form normally over a subset of transcription termination regions. There it mediates the recruitment of a specific, physiological binding partner, senataxin (SETX). Disruption of this complex led to R-loop-driven DNA damage at those loci as reflected by adjacent γ-H2AX accumulation and ssDNA breaks within the untranscribed strand of relevant R-loop structures. Genome-wide analysis revealed widespread BRCA1 binding enrichment at R-loop-rich termination regions (TRs) of actively transcribed genes. Strikingly, within some of these genes in BRCA1 null breast tumors, there are specific insertion/deletion mutations located close to R-loop-mediated BRCA1 binding sites within TRs. Thus, BRCA1/SETX complexes support a DNA repair mechanism that addresses R-loop-based DNA damage at transcriptional pause sites.
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•Endogenous BRCA1 and senataxin (SETX) interact in a BRCA1-driven process•BRCA1/SETX complexes are recruited to R-loop-associated termination regions (TRs)•BRCA1/SETX complexes suppress transcriptional DNA damage arising at nearby R-loops•BRCA1 breast cancers reveal indel mutations near BRCA1 TR binding regions
Transcriptional R-loops represent a potential threat to genome integrity. Hatchi et al. show that BRCA1, in partnership with SETX, is engaged in a DNA repair mechanism that deals with R-loop-associated genomic instability at transcriptional termination pause sites.
New DNA sequencing methods will soon make it possible to identify all germline variants in any individual at a reasonable cost. However, the ability of whole-genome sequencing to predict ...predisposition to common diseases in the general population is unknown. To estimate this predictive capacity, we use the concept of a "genometype." A specific genometype represents the genomes in the population conferring a specific level of genetic risk for a specified disease. Using this concept, we estimated the maximum capacity of whole-genome sequencing to identify individuals at clinically significant risk for 24 different diseases. Our estimates were derived from the analysis of large numbers of monozygotic twin pairs; twins of a pair share the same genometype and therefore identical genetic risk factors. Our analyses indicate that (i) for 23 of the 24 diseases, most of the individuals will receive negative test results; (ii) these negative test results will, in general, not be very informative, because the risk of developing 19 of the 24 diseases in those who test negative will still be, at minimum, 50 to 80% of that in the general population; and (iii) on the positive side, in the best-case scenario, more than 90% of tested individuals might be alerted to a clinically significant predisposition to at least one disease. These results have important implications for the valuation of genetic testing by industry, health insurance companies, public policy-makers, and consumers.
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