Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk

Funder: U.S. Department of Health & Human Services | National Institutes of Health (NIH)Abstract: Mammographic density (MD) phenotypes are strongly associated with breast cancer risk and highly heritable. In this GWAS meta-analysis of 24,192 women, we identify 31 MD loci at P < 5 × 10−8, tripling the number known to 46. Seventeen identified MD loci also are associated with breast cancer risk in an independent meta-analysis (P < 0.05). Mendelian randomization analyses show that genetic estimates of dense area (DA), nondense area (NDA), and percent density (PD) are all significantly associated with breast cancer risk (P < 0.05). Pathway analyses reveal distinct biological processes involving DA, NDA and PD loci. These findings provide additional insights into the genetic basis of MD phenotypes and their associations with breast cancer risk

Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is sensitive but not specific to determining treatment response in early stage triple-negative breast cancer (TNBC) patients. We propose an efficient computerized technique for assessing treatment response, specifically the residual tumor (RT) status and pathological complete response (pCR), in response to neoadjuvant chemotherapy. The proposed approach is based on Riesz wavelet analysis of pharmacokinetic maps derived from noninvasive DCE-MRI scans, obtained before and after treatment. We compared the performance of Riesz features with the traditional gray level co-occurrence matrices and a comprehensive characterization of the lesion that includes a wide range of quantitative features (e.g., shape and boundary). We investigated a set of predictive models ( ∼96 ) incorporating distinct combinations of quantitative characterizations and statistical models at different time points of the treatment and some area under the receiver operating characteristic curve (AUC) values we reported are above 0.8. The most efficient models are based on first-order statistics and Riesz wavelets, which predicted RT with an AUC value of 0.85 and pCR with an AUC value of 0.83, improving results reported in a previous study by ∼13% . Our findings suggest that Riesz texture analysis of TNBC lesions can be considered a potential framework for optimizing TNBC patient care

Case-control study of mammographic density and breast cancer risk using processed digital mammograms

Abstract Background Full-field digital mammography (FFDM) has largely replaced film-screen mammography in the US. Breast density assessed from film mammograms is strongly associated with breast cancer risk, but data are limited for processed FFDM images used for clinical care. Methods We conducted a case-control study nested among non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were aged 40 to 74 years and had screening mammograms acquired on Hologic FFDM machines. Cases (n = 297) were women with a first invasive breast cancer diagnosed after a screening FFDM. For each case, up to five controls (n = 1149) were selected, matched on age and year of FFDM and image batch number, and who were still under follow-up and without a history of breast cancer at the age of diagnosis of the matched case. Percent density (PD) and dense area (DA) were assessed by a radiological technologist using Cumulus. Conditional logistic regression was used to estimate odds ratios (ORs) for breast cancer associated with PD and DA, modeled continuously in standard deviation (SD) increments and categorically in quintiles, after adjusting for body mass index, parity, first-degree family history of breast cancer, breast area, and menopausal hormone use. Results Median intra-reader reproducibility was high with a Pearson’s r of 0.956 (range 0.902 to 0.983) for replicate PD measurements across 23 image batches. The overall mean was 20.02 (SD, 14.61) for PD and 27.63 cm2 (18.22 cm2) for DA. The adjusted ORs for breast cancer associated with each SD increment were 1.70 (95 % confidence interval, 1.41–2.04) for PD, and 1.54 (1.34–1.77) for DA. The adjusted ORs for each quintile were: 1.00 (ref.), 1.49 (0.91–2.45), 2.57 (1.54–4.30), 3.22 (1.91–5.43), 4.88 (2.78–8.55) for PD, and 1.00 (ref.), 1.43 (0.85–2.40), 2.53 (1.53–4.19), 2.85 (1.73–4.69), 3.48 (2.14–5.65) for DA. Conclusions PD and DA measured using Cumulus on processed FFDM images are positively associated with breast cancer risk, with similar magnitudes of association as previously reported for film-screen mammograms. Processed digital mammograms acquired for routine clinical care in a general practice setting are suitable for breast density and cancer research

Identification of 31 loci for mammographic density phenotypes and their associations with breast cancer risk

Funder: U.S. Department of Health & Human Services | National Institutes of Health (NIH)Abstract: Mammographic density (MD) phenotypes are strongly associated with breast cancer risk and highly heritable. In this GWAS meta-analysis of 24,192 women, we identify 31 MD loci at P < 5 × 10−8, tripling the number known to 46. Seventeen identified MD loci also are associated with breast cancer risk in an independent meta-analysis (P < 0.05). Mendelian randomization analyses show that genetic estimates of dense area (DA), nondense area (NDA), and percent density (PD) are all significantly associated with breast cancer risk (P < 0.05). Pathway analyses reveal distinct biological processes involving DA, NDA and PD loci. These findings provide additional insights into the genetic basis of MD phenotypes and their associations with breast cancer risk

The PATHFINDER Study: Assessment of the Implementation of an Investigational Multi-Cancer Early Detection Test into Clinical Practice

To examine the extent of the evaluation required to achieve diagnostic resolution and the test performance characteristics of a targeted methylation cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test, ~6200 participants ≥50 years with (cohort A) or without (cohort B) ≥1 of 3 additional specific cancer risk factors will be enrolled in PATHFINDER (NCT04241796), a prospective, longitudinal, interventional, multi-center study. Plasma cfDNA from blood samples will be analyzed to detect abnormally methylated DNA associated with cancer (i.e., cancer “signal”) and a cancer signal origin (i.e., tissue of origin). Participants with a “signal detected” will undergo further diagnostic evaluation per guiding physician discretion; those with a “signal not detected” will be advised to continue guideline-recommended screening. The primary objective will be to assess the number and types of subsequent diagnostic tests needed for diagnostic resolution. Based on microsimulations (using estimates of cancer incidence and dwell times) of the typical risk profiles of anticipated participants, the median (95% CI) number of participants with a “signal detected” result is expected to be 106 (87–128). Subsequent diagnostic evaluation is expected to detect 52 (39–67) cancers. The positive predictive value of the MCED test is expected to be 49% (39–58%). PATHFINDER will evaluate the integration of a cfDNA-based MCED test into existing clinical cancer diagnostic pathways. The study design of PATHFINDER is described here