Positive Predictive Value for Multitarget Stool DNA After Bariatric and Metabolic Surgery.

BACKGROUND AND AIMS: Bariatric and metabolic surgery (BMS) may adversely affect noninvasive stool tests for colorectal cancer (CRC) screening through several mechanisms. Multitarget stool DNA (mt-sDNA) is approved for CRC screening; however, performance in post-BMS patients is unknown. As the rates of BMS are anticipated to increase with rising incidence of obesity, it is important to evaluate mt-sDNA test performance among these patients. METHODS: In a multisite academic and community-based practice, we obtained mt-sDNA results from 10/2014 to 12/2019 through electronic records and an institutional BMS registry. Average CRC risk patients with BMS prior to a positive mt-sDNA underwent a detailed chart review. Follow-up colonoscopy findings were compared to those among BMS patients screened with colonoscopy alone and a historical cohort of patients without BMS, screened by mt-sDNA. The primary study endpoint was the positive predictive value (PPV) for advanced colorectal neoplasia. RESULTS: Among 336 average-risk patients who had mt-sDNA after BMS, mt-sDNA was positive in 49 (14.6%), 47/49 (96%) underwent follow-up colonoscopy, and the PPV for advanced neoplasia was 12/47 (25.5%). This is similar to the PPV for advanced colorectal neoplasia (425/1542, 28%) in a historical cohort of persons without prior BMS, screened by mt-sDNA at our center (P = .86). Among those who had prior BMS, the rate of advanced neoplasia was higher after mt-sDNA compared to screening colonoscopy alone. CONCLUSION: Despite anatomic and physiologic mechanisms that could alter blood or DNA content in stool, BMS does not appear to adversely affect the PPV of mt-sDNA.

Neoplasia Diagnosis After Multi-target Stool DNA Is Enhanced Among Lowest Baseline Detectors.

BACKGROUND AND AIMS: Variation in colorectal neoplasia detection limits the effectiveness of screening colonoscopy. By evaluating neoplasia detection rates of individual colonoscopists, we aimed to quantify the effects of pre-procedural knowledge of a positive (+) multi-target stool DNA (mt-sDNA) on colonoscopy quality metrics. METHODS: We retrospectively identified physicians who performed a high volume of + mt-sDNA colonoscopies; colorectal neoplasia at post-mt-sDNA colonoscopy was recorded. These colonoscopists were stratified into quartiles based on baseline adenoma detection rates. Baseline colonoscopy adenoma detection rates and sessile serrated lesion detection rates were compared to post-mt-sDNA colonoscopy neoplasia diagnosis rates among each quartile. Withdrawal times were measured from negative exams. RESULTS: During the study period (2014-17) the highest quartile of physicians by volume of post-mt-sDNA colonoscopies were evaluated. Among thirty-five gastroenterologists, their median screening colonoscopy adenoma detection rate was 32% (IQR, 28-39%) and serrated lesion detection rate was 13% (8-15%). After + mt-sDNA, adenoma diagnosis increased to 47% (36-56%) and serrated lesion diagnosis increased to 31% (17-42%) (both p < 0.0001). Median withdrawal time increased from 10 (7-13) to 12 (10-17) minutes (p < 0.0001) and was proportionate across quartiles. After + mt-sDNA, lower baseline detectors had disproportionately higher rates of adenoma diagnosis in female versus male patients (p = 0.048) and higher serrated neoplasia diagnosis rates among all patients (p = 0.0092). CONCLUSIONS: Knowledge of + mt-sDNA enriches neoplasia diagnosis compared to average risk screening exams. Adenomatous and serrated lesion diagnosis was magnified among those with lower adenoma detection rates. Awareness of the mt-sDNA result may increase physician attention during colonoscopy. Pre-procedure knowledge of a positive mt-sDNA test improves neoplasia diagnosis rates among colonoscopists with lower baseline adenoma detection rates, independent of withdrawal time.

Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning.

Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of background terminal duct lobular units (TDLUs), the structures from which breast cancers arise. Studies indicate that increased levels of age-related TDLU involution in BBD biopsies predict lower breast cancer risk, and therefore its assessment may have potential value in risk assessment and management. However, assessment of TDLU involution is time-consuming and difficult to standardize and quantitate. Accordingly, we developed a CNN to enable automated quantitative measurement of TDLU involution and tested its performance in 174 specimens selected from the pathology archives at Mayo Clinic, Rochester, MN. The CNN was trained and tested on a subset of 33 biopsies, delineating important tissue types. Nine quantitative features were extracted from delineated TDLU regions. Our CNN reached an overall dice-score of 0.871 (±0.049) for tissue classes versus reference standard annotation. Consensus of four reviewers scoring 705 images for TDLU involution demonstrated substantial agreement with the CNN method (unweighted κappa = 0.747 ± 0.01). Quantitative involution measures showed anticipated associations with BBD histology, breast cancer risk, breast density, menopausal status, and breast cancer risk prediction scores (p < 0.05). Our work demonstrates the potential to improve risk prediction for women with BBD biopsies by applying CNN approaches to generate automated quantitative evaluation of TDLU involution.

Accurate Nonendoscopic Detection of Barrett's Esophagus by Methylated DNA Markers: A Multisite Case Control Study.

INTRODUCTION: Nonendoscopic Barrett's esophagus (BE) screening may help improve esophageal adenocarcinoma outcomes. We previously demonstrated promising accuracy of methylated DNA markers (MDMs) for the nonendoscopic diagnosis of BE using samples obtained from a capsule sponge-on-string (SOS) device. We aimed to assess the accuracy of these MDMs in an independent cohort using a commercial grade assay. METHODS: BE cases had ≥ 1 cm of circumferential BE with intestinal metaplasia; controls had no endoscopic evidence of BE. The SOS device was withdrawn 8 minutes after swallowing, followed by endoscopy (the criterion standard). Highest performing MDMs from a previous study were blindly assessed on extracted bisulfite-converted DNA by target enrichment long-probe quantitative amplified signal (TELQAS) assays. Optimal MDM combinations were selected and analyzed using random forest modeling with in silico cross-validation. RESULTS: Of 295 patients consented, 268 (91%) swallowed the SOS device; 112 cases and 89 controls met the pre-established inclusion criteria. The median BE length was 6 cm (interquartile range 4-9), and 50% had no dysplasia. The cross-validated sensitivity and specificity of a 5 MDM random forest model were 92% (95% confidence interval 85%-96%) and 94% (95% confidence interval 87%-98%), respectively. Model performance was not affected by age, gender, or smoking history but was influenced by the BE segment length. SOS administration was well tolerated (median [interquartile range] tolerability 2 [0, 4] on 10 scale grading), and 95% preferred SOS over endoscopy. DISCUSSION: Using a minimally invasive molecular approach, MDMs assayed from SOS samples show promise as a safe and accurate nonendoscopic test for BE prediction.

Model for Predicting Breast Cancer Risk in Women With Atypical Hyperplasia.

Purpose Women with atypical hyperplasia (AH) on breast biopsy have an aggregate increased risk of breast cancer (BC), but existing risk prediction models do not provide accurate individualized estimates of risk in this subset of high-risk women. Here, we used the Mayo benign breast disease cohort to develop and validate a model of BC risk prediction that is specifically for women with AH, which we have designated as AH-BC. Patients and Methods Retrospective cohorts of women age 18 to 85 years with pathologically confirmed benign AH from Rochester, MN, and Nashville, TN, were used for model development and external validation, respectively. Clinical risk factors and histologic features of the tissue biopsy were selected using L1-penalized Cox proportional hazards regression. Identified features were included in a Fine and Gray regression model to estimate BC risk, with death as a competing risk. Model discrimination and calibration were assessed in the model-building set and an external validation set. Results The model-building set consisted of 699 women with AH, 142 of whom developed BC (median follow-up, 8.1 years), and the external validation set consisted of 461 women with 114 later BC events (median follow-up, 11.4 years). The final AH-BC model included three covariates: age at biopsy, age at biopsy squared, and number of foci of AH. At 10 years, the AH-BC model demonstrated good discrimination (0.63 [95% CI, 0.57 to 0.70]) and calibration (0.87 [95% CI, 0.66 to 1.24]). In the external validation set, the model showed acceptable discrimination (0.59 [95% CI, 0.51 to 0.67]) and calibration (0.91 [95% CI, 0.65 to 1.42]). Conclusion We have created a new model with which to refine BC risk prediction for women with AH. The AH-BC model demonstrates good discrimination and calibration, and it validates in an external data set.

NanoString-based breast cancer risk prediction for women with sclerosing adenosis.

PURPOSE: Sclerosing adenosis (SA), found in » of benign breast disease (BBD) biopsies, is a histological feature characterized by lobulocentric proliferation of acini and stromal fibrosis and confers a two-fold increase in breast cancer risk compared to women in the general population. We evaluated a NanoString-based gene expression assay to model breast cancer risk using RNA derived from formalin-fixed, paraffin-embedded (FFPE) biopsies with SA. METHODS: The study group consisted of 151 women diagnosed with SA between 1967 and 2001 within the Mayo BBD cohort, of which 37 subsequently developed cancer within 10 years (cases) and 114 did not (controls). RNA was isolated from benign breast biopsies, and NanoString-based methods were used to assess expression levels of 61 genes, including 35 identified by previous array-based profiling experiments and 26 from biological insight. Diagonal linear discriminant analysis of these data was used to predict cancer within 10 years. Predictive performance was assessed with receiver operating characteristic area under the curve (ROC-AUC) values estimated from 5-fold cross-validation. RESULTS: Gene expression prediction models achieved cross-validated ROC-AUC estimates ranging from 0.66 to 0.70. Performing univariate associations within each of the five folds consistently identified genes DLK2, EXOC6, KIT, RGS12, and SORBS2 as significant; a model with only these five genes showed cross-validated ROC-AUC of 0.75, which compared favorably to risk prediction using established clinical models (Gail/BCRAT: 0.57; BBD-BC: 0.67). CONCLUSIONS: Our results demonstrate that biomarkers of breast cancer risk can be detected in benign breast tissue years prior to cancer development in women with SA. These markers can be assessed using assay methods optimized for RNA derived from FFPE biopsy tissues which are commonly available.

Breast Cancer Risk and Progressive Histology in Serial Benign Biopsies.

Background: More than 1 million women per year in the United States with benign breast biopsies are known to be at elevated risk for breast cancer (BC), with risk stratified on histologic categories of epithelial proliferation. Here we assessed women who had serial benign biopsies over time and how changes in the histologic classification affected BC risk. Methods: In the Mayo Clinic Benign Breast Disease Cohort of 13 466 women, 1414 women had multiple metachronous benign biopsies (10.5%). Both initial and subsequent biopsies were assessed histologically. BC risk for clinical and prognostic factors was assessed using subdistribution models to account for competing risks, and logistic regression/Wilcoxon/chi-square tests to assess covariates. All statistical tests were two-sided. Results: Breast cancer risk for women with serial biopsies, stratified by histologic category in the later biopsies, was similar to women with a single biopsy. We found that changes in histological category between initial and subsequent biopsy statistically significantly impacted BC risk. Women with nonproliferative initial findings and subsequent proliferative findings had an increased risk (hazard ratio [HR] = 1.77, 95% confidence interval [CI] = 1.06 to 2.94, P = .03) compared with no change. Among women with proliferative disease without atypia at initial biopsy, risk decreased if later biopsy regressed to nonproliferative (HR = 0.49, 95% CI = 0.25 to 0.98) and increased if later biopsy showed progression to atypical hyperplasia (HR = 1.49, 95% CI = 0.73 to 3.05) compared with no change ( P = .04). Conclusions: We found that breast cancer risk increases in women with progressive epithelial proliferation over time and decreases in women whose biopsies show less proliferation. This finding has important implications for effective clinical management of the 100 000 women per year who have multiple benign breast biopsies.

Genetically Predicted Telomere Length is not Associated with Pancreatic Cancer Risk.

Background: Epidemiologic associations of leukocyte telomere length (LTL) and pancreatic ductal adenocarcinoma (PDAC) have been inconsistent owing, in part, to variation in telomere length (TL) assessment across studies. To overcome this limitation and address concerns of potential reverse causation, we used carriage of telomere-related alleles to genetically predict TL and examined its association with PDAC.Methods: A case-control study of 1,500 PDAC cases and 1,500 controls, frequency-matched on age and sex was performed. Eight of nine polymorphisms previously associated with variation in LTL were analyzed. Genetic risk scores (GRS) consisting of the TL-related polymorphisms were computed as the number of long TL alleles carried by an individual scaled to published kilobase pairs of TL associated with each allele. Participants were further categorized on the basis of the number of short TL alleles they carry across all eight SNPs. Associations were examined in additive and dominant models using logistic regression to calculate ORs and 95% confidence intervals (CI).Results: In age- and sex-adjusted models, one short TL allele (rs10936599, T) was associated with reduced risk, whereas another short TL allele (rs2736100, A) was associated with increased risk, with per-allele ORs of 0.89 (95% CI, 0.79-0.99) and 1.13 (95% CI, 1.01-1.24), respectively. No association was observed with GRS or short TL allele counts, and no associations were observed in the dominant models.Conclusions: Findings suggest that genetically predicted short TL is not associated with PDAC risk.Impact: Common genetic determinants of short TL do not appear to influence PDAC risk. Cancer Epidemiol Biomarkers Prev; 26(6); 971-4. ©2017 AACR.