A novel biosignature identifies patients with DCIS with high risk of local recurrence after breast conserving surgery and radiation therapy

PURPOSE: There is an unmet need to identify women diagnosed with ductal carcinoma in situ (DCIS) with a low risk of in-breast recurrence (IBR) after breast conserving surgery (BCS), which could omit radiation therapy (RT), and also to identify those with elevated IBR risk remaining after BCS plus RT. We evaluated a novel biosignature for a residual risk subtype (RRt) to help identify patients with elevated IBR risk after BCS plus RT. METHODS AND MATERIALS: Women with DCIS treated with BCS with or without RT at centers in the US, Australia, and Sweden (n = 926) were evaluated. Patients were classified into 3 biosignature risk groups using the decision score (DS) and the RRt category: (1) Low Risk (DS ≤2.8 without RRt), (2) Elevated Risk (DS \u3e2.8 without RRt), and (3) Residual Risk (DS \u3e2.8 with RRt). Total and invasive IBR rates were assessed by risk group and treatment. RESULTS: In patients at low risk, there was no significant difference in IBR rates with or without RT (total, P = .8; invasive IBR, P = .7), and there were low overall 10-year rates (total, 5.1%; invasive, 2.7%). In patients with elevated risk, IBR rates were decreased with RT (total: hazard ratio [HR], 0.25; P \u3c .001; invasive: HR, 0.28; P = .005); 10-year rates were 20.6% versus 4.9% (total) and 10.9% versus 3.1% (invasive). In patients with residual risk, although IBR rates decreased with RT after BCS (total: HR, 0.21; P \u3c .001; invasive: HR, 0.29; P = .028), IBR rates remained significantly higher after RT compared with patients with elevated risk (HR, 2.5; 95% CI, 1.2-5.4; P = .018), with 10-year rates of 42.1% versus 14.7% (total) and 18.3% versus 6.5% (invasive). CONCLUSIONS: The novel biosignature identified patients with 3 distinct risk profiles: Low Risk patients with a low recurrence risk with or without adjuvant RT, Elevated Risk patients with excellent outcomes after BCS plus RT, and Residual Risk patients with an elevated recurrence risk remaining after BCS plus RT, warranting potential intensified or alternative treatment approaches

Integrating predictive models and sensors to manage food stability in supply chains

Food products move through complex supply chains, which require effective logistics to ensure food safety and to maximize shelf-life. Predictive models offer an efficient means to monitor and manage the safety and quality of perishable foods, however models require environmental data to estimate changes in microbial growth and sensory attributes. Currently, several companies produce Time-Temperature Indicators that react at rates that closely approximate predictive models; these devices are simple and cost-effective for food companies. However, even greater outcomes could be realized using sensors that transfer data to predictive models in real-time. This report describes developments in predictive models designed for supply chain management, as well as advances in environmental sensors. Important innovation can be realized in both supply chain logistics and food safety management by integrating these technologies

Analytical validation of the 7-gene biosignature for prediction of recurrence risk and radiation therapy benefit for breast ductal carcinoma in situ

PurposeDuctal carcinoma in situ (DCIS), is a noninvasive breast cancer, representing 20-25% of breast cancer diagnoses in the USA. Current treatment options for DCIS include mastectomy or breast-conserving surgery (BCS) with or without radiation therapy (RT), but optimal risk-adjusted treatment selection remains a challenge. Findings from past and recent clinical trials have failed to identify a ‘low risk’ group of patients who do not benefit significantly from RT after BCS. To address this unmet need, a DCIS biosignature, DCISionRT (PreludeDx, Laguna Hills, CA), was developed and validated in multiple cohorts. DCISionRT is a molecular assay with an algorithm reporting a recurrence risk score for patients diagnosed with DCIS intended to guide DCIS treatment. In this study, we present results from analytical validity, performance assessment, and clinical performance validation and clinical utility for the DCISionRT test comprised of multianalyte assays with algorithmic analysis.MethodsThe analytical validation of each molecular assay was performed based on the Clinical and Laboratory Standards Institute (CLSI) guidelines Quality Assurance for Design Control and Implementation of Immunohistochemistry Assays and the College of American Pathologists/American Society of Clinical Oncology (CAP/ASCO) recommendations for analytic validation of immunohistochemical assays.ResultsThe analytic validation showed that the molecular assays that are part of DCISionRT test have high sensitivity, specificity, and accuracy/reproducibility (≥95%). The analytic precision of the molecular assays under controlled non-standard conditions had a total standard deviation of 6.6 (100-point scale), where the analytic variables (Lot, Machine, Run) each contributed <1% of the total variance. Additionally, the precision in the DCISionRT test result (DS) had a 95%CI ≤0.4 DS units under controlled non-standard conditions (Day, Lot, and Machine) for molecular assays over a wide range of clinicopathologic factor values. Clinical validation showed that the test identified 37% of patients in a low-risk group with a 10-year invasive IBR rate of ~3% and an absolute risk reduction (ARR) from RT of 1% (number needed to treat, NNT=100), while remaining patients with higher DS scores (elevated-risk) had an ARR for RT of 9% (NNT=11) and 96% clinical sensitivity for RT benefit.ConclusionThe analytical performance of the PreludeDx DCISionRT molecular assays was high in representative formalin-fixed, paraffin-embedded breast tumor specimens. The DCISionRT test has been analytically validated and has been clinically validated in multiple peer-reviewed published studies

Prognostic Risk Assessment and Prediction of Radiotherapy Benefit for Women with Ductal Carcinoma In Situ (DCIS) of the Breast, in a Randomized Clinical Trial (SweDCIS)

Prediction of radiotherapy (RT) benefit after breast-conserving surgery (BCS) for DCIS is crucial. The aim was to validate a biosignature, DCISionRT®, in the SweDCIS randomized trial. Women were randomly assigned to RT or not after BCS, between 1987 and 2000. Tumor blocks were collected, and slides were sent to PreludeDxTM for testing. In 504 women with complete data and negative margins, DCISionRT divided 52% women into Elevated (DS > 3) and 48% in Low (DS ≤ 3) Risk groups. In the Elevated Risk group, RT significantly decreased relative 10-year ipsilateral total recurrence (TotBE) and 10-year ipsilateral invasive recurrence (InvBE) rates, HR 0.32 and HR 0.24, with absolute decreases of 15.5% and 9.3%. In the Low Risk group, there were no significant risk differences observed with radiotherapy. Using a cutoff of DS > 3.0, the test was not predictive for RT benefit (p = 0.093); however, above DS > 2.8 RT benefit was greater for InvBE (interaction p = 0.038). Recurrences at 10 years without radiotherapy increased significantly per 5 DS units (TotBE HR:1.5 and InvBE HR:1.5). Continuous DS was prognostic for TotBE risk although categorical DS did not reach significance. Absolute 10-year TotBE and InvBE risks appear sufficiently different to indicate that DCISionRT can aid physicians in selecting individualized adjuvant DCIS treatment strategies. Further analyses are planned in combined cohorts to increase statistical power

Baseline Immune Biomarkers as Predictors of MBSR(BC) Treatment Success in Off-Treatment Breast Cancer Patients

Researchers focused on patient-centered medicine are increasingly trying to identify baseline factors that predict treatment success. Because the quantity and function of lymphocyte subsets change during stress, we hypothesized that these subsets would serve as stress markers and therefore predict which breast cancer patients would benefit most from mindfulness-based stress reduction (MBSR)-facilitated stress relief. The purpose of this study was to assess whether baseline biomarker levels predicted symptom improvement following an MBSR intervention for breast cancer survivors (MBSR[BC]). This randomized controlled trial involved 41 patients assigned to either an MBSR(BC) intervention group or a no-treatment control group. Biomarkers were assessed at baseline, and symptom change was assessed 6 weeks later. Biomarkers included common lymphocyte subsets in the peripheral blood as well as the ability of T cells to become activated and secrete cytokines in response to stimulation with mitogens. Spearman correlations were used to identify univariate relationships between baseline biomarkers and 6-week improvement of symptoms. Next, backward elimination regression models were used to identify the strongest predictors from the univariate analyses. Multiple baseline biomarkers were significantly positively related to 6-week symptom improvement. The regression models identified B-lymphocytes and interferon-γ as the strongest predictors of gastrointestinal improvement (p \u3c .01), +CD4+CD8 as the strongest predictor of cognitive/psychological (CP) improvement (p = .02), and lymphocytes and interleukin (IL)-4 as the strongest predictors of fatigue improvement (p \u3c .01). These results provide preliminary evidence of the potential to use baseline biomarkers as predictors to identify the patients likely to benefit from this intervention