Use of proteomic strategies to identify a risk classifier that estimates probability of distant recurrence in early-stage hormone receptor (HR)-positive breast cancer is relevant to physiological cellular function and therefore to intrinsic tumor biology. We used a 298-sample retrospective training set to develop an immunohistochemistry-based novel risk classifier called CanAssist-Breast (CAB) which combines 5 prognostically relevant biomarkers and 3 clinico-pathological parameters to arrive at probability of distant recurrence within 5 years from diagnosis. Five selected biomarkers, namely, CD44, ABCC4, ABCC11, N-cadherin, and pan-cadherin, were chosen based on their role in tumor metastasis. The chosen biomarkers represent the hallmarks of cancer and are distinct from other proliferation and gene expression–based prognostic signatures. The 3 clinico-pathological parameters integrated into the machine learning–based CAB algorithm are tumor size, tumor grade, and node status. These features are used to calculate a “CAB risk score” that classifies patients into low- or high-risk groups and predicts probability of distant recurrence in 5 years. Independent clinical validation of CAB in a retrospective study comprising 196 patients indicated that distant metastasis-free survival (DMFS) was significantly different in the 2 risk groups. The difference in DMFS between the low- and high-risk categories was 19% in the validation cohort (P = .0002). In multivariate analysis, CAB risk score was the most significant independent predictor of distant recurrence with a hazard ratio of 4.3 (P = .0003). CanAssist-Breast is a precise and unique machine learning–based proteomic risk-classifier that can assist in risk stratification of patients with early-stage HR+ breast cancer.

BackgroundSensitive and specific detection of malarial parasites is crucial in controlling the significant malaria burden in the developing world. Also important is being able to identify life threatening Plasmodium falciparum malaria quickly and accurately to reduce malaria related mortality. Existing methods such as microscopy and rapid diagnostic tests (RDTs) have major shortcomings. Here, we describe a new real-time PCR-based diagnostic test device at point-of-care service for resource-limited settings.MethodsTruenat® Malaria, a chip-based microPCR test, was developed by bigtec Labs, Bangalore, India, for differential identification of Plasmodium falciparum and Plasmodium vivax parasites. The Truenat Malaria tests runs on bigtec’s Truelab Uno® microPCR device, a handheld, battery operated, and easy-to-use real-time microPCR device. The performance of Truenat® Malaria was evaluated versus the WHO nested PCR protocol. The Truenat® Malaria was further evaluated in a triple-blinded study design using a sample panel of 281 specimens created from the clinical samples characterized by expert microscopy and a rapid diagnostic test kit by the National Institute of Malaria Research (NIMR). A comparative evaluation was done on the Truelab Uno® and a commercial real-time PCR system.ResultsThe limit of detection of the Truenat Malaria assay was found to be <5 parasites/μl for both P. falciparum and P. vivax. The Truenat® Malaria test was found to have sensitivity and specificity of 100% each, compared to the WHO nested PCR protocol based on the evaluation of 100 samples. The sensitivity using expert microscopy as the reference standard was determined to be around 99.3% (95% CI: 95.5–99.9) at the species level. Mixed infections were identified more accurately by Truenat Malaria (32 samples identified as mixed) versus expert microscopy and RDTs which detected 4 and 5 mixed samples, respectively.ConclusionThe Truenat® Malaria microPCR test is a valuable diagnostic tool and implementation should be considered not only for malaria diagnosis but also for active surveillance and epidemiological intervention.

Background CanAssist Breast (CAB) is a prognostic test for early stage hormone receptor‐positive (HR+), human epidermal growth factor receptor 2 negative (HER2−) breast cancer patients, validated on Indian and Caucasian patients. The 21‐gene signature Oncotype DX (ODX) is the most widely used commercially available breast cancer prognostic test. In the current study, risk stratification of CAB is compared with that done with ODX along with the respective outcomes of these patients. Methods A cohort of 109 early stage breast cancer patients who had previously taken the ODX test were retested with CAB, and the results respectively compared with old cut‐offs of ODX as well as cut‐offs suggested by TAILORx, a prospective randomized trial of ODX. Distant metastasis‐free survival after 5 years was taken as the end point. Results CanAssist Breast stratified 83.5% of the cohort into low‐risk and 16.5% into high‐risk. With the TAILORx cut‐offs, ODX stratified the cohort into 89.9% low‐risk and 10.1% into high‐risk. The low, intermediate, and high‐risk groups with ODX old cut‐offs were 62.4%, 31.2%, and 6.4%, respectively. The overall concordance of CAB with ODX using both cut‐offs is 75%‐76%, with ~82%‐83% concordance in the low‐risk category of these tests. The NPV of the low‐risk category of CAB was 93.4%, and of ODX with TAILORx cut‐offs was 91.8% and 89.7% with old cut‐offs. Conclusions Compared to the concordance reported for other tests, CAB shows high concordance with ODX, and in addition shows comparable performance in the patient outcomes in this cohort. CAB is thus an excellent and cost‐effective alternative to ODX.

541 Background: Treatment decisions for early stage HR+/HER2neu- breast cancer patients in the West routinely depend on prognostic tests that predict risk of recurrence. However, such tests are rarely used in Asia due to prohibitive costs and lack of validation data on Asian patients. Chemotherapy is thus often a default treatment leading to physiological and financial toxicity. To address these, we have developed CanAssist Breast (CAB) as an affordable IHC-based prognostic test, retrospectively validated on ~1400 patients, 63% South Asians and rest Caucasians. To date CAB has been prescribed by 180+ doctors across 30 cities in India for ~600 patients in clinics, enabling personalized treatment decisions. Methods: Primary surgical FFPE blocks and clinical follow-up data were obtained from hospitals. GraphPad Prism and MedCalc were respectively used for Kaplan-Meier survival analyses and Cox logistic regression to calculate hazard ratios. Results: The median age of diagnosis in the validation cohort was 56 years, 63% patients with stage II disease and 60% node negative tumors. Distant Metastasis Free Survival (DMFS) in the low-risk category of the validation cohort was 95%, and 84% in high-risk (P < 0.0001). Similar results were obtained with the Caucasian subgroup, as also with the chemotherapy-naive subgroup (30% of the cohort), demonstrating that risk stratification by CAB is unaffected by race or chemotherapy. Next, the performance of CAB was compared with Oncotype DX (ODX). 83% patients stratified as low risk by ODX (RS 0-25) in a sub-cohort of 109 were also stratified as low-risk by CAB. To assess the impact of CAB in treatment decision making, we assessed the data of 589 patients who have undergone CAB testing so far, 288 were identified as low-risk. 93% of these CAB low-risk patients were not given chemotherapy, demonstrating the clinical impact of CAB. Conclusions: CAB is the first test of its kind to be retrospectively validated in Asia. It shows high concordance with ODX in risk stratification of patients. CAB has been in clinical practice in India and near-India markets for 2 years and is helping clinicians and patients in making affordable treatment decisions.

Aims: Assessment of 'risk of recurrence' in ER+ breast cancer patients based on clinical parameters and existing hormone receptor signaling pathway and/or proliferation based biomarkers is insufficient, leading to treatment of majority of patients with chemotherapy. First generation risk identification tests like OncotypeDx and Mammaprint are not impactful in India and SE Asia as are largely prognostic with limited chemotherapy-predictivity and are prohibitively expensive. A cost-effective 'predictive' test which will accurately estimate the 'risk of recurrence' for a 'broader' (node - & +) set of breast cancer patients in low resource settings is urgently required. Materials and Methods: Using a retrospective training cohort of 300 node– and node+ patients, we developed 'CanAssist-Breast'- a Morphometric Immunohistochemistry based test comprising 5 biomarkers plus three clinical parameters (Tumor size, grade and node status) to arrive at 'CanAssist-Breast Score'. The risk stratification model was developed using cutting edge support vector based machine learning technology. CanAssist-Breast Score stratifies patients into an all actionable 'low or high' risk for recurrence, with no intermediate zone. CanAssist-Breast biomarkers include cancer stem cell markers, Cadherins, and ATP transporter proteins - all critical players in the various steps of chemotherapy resistance leading to metastasis. Results: We validated CanAssist-Breast in accordance with EGAPP recommendations which require that prognostic tests be validated both analytically and clinically prior to being utilized in patients. Analytical validation experiments were performed to assess 'variation' in the outcome prediction due to critical IHC variables. We tested inter-pathologists, sample, operator and laboratory site variation and found high concordance in the outcome predictions across all variables, confirming the robustness and reproducibility of the test. Extended clinical validation on 1000+ pre and post-menopausal cases shows NPV of 95%. The majority of patients in 'low risk' had Stage 2, Grade 2/3 disease over Stage 1, Grade 1 disease, demonstrating that CanAssist-Breast reclassifies patients who would be considered high risk clinically. In a head-to-head pilot study of 100 patients with Oncotype Dx, CanAssist-Breast test had about 80% concordance with Oncotype in the 'low risk' category. Importantly, CanAssist-Breast correctly stratified few recurred cases as 'high risk' which were called 'low risk' by Oncotype Dx and thus were not treated with chemotherapy. Conclusion: In conclusion, we have developed a robust, accurate and low-cost prognostic test to predict risk of recurrence and enable optimal treatment planning in patients with early stage Breast Cancer. Citation Format: SP S, Bakre MM, Ramkumar C, Basavaraj C, Attuluri A, Madhav L, Prakash C, Naidu N, Malpani S. Development and validation of a broad-based second generation multi marker “Morphometric IHC” test for optimal treatment planning of stage 1 and 2 breast cancer patients in low resource settings [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P3-08-10.