Comparison of breast cancer prognostic tests CanAssist Breast and Oncotype DX.

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.

Development of a Novel Proteomic Risk-Classifier for Prognostication of Patients With Early-Stage Hormone Receptor-Positive Breast Cancer.

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.

Differential Diagnosis of Malaria on Truelab Uno®, a Portable, Real-Time, MicroPCR Device for Point-Of-Care Applications.

BACKGROUND: Sensitive 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. METHODS: Truenat® 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. RESULTS: The 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. CONCLUSION: The 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.