Analytical validation of CanAssist-Breast: an immunohistochemistry based prognostic test for hormone receptor positive breast cancer patients

Autor: Naveen Krishnamurthy, Charusheila Ramkumar, Arun Kumar Attuluri, Aparna Gunda, Chandra Prakash V Serkad, Nirupama Naidu, Manjiri M Bakre, Suchita Kanaldekar, Lekshmi Madhav, Prathima R, Ljubomir Buturovic, Chetana Basavaraj
Rok vydání: 2018
Předmět:
0301 basic medicine
Oncology
Cancer Research
medicine.medical_specialty
Correlation coefficient
Analytical validation
Concordance
Breast Neoplasms
lcsh:RC254-282
Risk Assessment
03 medical and health sciences
0302 clinical medicine
Breast cancer
Internal medicine
Genetics
medicine
Biomarkers
Tumor
Humans
Breast
Repeatability
Bland–Altman plot
Reproducibility
Framingham Risk Score
business.industry
Patient Selection
Reproducibility of Results
medicine.disease
lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Prognosis
Immunohistochemistry
Tumor Burden
030104 developmental biology
Treatment Outcome
Receptors
Estrogen
Chemotherapy
Adjuvant
030220 oncology & carcinogenesis
Lymphatic Metastasis
Cohort
Female
CanAssist-breast
Neoplasm Grading
Neoplasm Recurrence
Local
business
Receptors
Progesterone
Research Article
Zdroj: BMC Cancer
BMC Cancer, Vol 19, Iss 1, Pp 1-10 (2019)
ISSN: 1471-2407
Popis: Background CanAssist-Breast is an immunohistochemistry based test that predicts risk of distant recurrence in early-stage hormone receptor positive breast cancer patients within first five years of diagnosis. Immunohistochemistry gradings for 5 biomarkers (CD44, ABCC4, ABCC11, N-Cadherin and pan-Cadherins) and 3 clinical parameters (tumor size, tumor grade and node status) of 298 patient cohort were used to develop a machine learning based statistical algorithm. The algorithm generates a risk score based on which patients are stratified into two groups, low- or high-risk for recurrence. The aim of the current study is to demonstrate the analytical performance with respect to repeatability and reproducibility of CanAssist-Breast. Methods All potential sources of variation in CanAssist-Breast testing involving operator, run and observer that could affect the immunohistochemistry performance were tested using appropriate statistical analysis methods for each of the CanAssist-Breast biomarkers using a total 309 samples. The cumulative effect of these variations in the immunohistochemistry gradings on the generation of CanAssist-Breast risk score and risk category were also evaluated. Intra-class Correlation Coefficient, Bland Altman plots and pair-wise agreement were performed to establish concordance on IHC gradings, risk score and risk categorization respectively. Results CanAssist-Breast test exhibited high levels of concordance on immunohistochemistry gradings for all biomarkers with Intra-class Correlation Coefficient of ≥0.75 across all reproducibility and repeatability experiments. Bland-Altman plots demonstrated that agreement on risk scores between the comparators was within acceptable limits. We also observed > 90% agreement on risk categorization (low- or high-risk) across all variables tested. Conclusions The extensive analytical validation data for the CanAssist-Breast test, evaluating immunohistochemistry performance, risk score generation and risk categorization showed excellent agreement across variables, demonstrating that the test is robust. Electronic supplementary material The online version of this article (10.1186/s12885-019-5443-5) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
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