Development of CanAssist Breast

OncoStem Diagnostics has developed an immunohistochemistry(IHC)-based novel risk classifier called CanAssist Breast which combines five prognostically relevant biomarkers and three clinicopathological parameters to arrive at the probability of distant recurrence within five years from diagnosis.

CanAssist Breast is a first of its kind IHC-based prognostic test developed using machine learning-based techniques. These machine learning techniques allow assessment of non-linear interactions between biomarkers, which is critical given crosstalk between signaling pathways in cancer.

The CanAssist Breast algorithm produces a risk score ranging from 0-100. A cut-off of 15.5 is applied to stratify patients into either low-risk (score ≤15.5) or high-risk (score >15.5).

Analytical Validation

CanAssist Breast has undergone rigorous validation to demonstrate the precision of the test. The validation strategy was adopted from various international guidelines to assess the impact of multiple variables like operator, observer, run, etc.

All IHC protocols used in CanAssist Breast testing are extensively validated and involve stringent QC.

Subjectivity attributable to IHC scoring was evaluated and found to be minimal among OncoStem’s panel of experienced pathologists.

CanAssist Breast is performed at the reference laboratory in Bangalore, India which is accredited by the College of American Pathologists (CAP) and ISO 13485 certified.

Clinical Validation

CanAssist Breast has been validated in a retrospective multi-centric clinical study (10+ centers), involving a cohort of close to 1000 patients (85% Asian and 15% Caucasian).

Approximately 40% of the validation cohort was aged 50 or younger, and 66% were Stage II patients, representative of Indian breast cancer patients who tend to be younger and diagnosed with more advanced disease than their Western counterparts.