A comparative analysis of recurrence risk predictions in ER+/HER2- early breast cancer using NHS Nottingham Prognostic Index, PREDICT, and CanAssist Breast.

AIMS: Clinicians use multi-gene/biomarker prognostic tests and free online tools to optimize treatment in early ER+/HER2- breast cancer. Here we report the comparison of recurrence risk predictions by CanAssist Breast (CAB), Nottingham Prognostic Index (NPI), and PREDICT along with the differences in the performance of these tests across Indian and European cohorts. METHODS: Current study used a retrospective cohort of 1474 patients from Europe, India, and USA. NPI risk groups were categorized into three prognostic groups, good (GPG-NPI index ≤ 3.4) moderate (MPG 3.41-5.4), and poor (PPG > 5.4). Patients with chemotherapy benefit of < 2% were low-risk and ≥ 2% high-risk by PREDICT. We assessed the agreement between the CAB and NPI/PREDICT risk groups by kappa coefficient. RESULTS: Risk proportions generated by all tools were: CAB low:high 74:26; NPI good:moderate:poor prognostic group- 38:55:7; PREDICT low:high 63:37. Overall, there was a fair agreement between CAB and NPI[κ = 0.31(0.278-0.346)]/PREDICT [κ = 0.398 (0.35-0.446)], with a concordance of 97%/88% between CAB and NPI/PREDICT low-risk categories. 65% of NPI-MPG patients were called low-risk by CAB. From PREDICT high-risk patients CAB segregated 51% as low-risk, thus preventing over-treatment in these patients. In cohorts (European) with a higher number of T1N0 patients, NPI/PREDICT segregated more as LR compared to CAB, suggesting that T1N0 patients with aggressive biology are missed out by online tools but not by the CAB. CONCLUSION: Data shows the use of CAB in early breast cancer overall and specifically in NPI-MPG and PREDICT high-risk patients for making accurate decisions on chemotherapy use. CAB provided unbiased risk stratification across cohorts of various geographies with minimal impact by clinical parameters.

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

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.

The usefulness of CanAssist Breast over Ki67 in breast cancer recurrence risk assessment.

BACKGROUND: Assessment of Ki67 by immunohistochemistry (IHC) has limited utility in clinical practice owing to analytical validity issues. According to International Ki67 Working Group (IKWG) guidelines, treatment should be guided by a prognostic test in patients expressing intermediate Ki67 range, >5%-<30%. The objective of the study is to compare the prognostic performance of CanAssist Breast (CAB) with that of Ki67 across various Ki67 prognostic groups. METHODS: The cohort had 1701 patients. Various risk groups were compared for the distant relapse-free interval (DRFi) derived from Kaplan-Meier survival analysis. As per IKWG, patients are categorized into three risk groups: low-risk (<5%), intermediate risk (>5%-<30%), and high-risk (>30%). CAB generates two risk groups, low and high risk based on a predefined cutoff. RESULTS: In the total cohort, 76% of the patients were low risk (LR) by CAB as against 46% by Ki67 with a similar DRFi of 94%. In the node-negative sub-cohort, 87% were LR by CAB with a DRFi of 97% against 49% by Ki67 with a DRFi of 96%. In subgroups of patients with T1 or N1 or G2 tumors, Ki67-based risk stratification was not significant while it was significant by CAB. In the intermediate Ki67 (>5%-<30%) category up to 89% (N0 sub-cohort) were LR by CAB and the percentage of LR patients was 25% (p < 0.0001) higher compared to NPI or mAOL. In the low Ki67 (≤5%) group, up to 19% were segregated as high-risk by CAB with 86% DRFi suggesting the requirement of chemotherapy in these low Ki67 patients. CONCLUSION: CAB provided superior prognostic information in various Ki67 subgroups, especially in the intermediate Ki67 group.

A retrospective validation of CanAssist Breast in European early-stage breast cancer patient cohort.

CanAssist Breast (CAB), a prognostic test uses immunohistochemistry (IHC) approach coupled with artificial intelligence-based machine learning algorithm for prognosis of early-stage hormone-receptor positive, HER2/neu negative breast cancer patients. It was developed and validated in an Indian cohort. Here we report the first blinded validation of CAB in a multi-country European patient cohort. FFPE tumor samples from 864 patients were obtained from-Spain, Italy, Austria, and Germany. IHC was performed on these samples, followed by recurrence risk score prediction. The outcomes were obtained from medical records. The performance of CAB was analyzed by hazard ratios (HR) and Kaplan Meier curves. CAB stratified European cohort (n = 864) into distinct low- and high-risk groups for recurrence (P < 0.0001) with HR of 3.32 (1.85-5.93) like that of mixed (India, USA, and Europe) (n = 1974), 3.43 (2.34-4.93) and Indian cohort (n = 925), 3.09 (1.83-5.21). CAB provided significant prognostic information (P < 0.0001) in women aged ≤ 50 (HR: 4.42 (1.58-12.3), P < 0.0001) and >50 years (HR: 2.93 (1.44-5.96), P = 0.0002). CAB had an HR of 2.57 (1.26-5.26), P = 0.01) in women with N1 disease. CAB stratified significantly higher proportions (77%) as low-risk over IHC4 (55%) (P < 0.0001). Additionally, 82% of IHC4 intermediate-risk patients were stratified as low-risk by CAB. Accurate risk stratification of European patients by CAB coupled with its similar performance inIndian patients shows that CAB is robust and functions independent of ethnic differences. CAB can potentially prevent overtreatment in a greater number of patients compared to IHC4 demonstrating its usefulness for adjuvant systemic therapy planning in European breast cancer patients.

Parathyroid hormone-related peptide is a naturally occurring, protein kinase A-dependent angiogenesis inhibitor.

Angiogenesis is a highly regulated process that results from the sequential actions of naturally occurring stimulators and inhibitors. Here, we show that parathyroid hormone-related peptide, a peptide hormone derived from normal and tumor cells that regulates bone metabolism and vascular tone, is a naturally occurring angiogenesis inhibitor. Parathyroid hormone-related peptide or a ten-amino-acid peptide from its N terminus inhibits endothelial cell migration in vitro and angiogenesis in vivo by activating endothelial cell protein kinase A. Activation of protein kinase A inhibits cell migration and angiogenesis by inhibiting the small GTPase Rac. In contrast, inhibition of protein kinase A reverses the anti-migratory and anti-angiogenic properties of parathyroid hormone-related peptide. These studies show that parathyroid hormone-related peptide is a naturally occurring angiogenesis inhibitor that functions by activation of protein kinase A.

Validation of CanAssist Breast immunohistochemistry biomarkers on an automated platform and its applicability in tissue microarray.

CanAssist Breast (CAB) is a prognostic test for early-stage hormone receptor-positive invasive breast cancer. The test involves performing immunohistochemical (IHC) analysis for five biomarkers, namely CD44, ABCC4, ABCC11, N-cadherin, and pan-cadherin. In addition to IHC grading information, three clinical features, i.e., tumor size, grade, and lymph node status, serve as input into the machine learning-based algorithm to generate the CAB risk score. CAB was developed and initially validated using manual IHC. This study's objectives included: i) automate CAB IHC on an autostainer and establish its performance equivalence with manual IHC ii) validate CAB test using samples in Tissue MicroArray (TMA) format. IHC for CAB biomarkers was standardized on Ventana BenchMark XT autostainer. Two IHC methods were compared for IHC gradings and corresponding CAB risk scores/risk categories. A concordance analysis was done using MedCalcTM software. The manual and automated IHC staining methods exhibited a high level of concordance on IHC gradings for 40 cases with an Intra-class Correlation Coefficient (ICC) of >0.85 for 4 of 5 biomarkers. 100% concordance was achieved in risk categorization (low- or high-risk), with very good agreement between the risk scores demonstrated by a kappa statistic of 0.83. TMA versus whole tissue section concordance was analyzed using 45 samples on an autostainer, and the data showed 92% concordance in terms of risk category. The results confirm the equivalence between manual and automated staining methods and demonstrate the utility of TMA as an acceptable format for CanAssist Breast testing.

CanAssist Breast Impacting Clinical Treatment Decisions in Early-Stage HR+ Breast Cancer Patients: Indian Scenario.

CanAssist Breast (CAB) has thus far been validated on a retrospective cohort of 1123 patients who are mostly Indians. Distant metastasis-free survival (DMFS) of more than 95% was observed with significant separation (P < 0.0001) between low-risk and high-risk groups. In this study, we demonstrate the usefulness of CAB in guiding physicians to assess risk of cancer recurrence and to make informed treatment decisions for patients. Of more than 500 patients who have undergone CAB test, detailed analysis of 455 patients who were treated based on CAB-based risk predictions by more than 140 doctors across India is presented here. Majority of patients tested had node negative, T2, and grade 2 disease. Age and luminal subtypes did not affect the performance of CAB. On comparison with Adjuvant! Online (AOL), CAB categorized twice the number of patients into low risk indicating potential of overtreatment by AOL-based risk categorization. We assessed the impact of CAB testing on treatment decisions for 254 patients and observed that 92% low-risk patients were not given chemotherapy. Overall, we observed that 88% patients were either given or not given chemotherapy based on whether they were stratified as high risk or low risk for distant recurrence respectively. Based on these results, we conclude that CAB has been accepted by physicians to make treatment planning and provides a cost-effective alternative to other similar multigene prognostic tests currently available.

The usefulness of CanAssist breast in the assessment of recurrence risk in patients of ethnic Indian origin.

Accurate recurrence risk assessment in hormone receptor positive, HER2/neu negative breast cancer is critical to plan precise therapy. CanAssist Breast (CAB) assesses recurrence risk based on tumor biology using artificial intelligence-based approach. We report CAB risk assessment correlating with disease outcomes in multiple clinically high- and low-risk subgroups. In this retrospective cohort of 925 patients [median age-54 (22-86)] CAB had hazard ratio (HR) of 3 (1.83-5.21) and 2.5 (1.45-4.29), P = 0.0009) in univariate and multivariate analysis. CAB's HR in sub-groups with the other determinants of outcome, T2 (HR: 2.79 (1.49-5.25), P = 0.0001); age [< 50 (HR: 3.14 (1.39-7), P = 0.0008)]. Besides application in node-negative patients, CAB's HR was 2.45 (1.34-4.47), P = 0.0023) in node-positive patients. In clinically low-risk patients (N0 tumors up to 5 cms) (HR: 2.48 (0.79-7.8), P = 0.03) and with luminal-A characteristics (HR: 4.54 (1-19.75), P = 0.004), CAB identified >16% as high-risk with recurrence rates of up to 12%. In clinically high-risk patients (T2N1 tumors (HR: 2.65 (1.31-5.36), P = 0.003; low-risk DMFS: 92.66 ± 1.88) and in women with luminal-B characteristics (HR: 3.24; (1.69-6.22), P < 0.0001; low-risk DMFS: 93.34 ± 1.34)), CAB identified >64% as low-risk. Thus, CAB prognostication was significant in women with clinically low- and high-risk disease. The data imply the use of CAB for providing helpful information to stratify tumors based on biology incorporated with clinical features for Indian patients, which can be extrapolated to regions with similarly characterized patients, South-East Asia.

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.

Clinical validation of an immunohistochemistry-based CanAssist-Breast test for distant recurrence prediction in hormone receptor-positive breast cancer patients.

CanAssist-Breast (CAB) is an immunohistochemistry (IHC)-based prognostic test for early-stage Hormone Receptor (HR+)-positive breast cancer patients. CAB uses a Support Vector Machine (SVM) trained algorithm which utilizes expression levels of five biomarkers (CD44, ABCC4, ABCC11, N-Cadherin, and Pan-Cadherin) and three clinical parameters such as tumor size, grade, and node status as inputs to generate a risk score and categorizes patients as low- or high-risk for distant recurrence within 5 years of diagnosis. In this study, we present clinical validation of CAB. CAB was validated using a retrospective cohort of 857 patients. All patients were treated either with endocrine therapy or chemoendocrine therapy. Risk categorization by CAB was analyzed by calculating Distant Metastasis-Free Survival (DMFS) and recurrence rates using Kaplan-Meier survival curves. Multivariate analysis was performed to calculate Hazard ratios (HR) for CAB high-risk vs low-risk patients. The results showed that Distant Metastasis-Free Survival (DMFS) was significantly different (P-0.002) between low- (DMFS: 95%) and high-risk (DMFS: 80%) categories in the endocrine therapy treated alone subgroup (n = 195) as well as in the total cohort (n = 857, low-risk DMFS: 95%, high-risk DMFS: 84%, P < 0.0001). In addition, the segregation of the risk categories was significant (P = 0.0005) in node-positive patients, with a difference in DMFS of 12%. In multivariate analysis, CAB risk score was the most significant predictor of distant recurrence with hazard ratio of 3.2048 (P < 0.0001). CAB stratified patients into discrete risk categories with high statistical significance compared to Ki-67 and IHC4 score-based stratification. CAB stratified a higher percentage of the cohort (82%) as low-risk than IHC4 score (41.6%) and could re-stratify >74% of high Ki-67 and IHC4 score intermediate-risk zone patients into low-risk category. Overall the data suggest that CAB can effectively predict risk of distant recurrence with clear dichotomous high- or low-risk categorization.

Genomic and proteomic characterization of embryonic stem cells.

Stem cell biology, like all areas of cell biology, has been significantly affected by the arrival of the genomics era. The rendering of the human and mouse genome sequences and the development of attendant technologies have made it possible to comprehensively explore embryonic stem cell biology at the molecular level. Recently, there has been emphasis on global characterization of the transcriptome, epigenome, and proteome of embryonic stem cells. These omic evaluations of embryonic stem cells are leading to improved methods for cell-based therapies and are advancing our basic understanding of early embryonic development.

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.