Androgen Receptor mRNA levels determine the prognosis in triple-negative breast cancer patients.

BACKGROUND: Anti-Androgen Receptor (AR) therapy holds promise for a subset of AR expressing triple-negative breast cancer (TNBC) patients. However, current AR assays are suboptimal in detecting the dynamic range of AR expression, contributing to its controversial role in TNBC disease prognosis. This study is aimed at evaluating the feasibility of qRT-PCR to sensitively and robustly detect AR mRNA levels for prognostication. METHODS: mRNA expression profiling was performed on FFPE blocks from a retrospective cohort of 101 TNBC patients using qRT-PCR and compared with AR protein expression by immunohistochemistry . Statistical analyses included Spearman's rank correlation, Chi-square and Kaplan-Meier analyses. Distant Metastasis Free Survival was used as the end point in survival analysis. RESULTS: AR mRNA expression was observed in 34/101 patients (34%) whereas 12/80 cases (15%) were positive by IHC. qRT-PCR could thus detect more AR positive patients as compared to IHC, with 75% (9/12) concordance between the two methods. Co-expression of GATA3 and FOXA1 mRNA was observed in 85 and 88% of AR mRNA positive tumors, respectively. AR mRNA positivity was significantly correlated with age at disease onset (p = 0.02), high FOXA1/GATA3 (p < 0.05) and distant recurrence. AR mRNA positive patients had poorer DMFS (43%; p = 0.002). DMFS dropped further to 26% (p = 0.006) in AR (+)/high FOXA1/GATA3 patients. AR mRNA expression together with node positivity had the worst DMFS (23%; p < 0.0001) compared to patients who were either positive for any one of these, or negative for both AR and node status. Low Ki67 mRNA with AR mRNA positivity also had poorer DMFS (39%; p = 0.001) compared to patients expressing low Ki67 with no AR mRNA expression. CONCLUSION: qRT-PCR was more sensitive and reliable in detecting the dynamic expression levels of AR compared to IHC and this variation could be explained by the higher sensitivity of the former method. High AR mRNA expression was strongly associated with expression of AR protein, high FOXA1/GATA3 mRNA, and with poor prognosis. qRT-PCR was more efficient in detecting the AR positive cases compared to IHC. A distinct signature involving high GATA3/FOXA1, low Ki67, and node positivity in AR mRNA positive tumors correlated with poor prognosis. Thus, AR mRNA screening can serve as an effective prognostic marker along with offering potential targeted therapy options for TNBC.

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.

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.

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.

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.

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.

High prevalence of maternal hypothyroidism despite adequate iodine status in Indian pregnant women in the first trimester.

BACKGROUND: Iodine requirements are increased during pregnancy to maintain maternal and fetal euthyroidism. There have been recent improvements in iodized salt coverage in India, but whether iodized salt is sufficient to sustain iodine requirements during pregnancy remains uncertain. Our aims were to measure thyroid status in first trimester pregnant women in southern India and assess potential determinants of thyroid function, including iodine status, thyroid autoimmunity, dietary patterns, body weight, and anemia. METHODS: This was a cross-sectional study among 334 pregnant women of ≤ 14 weeks' gestation, in Bangalore, India. We measured anthropometrics, urinary iodine concentration (UIC), maternal thyroid volume (by ultrasound), and thyroid function. We applied a thyrotropin (TSH) upper limit of 2.5 mIU/L to classify thyroid insufficiency. Using a questionnaire, we obtained sociodemographic and dietary data, obstetric history, and use of iodized salt and iodine supplements. RESULTS: Among the women, the mean (standard deviation) gestational age was 10.3 (2.5) weeks, 67% were nulliparous, 21% were vegetarian, 19% were anemic, and 23% were overweight or obese. Iodized salt was used by 98% of women, and they were iodine sufficient: median UIC (range) was 184.2 µg/L (8.1-1152 µg/L) and all had a normal thyroid volume. However, 18% of the women had thyroid insufficiency: 3.7% had overt hypothyroidism (83% with positive TPO-Ab), 9.2% had subclinical hypothyroidism, and 5.2% had hypothyroxinemia. Women consuming vegetarian diets did not have significantly lower iodine intakes or higher risk of hypothyroidism than those consuming mixed diets, but overweight/obesity and anemia predicted thyroid insufficiency. CONCLUSION: In this urban population of southern India, pregnant women have adequate iodine status in the first trimester. Despite this, many have thyroid insufficiency, and the prevalence of overt hypothyroidism is more than fivefold higher than reported in other iodine sufficient populations of pregnant women.

EGFR targeting monoclonal antibody combines with an mTOR inhibitor and potentiates tumor inhibition by acting on complementary signaling hubs.

Nimotuzumab, an anti-epidermal growth factor receptor (anti-EGFR) monoclonal antibody, has been used extensively in many solid tumors and confers significant survival advantage. The antibody has limited skin toxicity and is generally well tolerated. Similar to other anti-EGFR therapies, patients may relapse a few months after treatment. In this study we show for the first time, the use of Nimotuzumab along with Sirolimus has synergistic effect on tumor inhibition as compared with the drugs used individually, in Nimotuzumab responsive and nonresponsive cell lines. In vitro studies prove that while Sirolimus (25 nmol/L) affects the signal downstream to mammalian target of rapamycin (mTOR), Nimotuzumab (83 nmol/L) downregulates pTYR, pMAPK and pSTAT3 by 40%, 20% and 30%, respectively. The combination, targeting these two different signaling hubs, may be associated with the synergistic inhibition observed. In vivo, the use of half human therapeutic equivalent doses for both the drugs substantially reduces tumors established in nude as well as severe combined immunodeficiency (SCID) mice by EGFR overexpressing A-431 cells. The drug combination reduces cell proliferation and the expression of signal transduction molecules. Treated tumors are better differentiated as compared with those established in the control mice. Tumor microarray demonstrates that Nimotuzumab and the combination groups segregate independently to the Sirolimus and the control treatment. The combination uniquely downregulated 55% of the altered tumor genes, extending beyond the typical pathways associated with Nimotuzumab and Sirolimus downstream pathways inhibition. These results would suggest that this nontoxic drug combination improves therapeutic benefit even in patients with low-EGFR expression and severely immunocompromised because of their current medication.

Nimotuzumab with chemoradiation confers a survival advantage in treatment-naïve head and neck tumors over expressing EGFR.

Head and neck cancer associated with the chewing of betel preparations, including tobacco, is common to South East Asia. We report a Phase IIB study in which ninety-two treatment naïve patients with advanced squamous cell carcinoma received standard therapy with or without an anti-Epidermal Growth Factor Receptor (EGFR) monoclonal antibody (Nimotuzumab). In pretreatment samples, the tissue expression of ErbB family proteins and downstream molecules, including their association with clinical response and survival. Marker expression in tumor adjacent sections was evaluated by immunohistochemistry. The clinical benefit of Nimotuzumab (200 mg/dose, once a week for six weeks) in combination with radiotherapy or chemoradiation was assessed with respect to EGFR expression and intensity. Two antibodies, which recognized independent epitopes, were used to assess EGFR expression levels by immunohistochemistry. EGFR detection using mR3, an antibody with similar specificity to Nimotuzumab, correlated significantly with the expression of ErbB3 (p<0.05), PCNA and pMAPK (p<0.001). Although EGFR expression showed a significant relationship to patient survival in patients treated with Nimotuzumab and chemoradiation (p=0.02), pMAPK expression did not (p=0.07). Interestingly, EGFR overexpression (as defined by mR3) correlated directly with overall survival in this group (p=0.01). This data supports a model of basal activation of the EGFR signal transduction pathway in these oropharyngeal tumors. Detection of EGFR by immunohistochemistry could define a subset of treatment naïve Head and Neck cancer patients who may benefit from receiving EGFR targeted therapies in combination with chemoradiation.