Anti-tumor effects of retinoids combined with trastuzumab or tamoxifen in breast cancer cells: induction of apoptosis by retinoid/trastuzumab combinations

INTRODUCTION: HER2 and estrogen receptor (ER) are important in breast cancer and are therapeutic targets of trastuzumab (Herceptin) and tamoxifen, respectively. Retinoids inhibit breast cancer growth, and modulate signaling by HER2 and ER. We hypothesized that treatment with retinoids and simultaneous targeting of HER2 and/or ER may have enhanced anti-tumor effects. METHODS: The effects of retinoids combined with trastuzumab or tamoxifen were examined in two human breast cancer cell lines in culture, BT474 and SKBR3. Assays of proliferation, apoptosis, differentiation, cell cycle distribution, and receptor signaling were performed. RESULTS: In HER2-overexpressing/ER-positive BT474 cells, combining all-trans retinoic acid (atRA) with tamoxifen or trastuzumab synergistically inhibited cell growth, and altered cell differentiation and cell cycle. Only atRA/trastuzumab-containing combinations induced apoptosis. BT474 and HER2-overexpressing/ER-negative SKBR3 cells were treated with a panel of retinoids (atRA, 9-cis-retinoic acid, 13-cis-retinoic acid, or N-(4-hydroxyphenyl) retinamide (fenretinide) (4-HPR)) combined with trastuzumab. In BT474 cells, none of the single agents except 4-HPR induced apoptosis, but again combinations of each retinoid with trastuzumab did induce apoptosis. In contrast, the single retinoid agents did cause apoptosis in SKBR3 cells; this was only modestly enhanced by addition of trastuzumab. The retinoid drug combinations altered signaling by HER2 and ER. Retinoids were inactive in trastuzumab-resistant BT474 cells. CONCLUSIONS: Combining retinoids with trastuzumab maximally inhibits cell growth and induces apoptosis in trastuzumab-sensitive cells. Treatment with such combinations may have benefit for breast cancer patients

Low-level expression of HER2 and CK19 in normal peripheral blood mononuclear cells: relevance for detection of circulating tumor cells

<p>Abstract</p> <p>Background</p> <p>Detection of circulating tumor cells (CTC) in the blood of cancer patients may have prognostic and predictive significance. However, background expression of 'tumor specific markers' in peripheral blood mononuclear cells (PBMC) may confound these studies. The goal of this study was to identify the origin of Cytokeratin 19 (CK19) and HER-2 signal in PBMC and suggest an approach to enhance techniques involved in detection of CTC in breast cancer patients.</p> <p>Methods</p> <p>PBMC from healthy donors were isolated and fractionated into monocytes, lymphocytes, natural killer cells/granulocytes and epithelial populations using immunomagnetic selection and fluorescent cell-sorting for each cell type. RNA isolated from each fraction was analyzed for CK19, HER2 and Beta 2 microglobulin (B2M) using real-time qRT-PCR. Positive selection for epithelial cells and negative selection for NK/granulocytes were used in an attempt to reduce background expression of CK19 and HER2 markers.</p> <p>Results</p> <p>In normal PBMC, CK19 was expressed in the lymphocyte population while HER-2 expression was highest in the NK/granulocyte population. Immunomagnetic selection for epithelial cells reduced background CK19 signal to a frequency of <5% in normal donors. Using negative selection, the majority (74–98%) of HER2 signal could be removed from PBMC. Positive selection methods are variably effective at reducing these background signals.</p> <p>Conclusion</p> <p>We present a novel method to improve the specificity of the traditional method of detecting CTC by identifying the source of the background signals and reducing them by negative immunoselection. Further studies are warranted to improve sensitivity and specificity of methods of detecting CTC will prove to be useful tools for clinicians in determining prognosis and monitoring treatment responses of breast cancer patients.</p

Recursive SVM Feature Selection and Sample Classification for Mass-Spectrometry and Microarray Data

Background: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. Results: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. Conclusion: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.Statistic

Optimal tumor sampling for immunostaining of biomarkers in breast carcinoma

IntroductionBiomarkers, such as Estrogen Receptor, are used to determine therapy and prognosis in breast carcinoma. Immunostaining assays of biomarker expression have a high rate of inaccuracy; for example, estimates are as high as 20% for Estrogen Receptor. Biomarkers have been shown to be heterogeneously expressed in breast tumors and this heterogeneity may contribute to the inaccuracy of immunostaining assays. Currently, no evidence-based standards exist for the amount of tumor that must be sampled in order to correct for biomarker heterogeneity. The aim of this study was to determine the optimal number of 20X fields that are necessary to estimate a representative measurement of expression in a whole tissue section for selected biomarkers: ER, HER-2, AKT, ERK, S6K1, GAPDH, Cytokeratin, and MAP-Tau.MethodsTwo collections of whole tissue sections of breast carcinoma were immunostained for biomarkers. Expression was quantified using the Automated Quantitative Analysis (AQUA) method of quantitative immunofluorescence. Simulated sampling of various numbers of fields (ranging from one to thirty five) was performed for each marker. The optimal number was selected for each marker via resampling techniques and minimization of prediction error over an independent test set.ResultsThe optimal number of 20X fields varied by biomarker, ranging between three to fourteen fields. More heterogeneous markers, such as MAP-Tau protein, required a larger sample of 20X fields to produce representative measurement.ConclusionsThe optimal number of 20X fields that must be sampled to produce a representative measurement of biomarker expression varies by marker with more heterogeneous markers requiring a larger number. The clinical implication of these findings is that breast biopsies consisting of a small number of fields may be inadequate to represent whole tumor biomarker expression for many markers. Additionally, for biomarkers newly introduced into clinical use, especially if therapeutic response is dictated by level of expression, the optimal size of tissue sample must be determined on a marker-by-marker basis

Molecular Heterogeneity and Response to Neoadjuvant Human Epidermal Growth Factor Receptor 2 Targeting in CALGB 40601, a Randomized Phase III Trial of Paclitaxel Plus Trastuzumab With or Without Lapatinib

Dual human epidermal growth factor receptor 2 (HER2) targeting can increase pathologic complete response rates (pCRs) to neoadjuvant therapy and improve progression-free survival in metastatic disease. CALGB 40601 examined the impact of dual HER2 blockade consisting of trastuzumab and lapatinib added to paclitaxel, considering tumor and microenvironment molecular features

PAM50 proliferation score as a predictor of weekly paclitaxel benefit in breast cancer

To identify a group of patients who might benefit from the addition of weekly paclitaxel to conventional anthracycline-containing chemotherapy as adjuvant therapy of node-positive operable breast cancer. The predictive value of PAM50 subtypes and the 11-gene proliferation score contained within the PAM50 assay were evaluated in 820 patients from the GEICAM/9906 randomized phase III trial comparing adjuvant FEC to FEC followed by weekly paclitaxel (FEC-P). Multivariable Cox regression analyses of the secondary endpoint of overall survival (OS) were performed to determine the significance of the interaction between treatment and the (1) PAM50 subtypes, (2) PAM50 proliferation score, and (3) clinical and pathological variables. Similar OS analyses were performed in 222 patients treated with weekly paclitaxel versus paclitaxel every 3 weeks in the CALGB/9342 and 9840 metastatic clinical trials. In GEICAM/9906, with a median follow up of 8.7 years, OS of the FEC-P arm was significantly superior compared to the FEC arm (unadjusted HR = 0.693, p = 0.013). A benefit from paclitaxel was only observed in the group of patients with a low PAM50 proliferation score (unadjusted HR = 0.23, p < 0.001; and interaction test, p = 0.006). No significant interactions between treatment and the PAM50 subtypes or the various clinical–pathological variables, including Ki-67 and histologic grade, were identified. Finally, similar OS results were obtained in the CALGB data set, although the interaction test did not reach statistical significance (p = 0.109). The PAM50 proliferation score identifies a subset of patients with a low proliferation status that may derive a larger benefit from weekly paclitaxel. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10549-013-2416-2) contains supplementary material, which is available to authorized users

Addressing the dichotomy between individual and societal approaches to personalised medicine in oncology

Academic, industry, regulatory leaders and patient advocates in cancer clinical research met in November 2018 at the Innovation and Biomarkers in Cancer Drug Development meeting in Brussels to address the existing dichotomy between increasing calls for personalised oncology approaches based on individual molecular profiles and the need to make resource and regulatory decisions at the societal level in differing health-care delivery systems around the globe. Novel clinical trial designs, the utility and limitations of real-world evidence (RWE) and emerging technologies for profiling patient tumours and tumour-derived DNA in plasma were discussed. While randomised clinical trials remain the gold standard approach to defining clinical utility of local and systemic therapeutic interventions, the broader adoption of comprehensive tumour profiling and novel trial designs coupled with RWE may allow patient and physician autonomy to be appropriately balanced with broader assessments of safety and overall societal benefit. (C) 2019 Published by Elsevier Ltd

Molecular subtypes of breast cancer in relation to paclitaxel response and outcomes in women with metastatic disease: results from CALGB 9342

INTRODUCTION: The response to paclitaxel varies widely in metastatic breast cancer. We analyzed data from CALGB 9342, which tested three doses of paclitaxel in women with advanced disease, to determine whether response and outcomes differed according to HER2, hormone receptor, and p53 status. METHODS: Among 474 women randomly assigned to paclitaxel at a dose of 175, 210, or 250 mg/m(2), adequate primary tumor tissue was available from 175. Immunohistochemistry with two antibodies and fluorescence in situ hybridization were performed to evaluate HER2 status; p53 status was determined by immunohistochemistry and sequencing. Hormone receptor status was obtained from pathology reports. RESULTS: Objective response rate was not associated with HER2 or p53 status. There was a trend toward a shorter median time to treatment failure among women with HER2-positive tumors (2.3 versus 4.2 months; P = 0.067). HER2 status was not related to overall survival (OS). Hormone receptor expression was not associated with differences in response but was associated with longer OS (P = 0.003). In contrast, women with p53 over-expression had significantly shorter OS than those without p53 over-expression (11.5 versus 14.4 months; P = 0.002). In addition, triple negative tumors were more frequent in African-American than in Caucasian patients, and were associated with a significant reduction in OS (8.7 versus 12.9 months; P = 0.008). CONCLUSION: None of the biomarkers was predictive of treatment response in women with metastatic breast cancer; however, survival differed according to hormone receptor and p53 status. Triple negative tumors were more frequent in African-American patients and were associated with a shorter survival

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5 %-~20 % improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features