Neoadjuvant chemotherapy in women with large and locally advanced breast cancer: chemoresistance and prediction of response to drug therapy

Enhanced full-inversion-based ultrasound elastography for evaluating tumor response to neoadjuvant chemotherapy in patients with locally advanced breast cancer

PURPOSE

An enhanced ultrasound elastography technique is proposed for early assessment of locally advanced breast cancer (LABC) response to neoadjuvant chemotherapy (NAC).

METHODS

The proposed elastography technique inputs ultrasound radiofrequency data obtained through tissue quasi-static stimulation and adapts a strain refinement algorithm formulated based on fundamental principles of continuum mechanics, coupled with an iterative inverse finite element method to reconstruct the breast Young's modulus (E) images. The technique was explored for therapy response assessment using data acquired from 25 LABC patients before and at weeks 1, 2, and 4 after the NAC initiation (100 scans). The E ratio of tumor to the surrounding tissue was calculated at different scans and compared to the baseline for each patient. Patients' response to NAC was determined many months later using standard clinical and histopathological criteria.

RESULTS

Reconstructed E ratio changes obtained as early as one week after the NAC onset demonstrate very good separation between the two cohorts of responders and non-responders to NAC. Statistically significant differences were observed in the E ratio changes between the two patient cohorts at weeks 1 to 4 after treatment (p-value < 0.001; statistical power greater than 97%). A significant difference in axial strain ratio changes was observed only at week 4 (p-value = 0.01; statistical power = 76%). No significant difference was observed in tumor size changes at weeks 1, 2 or 4.

CONCLUSION

The proposed elastography technique demonstrates a high potential for chemotherapy response monitoring in LABC patients and superior performance compared to strain imaging.

Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies

Complete pathological response (pCR) to neoadjuvant chemotherapy (NAC) is a prognostic factor for breast cancer (BC) patients and is correlated with improved survival. However, pCR rates are variable to standard NAC, depending on BC subtype. This study investigates quantitative digital histopathology coupled with machine learning (ML) to predict NAC response a priori. Clinicopathologic data and digitized slides of BC core needle biopsies were collected from 149 patients treated with NAC. The nuclei within the tumor regions were segmented on the histology images of biopsy samples using a weighted U-Net model. Five pathomic feature subsets were extracted from segmented digitized samples, including the morphological, intensity-based, texture, graph-based and wavelet features. Seven ML experiments were conducted with different feature sets to develop a prediction model of therapy response using a gradient boosting machine with decision trees. The models were trained and optimized using a five-fold cross validation on the training data and evaluated using an unseen independent test set. The prediction model developed with the best clinical features (tumor size, tumor grade, age, and ER, PR, HER2 status) demonstrated an area under the ROC curve (AUC) of 0.73. Various pathomic feature subsets resulted in models with AUCs in the range of 0.67 and 0.87, with the best results associated with the graph-based and wavelet features. The selected features among all subsets of the pathomic and clinicopathologic features included four wavelet and three graph-based features and no clinical features. The predictive model developed with these features outperformed the other models, with an AUC of 0.90, a sensitivity of 85% and a specificity of 82% on the independent test set. The results demonstrated the potential of quantitative digital histopathology features integrated with ML methods in predicting BC response to NAC. This study is a step forward towards precision oncology for BC patients to potentially guide future therapies.

Deep learning of quantitative ultrasound multi-parametric images at pre-treatment to predict breast cancer response to chemotherapy

In this study, a novel deep learning-based methodology was investigated to predict breast cancer response to neo-adjuvant chemotherapy (NAC) using the quantitative ultrasound (QUS) multi-parametric imaging at pre-treatment. QUS multi-parametric images of breast tumors were generated using the data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for NAC followed by surgery. The ground truth response to NAC was identified for each patient after the surgery using the standard clinical and pathological criteria. Two deep convolutional neural network (DCNN) architectures including the residual network and residual attention network (RAN) were explored for extracting optimal feature maps from the parametric images, with a fully connected network for response prediction. In different experiments, the features maps were derived from the tumor core only, as well as the core and its margin. Evaluation results on an independent test set demonstrate that the developed model with the RAN architecture to extract feature maps from the expanded parametric images of the tumor core and margin had the best performance in response prediction with an accuracy of 88% and an area under the receiver operating characteristic curve of 0.86. Ten-year survival analyses indicate statistically significant differences between the survival of the responders and non-responders identified based on the model prediction at pre-treatment and the standard criteria at post-treatment. The results of this study demonstrate the promising capability of DCNNs with attention mechanisms in predicting breast cancer response to NAC prior to the start of treatment using QUS multi-parametric images.

Breast cancer resistance to chemotherapy: When should we suspect it and how can we prevent it?

Chemotherapy is an essential treatment for breast cancer, inducing cancer cell death. However, chemoresistance is a problem that limits the effectiveness of chemotherapy. Many factors influence chemoresistance, including drug inactivation, changes in drug targets, overexpression of ABC transporters, epithelial-to-mesenchymal transitions, apoptotic dysregulation, and cancer stem cells. The effectiveness of chemotherapy can be assessed clinically and pathologically. Clinical response evaluation is based on physical examination or imaging (mammography, ultrasonography, computed tomography scan, or magnetic resonance imaging) and includes tumor size changes after chemotherapy. Pathological response evaluation is a method based on tumor residues in histopathological preparations. We should be suspicious of chemoresistance if there are no significant changes clinically according to the Response Evaluation Criteria in Solid Tumors and World Health Organization criteria or pathological changes according to the Miller and Payne criteria, especially after 2–3 cycles of chemotherapy treatments. Chemoresistance is mostly detected after the administration of chemotherapy drugs. No reliable parameters or biomarkers can predict chemotherapy responses appropriately and effectively. Well-known parameters such as cancer type, grade, subtype, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, Ki-67, and MDR-1/P-gP have been used for selecting chemotherapy regimens. Some new methods for predicting chemoresistance include chemosensitivity and chemoresistance assays, multigene expressions, and positron emission tomography assays. The latest approaches are based on evaluation of molecular processes and the metabolic activity of cancer cells. Some methods for preventing chemoresistance include using the right regimen, using some combination of chemotherapy methods, conducting adequate monitoring, and using drugs that could prevent the emergence of multidrug resistance.

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Chemotherapy is an essential treatment in the management of breast cancer.

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Chemotherapy is carried out based on the selection of regimens for the specific individual and tumor characteristics.

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Combination therapy, monitoring, and evaluation are used to prevent chemoresistance.

Characterizing intra-tumor regions on quantitative ultrasound parametric images to predict breast cancer response to chemotherapy at pre-treatment

The efficacy of quantitative ultrasound (QUS) multi-parametric imaging in conjunction with unsupervised classification algorithms was investigated for the first time in characterizing intra-tumor regions to predict breast tumor response to chemotherapy before the start of treatment. QUS multi-parametric images of breast tumors were generated using the ultrasound radiofrequency data acquired from 181 patients diagnosed with locally advanced breast cancer and planned for neo-adjuvant chemotherapy followed by surgery. A hidden Markov random field (HMRF) expectation maximization (EM) algorithm was applied to identify distinct intra-tumor regions on QUS multi-parametric images. Several features were extracted from the segmented intra-tumor regions and tumor margin on different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker consisting of four features for response prediction. Evaluation results on an independent test set indicated that the developed biomarker coupled with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patient at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In comparison, the biomarkers consisted of the features derived from the entire tumor core (without consideration of the intra-tumor regions), and the entire tumor core and the tumor margin could predict the treatment response of patients with an accuracy of 74.5% and 76.4%, and an AUC of 0.79 and 0.76, respectively. Standard clinical features could predict the therapy response with an accuracy of 69.1% and an AUC of 0.6. Long-term survival analyses indicated that the patients predicted by the developed model as responders had a significantly better survival compared to the non-responders. Similar findings were observed for the two response cohorts identified at post-treatment based on standard clinical and pathological criteria. The results obtained in this study demonstrated the potential of QUS multi-parametric imaging integrated with unsupervised learning methods in identifying distinct intra-tumor regions in breast cancer to characterize its responsiveness to chemotherapy prior to the start of treatment.

Targeting PKM2 promotes chemosensitivity of breast cancer cells in vitro and in vivo

BACKGROUND

Pyruvate kinase M2 (PKM2) was overexpressed in many cancers, and high PKM2 expression was related with poor prognosis and chemoresistance.

OBJECTIVE

We investigated the expression of PKM2 in breast cancer and analyzed the relation of PKM2 expression with chemotherapy resistance to the neoadjuvant chemotherapy (NAC). We also investigated whether PKM2 could reverse chemoresistance in breast cancer cells in vitro and in vivo.

METHODS

Immunohistochemistry (IHC) was performed in 130 surgical resected breast cancer tissues. 78 core needle biopsies were collected from breast cancer patients before neoadjuvant chemotherapy. The relation of PKM2 expression and multi-drug resistance to NAC was compared. The effect of PKM2 silencing or overexpression on Doxorubicin (DOX) sensitivity in the MCF-7 cells in vitro and in vivo was compared.

RESULTS

PKM2 was intensively expressed in breast cancer tissues compared to adjacent normal tissues. In addition, high expression of PKM2 was associated with poor prognosis in breast cancer patients. The NAC patients with high PKM2 expression had short survival. PKM2 was an independent prognostic predictor for surgical resected breast cancer and NAC patients. High PKM2 expression was correlated with neoadjuvant treatment resistance. High PKM2 expression significantly distinguished chemoresistant patients from chemosensitive patients. In vitro and in vivo knockdown of PKM2 expression decreases the resistance to DOX in breast cancer cells in vitro and tumors in vivo.

CONCLUSION

PKM2 expression was associated with chemoresistance of breast cancers, and could be used to predict the chemosensitivity. Furthermore, targeting PKM2 could reverse chemoresistance, which provides an effective treatment methods for patients with breast cancer.

FABP7 is a potential biomarker to predict response to neoadjuvant chemotherapy for breast cancer

BACKGROUND

Early prediction of response to neoadjuvant chemotherapy (NAC) is critical in choosing appropriate chemotherapeutic regimen for patients with locally advanced breast cancer. Herein, we sought to identify potential biomarkers to predict the response to neoadjuvant chemotherapy for breast cancer patients.

METHODS

Three genomic profiles acquired by microarray analysis from subjects with or without residual tumors after NAC downloaded from the GEO database were used to screen the differentially expressed genes (DEGs). An array of public databases, including ONCOMINE, cBioportal, Breast Cancer Gene Expression Miner v4.0, and the Kaplan Meir-plotter, etc., were used to evaluate the potential functions, related signaling pathway, as well as prognostic values of FABP7 in breast cancer. Anti-cancer drug sensitivity assay, real-time PCR, flow cytometry and western-blotting assays were used to investigate the function of FABP7 in breast cancer cells and examine the relevant mechanism.

RESULTS

Two differentially expressed genes, including FABP7 and ESR1, were identified to be potential indicators of response to anthracycline and taxanes for breast cancer. FABP7 was associated with better chemotherapeutic response, while ESR1 was associated with poorer chemotherapeutic effectiveness. Generally, the expression of FABP7 was significantly lower in breast cancer than normal tissue samples. FABP7 mainly high expressed in ER-negative breast tumor and might regulate cell cycle to enhance chemosensitivity. Moreover, elevated FABP7 expression increased the percentage of cells at both S and G2/M phase in MDA-MB-231-ADR cells, and decreased the percentage of cells at G0/G1 phase, as compared to control group. Western-blotting results showed that elevated FABP7 expression could increase Skp2 expression, while decrease Cdh1 and p27kip1 expression in MDA-MB-231-ADR cells. In addition, FABP7 was correlated to longer recurrence-free survival (RFS) in BC patients with ER-negative subtype of BC treated with chemotherapy.

CONCLUSION

FABP7 is a potential favorable biomarker and predicts better response to NAC in breast cancer patients. Future study on the predictive value and detail molecular mechanisms of FABP7 in contribution to chemosensitivity in breast cancer is warranted.

Effect of quercetin on the anti-tumor activity of cisplatin in EMT6 breast tumor-bearing mice

OBJECTIVE

The purpose of this study was to determine the effect of quercetin on the antitumor activity of cisplatin and its side-effects.

METHODS

EMT6 cells, a mouse breast cancer cell line, were injected subcutaneously in mice to generate a breast tumor-bearing mouse model. Experimental groups were divided into four groups: control (C), quercetin (Q), cisplatin (CP), and cisplatin+quercetin (CP+Q).

RESULTS

The tumor volume of the CP+Q group was significantly lower than that of the CP group. Serum blood urea nitrogen and creatinine levels in the CP+Q group were lower than those in the CP group. Renal γ-glutamyltranspeptidase and alkaline phosphatase activities were significantly higher in the CP+Q group than in the CP group, and the content of renal thiobarbituric acid reactive substance was significantly lower in the CP+Q group than that in the CP group. These results suggested that quercetin and cisplatin synergistically increased cellular toxicity in breast cancer cells and mediated cancer growth inhibition, thereby enhancing the antitumor effect of cisplatin compared to when only cisplatin was administered. Quercetin also reduced renal toxicity, which arose as a potential a side effect of cisplatin.

CONCLUSION

The enhanced antitumor effect of cisplatin and decreased renal toxicity after quercetin treatment suggested the applicability of quercetin as an adjuvant for chemotherapeutic agents.

miR-141 confers docetaxel chemoresistance of breast cancer cells via regulation of EIF4E expression

Resistance to docetaxel, a chemotherapy drug for breast cancer (BC) treatment, occurs in ~50% of patients, and the underlying molecular mechanisms of drug resistance are not fully understood. Gene regulation through miR-141 has been proven to play an important role in cancer drug resistance. The present study investigated the role of miR-141 expression in BC cells of acquired docetaxel resistance. Inhibition of miR-141 enhanced the response to docetaxel in docetaxel-resistant cells (MCF-7/DTX and MDA-MB-231/DTX, respectively), whereas overexpression of miR-141 confered resistance in docetaxel-sensitive cells (MCF-7 and MDA-MB-231, respectively). By directly targeting the eukaryotic translation initiation factor 4E (EIF4E) mRNA, miR-141 acts on genes that are necessary for drug induced apoptosis rendering the cells drug resistant. Modulation of miR-141 expression was correlated with EIF4E expression changes and a direct interaction of miR-141 with EIF4E was shown by a luciferase assay. Thus, the present study is the first to show an increased expression of miR-141 in an acquired model of docetaxel resistance in BC. This serves as a mechanism of acquired docetaxel resistance in BC cells, possibly through direct interactions with EIF4E, therefore presenting a potential therapeutic target for the treatment of docetaxel resistant BC.

Expression of nuclear factor kappa B (NF-κB) as a predictor of poor pathologic response to chemotherapy in patients with locally advanced breast cancer

BACKGROUND

NF-κB inhibits apoptosis through induction of antiapoptotic proteins and suppression of proapoptotic genes. Various chemotherapy agents induce NF-κB translocation and target gene activation. We conducted the present study to assess the predictive value of NF-κB regarding pathologic responses after receiving neoadjuvant chemotherapy.

MATERIALS AND METHODS

We enrolled 131 patients with locally advanced invasive ductal breast carcinoma. Immunohistochemistry (IHC) was used to detect NF-κB expression. Evaluation of pathologic response was elaborated with the Ribero classification.

RESULTS

Expression of NF-κB was significantly associated with poor pathological response (p=0.02). From the multivariate analysis, it was found that the positive expression of NF-κB yielded RR=1.74 (95%CI 0.77 to 3.94).

CONCLUSIONS

NF-κB can be used as a predictor of poor pathological response after neoadjuvant chemotherapy.

Anti-tumor activity of novel biisoquinoline derivatives against breast cancers

Breast cancer is classified into three groups according to its expression of hormone/growth factor receptors: (i) estrogen receptor (ER) and progesterone receptor (PR)-positive; (ii) human epidermal growth factor receptor 2 (HER2)-positive; and (iii) ER, PR, and HER2-negative (triple-negative). A series of methoxy-substituted biisoquinoline compounds have been synthesized as a potential chemotherapeutic agent for the triple-negative breast cancers which are especially challenging to manage. Structure activity relationship study revealed that rigid 6,6'-dimethoxy biisoquinoline imidazolium compound (1c, DH20931) exhibited the significant growth inhibitory effects on both triple-positive and triple-negative human breast cancer cell lines with IC50 in the range of 0.3-3.9 μM. The 1c (DH20931) is more potent than structurally related noscapine for growth inhibition of MCF7 cell line (IC50=1.3 vs 57 μM) and MDA-MB231 cell line (IC50=3.9 vs 64 μM).

Carbonic anhydrase 9 is associated with chemosensitivity and prognosis in breast cancer patients treated with taxane and anthracycline

Background

Neoadjuvant chemotherapy (NAC) is one of the standard care regimens for patients with resectable early-stage breast cancer. It would be advantageous to determine the chemosensitivity of tumors before initiating NAC. One of the parameters potentially compromising such chemosensitivity would be a hypoxic microenvironment of cancer cells. The aim of this study was thus to clarify the correlation between expression of the hypoxic marker carbonic anhydrase-9 (CA9) and chemosensitivity to NAC as well as prognosis of breast cancer patients.

Methods

A total of 102 patients with resectable early-stage breast cancer was treated with NAC consisting of FEC (5-fluorouracil, epirubicin, and cyclophosphamide) followed by weekly paclitaxel before surgery. Core needle biopsy (CNB) specimens and resected tumors were obtained from all patients before and after NAC, respectively. Chemosensitivity to NAC and the prognostic potential of CA9 expression were evaluated by immunohistochemistry.

Results

CA9 positivity was detected in the CNB specimens from 47 (46%) of 102 patients. The CA9 expression in CNB specimens was significantly correlated with pathological response, lymph node metastasis, and lymph-vascular invasion. Multivariate analysis revealed that the CA9 expression in CNB specimens was an independent predictive factor for pathological response. The Kaplan-Meier survival curve revealed a significant negative correlation (p = 0.013) between the disease-free survival (DFS) and the CA 9 expression in resected tissues after NAC. Multivariate regression analyses indicated that the CA9 expression in resected tissues was an independent prognostic factor for DFS.

Conclusions

CA9 expression in CNB specimens is a useful marker for predicting chemosensitivity, and CA9 expression in resected tissue is prognostic of DFS in patients with resectable early-stage breast cancer treated by sequential FEC and weekly paclitaxel prior to resection.

Hyaluronan-CD44 interaction promotes c-Jun signaling and miRNA21 expression leading to Bcl-2 expression and chemoresistance in breast cancer cells

MicroRNA-21 (miR-21) is associated with the development of solid tumors progression including breast cancer. In this study we investigated matrix hyaluronan (HA)-CD44 (a primary HA receptor) interaction with c-Jun N-Terminal Kinase (JNK)/c-Jun signaling in MDA-MB-468 breast cancer cells [a triple-negative (estrogen receptor-negative/progesterone receptor-negative/HER2-negative) breast cancer cell line]. Our results indicated that HA binding to CD44 promotes c-Jun nuclear translocation and transcriptional activation. Further analyses revealed that miR-21 is regulated by an upstream promoter containing AP1 binding site(s), and chromatin immunoprecipitation (CHIP) assays demonstrated that stimulation of miR-21 expression by HA/CD44 interaction is c-Jun-dependent in these breast cancer cells. This process results in an increase of the anti-apoptosis protein Bcl-2 and upregulation of inhibitors of the apoptosis family of proteins (IAPs) as well as chemoresistance in MDA-MB-468 cells. Treatment with c-Jun specific small interfering RNAs effectively blocks HA-mediated c-Jun signaling and abrogates miR-21 production as well as causes downregulation of survival proteins (Bcl-2 and IAPs) and enhancement of chemosensitivity. In addition, our results demonstrated that anti-miR-21 inhibitor not only downregulates Bcl-2/IAP expression but also increases chemosensitivity in HA-treated breast cancer cells. Together, these findings suggest that the HA/CD44-induced c-Jun signaling plays a pivotal role in miR-21 production leading to survival protein (Bcl-2/IAP) upregulation and chemoresistance in triple negative breast cancer cells such as MDA-MB-468 cell line. This novel HA/CD44-mediated c-Jun signaling pathway and miR-21 production provide a new drug target for the future intervention strategies to treat breast cancer.

Survivin up-regulates the expression of breast cancer resistance protein (BCRP) through attenuating the suppression of p53 on NF-κB expression in MCF-7/5-FU cells

Both breast cancer resistance protein (BCRP, ABCG2) and apoptosis-related molecules are involved in the development of multidrug resistance (MDR) in cancer cells. However, the association of BCRP with apoptosis-related molecules in the development of MDR is unknown. In this study, we investigated the changes in expression levels of BCRP, Survivin, p53, Bcl-2, Bcl-xL or Bax in cultured MCF-7 and MCF-7/5-FU cells, and explored whether these changes affected the expressions of BCRP. Our data showed that the protein and mRNA expression levels of BCRP, Survivin and Bcl-2 were significantly higher in MCF-7/5-FU cells than in MCF-7 cells, while p53 expression lower in MCF-7/5-FU cells than in MCF-7 cells. Knockdown of Survivin or Bcl-2 in MCF-7/5-FU cells and overexpression of Survivin in MCF-7 cells revealed that Survivin had significant association with BCRP expression. Luciferase reporter gene assays showed that Survivin up-regulated BCRP expression at transcriptional level and this response was mediated through NF-κB(p50) pathway. However, may be due to the physical interaction between p53 and Survivin, p53 directly affected Survivin-regulated BCRP expressions. Interestingly, we found that Survivin would suppress p53 expression. Furthermore, our data revealed that Survivin itself had no apparent effect on NF-κB(p50) and BCRP expression when knockdown of p53 in MCF-7 cells; whereas p53 exerted significant inhibitory action on these when knockdown of Survivin. In conclusion, through down regulation of p53 expression, Survivin attenuates the suppressing effect of p53 on NF-κB(p50) expression and then enhances BCRP expression. This may represent a novel strategy for reversal of BCRP drug transporter activity to modulate MDR in cancer cells.

CD133 is a useful surrogate marker for predicting chemosensitivity to neoadjuvant chemotherapy in breast cancer

Background

Neoadjuvant chemotherapy (NAC) is a standard care regimen for patients with breast cancer. However, the pathologic complete response (pCR) rate remains at 30%. We hypothesized that a cancer stem cell marker may identify NAC-resistant patients, and evaluated CD133 and ALDH1 as a potential surrogate marker for breast cancer. The aim of this study was to find a surrogate maker to predict chemosensitivity of NAC for breast cancer.

Methodology/Findings

A total of 102 patients with breast cancer were treated with NAC consisting of epirubicin followed by paclitaxel. Core needle biopsy (CNB) specimens and resected tumors were obtained from all patients before and after NAC, respectively. Chemosensitivity and prognostic potential of CD133 or ALDH1 expression was evaluated by immunohistochemistry. Clinical CR (cCR) and pCR rates were 18% (18/102) and 29% (30/102), respectively. Forty-seven (46%) patients had CD133-positive tumors before NAC, and CD133 expression was significantly associated with a low pCR rate (p = 0.035) and clinical non-responders. Multivariate analysis revealed that CD133 expression was significantly (p = 0.03) related to pCR. Recurrence was more frequent in patients with CD133-positive tumors (21/47, 45%) than that in patients with CD133-negative tumors (7/55, 13%). The number of patients with CD133-positive tumors (62%) after NAC was higher than that (46%) before NAC. Furthermore, most patients with CD133-positive tumors before NAC maintained the same status after NAC.

Conclusion/Significance

CD133 before NAC might be a useful marker for predicting the effectiveness of NAC and recurrence of breast cancer after NAC.

Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response

Background:

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contains crucial information about tumour heterogeneity and the transport limitations that reduce drug efficacy. Mathematical modelling of drug delivery and cellular responsiveness based on underutilised DCE-MRI data has the unique potential to predict therapeutic responsiveness for individual patients.

Methods:

To interpret DCE-MRI data, we created a modelling framework that operates over multiple time and length scales and incorporates intracellular metabolism, nutrient and drug diffusion, trans-vascular permeability, and angiogenesis. The computational methodology was used to analyse DCE-MR images collected from eight breast cancer patients at Baystate Medical Center in Springfield, MA.

Results:

Computer simulations showed that trans-vascular transport was correlated with tumour aggressiveness because increased vessel growth and permeability provided more nutrients for cell proliferation. Model simulations also indicate that vessel density minimally affects tissue growth and drug response, and nutrient availability promotes growth. Finally, the simulations indicate that increased transport heterogeneity is coupled with increased tumour growth and poor drug response.

Conclusion:

Mathematical modelling based on DCE-MRI has the potential to aid treatment decisions and improve overall cancer care. This model is the critical first step in the creation of a comprehensive and predictive computational method.

Hyaluronan-CD44 interaction with protein kinase C(epsilon) promotes oncogenic signaling by the stem cell marker Nanog and the Production of microRNA-21, leading to down-regulation of the tumor suppressor protein PDCD4, anti-apoptosis, and chemotherapy resistance in breast tumor cells

Multidrug resistance and disease relapse is a challenging clinical problem in the treatment of breast cancer. In this study, we investigated the hyaluronan (HA)-induced interaction between CD44 (a primary HA receptor) and protein kinase Cepsilon (PKCepsilon), which regulates a number of human breast tumor cell functions. Our results indicate that HA binding to CD44 promotes PKCepsilon activation, which, in turn, increases the phosphorylation of the stem cell marker, Nanog, in the breast tumor cell line MCF-7. Phosphorylated Nanog is then translocated from the cytosol to the nucleus and becomes associated with RNase III DROSHA and the RNA helicase p68. This process leads to microRNA-21 (miR-21) production and a tumor suppressor protein (e.g. PDCD4 (program cell death 4)) reduction. All of these events contribute to up-regulation of inhibitors of apoptosis proteins (IAPs) and MDR1 (multidrug-resistant protein), resulting in anti-apoptosis and chemotherapy resistance. Transfection of MCF-7 cells with PKCepsilon or Nanog-specific small interfering RNAs effectively blocks HA-mediated PKCepsilon-Nanog signaling events, abrogates miR-21 production, and increases PDCD4 expression/eIF4A binding. Subsequently, this PKCepsilon-Nanog signaling inhibition causes IAP/MDR1 down-regulation, apoptosis, and chemosensitivity. To further evaluate the role of miR-21 in oncogenesis and chemoresistance, MCF-7 cells were also transfected with a specific anti-miR-21 inhibitor in order to silence miR-21 expression and inhibit its target functions. Our results indicate that anti-miR-21 inhibitor not only enhances PDCD4 expression/eIF4A binding but also blocks HA-CD44-mediated tumor cell behaviors. Thus, this newly discovered HA-CD44 signaling pathway should provide important drug targets for sensitizing tumor cell apoptosis and overcoming chemotherapy resistance in breast cancer cells.

Hyaluronan-mediated CD44 interaction with p300 and SIRT1 regulates beta-catenin signaling and NFkappaB-specific transcription activity leading to MDR1 and Bcl-xL gene expression and chemoresistance in breast tumor cells

In this study we have investigated hyaluronan (HA)-mediated CD44 (an HA receptor) interactions with p300 (a histone acetyltransferase) and SIRT1 (a histone deacetylase) in human breast tumor cells (MCF-7 cells). Specifically, our results indicate that HA binding to CD44 up-regulates p300 expression and its acetyltransferase activity that, in turn, promotes acetylation of beta-catenin and NFkappaB-p65 leading to activation of beta-catenin-associated T-cell factor/lymphocyte enhancer factor transcriptional co-activation and NFkappaB-specific transcriptional up-regulation, respectively. These changes then cause the expression of the MDR1 (P-glycoprotein/P-gp) gene and the anti-apoptotic gene Bcl-x(L) resulting in chemoresistance in MCF-7 cells. Our data also show that down-regulation of p300, beta-catenin, or NFkappaB-p65 in MCF-7 cells (by transfecting cells with p300-, beta-catenin-, or NFkappaB-p65-specific small interfering RNA) inhibits the HA/CD44-mediated beta-catenin/NFkappaB-p65 acetylation and abrogates the aforementioned transcriptional activities. Subsequently, there is a significant decrease in both MDR1 and Bcl-x(L) gene expression and an enhancement in caspase-3 activity and chemosensitivity in the breast tumor cells. Further analyses indicate that activation of SIRT1 (deacetylase) by resveratrol (a natural antioxidant) induces SIRT1-p300 association and acetyltransferase inactivation, leading to deacetylation of HA/CD44-induced beta-catenin and NFkappaB-p65, inhibition of beta-catenin-T-cell factor/lymphocyte enhancer factor and NFkappaB-specific transcriptional activation, and the impairment of MDR1 and Bcl-x(L) gene expression. All these multiple effects lead to an activation of caspase-3 and a reduction of chemoresistance. Together, these findings suggest that the interactions between HA/CD44-stimulated p300 (acetyltransferase) and resveratrol-activated SIRT1 (deacetylase) play pivotal roles in regulating the balance between cell survival versus apoptosis, and multidrug resistance versus sensitivity in breast tumor cells.

Annexins in human breast cancer: Possible predictors of pathological response to neoadjuvant chemotherapy

Neoadjuvant chemotherapy is used in women who have large or locally advanced breast cancers. However, up to 70% of women who receive neoadjuvant chemotherapy fail to achieve a complete pathological response in their primary tumour (a surrogate marker of long-term survival). Five proteins, previously identified to be linked with chemoresistance in our in vitro experiments, were identified histochemically in pre-treatment core needle biopsies from 40 women with large or locally advanced breast cancers. Immunohistochemical staining with the five proteins showed no single protein to be a predictor of response to chemotherapy. However, pre-treatment breast cancer specimens that were annexin-A2 positive but annexin-A1 negative correlated with a poor pathological response (p=0.04, Fisher's exact test). The mechanisms by which annexins confer chemoresistance have not been identified, but may be due to inhibition of apoptosis. Annexin-A1 has been shown to enhance apoptosis, whilst annexin-A2, by contrast, inhibits apoptosis.

Clinical response after two cycles compared to HER2, Ki-67, p53, and bcl-2 in independently predicting a pathological complete response after preoperative chemotherapy in patients with operable carcinoma of the breast

Introduction

To investigate the predictive value of clinical and biological markers for a pathological complete remission after a preoperative dose-dense regimen of doxorubicin and docetaxel, with or without tamoxifen, in primary operable breast cancer.

Methods

Patients with a histologically confirmed diagnosis of previously untreated, operable, and measurable primary breast cancer (tumour (T), nodes (N) and metastases (M) score: T2-3(≥ 3 cm) N0-2 M0) were treated in a prospectively randomised trial with four cycles of dose-dense (bi-weekly) doxorubicin and docetaxel (ddAT) chemotherapy, with or without tamoxifen, prior to surgery. Clinical and pathological parameters (menopausal status, clinical tumour size and nodal status, grade, and clinical response after two cycles) and a panel of biomarkers (oestrogen and progesterone receptors, Ki-67, human epidermal growth factor receptor 2 (HER2), p53, bcl-2, all detected by immunohistochemistry) were correlated with the detection of a pathological complete response (pCR).

Results

A pCR was observed in 9.7% in 248 patients randomised in the study and in 8.6% in the subset of 196 patients with available tumour tissue. Clinically negative axillary lymph nodes, poor tumour differentiation, negative oestrogen receptor status, negative progesterone receptor status, and loss of bcl-2 were significantly predictive for a pCR in a univariate logistic regression model, whereas in a multivariate analysis only the clinical nodal status and hormonal receptor status provided significantly independent information. Backward stepwise logistic regression revealed a response after two cycles, with hormone receptor status and lymph-node status as significant predictors. Patients with a low percentage of cells stained positive for Ki-67 showed a better response when treated with tamoxifen, whereas patients with a high percentage of Ki-67 positive cells did not have an additional benefit when treated with tamoxifen. Tumours overexpressing HER2 showed a similar response to that in HER2-negative patients when treated without tamoxifen, but when HER2-positive tumours were treated with tamoxifen, no pCR was observed.

Conclusion

Reliable prediction of a pathological complete response after preoperative chemotherapy is not possible with clinical and biological factors routinely determined before start of treatment. The response after two cycles of chemotherapy is a strong but dependent predictor. The only independent factor in this subset of patients was bcl-2.

Trial registration number

NCT00543829