Value of 18FDG PET for prediction of therapeutic response in HER2+ breast cancer: Interim analysis

¹⁸F-FDG PET is recommended for the initial staging of locally advanced breast cancer. Studies have shown the prognostic value of ¹⁸F-FDG PET for staging, but its ability to predict pathological complete response remains uncertain. Our objective was to determine the predictive value of early therapeutic evaluation by ¹⁸F-FDG PET for HER2-positive breast cancer patients. We studied a subpopulation of the prospective and multicentric French NeoTOP trial, at interim analysis. All patients were eligible for neoadjuvant chemotherapy. A ¹⁸F-FDG PET was performed at baseline and then after one cycle of neoadjuvant treatment. ¹⁸F-FDG PET were studied by three conventional methods (two visuals and one quantitative) and by textural analysis. Complete pathological response on surgical samples corresponded to grades 1/2 of Chevallier's classification and no responders corresponded to grades 3/4 of Chevallier. Pathological results were available for 21 patients. SUV_max decreased by 55% after one cycle of chemotherapy. No difference between groups was found with visuals, conventional quantitative and textural analysis of tumour uptake evaluations. Early therapeutic evaluation by ¹⁸F-FDG PET for HER2-positive breast cancer was not predictive of pathological therapeutic response after neoadjuvant treatment, with conventional study or textural analyses in this interim analysis.     

Efficacy and clinical monitoring strategies for immune checkpoint inhibitors and targeted cytokine immunotherapy for locally advanced and metastatic colorectal cancer

Colorectal cancer (CRC) is the fourth most common cancer type and is the second leading cause of cancer deaths annually in the United States. Conventional treatment options include postoperative (adjuvant) and preoperative (neoadjuvant) chemotherapy and radiotherapy. Although these treatment modalities have shown to decrease tumor burden, a major limitation to chemothearpy/radiotherapy is the high recurrence rate in patients. Immune-modulation strategies have emerged as a promising new therapeutic avenue to reduce this recurrence rate while minimizing undesirable systemic side effects. This review will focus specifically on the mechanisms of monoclonal antibodies: immune checkpoint inhibitors and cytokines, as well as current drugs approved by the Food and Drug Administration (FDA) and new clinical/pre-clinical trials. Finally, this review will investigate emerging methods used to monitor tumor response post-treatment.     

Differential Network Analysis Applied to Preoperative Breast Cancer Chemotherapy Response

In silico approaches are increasingly considered to improve breast cancer treatment. One of these treatments, neoadjuvant TFAC chemotherapy, is used in cases where application of preoperative systemic therapy is indicated. Estimating response to treatment allows or improves clinical decision-making and this, in turn, may be based on a good understanding of the underlying molecular mechanisms. Ever increasing amounts of high throughput data become available for integration into functional networks. In this study, we applied our software tool ExprEssence to identify specific mechanisms relevant for TFAC therapy response, from a gene/protein interaction network. We contrasted the resulting active subnetwork to the subnetworks of two other such methods, OptDis and KeyPathwayMiner. We could show that the ExprEssence subnetwork is more related to the mechanistic functional principles of TFAC therapy than the subnetworks of the other two methods despite the simplicity of ExprEssence. We were able to validate our method by recovering known mechanisms and as an application example of our method, we identified a mechanism that may further explain the synergism between paclitaxel and doxorubicin in TFAC treatment: Paclitaxel may attenuate MELK gene expression, resulting in lower levels of its target MYBL2, already associated with doxorubicin synergism in hepatocellular carcinoma cell lines. We tested our hypothesis in three breast cancer cell lines, confirming it in part. In particular, the predicted effect on MYBL2 could be validated, and a synergistic effect of paclitaxel and doxorubicin could be demonstrated in the breast cancer cell lines SKBR3 and MCF-7.     

A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions

An ability to predict the likelihood of cellular response towards particular chemotherapeutic agents based upon protein expression patterns could facilitate the identification of biological molecules with previously undefined roles in the process of chemoresistance/chemosensitivity, and if robust enough these patterns might also be exploited towards the development of novel predictive assays. To ascertain whether proteomic based molecular profiling in conjunction with artificial neural network (ANN) algorithms could be applied towards the specific recognition of phenotypic patterns between either control or drug treated and chemosensitive or chemoresistant cellular populations, a combined approach involving MALDI-TOF matrix-assisted laser desorption/ionization-time of flight mass spectrometry, Ciphergen protein chip technology and ANN algorithms have been applied to specifically identify proteomic 'fingerprints' indicative of treatment regimen for chemosensitive (MCF-7, T47D) and chemoresistant (MCF-7/ADR) breast cancer cell lines following exposure to Doxorubicin or Paclitaxel. The results indicate that proteomic patterns can be identified by ANN algorithms to correctly assign 'class' for treatment regimen (e.g. control/drug treated or chemosensitive/chemoresistant) with a high degree of accuracy using boot-strap statistical validation techniques and that biomarker ion patterns indicative of response/non-response phenotypes are associated with MCF-7 and MCF-7/ADR cells exposed to Doxorubicin. We have also examined the predictive capability of this approach towards MCF-7 and T47D cells to ascertain whether prediction could be made based upon treatment regimen irrespective of cell lineage. Models were identified that could correctly assign class (control or Paclitaxel treatment) for 35/38 samples of an independent dataset. A similar level of predictive capability was also found (> 92%; n = 28) when proteomic patterns derived from the drug resistant cell line MCF-7/ADR were compared against those derived from MCF-7 and T47D as a model system of drug resistant and drug sensitive phenotypes. This approach might offer a potential methodology for predicting the biological behaviour of cancer cells towards particular chemotherapeutics and through protein isolation and sequence identification could result in the identification of biological molecules associated with chemosensitive/chemoresistance tumour phenotypes.     

Pathway-Based Genomics Prediction using Generalized Elastic Net

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.     

Mechanisms of resistance to anti-angiogenesis therapies

Angiogenesis, the formation of new blood vessels from preexisting ones, provides oxygen and nutrients to actively proliferating tumor cells. Hence, it represents a critical aspect of tumor progression and metastasis. Because inhibition of angiogenesis represents a major approach to cancer treatment, the development of inhibitors of angiogenesis is a major challenge. The first FDA approved anti-angiogenic drug bevacizumab, a humanized monoclonal antibody directed against the Vascular Endothelial Growth Factor (VEGF), has been approved for the treatment of metastatic colorectal, lung, breast, and kidney cancers. The encouraging results have lead to the development, in the past few years, of other agents targeting angiogenic pathways as potent anti-cancer drugs and a number of them have been approved for metastatic breast, lung, kidney, and central nervous system cancers. Despite a statistically significant increase in progression free survival, which has accelerated FDA approval, no major benefit to overall survival was described and patients inevitably relapsed due to acquired resistance. However, while progression free survival was increased by only a few months for the majority of the patients, some clearly benefited from the treatment with a real increase in life span. The objective of this review is to present an overview of the different treatments targeting angiogenesis, their efficacy and the mechanisms of resistance that have been identified in different cancer types. It is essential to understand how resistance (primary or acquired over time) develops and how it may be overcome.     

A computational approach to analyze the mechanism of action of the kinase inhibitor bafetinib

Prediction of drug action in human cells is a major challenge in biomedical research. Additionally, there is strong interest in finding new applications for approved drugs and identifying potential side effects. We present a computational strategy to predict mechanisms, risks and potential new domains of drug treatment on the basis of target profiles acquired through chemical proteomics. Functional protein-protein interaction networks that share one biological function are constructed and their crosstalk with the drug is scored regarding function disruption. We apply this procedure to the target profile of the second-generation BCR-ABL inhibitor bafetinib which is in development for the treatment of imatinib-resistant chronic myeloid leukemia. Beside the well known effect on apoptosis, we propose potential treatment of lung cancer and IGF1R expressing blast crisis.     

Correlation between baseline 18F-FDG PET/CT features and pathological complete response after neoadjuvant chemotherapy in early triple negative breast cancer

Aim of the studyTo evaluate correlation between metabolic and textural parameters on baseline ¹⁸F-FDG PET/CT and pathological response after neoadjuvant chemotherapy in non-metastatic triple negative breast cancer (TNBC).MethodsAll consecutive non-metastatic TNBC women treated by neoadjuvant chemotherapy followed by breast surgery who underwent ¹⁸F-FDG PET/CT examination at diagnosis between 2012 and 2018 were retrospectively included. Metabolic parameters (SUVmax, SUVmean, SUVpeak, MTV, TLG) of the primary tumour and lymph nodes, and textural features (entropy, homogeneity, SRE, LRE, LGZE, HGZE) of the primary tumour were collected. Pathological response was defined according to Sataloff classification.ResultsSeventy-four patients were enrolled. In univariate analysis, metabolic and textural features of the primary breast lesion or metabolic parameters of regional lymph nodes were not predictive of pathological complete response after neoadjuvant chemotherapy.ConclusionMetabolic and textural features of baseline PET/CT do not seem to predict pathological response after neoadjuvant chemotherapy in non-metastatic triple negative breast cancer.     

乳腺癌新辅助化疗疗效评价体系

目前新辅助化疗已广泛应用于乳腺癌的治疗,可降低肿瘤分期,提高手术切除率和增加保乳手术的机会。恰当的新辅助化疗疗效评价不仅可以指导患者治疗方案和预测预后,还可对不同药物的疗效提供可靠的评估。目前新辅助化疗评估主要采用临床检查如触诊、超声、钼靶X线、计算机断层显像、磁共振成像及病理学检查确定肿瘤体积变化,可分为临床评估和病理学评估。两者均有多种体系标准,未形成统一公认的标准。临床试验中采用较多的标准有WHO和RECIST等临床评价标准以及MP标准和JBCS标准等病理学评价标准。本文就乳腺癌新辅助化疗疗效评估体系进行总结。     

A serum protein profile predictive of the resistance to neoadjuvant chemotherapy in advanced breast cancers

Prediction of the responses to neoadjuvant chemotherapy (NACT) can improve the treatment of patients with advanced breast cancer. Genes and proteins predictive of chemoresistance have been extensively studied in breast cancer tissues. However, noninvasive serum biomarkers capable of such prediction have been rarely exploited. Here, we performed profiling of N-glycosylated proteins in serum from fifteen advanced breast cancer patients (ten patients sensitive to and five patients resistant to NACT) to discover serum biomarkers of chemoresistance using a label-free liquid chromatography-tandem MS method. By performing a series of statistical analyses of the proteomic data, we selected thirteen biomarker candidates and tested their differential serum levels by Western blotting in 13 independent samples (eight patients sensitive to and five patients resistant to NACT). Among the candidates, we then selected the final set of six potential serum biomarkers (AHSG, APOB, C3, C9, CP, and ORM1) whose differential expression was confirmed in the independent samples. Finally, we demonstrated that a multivariate classification model using the six proteins could predict responses to NACT and further predict relapse-free survival of patients. In summary, global N-glycoproteome profile in serum revealed a protein pattern predictive of the responses to NACT, which can be further validated in large clinical studies.     

Prediction of drug combinations by integrating molecular and pharmacological data

Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.     

Place de l’imagerie dans l’évaluation de l’efficacité des traitements dans le cancer du sein

This paper describes, from the current literature, the role of various imaging methods to assess the response to therapy in breast cancer. Two different clinical situations are considered: neoadjuvant chemotherapy of locally advanced breast cancer and the metastastic breast cancer. Significant clinical data are available for three criteria: the volume of the tumour, the uptake of fluorodeoxyglucose using PET and the perfusion of the tumor evaluated either by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) or by PET using ¹⁵O water. ¹⁸F FDG PET allows prediction of the response after one or two cycles of neoadjuvant chemotherapy. New approaches will offer opportunities to refine the role of imaging in monitoring the response to chemotherapy. PET using thymidine as biomarker is promising in assessing the tissular proliferation. Estrogen analogs could be used to predict hormonally responsive breast cancer. Many other approaches, although less developed, might offer new insights in the response to therapy of breast cancer like magnetic resonance spectroscopy or optical imaging of hemoglobin oxygenation. Imaging also offers potential of monitoring the down-regulation of specialized receptors of the cell membrane in response to treatment: the most studied receptor in preclinical model has been the human epidermal growth factor receptor type 2 (HER2). Integrin, a family of cell adhesion receptor, is also an important target for imaging. Apoptosis, multidrug resistance and hypoxia can also be studied using appropriate biomarkers. To allow reliable multicenter trials of new drugs, these different imaging approaches still require an improved standardization of image acquisition and processing.     

Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model

The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.     

A systematic analysis of FDA-approved anticancer drugs

Background

The discovery of novel anticancer drugs is critical for the pharmaceutical research and development, and patient treatment. Repurposing existing drugs that may have unanticipated effects as potential candidates is one way to meet this important goal. Systematic investigation of efficient anticancer drugs could provide valuable insights into trends in the discovery of anticancer drugs, which may contribute to the systematic discovery of new anticancer drugs.

Results

In this study, we collected and analyzed 150 anticancer drugs approved by the US Food and Drug Administration (FDA). Based on drug mechanism of action, these agents are divided into two groups: 61 cytotoxic-based drugs and 89 target-based drugs. We found that in the recent years, the proportion of targeted agents tended to be increasing, and the targeted drugs tended to be delivered as signal drugs. For 89 target-based drugs, we collected 102 effect-mediating drug targets in the human genome and found that most targets located on the plasma membrane and most of them belonged to the enzyme, especially tyrosine kinase. From above 150 drugs, we built a drug-cancer network, which contained 183 nodes (150 drugs and 33 cancer types) and 248 drug-cancer associations. The network indicated that the cytotoxic drugs tended to be used to treat more cancer types than targeted drugs. From 89 targeted drugs, we built a cancer-drug-target network, which contained 214 nodes (23 cancer types, 89 drugs, and 102 targets) and 313 edges (118 drug-cancer associations and 195 drug-target associations). Starting from the network, we discovered 133 novel drug-cancer associations among 52 drugs and 16 cancer types by applying the common target-based approach. Most novel drug-cancer associations (116, 87%) are supported by at least one clinical trial study.

Conclusions

In this study, we provided a comprehensive data source, including anticancer drugs and their targets and performed a detailed analysis in term of historical tendency and networks. Its application to identify novel drug-cancer associations demonstrated that the data collected in this study is promising to serve as a fundamental for anticancer drug repurposing and development.

     

Proteomic identification of predictive biomarkers of resistance to neoadjuvant chemotherapy in luminal breast cancer: a possible role for 14-3-3 theta/tau and tBID?

Introduction

Chemotherapy resistance is a major obstacle in effective neoadjuvant treatment for estrogen receptor-positive breast cancer. The ability to predict tumour response would allow chemotherapy administration to be directed towards only those patients who would benefit, thus maximising treatment efficiency. We aimed to identify putative protein biomarkers associated with chemotherapy resistance, using fresh tumour samples with antibody microarray analysis and then to perform pilot clinical validation experiments.

Materials and methods

Chemotherapy resistant and chemotherapy sensitive tumour samples were collected from breast cancer patients who had received anthracycline based neoadjuvant therapy consisting of epirubicin with cyclophosphamide followed by docetaxel. A total of 5 comparative proteomics experiments were performed using invasive ductal carcinomas which demonstrated estrogen receptor positivity (luminal subtype). Protein expression was compared between chemotherapy resistant and chemotherapy sensitive tumour samples using the Panorama XPRESS Profiler725 antibody microarray containing 725 antibodies from a wide variety of cell signalling and apoptosis pathways. A pilot series of archival samples was used for clinical validation of putative predictive biomarkers.

Results

AbMA analysis revealed 38 differentially expressed proteins which demonstrated at least 1.8 fold difference in expression in chemotherapy resistant tumours and 7 of these proteins (Zyxin, 14-3-3 theta/tau, tBID, Pinin, Bcl-xL, RIP and MyD88) were found in at least 2 experiments. Clinical validation in a pilot series of archival samples revealed 14-3-3 theta/tau and tBID to be significantly associated with chemotherapy resistance.

Conclusions

For the first time, antibody microarrays have been used to identify proteins associated with chemotherapy resistance using fresh breast cancer tissue. We propose a potential role for 14-3-3 theta/tau and tBID as predictive biomarkers of neoadjuvant chemotherapy resistance in breast cancer. Further validation in a larger sample series is now required.     

Gene Expression Profiling Reveals Epithelial Mesenchymal Transition (EMT) Genes Can Selectively Differentiate Eribulin Sensitive Breast Cancer Cells

Objectives

Eribulin mesylate is a synthetic macrocyclic ketone analog of the marine sponge natural product halichondrin B. Eribulin is a mechanistically unique inhibitor of microtubule dynamics. In this study, we investigated whether selective signal pathways were associated with eribulin activity compared to paclitaxel, which stabilizes microtubules, based on gene expression profiling of cell line panels of breast, endometrial, and ovarian cancer in vitro.

Results

We determined the sets of genes that were differentially altered between eribulin and paclitaxel treatment in breast, endometrial, and ovarian cancer cell line panels. Our unsupervised clustering analyses revealed that expression profiles of gene sets altered with treatments were correlated with the in vitro antiproliferative activities of the drugs. Several tubulin isotypes had significantly lower expression in cell lines treated with eribulin compared to paclitaxel. Pathway enrichment analyses of gene sets revealed that the common pathways altered between treatments in the 3 cancer panels were related to cytoskeleton remodeling and cell cycle regulation. The epithelial-mesenchymal transition (EMT) pathway was enriched in genes with significantly altered expression between the two drugs for breast and endometrial cancers, but not for ovarian cancer. Expression of genes from the EMT pathway correlated with eribulin sensitivity in breast cancer and with paclitaxel sensitivity in endometrial cancer. Alteration of expression profiles of EMT genes between sensitive and resistant cell lines allowed us to predict drug sensitivity for breast and endometrial cancers.

Conclusion

Gene expression analysis showed that gene sets that were altered between eribulin and paclitaxel correlated with drug in vitro antiproliferative activities in breast and endometrial cancer cell line panels. Among the panels, breast cancer provided the strongest differentiation between eribulin and paclitaxel sensitivities based on gene expression. In addition, EMT genes were predictive of eribulin sensitivity in the breast and endometrial cancer panels.     

Gene expression and pathologic response to neoadjuvant chemotherapy in breast cancer

Pathologic complete response after neoadjuvant systemic treatment appears to be a valid surrogate for better overall survival in breast cancer patients. Currently, together with standard clinicopathologic assessment, novel molecular biomarkers are being exhaustively tested in order to look into the heterogeneity of breast cancer. The aim of our study was to examine an association between 23-gene real-time-PCR expression assay including ABCB1, ABCC1, BAX, BBC3, BCL2, CASP3, CYP2D6, ERCC1, FOXC1, GAPDH, IGF1R, IRF1, MAP2, MAPK 8, MAPK9, MKI67, MMP9, NCOA3, PARP1, PIK3CA, TGFB3, TOP2A, and YWHAZ receptor status of breast cancer core biopsies sampled before neoadjuvant chemotherapy (anthracycline and taxanes) and pathologic response. Core-needle biopsies were collected from 42 female patients with inoperable locally advanced breast cancer or resectable tumors suitable for downstaging, before any treatment. Expressions of 23 genes were determined by means of TagMan low density arrays. Analysis of variance was used to select genes with discriminatory potential between receptor subtypes. We introduced a correction for false discovery rates (presented as q values) due to multiple hypothesis testing. Statistical analysis showed that seven genes out of a 23-gene real-time-PCR expression assay differed significantly in relation to pathologic response regardless of breast cancer subtypes. Among these genes, we identified: BAX (p = 0.0146), CYP2D6 (p = 0.0063), ERCC1 (p = 0.0231), FOXC1 (p = 0.0048), IRF1 (p = 0.0022), MAP2 (p = 0.0011), and MKI67 (p = 0.0332). The assessment of core biopsy gene profiles and receptor-based subtypes, before neoadjuvant therapy seems to predict response or resistance and to define new signaling pathways to provide more powerful classifiers in breast cancer, hence the need for further research.