Molecular and protein markers for clinical decision making in breast cancer: today and tomorrow

Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form

BACKGROUND AND OBJECTIVE

Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decision-making processes in the treatment of this malignancy. Though several machine learning models analyzing both clinical and histopathological information have been developed in literature to address this task, these approaches turned out to be unsuitable for describing this problem.

METHODS

In this study, we designed a novel artificial intelligence-based approach which converts clinical information into an image-form to be analyzed through Convolutional Neural Networks. Specifically, we predicted the occurrence of an Invasive Disease Event at both 5-year and 10-year follow-ups of 696 female patients with a first invasive breast cancer diagnosis enrolled at IRCCS "Giovanni Paolo II" in Bari, Italy. After transforming each patient, represented by a vector of clinical information, to an image form, we extracted low-level quantitative imaging features by means of a pre-trained Convolutional Neural Network, namely, AlexNET. Then, we classified breast cancer patients in the two classes, namely, Invasive Disease Event and non-Invasive Disease Event, via a Support Vector Machine classifier trained on a subset of significative features previously identified.

RESULTS

Both 5-year and 10-year models resulted particularly accurate in predicting breast cancer recurrence event, achieving an AUC value of 92.07% and 92.84%, an accuracy of 88.71% and 88.82%, a sensitivity of 86.83% and 88.06%, a specificity of 89.55% and 89.3%, a precision of 71.93% and 84.82%, respectively.

CONCLUSIONS

This is the first study proposing an approach which converts clinical information into an image-form to develop a decision support system for identifying patients at high risk of occurrence of an Invasive Disease Event, and then defining personalized oncological therapeutic treatments for breast cancer patients.

Evaluation of Breast Cancer Tumor-Infiltrating Lymphocytes on Ultrasound Images Based on a Novel Multi-Cascade Residual U-Shaped Network

OBJECTIVE

Breast cancer has become the leading cancer of the 21st century. Tumor-infiltrating lymphocytes (TILs) have emerged as effective biomarkers for predicting treatment response and prognosis in breast cancer. The work described here was aimed at designing a novel deep learning network to assess the levels of TILs in breast ultrasound images.

METHODS

We propose the Multi-Cascade Residual U-Shaped Network (MCRUNet), which incorporates a gray feature enhancement (GFE) module for image reconstruction and normalization to achieve data synergy. Additionally, multiple residual U-shaped (RSU) modules are cascaded as the backbone network to maximize the fusion of global and local features, with a focus on the tumor's location and surrounding regions. The development of MCRUNet is based on data from two hospitals and uses a publicly available ultrasound data set for transfer learning.

RESULTS

MCRUNet exhibits excellent performance in assessing TILs levels, achieving an area under the receiver operating characteristic curve of 0.8931, an accuracy of 85.71%, a sensitivity of 83.33%, a specificity of 88.64% and an F1 score of 86.54% in the test group. It outperforms six state-of-the-art networks in terms of performance.

CONCLUSION

The MCRUNet network based on breast ultrasound images of breast cancer patients holds promise for non-invasively predicting TILs levels and aiding personalized treatment decisions.

Biomarkers in Breast Cancer: An Old Story with a New End

Breast cancer is the second most frequent cancer in the world. It is a heterogeneous disease and the leading cause of cancer mortality in women. Advances in molecular technologies allowed for the identification of new and more specifics biomarkers for breast cancer diagnosis, prognosis, and risk prediction, enabling personalized treatments, improving therapy, and preventing overtreatment, undertreatment, and incorrect treatment. Several breast cancer biomarkers have been identified and, along with traditional biomarkers, they can assist physicians throughout treatment plan and increase therapy success. Despite the need of more data to improve specificity and determine the real clinical utility of some biomarkers, others are already established and can be used as a guide to make treatment decisions. In this review, we summarize the available traditional, novel, and potential biomarkers while also including gene expression profiles, breast cancer single-cell and polyploid giant cancer cells. We hope to help physicians understand tumor specific characteristics and support decision-making in patient-personalized clinical management, consequently improving treatment outcome.

A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification

Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions of the occurrence of breast cancer IDEs at both 5- and 10-years. The method is based on the concept of voting among multiple models to give a final prediction for each individual patient. Promising results were achieved on a cohort of 529 patients, whose data, related to primary breast cancer, were provided by Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Our proposal greatly improves the performances returned by the baseline original model, i.e., without voting, finally reaching a median AUC value of 77.1% and 76.3% for the IDE prediction at 5-and 10-years, respectively. Finally, the proposed approach allows to promote more intelligible decisions and then a greater acceptability in clinical practice since it returns an explanation of the IDE prediction for each individual patient through the voting procedure.

Understanding Breast Cancers through Spatial and High-Resolution Visualization Using Imaging Technologies

Breast cancer is the most common cancer affecting women worldwide. Although many analyses and treatments have traditionally targeted the breast cancer cells themselves, recent studies have focused on investigating entire cancer tissues, including breast cancer cells. To understand the structure of breast cancer tissues, including breast cancer cells, it is necessary to investigate the three-dimensional location of the cells and/or proteins comprising the tissues and to clarify the relationship between the three-dimensional structure and malignant transformation or metastasis of breast cancers. In this review, we aim to summarize the methods for analyzing the three-dimensional structure of breast cancer tissue, paying particular attention to the recent technological advances in the combination of the tissue-clearing method and optical three-dimensional imaging. We also aimed to identify the latest methods for exploring the relationship between the three-dimensional cell arrangement in breast cancer tissues and the gene expression of each cell. Finally, we aimed to describe the three-dimensional imaging features of breast cancer tissues using noninvasive photoacoustic imaging methods.

Immunometabolism characteristics and a potential prognostic risk model associated with TP53 mutations in breast cancer

TP53, a gene with high-frequency mutations, plays an important role in breast cancer (BC) development through metabolic regulation, but the relationship between TP53 mutation and metabolism in BC remains to be explored. Our study included 1,066 BC samples from The Cancer Genome Atlas (TCGA) database, 415 BC cases from the Gene Expression Omnibus (GEO) database, and two immunotherapy cohorts. We identified 92 metabolic genes associated with TP53 mutations by differential expression analysis between TP53 mutant and wild-type groups. Univariate Cox analysis was performed to evaluate the prognostic effects of 24 TP53 mutation-related metabolic genes. By unsupervised clustering and other bioinformatics methods, the survival differences and immunometabolism characteristics of the distinct clusters were illustrated. In a training set from TCGA cohort, we employed the least absolute shrinkage and selection operator (LASSO) regression method to construct a metabolic gene prognostic model associated with TP53 mutations, and the GEO cohort served as an external validation set. Based on bioinformatics, the connections between risk score and survival prognosis, tumor microenvironment (TME), immunotherapy response, metabolic activity, clinical characteristics, and gene characteristics were further analyzed. It is imperative to note that our model is a powerful and robust prognosis factor in comparison to other traditional clinical features and also has high accuracy and clinical usefulness validated by receiver operating characteristic (ROC) and decision curve analysis (DCA). Our findings deepen our understanding of the immune and metabolic characteristics underlying the TP53 mutant metabolic gene profile in BC, laying a foundation for the exploration of potential therapies targeting metabolic pathways. In addition, our model has promising predictive value in the prognosis of BC.

Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in breast cancer

Background

Breast cancer (BC) is the most common malignant tumor worldwide. Apoptosis and hypoxia are involved in the progression of BC, but reliable biomarkers for these have not been developed. We hope to explore a gene signature that combined apoptosis and hypoxia‐related genes (AHGs) to predict BC prognosis and immune infiltration.

Methods

We collected the mRNA expression profiles and clinical data information of BC patients from The Cancer Genome Atlas database. The gene signature based on AHGs was constructed using the univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analysis. The associations between risk scores, immune infiltration, and immune checkpoint gene expression were studied using single‐sample gene set enrichment analysis. Besides, gene signature and independent clinicopathological characteristics were combined to establish a nomogram. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on the potential functions of AHGs.

Results

We identified a 16‐AHG signature (AGPAT1, BTBD6, EIF4EBP1, ERRFI1, FAM114A1, GRIP1, IRF2, JAK1, MAP2K6, MCTS1, NFKBIA, NFKBIZ, NUP43, PGK1, RCL1, and SGCE) that could independently predict BC prognosis. The median score of the risk model divided the patients into two subgroups. By contrast, patients in the high‐risk group had poorer prognosis, less abundance of immune cell infiltration, and expression of immune checkpoint genes. The gene signature and nomogram had good predictive effects on the overall survival of BC patients. GO and KEGG analyses revealed that the differential expression of AHGs may be closely related to tumor immunity.

Conclusion

We established and verified a 16‐AHG BC signature which may help predict prognosis, assess potential immunotherapy benefits, and provide inspiration for future research on the functions and mechanisms of AHGs in BC.

Correlation of ER, PR, and HER2 at protein and mRNA levels in the Asian patients with operable breast cancer

Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. The estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) are the important biomarkers in the prognosis of breast cancer, and their expression is used to categorize breast cancer into subtypes. We aimed to analyze the concordance among ER, PR, and HER2 expression levels and breast cancer subtyping results obtained by immunohistochemistry (IHC, for protein) and reverse transcriptase-polymerase chain reaction (RT-PCR, for mRNA) and to assess the recurrence-free survival (RFS) of the different subtypes as determined by the two methods. We compared biomarker expression by IHC and RT-PCR in 397 operable breast cancer patients and categorized all patients into luminal, HER2, and triple-negative (TN) subtypes. The concordance of biomarker expression between the two methods was 81.6% (κ = 0.4075) for ER, 87.2% (κ = 0.5647) for PR, and 79.1% (κ = 0.2767) for HER2. The κ-statistic was 0.3624 for the resulting luminal, HER2, and TN subtypes. The probability of 5-year RFS was 0.78 for the luminal subtype versus 0.77 for HER2 and 0.51 for TN, when determined by IHC (P=0.007); and 0.80, 0.71, and 0.61, respectively, when determined by the RT-PCR method (P=0.008). Based on the current evidence, subtyping by RT-PCR performs similar to conventional IHC with regard to the 5-year prognosis. The PCR method may thus provide a complementary means of subtyping when IHC results are ambiguous.

Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling

For a patient affected by breast cancer, after tumor removal, it is necessary to decide which adjuvant therapy is able to prevent tumor relapse and formation of metastases. A prediction of the outcome of adjuvant therapy tailored for the patient is hard, due to the heterogeneous nature of the disease. We devised a methodology for predicting 5-years survival based on the new machine learning paradigm of coherent voting networks, with improved accuracy over state-of-the-art prediction methods. The 'coherent voting communities' metaphor provides a certificate justifying the survival prediction for an individual patient, thus facilitating its acceptability in practice, in the vein of explainable Artificial Intelligence. The method we propose is quite flexible and applicable to other types of cancer.

Recent progress in smartphone-based techniques for food safety and the detection of heavy metal ions in environmental water

Emerging smartphone-based point-of-care tests (POCTs) are cost-effective, precise, and easy to implement in resource-limited areas. Thus, they are considered a potential alternative to conventional diagnostic testing. This review explores food safety and the detection of metal ions in environmental water based on unprecedented smartphone technology. Specifically, we provide an overview of various methods used for target analyte detection (antibiotics, enzymes, mycotoxins, pathogens, pesticides, small molecules, and metal ions), such as colorimetric, fluorescence, microscopic imaging, and electrochemical methods. This paper performs a comprehensive review of smartphone-based POCTs developed in the last three years (2018-2020) and evaluates their relative advantages and limitations. Moreover, we discuss the imperative role of new technology in the progress of POCTs. Sensor materials (metal nanoparticles, carbon dots, quantum dots, organic substrates, etc.) and detection techniques (paper-based, later flow assay, microfluidic platform, etc.) involved in POCTs based on smartphones, and the challenges faced by these techniques, are addressed.

Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC. Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis. Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC. Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

CancerEMC: frontline non-invasive cancer screening from circulating protein biomarkers and mutations in cell-free DNA

MOTIVATION

The early detection of cancer through accessible blood tests can foster early patient interventions. Although there are developments in cancer detection from cell-free DNA (cfDNA), its accuracy remains speculative. Given its central importance with broad impacts, we aspire to address the challenge.

METHODS

A bagging Ensemble Meta Classifier (CancerEMC) is proposed for early cancer detection based on circulating protein biomarkers and mutations in cfDNA from the blood. CancerEMC is generally designed for both binary cancer detection and multi-class cancer type localization. It can address the class imbalance problem in multi-analyte blood test data based on robust oversampling and adaptive synthesis techniques.

RESULTS

Based on the clinical blood test data, we observe that the proposed CancerEMC has outperformed other algorithms and state-of-the-arts studies (including CancerSEEK published in Science, 2018) for cancer detection. The results reveal that our proposed method (i.e., CancerEMC) can achieve the best performance result for both binary cancer classification with 99.1748% accuracy (AUC = 0.999) and localized multiple cancer detection with 74.1214% accuracy (AUC = 0.938). For addressing the data imbalance issue with oversampling techniques, the accuracy can be increased to 91.4966% (AUC = 0.992), where the state-of-the-art method can only be estimated at 69.64% (AUC = 0.921). Similar results can also be observed on independent and isolated testing data.

AVAILABILITY

https://github.com/saifurcubd/Cancer-Detection.

Breast cancer prognosis signature: linking risk stratification to disease subtypes

Breast cancer is a very complex and heterogeneous disease with variable molecular mechanisms of carcinogenesis and clinical behaviors. The identification of prognostic risk factors may enable effective diagnosis and treatment of breast cancer. In particular, numerous gene-expression-based prognostic signatures were developed and some of them have already been applied into clinical trials and practice. In this study, we summarized several representative gene-expression-based signatures with significant prognostic value and separately assessed their ability of prognosis prediction in their originally targeted populations of breast cancer. Notably, many of the collected signatures were originally designed to predict the outcomes of estrogen receptor positive (ER+) patients or the whole breast cancer cohort; there are no typical signatures used for the prognostic prediction in a specific population of patients with the intrinsic subtype. We thus attempted to identify subtype-specific prognostic signatures via a computational framework for analyzing multi-omics profiles and patient survival. For both the discovery and an independent data set, we confirmed that subtype-specific signature is a strong and significant independent prognostic factor in the corresponding cohort. These results indicate that the subtype-specific prognostic signature has a much higher resolution in the risk stratification, which may lead to improved therapies and precision medicine for patients with breast cancer.

Prognostic role of high cathepsin D expression in breast cancer: a systematic review and meta-analysis

Background:

High cathepsin D has been associated with poor prognosis in breast cancer; however, the results of many studies are controversial. Here, we assessed the association between high cathepsin D levels and worse breast cancer prognosis by conducting a meta-analysis.

Methods:

A comprehensive search strategy was used to search relevant literature in PUBMED and EMBASE by September 2018. The meta-analysis was performed in Review Manager 5.3 using hazard ratios (HRs) with 95% confidence intervals (CIs).

Results:

A total of 15,355 breast cancer patients from 26 eligible studies were included in this meta-analysis. Significant associations between elevated high cathepsin D and poor overall survival (OS) (HR = 1.61, 95% CI: 1.35–1.92, p < 0.0001) and disease-free survival (DFS) (HR = 1.52, 95% CI: 1.31–2.18, p < 0.001) were observed. In the subgroup analysis for DFS, high cathepsin D was significantly associated with poor prognosis in node-positive patients (HR = 1.38, 95% CI: 1.25–1.71, p < 0.00001), node-negative patients (HR = 1.78, 95% CI: 1.39–2.27, p < 0.0001), early stage patients (HR = 1.73, 95% CI: 1.34–2.23, p < 0.0001), and treated with chemotherapy patients (HR = 1.60, 95% CI: 1.21–2.12, p < 0.001). Interestingly, patients treated with tamoxifen had a low risk of relapse when their cathepsin D levels were high (HR = 0.71, 95% CI: 0.52–0.98, p = 0.04) and a high risk of relapse when their cathepsin D levels were low (HR = 1.50, 95% CI: 1.22–1.85, p = 0.0001).

Conclusions:

Our meta-analysis suggests that high expression levels of cathepsin D are associated with a poor prognosis in breast cancer. Based on our subgroup analysis, we believe that cathepsin D can act as a marker for poor breast cancer prognosis and also as a therapeutic target for breast cancer.

Proteomics-Based Identification of Diagnostic Biomarkers Related to Risk Factors and Pathogenesis of Ischemic Stroke

Ischemic stroke is caused by blood clot formation and consequent vessel blockage. Proteomic approaches provide a cost-effective alternative to current diagnostic methods, including computerized tomography (CT) scans and magnetic resonance imaging (MRI). To identify diagnostic biomarkers associated with ischemic stroke risk factors, we performed individual proteomic analysis of serum taken from 20 healthy controls and 20 ischemic stroke patients. We then performed SWATH analysis, a data-independent method, to assess quantitative changes in protein expression between the two experimental conditions. Our analysis identified several candidate protein biomarkers, 11 of which were validated by multiple reaction monitoring (MRM) analysis as novel diagnostic biomarkers associated with ischemic stroke risk factors. Our study identifies new biomarkers associated with the risk factors and pathogenesis of ischemic stroke which, to the best of our knowledge, were previously unknown. These markers may be effective in not only the diagnosis but also the prevention and management of ischemic stroke.

Advanced Near-Infrared Light for Monitoring and Modulating the Spatiotemporal Dynamics of Cell Functions in Living Systems

Light-based technique, including optical imaging and photoregulation, has become one of the most important tools for both fundamental research and clinical practice, such as cell signal sensing, cancer diagnosis, tissue engineering, drug delivery, visual regulation, neuromodulation, and disease treatment. In particular, low energy near-infrared (NIR, 700-1700 nm) light possesses lower phototoxicity and higher tissue penetration depth in living systems as compared with ultraviolet/visible light, making it a promising tool for in vivo applications. Currently, the NIR light-based imaging and photoregulation strategies have offered a possibility to real-time sense and/or modulate specific cellular events in deep tissues with subcellular accuracy. Herein, the recent progress with respect to NIR light for monitoring and modulating the spatiotemporal dynamics of cell functions in living systems are summarized. In particular, the applications of NIR light-based techniques in cancer theranostics, regenerative medicine, and neuroscience research are systematically introduced and discussed. In addition, the challenges and prospects for NIR light-based cell sensing and regulating techniques are comprehensively discussed.

Absence of Nuclear p16 Is a Diagnostic and Independent Prognostic Biomarker in Squamous Cell Carcinoma of the Cervix

The tumor-suppressor protein p16 is paradoxically overexpressed in cervical cancer (CC). Despite its potential as a biomarker, its clinical value and the reasons for its failure in tumor suppression remain unclear. Our purpose was to determine p16 clinical and biological significance in CC. p16 expression pattern was examined by immunohistochemistry in 78 CC cases (high-grade squamous intraepithelial lesions (HSILs) and squamous cell carcinomas of the cervix –SCCCs). CC cell proliferation and invasion were monitored by real-time cell analysis and Transwell^® invasion assay, respectively. Cytoplasmic p16 interactors were identified from immunoprecipitated extracts by liquid chromatography-tandem mass spectrometry, and colocalization was confirmed by double-immunofluorescence. We observed that SCCCs showed significantly more cytoplasmic than nuclear p16 expression than HSILs. Importantly, nuclear p16 absence significantly predicted poor outcome in SCCC patients irrespective of other clinical parameters. Moreover, we demonstrated that cytoplasmic p16 interacted with CDK4 and other unreported proteins, such as BANF1, AKAP8 and AGTRAP, which could sequester p16 to avoid nuclear translocation, and then, impair its anti-tumor function. Our results suggest that the absence of nuclear p16 could be a diagnostic biomarker between HSIL and SCCC, and an independent prognostic biomarker in SCCC; and explain why p16 overexpression fails to stop CC growth.

A preliminary report of head-to-head comparison of 18-gene-based clinical-genomic model and oncotype DX 21-gene assay for predicting recurrence of early-stage breast cancer

Background

The information of Oncotype DX applied in Asian breast cancer patients is limited. A recurrence index for distant recurrence (RI-DR) has been developed for early-stage breast cancer (EBC) from tumor samples in Chinese patients. In this study, we compared the prognostic performance of the Oncotype DX (ODx) recurrence score (RS) with the RI-DR for any recurrence risk type.

Materials and methods

One hundred thirty-eight (138) patients with hormone receptor-positive and human epidermal growth factor receptor 2-negative EBC who were previously tested with ODx were included for testing with the RI-DR. The cutoff score to partition the low- and high-risk patients was 26 for RS and 36 for RI-DR. The primary endpoint was recurrence-free survival (RFS).

Results

The concordance between the RI-DR and RS was 83% in N0 patients and 81% in node-positive patients when the RS score cutoff was set at 26. With a median follow-up interval of 36.8 months, the 4-year RFS for the high- and low-risk groups categorized by the RS were 61.9% and 95.0%, respectively (hazard ratio: 10.6, 95.0% confidence interval [CI]: 1.8–62.9). The 4-year RFS in the high- and low-risk groups categorized by the RI-DR were 72.6% and 98.5%, respectively (hazard ratio: 18.9, 95% CI: 1.8–138.8).

Conclusion

This paper illustrated the performance of RI-DR and ODx RS in breast cancer women in Taiwan. There was high concordance between the RI-DR and RS. The RI-DR is not inferior to the RS in predicting RFS in EBC patients. This study will fill the gap between the current and best practice in Chinese patients.

An integrative view on breast cancer signature panels

Introduction: Breast cancer is heterogeneous with distinct clinical outcomes. Diverse types of markers are available on the market for breast cancer prognosis, diagnosis, and therapeutics, with distinct assay approaches. These, though they enlarge our selection pool for characterizing breast cancer patients and help improve the precision on the therapeutics, they can complicate our understanding and choice of marker panels. Areas covered: This review aims at classifying the commonly used marker panels according to their functionalities and detection approaches, comparing their advantages and disadvantages, and identifying their shared features to gain a comprehensive understanding of the diversified molecular profiles that drive breast cancer heterogeneity. Expert opinion: Our effort will contribute as a guidebook for clinicians on the use of breast cancer signature panels for disease management, and for researchers on the establishment of novel marker panels with improved precision and reduced complexity. We propose that collectively analyzing all available marker panels is equally important as investigating on entirely novel marker panels. Advances in technologies capturing signals from multiple levels are of practical importance in breaking through limitations on translating markers into clinical use.

Early Cancer Detection from Multianalyte Blood Test Results

The early detection of cancers has the potential to save many lives. A recent attempt has been demonstrated successful. However, we note several critical limitations. Given the central importance and broad impact of early cancer detection, we aspire to address those limitations. We explore different supervised learning approaches for multiple cancer type detection and observe significant improvements; for instance, one of our approaches (i.e., CancerA1DE) can double the existing sensitivity from 38% to 77% for the earliest cancer detection (i.e., Stage I) at the 99% specificity level. For Stage II, it can even reach up to about 90% across multiple cancer types. In addition, CancerA1DE can also double the existing sensitivity from 30% to 70% for detecting breast cancers at the 99% specificity level. Data and model analysis are conducted to reveal the underlying reasons. A website is built at http://cancer.cs.cityu.edu.hk/.

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We propose an approach (CancerA1DE) to detect early cancers from blood

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CancerA1DE doubles the existing sensitivity for the stage I cancer detection

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For stage II cancers, it can reach up to 90% across multiple cancer types

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The related software is opened and released for future follow-up works

EndoPredict versus uPA/PAI-1 in breast cancer: Comparison of markers and association with clinicopathological parameters

We retrospectively investigated concordance of EndoPredict (EPclin) with urokinase plasminogen activator and plasminogen activator inhibitor-1 (uPA/PAI-1) in 72 breast cancer patients and compared the results with grading, molecular subtype and chemotherapy recommendation. Compared to uPA/PAI-1, EPclin proved to be more conservative concerning correlation with clinicopathological parameters and was significantly associated with the recommendation of adjuvant chemotherapy.

Mitotic count can predict tamoxifen benefit in postmenopausal breast cancer patients while Ki67 score cannot

Background

Controversy exists for the use of Ki67 protein expression as a predictive marker to select patients who do or do not derive benefit from adjuvant endocrine therapy. Whether other proliferation markers, like Cyclin D1, and mitotic count can also be used to identify those estrogen receptor α (ERα) positive breast cancer patients that derive benefit from tamoxifen is not well established. We tested the predictive value of these markers for tamoxifen benefit in ERα positive postmenopausal breast cancer patients.

Methods

We collected primary tumor blocks from 563 ERα positive patients who had been randomized between tamoxifen (1 to 3 years) vs. no adjuvant therapy (IKA trial) with a median follow-up of 7.8 years. Mitotic count, Ki67 and Cyclin D1 protein expression were centrally assessed by immunohistochemistry on tissue microarrays. In addition, we tested the predictive value of CCND1 gene copy number variation using MLPA technology. Multivariate Cox proportional hazard models including interaction between marker and treatment were used to test the predictive value of these markers.

Results

Patients with high Ki67 (≥5%) as well as low (< 5%) expressing tumors equally benefitted from adjuvant tamoxifen (adjusted hazard ratio (HR) 0.5 for both groups)(p for interaction 0.97). We did not observe a significant interaction between either Cyclin D1 or Ki67 and tamoxifen, indicating that the relative benefit from tamoxifen was not dependent on the level of these markers. Patients with tumors with low mitotic count derived substantial benefit from tamoxifen (adjusted HR 0.24, p <  0.0001), while patients with tumors with high mitotic count derived no significant benefit (adjusted HR 0.64, p = 0.14) (p for interaction 0.03).

Conclusion

Postmenopausal breast cancer patients with high Ki67 counts do significantly benefit from adjuvant tamoxifen, while those with high mitotic count do not. Mitotic count is a better selection marker for reduced tamoxifen benefit than Ki67.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4516-1) contains supplementary material, which is available to authorized users.

Clinical and economic impact of the 21-gene recurrence score assay in adjuvant therapy decision making in patients with early-stage breast cancer: pooled analysis in 4 Basque Country university hospitals

Purpose

The 21-gene recurrence score (RS) is a genomic test developed as a prognostic and predictive tool to improve the treatment decision making in cases of estrogen receptor-positive and human epidermal growth factor receptor 2-negative early-stage breast cancer. This study examined the clinical and economic impact of its use in 4 Basque Country university hospitals.

Methods

Taking into consideration the RS result, we recorded the recommended initial systemic adjuvant therapy (endocrine therapy with or without chemotherapy) according to standard clinicopathologic factors and the final decision about chemotherapy. Then, if the RS was high, chemotherapy was recommended; it was not recommended if the RS was low; for those with an intermediate RS, clinicopathologic factors were considered, and the initial recommendation based on those factors was maintained. In addition, the probability of switching treatment was calculated. Then, we developed an economic evaluation by measuring the treatment’s incremental short-term budget impact from both the societal perspective and that of the Basque Health System. Patients’ characteristics and chemotherapy use were analyzed using logistic regressions and receiver operating characteristic curves.

Results

Without an RS, chemotherapy would have been prescribed to 56% of 401 patients, but, with RS use, that percentage decreased to 25. The overall rate of decision change was 35.4%. Test inclusion led to a reduction in chemotherapy costs of €922 per patient in the total population. Although this reduction did not entirely offset the cost of the test, the productivity loss per patient was reduced by €1,977.

Conclusion

The 21-gene RS test significantly changed the indication for chemotherapy. As chemotherapy treatments with no benefit were avoided, patients’ quality of life was improved. The short-term economic impact was negative for the Basque Health Service, but savings resulted when sick-leave costs were included.

Novel Predictors of Breast Cancer Survival Derived from miRNA Activity Analysis

Purpose: Breast cancer is among the leading causes of cancer-related death; discovery of novel prognostic markers is needed to improve outcomes. Combining systems biology and epidemiology, we investigated miRNA-associated genes and breast cancer survival in a well-characterized population-based study.Experimental Design: A recently developed algorithm, ActMiR, was used to identify key miRNA "activities" corresponding to target gene degradation, which were predictive of breast cancer mortality in published databases. We profiled miRNA-associated genes in tumors from our well-characterized population-based cohort of 606 women with first primary breast cancer. Cox proportional hazards models were used to estimate HRs and 95% confidence intervals (CI), after 15+ years of follow-up with 119 breast cancer-specific deaths.Results: miR-500a activity was identified as a key miRNA for estrogen receptor-positive breast cancer mortality using public databases. From a panel of 161 miR-500a-associated genes profiled, 73 were significantly associated with breast cancer-specific mortality (FDR < 0.05) in our population, among which two clusters were observed to have opposing directions of association. For example, high level of SUSD3 was associated with reduced breast cancer-specific mortality (HR = 0.3; 95% CI, 0.2-0.4), whereas the opposite was observed for TPX2 (HR = 2.7; 95% CI, 1.8-3.9). Most importantly, we identified set of genes for which associations with breast cancer-specific mortality were independent of known prognostic factors, including hormone receptor status and PAM50-derived risk-of-recurrence scores. These results are validated in independent datasets.Conclusions: We identified novel markers that may improve prognostic efficiency while shedding light on molecular mechanisms of breast cancer progression. Clin Cancer Res; 24(3); 581-91. ©2017 AACR.

Long non-coding RNAs as prognostic markers in human breast cancer

Long non-coding RNAs (lncRNAs) have been recently shown to play an important role in gene regulation and normal cellular functions, and disease processes. However, despite the overwhelming number of lncRNAs identified to date, little is known about their role in cancer for vast majority of them. The present study aims to determine whether lncRNAs can serve as prognostic markers in human breast cancer. We interrogated the breast invasive carcinoma dataset of the Cancer Genome Atlas (TCGA) at the cBioPortal consisting of ~ 1,000 cases. Among 2,730 lncRNAs analyzed, 577 lncRNAs had alterations ranging from 1% to 32% frequency, which include mutations, alterations of copy number and RNA expression. We found that deregulation of 11 lncRNAs, primarily due to copy number alteration, is associated with poor overall survival. At RNA expression level, upregulation of 4 lncRNAs (LINC00657, LINC00346, LINC00654 and HCG11) was associated with poor overall survival. A third signature consists of 9 lncRNAs (LINC00705, LINC00310, LINC00704, LINC00574, FAM74A3, UMODL1-AS1, ARRDC1-AS1, HAR1A, and LINC00323) and their upregulation can predict recurrence. Finally, we selected LINC00657 to determine their role in breast cancer, and found that LINC00657 knockout significantly suppresses tumor cell growth and proliferation, suggesting that it plays an oncogenic role. Together, these results highlight the clinical significance of lncRNAs, and thus, these lncRNAs may serve as prognostic markers for breast cancer.

Identification of prognostic signature in cancer based on DNA methylation interaction network

BACKGROUND

The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites.

METHODS

In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes.

RESULTS

We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme). In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures.

CONCLUSIONS

A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.

The MAGIC survey in hormone receptor positive (HR+), HER2-negative (HER2-) breast cancer: When might multigene assays be of value?

BACKGROUND

A modest proportion of patients with early stage hormone receptor-positive (HR+), HER2-negative (HER2-) breast cancer benefit from adjuvant chemotherapy. Traditionally, treatment recommendations are based on clinical/pathologic criteria that are not predictive of chemotherapy benefit. Multigene assays provide prognostic and predictive information that can help to make more informed treatment decisions. The MAGIC survey evaluated international differences in treatment recommendations, how traditional parameters are used for making treatment choices, and for which patients treating physicians feel most uncertain about their decisions.

METHODS

The MAGIC survey captured respondents' demographics, practice patterns, relevance of traditional parameters for treatment decisions, and use of or interest in using multigene assays. Using this information, a predictive model was created to simulate treatment recommendations for 672 patient profiles.

RESULTS

The survey was completed by 911 respondents (879 clinicians, 32 pathologists) from 52 countries. Chemo-endocrine therapy was recommended more often than endocrine therapy alone, but there was substantial heterogeneity in treatment recommendations in 52% of the patient profiles; approximately every fourth physician provided a different treatment recommendation. The majority of physicians indicated they wanted to use multigene assays clinically. Lack of reimbursement/availability were the main reasons for non-usage.

CONCLUSIONS

The survey reveals substantial heterogeneity in treatment recommendations. Physicians have uncertainty in treatment recommendations in a high proportion of patients with intermediate risk features using traditional parameters. In HR+, HER2- patients with early disease the findings highlight the need for additional markers that are both prognostic and predictive of chemotherapy benefit that may support more-informed treatment decisions.

Selecting postoperative adjuvant systemic therapy for early stage breast cancer: A critical assessment of commercially available gene expression assays

Risk stratification of patients with early stage breast cancer may support adjuvant chemotherapy decision-making. This review details the development and validation of six multi-gene classifiers, each of which claims to provide useful prognostic and possibly predictive information for early stage breast cancer patients. A careful assessment is presented of each test's analytical validity, clinical validity, and clinical utility, as well as the quality of evidence supporting its use.

Low utility of Oncotype DX® in the clinic

Precision medicine tools are currently making their way into the clinic and being utilized to diagnose, prognose, and individualize cancer care. The multi-gene expression-based assay, Oncotype DX® (ODX), is a genomic tumor profiling tool that determines the expression of 21 tumor- associated genes; it helps determine the risk for distant recurrence and whether chemotherapy is an appropriate course of treatment in patients with early stage, estrogen receptor (ER) positive, HER2 negative, and lymph node negative (or 1-3 positive lymph nodes) invasive BCa. The aim of this study was to determine the overall utilization and uptake of the ODX genomic test in a cross-sectional analysis of the Virginia Tumor registry, compare utilization in African Americans (AAs) and Caucasian Americans (CAs), and determine the profile of patients referred for testing. Caucasian (89.7%) patients made up the majority of the ODX testers compared to AAs (10.3%) (P < 0.0001). Those who received ODX testing were less likely to have higher grade and higher stage tumors, and were less likely to be ER negative (RR = 0.21, 95% CI: 0.01-0.31), progesterone receptor (PR) negative (RR = 0.35, 95% CI: 0.27-0.45), HER2 amplified (RR = 0.27, 95% CI: 0.17-0.43), or triple negative (RR = 0.21, 95% CI: 0.14-0.33). Of the patients that were eligible (n = 3924), 10.5% (n = 412) received ODX testing. Specifically, 11.7% of the Caucasian patients and 5.1% of AAs patients received ODX testing (P < 0.001). Our analysis confirmed that the utilization of ODX was low and that AAs were much less likely to receive ODX testing.

Intracellular Expression of PAI-1 Specific Aptamers Alters Breast Cancer Cell Migration, Invasion and Angiogenesis

Plasminogen activator inhibitor-1 (PAI-1) is elevated in various cancers, where it has been shown to effect cell migration and invasion and angiogenesis. While, PAI-1 is a secreted protein, its intercellular levels are increased in cancer cells. Consequently, intracellular PAI-1 could contribute to cancer progression. While various small molecule inhibitors of PAI-1 are currently being investigated, none specifically target intracellular PAI-1. A class of inhibitors, termed aptamers, has been used effectively in several clinical applications. We previously generated RNA aptamers that target PAI-1 and demonstrated their ability to inhibit extracellular PAI-1. In the current study we explored the effect of these aptamers on intracellular PAI-1. We transiently transfected the PAI-1 specific aptamers into both MDA-MB-231 human breast cancer cells, and human umbilical vein endothelial cells (HUVECs) and studied their effects on cell migration, invasion and angiogenesis. Aptamer expressing MDA-MB-231 cells exhibited a decrease in cell migration and invasion. Additionally, intracellular PAI-1 and urokinase plasminogen activator (uPA) protein levels decreased, while the PAI-1/uPA complex increased. Moreover, a significant decrease in endothelial tube formation in HUVECs transfected with the aptamers was observed. In contrast, conditioned media from aptamer transfected MDA-MB-231 cells displayed a slight pro-angiogenic effect. Collectively, our study shows that expressing functional aptamers inside breast and endothelial cells is feasible and may exhibit therapeutic potential.

Multimodal Assessment of Estrogen Receptor mRNA Profiles to Quantify Estrogen Pathway Activity in Breast Tumors

BACKGROUND

Molecular markers have transformed our understanding of the heterogeneity of breast cancer and have allowed the identification of genomic profiles of estrogen receptor (ER)-α signaling. However, our understanding of the transcriptional profiles of ER signaling remains inadequate. Therefore, we sought to identify the genomic indicators of ER pathway activity that could supplement traditional immunohistochemical (IHC) assessments of ER status to better understand ER signaling in the breast tumors of individual patients.

MATERIALS AND METHODS

We reduced ESR1 (gene encoding the ER-α protein) mRNA levels using small interfering RNA in ER⁺ MCF7 breast cancer cells and assayed for transcriptional changes using Affymetrix HG U133 Plus 2.0 arrays. We also compared 1034 ER⁺ and ER^- breast tumors from publicly available microarray data. The principal components of ER activity generated from these analyses and from other published estrogen signatures were compared with ESR1 expression, ER-α IHC, and patient survival.

RESULTS

Genes differentially expressed in both analyses were associated with ER-α IHC and ESR1 mRNA expression. They were also significantly enriched for estrogen-driven molecular pathways associated with ESR1, cyclin D1 (CCND1), MYC (v-myc avian myelocytomatosis viral oncogene homolog), and NFKB (nuclear factor kappa B). Despite their differing constituent genes, the principal components generated from these new analyses and from previously published ER-associated gene lists were all associated with each other and with the survival of patients with breast cancer treated with endocrine therapies.

CONCLUSION

A biomarker of ER-α pathway activity, generated using ESR1-responsive mRNAs in MCF7 cells, when used alongside ER-α IHC and ESR1 mRNA expression, could provide a method for further stratification of patients and add insight into ER pathway activity in these patients.

A four gene signature predicts benefit from anthracyclines: evidence from the BR9601 and MA.5 clinical trials

Chromosome instability (CIN) in solid tumours results in multiple numerical and structural chromosomal aberrations and is associated with poor prognosis in multiple tumour types. Recent evidence demonstrated CEP17 duplication, a CIN marker, is a predictive marker of anthracycline benefit. An analysis of the BR9601 and MA.5 clinical trials was performed to test the role of existing CIN gene expression signatures as predictive markers of anthracycline sensitivity in breast cancer. Univariate analysis demonstrated, high CIN25 expression score was associated with improved distant relapse free survival (DRFS) (HR: 0.74, 95% CI 0.54-0.99, p = 0.046). High tumour CIN70 and CIN25 scores were associated with aggressive clinicopathological phenotype and increased sensitivity to anthracycline therapy compared to low CIN scores. However, in a prospectively planned multivariate analysis only pathological grade, nodal status and tumour size were significant predictors of outcome for CIN25/CIN70. A limited gene signature was generated, patients with low tumour CIN4 scores benefited from anthracycline treatment significantly more than those with high CIN4 scores (HR 0.37, 95% CI 0.20-0.56, p = 0.001). In multivariate analyses the treatment by marker interaction for CIN4/anthracyclines demonstrated hazard ratio of 0.35 (95% CI 0.15-0.80, p = 0.012) for DRFS. This data shows CIN4 is independent predictor of anthracycline benefit for DRFS in breast cancer.

Increased angiogenesis is associated with a 32-gene expression signature and 6p21 amplification in aggressive endometrial cancer

Background

Angiogenesis is a hallmark of cancer. The aim of this study was to explore whether microvessel proliferation is associated with gene expression profiles or copy number alterations in endometrial cancer.

Methods

A prospective series of endometrial carcinomas was studied for angiogenesis markers, gene expression profiles, and gene copy number data. For validation, an independent series of endometrial carcinomas as well as an external cohort of endometrial cancer patients were examined by gene expression microarrays.

Results

Increased microvessel proliferation (MVP) was associated with aggressive tumor features and reduced survival, and a 32-gene expression signature was found to separate tumors with high versus low MVP. An increased 32-gene signature score was confirmed to associate with high-grade tumor features and reduced survival by independent cohorts. Copy number studies revealed that amplification of the 6p21 region was significantly associated with MVP, a high 32-gene score, as well as reduced survival.

Conclusion

Increased MVP was significantly associated with aggressive endometrial cancer and reduced survival. Integrated analyses demonstrated significant associations between increased vascular proliferation, amplification of the 6p21 region, VEGF-A mRNA expression, and the 32-gene angiogenesis signature. Our findings indicate amplification of 6p21 as a possible driver of tumor vascular proliferation in endometrial cancer.

Building a Molecular Trap for a Serine Protease from Aptamer and Peptide Modules

In drug development, molecular intervention strategies are usually based on interference with a single protein function, such as enzyme activity or receptor binding. However, in many cases, protein drug targets are multifunctional, with several molecular functions contributing to their pathophysiological actions. Aptamers and peptides are interesting synthetic building blocks for the design of multivalent molecules capable of modulating multiple functions of a target protein. Here, we report a molecular trap with the ability to interfere with the activation, catalytic activity, receptor binding, etc. of the serine protease urokinase-type plasminogen activator (uPA) by a rational combination of two RNA aptamers and a peptide with different inhibitory properties. The assembly of these artificial inhibitors into one molecule enhanced the inhibitory activity between 10- and 10,000-fold toward several functions of uPA. The study highlights the potential of multivalent designs and illustrates how they can easily be constructed from aptamers and peptides using nucleic acid engineering, chemical synthesis, and bioconjugation chemistry. By aptamer to aptamer and aptamer to peptide conjugation, we created, to the best of our knowledge, the first trivalent molecule which combines three artificial inhibitors binding to three different sites in a protein target. We hypothesize that by simultaneously preventing all of the functional interactions and activities of the target protein, this approach may represent an alternative to siRNA technology for a functional knockout.

The Predictive Link between Matrix and Metastasis

Cancer spread (metastasis) is responsible for 90% of cancer-related fatalities. Informing patient treatment to prevent metastasis, or kill all cancer cells in a patient's body before it becomes metastatic is extremely powerful. However, aggressive treatment for all non-metastatic patients is detrimental, both for quality of life concerns, and the risk of kidney or liver-related toxicity. Knowing when and where a patient has metastatic risk could revolutionize patient treatment and care. In this review, we attempt to summarize the key work of engineers and quantitative biologists in developing strategies and model systems to predict metastasis, with a particular focus on cell interactions with the extracellular matrix (ECM), as a tool to predict metastatic risk and tropism.

Transcriptomics in cancer diagnostics: developments in technology, clinical research and commercialization

Transcriptomic technologies are evolving to diagnose cancer earlier and more accurately to provide greater predictive and prognostic utility to oncologists and patients. Digital techniques such as RNA sequencing are replacing still-imaging techniques to provide more detailed analysis of the transcriptome and aberrant expression that causes oncogenesis, while companion diagnostics are developing to determine the likely effectiveness of targeted treatments. This article examines recent advancements in molecular profiling research and technology as applied to cancer diagnosis, clinical applications and predictions for the future of personalized medicine in oncology.

Clinical and molecular complexity of breast cancer metastases

Clinical oncology is advancing toward a more personalized treatment orientation, making the need to understand the biology of metastasis increasingly acute. Dissecting the complex molecular, genetic and clinical phenotypes underlying the processes involved in the development of metastatic disease, which remains the principal cause of cancer-related deaths, could lead to the identification of more effective prognostication and targeted approaches to prevent and treat metastases. The past decade has witnessed significant progress in the field of cancer metastasis research. Clinical and technological milestones have been reached which have tremendously enriched our understanding of the complex pathways undertaken by primary tumors to progress into lethal metastases and how some of these processes might be amenable to therapy. The aim of this review article is to highlight the recent advances toward unraveling the clinical and molecular complexity of breast cancer metastases. We focus on genes mediating breast cancer metastases and organ-specific tropism, and discuss gene signatures for prediction of metastatic disease. The challenges of translating this information into clinically applicable tools for improving the prognostication of the metastatic potential of a primary breast tumor, as well as for therapeutic interventions against latent and active metastatic disease are addressed.

Prospective cohort study using the breast cancer spheroid model as a predictor for response to neoadjuvant therapy--the SpheroNEO study

Background

Aim of this prospective study was to predict response to neoadjuvant therapy in breast cancer patients using an in vitro breast cancer spheroid model.

Methods

Three-dimensional spheroids were directly generated from fresh breast tumor biopsies of 78 patients eligible for neoadjuvant therapy. Cell survival was measured after in vitro exposure to the equivalent therapeutic agents in the breast cancer spheroid model. Treatment results in vitro were correlated with pathological complete response (pCR, i.e. ypT0 ypN0) determined at surgery.

Results

A mean cell survival of 21.8 % was found in the breast cancer spheroid model for 22 patients with pCR versus 63.8 % in 56 patients without pCR (P = .001). The area under the receiver operator characteristic curve to predict pCR was 0.86 (95 % CI: 0.77 to 0.96) for cell survival in vitro compared to 0.80 (95 % CI: 0.70 to 0.90) for a combined model of conventional factors (hormone- and HER2 receptor, and age). A cutoff at 35 % cell survival for the spheroid model was proposed. Out of the 32 patients with values below this threshold, 21 patients (65.6 %) and one patient (2.2 %) with a cell survival greater than 35 % achieved pCR respectively; (sensitivity 95.5 % (95 % CI: 0.86 to 1.00); specificity 80.4 % (95 % CI: 0.70 to 0.91)). Extent of residual disease positively correlated with increased cell survival (P = .021).

Conclusion

The breast cancer spheroid model proved to be a highly sensitive and specific predictor for pCR after neoadjuvant chemotherapy in breast cancer patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1491-7) contains supplementary material, which is available to authorized users.

The region-of-interest size impacts on Ki67 quantification by computer-assisted image analysis in breast cancer

Therapeutic decision-making in breast cancer depends on histopathologic biomarkers and is influenced by the Ki67 proliferation index. Computer-assisted image analysis (CAIA) promises to improve Ki67 quantification. Several commercial applications have been developed for semiautomated CAIA-based Ki67 quantification, many of which rely on measurements in user-defined regions of interest (ROIs). Because of intratumoral proliferative heterogeneity, definition of the ROI is an important step in the analytical procedure. This study explores the ROI size impacts on Ki67 quantification. Whole-slide sections of 100 breast cancers were immunostained with the anti-Ki67 antibody 30-9 and were analyzed on the iScan Coreo digital pathology platform using a Food and Drug Administration-cleared Ki67 quantification software version v5.3 (Virtuoso; Ventana, Tucson, TX). For each case, the Ki67 labeling index (LI) was determined in multiple ROIs of gradually increasing size centered around a high-proliferation area. The spatial Ki67 decline was modeled with nonlinear regression. Depending on the ROI size, the median Ki67 LI varied between 55% and 15%. The proportion of tumors classified as Ki67 low according to the St Gallen 2013/2015 cutoff increased from 2% to 56%, as the ROI size increased from 50 to 10,000 cells captured. The interrater reliability of conventional Ki67 assessment versus CAIA-based Ki67 quantification was also dependent on the ROI size and varied between slight and almost perfect agreement (Cohen κ = 0.06-0.85). In conclusion, the ROI size is a critically important parameter for semiautomated Ki67 quantification by CAIA. Ki67 LIs determined on platforms like iScan Coreo/Virtuoso require an ROI size adjustment, for which we offer a downloadable data transformation tool.

Protein-binding RNA aptamers affect molecular interactions distantly from their binding sites

Nucleic acid aptamer selection is a powerful strategy for the development of regulatory agents for molecular intervention. Accordingly, aptamers have proven their diligence in the intervention with serine protease activities, which play important roles in physiology and pathophysiology. Nonetheless, there are only a few studies on the molecular basis underlying aptamer-protease interactions and the associated mechanisms of inhibition. In the present study, we use site-directed mutagenesis to delineate the binding sites of two 2´-fluoropyrimidine RNA aptamers (upanap-12 and upanap-126) with therapeutic potential, both binding to the serine protease urokinase-type plasminogen activator (uPA). We determine the subsequent impact of aptamer binding on the well-established molecular interactions (plasmin, PAI-1, uPAR, and LRP-1A) controlling uPA activities. One of the aptamers (upanap-126) binds to the area around the C-terminal α-helix in pro-uPA, while the other aptamer (upanap-12) binds to both the β-hairpin of the growth factor domain and the kringle domain of uPA. Based on the mapping studies, combined with data from small-angle X-ray scattering analysis, we construct a model for the upanap-12:pro-uPA complex. The results suggest and highlight that the size and shape of an aptamer as well as the domain organization of a multi-domain protein such as uPA, may provide the basis for extensive sterical interference with protein ligand interactions considered distant from the aptamer binding site.

FOXA1 Expression Significantly Predict Response to Chemotherapy in Estrogen Receptor-Positive Breast Cancer Patients

PURPOSE

Most estrogen receptor (ER)-positive breast cancer responds poorly to chemotherapy and no single cost-effective biomarker capable of selecting chemosensitive ones has been found yet. We investigated FOXA1 for its role in predicting chemosensitivity of this subgroup in neoadjuvant chemotherapy settings.

METHODS

We reviewed pathologic slides of 123 patients who were diagnosed with ER-positive breast cancer on core needle biopsy and underwent neoadjuvant chemotherapy at our institution between 2002 and 2012. FOXA1 expression and pathologic response were evaluated. We then statistically analyzed FOXA1 expression and its relationship with chemosensitivity.

RESULTS

FOXA1 expression before NAC was correlated with poor chemoresponse in ER-positive as well as luminal A and luminal B breast cancer patients (p = 0.002, 0.001, and 0.049 respectively). Significant association between change of FOXA1 staining position after NAC and chemosensitivity also was observed (p = 0.024). Multivariate analysis identified FOXA1 expression before NAC as an independent predictor of chemosensitivity in ER-positive and luminal A breast cancer patients [p = 0.002; relative risk (RR) 0.163; 95 % confidence interval (CI) 0.053-0.500, and p = 0.002; RR 0.055; 95 % CI 0.008-0.353, respectively]. Additionally, change of FOXA1 staining position after NAC was shown to be an independent predictor of chemoresponse in luminal B subtype breast cancer patients (p = 0.012; RR 0.153; 95 % CI 0.035-0.665).

CONCLUSIONS

FOXA1 expression can independently predict chemosensitivity of ER-positive breast cancer patients.

Genomic predictor of residual risk of recurrence after adjuvant chemotherapy and endocrine therapy in high risk estrogen receptor-positive breast cancers

A subset of early stage estrogen receptor (ER)-positive breast cancers considered "high risk" for recurrence with endocrine therapy alone by current genomic prognostic predictors, such as Oncotype DX, is no longer high risk after receiving adjuvant chemotherapy. We hypothesized that a recently described gene expression-based outcome predictor adjuvant chemotherapy and endocrine therapy sensitivity (ACES) could re-stratify these patients into high and low risk groups for relapse when treated with both chemo- and endocrine therapies. ACES involves four separate modules (endocrine sensitivity, chemotherapy sensitivity, chemotherapy resistance, and survival prediction) that yield a prediction for good or poor outcome with current standard of care multimodality therapy. ACES was applied to Affymetrix gene expression data from 2 retrospectively collected ER-positive and HER2-negative patient cohorts that were uniformly treated with adjuvant endocrine and chemotherapy (n = 250). Each sample was first risk stratified by a genomic surrogate of Oncotype DX, and the high risk patients (n = 76) were re-stratified by ACES. Recurrence-free survival (RFS) was evaluated with ACES risk categories. The Oncotype DX high risk but ACES good prognosis patients (n = 24, 32%) had an RFS of 95% compared to 76% in the poor prognosis group (n = 52; log-rank p = 0.033) at 5 years. ACES risk category remained an independent predictor in multivariate analysis after adjusting for age, T-stage, and lymph node involvement at diagnosis (hazard ratio 0.15; p = 0.072). Tertiary risk prediction that takes into account chemotherapy and endocrine sensitivity, and baseline prognosis may help identify high risk ER-positive patients who have excellent survival after chemotherapy.