Breast cancer classification by proteomic technologies: current state of knowledge

Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results

BACKGROUND

To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes.

METHODS

One hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n = 49; luminal B, n = 8; HER2-enriched, n = 11; triple negative, n = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n = 17; luminal B, n = 17; triple negative, n = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference.

RESULTS

In the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%).

CONCLUSIONS

In this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.

ECM Remodeling in Breast Cancer with Different Grade: Contribution of 2D-DIGE Proteomics

Tumor extracellular matrix (ECM) plays a pivotal role in outcome of breast cancer (BC) patients. Overexpression of 58 genes, encoding 43 structural ECM proteins, has been identified to determine a specific cluster of BC with accelerated metastatic potential only in the undifferentiated (grade III) phenotype. The scope of this study is to characterize protein repertoire able to predict patient outcome in BC according to ECM gene expression pattern and histological grade. The differential proteomic analysis is based on 2D-differential gel electrophoresis, MALDI-MS, bioinformatics, and immunoblotting. Results suggest a relationship among ECM remodeling, signal mechanotransduction, and metabolic rewiring in BCs characterized by a specific mRNA ECM signature and identified a set of dysregulated proteins characteristic of hormone receptors expression as fibrinogen-β chain, collagen α-1(VI) chain, and α-1B-glycoprotein. Furthermore, in triple negative tumors with ECM signature, the FGG and α5β1/αvβ3 integrins increase whereas detyrosinated α-tubulin and mimecan decrease leading to unorganized integrin presentation involving focal adhesion kinase, activation of Rho GTPases associated to epithelial mesenchymal transition. In hormone receptors negative BCs characterized by a specific ECM gene cluster, the differentially regulated proteins, identified in the present study, can be potentially relevant to predict patient's outcome.

APE1 and NPM1 protect cancer cells from platinum compounds cytotoxicity and their expression pattern has a prognostic value in TNBC

Background

Triple negative breast cancer (TNBC) is a breast cancer subgroup characterized by a lack of hormone receptors’ expression and no HER2 overexpression. These molecular features both drastically reduce treatment options and confer poor prognosis. Platinum (Pt)-salts are being investigated as a new therapeutic strategy. The base excision repair (BER) pathway is important for resistance to Pt-based therapies. Overexpression of APE1, a pivotal enzyme of the BER pathway, as well as the expression of NPM1, a functional regulator of APE1, are associated with poor outcome and resistance to Pt-based therapies.

Methods

We evaluated the role of NPM1, APE1 and altered NPM1/APE1 interaction in the response to Pt-salts treatment in different cell lines: APE1 knockout (KO) cells, NPM1 KO cells, cell line models having an altered APE1/NPM1 interaction and HCC70 and HCC1937 TNBC cell lines, having different levels of APE1/NPM1. We evaluated the TNBC cells response to new chemotherapeutic small molecules targeting the endonuclease activity of APE1 or the APE1/NPM1 interaction, in combination with Pt-salts treatments. Expression levels’ correlation between APE1 and NPM1 and their impact on prognosis was analyzed in a cohort of TNBC patients through immunohistochemistry. Bioinformatics analysis, using TCGA datasets, was performed to predict a molecular signature of cancers based on APE1 and NPM1 expression.

Results

APE1 and NPM1, and their interaction as well, protect from the cytotoxicity induced by Pt-salts treatment. HCC1937 cells, having higher levels of APE1/NPM1 proteins, are more resistant to Pt-salts treatment compared to the HCC70 cells. A sensitization effect by APE1 inhibitors to Pt-compounds was observed. The association of NPM1/APE1 with cancer gene signatures highlighted alterations concerning cell-cycle dependent proteins.

Conclusions

APE1 and NPM1 protect cancer cells from Pt-compounds cytotoxicity, suggesting a possible improvement of the activity of Pt-based therapy for TNBC, using the NPM1 and APE1 proteins as secondary therapeutic targets. Based on positive or negative correlation with APE1 and NPM1 gene expression levels, we finally propose several TNBC gene signatures that should deserve further attention for their potential impact on TNBC precision medicine approaches.

Electronic supplementary material

The online version of this article (10.1186/s13046-019-1294-9) contains supplementary material, which is available to authorized users.

Pharmacokinetics and tissue distribution of Ebracteolatain A, a potential anti-cancer compound, as determined by an optimized ultra-performance liquid chromatography tandem mass spectrometry method

Ebracteolatain A is a phloroglucinol derivative from the root of Euphorbia ebracteolata Hayata, a Traditional Chinese Medicine also known as Langdu. It has been shown to have good inhibitory effects in breast cancer cells. In this study, a simple, rapid, sensitive, and specific ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated to study the pharmacokinetics (PKs) and tissue distribution of Ebracteolatain A in rats. Ebracteolatain A and Magnolol (internal standard) were extracted by the simple protein precipitation extraction technique using methanol as the precipitating solvent. Chromatographic separation was performed using the Agilent Poroshell 120 EC-C₁₈ column with a mobile phase of acetonitrile:0.1% formic acid (70:30, v/v). The protonated analyte was quantitated in negative ionization by MS/MS via multiple reaction monitoring mode. The assay exhibited a linear dynamic range of 2-2000 ng/mL for Ebracteolatain A in biological samples. The lower limit of quantitation was 2 ng/mL. Non-compartmental PK parameters indicated that Ebracteolatain A was well absorbed into the systemic circulation. The absolute bioavailability of Ebracteolatain A was greater when administered by intraperitoneal administration than by oral administration. The tissue distribution study showed that Ebracteolatain A was distributed in the heart, liver, spleen, lung, kidney, brain, stomach, intestine, uterus, ovary, and breast after intravenous injection. The results of this study further our understanding of the in vivo anti-cancer activity of Ebracteolatain A, and shed light on pharmacological strategies that may be useful for the development of novel breast cancer therapeutics.

Comparative prognostic analysis for triple-negative breast cancer with metaplastic and invasive ductal carcinoma

AIMS

Triple-negative breast cancer comprises different histological subtypes, including metaplastic breast cancer (MBC) and ductal carcinomas (IDCs). The purpose of this study was to compare triple-negative MBC (TN-MBC) with triple-negative IDC (TN-IDC) in terms of survival and predictive factors.

METHODS

With access to the Surveillance, Epidemiology and End Result (SEER) database, a total of 19 383 patients met the eligibility criteria. Clinicopathological characteristics were compared between groups using the χ² test. Univariate and multivariate analyses were applied to evaluate the disease-specific survival (DSS) and overall survival (OS). Subgroup analyses summarised the hazard ratios of TN-MBC versus TN-IDC using a forest plot.

RESULTS

A total of 586 patients with TN-MBC and 18 797 with TN-IDC were included in this study. Patients with TN-MBC were older and presented with larger tumour sizes, relatively rare lymph node positive disease, and had received more chemotherapy. Compared with TN-IDC, the TN-MBC group showed a significantly poorer prognosis before and after the 1:3 matched case-control analysis. Further subgroup analysis indicated that patients with TN-MBC were older, were from specific races, and those with distant metastasis and not receiving radiotherapy had worse prognosis than patients with TN-IDC in terms of DFS and OS.

CONCLUSION

Our results showed that patients with TN-MBC had unique clinicopathological characteristics and poorer prognostic subtype compared with TN-IDC. This improves our understanding of the clinicopathological and prognostic features of this rare entity but also provides more convincing therapeutic guidelines for TN-MBC in patients with breast cancer.

Identification and validation of DOCK4 as a potential biomarker for risk of bone metastasis development in patients with early breast cancer

Skeletal metastasis occurs in around 75% of advanced breast cancers, with the disease incurable once cancer cells disseminate to bone, but there remains an unmet need for biomarkers to identify patients at high risk of bone recurrence. This study aimed to identify such a biomarker and to assess its utility in predicting response to adjuvant zoledronic acid (zoledronate). We used quantitative proteomics (stable isotope labelling by amino acids in cell culture-mass spectrometry; SILAC-MS) to compare protein expression in a bone-homing variant (BM1) of the human breast cancer cell line MDA-MB-231 with parental non-bone-homing cells to identify novel biomarkers for risk of subsequent bone metastasis in early breast cancer. SILAC-MS showed that dedicator of cytokinesis protein 4 (DOCK4) was upregulated in bone-homing BM1 cells, confirmed by western blotting. BM1 cells also had enhanced invasive ability compared with parental cells, which could be reduced by DOCK4-shRNA. In a training tissue microarray (TMA) comprising 345 patients with early breast cancer, immunohistochemistry followed by Cox regression revealed that high DOCK4 expression correlated with histological grade (p = 0.004) but not oestrogen receptor status (p = 0.19) or lymph node involvement (p = 0.15). A clinical validation TMA used tissue samples and the clinical database from the large AZURE adjuvant study (n = 689). Adjusted Cox regression analyses showed that high DOCK4 expression in the control arm (no zoledronate) was significantly prognostic for first recurrence in bone (HR 2.13, 95%CI 1.06-4.30, p = 0.034). No corresponding association was found in patients who received zoledronate (HR 0.812, 95%CI 0.176-3.76, p = 0.790), suggesting that treatment with zoledronate may counteract the higher risk for bone relapse from high DOCK4-expressing tumours. High DOCK4 expression was not associated with metastasis to non-skeletal sites when these were assessed collectively. In conclusion, high DOCK4 in early breast cancer is significantly associated with aggressive disease and with future bone metastasis and is a potentially useful biomarker for subsequent bone metastasis risk. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Breast Cancer Molecular Subtype Prediction by Mammographic Radiomic Features

RATIONALE AND OBJECTIVES

This study aimed to investigate whether quantitative radiomic features extracted from digital mammogram images are associated with molecular subtypes of breast cancer.

MATERIALS AND METHODS

In this institutional review board-approved retrospective study, we collected 331 Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 29 triple-negative, 45 human epidermal growth factor receptor 2 (HER2)-enriched, 36 luminal A, and 221 luminal B lesions. A set of 39 quantitative radiomic features, including morphologic, grayscale statistic, and texture features, were extracted from the segmented lesion area. Three binary classifications of the subtypes were performed: triple-negative vs non-triple-negative, HER2-enriched vs non-HER2-enriched, and luminal (A + B) vs nonluminal. The Naive Bayes machine learning scheme was employed for the classification, and the least absolute shrink age and selection operator method was used to select the most predictive features for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve and accuracy.

RESULTS

The model that used the combination of both the craniocaudal and the mediolateral oblique view images achieved the overall best performance than using either of the two views alone, yielding an area under receiver operating characteristic curve (or accuracy) of 0.865 (0.796) for triple-negative vs non-triple-negative, 0.784 (0.748) for HER2-enriched vs non-HER2-enriched, and 0.752 (0.788) for luminal vs nonluminal subtypes. Twelve most predictive features were selected by the least absolute shrink age and selection operator method and four of them (ie, roundness, concavity, gray mean, and correlation) showed a statistical significance (P< .05) in the subtype classification.

CONCLUSIONS

Our study showed that quantitative radiomic imaging features of breast tumor extracted from digital mammograms are associated with breast cancer subtypes. Future larger studies are needed to further evaluate the findings.

Development and Validation of Nomograms Predictive of Axillary Nodal Status to Guide Surgical Decision-Making in Early-Stage Breast Cancer

Purpose: To develop and validate nomogram models using noninvasive imaging parameters with related clinical variables to predict the extent of axillary nodal involvement and stratify treatment options based on the essential cut-offs for axillary surgery according to the ACOSOG Z0011 criteria.

Materials and Methods: From May 2007 to December 2017, 1799 patients who underwent preoperative breast and axillary magnetic resonance imaging (MRI) were retrospectively studied. Patients with data on axillary ultrasonography (AUS) were enrolled. The MRI images were interpreted according to Breast Imaging Reporting and Data system (BI-RADS). Using logistic regression analyses, nomograms were developed to visualize the associations between the predictors and each lymph node (LN) status endpoint. Predictive performance was assessed based on the area under the receiver operating characteristic curve (AUC). Bootstrap resampling was performed for internal validation. Goodness-of-fit of the models was evaluated using the Hosmer-Lemeshow test.

Results: Of 397 early breast cancer patients, 200 (50.4%) had disease-free axilla, 119 (30.0%) had 1 or 2 positive LNs, and 78 (19.6%) had ≥3 positive LNs. Patient age, MRI features (mass margin, LN margin, presence/absence of LN hilum, and LN symmetry/asymmetry), and AUS descriptors (presence of cortical thickening or hilum) were identified as predictors of nodal disease. Nomograms with these predictors showed good calibration and discrimination; the AUC was 0.809 for negative axillary node (N0) vs. any LN metastasis, 0.749 for 1 or 2 involved nodes vs. N0, and 0.874 for ≥3 nodes vs. ≤2 metastatic nodes. The predictive ability of the 3 nomograms with additional pathological variables was significantly greater.

Conclusion: The nomograms could predict the extent of ALN metastasis and facilitate decision-making preoperatively.

BET proteins regulate homologous recombination-mediated DNA repair: BRCAness and implications for cancer therapy

Bromodomain and Extra-Terminal (BET) proteins are historically involved in regulating gene expression and BRD4 was recently found to be involved in DNA damage regulation. Aims of our study were to assess BRD4 regulation in homologous recombination-mediated DNA repair and to explore novel clinical strategies through the combinations of the pharmacological induction of epigenetic BRCAness in BRCA1 wild-type triple negative breast cancer (TNBC) cells by means of BET inhibitors and compounds already available in clinic. Performing a dual approach (chromatin immunoprecipitation and RNA interference), the direct relationship between BRD4 and BRCA1/RAD51 expression was confirmed in TNBC cells. Moreover, BRD4 pharmacological inhibition using two BET inhibitors (JQ1 and GSK525762A) induced a dose-dependent reduction in BRCA1 and RAD51 levels and is able to hinder homologous recombination-mediated DNA damage repair, generating a BRCAness phenotype in TNBC cells. Furthermore, BET inhibition impaired the ability of TNBC cells to overcome the increase in DNA damage after platinum salts (i.e., CDDP) exposure, leading to massive cell death, and triggered synthetic lethality when combined with PARP inhibitors (i.e., AZD2281). Altogether, the present study confirms that BET proteins directly regulate the homologous recombination pathway and their inhibition induced a BRCAness phenotype in BRCA1 wild-type TNBC cells. Noteworthy, being this strategy based on drugs already available for human use, it is rapidly transferable and could potentially enable clinicians to exploit platinum salts and PARP inhibitors-based treatments in a wider population of TNBC patients and not just in a specific subgroup, after validating clinical trials.

Lymph node metastasis and high serum CEA are important prognostic factors in hormone receptor positive and HER2 negative breast cancer

In recent years, treatment options for breast cancer have increased, and prognosis has improved since the 1990s. The present study examined the prognosis for recurrence of breast cancer between 2006 and 2009, in comparison with the results of past treatments, and sought to guide future treatment strategies by elucidating present prognostic factors. A total of 662 patients with breast cancer stage 0-III who underwent surgery at Kitasato University Hospital between January 2006 and March 2009 were included. Cases were classified into four subtypes, based on the presence or absence of hormone receptors and human epidermal growth factor receptor 2 (HER2). Factors associated with recurrence and prognosis were then examined. The 5-year recurrence-free survival (RFS) was 94.9% and the 5-year disease-specific survival (DSS) was 98.4%. Factors related to RFS were pathological lymph node (pN) positive [hazard ratio (HR)=2.85, P=0.001], clinical lymph node (cN) positive (HR=2.28, P<0.01), and hormone receptor negative (HR=1.83, P<0.05). Factors associated with DSS were cN positive (HR=4.55, P<0.01), pN positive (HR=3.40, P<0.05), higher preoperative serum carcinoembryonic antigen (CEA) (HR=3.04, P<0.05), and hormone receptor negative (HR=2.32, P<0.05). In the hormone receptor positive HER2 negative, cN-positive/pN-positive breast cancer group, RFS and DSS were poorer compared with the other groups. In this group, preoperative high CEA level was a poor prognostic factor. The prognosis for hormone receptor positive HER2-negative breast cancer has improved significantly since the 1990s. On the other hand, the prognosis for cN-positive/pN-positive breast cancer was poor. Pre-treatment serum CEA positive cases exhibited a particularly poor prognosis.

Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging

OBJECTIVES

To study the added value of mean and entropy of apparent diffusion coefficient (ADC) values at standard (800 s/mm²) and high (1500 s/mm²) b-values obtained with diffusion-weighted imaging in identifying histologic phenotypes of invasive ductal breast cancer (IDC) with MR imaging.

METHODS

One hundred thirty-four IDC patients underwent diffusion-weighted imaging with b-values of 800 and 1500 s/mm², and corresponding ADC₈₀₀ and ADC₁₅₀₀ maps were generated. Mean and entropy of volumetric ADC values were compared with molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67). Associations among morphologic features, ADC metrics, and phenotypes (luminal A, luminal B [HER2 negative], luminal B [HER2 positive], HER2 positive, and triple negative) were evaluated.

RESULTS

Mean ADC values were significantly decreased in ER-positive, PR-positive, and HER2-negative tumors (p < 0.01). Ki-67 ≥ 20% tumors demonstrated significantly higher ADC entropy values compared with Ki-67 < 20% tumors (p < 0.001). Luminal A subtype tended to display lower ADC entropy values compared with other subtypes, while HER2-positive subtype tended to display higher mean ADC values. ADC₁₅₀₀ entropy provided superior diagnostic performance over ADC₈₀₀ entropy (p = 0.04). Independent risk factors were ADC₁₅₀₀ entropy (p = 0.002) associated with luminal A, irregular mass shape (p = 0.018) and ADC₁₅₀₀ entropy (p = 0.022) with luminal B (HER2 positive), mean ADC₁₅₀₀ (p = 0.018) with HER2 positive, and smooth mass margin (p = 0.012) and rim enhancement (p = 0.003) with triple negative.

CONCLUSIONS

Mean and entropy of ADC values provided complementary information and added value for evaluating IDC histologic phenotypes. High-b-value ADC₁₅₀₀ may facilitate better phenotype discrimination.

KEY POINTS

• ADC metrics are associated with molecular marker status in IDC. • ADC ₁₅₀₀ improves differentiation of histologic phenotypes compared with ADC ₈₀₀ . • ADC metrics add value to morphologic features in IDC phenotyping.

Preliminary Study on Molecular Subtypes of Breast Cancer Based on Magnetic Resonance Imaging Texture Analysis

OBJECTIVE

The aim of the study was to investigate the molecular subtypes of breast cancer based on the texture features derived from magnetic resonance images (MRIs).

METHODS

One hundred seven patients with preoperative confirmed breast cancer were recruited. One hundred eight breast lesions were divided into 4 subtypes according to the status of estrogen receptor, progesterone receptor, human epidermal growth factor receptor type 2, and Ki67. Fisher discriminant analysis was performed on the texture features that extracted from the enhanced high-resolution T1-weighted images and diffusion weighted images to establish the classification model of molecular subtypes.

RESULTS

The differentiation accuracies of Fisher discriminant analysis on the enhanced high-resolution T1-weighted images were 82.8% and 86.4% for 1.5T and 3.0T imaging. Fisher discriminant analysis on diffusion weighted imaging texture features were achieved with a classification ability of 73.4% and 88.6%. The combined discriminant results for 2 kinds magnetic resonance images were 95.0%, 97.7% in 1.5T and 3.0T imaging, respectively.

CONCLUSIONS

The fine results indicated a promising approach to predict the molecular subtypes of breast cancer.

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. ^© RSNA, 2018.

Predicting the molecular subtype of breast cancer based on mammography and ultrasound findings

AIM

To determine the correlation between mammography and ultrasound features of breast cancer with molecular subtypes and to calculate the predictive value of these features.

MATERIALS AND METHOD

This is a prospective study of consecutive patients with breast cancer presenting between January 2016 and July 2017, who underwent mammography and/or ultrasound of breast and excision of the breast mass. Patients with contralateral breast mass, metastases, h/o prior cancer treatment, and other malignancies were excluded. On mammography, the presence or absence of microcalcification was noted. On ultrasound examination size, margins, microcalcification, posterior acoustic features, vascularity, and axillary nodes were assessed. Margins were categorized into circumscribed and non-circumscribed. Posterior acoustic features were classified into four categories: shadowing, enhancement, mixed, and no changes. Vascularity was assessed based on Adler's index into grades 0, 1, 2, and 3. Grades 0 and 1 were considered low and 2 and 3 were high.

RESULTS

Tumors with non-circumscribed margins and posterior acoustic shadowing were likely to be luminal A or B subtype of breast cancer [odds ratio (OR) 5.78; 95% confidence interval (CI) 3.68-9.80; P < 0.0001]. Tumors with non-circumscribed margins, posterior acoustic shadowing, and high vascularity were more likely to be luminal B subtype (OR 2.88; 95% CI 2-4.14; P- <0.0001). Tumors with microcalcification and posterior mixed acoustic pattern were strongly associated to be HER2-positive (OR 5.48; 95% CI 3.06-9.80; P < 0.0001). Tumors with circumscribed margins and posterior acoustic enhancement were highly suggestive of triple-negative breast cancer (OR 7.06; 95% CI 4.64-10.73; P < 0.0001).

CONCLUSION

Microcalcification detected on mammography and certain ultrasound features such as circumscribed or non-circumscribed margins, posterior acoustic features, and vascularity are strongly correlated in predicting the molecular subtypes of breast cancer, and thus may further expand the role of conventional breast imaging.

Background, current role, and potential applications of radiogenomics

With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors.

LEVEL OF EVIDENCE

5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.

Effects of FSTL1 on cell proliferation in breast cancer cell line MDA‑MB‑231 and its brain metastatic variant MDA‑MB‑231‑BR

In the past decades, altered Follistatin-like 1 (FSTL1) expression has been documented in a variety of cancers, while its functional roles are poorly understood. Particularly in breast cancer, the expression of FSTL1 and its signaling pathway remain to be determined. In the present study, an elevated FSTL1 expression and a supressed cell proliferation were detected in a specific brain metastatic cell line MDA-MB-231-BR (231-BR), compared with its parental cell line MDA-MB-231. However, this protein was hardly detected in the other three breast cancer cell lines. Next, lentiviral vectors encoding FSTL1 or FSTL1 specific shRNAs were used to overexpress or knock down FSTL1 in MDA-MB-231 or 231-BR, respectively (MDA-MB-231^FSTL1 or 231-BR^{sh FSTL1}). Results showed that overexpression of FSTL1 inhibited MDA-MB-231 cell proliferation, while knockdown of FSTL1 in 231-BR cells promotes cell proliferation, compared with their corresponding control groups. These results were further confirmed in nude mouse xenografts. The tumor volume in 231-BR cell-bearing mice was significantly smaller than that of MDA-MB-231 group, and reduction of tumor volume was detected in MDA-MB-231^FSTL1 cell-bearing mice compared with the control group. Previous studies revealed that TGF-β-Smad2/3 signaling pathway was activated in 231-BR and MDA-MB-231^FSTL1 cells, which may contribute to the inhibited cell proliferation. In addition, Smad3 knockdown could restore the inhibition of cell proliferation induced by FSTL1 overexpression in MDA-MB-231^FSTL1 cells, indicating that the anti-proliferative effect of FSTL1 overexpression may be associated with Smad3 involved TGF-β signaling pathway regulation. This study identified FSTL1 as an inhibitor of cell proliferation in MDA-MB-231 and 231-BR cell lines, which may provide new insights into the development and management of breast cancer.

Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences

Most implementations of mass spectrometry-based proteomics involve enzymatic digestion of proteins, expanding the analysis to multiple proteolytic peptides for each protein. Currently, there is no consensus of how to summarize peptides' abundances to protein concentrations, and such efforts are complicated by the fact that error control normally is applied to the identification process, and do not directly control errors linking peptide abundance measures to protein concentration. Peptides resulting from suboptimal digestion or being partially modified are not representative of the protein concentration. Without a mechanism to remove such unrepresentative peptides, their abundance adversely impacts the estimation of their protein's concentration. Here, we present a relative quantification approach, Diffacto, that applies factor analysis to extract the covariation of peptides' abundances. The method enables a weighted geometrical average summarization and automatic elimination of incoherent peptides. We demonstrate, based on a set of controlled label-free experiments using standard mixtures of proteins, that the covariation structure extracted by the factor analysis accurately reflects protein concentrations. In the 1% peptide-spectrum match-level FDR data set, as many as 11% of the peptides have abundance differences incoherent with the other peptides attributed to the same protein. If not controlled, such contradicting peptide abundance have a severe impact on protein quantifications. When adding the quantities of each protein's three most abundant peptides, we note as many as 14% of the proteins being estimated as having a negative correlation with their actual concentration differences between samples. Diffacto reduced the amount of such obviously incorrectly quantified proteins to 1.6%. Furthermore, by analyzing clinical data sets from two breast cancer studies, our method revealed the persistent proteomic signatures linked to three subtypes of breast cancer. We conclude that Diffacto can facilitate the interpretation and enhance the utility of most types of proteomics data.

Relationships Between MRI Breast Imaging-Reporting and Data System (BI-RADS) Lexicon Descriptors and Breast Cancer Molecular Subtypes: Internal Enhancement is Associated with Luminal B Subtype

The aim of this study was to determine the associations between breast MRI findings using the Breast Imaging-Reporting and Data System (BI-RADS) lexicon descriptors and breast cancer molecular subtypes. In this retrospective, IRB-approved, single institution study MRIs from 278 women with breast cancer were reviewed by one of six fellowship-trained breast imagers. Readers reported BI-RADS descriptors for breast masses (shape, margin, internal enhancement) and non-mass enhancement (distribution, internal enhancement). Pathology reports were reviewed for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). Surrogates were used to categorize tumors by molecular subtype: ER/PR+, HER2- (luminal A); ER/PR+, HER2+ (luminal B); ER/PR-, HER2+ (HER2); ER/PR/HER2- (basal). A univariate logistic regression model was developed to identify associations between BI-RADS descriptors and molecular subtypes. Internal enhancement for mass and non-mass enhancement was combined for analysis. There was an association between mass shape and basal subtype (p = 0.039), which was more frequently round (17.1%) than other subtypes (range: 0-8.3%). In addition, there was an association between mass margin and HER2 subtype (p = 0.040), as HER2 cancers more frequently had a smooth margin (33.3%) than other subtypes (range: 4.2-17.1%). Finally, there was an association between internal enhancement and luminal B subtype (p = 0.003), with no cases of luminal B cancer demonstrating homogeneous internal enhancement versus a range of 10.9-23.5% for other subtypes. There are associations between breast cancer molecular subtypes and lesion appearance on MRI using the BI-RADS lexicon.

Bringing 3D tumor models to the clinic - predictive value for personalized medicine

Current decision-guiding algorithms in cancer drug treatment are based on decades of research and numerous clinical trials. For the majority of patients, this data is successfully applied for a systemic disease management. For a number of patients however, treatment stratification according to clinically based risk criteria will not be sufficient. The most effective treatment options are ideally identified prior to the start of clinical drug therapy. This review will discuss the implementation of three-dimensional (3D) cell culture models as a preclinical testing paradigm for the efficacy of clinical cancer treatment. Patient tumor-derived cells in 3D cultures duplicate the individual tumor microenvironment with a minimum of confounding factors. Clinical implementation of such personalized tumor models requires a high quality of methodological and clinical validation comparable to other biomarkers. A non-systematic literature search demonstrated the small number of prospective studies that have been conducted in this area of research. This may explain the current reluctance of many physicians and insurance providers in implementing this type of assay into the clinical diagnostic routine despite potential benefit for patients. Achieving valid and reproducible results with a high level of evidence is central in improving the acceptance of preclinical 3D tumor models.

Is There a Correlation between the Presence of a Spiculated Mass on Mammogram and Luminal A Subtype Breast Cancer?

Objective

To determine whether the appearance of a spiculated mass on a mammogram is associated with luminal A subtype breast cancer and the factors that may influence the presence or absence of the spiculated mass.

Materials and Methods

Three hundred seventeen (317) patients who underwent image-guided or surgical biopsy between December 2014 and April 2015 were included in the study. Radiologists conducted retrospective assessments of the presence of spiculated masses according to the criteria of Breast Imaging Reporting and Data System. We used combinations of estrogen receptor (ER), progesterone receptor (PR), human epithelial growth factor receptor 2 (HER2), and Ki67 as surrogate markers to identify molecular subtypes of breast cancer. Pearson chi-square test was employed to measure statistical significance of correlations. Furthermore, we built a bi-variate logistic regression model to quantify the relative contribution of the factors that may influence the presence or absence of the spiculated mass.

Results

Seventy-one percent (71%) of the spiculated masses were classified as luminal A. Masses classified as luminal A were 10.3 times more likely to be presented as spiculated mass on a mammogram than all other subtypes. Patients with low Ki67 index (< 14%) and HER2 negative were most likely to present with a spiculated mass on their mammograms (p <0.001) than others. The hormone receptor status (ER and PR), pathology grade, overall breast composition, were all associated with the presence of a spiculated mass, but with less weight in contribution than Ki67 and HER2.

Conclusion

We observed an association between the luminal A subtype of invasive breast cancer and the presence of a spiculated mass on a mammogram. It is hypothesized that lower Ki67 index and HER2 negativity may be the most significant factors in the presence of a spiculated mass.

Immunoprofile from tissue microarrays to stratify familial breast cancer patients

Familial breast cancer (BC) is a heterogeneous disease with variable prognosis. The identification of an immunoprofile is important to predict tumor behavior for the routine clinical management of familial BC patients. Using immunohistochemistry on tissue microarrays, we studied 95 familial BCs in order to analyze the expression of some biomarkers involved in different pathways. We used unsupervised hierarchical clustering analyses (HCA), performed using the immunohistochemical score data, to define an immunoprofile able to characterize these tumors. The analyses on 95 and then on a subset of 45 tumors with all biomarkers contemporarily evaluable, revealed the same biomarker and patient clusters. Focusing on the 45 tumors we identified a group of patients characterized by the low expression of estrogen receptor (P = 0.009), progesterone receptor (P < 0.001), BRCA1 (P = 0.005), nuclear Na⁺/H⁺ exchanger regulatory factor 1 (NHERF1) (P = 0.026) and hypoxia inducible factor-1 alpha (P < 0.001), and also by the higher expression of MIB1 (P = 0.043), cytoplasmic NHERF1 (P = 0.004), cytoplasmic BRCT-repeat inhibitor of hTERT expression (P = 0.001), vascular endothelial growth factor (VEGF) (P = 0.024) and VEGF receptor-1 (P = 0.029). This immunoprofile identified a more aggressive tumor phenotype associated also with a larger tumor size (P = 0.012) and G3 grade (P = 0.006), confirmed by univariate and multivariate analyses. In conclusion, the clinical application of HCA of immunohistochemical data could allow the assessment of prognostic biomarkers to be used simultaneously. The 10 protein expression panel might be used to identify the more aggressive tumor phenotype in familial BC and to direct patients towards a different clinical therapy.

Proteomics and peptidomics: moving toward precision medicine in urological malignancies

Urological malignancies are a major cause of morbidity and mortality worldwide. Advances in early detection, diagnosis, prognosis and prediction of treatment response can significantly improve patient care. Proteomic and peptidomic profiling studies are at the center of kidney, prostate and bladder cancer biomarker discovery and have shown great promise for improved clinical assessment. Mass spectrometry (MS) is the most widely employed method for proteomic and peptidomic analyses. A number of MS platforms have been developed to facilitate accurate identification of clinically relevant markers in various complex biological samples including tissue, urine and blood. Furthermore, protein profiling studies have been instrumental in the successful introduction of several diagnostic multimarker tests into the clinic. In this review, we will provide a brief overview of high-throughput technologies for protein and peptide based biomarker discovery. We will also examine the current state of kidney, prostate and bladder cancer biomarker research as well as review the journey toward successful clinical implementation.

Cell-specific biomarkers and targeted biopharmaceuticals for breast cancer treatment

Breast cancer is the second leading cause of cancer death among women, and its related treatment has been attracting significant attention over the past decades. Among the various treatments, targeted therapy has shown great promise as a precision treatment, by binding to cancer cell-specific biomarkers. So far, great achievements have been made in targeted therapy of breast cancer. In this review, we first discuss cell-specific biomarkers, which are not only useful for classification of breast cancer subtyping but also can be utilized as goals for targeted therapy. Then, the innovative and generic-targeted biopharmaceuticals for breast cancer, including monoclonal antibodies, non-antibody proteins and small molecule drugs, are reviewed. Finally, we provide our outlook on future developments of biopharmaceuticals, and provide solutions to problems in this field.

Do platinum salts fit all triple negative breast cancers?

Triple-negative breast cancer (TNBC) is an aggressive disease with limited treatment options and poor prognosis once metastatic. Pre-clinical and clinical data suggest that TNBC could be more sensitive to platinum-based chemotherapy, especially among BRCA1/2-mutated patients. In recent years, several randomised trials have been conducted to evaluate platinum efficacy in both early-stage and advanced TNBC, with conflicting results especially for long-term outcomes. Experimental studies are now focusing on identifying biomarkers of response to help selecting patients who may benefit most from platinum-based therapies, including BRCA1/2 mutational status and genomic instability signatures (such as HRD-LOH or HRD-LST scores). A standard therapy for TNBC is still missing and platinum-based regimens represent an emerging therapeutic option for selected patients with a defect in the homologous recombination repair system. The identification of these patients through validated biomarker assays will be crucial to optimize the use of currently approved agents in TNBC.

A microscopic landscape of the invasive breast cancer genome

Histologic grade is one of the most important microscopic features used to predict the prognosis of invasive breast cancer and may serve as a marker for studying cancer driving genomic abnormalities in vivo. We analyzed whole genome sequencing data from 680 cases of TCGA invasive ductal carcinomas of the breast and correlated them to corresponding pathology information. Ten genetic abnormalities were found to be statistically associated with histologic grade, including three most prevalent cancer driver events, TP53 and PIK3CA mutations and MYC amplification. A distinct genetic interaction among these genomic abnormalities was revealed as measured by the histologic grading score. While TP53 mutation and MYC amplification were synergistic in promoting tumor progression, PIK3CA mutation was found to have alleviated the oncogenic effect of either the TP53 mutation or MYC amplification, and was associated with a significant reduction in mitotic activity in TP53 mutated and/or MYC amplified breast cancer. Furthermore, we discovered that different types of genetic abnormalities (mutation versus amplification) within the same cancer driver gene (PIK3CA or GATA3) were associated with opposite histologic changes in invasive breast cancer. In conclusion, our study suggests that histologic grade may serve as a biomarker to define cancer driving genetic events in vivo.

Homogeneous Biosensing Based on Magnetic Particle Labels

The growing availability of biomarker panels for molecular diagnostics is leading to an increasing need for fast and sensitive biosensing technologies that are applicable to point-of-care testing. In that regard, homogeneous measurement principles are especially relevant as they usually do not require extensive sample preparation procedures, thus reducing the total analysis time and maximizing ease-of-use. In this review, we focus on homogeneous biosensors for the in vitro detection of biomarkers. Within this broad range of biosensors, we concentrate on methods that apply magnetic particle labels. The advantage of such methods lies in the added possibility to manipulate the particle labels by applied magnetic fields, which can be exploited, for example, to decrease incubation times or to enhance the signal-to-noise-ratio of the measurement signal by applying frequency-selective detection. In our review, we discriminate the corresponding methods based on the nature of the acquired measurement signal, which can either be based on magnetic or optical detection. The underlying measurement principles of the different techniques are discussed, and biosensing examples for all techniques are reported, thereby demonstrating the broad applicability of homogeneous in vitro biosensing based on magnetic particle label actuation.

Noninvasive strategies for breast cancer early detection

Breast cancer screening and presurgical diagnosis are currently based on mammography, ultrasound and more sensitive imaging technologies; however, noninvasive biomarkers represent both a challenge and an opportunity for early detection of cancer. An extensive number of potential breast cancer biomarkers have been discovered by microarray hybridization or sequencing of circulating DNA, noncoding RNA and blood cell RNA; multiplex analysis of immune-related molecules and mass spectrometry-based approaches for high-throughput detection of protein, endogenous peptides, circulating and volatile metabolites. However, their medical relevance and their translation to clinics remain to be exploited. Once they will be fully validated, cancer biomarkers, used in combination with the current and emerging imaging technologies, represent an avenue to a personalized breast cancer diagnosis.

What gastric cancer proteomic studies show about gastric carcinogenesis?

Gastric cancer is a complex, heterogeneous, and multistep disease. Over the past decades, several studies have aimed to determine the molecular factors that lead to gastric cancer development and progression. After completing the human genome sequencing, proteomic technologies have presented rapid progress. Differently from the relative static state of genome, the cell proteome is dynamic and changes in pathologic conditions. Proteomic approaches have been used to determine proteome profiles and identify differentially expressed proteins between groups of samples, such as neoplastic and nonneoplastic samples or between samples of different cancer subtypes or stages. Therefore, proteomic technologies are a useful tool toward improving the knowledge of gastric cancer molecular pathogenesis and the understanding of tumor heterogeneity. This review aimed to summarize the proteins or protein families that are frequently identified by using high-throughput screening methods and which thus may have a key role in gastric carcinogenesis. The increased knowledge of gastric carcinogenesis will clearly help in the development of new anticancer treatments. Although the studies are still in their infancy, the reviewed proteins may be useful for gastric cancer diagnosis, prognosis, and patient management.

Synthesis and Cytotoxic Effect of Some Novel 1,2-Dihydropyridin-3-carbonitrile and Nicotinonitrile Derivatives

1-(2,4-Dichlorophenyl)-3-(4-fluorophenyl)propen-1-one (1) was prepared and reacted with an active methylene compound (ethyl cyanoacetate) in the presence of ammonium acetate to give the corresponding cyanopyridone 2. Compound 2 reacted with hydrazine hydrate, malononitrile, ethyl bromoacetate and phosphorous oxychloride to afford compounds 4 and 7–11, respectively. The 2-chloropyridine derivative 11 reacted with different primary amines, namely benzyl amine, piperonyl amine, 1-phenylethyl amine, and/or the secondary amines 2-methyl-pipridine and morpholine to give the corresponding derivatives 12–15. Hydrazinolysis of chloropyridine derivative 11 with hydrazine hydrate afforded the corresponding hydrazino derivative 17. Condensation of compound 17 with ethyl acetoacetate, acetylacetone, isatin and different aldehydes gave the corresponding derivatives 18–21. Some of newly synthesized compounds were screened for cytotoxic activity against three tumor cell lines. The results indicated that compounds 8 and 16 showed the best results, exhibiting the highest inhibitory effects towards the three tumor cell lines, which were higher than that of the reference doxorubicin and these compounds were non-cytotoxic towards normal cells (IC₅₀ values > 100 μg/mL).

Selectivity of Pinus sylvestris extract and essential oil to estrogen-insensitive breast cancer cells Pinus sylvestris against cancer cells

Background:

So far, the anticancer action of pine tree extracts has mainly been shown for the species distributed widely around the Asian countries.

Objective:

Therefore, this study was performed to examine the potential cytotoxicity of Scots pine (Pinus sylvestris L.) native also to the European region and growing widely in Estonia.

Materials and Methods:

The cytotoxic activity of methanol extract and essential oil of Scots pine needles was determined by sulforhodamine B assay in different human cancer cell lines.

Results:

This needle extract was found to suppress the viability of several human cancer cell lines showing some selectivity to estrogen receptor negative breast cancer cells, MDA-MB-231(half maximal inhibitory concentration [IC₅₀] 35 μg/ml) in comparison with estrogen receptor-positive breast cancer cells, MCF-7 (IC₅₀ 86 μg/ml). It is the strongest cytotoxic effect at all measured, thus far for the needles and leaves extracts derived from various pine species, and is also the first study comparing the anticancer effects of pine tree extracts on molecularly different human breast cancer cells. The essential oil showed the stronger cytotoxic effect to both negative and positive breast cancer cell lines (both IC₅₀ 29 μg/ml) than pine extract (IC₅₀ 42 and 80 μg/ml, respectively).

Conclusion:

The data from this report indicate that Scots pine needles extract and essential oil exhibits some potential as chemopreventive or chemotherapeutic agent for mammary tumors unresponsive to endocrine treatment.

Breast MRI radiogenomics: Current status and research implications

Breast magnetic resonance imaging (MRI) radiogenomics is an emerging area of research that has the potential to directly influence clinical practice. Clinical MRI scanners today are capable of providing excellent temporal and spatial resolution, which allows extraction of numerous imaging features via human extraction approaches or complex computer vision algorithms. Meanwhile, advances in breast cancer genetics research has resulted in the identification of promising genes associated with cancer outcomes. In addition, validated genomic signatures have been developed that allow categorization of breast cancers into distinct molecular subtypes as well as predict the risk of cancer recurrence and response to therapy. Current radiogenomics research has been directed towards exploratory analysis of individual genes, understanding tumor biology, and developing imaging surrogates to genetic analysis with the long-term goal of developing a meaningful tool for clinical care. The background of breast MRI radiogenomics research, image feature extraction techniques, approaches to radiogenomics research, and promising areas of investigation are reviewed. J. Magn. Reson. Imaging 2016;43:1269-1278.

Quantitative tissue proteomic investigation of invasive ductal carcinoma of breast with luminal B HER2 positive and HER2 enriched subtypes towards potential diagnostic and therapeutic biomarkers

Worldwide, breast cancer is one of the frequently diagnosed cancers in women with high mortality if not diagnosed at early stage. Although biomarker discoveries through various proteomic approaches have been studied in breast cancer, a limited number of studies have explored the invasive ductal carcinoma with Luminal B HER2 positive (LB) and HER2 enriched (HE) subtypes. The present study employed the complementary quantitative proteomic approaches to find a panel of markers that could discriminate LB and HE subtypes as well as early (ES) and late stages (LS) of these subtypes. A total of 67 and 68 differentially expressed proteins were identified by DIGE for the subtype and stage wise categories, respectively. Multivariate statistical analysis was employed to identify the set of most significant proteins, which could discriminate between these two subtypes and also early and late stages under study. Immunoblotting and MRM based validation in a separate cohort of samples confirmed that panel of biosignatures for LB are APOA1, GELS, HS90B, EF1A1, NHRF1 and PRDX3 and for HE are PRDX1, CATD, CALR, ATPB and CH60. For the diagnosis of early and late stages the potential markers are TPM4, CATD, PRDX3, ANXA3, HSPB1 and CALR, TRFE, GELS, CH60, CAPG, NHRF1, 1433G, GRP78 respectively.

Genome-Wide Screening of mRNA Expression in Leprosy Patients

Leprosy, an infectious disease caused by Mycobacterium leprae, affects millions of people worldwide. However, little is known regarding its molecular pathophysiological mechanisms. In this study, a comprehensive assessment of human mRNA was performed on leprosy skin lesions by using DNA chip microarrays, which included the entire spectrum of the disease along with its reactional states. Sixty-six samples from leprotic lesions (10TT, 10BT, 10BB, 10BL, 4LL, 14R1, and 10R2) and nine skin biopsies from healthy individuals were used as controls (CC) (ages ranged from 06 to 83 years, 48 were male and 29 female). The evaluation identified 1580 differentially expressed mRNAs [Fold Change (FC) ≥ 2.0, p ≤ 0.05] in diseased lesions vs. healthy controls. Some of these genes were observed in all forms of the disease (CD2, CD27, chit1, FA2H, FAM26F, GZMB, MMP9, SLAMF7, UBD) and others were exclusive to reactional forms (Type "1" reaction: GPNMB, IL1B, MICAL2, FOXQ1; Type "2" reaction: AKR1B10, FAM180B, FOXQ1, NNMT, NR1D1, PTX3, TNFRSF25). In literature, these mRNAs have been associated with numerous pathophysiological processes and signaling pathways and are present in a large number of diseases. The role of these mRNAs maybe studied in the context of developing new diagnostic markers and therapeutic targets for leprosy.

Mass spectrometry-based proteomics: from cancer biology to protein biomarkers, drug targets, and clinical applications

Proteomics is optimally suited to bridge the gap between genomic information on the one hand and biologic functions and disease phenotypes at the other, since it studies the expression and/or post-translational modification (especially phosphorylation) of proteins--the major cellular players bringing about cellular functions--at a global level in biologic specimens. Mass spectrometry technology and (bio)informatic tools have matured to the extent that they can provide high-throughput, comprehensive, and quantitative protein inventories of cells, tissues, and biofluids in clinical samples at low level. In this article, we focus on next-generation proteomics employing nanoliquid chromatography coupled to high-resolution tandem mass spectrometry for in-depth (phospho)protein profiling of tumor tissues and (proximal) biofluids, with a focus on studies employing clinical material. In addition, we highlight emerging proteogenomic approaches for the identification of tumor-specific protein variants, and targeted multiplex mass spectrometry strategies for large-scale biomarker validation. Below we provide a discussion of recent progress, some research highlights, and challenges that remain for clinical translation of proteomic discoveries.

Pit-1 inhibits BRCA1 and sensitizes human breast tumors to cisplatin and vitamin D treatment

The POU class 1 homeobox 1 (POU1F1, also known as Pit-1), pertaining to the Pit-Oct-Unc (POU) family of transcription factors, has been related to tumor growth and metastasis in breast. However, its role in response to breast cancer therapy is unknown. We found that Pit-1 down-regulated DNA-damage and repair genes, and specifically inhibited BRCA1 gene expression, sensitizing breast cancer cells to DNA-damage agents. Administration of 1α, 25-dihydroxy-3-epi-vitamin D3 (3-Epi, an endogenous low calcemic vitamin D metabolite) reduced Pit-1 expression, and synergized with cisplatin, thus, decreasing cell proliferation and apoptosis in vitro, and reducing tumor growth in vivo. In addition, fifteen primary cultures of human breast tumors showed significantly decreased proliferation when treated with 3-Epi+cisplatin, compared to cisplatin alone. This response positively correlated with Pit-1 levels. Our findings demonstrate that high levels of Pit-1 and reduced BRCA1 levels increase breast cancer cell susceptibility to 3-Epi+cisplatin therapy.

Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms

PURPOSE

To identify associations between semiautomatically extracted MRI features and breast cancer molecular subtypes.

METHODS

We analyzed routine clinical pre-operative breast MRIs from 275 breast cancer patients at a single institution in this retrospective, Institutional Review Board-approved study. Six fellowship-trained breast imagers reviewed the MRIs and annotated the cancers. Computer vision algorithms were then used to extract 56 imaging features from the cancers including morphologic, texture, and dynamic features. Surrogate markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor-2 [HER2]) were used to categorize tumors by molecular subtype: ER/PR+, HER2- (luminal A); ER/PR+, HER2+ (luminal B); ER/PR-, HER2+ (HER2); ER/PR/HER2- (basal). A multivariate analysis was used to determine associations between the imaging features and molecular subtype.

RESULTS

The imaging features were associated with both luminal A (P = 0.0007) and luminal B (P = 0.0063) molecular subtypes. No association was found for either HER2 (P = 0.2465) or basal (P = 0.1014) molecular subtype and the imaging features. A P-value of 0.0125 (0.05/4) was considered significant.

CONCLUSION

Luminal A and luminal B molecular subtype breast cancer are associated with semiautomatically extracted features from routine contrast enhanced breast MRI.

Pattern of metastasis and outcome in patients with breast cancer

There is growing evidence about differences in metastatic spread among breast cancer (BC) biologic subtypes (BS). Aim of this study was to analyze the pattern of metastasization according to BS and to explore the corresponding prognosis. A series of 544 consecutive patients receiving anticancer therapy for metastatic BC from 2004 to 2013, was analyzed. BS were defined by immunohistochemistry according to St Gallen 2013 criteria. Association between BS and the different distant localizations was analyzed. Prognosis was described in terms of overall survival (OS), progression free survival (PFS) and post progression survival (PPS). Results were reported taking luminal A BC as reference. Triple negative BC showed a higher tropism for lung (OR 4.30 95% CI 1.41-13.1), while non luminal HER2 subtype was associated with a higher rate of liver metastases (OR 3.61 95% CI 1.36-9.58). All subtypes were associated with a lower risk of bone-only localization. Central nervous system (CNS) involvement was more common in HER2 positive BC (OR 6.3, 95% CI 1.08-36.66). Liver, lung and CNS involvement influenced negatively OS (HR 1.64, 95% CI 1.29-2.07; HR 1.49, 95% CI 1.18-1.90; HR 2.891, 95% CI 1.85-4.51, respectively) and PFS (HR 1.39, 95% CI 1.13-1.71; HR 1.26, 95% CI 1.02-1.55; HR 1.75, 95% CI 1.12-2.71, respectively). Multivariate analysis confirmed liver involvement as independent predictor of worse OS (HR 1.64, 95% CI 1.15-2.34). Stratification by metastatic pattern showed significant differences in terms of PPS but not in terms of PFS. The study suggests that BS may be characterized by typical patterns of metastatic spread and have different impact on clinical outcome.

Proteomics reveals the importance of the dynamic redistribution of the subcellular location of proteins in breast cancer cells

At the molecular level, living cells are enormously complicated complex adaptive systems in which intertwined genomic, transcriptomic, proteomic and metabolic networks all play a crucial role. At the same time, cells are spatially heterogeneous systems in which subcellular compartmentalization of different functions is ubiquitous and requires efficient cross-compartmental communication. Dynamic redistribution of multitudinous proteins to different subcellular locations in response to cellular functional state is increasingly recognized as a crucial characteristic of cellular function that seems to be at least as important as overall changes in protein abundance. Characterization of the subcellular spatial dynamics of protein distribution is a major challenge for proteomics and recent results with MCF7 breast cancer cells suggest that this may be of particular importance for cancer cells.