Breast cancer classification by proteomic technologies: current state of knowledge

The Value of Whole-Volume Radiomics Machine Learning Model Based on Multiparametric MRI in Predicting Triple-Negative Breast Cancer

OBJECTIVE

This study aimed to investigate the value of radiomics analysis in the precise diagnosis of triple-negative breast cancer (TNBC) based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) maps.

METHODS

This retrospective study included 326 patients with pathologically proven breast cancer (TNBC: 129, non-TNBC: 197). The lesions were segmented using the ITK-SNAP software, and whole-volume radiomics features were extracted using a radiomics platform. Radiomics features were obtained from DCE-MRI and ADC maps. The least absolute shrinkage and selection operator regression method was employed for feature selection. Three prediction models were constructed using a support vector machine classifier: Model A (based on the selected features of the ADC maps), Model B (based on the selected features of DCE-MRI), and Model C (based on the selected features of both combined). Receiver operating characteristic curves were used to evaluate the diagnostic performance of the conventional MR image model and the 3 radiomics models in predicting TNBC.

RESULTS

In the training dataset, the AUCs for the conventional MR image model and the 3 radiomics models were 0.749, 0.801, 0.847, and 0.896. The AUCs for the conventional MR image model and 3 radiomics models in the validation dataset were 0.693, 0.742, 0.793, and 0.876, respectively.

CONCLUSIONS

Radiomics based on the combination of whole volume DCE-MRI and ADC maps is a promising tool for distinguishing between TNBC and non-TNBC.

Application of DCE-MRI radiomics and heterogeneity analysis in predicting luminal and non-luminal subtypes of breast cancer

Purpose

The aim of this study was to explore the application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and heterogeneity analysis in the differentiation of molecular subtypes of luminal and non-luminal breast cancer.

Methods

In this retrospective study, 388 female breast cancer patients (48.37 ± 9.41 years) with luminal (n = 190) and non-luminal (n = 198) molecular subtypes who received surgical treatment at Jilin Cancer Hospital were recruited from January 2019 to June 2023. All patients underwent breast MRI scan and DCE scan before surgery. The patients were then divided into a training set (n = 272) and a validation set (n = 116) in a 7:3 ratio. The three-dimensional texture feature parameters of the breast lesion areas were extracted. Four tumor heterogeneity parameters, including type I curve proportion, type II curve proportion, type III curve proportion and tumor heterogeneity values were calculated and normalized. Five machine learning (ML) models, including the logistic regression, naive Bayes algorithm (NB), k-nearest neighbor (KNN), decision tree algorithm (DT) and extreme gradient boosting (XGBoost) model were used to process the training data and were further validated. The best ML model was selected according to the performance in the validation set.

Results

In luminal subtype breast lesions, type III curve proportion and heterogeneity index were significantly lower than the corresponding parameters of the non-luminal subtype lesions both in the training set and validation set. Eight features together with four heterogeneity-related parameters with significant differences between luminal and non-luminal groups were retained as radiomics signatures for constructing the prediction model. The logistic regression ML model achieved the best performance in the validation set with the highest area under the curve value (0.93), highest accuracy (86.94%), sensitivity (87.55%) and specificity (86.25%).

Conclusion

The radiomics and heterogeneity analysis based on the DCE-MRI exhibit good application value in discriminating luminal and non-luminal subtype breast cancer. The logistic regression model demonstrates the best predictive performance among various machine learning models.

Clinically Significant BRCA1 and BRCA2 Germline Variants in Breast Cancer-A Single-Center Experience

BACKGROUND

Conditions associated with BRCA1/2 pathogenic (PVs) or likely pathogenic variants (LPVs) are often severe. The early detection of carrier status is ideal, as it provides options for effective case management.

MATERIALS AND METHODS

The study involved 58 patients with a personal and familial history of breast cancer (BC) who underwent genetic testing at the Regional Centre for Medical Genetics Dolj over a three-year period. An immunohistochemical panel (HER2, ER, PR, and Ki-67) was used to define the molecular subtypes of breast tumors. The AmpliSeq for Illumina BRCA Panel was used to evaluate germline variants in the BRCA1 and BRCA2 genes in patients with BC. The χ2 test and Fisher's exact test were used to compare the different parameters studied.

RESULTS

Our findings revealed that 15.5% of the patients carried either BRCA1 or BRCA2 PVs or LPVs. BRCA1 carriers had aggressive tumors whereas BRCA2 carriers had rather low-grade tumors.

CONCLUSIONS

The study revealed that PVs in both BRCA genes have a significant frequency among BC patients in our region, and BRCA1 carriers tend to develop more aggressive tumors than carriers of BRCA2 PVs and patients with no germline PVs in either of the two genes. These observations could provide new epidemiologic data for this disease in our region and contribute further to the development of national screening strategies.

Early detection of breast cancer through the diagnosis of Nipple Aspirate Fluid (NAF)

The development of breast cancer has been mainly reported in women who have reached the post-menopausal stage; therefore, it is the primary factor responsible for death amongst postmenopausal women. However, if treated on time it has shown a survival rate of 20 years in about two-thirds of women. Cases of breast cancer have also been reported in younger women and the leading cause in them is their lifestyle pattern or they may be carriers of high penetrance mutated genes. Premenopausal women who have breast cancer have been diagnosed with aggressive build-up of tumors and are therefore at more risk of loss of life. Mammography is an effective way to test for breast cancer in women after menopause but is not so effective for premenopausal women or younger females. Imaging techniques like contrast-enhanced MRI can up to some extent indicate the presence of a tumor but it cannot adequately differentiate between benign and malignant tumors. Although the 'omics' strategies continuing for the last 20 years have been helpful at the molecular level in enabling the characteristics and proper understanding of such tumors over long-term longitudinal monitoring. Classification, diagnosis, and prediction of the outcomes have been made through tissue and serum biomarkers but these also fail to diagnose the disease at an early stage. Considerably there is no adequate detection technique present globally that can help early detection and provide adequate specificity, safety, sensitivity, and convenience for the younger and premenopausal women, thereby it becomes necessary to take early measures and build efficient tools and techniques for the same. Through biopsies of nipple aspirate fluid (NAF) biomarker profiling can be performed. It is a naturally secreted fluid from the cells of epithelium found in the breast. Nowadays, home-based liquid biopsy collection kits are also available through which a routine check on breast health can be performed with the help of NAF. Herein, we will review the biomarker screening liquid biopsy, and the new emerging technologies for the examination of cancer at an early stage, especially in premenopausal women.

Understanding the mechanistic pathways and clinical aspects associated with protein and gene based biomarkers in breast cancer

Cancer is one of the most widespread and severe diseases with a huge mortality rate. In recent years, the second-leading mortality rate of any cancer globally has been breast cancer, which is one of the most common and deadly cancers found in women. Detecting breast cancer in its initial stages simplifies treatment, decreases death risk, and recovers survival rates for patients. The death rate for breast cancer has risen to 0.024 % in some regions. Sensitive and accurate technologies are required for the preclinical detection of BC at an initial stage. Biomarkers play a very crucial role in the early identification as well as diagnosis of women with breast cancer. Currently, a wide variety of cancer biomarkers have been discovered for the diagnosis of cancer. For the identification of these biomarkers from serum or other body fluids at physiological amounts, many detection methods have been developed. In the case of breast cancer, biomarkers are especially helpful in discovering those who are more likely to develop the disease, determining prognosis at the time of initial diagnosis and choosing the best systemic therapy. In this study we have compiled various clinical aspects and signaling pathways associated with protein-based biomarkers and gene-based biomarkers.

Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.

Clinicopathological characteristics and prognosis of triple-negative breast cancer invasive ductal carcinoma with ductal carcinoma in situ

PURPOSE

The purpose of this study is to compare and analyze the clinicopathological characteristics and prognosis of patients with invasive ductal carcinoma coexisting with ductal carcinoma in situ (IDC-DCIS) and invasive ductal carcinoma (IDC) in triple-negative breast cancer (TNBC), and to explore the factors affecting the prognosis, so as to provide new ideas for clinical diagnosis and treatment of these patients.

METHODS

The patients with TNBC underwent surgery in the Department of Breast Surgery of Harbin Medical University Cancer Hospital from October 2012 to December 2018 were retrospectively analyzed and divided into IDC-DCIS group and IDC group. The clinicopathological characteristics and prognosis of the two groups were compared. P < 0.05 was considered statistically significant.

RESULTS

A total of 358 patients were enrolled. There were significant differences in age (P = 0.002), family history (P = 0.016), menopausal status (P = 0.003), KI-67% (P < 0.001), lymphovascular invasion (P = 0.010), histologic grade of IDC (P < 0.001) and multifocal (P < 0.001) between the two groups. The disease-free survival (DFS) of the IDC-DCIS group was better than that of the IDC group (the 5-year DFS was 87.9% vs. 82.6%, P = 0.045), but the overall survival (OS) of the two groups was not statistically significant (the 5-year OS was 96.2% vs. 96.0%, P = 0.573). In addition, the coexistence of DCIS (P = 0.030), lymph node pathologic stage (P = 0.001), tumor location (P = 0.011), and adjuvant chemotherapy (P < 0.001) were independent prognostic factors for DFS.

CONCLUSION

In TNBC, the IDC-DCIS group had less invasive biological characteristics. The DFS of the IDC-DCIS group was better than that of the IDC group, but there was no statistical difference in OS between the two groups. In addition, the coexistence of DCIS, lymph node stage, tumor location and adjuvant chemotherapy may be independent prognostic factors for DFS.

A comprehensive review on the impact of calcium and vitamin D insufficiency and allied metabolic disorders in females

Calcium is imperative in maintaining a quality life, particularly during later ages. Its deficiency results in a wide range of metabolic disorders such as dental changes, cataracts, alterations in brain function, and osteoporosis. These deficiencies are more pronounced in females due to increased calcium turnover throughout their life cycle, especially during pregnancy and lactation. Vitamin D perform a central role in the metabolism of calcium. Recent scientific interventions have linked calcium with an array of metabolic disorders in females including hypertension, obesity, premenstrual dysphoric disorder, polycystic ovary syndrome (PCOS), multiple sclerosis, and breast cancer. This review encompasses these female metabolic disorders with special reference to calcium and vitamin D deficiency. This review article aims to present and elaborate on available data regarding the worldwide occurrence of insufficient calcium consumption in females and allied health risks, to provide a basis for formulating strategies and population-level scientific studies to adequately boost calcium intake and position where required.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Omics-Based Investigations of Breast Cancer

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

DenseNet_ HybWWoA: A DenseNet-Based Brain Metastasis Classification with a Hybrid Metaheuristic Feature Selection Strategy

Brain metastases (BM) are the most severe consequence of malignancy in the brain, resulting in substantial illness and death. The most common primary tumors that progress to BM are lung, breast, and melanoma. Historically, BM patients had poor clinical outcomes, with limited treatment options including surgery, stereotactic radiation therapy (SRS), whole brain radiation therapy (WBRT), systemic therapy, and symptom control alone. Magnetic Resonance Imaging (MRI) is a valuable tool for detecting cerebral tumors, though it is not infallible, as cerebral matter is interchangeable. This study offers a novel method for categorizing differing brain tumors in this context. This research additionally presents a combination of optimization algorithms called the Hybrid Whale and Water Waves Optimization Algorithm (HybWWoA), which is used to identify features by reducing the size of recovered features. This algorithm combines whale optimization and water waves optimization. The categorization procedure is consequently carried out using a DenseNet algorithm. The suggested cancer categorization method is evaluated on a number of factors, including precision, specificity, and sensitivity. The final assessment findings showed that the suggested approach exceeded the authors' expectations, with an F1-score of 97% and accuracy, precision, memory, and recollection of 92.1%, 98.5%, and 92.1%, respectively.

Multimodal ultrasound features of breast cancers: correlation with molecular subtypes

OBJECTIVES

To investigate whether multimodal intratumour and peritumour ultrasound features correlate with specific breast cancer molecular subtypes.

METHODS

From March to November 2021, a total of 85 patients with histologically proven breast cancer underwent B-mode, real-time elastography (RTE), colour Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS). The time intensity curve (TIC) of CEUS was obtained, and the peak and time to peak (TTP) were analysed. Chi-square and binary logistic regression were used to analyse the connection between multimodal ultrasound imaging features and breast cancer molecular subtype.

RESULTS

Among 85 breast cancers, the subtypes were as follows: luminal A (36 cases, 42.4%), luminal B (20 cases, 23.5%), human epidermal growth factor receptor-2 positive (HER2+) (16 cases, 18.8%), and triple negative breast cancer (TNBC) (13 cases, 15.3%). Binary logistic regression models showed that RTE (P < 0.001) and CDFI (P = 0.036) were associated with the luminal A cancer subtype (C-index: 0.741), RTE (P = 0.016) and the peak ratio between intratumour and corpus mamma (P = 0.036) were related to the luminal B cancer subtype (C-index: 0.788). The peak ratio between peritumour and intratumour (P = 0.039) was related to the HER2 + cancer subtype (C-index: 0.859), and CDFI (P = 0.002) was associated with the TNBC subtype (C-index: 0.847).

CONCLUSIONS

Multimodal ultrasound features could be powerful predictors of specific breast cancer molecular subtypes. The intra- and peritumour CEUS features play assignable roles in separating luminal B and HER2 + breast cancer subtypes.

Whole-Tumor ADC Texture Analysis Is Able to Predict Breast Cancer Receptor Status

There are different breast cancer molecular subtypes with differences in incidence, treatment response and outcome. They are roughly divided into estrogen and progesterone receptor (ER and PR) negative and positive cancers. In this retrospective study, we included 185 patients augmented with 25 SMOTE patients and divided them into two groups: the training group consisted of 150 patients and the validation cohort consisted of 60 patients. Tumors were manually delineated and whole-volume tumor segmentation was used to extract first-order radiomic features. The ADC-based radiomics model reached an AUC of 0.81 in the training cohort and was confirmed in the validation set, which yielded an AUC of 0.93, in differentiating ER/PR positive from ER/PR negative status. We also tested a combined model using radiomics data together with ki67% proliferation index and histological grade, and obtained a higher AUC of 0.93, which was also confirmed in the validation group. In conclusion, whole-volume ADC texture analysis is able to predict hormonal status in breast cancer masses.

Choice of High-Throughput Proteomics Method Affects Data Integration with Transcriptomics and the Potential Use in Biomarker Discovery

In recent years, several advances have been achieved in breast cancer (BC) classification and treatment. However, overdiagnosis, overtreatment, and recurrent disease are still significant causes of complication and death. Here, we present the development of a protocol aimed at parallel transcriptome and proteome analysis of BC tissue samples using mass spectrometry, via Data Dependent and Independent Acquisitions (DDA and DIA). Protein digestion was semi-automated and performed on flowthroughs after RNA extraction. Data for 116 samples were acquired in DDA and DIA modes and processed using MaxQuant, EncyclopeDIA, or DIA-NN. DIA-NN showed an increased number of identified proteins, reproducibility, and correlation with matching RNA-seq data, therefore representing the best alternative for this setup. Gene Set Enrichment Analysis pointed towards complementary information being found between transcriptomic and proteomic data. A decision tree model, designed to predict the intrinsic subtypes based on differentially abundant proteins across different conditions, selected protein groups that recapitulate important clinical features, such as estrogen receptor status, HER2 status, proliferation, and aggressiveness. Taken together, our results indicate that the proposed protocol performed well for the application. Additionally, the relevance of the selected proteins points to the possibility of using such data as a biomarker discovery tool for personalized medicine.

Noninvasive assessment of breast cancer molecular subtypes on multiparametric MRI using convolutional neural network with transfer learning

BACKGROUND

To evaluate the performances of multiparametric MRI-based convolutional neural networks (CNNs) for the preoperative assessment of breast cancer molecular subtypes.

METHODS

A total of 136 patients with 136 pathologically confirmed invasive breast cancers were randomly divided into training, validation, and testing sets in this retrospective study. The CNN models were established based on contrast-enhanced T₁ -weighted imaging (T₁ C), Apparent diffusion coefficient (ADC), and T₂ -weighted imaging (T₂ W) using the training and validation sets. The performances of CNN models were evaluated on the testing set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the performance.

RESULTS

For the separation of each subtype from other subtypes on the testing set, the T₁ C-based models yielded AUCs from 0.762 to 0.920; the ADC-based models yielded AUCs from 0.686 to 0.851; and the T₂ W-based models achieved AUCs from 0.639 to 0.697.

CONCLUSION

T₁ C-based models performed better than ADC-based models and T₂ W-based models in assessing the breast cancer molecular subtypes. The discriminating performances of our CNN models for triple negative and human epidermal growth factor receptor 2-enriched subtypes were better than that of luminal A and luminal B subtypes.

Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions

Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with -omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).

Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning

Background

Most studies of molecular subtype prediction in breast cancer were mainly based on two-dimensional MRI images, the predictive value of three-dimensional volumetric features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting breast cancer molecular subtypes has not been thoroughly investigated. This study aimed to look into the role of features derived from DCE-MRI and how they could be combined with clinical data to predict invasive ductal breast cancer molecular subtypes.

Methods

From January 2019 to December 2021, 190 Chinese women with invasive ductal breast cancer were studied (32 triple-negative, 59 HER2-enriched, and 99 luminal lesions) in this institutional review board-approved retrospective cohort study. The image processing software extracted 1130 quantitative radiomic features from the segmented lesion area, including shape-based, first-order statistical, texture, and wavelet features. Three binary classifications of the subtypes were performed: triple-negative vs. non-triple-negative, HER2-overexpressed vs. non-HER2-overexpressed, and luminal (A + B) vs. non-luminal. For the classification, five machine learning methods (random forest, logistic regression, support vector machine, naïve Bayes, and eXtreme Gradient Boosting) were employed. The classifiers were chosen using the least absolute shrinkage and selection operator method. The area evaluated classification performance under the receiver operating characteristic curve, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean.

Results

EXtreme Gradient Boosting model showed the best performance in luminal and non-luminal groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8282, 0.7524, 0.6542, 0.6964, 0.6086, 0.3458, 0.8524 and 0.7016, respectively. Meanwhile, the random forest model showed the best performance in HER2-overexpressed and non-HER2-overexpressed groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.8054, 0.2941, 0.9744, 0.7679, 0.4348, 0.0256, 0.8333 and 0.5353, respectively. Furthermore, eXtreme Gradient Boosting model showed the best performance in the triple-negative and non-triple-negative groups, with AUC, sensitivity, specificity, accuracy, F1-Score, false positive rate, precision, and geometric mean of 0.9031, 0.9362, 0.4444, 0.8571, 0.9167, 0.5556, 0.8980 and 0.6450.

Conclusion

Clinical data and three-dimension imaging features from DCE-MRI were identified as potential biomarkers for distinguishing between three molecular subtypes of invasive ductal carcinomas breast cancer. In the future, more extensive studies will be required to evaluate the findings.

Correlation between tumor to liver SUV ratio and molecular subtypes of invasive breast carcinoma in PET CT

Background

Breast cancer is known to be one of the most cancer affecting women around the globe and the second most common cancer in general. In third worlds countries, breast cancer is the most cause of cancer death. Early diagnosis and accurate follow-up of these patients affect the management. There are multiple prognostic factors most important one is the immunohistochemical molecular markers in the specimens including human epidermal growth factor, progesterone, and estrogen receptors (HER2, PR, ER). In breast cancer, the HER2 positive molecular subtype is associated with a bad prognosis and aggressive histological features, yet while following neoadjuvant chemotherapy, it achieves an increased pathological complete response rate. 18F-fluoro-2-deoxy-d-glucose positron emission tomography (FDG PET) has proved to be an effective and accurate imaging technique for lymph node and distant metastasis assessment, tumor staging, restaging of recurrence, treatment response, and follow-up. In breast cancer, tumor molecular subtype, tumor size, proliferation index, and histological grade correlated with 18F-fluoro-2-deoxy-d-glucose (FDG) uptake. This study evaluates the possible correlation between tumor to liver and tumor to spleen (standardized uptake value) SUV max ratio and the four different molecular subtypes in patients with pathologically proven primary breast cancer.

Results

Tumor to liver and tumor to spleen SUV max ratio (TLR, TSR) was a significant parameter for HER2 molecular subtype identification (P value = 0.0005 and 0.014 respectively) and luminal A molecular subtype identification (P value = 0.016 and 0.037 respectively). The specificity, sensitivity, and area under the receiver operating-characteristic curve (AUC) of TLR parameters for HER2-positive subtype identification were 89.4%, 83.3%, and 0.89, respectively. The specificity, sensitivity, and AUC of the TSR parameter for HER2-positive subtype identification were 57.9%, 100%, and 0.83, respectively.

Conclusions

TLR and TSR appeared to be valuable for HER2- and luminal A molecular subtype detection. thus, 18F-FDG PET/CT could be a beneficial tool for prediction of tumor biological characteristics that help in management of breast cancer patients.

LLL12B, a Novel Small-Molecule STAT3 Inhibitor, Induces Apoptosis and Suppresses Cell Migration and Tumor Growth in Triple-Negative Breast Cancer Cells

Persistent STAT3 signaling plays a pivotal role in human tumor malignancy, including triple-negative breast cancer (TNBC). There are few treatment options currently available for TNBC; thus, given its importance to cancer, STAT3 is a potential cancer therapeutic target and is the focus of drug discovery efforts. In this study, we tested a novel orally bioavailable small-molecule STAT3 inhibitor, LLL12B, in human MDA-MB-231, SUM159, and murine 4T1 TNBC cell lines. TNBC cells frequently expressed persistent STAT3 phosphorylation and their cell viability was sensitive to STAT3 knockdown by siRNA. LLL12B selectively inhibited the IL-6-mediated induction of STAT3 phosphorylation, but had little effect on the IFN-γ-mediated induction of STAT1 phosphorylation nor the EGF-mediated induction of ERK phosphorylation. In addition, targeting STAT3 with LLL12B induced apoptosis, reduced colony formation ability, and inhibited cell migration in TNBC cells. Furthermore, LLL12B suppressed the tumor growth of the MDA-MB-231 TNBC cells in a mammary fat pad mouse tumor model in vivo. Together, our findings support the concept that targeting persistent STAT3 signaling using the novel small-molecule LLL12B is a potential approach for TNBC therapy.

Expression of SOX10 in Triple-Negative Breast Carcinoma in Pakistan

Background

The term triple-negative breast cancer (TNBC) refers to a particular class of aggressive, poorly differentiated neoplasms that show the absence of estrogen (ER), progesterone (PR), and human epidermal growth factor receptor 2 (HER2) antibodies. SOX10 (SRY-related HMG-box 10) is a nuclear transcription factor that is commonly used to identify cancers of neural origin, but it has recently been linked to TNBC. The purpose of this study is to determine SOX10 expression in TNBC, its association with tumor grade for molecular categorization, and to determine the diagnostic significance of SOX10 in TNBC at the metastatic site in the case of an unknown primary.

Methodology

SOX10 was used to stain a tissue microarray of 100 patients. According to the tumor grade, SOX10 staining was classified as negative (<1%), patchy (1-10%), focal (10-70%), and diffuse (70-100%). SPSS version 22 (IBM Corp., Armonk, NY, USA) software was used to conduct the statistical analysis.

Results

The expression of SOX10 regarding positivity and intensity was higher in high-grade tumors than in intermediate-grade tumors (p = 0.001 and p = 0.007, respectively).

Conclusions

SOX10 is a reliable novel marker for the diagnosis of TNBC and has diagnostic utility in the unknown primary at the metastatic site.

Correlation of dynamic contrast-enhanced MRI and diffusion-weighted MR imaging with prognostic factors and subtypes of breast cancers

Objective

To determine the preoperative magnetic resonance imaging (MRI) findings of breast cancer on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DWI) in different molecular subtypes.

Materials and methods

A retrospective study was conducted on 116 breast cancer subjects who underwent preoperative MRI and surgery or biopsy. Three radiologists retrospectively assessed the morphological and kinetic characteristics on DCE-MRI and tumor detectability on DWI, by using apparent diffusion coefficient (ADC) values of lesions. The clinicopathologic and MRI features of four subtypes were compared. The correlation between clinical and MRI findings with molecular subtypes was evaluated using the chi-square and ANOVA tests, while the Mann–Whitney test was used to analyze the relationship between ADC and prognostic factors.

Results

One hundred and sixteen women diagnosed with breast cancer confirmed by surgery or biopsy had the following subtypes of breast cancer: luminal A (27, 23.3%), luminal B (56, 48.2%), HER2 positive (14, 12.1%), and triple-negative breast cancer (TNBC) (19, 16.4%), respectively. Among the subtypes, significant differences were found in axillary node metastasis, histological grade, tumor shape, rim enhancement, margin, lesion type, intratumoral T2 signal intensity, Ki-67 index, and paratumoral enhancement (p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p < 0.001, and p = 0.02, respectively). On DWI, the mean ADC value of TNBC (0.910 × 10⁻³ mm²/s) was the lowest compared to luminal A (1.477×10⁻³ mm²/s), luminal B (0.955 × 10⁻³ mm²/s), and HER2 positive (0.996 × 10⁻³ mm²/s) (p < 0.001). Analysis of the correlation between different prognostic factors and ADC value showed that only axillary lymph node status and ADC value had a statistically significant difference (p = 0.009).

Conclusion

The morphologic features of MRI can be used as imaging biomarkers to identify the molecular subtypes of breast cancer. In addition, quantitative assessments of ADC values on DWI may also provide biological clues about molecular subtypes.

A deep learning approach with subregion partition in MRI image analysis for metastatic brain tumor

Purpose

To propose a deep learning network with subregion partition for predicting metastatic origins and EGFR/HER2 status in patients with brain metastasis.

Methods

We retrospectively enrolled 140 patients with clinico-pathologically confirmed brain metastasis originated from primary NSCLC (n = 60), breast cancer (BC, n = 60) and other tumor types (n = 20). All patients underwent contrast-enhanced brain MRI scans. The brain metastasis was subdivided into phenotypically consistent subregions using patient-level and population-level clustering. A residual network with a global average pooling layer (RN-GAP) was proposed to calculate deep learning-based features. Features from each subregion were selected with least absolute shrinkage and selection operator (LASSO) to build logistic regression models (LRs) for predicting primary tumor types (LR-NSCLC for the NSCLC origin and LR-BC for the BC origin), EGFR mutation status (LR-EGFR) and HER2 status (LR-HER2).

Results

The brain metastasis can be partitioned into a marginal subregion (S1) and an inner subregion (S2) in the MRI image. The developed models showed good predictive performance in the training (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.860 vs. 0.909 vs. 0.850 vs. 0.900) and validation (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.819 vs. 0.872 vs. 0.750 vs. 0.830) set.

Conclusion

Our proposed deep learning network with subregion partitions can accurately predict metastatic origins and EGFR/HER2 status of brain metastasis, and hence may have the potential to be non-invasive and preoperative new markers for guiding personalized treatment plans in patients with brain metastasis.

Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer

OBJECTIVES

To develop and validate an ultrasound elastography radiomics nomogram for preoperative evaluation of the axillary lymph node (ALN) burden in early-stage breast cancer.

METHODS

Data of 303 patients from hospital #1 (training cohort) and 130 cases from hospital #2 (external validation cohort) between Jun 2016 and May 2019 were enrolled. Radiomics features were extracted from shear-wave elastography (SWE) and corresponding B-mode ultrasound (BMUS) images. The minimum redundancy maximum relevance and least absolute shrinkage and selection operator algorithms were used to select ALN status-related features. Proportional odds ordinal logistic regression was performed using the radiomics signature together with clinical data, and an ordinal nomogram was subsequently developed. We evaluated its performance using C-index and calibration.

RESULTS

SWE signature, US-reported LN status, and molecular subtype were independent risk factors associated with ALN status. The nomogram based on these variables showed good discrimination in the training (overall C-index: 0.842; 95%CI, 0.773-0.879) and the validation set (overall C-index: 0.822; 95%CI, 0.765-0.838). For discriminating between disease-free axilla (N0) and any axillary metastasis (N + (≥ 1)), it achieved a C-index of 0.845 (95%CI, 0.777-0.914) for the training cohort and 0.817 (95%CI, 0.769-0.865) for the validation cohort. The tool could also discriminate between low (N + (1-2)) and heavy metastatic ALN burden (N + (≥ 3)), with a C-index of 0.827 (95%CI, 0.742-0.913) in the training cohort and 0.810 (95%CI, 0.755-0.864) in the validation cohort.

CONCLUSION

The radiomics model shows favourable predictive ability for ALN staging in patients with early-stage breast cancer, which could provide incremental information for decision-making.

KEY POINTS

• Radiomics analysis helps radiologists to evaluate the axillary lymph node status of breast cancer with accuracy. • This multicentre retrospective study showed that radiomics nomogram based on shear-wave elastography provides incremental information for risk stratification. • Treatment can be given with more precision based on the model.

Intramammary edema of invasive breast cancers on MRI T2-weighted fat suppression sequence: Correlation with molecular subtypes and clinical-pathologic prognostic factors

OBJECTIVE

To investigate the associations between different patterns of intramammary edema on preoperative 3.0 T magnetic resonance imaging (MRI) T₂-weighted fat suppression sequence and invasive breast cancer molecular subtypes and clinical-pathologic prognostic factors.

METHODS

Between May 2014 and December 2020, 191 patients with invasive breast cancer who had undergone preoperative MRI and mastectomy or breast-conserving surgery were retrospectively enrolled. The relationships between different patterns of intramammary edema and invasive breast cancer molecular subtypes and clinical-pathologic features were evaluated using the Student's t-test or Mann-Whitney U test and the χ2 test or Fisher's exact test.

RESULTS

Patients with luminal B (HER2 positive), HER2-enriched and triple negative breast cancers respectively had different patterns of intramammary edema (P < 0.001). There was a significant association between intramammary edema and clinical-pathologic factors, including larger tumor size, higher Ki-67 index, lymph node metastasis and lymphovascular invasion (all P < 0.001).

CONCLUSIONS

Intramammary edema may provide added values of predicting molecular subtypes and clinical-pathologic prognosis, enhancing the ability to individualize the treatment of patients with invasive breast cancer.

Magnetic Resonance Imaging Phenotypes of Breast Cancer Molecular Subtypes: A Systematic Review

OBJECTIVE

Magnetic resonance imaging (MRI) is the most sensitive imaging modality in detecting breast cancer. The purpose of this systematic review is to investigate the role of human extracted MRI phenotypes in classifying molecular subtypes of breast cancer.

METHODS

We performed a literature search of published articles on the application of MRI phenotypic features in invasive breast cancer molecular subtype classifications by radiologists' interpretation on Medline Complete, Pubmed, and Google scholar from 1st January 2000 to 31st March 2021. Of the 1453 literature identified, 42 fulfilled the inclusion criteria.

RESULTS

All studies were case-controlled, retrospective study and research-based. The majority of the studies assessed the MRI features using American College of Radiology- Breast Imaging Reporting and Data System (ACR-BIRADS) classification and using dynamic contrast-enhanced (DCE) kinetic features, Apparent Diffusion Coefficient (ADC) values, and T2 sequence. Most studies divided invasive breast cancer into 4 main subtypes, luminal A, luminal B, HER2, and triple-negative (TN) cancers, and used 2 readers. We present a summary of the radiologists' extracted breast MRI phenotypical features and their correlating breast cancer subtypes classifications. The characteristic features are morphology, enhancement kinetics, and T2 signal intensity. We found that the TN subtype has the most distinctive MRI features compared to the other subtypes and luminal A and B have many similar features.

CONCLUSION

The MRI features which are predictive of each subtype are the morphology, internal enhancement features, and T2 signal intensity, predominantly between TN and the rest. Radiologists' visual interpretation of some of MRI features may offer insight into the respective invasive breast cancer molecular subtype. However, current evidence are still limited to "suggestive" features instead of a diagnostic standard.  Further research is recommended to explore this potential application, for example, by augmentation of radiologists' visual interpretation by artificial intelligence.

Bioinformatics Approach for Data Capturing: The Case of Breast Cancer

Background:

With the rapid evolution in advanced computer systems and various statistical algorithms, it is now a days possible to analyze complex biological data. Bioinformatics is an interface between computational and biological assemblies. It is applied in various fields of biological as well as medical sciences.

Aim:

The manuscript aims to summarize the developments in the field of breast cancer research through the applications of bioinformatics.

Methods:

Various search engines like google, science direct, Scopus, PubMed, etc., were used for the literature survey.

Results:

It describes the bioinformatics analysis tools and models, which include mainly artificial neural network models.

Conclusion:

Bioinformatics is the evolutionary approach that is used for the capturing of data from the various case studies related to breast cancer.

Intra- and peritumoral radiomics on assessment of breast cancer molecular subtypes based on mammography and MRI

PURPOSE

This study aimed to investigate the efficacy of digital mammography (DM), digital breast tomosynthesis (DBT), diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI separately and combined in the prediction of molecular subtypes of breast cancer.

METHODS

A total of 241 patients were enrolled and underwent breast MD, DBT, DW and DCE scans. Radiomics features were calculated from intra- and peritumoral regions, and selected with least absolute shrinkage and selection operator (LASSO) regression to develop radiomics signatures (RSs). Prediction performance of intra- and peritumoral regions in the four modalities were evaluated and compared with area under the receiver-operating characteristic (ROC) curve (AUC), specificity and sensitivity as comparison metrics.

RESULTS

The RSs derived from combined intra- and peritumoral regions improved prediction AUCs compared with those from intra- or peritumoral regions alone. DM plus DBT generated better AUCs than the DW plus DCE on predicting Luminal A and Luminal B in the training (Luminal A: 0.859 and 0.805; Luminal B: 0.773 and 0.747) and validation (Luminal A: 0.906 and 0.853; Luminal B: 0.807 and 0.784) cohort. For the prediction of HER2-enriched and TN, the DW plus DCE yielded better AUCs than the DM plus DBT in the training (HER2-enriched: 0.954 and 0.857; TN: 0.877 and 0.802) and validation (HER2-enriched: 0.974 and 0.907; TN: 0.938 and 0.874) cohort.

CONCLUSIONS

Peritumoral regions can provide complementary information to intratumoral regions for the prediction of molecular subtypes. Compared with MRI, the mammography showed higher AUCs for the prediction of Luminal A and B, but lower AUCs for the prediction of HER2-enriched and TN.

Significant Diagnostic and Prognostic Value of FLAD1 and Related MicroRNAs in Breast Cancer after a Pan-Cancer Analysis

The identification of biomarkers plays an important role in the diagnosis and prognosis of cancers. In this study, we explored the diagnostic and prognostic value of the FLAD1 expression across pan-cancer analysis from online databases (Oncomine, cBioPortal, Breast Cancer Gene-Expression Miner, UALCAN, GEO, BCIP, TNMplot, ENCORI, Kaplan-Meier Plotter, and LinkedOmics). We found that FLAD1 was overexpressed in a number of different kinds of cancers, especially in breast cancer, and higher FLAD1 expression level was associated with the HER+, p53 mutant, node-involved, NPI stage 3, basal-like, and triple-negative groups compared with the other subgroups of breast cancer. The FLAD1 expression levels were higher in patients that were 21–40 years old than those in patients of other ages and were higher in the African-American group than in the Caucasian group. We also analyzed the FLAD1-related microRNAs and their prognostic values in breast cancer. This study highlights the significance of FLAD1 in cancers and provides evidence for its potential as a biomarker for the diagnosis and prognosis of cancers.

Serum N-glycan profiles differ for various breast cancer subtypes

Breast cancer is the most prevalent cancer in women. Early detection of this disease improves survival and therefore population screenings, based on mammography, are performed. However, the sensitivity of this screening modality is not optimal and new screening methods, such as blood tests, are being explored. Most of the analyses that aim for early detection focus on proteins in the bloodstream. In this study, the biomarker potential of total serum N-glycosylation analysis was explored with regard to detection of breast cancer. In an age-matched case-control setup serum protein N-glycan profiles from 145 breast cancer patients were compared to those from 171 healthy individuals. N-glycans were enzymatically released, chemically derivatized to preserve linkage-specificity of sialic acids and characterized by high resolution mass spectrometry. Logistic regression analysis was used to evaluate associations of specific N-glycan structures as well as N-glycosylation traits with breast cancer. In a case-control comparison three associations were found, namely a lower level of a two triantennary glycans and a higher level of one tetraantennary glycan in cancer patients. Of note, various other N-glycomic signatures that had previously been reported were not replicated in the current cohort. It was further evaluated whether the lack of replication of breast cancer N-glycomic signatures could be partly explained by the heterogenous character of the disease since the studies performed so far were based on cohorts that included diverging subtypes in different numbers. It was found that serum N-glycan profiles differed for the various cancer subtypes that were analyzed in this study.

Molecular markers, pathology, and ultrasound features of invasive breast cancer

INTRODUCTION

Ultrasound is commonly used in breast cancer screening and diagnosis. The use of ultrasound features to predict the subtypes of invasive breast cancer is of great clinical significance, since it facilitates a fast and early diagnosis and treatment. The correlation between breast lesion ultrasound features and the breast cancer subtypes requires further investigation.

METHODS

388 patients with invasive breast cancer were retrospectively analyzed by two sonographers. The tumor size, shape, margin, echogenicity, echotexture, posterior echo attenuation microcalcification, and blood vessel density were recorded. The correlation between the tumor ER, PR, HER2, and Ki67 status, the molecular subtypes, and the ultrasound features was analyzed using the chi-square test, Fisher's exact test, and multiple logistic regression.

RESULTS

ER and PR positivity were correlated with a low histologic grade, lymph node metastasis, and smaller-sized tumors. A hyperechoic or a mixed echogenicity was rare in the tumors of all groups but was enriched in the ER and PR tumors (9.57% and 7.64%, respectively, p < 0.01). A high percentage of posterior echo attenuation was found in the Ki67 low (53.94%) and ER+ (51.28%) tumors. Furthermore, heterogeneous and microcalcifications were enriched in HER2-positive tumors. In terms of the molecular subtypes, the luminal A subtype group had the lowest lymph node positivity and the smallest primary tumor size. The luminal B subtype had the lowest percentage of hyperechoic or mixed tumors. The HER2 subtype was positively correlated with microcalcification. Finally, TNBC showed the highest percentage of hyperechoic or mixed tumors and the lowest percentage of posterior echo attenuation and microcalcification.

CONCLUSION

Tumor pathologic and ultrasound features were correlated with invasive breast tumor molecular marker positivity and its molecular subtypes.

Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes

Purpose

To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping.

Procedures

In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, 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 applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard.

Results

For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM).

Conclusions

Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.

ANKLE1 as New Hotspot Mutation for Breast Cancer in Indian Population and Has a Role in DNA Damage and Repair in Mammalian Cells

Breast cancer has replaced cervical cancer as being the most common and having the highest mortality among women in India. ANKLE gene is conserved among organisms during evolutionary succession and is a member of LEM family proteins in lower metazoans and is involved in critical functions in the nuclear architecture, gene expression and cell signaling. ANKLE1 is the human orthologous of LEM-3 and is involved in DNA damage response and DNA repair. Whole Exome Sequencing (WES) of paired breast cancer samples was performed and ANKLE1 was found to be a new possible hotspot for predisposition of breast cancer. The mass array genotyping for breast cancer variant rs2363956 further confirmed the ANKLE1 association with the studied population of breast cancer. To elucidate the role of ANKLE1 in DNA damage, it was knocked down in MCF-7 breast cancer cell line and the expression of γH2AX was assessed. ANKLE1 knockdown cells displayed elevated levels of γ-H2AX foci in response to the cisplatin induced replication stress. The localization pattern of ANKLE1 further emphasized the role of ANKLE1 in DNA repair process. We observed that ANKLE1 is required for maintaining genomic stability and plays a role in DNA damage and repair process. These findings provided a molecular basis for the suspected role of ANKLE1 in human breast cancer and suggested an important role of this gene in controlling breast cancer development among women in India.

High-throughput proteomics of breast cancer interstitial fluid: identification of tumor subtype-specific serologically relevant biomarkers

Despite significant advancements in breast cancer (BC) research, clinicians lack robust serological protein markers for accurate diagnostics and tumor stratification. Tumor interstitial fluid (TIF) accumulates aberrantly externalized proteins within the local tumor space, which can potentially gain access to the circulatory system. As such, TIF may represent a valuable starting point for identifying relevant tumor-specific serological biomarkers. The aim of the study was to perform comprehensive proteomic profiling of TIF to identify proteins associated with BC tumor status and subtype. A liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of 35 TIFs of three main subtypes: luminal (19), Her2 (4), and triple-negative (TNBC) (12) resulted in the identification of > 8800 proteins. Unsupervised hierarchical clustering segregated the TIF proteome into two major clusters, luminal and TNBC/Her2 subgroups. High-grade tumors enriched with tumor infiltrating lymphocytes (TILs) were also stratified from low-grade tumors. A consensus analysis approach, including differential abundance analysis, selection operator regression, and random forest returned a minimal set of 24 proteins associated with BC subtypes, receptor status, and TIL scoring. Among them, a panel of 10 proteins, AGR3, BCAM, CELSR1, MIEN1, NAT1, PIP4K2B, SEC23B, THTPA, TMEM51, and ULBP2, was found to stratify the tumor subtype-specific TIFs. In particular, upregulation of BCAM and CELSR1 differentiates luminal subtypes, while upregulation of MIEN1 differentiates Her2 subtypes. Immunohistochemistry analysis showed a direct correlation between protein abundance in TIFs and intratumor expression levels for all 10 proteins. Sensitivity and specificity were estimated for this protein panel by using an independent, comprehensive breast tumor proteome dataset. The results of this analysis strongly support our data, with eight of the proteins potentially representing biomarkers for stratification of BC subtypes. Five of the most representative proteomics databases currently available were also used to estimate the potential for these selected proteins to serve as putative serological markers.

Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis

We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.

Precise discrimination of Luminal A breast cancer subtype using an aptamer in vitro and in vivo

Precise discrimination of breast cancer remains a challenge in clinical medicine, which depends on the development of novel specific molecular probes. However, the current technologies and antibodies cannot achieve precise discrimination of breast cancer subtypes very well. To address this problem, a novel truncated DNA aptamer MF3Ec was developed in this work. Aptamer MF3Ec exhibited high specificity and binding affinity against MCF-7 breast cancer cells with a K_d value of 18.95 ± 2.9 nM which is four times lower than that of the original aptamer, and could work at 4 °C, 25 °C and 37 °C with no obvious differences. Besides, aptamer MF3Ec displayed better stability in serum samples with a long existence time of about 12 h. Moreover, fluorescence imaging experiments indicated that aptamer MF3Ec was able to distinguish MCF-7 breast cancer cells from SK-BR-3, MDA-MB-231 and MCF-10A breast cancer cell subtypes, and differentiate the tumor-bearing mice and xenografted tissue sections of MCF-7 breast cancer cells from those of MDA-MB-231 and SK-BR-3 breast cancer cells in vivo and in vitro, respectively. Finally, clinical experiments indicated that aptamer MF3Ec could distinguish Luminal A breast cancer subtype from Luminal B (HER2+), HER2-enriched, and triple-negative breast cancer subtypes, para-carcinoma tissues and normal breast tissues. Collectively, all these results suggest that aptamer MF3Ec is a promising probe for precise discrimination and targeted therapy of Luminal A breast cancer molecular subtype.

Distant metastasis prediction via a multi-feature fusion model in breast cancer

This study aimed to develop a model that fused multiple features (multi-feature fusion model) for predicting metachronous distant metastasis (DM) in breast cancer (BC) based on clinicopathological characteristics and magnetic resonance imaging (MRI). A nomogram based on clinicopathological features (clinicopathological-feature model) and a nomogram based on the multi-feature fusion model were constructed based on BC patients with DM (n=67) and matched patients (n=134) without DM. DM was diagnosed on average (17.31±13.12) months after diagnosis. The clinicopathological-feature model included seven features: reproductive history, lymph node metastasis, estrogen receptor status, progesterone receptor status, CA153, CEA, and endocrine therapy. The multi-feature fusion model included the same features and an additional three MRI features (multiple masses, fat-saturated T2WI signal, and mass size). The multi-feature fusion model was relatively better at predicting DM. The sensitivity, specificity, diagnostic accuracy and AUC of the multi-feature fusion model were 0.746 (95% CI: 0.623-0.841), 0.806 (0.727-0.867), 0.786 (0.723-0.841), and 0.854 (0.798-0.911), respectively. Both internal and external validations suggested good generalizability of the multi-feature fusion model to the clinic. The incorporation of MRI factors significantly improved the specificity and sensitivity of the nomogram. The constructed multi-feature fusion nomogram may guide DM screening and the implementation of prophylactic treatment for BC.

Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry

Accurate classification of breast tumors is vital for patient management decisions and enables more precise cancer treatment. Here, we present a quantitative proteotyping approach based on sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry and establish key proteins for breast tumor classification. The study is based on 96 tissue samples representing five conventional breast cancer subtypes. SWATH proteotype patterns largely recapitulate these subtypes; however, they also reveal varying heterogeneity within the conventional subtypes, with triple negative tumors being the most heterogeneous. Proteins that contribute most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2 and are associated with estrogen receptor (ER) status, tumor grade status, and HER2 status. Although these three key proteins exhibit high levels of correlation with transcript levels (R > 0.67), general correlation did not exceed R = 0.29, indicating the value of protein-level measurements of disease-regulated genes. Overall, this study highlights how cancer tissue proteotyping can lead to more accurate patient stratification.

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Proteotyping of 96 breast tumors by SWATH mass spectrometry

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Three key proteins for breast tumor classification

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Varying degrees of heterogeneity within conventional breast cancer subtypes

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Generally modest correlation between protein and transcript levels in tumor tissue

Screening detects a myriad of breast disease - refining practice will increase effectiveness and reduce harm

For many individuals, the term 'cancer' equates to a disease that if untreated will progress, spread from the area initially affected and ultimately cause death. 'Breast cancer', however, is a diverse of range of pathological entities, incorporating indolent to fast-growing and aggressive lesions, with varying histological patterns, clinical presentations, treatment responses and outcomes. Screening for malignancy is based on the assumption that cancer has a gradual, orderly progression and that detecting lesions earlier in their natural history, and intervening, will reduce mortality. The natural history of epithelial atypia, ductal carcinoma in situ and even invasive breast cancer is poorly understood, but widely variable. We believe that population breast screening methodology needs to change to focus on diagnosis of lesions of greatest clinical relevance.

Non-Invasive Assessment of Hypoxia and Neovascularization with MRI for Identification of Aggressive Breast Cancer

The aim of this study was to investigate the potential of magnetic resonance imaging (MRI) for a non-invasive synergistic assessment of tumor microenvironment (TME) hypoxia and induced neovascularization for the identification of aggressive breast cancer. Fifty-three female patients with breast cancer underwent multiparametric breast MRI including quantitative blood-oxygen-level-dependent (qBOLD) imaging for hypoxia and vascular architecture mapping for neovascularization. Quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO2), mitochondrial oxygen tension (mitoPO2), microvessel radius (VSI), microvessel density (MVD), and microvessel type indicator (MTI) were calculated. Histopathology was the standard of reference. Histopathological markers (vascular endothelial growth factor receptor 1 (FLT1), podoplanin, hypoxia-inducible factor 1-alpha (HIF-1alpha), carbonic anhydrase 9 (CA IX), vascular endothelial growth factor C (VEGF-C)) were used to confirm imaging biomarker findings. Univariate and multivariate regression analyses were performed to differentiate less aggressive luminal from aggressive non-luminal (HER2-positive, triple negative) malignancies and assess the interplay between hypoxia and neoangiogenesis markers. Aggressive non-luminal cancers (n = 40) presented with significantly higher MRO2 (i.e., oxygen consumption), lower mitoPO2 values (i.e., hypoxia), lower MTI, and higher MVD than less aggressive cancers (n = 13). Data suggest that a model derived from OEF, mitoPO2, and MVD can predict tumor proliferation rate. This novel MRI approach, which can be easily implemented in routine breast MRI exams, aids in the non-invasive identification of aggressive breast cancer.

Performance of preoperative breast MRI based on breast cancer molecular subtype

PURPOSE

To assess the performance of preoperative breast MRI biopsy recommendations based on breast cancer molecular subtype.

METHODS

All preoperative breast MRIs at a single academic medical center from May 2010 to March 2014 were identified. Reports were reviewed for biopsy recommendations. All pathology reports were reviewed to determine biopsy recommendation outcomes. Molecular subtypes were defined as Luminal A (ER/PR+ and HER2-), Luminal B (ER/PR+ and HER2+), HER2 (ER-, PR- and HER2+), and Basal (ER-, PR-, and HER2-). Logistic regression assessed the probability of true positive versus false positive biopsy and mastectomy versus lumpectomy.

RESULTS

There were 383 patients included with a molecular subtype distribution of 253 Luminal A, 44 Luminal B, 20 HER2, and 66 Basal. Two hundred and thirteen (56%) patients and 319 sites were recommended for biopsy. Molecular subtype did not influence the recommendation for biopsy (p = 0.69) or the number of biopsy site recommendations (p = 0.30). The positive predictive value for a biopsy recommendation was 42% overall and 46% for Luminal A, 43% for Luminal B, 36% for HER2, and 29% for Basal subtype cancers. The multivariate logistic regression model showed no difference in true positive biopsy rate based on molecular subtype (p = 0.78). Fifty-one percent of patients underwent mastectomy and the multivariate model demonstrated that only a true positive biopsy (odds ratio: 5.3) was associated with higher mastectomy rates.

CONCLUSION

Breast cancer molecular subtype did not influence biopsy recommendations, positive predictive values, or surgical approaches. Only true positive biopsies increased the mastectomy rate.

Development of a Non-invasive Assessment of Hypoxia and Neovascularization with Magnetic Resonance Imaging in Benign and Malignant Breast Tumors: Initial Results

PURPOSE

To develop a novel magnetic resonance imaging (MRI) approach for the noninvasive assessment of hypoxia and neovascularization in breast tumors.

PROCEDURES

In this IRB-approved prospective study, 20 patients with suspicious breast lesions (BI-RADS 4/5) underwent multiparametric breast MRI including quantitative BOLD (qBOLD) and vascular architecture mapping (VAM). Custom-made in-house MatLab software was used for qBOLD and VAM data postprocessing and calculation of quantitative MRI biomarker maps of oxygen extraction fraction (OEF), metabolic rate of oxygen (MRO₂), and mitochondrial oxygen tension (mitoPO₂) to measure tissue hypoxia and neovascularization including vascular architecture including microvessel radius (VSI), density (MVD), and type (MTI). Histopathology was used as standard of reference. Appropriate statistics were performed to assess and compare correlations between MRI biomarkers for hypoxia and neovascularization.

RESULTS

qBOLD and VAM data with good quality were obtained from all patients with 13 invasive ductal carcinoma (IDC) and 7 benign breast tumors with a lesion diameter of at least 10 mm in all spatial directions. MRI biomarker maps of oxygen metabolism and neovascularization demonstrated intratumoral spatial heterogeneity with a broad range of biomarker values. Bulk tumor neovasculature consisted of draining venous microvasculature with slow flowing blood. High OEF and low mitoPO₂ were associated with low MVD and vice versa. The heterogeneous pattern of MRO₂ values showed spatial congruence with VSI. IDCs showed significantly higher MRO₂ (P = 0.007), lower mitoPO₂ (P = 0.021), higher MVD (P = 0.005), and lower (i.e., more pathologic) MTI (P = 0.001) compared with benign breast tumors. These results indicate that IDCs consume more oxygen and are more hypoxic and neovascularized than benign tumors.

CONCLUSIONS

We developed a novel MRI approach for the noninvasive assessment of hypoxia and neovascularization in benign and malignant breast tumors that can be easily integrated in a diagnostic MRI protocol and provides insight into intratumoral heterogeneity.

The association between MRI findings and breast cancer subtypes: focused on the combination patterns on diffusion-weighted and T2-weighted images

PURPOSE

To assess morphology on diffusion-weighted imaging (DWI) and intratumoral signal intensity (SI) on T2-weighted images (T2WI) of breast carcinomas, and to evaluate the association between the combined DWI and T2WI findings and breast cancer subtypes.

METHODS

Two hundred and eighty breast cancer patients who underwent breast MRI prior to therapy were included in this retrospective study. All had invasive carcinomas, which were classified into five subtypes: Luminal A-like (n = 149), Luminal B-like (n = 63), Hormone receptor-positive HER2 (n = 31), Hormone receptor-negative HER2 (n = 13), or Triple-negative (TN) (n = 24). Based on the morphology on DWI, the tumors were classified into two patterns: DWI-homogeneous or DWI-heterogeneous. If DWI-heterogeneous, an assessment of intratumoral SI on T2WI was performed: tumors with intratumoral high/low SI on T2WI were classified as Hete-H/Hete-L, respectively. The associations between (1) the morphological patterns on DWI and the five subtypes, and (2) the intratumoral SI patterns on T2WI and the five subtypes in DWI-heterogeneous were evaluated.

RESULTS

There was a significant association between (1) the morphological patterns on DWI and the five subtypes (p < 0.0001), and (2) the intratumoral SI patterns on T2WI and the five subtypes in DWI-heterogeneous (p < 0.0001). DWI-homogeneous was dominant in Luminal A-like (67.1%), and Hete-H was dominant in TN type (75%). Hete-H, suggesting the presence of intratumoral necrosis, included high proliferative and/or aggressive subtypes more frequently (80%) than Hete-L, suggesting the presence of fibrotic focus. Fibrotic focus was seen more commonly in the luminal subtypes.

CONCLUSION

The combined findings on DWI and T2WI revealed breast carcinomas that were associated with particular subtypes.

Comparing blood versus tissue-based biomarkers expression in breast cancer patients

Molecular markers have been used as a tool for diagnostic approaches, staging, and evaluation of therapeutic responses in patients with cancer. Cancer molecular markers can also help clinicians to make decision on therapy and prognosis evaluation at the time of diagnosis. In the early diagnosis of breast cancer (BC), estrogen and progesterone receptors (ER/PR) expression levels should be determined through immunohistochemistry (IHC). In molecular genetics, there are some important tissue-based markers that can also be found in blood, such as Mammaglobin (MAM), Cytokeratin 19 (CK19), Mucin (MUC), Proto-oncogene (c-Myc), antigen Ki-67 (Ki67), and Carcinoembryonic antigen (CEA). In this study, the positive level of the marker genes in both blood and tissue of the BC patients were compared using reverse transcription polymerase chain reaction (RT-PCR) method. In addition, the importance of blood vs. tissue-based markers in BC diagnosis also demonstrated and is discussed. CEA (O), ERβ, CK19 and, c-Myc molecular markers were significantly different between blood of normal and patients while there was no significant difference of these markers in tissue samples. Blood-based biomarkers can be used for the early diagnosis of BC. Comparing blood versus tissue-based biomarkers indicated that there are correlations between markers in blood and tissue, since blood markers can be substitute to tissue markers in BC patients in the future.

Use of Lectin-based Affinity Techniques in Breast Cancer Glycoproteomics: A Review

Changes in glycoprotein content, altered glycosylations, and aberrant glycan structures are increasingly recognized as cancer hallmarks. Because breast cancer is one of the most common causes of cancer deaths in the world, it is highly urgent to find other reliable biomarkers for its initial diagnosis and to learn as much as possible about this disease. In this Review, the applications of lectins to a screening of potential breast cancer biomarkers published during recent years are overviewed. These data provide a deeper insight into the use of modern strategies, technologies, and scientific knowledge in glycoproteomic breast cancer research. Particular attention is concentrated on the use of lectin-based affinity techniques, applied independently or most frequently in combination with mass spectrometry, as an effective tool for the targeting, separation, and reliable identification of glycoprotein molecules. Individual procedures and lectins used in published glycoproteomic studies of breast-cancer-related glycoproteins are discussed. The summarized approaches have the potential for use in diagnostic and predictive applications. Finally, the use of lectins is briefly discussed from the view of their future applications in the analysis of glycoproteins in cancer.

BRCAness as an Important Prognostic Marker in Patients with Triple-Negative Breast Cancer Treated with Neoadjuvant Chemotherapy: A Multicenter Retrospective Study

Triple-negative breast cancer (TNBC) has several subtypes. The identification of markers associated with recurrence and poor prognosis in patients with TNBC is urgently needed. BRCAness is a set of traits in which BRCA1 dysfunction, arising from gene mutation, methylation, or deletion, results in DNA repair deficiency. In the current study, we evaluated the clinical significance and prognosis of BRCAness in a multicenter retrospective study. Ninety-four patients with TNBC treated with neoadjuvant chemotherapy were enrolled from three university hospitals for this retrospective study. BRCAness was evaluated in 94 core needle biopsy (CNB) specimens prior to neoadjuvant chemotherapy and 49 surgical specimens without pathological complete response (pCR). The samples were assessed using multiplex ligation-dependent probe amplification, and the amplicons were scored. Of the 94 patients, 51 had BRCAness in CNB specimens. There were no significant differences in pCR rates or recurrence between the BRCAness and non-BRCAness groups. Among surgical specimens, the BRCAness group had a significantly shorter recurrence-free survival and overall survival compared with the non-BRCAness group. The BRCAness of surgical specimens was found to be an important marker to predict prognosis in patients with TNBC after neoadjuvant chemotherapy. A clinical trial to assess the clinical impact of carboplatin with BRCAness is planned.

Comparative proteomic analysis of different stages of breast cancer tissues using ultra high performance liquid chromatography tandem mass spectrometer

BACKGROUND

Breast cancer is the fifth most prevalent cause of death among women worldwide. It is also one of the most common types of cancer among Malaysian women. This study aimed to characterize and differentiate the proteomics profiles of different stages of breast cancer and its matched adjacent normal tissues in Malaysian breast cancer patients. Also, this study aimed to construct a pertinent protein pathway involved in each stage of cancer.

METHODS

In total, 80 samples of tumor and matched adjacent normal tissues were collected from breast cancer patients at Seberang Jaya Hospital (SJH) and Kepala Batas Hospital (KBH), both in Penang, Malaysia. The protein expression profiles of breast cancer and normal tissues were mapped by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). The Gel-Eluted Liquid Fractionation Entrapment Electrophoresis (GELFREE) Technology System was used for the separation and fractionation of extracted proteins, which also were analyzed to maximize protein detection. The protein fractions were then analyzed by tandem mass spectrometry (LC-MS/MS) analysis using LC/MS LTQ-Orbitrap Fusion and Elite. This study identified the proteins contained within the tissue samples using de novo sequencing and database matching via PEAKS software. We performed two different pathway analyses, DAVID and STRING, in the sets of proteins from stage 2 and stage 3 breast cancer samples. The lists of molecules were generated by the REACTOME-FI plugin, part of the CYTOSCAPE tool, and linker nodes were added in order to generate a connected network. Then, pathway enrichment was obtained, and a graphical model was created to depict the participation of the input proteins as well as the linker nodes.

RESULTS

This study identified 12 proteins that were detected in stage 2 tumor tissues, and 17 proteins that were detected in stage 3 tumor tissues, related to their normal counterparts. It also identified some proteins that were present in stage 2 but not stage 3 and vice versa. Based on these results, this study clarified unique proteins pathways involved in carcinogenesis within stage 2 and stage 3 breast cancers.

CONCLUSIONS

This study provided some useful insights about the proteins associated with breast cancer carcinogenesis and could establish an important foundation for future cancer-related discoveries using differential proteomics profiling. Beyond protein identification, this study considered the interaction, function, network, signaling pathway, and protein pathway involved in each profile. These results suggest that knowledge of protein expression, especially in stage 2 and stage 3 breast cancer, can provide important clues that may enable the discovery of novel biomarkers in carcinogenesis.

Breast Cancer Radiogenomics: Current Status and Future Directions

Radiogenomics is an area of research that aims to identify associations between imaging phenotypes ("radio-") and tumor genome ("-genomics"). Breast cancer radiogenomics research in particular has been an especially prolific area of investigation in recent years as evidenced by the wide number and variety of publications and conferences presentations. To date, research has primarily been focused on dynamic contrast enhanced pre-operative breast MRI and breast cancer molecular subtypes, but investigations have extended to all breast imaging modalities as well as multiple additional genetic markers including those that are commercially available. Furthermore, both human and computer-extracted features as well as deep learning techniques have been explored. This review will summarize the specific imaging modalities used in radiogenomics analysis, describe the methods of extracting imaging features, and present the types of genomics, molecular, and related information used for analysis. Finally, the limitations and future directions of breast cancer radiogenomics research will be discussed.

The plasma peptides of breast versus ovarian cancer

Background

There is a need to demonstrate a proof of principle that proteomics has the capacity to analyze plasma from breast cancer versus other diseases and controls in a multisite clinical trial design. The peptides or proteins that show a high observation frequency, and/or precursor intensity, specific to breast cancer plasma might be discovered by comparison to other diseases and matched controls. The endogenous tryptic peptides of breast cancer plasma were compared to ovarian cancer, female normal, sepsis, heart attack, Alzheimer's and multiple sclerosis along with the institution-matched normal and control samples collected directly onto ice.

Methods

Endogenous tryptic peptides were extracted from individual breast cancer and control EDTA plasma samples in a step gradient of acetonitrile, and collected over preparative C18 for LC-ESI-MS/MS with a set of LTQ XL linear quadrupole ion traps working together in parallel to randomly and independently sample clinical populations. The MS/MS spectra were fit to fully tryptic peptides or phosphopeptides within proteins using the X!TANDEM algorithm. The protein observation frequency was counted using the SEQUEST algorithm after selecting the single best charge state and peptide sequence for each MS/MS spectra. The observation frequency was subsequently tested by Chi Square analysis. The log₁₀ precursor intensity was compared by ANOVA in the R statistical system.

Results

Peptides and/or phosphopeptides of common plasma proteins such as APOE, C4A, C4B, C3, APOA1, APOC2, APOC4, ITIH3 and ITIH4 showed increased observation frequency and/or precursor intensity in breast cancer. Many cellular proteins also showed large changes in frequency by Chi Square (χ² > 100, p < 0.0001) in the breast cancer samples such as CPEB1, LTBP4, HIF-1A, IGHE, RAB44, NEFM, C19orf82, SLC35B1, 1D12A, C8orf34, HIF1A, OCLN, EYA1, HLA-DRB1, LARS, PTPDC1, WWC1, ZNF562, PTMA, MGAT1, NDUFA1, NOGOC, OR1E1, OR1E2, CFI, HSA12, GCSH, ELTD1, TBX15, NR2C2, FLJ00045, PDLIM1, GALNT9, ASH2L, PPFIBP1, LRRC4B, SLCO3A1, BHMT2, CS, FAM188B2, LGALS7, SAT2, SFRS8, SLC22A12, WNT9B, SLC2A4, ZNF101, WT1, CCDC47, ERLIN1, SPFH1, EID2, THOC1, DDX47, MREG, PTPRE, EMILIN1, DKFZp779G1236 and MAP3K8 among others. The protein gene symbols with large Chi Square values were significantly enriched in proteins that showed a complex set of previously established functional and structural relationships by STRING analysis. An increase in mean precursor intensity of peptides was observed for QSER1 as well as SLC35B1, IQCJ-SCHIP1, MREG, BHMT2, LGALS7, THOC1, ANXA4, DHDDS, SAT2, PTMA and FYCO1 among others. In contrast, the QSER1 peptide QPKVKAEPPPK was apparently specific to ovarian cancer.

Conclusion

There was striking agreement between the breast cancer plasma peptides and proteins discovered by LC-ESI-MS/MS with previous biomarkers from tumors, cells lines or body fluids by genetic or biochemical methods. The results indicate that variation in plasma peptides from breast cancer versus ovarian cancer may be directly discovered by LC-ESI-MS/MS that will be a powerful tool for clinical research. It may be possible to use a battery of sensitive and robust linear quadrupole ion traps for random and independent sampling of plasma from a multisite clinical trial.