Non-invasive Detection Of Breast Cancer Research Articles

Machine learning unveils an immune-related DNA methylation profile in germline DNA from breast cancer patients

BackgroundThere is an unmet need for precise biomarkers for early non-invasive breast cancer detection. Here, we aimed to identify blood-based DNA methylation biomarkers that are associated with breast cancer.MethodsDNA methylation profiling was performed for 524 Asian Chinese individuals, comprising 256 breast cancer patients and 268 age-matched healthy controls, using the Infinium MethylationEPIC array. Feature selection was applied to 649,688 CpG sites in the training set. Predictive models were built by training three machine learning models, with performance evaluated on an independent test set. Enrichment analysis to identify transcription factors binding to regions associated with the selected CpG sites and pathway analysis for genes located nearby were conducted.ResultsA methylation profile comprising 51 CpGs was identified that effectively distinguishes breast cancer patients from healthy controls achieving an AUC of 0.823 on an independent test set. Notably, it outperformed all four previously reported breast cancer-associated methylation profiles. Enrichment analysis revealed enrichment of genomic loci associated with the binding of immune modulating AP-1 transcription factors, while pathway analysis of nearby genes showed an overrepresentation of immune-related pathways.ConclusionThis study has identified a breast cancer-associated methylation profile that is immune-related to potential for early cancer detection.

Abstract PO1-28-06: Development of a plasma-based real-time qPCR gene expression assay for targeted screening and diagnosis of early-stage breast cancer

Abstract Background Over 12 million US women undergo follow-up breast imaging and exams annually due to breast cancer suspicions, incurring an $8 billion total cost, of which $2.18 billion is attributed to false-positive breast biopsies. Recent updates published to the Mammography Quality Standards Act (MQSA) now recommend supplemental imaging for women with heterogeneously dense and extremely dense breasts, regardless of suspicious findings. Given sensitivity limitations associated with mammography in dense-breasted women, as low as 30-47%, and non-specificity associated with ultrasound and MRI, these recent recommendations are likely to further strain the healthcare system and place substantial burden on patients and providers. With an estimated 25 million dense-breasted women who regularly screen for breast cancer now considered at-risk, there is a critical need for affordable and highly accurate testing, offering better specificity without sacrificing sensitivity. To address this gap, we developed an affordable and accurate plasma-based gene expression assay using real-time qPCR for targeted screening and diagnosis of early-stage breast cancer. Methods In previous studies, 26 cross-correlated mRNA gene targets were discovered and independently validated for non-invasive breast cancer detection across five independent patient cohorts using microarray gene expression profiling, comprised of peripheral blood mononuclear cells (n=337) and saliva (n=20), demonstrating strong efficacy, reproducibility, and concordance across bio-fluids and assay platforms in all studies. A sixth independent validation cohort comprised of 203 plasma samples was subsequently obtained for real-time qPCR clinical assay development and validation in our CLIA laboratory. The CLIA validation cohort was designed to be representative of different diagnosis status, stage, and ethnicity, and included Caucasian, Black/African American, Hispanic, and Asian women. After normalizing mRNA expression and clustering analysis, the signature was refined to 8 target genes and was assessed in a cohort of 87 plasma samples [Table 1] to verify the assay’s diagnostic performance for stage I breast cancer detection, under locked laboratory protocols. We leveraged machine learning methods derived from XGBoost classification, a supervised-learning algorithm that uses sequentially built shallow decision trees to provide accurate results and avoidance of overfitting. Results The XGBoost model, selecting from the 8 mRNA gene targets and patient age, achieved >99% sensitivity, 89% specificity for stage I breast cancer detection in the held-out test set, with an overall diagnostic accuracy of 94.5%. Normalized data showed significant differences in gene expression between healthy controls and stage I patients, and distinct clustering was observed for the 8 mRNA gene signature, including patient age. Conclusions Our plasma-based real-time qPCR gene expression clinical assay, with machine learning, is positioned as a highly accurate non-invasive tool for targeted screening and diagnosis of early-stage breast cancer. Furthermore, a clinically validated assay in this space addresses the need for more targeted diagnostics, while serving as a promising tool for clinicians as they make critical care decisions across the breast health continuum, accelerating time to earlier and more precise intervention and treatment, mitigating unnecessary healthcare expenditures, and reducing the mental, emotional, and financial burden on patients and their families. Citation Format: Martin Keiser, Elizabeth Cormier-May, Matthew Alderdice, Joy Kavanagh, William Guesdon, Heather Healy, Nathalie Jean-Charles, Jay Harness. Development of a plasma-based real-time qPCR gene expression assay for targeted screening and diagnosis of early-stage breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-28-06.

Non-Invasive Breast Cancer Detection Using Electrical Impedance Tomography: Design, Analysis and Comparison of Reconstruction Algorithms

Non-Invasive Breast Cancer Detection Using Electrical Impedance Tomography: Design, Analysis and Comparison of Reconstruction Algorithms

Non-Invasive Detection of Breast Cancer by Low-Coverage Whole-Genome Sequencing from Plasma

Non-Invasive Detection of Breast Cancer by Low-Coverage Whole-Genome Sequencing from Plasma

Neutrosophic-based Machine Learning Techniques for Analysis and Diagnosis the Breast Cancer

Approximately one in eight women will get breast cancer in their lifetime. Because of the risks associated with radiation exposure, various women choose to avoid getting detected with breast cancer. Non-invasive breast cancer detection methods have limitations concerning the safety of radiation exposure and the accuracy with which tumors in the breast are diagnosed. Machine learning methods are commonly used to diagnose breast cancer. This paper applied three different machine learning methods like KNN, Naïve Bayes, and ID3. These methods are applied to the Wisconsin Breast Cancer dataset. In the process of categorization, data with unbalanced classes is problematic because methods are more probable to categorize fresh observations to the majority class since the likelihood of cases forming the plurality class is considerably high. So neutrosophic set is used to overcome the vague and uncertain data. This paper used single-valued neutrosophic numbers to evaluate the criteria. This paper used ROC and accuracy to evaluate the methods. The KNN has a 96.7%, Naïve Bayes has a 95.2%, and ID3 has a 95.3% accuracy.

Indocyanine Green-Conjugated Superparamagnetic Iron Oxide Nanoworm for Multimodality Breast Cancer Imaging.

Breast cancer is the leading cause of cancer-associated deaths among women. Techniques for non-invasive breast cancer detection and imaging are urgently needed. Multimodality breast cancer imaging is attractive since it can integrate advantages from several modalities, enabling more accurate cancer detection. In order to accomplish this, indocyanine green (ICG)-conjugated superparamagnetic iron oxide nanoworm (NW-ICG) has been synthesized as a contrast agent. When evaluated in a spontaneous mouse breast cancer model, NW-ICG gave a large tumor to normal tissue contrasts in multiple imaging modalities including magnetic particle imaging, near-infrared fluorescence imaging, and photoacoustic imaging, providing more comprehensive detection and imaging of breast cancer. Thus, NW-ICGs are an attractive platform for non-invasive breast cancer diagnosis.

Znhit1 and HIF-2α are correlated with cancer stem cell markers in breast cancer patients

Epigenetic alterations have emerged as fundamental players in development and progression of breast cancer (BC). A hypoxic tumour microenvironment regulates the stemness phenotype in breast cancer stem cells (BCSCs). The aim of this study was to investigate Znhit1 and HIF-2α gene expression in breast cancer tissues as well as their relation to CSCs markers LGR5, ALDH1A1 and β-catenin in tissue and serum of BC patients. The present study included 160 females divided into two groups, group I: 80 healthy females served as control group and group II: 80 breast cancer patients. Gene expression of tissue Znhit1 and HIF-2α was determined by qRT-PCR. Tissue and serum ALDH1A1, LGR5 and β-catenin levels were determined by ELISA. We found that gene expression of Znhit1 was significantly downregulated in BC tissues. Moreover, it was significantly negatively correlated with clinical stage and β-catenin levels in BC patients. Regarding HIF-2α, gene expression of HIF-2α was significantly upregulated in BC tissues. Moreover, it was significantly positively correlated with Her-2/neu expression and β-catenin levels in BC patients. Based upon our results, Znhit1 and HIF-2α may serve as novel therapeutic targets for BC therapy. Additionally, each of serum ALDH1A1, LGR5 and β-catenin may play a crucial role in non-invasive detection of BC with a high specificity and sensitivity.

An adaptive window-based hybrid artifact removal method for Ultra-Wide Band imaging enhancement of early breast cancer detection

At present, many studies have shown that breast cancer is the primary factor affecting women's health all over the world. In the past decades, ultra-wide band (UWB) microwave imaging has been widely studied as a non-invasive breast cancer detection tool through abundant researches and engineering applications. Generally, the artifact caused by skin and other factors will significantly affect the accuracy of target detection in this technology. Therefore, how to eliminate them is crucial. In this paper, an adaptive window-based hybrid artifact removal (AW-HAR) approach is proposed. It consists of adaptive time window division, revised two-stage filtering, and Savitzky-Golay (S-G) smoothing. This method pre-analyzes the specificity of each channel and divides them into several time windows adaptively. Then adjusted two-stage filtering is carried out on each window to extract partial tumor information. Finally, the fragmented time sequences are integrated and S-G smoothing is applied. The simulation and phantom experiments demonstrate that the AW-HAR has favorable performance in terms of clutter suppression and image clarity. It effectively suppresses surface disturbance and irrelevant response, enhancing the region of interest (ROI) in microwave imaging.

Sensorial innovation for non-invasive breast cancer detection.

Breast cancer represents a great challenge due to the context of gender, social and cultural aspects, as well as of the possibility of making accessible early detection methods. Currently, detection mostly relies on image tests (invasive procedures). However, due to new and numerous cases detected in young women, and detected in such advanced stages that only palliative care may be offered, there is opportunity to develop new, innovative and promising detection tests. In order to find solutions to the organizational process of tumor cells, and adapt them for early detection, biomimesis eases the development of new non-invasive approaches and innovative designs. Nowadays, the volatile organic compounds detection (also called volatolome) emitted by tumoral cells can be detected with the use of biological and/or artificial intelligent noses. This approach may represent a real opportunity for the early detection of cancer and several pathologies. There is no doubt that novel test for early detection of different diseases will be soon reality.

Abstract P4-01-25: Blood-based DNA methylation as epigenetic biomarkers for non-invasive detection of breast cancer

Abstract Background: DNA methylation significantly contributes to all hallmarks of cancer. In particular, promoter hypermethylation has been identified as a potential marker for early detection of cancer, therapy monitoring, assessment of prognosis or prediction of therapy response. Here, we identified and validated breast cancer-specific methylation markers for diagnosis of the disease with high sensitivity and specificity. Methods: 151 bio-banked samples were obtained for a randomized blind study; 65 subjects diagnosed with breast cancer (Stage I to IV), 15 subjects with benign breast disease, 32 subjects diagnosed with other cancer types (11 colorectal, 9 liver and 12 lung cancer), and 39 healthy donor samples. Cell-free DNA was then extracted from the samples, bisulfite converted, and DNA methylation was quantified by using the IvyGene Platform. Finally, the raw data were collected and analyzed to evaluate the test performance. Results: A total of 53 of the 65 breast cancer samples were correctly identified for an overall calculated sensitivity of 89% with little difference between the sensitivity of detecting Stage I to Stage IV breast cancer (range 88% to 94%). The breast cancer subjects were correctly identified with 96% specificity. Additionally, 28 of 32 samples drawn from healthy donor subjects (specificity 95%) and all 15 samples drawn from subjects diagnosed with benign breast disease (specificity 100%) were also correctly identified. Of the samples drawn from subjects with cancer other than breast cancer, over 96% of lung cancer, colorectal cancer and liver cancer samples were correctly identified as not breast cancer. Conclusion: The results demonstrate the high diagnostic potential of ctDNA methylation markers in the blood for the detection of breast cancer. In addition, a quantitative analysis of ctDNA provides an opportunity for non-invasive detection of cancer and it's benefits can be anticipated to improve patient management and healthcare quality.

Abstract LB-226: Exosomal liquid biopsy reveals mRNA and lincRNA biomarkers in early stage breast cancer patient plasma

Abstract Introduction Breast cancer is the most prevalent cancer in women, with approximately 250,000 diagnoses per year in the US. Non-invasive detection of breast cancer is of critical importance but has proven challenging due to the rate of false-positive diagnoses with current tests. Liquid biopsies including circulating tumor cells (CTCs) or cell-free DNA (cfDNA) have struggled with the detection of early stage disease. For example, a recent multi-analyte (cfDNA+protein) analysis, ‘cancerSEEK', achieved a sensitivity of just 33% in breast cancer, highlighting the challenges facing the development of more sensitive and specific diagnostics for this disease. Exosome-based liquid biopsy is a promising approach for minimally-invasive and highly sensitive diagnostics and it has been demonstrated that combining exosomal RNA and cfDNA greatly enhances mutation detection compared to profiling cfDNA alone. While most liquid biopsies profile mutations, studying RNA abundance in exosomes adds a new dimension to these non-invasive diagnostics. To date, much of the focus on exosomal RNA expression profiling has been on the small-RNA fraction. Here we demonstrate that whole-transcriptome profiling of mRNAs and lincRNAs greatly expands the landscape of potential biomarkers to clinically actionable genes. Results We have developed a novel platform designed to perform long-RNA sequencing on transcripts obtained from exosomes. We used this platform to compare expression profiles of total plasma exosomes versus subpopulations enriched for breast cancer-derived exosomes (CDE) versus depleted of non-cancer exosomes (NCE). The NCE-depleted and CDE-enriched exosomes equally outperformed total plasma exosomes, each detecting significantly more genes exhibiting breast cancer vs. healthy expression differences. We performed NCE-depletion on 1.5 mL of input plasma from 15 stage I & II ER+/Her2- breast cancer patients and 12 healthy women matched for age & menopausal-status. RNA-seq data from these samples detected over 10,000 mRNAs and over 1,000 lincRNAs. Of these, we observed significantly increased abundance in over 100 mRNAs and lincRNAs in these early stage breast cancer patients. These mRNAs are enriched for gene-sets including those previously implicated in ‘breast cancer', ‘chromatin remodeling', and ‘immune response'. We also performed RNA-seq on formalin-fixed paraffin-embedded (FFPE) samples from healthy and matched breast cancer tissue. The >100 genes found to be more abundant in breast cancer plasma exosomes significantly (p<0.05) separate the FFPE samples into two clusters corresponding to breast cancer patients and normal individuals, lending confidence to the exosomal signature. Conclusions This preliminary analysis highlights the exciting potential of exosomal long RNA based liquid biopsy for non-invasive early-stage detection of breast cancer. The platform is readily applicable to other disease areas and other biofluids such as urine or CSF. Citation Format: Sudipto K. Chakrabortty, Robert R. Kitchen, Christine M. Coticchia, Vasisht R. Tadigotla, Erez Eitan, Elena Castellanos-Rizaldos, Lisa Bedford, Sunita Badola, Michael D. Valentino, Nicholas Colafemina, Hidefumi Uchiyama, Mario Morken, Miguel Williams, Sylvie Vincent, Hadi Danaee, Seth Yu, Johan Skog. Exosomal liquid biopsy reveals mRNA and lincRNA biomarkers in early stage breast cancer patient plasma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-226.

Early non-invasive detection of breast cancer using exhaled breath and urine analysis

The main focus of this pilot study is to develop a statistical approach that is suitable to model data obtained by different detection methods. The methods used in this study examine the possibility to detect early breast cancer (BC) by exhaled breath and urine samples analysis.Exhaled breath samples were collected from 48 breast cancer patients and 45 healthy women that served as a control group. Urine samples were collected from 37 patients who were diagnosed with breast cancer based on physical or mammography tests prior to any surgery, and from 36 healthy women. Two commercial electronic noses (ENs) were used for the exhaled breath analysis. Urine samples were analyzed using Gas-Chromatography Mass-Spectrometry (GC-MS).Statistical analysis of results is based on an artificial neural network (ANN) obtained following feature extraction and feature selection processes. The model obtained allows classification of breast cancer patients with an accuracy of 95.2% ± 7.7% using data of one EN, and an accuracy of 85% for the other EN and for urine samples.The developed statistical analysis method enables accurate classification of patients as healthy or with BC based on simple non-invasive exhaled breath and a urine sample analysis. This study demonstrates that available commercial ENs can be used, provided that the data analysis is carried out using an appropriate scheme.

Abstract P2-02-03: Assessment of an immune response panel of serum protein biomarkers for the non-invasive detection of breast cancer

Abstract Background: Breast cancer screening guidelines (ACS) recommend annual mammography for asymptomatic women ages 45 to 54 and once every two years for women age 55 and older. Women with suspicious screening mammograms are recommended for a diagnostic mammogram and may also undergo MRI or ultrasound. Ultimately, unresolved suspicious findings frequently result in the recommendation of a breast biopsy. Approximately 10% of suspicious diagnostic mammograms are recommended for breast biopsies and a large majority of these biopsies yield benign results. We conducted a screen for serum protein biomarkers and identified a novel panel for the non-invasive detection of breast cancer with the goal of developing a diagnostic test that can reduce the number of patients with benign pathology undergoing invasive biopsies. Methods: Serum samples were collected at 4 US sites from women with suspicious diagnostic mammogram findings (primarily BI-RADS category 4 and breast composition b/c) undergoing biopsy for evaluation of a potential malignancy. Serum samples from 136 patients (87 benign pathology and 49 malignant pathology) were evaluated on the olink® Proteomics Immune Response Panel (92 analytes). Statistical screening methodologies, such as individual t-tests with control for false discovery, were used to identify markers with the potential to distinguish benign from malignant pathology. The candidate markers were further studied and combined using generalized linear modeling to develop potential diagnostic models. Results: A 19-marker model resulted in an AUC of 0.94 with a sensitivity of 90% and a specificity of 80%. A 12-marker model resulted in an AUC of 0.93, yielding a sensitivity of 90% with a specificity of 77%. Conclusions: This study reveals a novel panel of serum protein biomarkers that may allow for the non-invasive and sensitive detection of breast cancer in patients presenting with suspicious findings on mammography, thus reducing the need for invasive biopsies. Citation Format: McQuary PR, Copeland KAF, Sheibani NM, Friedman LA, Korn RL, Fiorica JV, Lourenco AP, Struthers SE, Hesterberg LK. Assessment of an immune response panel of serum protein biomarkers for the non-invasive detection of breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-02-03.

Structural design of porous photonic crystal fibres

Researchers at the University of Adelaide, Australia, and the Islamic University of Technology, Bangladesh, report the optimum design for a porous photonic crystal fibre capable of terahertz frequency transmission. Photonic crystal fibres (PCFs), also known as micro-structured optical fibres (MOFs), are a new class of optical fibre. The significance of PCFs is their remarkable optical properties, such as providing single mode operation, high birefringence, and controllable chromatic dispersion characteristics, which are not obtainable using conventional optical fibres. As a result, PCFs are used extensively in different medical imaging applications, for example, in Optical Coherence Tomography (OCT). OCT is an imaging technology that allows non-invasive cross-sectional imaging of biological tissue. Bringing such techniques into the terahertz domain will be assisted by the development of PCFs that operate in the terahertz regime. There are different types of PCFs, including solid core PCF, hollow core PCF and porous core PCF. A solid core PCF experiences large material loss, which is a property that makes these PCFs unsuitable for terahertz signal transmission. In a hollow core PCF, the loss is greatly reduced, but such fibres operate over a narrow frequency range. To overcome the described disadvantages of solid core and hollow core fibres, porous core fibres were introduced. A porous core fibre contains a number of micro-structured air holes in the core. The advantage of the porous structure is that the fibre global parameters, including air hole size, pitch distance (centre-to-centre distance between air holes), core diameter and shape of air holes can be tuned as required for different applications. Thus, for porous fibre PCFs, the effective material loss, birefringence, dispersion, confinement loss, numerical aperture and other modal properties can be obtained by design. Nowadays, PCFs are used in the field of sensing, biomedical imaging, time domain spectroscopy, security, detecting DNA hybridisation and bio-sensing, skin cancer and breast cancer detection, and in communication applications. However, there are no previously reported articles on terahertz PCFs with sufficient numerical aperture (NA) to be used for OCT applications. In this issue of Electronics Letters, Saiful Islam and colleagues propose a terahertz PCF with high birefringence, high NA, and near zero dispersion variation. The measured high birefringence is reported to be due to the elliptical-shaped asymmetrical structure of the air holes inside the core. The asymmetrical structure of air holes created a refractive index difference between the polarisation modes, thus generating birefringence. The observation of high NA is reported to be due to the high birefringence and high modal effective area. Moreover, flattened dispersion is reported due to the waveguide structure and chosen materials that the waveguide was formed of. The kagome-based PCF has the advantage that it has a significantly lower confinement loss than other PCF structures. Terahertz time-domain spectroscopy set-up in the National T-Ray lab at the University of Adelaide. Saiful Islam at the University of Adelaide. The reported simulations in this Letter have allowed for rapid design optimisation that can now be experimentally tested. The major challenge of a PCF design is to anticipate difficulties in fabrication. Thus, the fabrication possibilities of a PCF must be addressed. For accuracy, obtaining correct parameters to describe the structural and optical properties of the fibre, such as refractive index, material absorption loss, relative permittivity and relative permeability, is of great importance. S. Islam states that ‘‘Our PCF design was produced with ease of fabrication in mind, allowing future realisation. Thus, applications in THz-OCT, chemical sensing, and multichannel communications are envisaged.’’ S. Islam and colleagues are currently using photonic crystal fibres to design chemical sensors for skin cancer detection. Non-invasive breast cancer detection is possible via detecting the presence of certain protein markers in human saliva. The authors are also designing sensors to detect hazardous chemicals for security applications. ‘‘We are planning to extend our PCF designs with the use of graphene or gold coasting to exploit surface plasmons for enhanced sensing’’, explains S. Islam. In practice, PCFs are limited to reasonably simple geometries due to difficulties in fabrication. However, emerging techniques exploiting developments in 3D printing are promising methods to overcome current limitations. S. Islam predicts that ‘‘It is possible that in the next decade will we begin to see more sophisticated PCFs with remarkable properties that are not deemed practical in today's technology.’’

Pre-clinical study of a TNFR1-targeted 18F probe for PET imaging of breast cancer

Tumor necrosis factor receptor 1 (TNFR1) is overexpressed in several varieties of carcinoma, including breast cancer. WH701 (Ala-Thr-Ala-Gln-Ser-Ala-Tyr-Gly), which was identified by phage display, can specifically bind to TNFR1. In this study, we labeled WH701 with 18F and investigated its tumor diagnostic value. WH701 was synthesized by standard Fmoc-solid phase synthetic protocols and conjugated by NOTA-NHS. NOTA-WH701 was radiolabeled with 18F using NOTA-AlF chelation reaction. The tumor target properties were evaluated in vitro and in vivo using MCF-7 xenografts and inflammation models. [18F]AlF-NOTA-WH701 was labeled in 25min with a decay-corrected yield of 38.1±4.8% (n=5) and a specific activity of 10.4-13.0GBq/μmol. WH701 had relatively high affinity for MCF-7 cells in vitro and [18F]AlF-NOTA-WH701 displayed relatively high tumor uptake in vivo. The tumor to muscle ratio was 4.25±0.56 at 30min post-injection (p.i.); further, there was a significant difference between the tumor/muscle and inflammation/muscle (3.22±0.56) ratio, which could differentiate the tumor and inflammation. The tumor uptake of [18F]AlF-NOTA-WH701 could be inhibited by 71.1% by unlabeled WH701 at 30min p.i. We have developed a promising PET tracer [18F]AlF-NOTA-WH701 for the noninvasive detection of breast cancer in vivo.

Abstract P5-03-05: Development of a panel of serum-based protein biomarkers for the non-invasive detection of breast cancer in BI-RADS category 4 patients

Abstract Background: Current breast cancer screening guidelines call for annual mammography for asymptomatic women age 45 to 54 and once every two years for women age 55 and older. Women with suspicious screening mammograms are recommended for a diagnostic mammogram and may also undergo MRI or ultrasound. Ultimately, suspicious findings unresolved by imaging typically result in the recommendation of a breast biopsy. Approximately 10% of suspicious diagnostic mammograms are recommended for breast biopsies and 67% to 95% of these biopsies yield negative results. With the goal of reducing the number of patients with benign pathology undergoing invasive biopsies, we conducted a screen for serum protein biomarkers and identified a novel panel for the non-invasive detection of breast cancer. Methods: Serum samples were collected at two sites from women with suspicious diagnostic mammogram findings (primarily BI-RADS category 4) undergoing biopsy for the evaluation of a potential malignancy. Serum samples from 100-patients (50 benign pathology and 50 malignant pathology) were evaluated on the SOMAscan Assay 1.3k, which measures levels of 1,310 different protein analytes. Statistical screening methodologies, such as individual t-tests with control for false discovery, were used to identify markers with the potential to distinguish benign from malignant pathology. The candidate markers were further studied and combined using generalized linear modeling to develop three potential diagnostic models. K-fold cross validation was used to guard against over fitting of the models. Results: A 15-marker model resulted in an AUC of 0.92 with a sensitivity of 90% and specificity of 76%. Two 6-marker models (with 4 markers in common) each resulted in AUC of 0.85, yielding a sensitivity of 90% with a specificity of 56% or 64%. Conclusions: This study reveals a novel panel of serum protein biomarkers that may allow for the non-invasive and sensitive detection of breast cancer in BI-RADS category 4 patients. A multicenter study is underway to further refine and validate this panel in a larger set of prospectively collected patient samples. Citation Format: Chapman KB, Copeland K, Kidd J, Qiu L, Sheibani N, Tam O, Friedman L, Korn R, Fiorica J, Lourenco A, Suthers S, Hesterberg L. Development of a panel of serum-based protein biomarkers for the non-invasive detection of breast cancer in BI-RADS category 4 patients [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-03-05.

In Vivo Electrical Conductivity Contrast Imaging in a Mouse Model of Cancer Using High-Frequency Magnetoacoustic Tomography With Magnetic Induction (hfMAT-MI).

Cancerous tissues have electrical-conductivity signatures different from normal tissues, which contain potentially useful information for early detection. Despite recent advancements in electrical-conductivity imaging and its applications, imaging electrical conductivities with high spatial resolution remains a challenge for non-invasive diagnosis of early-stage cancer. Among the various electrical-conductivity imaging methods, magnetoacoustic tomography with magnetic induction (MAT-MI) is a promising technology for non-invasive detection of breast cancer. However, previous efforts to use MAT-MI for cancer imaging have suffered due to insufficient spatial resolution. In this work, we have developed a high-frequency MAT-MI (hfMAT-MI) system with a 2-D spatial resolution of 1 mm, a significant improvement over previous methods. Furthermore, we demonstrated the performance of this method using an in vivo cancer model in nude mice with human breast xenograft hindlimb tumors. hfMAT-MI was able to resolve not only the boundaries between cancerous and healthy tissues, but also the tumors' internal structures. Importantly, we were able to track a growing tumor using our hfMAT-MI method for the first time in an in vivo mouse model, demonstrating the promise of this magneto-acoustic imaging system for effective detection and diagnosis of early-stage breast cancer.

Integrative System of Fast Photoacoustic Imaging

Abstract An integrative fast (Photoacoustic) PA imaging system based on multi-element linear ultrasonic transducer array was developed, which integrates laser delivery, photoacoustic excitation and photoacoustic detection into a portable system. It collects PA signals by a multi-element linear transducer array in a reflection mode. The PA images with high spatial resolution and high contrast were obtained. Compared to other existing PA imaging methods, the integrative PA imaging system is characterized by rapidness, convenience and high practicality. The integrative system is mobile and portable, and it is suitable for imaging samples in natural condition with various different shapes. It will provide a novel and effective PA imaging approach for clinic diagnosis of neoplasm and tissue functional imaging in vivo, and has potential to be developed into a practical apparatus used in the early non-invasive detection of breast-cancer.

P2-09-03: Breast Cancer Detection Using a Novel Functional Imaging Device Equipped with Multiparametric Computer Analysis.

Abstract Introduction: A novel prototype system performing functional three-dimensional (3D) infrared imaging (RUTH), coupled with multi-parametric computer analysis (MIRA), was evaluated for non-invasive breast cancer detection. The technique utilizes non-ionizing radiation and automatically provides risk assessment based on parameters derived from a clinically known training set. Materials and methods: Following IRB approval, prospective multi-center, two-armed blinded evaluation, was performed on 102 women, which included 43 patients with biopsy proven breast cancer and 59 women with a normal (BI-RADS 1) mammogram. RUTH imaging (Real Imaging Ltd, Israel) was acquired on a continuous temporal timeline for 5 minutes during which a cold-stress test was applied for one minute. Acquired data were analyzed using multiple computed algorithms (MIRA) which were combined to develop a master algorithm for risk assessment of breast cancer. Analysis was blinded to the clinical status. Results were displayed as risk assessment scale with 100 being suspicious for breast cancer and −100 inferring normal healthy breasts. Results Thirty-nine of 43 cancers (91.7%) were identified using this novel technology. The sensitivity and specificity of RUTH coupled with MIRA for detecting breast cancer were 90.7% and 67.8% respectively. The AUC defined by the ROC curve was 0.832. Twenty-seven of the 39 (70%) detected cancers were smaller than 20mm in size. Conclusions: This preliminary study shows adequate performance of the novel technology examined for detecting breast cancer. Performance is comparable to the reported performance of new adjunctive breast imaging technologies such as tomosynthesis and contrast-enhanced mammography, and superior to the published sensitivity of mammography. Our results warrant further evaluation of the RUTH and MIRA technologies in detecting and characterizing breast cancer. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P2-09-03.

Discovery and Preclinical Validation of Salivary Transcriptomic and Proteomic Biomarkers for the Non-Invasive Detection of Breast Cancer

BackgroundA sensitive assay to identify biomarkers using non-invasively collected clinical specimens is ideal for breast cancer detection. While there are other studies showing disease biomarkers in saliva for breast cancer, our study tests the hypothesis that there are breast cancer discriminatory biomarkers in saliva using de novo discovery and validation approaches. This is the first study of this kind and no other study has engaged a de novo biomarker discovery approach in saliva for breast cancer detection. In this study, a case-control discovery and independent preclinical validations were conducted to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for breast cancer detection.Methodology/Principal FindingsSalivary transcriptomes and proteomes of 10 breast cancer patients and 10 matched controls were profiled using Affymetrix HG-U133-Plus-2.0 Array and two-dimensional difference gel electrophoresis (2D-DIGE), respectively. Preclinical validations were performed to evaluate the discovered biomarkers in an independent sample cohort of 30 breast cancer patients and 63 controls using RT-qPCR (transcriptomic biomarkers) and quantitative protein immunoblot (proteomic biomarkers). Transcriptomic and proteomic profiling revealed significant variations in salivary molecular biomarkers between breast cancer patients and matched controls. Eight mRNA biomarkers and one protein biomarker, which were not affected by the confounding factors, were pre-validated, yielding an accuracy of 92% (83% sensitive, 97% specific) on the preclinical validation sample set.ConclusionsOur findings support that transcriptomic and proteomic signatures in saliva can serve as biomarkers for the non-invasive detection of breast cancer. The salivary biomarkers possess discriminatory power for the detection of breast cancer, with high specificity and sensitivity, which paves the way for prediction model validation study followed by pivotal clinical validation.