A Noninvasive Blood-based Combinatorial Proteomic Biomarker Assay to Detect Breast Cancer in Women over age 50 with BI-RADS 3, 4, or 5 Assessment

Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study

BACKGROUND

Patients with a Breast Imaging Reporting and Data System (BI-RADS) 4 mammogram are currently recommended for biopsy. However, 70-80% of the biopsies are negative/benign. In this study, we developed a deep learning classification algorithm on mammogram images to classify BI-RADS 4 suspicious lesions aiming to reduce unnecessary breast biopsies.

MATERIALS AND METHODS

This retrospective study included 847 patients with a BI-RADS 4 breast lesion that underwent biopsy at a single institution and included 200 invasive breast cancers, 200 ductal carcinoma in-situ (DCIS), 198 pure atypias, 194 benign, and 55 atypias upstaged to malignancy after excisional biopsy. We employed convolutional neural networks to perform 4 binary classification tasks: (I) benign vs. all atypia + invasive + DCIS, aiming to identify the benign cases for whom biopsy may be avoided; (II) benign + pure atypia vs. atypia-upstaged + invasive + DCIS, aiming to reduce excision of atypia that is not upgraded to cancer at surgery; (III) benign vs. each of the other 3 classes individually (atypia, DCIS, invasive), aiming for a precise diagnosis; and (IV) pure atypia vs. atypia-upstaged, aiming to reduce unnecessary excisional biopsies on atypia patients.

RESULTS

A 95% sensitivity for the "higher stage disease" class was ensured for all tasks. The specificity value was 33% in Task I, and 25% in Task II, respectively. In Task III, the respective specificity value was 30% (vs. atypia), 30% (vs. DCIS), and 46% (vs. invasive tumor). In Task IV, the specificity was 35%. The AUC values for the 4 tasks were 0.72, 0.67, 0.70/0.73/0.72, and 0.67, respectively.

CONCLUSION

Deep learning of digital mammograms containing BI-RADS 4 findings can identify lesions that may not need breast biopsy, leading to potential reduction of unnecessary procedures and the attendant costs and stress.

Circulating tumor biomarkers in early-stage breast cancer: characteristics, detection, and clinical developments

Breast cancer is the most common form of cancer in women, contributing to high rates of morbidity and mortality owing to the ability of these tumors to metastasize via the vascular system even in the early stages of progression. While ultrasonography and mammography have enabled the more reliable detection of early-stage breast cancer, these approaches entail high rates of false positive and false negative results Mammograms also expose patients to radiation, raising clinical concerns. As such, there is substantial interest in the development of more accurate and efficacious approaches to diagnosing breast cancer in its early stages when patients are more likely to benefit from curative treatment efforts. Blood-based biomarkers derived from the tumor microenvironment (TME) have frequently been studied as candidate targets that can enable tumor detection when used for patient screening. Through these efforts, many promising biomarkers including tumor antigens, circulating tumor cell clusters, microRNAs, extracellular vesicles, circulating tumor DNA, metabolites, and lipids have emerged as targets that may enable the detection of breast tumors at various stages of progression. This review provides a systematic overview of the TME characteristics of early breast cancer, together with details on current approaches to detecting blood-based biomarkers in affected patients. The limitations, challenges, and prospects associated with different experimental and clinical platforms employed in this context are also discussed at length.

Redefining serological diagnostics with immunoaffinity proteomics

Serological diagnostics is generally defined as the detection of specific human immunoglobulins developed against viral, bacterial, or parasitic diseases. Serological tests facilitate the detection of past infections, evaluate immune status, and provide prognostic information. Serological assays were traditionally implemented as indirect immunoassays, and their design has not changed for decades. The advantages of straightforward setup and manufacturing, analytical sensitivity and specificity, affordability, and high-throughput measurements were accompanied by limitations such as semi-quantitative measurements, lack of universal reference standards, potential cross-reactivity, and challenges with multiplexing the complete panel of human immunoglobulin isotypes and subclasses. Redesign of conventional serological tests to include multiplex quantification of immunoglobulin isotypes and subclasses, utilize universal reference standards, and minimize cross-reactivity and non-specific binding will facilitate the development of assays with higher diagnostic specificity. Improved serological assays with higher diagnostic specificity will enable screenings of asymptomatic populations and may provide earlier detection of infectious diseases, autoimmune disorders, and cancer. In this review, we present the major clinical needs for serological diagnostics, overview conventional immunoassay detection techniques, present the emerging immunoassay detection technologies, and discuss in detail the advantages and limitations of mass spectrometry and immunoaffinity proteomics for serological diagnostics. Finally, we explore the design of novel immunoaffinity-proteomic assays to evaluate cell-mediated immunity and advance the sequencing of clinically relevant immunoglobulins.

Autoantibodies as biomarkers for breast cancer diagnosis and prognosis

Breast cancer is the most common cancer in women worldwide and is a substantial public health problem. Screening for breast cancer mainly relies on mammography, which leads to false positives and missed diagnoses and is especially non-sensitive for patients with small tumors and dense breasts. The prognosis of breast cancer is mainly classified by tumor, node, and metastasis (TNM) staging, but this method does not consider the molecular characteristics of the tumor. As the product of the immune response to tumor-associated antigens, autoantibodies can be detected in peripheral blood and can be used as noninvasive, presymptomatic, and low-cost biomarkers. Therefore, autoantibodies can provide a possible supplementary method for breast cancer screening and prognosis classification. This article introduces the methods used to detect peripheral blood autoantibodies and the research progress in the screening and prognosis of breast cancer made in recent years to provide a potential direction for the examination and treatment of breast cancer.

Screening anlotinib responders via blood-based proteomics in non-small cell lung cancer

Anlotinib has been demonstrated to be effective in advanced non-small cell lung cancer (NSCLC) patients. The response stratification of anlotinib remains unclear. In this study, plasma samples from 28 anlotinib-treated NSCLC patients (discovery cohort: 14 responders and 14 non-responders) were subjected to proteomic analysis, and plasma samples from 35 anlotinib-treated NSCLC patients (validation cohort) were subjected to validation analysis. Liquid chromatography-tandem mass spectrometry analysis was performed on samples with different time points, namely baseline (BL), best response (BR), and progression disease (PD). Bioinformatics analysis was performed to screen for the underlying differential proteins. Enzyme-linked immunosorbent assay was performed to detect plasma ARHGDIB, FN1, CDH1, and KNG1 levels respectively. The Kaplan-Meier survival analysis was used for biomarker-based responsive stratification. Our results indicated that differential proteins between responders and non-responders showed that proteomic technology potentially contributes to biomarker screening in plasma samples at BL. Furthermore, our results suggested that the detection of plasma ARHGDIB, FN1, CDH1, and KNG1 levels have potential predictive value for anlotinib response both in the discovery cohort and validation cohort. Collectively, this study offers novel insights into the value of plasma biomarker screening via proteomic examination and suggests that plasma ARHGDIB, FN1, CDH1, and KNG1 levels could be used as biomarkers for anlotinib stratification in NSCLC patients.

[Clinical Value of Autoantibody Prognostic Markers in Tumor Immune Checkpoint Inhibitor Therapy]

Serum autoantibody markers have the advantages of easy specimen acquisition, simple detection technology and dynamic real-time monitoring. With the wide application of immune checkpoint inhibitors in the treatment of malignant tumors, autoantibody markers in predicting tumor immune checkpoint inhibitors efficacy and forecasting irAEs (immune related adverse events) show good prediction of potential. This review mainly focused on the progress of autoantibody markers in the prediction of therapeutic effect and the monitoring of irAE in tumor immunotherapy.
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Current advances in prognostic and diagnostic biomarkers for solid cancers: Detection techniques and future challenges

Solid cancers are one of the leading causes of cancer related deaths, characterized by rapid growth of tumour, and local and distant metastases. Current advances on multimodality care have substantially improved local control and metastasis-free survival of patients by resection of primary tumour. The major concern in disease prognosis is the timely detection of resectable or metastatic tumour, thus reinforcing the need for identification of biomarkers for premalignant lesions of solid cancer. This ultimately improves the outcome for the patients. Therefore, the purpose of this review is to update the recent advancements on prognostic and diagnostic biomarkers to enhance early detection of common solid cancers including, breast, lung, colorectal, prostate and stomach cancer. We also provide an insight into Food and Drug Administration (FDA)-approved solid cancers biomarkers; various conventional techniques used for detection of prognostic and diagnostic biomarkers and discuss approaches to turn challenges in this field into opportunities.

Breast cancer glycan biomarkers: their link to tumour cell metabolism and their perspectives in clinical practice

INTRODUCTION

Breast cancer (BCa) is the most common cancer type diagnosed in women and 5^th most common cause of deaths among all cancer deaths despite the fact that screening program is at place. This is why novel diagnostics approaches are needed in order to decrease number of BCa cases and disease mortality.

AREAS COVERED

In this review paper, we aim to cover some basic aspects regarding cellular metabolism and signalling in BCa behind altered glycosylation. We also discuss novel exciting discoveries regarding glycan-based analysis, which can provide useful information for better understanding of the disease. The final part deals with clinical usefulness of glycan-based biomarkers and the clinical performance of such biomarkers is compared to already approved BCa biomarkers and diagnostic tools based on imaging.

EXPERT OPINION

Recent discoveries suggest that glycan-based biomarkers offer high accuracy for possible BCa diagnostics in blood, but also for better monitoring and management of BCa patients. The review article was written using Web of Science search engine to include articles published between 2019 and 2021.

IgM and IgA augmented autoantibody signatures improve early-stage detection of colorectal cancer prior to nodal and distant spread

Objectives

Tumor‐associated autoantibodies (AAbs) in individuals with cancer can precede clinical diagnosis by several months to years. The objective of this study was to determine whether the primary immune response in form of IgM and gut mucosa‐associated IgA can aid IgG AAbs in the detection of early‐stage colorectal cancer (CRC).

Methods

We developed a novel protein array comprising 492 antigens seropositive in CRC. The array was used to profile IgG, IgM and IgA antibody signatures in 99 CRC patients and 99 sex‐ and age‐matched non‐cancer controls. A receiver operating curve (ROC), Kaplan–Meier survival analysis and univariate and multivariate Cox regression analyses were conducted.

Results

We identified a panel of 16 multi‐isotype AAbs with a cumulative sensitivity of 91% and specificity of 74% (AUC 0.90, 95% CI: 0.850–0.940) across all CRC stages. IgM and IgG isotypes were conversely associated with disease stage with IgM contributing significantly to improved stage I and II sensitivity of 96% at 78% specificity (AUC 0.928, 95% CI: 0.884–0.973). A single identified IgA AAb reached an overall sensitivity of 5% at 99% specificity (AUC 0.520, 95% CI: 0.440–0.601) balanced across all CRC stages. Kaplan–Meier analysis revealed that se33‐1 (ZNF638) IgG AAbs were associated with reduced 5‐year overall survival (log‐rank test, P = 0.012), whereas cumulative IgM isotype signatures were associated with improved 5‐year overall survival (log‐rank test, P = 0.024).

Conclusion

IgM AAbs are associated with early‐stage colorectal cancer. Combining IgG, IgM and IgA AAbs is a novel strategy to improve early diagnosis of cancers.

The Evolution of Our Understanding of the Biology of Cancer Is the Key to Avoiding Overdiagnosis and Overtreatment

There has been a tremendous evolution in our thinking about cancer since the 1880s. Breast cancer is a particularly good example to evaluate the progress that has been made and the new challenges that have arisen due to screening that inadvertently identifies indolent lesions. The degree to which overdiagnosis is a problem depends on the reservoir of indolent disease, the disease heterogeneity, and the fraction of the tumors that have aggressive biology. Cancers span the spectrum of biological behavior, and population-wide screening increases the detection of tumors that may not cause harm within the patient's lifetime or may never metastasize or result in death. Our approach to early detection will be vastly improved if we understand, address, and adjust to tumor heterogeneity. In this article, we use breast cancer as a case study to demonstrate how the approach to biological characterization, diagnostics, and therapeutics can inform our approach to screening, early detection, and prevention. Overdiagnosis can be mitigated by developing diagnostics to identify indolent disease, incorporating biology and risk assessment in screening strategies, changing the pathology rules for tumor classification, and refining the way we classify precancerous lesions. The more the patterns of cancers can be seen across other cancers, the more it is clear that our approach should transcend organ of origin. This will be particularly helpful in advancing the field by changing both our terminology for what is cancer and also by helping us to learn how best to mitigate the risk of the most aggressive cancers.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Autoantibodies in Early Detection of Breast Cancer

In spite of the progress made in treatment and early diagnosis, breast cancer remains a major public health issue worldwide. Although modern image-based screening modalities have significantly improved early diagnosis, around 15% to 20% of breast cancers still go undetected. In underdeveloped countries, lack of resources and cost concerns prevent implementing mammography for routine screening. Noninvasive, low-cost, blood-based markers for early breast cancer diagnosis would be an invaluable alternative that would complement mammography screening. Tumor-specific autoantibodies are excellent biosensors that could be exploited to monitor disease-specific changes years before disease onset. Although clinically informative autoantibody markers for early breast cancer screening have yet to emerge, progress has been made in the development of tools to discover and validate promising autoantibody signatures. This review focuses on the current progress toward the development of autoantibody-based early screening markers for breast cancer.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Non-Invasive Biomarkers for Early Detection of Breast Cancer

Breast cancer is the most common cancer in women worldwide. Accurate early diagnosis of breast cancer is critical in the management of the disease. Although mammogram screening has been widely used for breast cancer screening, high false-positive and false-negative rates and radiation from mammography have always been a concern. Over the last 20 years, the emergence of "omics" strategies has resulted in significant advances in the search for non-invasive biomarkers for breast cancer diagnosis at an early stage. Circulating carcinoma antigens, circulating tumor cells, circulating cell-free tumor nucleic acids (DNA or RNA), circulating microRNAs, and circulating extracellular vesicles in the peripheral blood, nipple aspirate fluid, sweat, urine, and tears, as well as volatile organic compounds in the breath, have emerged as potential non-invasive diagnostic biomarkers to supplement current clinical approaches to earlier detection of breast cancer. In this review, we summarize the current progress of research in these areas.

Identification of blood protein biomarkers for breast cancer staging by integrative transcriptome and proteome analyses

Breast cancer is the most common malignancy for women. Accurate prediction of breast cancer and its pathological stages is important for treatment decision-making. Although many studies have focused on discovering circulating biomarkers of breast cancer, no such biomarkers have been reported for different stages of this disease. In this study, we identified blood protein biomarkers for each stage of breast cancer by analyzing transcriptome and proteome data from patients. Analysis of the TCGA transcriptome datasets revealed that a large number of genes were differentially expressed in tumor samples of each stage of breast cancer compared with adjacent normal tissues. Blood-secretory proteins encoded by these genes were then predicted by bioinformatics programs. Furthermore, iTRAQ-based proteomic analysis was conducted for plasma samples of breast cancer patients with different stages. A portion of predicted blood-secretory proteins could be detected and verified differentially expressed. Finally, several proteins were chosen as potential blood protein biomarkers for different stages of breast cancer due to their consistent expression patterns at both mRNA and protein levels. Overall, our data provide new insights into diagnosis and classification of breast cancer as well as selection of optimal treatments. SIGNIFICANCE: We identified blood protein biomarkers for each stage of breast cancer by analyzing tissue-based transcriptome and blood-based proteome data from patients. To our knowledge, this is the first time to try to identify blood protein biomarkers for different stages of breast cancer via these integrative analyses. Our data may provide new insights into diagnosis and classification of breast cancer as well as selection of optimal treatment.

The Use of Immunohistochemistry, Fluorescence in situ Hybridization, and Emerging Epigenetic Markers in the Diagnosis of Malignant Pleural Mesothelioma (MPM): A Review

Malignant pleural mesothelioma (MPM) is an aggressive asbestos related disease that is generally considered to be difficult to diagnose, stage and treat. The diagnostic process is continuing to evolve and requires highly skilled pathology input, and generally an extensive list of biomarkers for definitive diagnosis. Diagnosis of MPM requires histological evidence of invasion by malignant mesothelial cells often confirmed by various immunohistochemical biomarkers in order to separate it from pleural metastatic carcinoma. Often when invasion of neoplastic mesothelial cells into adjacent tissue is not apparent, further immunohistochemical testing - namely BAP1 and MTAP, as well as FISH testing for loss of p16 (CDKN2A) are used to separate reactive mesothelial proliferation due to benign processes, from MPM. Various combinations of these markers, such as BAP1 and/or MTAP immunohistochemistry alongside FISH testing for loss of p16, have shown excellent sensitivity and specificity in the diagnosis of MPM. Additionally, over the recent years, research into epigenetic marker use in the diagnosis of MPM has gained momentum. Although still in their research stages, various markers in DNA methylation, long non-coding RNA, micro RNA, circular RNA, and histone modifications have all been found to support diagnosis of MPM with generally good sensitivity and specificity. Many of these studies are however, limited by small sample sizes or other study limitations and further research into the area would be beneficial. Epigenetic markers show promise for use in the future to facilitate the diagnosis of MPM.

Validation of a Novel Modified Aptamer-Based Array Proteomic Platform in Patients with End-Stage Renal Disease

End stage renal disease (ESRD) is characterized by complex metabolic abnormalities, yet the clinical relevance of specific biomarkers remains unclear. The development of multiplex diagnostic platforms is creating opportunities to develop novel diagnostic and therapeutic approaches. SOMAscan is an innovative multiplex proteomic platform which can measure >1300 proteins. In the present study, we performed SOMAscan analysis of plasma samples and validated the measurements by comparison with selected biomarkers. We compared concentrations of SOMAscan-measured prostate specific antigen (PSA) between males and females, and validated SOMAscan concentrations of fibroblast growth factor 23 (FGF23), FGF receptor 1 (FGFR1), and FGFR4 using Enzyme-Linked immunosorbent assay (ELISA). The median (25th and 75th percentile) SOMAscan PSA level in males and females was 4304.7 (1815.4 to 7259.5) and 547.8 (521.8 to 993.4) relative fluorescence units (p = 0.002), respectively, suggesting biological plausibility. Pearson correlation between SOMAscan and ELISA was high for FGF23 (R = 0.95, p < 0.001) and FGFR4 (R = 0.69, p < 0.001), indicating significant positive correlation, while a weak correlation was found for FGFR1 (R = 0.13, p = 0.16). In conclusion, there is a good to near-perfect correlation between SOMAscan and standard immunoassays for FGF23 and FGFR4, but not for FGFR1. This technology may be useful to simultaneously measure a large number of plasma proteins in ESRD, and identify clinically important prognostic markers to predict outcomes.