Autoantibodies in early detection of breast cancer

Abstract

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 15–20% of breast cancers still go undetected. In underdeveloped countries, lack of resources and cost concerns prevent implementing mammography for routine screening. Non-invasive, 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. While clinically informative autoantibody markers for early breast cancer screening are 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 towards the development of autoantibody-based early screening markers for breast cancer.

Introduction

Breast cancer is the leading cancer type among women with over 2 million new cases expected annually worldwide. In 2020, it is estimated over 279, 000 new cases of breast cancer will be diagnosed in the United States and over 42,000 may succumb to the disease(1). Based on recent reports, the death rate for breast cancer has dropped by 40% between 1989 and 2017 (1, 2). Advancement in treatment and early detection has contributed to this decrease in mortality rate (3). This emphasizes the importance of early detection and screening for timely intervention and better therapeutic outcomes. For instance, the 5-year relative survival rate for 44% of patients with breast cancer approaches 100% if diagnosed at stage 1, but decreases to 26% with stage IV(3). In the US, mammography and physical exams are widely used screening methods for breast cancer (4). For an average-risk woman, screening mammography has the benefit of reducing breast cancer mortality by 40% and thus improving survival (5–8). Although modern screening digital mammography has improved the sensitivity of breast cancer detection (86.9% vs 78.7% pre-digital era), it does not detect all breast cancers (9). Cancers in women with high breast density are often obscured by dense breast tissues(10). Some breast carcinomas tend to grow along the normal breast architecture making them difficult to detect with mammography (8). False-positive results are one of the most common issues encountered in mammography especially among young women and women with dense breasts which leads to follow-up studies including biopsies (11, 12). In the global health setting, low and middle-income countries have a lower frequency of mammography as a population-based screening tool due to affordability, inadequate resources, lack of medical education, and various other logistical limitations. Therefore, there is an intense effort in the search for simple, rapid, and cost-effective blood-based biomarkers for early detection of breast cancers which can be used in parallel with mammography. Many circulating biomarkers including proteins, autoantibodies (AAbs), circulating tumor cells, microRNAs, circulating tumor DNA, and exosomes have been investigated as promising tools to fill this clinical niche (13–17). This review will mainly focus on the development and progress made on tumor-specific AAbs for diagnosis and early detection of breast cancer.

Autoantibodies as Potential Biomarkers

Cancers can induce an immunological response resulting in the production of AAbs directed against self-antigens. Tumor-associated antigens can have abnormal structures, altered protein expression levels, or changes in post-translational modifications (glycosylation, acetylation, methylation, phosphorylation, etc.) that are no longer recognized as “self” by the immune system, thus triggering the production of AAbs (18–20). These AAbs can be exploited as sensors to monitor disease-related proteomic changes to develop useful diagnostic assays. AAbs possess many attractive features as a diagnostic marker for early detection. First, compared to other serum proteins, AAbs are highly stable and less prone to proteolysis making sample processing much easier(21). Second, AAbs may show persistent response over time since they are known to circulate for extended periods as opposed to tumor antigens. Tumor antigens suffer from low concentrations and brief circulation time due to degradation and rapid clearance (21, 22). Third, AAbs are detectable in archived samples and have well-characterized secondary reagents for easy identification, facilitating the development of cost-effective screening tools easily adaptable in a clinical setting. Finally, tumor AAbs are produced early in the tumorigenesis process and have been detected several years before the development of clinical symptoms (23–25). To be clinically useful as an early diagnostic marker the AAbs should allow clear discrimination against the healthy and disease state preferably at the early stages of cancer(22). Moreover, the screening AAbs should be able to distinguish breast cancer patients with high accuracy, sensitivity, and specificity, and therefore quantitative parameters should be established to clearly discriminate positive and negative tests(26). A better way to determine if selected AAbs will make a good early screening marker is to select a series of cutoff values for the assay and determine the specificity and sensitivity. This can be plotted in a receiver operating characteristic (ROC) curve to assess the diagnostic parameters including the area under the ROC curve (AUC) to establish a cut-off value for positivity for early screening(26). The ideal AUC would be 1.0 and the ideal specificity and sensitivity values would each be 100%. But such numbers are virtually never achievable in real circumstances. In most cases, there is a tradeoff between specificity and sensitivity, wherein finding conditions to increase one often diminishes the other. This often depends on the intended application. If the test is to be used as a screening assay, where the greatest cost of error is the missed detection of disease, then optimizing sensitivity is paramount. This is especially true if another test, such as an imaging study, can provide specificity in a second round. In the case of AAbs, it is sometimes possible to combine multiple AAbs, each having good specificity, to improve sensitivity while still maintaining high specificity. Also, it is highly recommended that all AAb studies follow the five-phase schema and the Prospective Sample Collection Retrospective Evaluation (PRoBE) guidelines to vigorously evaluate new diagnostic and screening markers before implementing them as clinical tools (27, 28).

Methods to Identify and Validate Autoantibodies in Breast Cancer

During the early years, AAb discovery studies were performed on small-scale, targeting just a few tumor antigens. Many studies lacked a systemic approach and failed to validate markers beyond the discovery stage. To develop a successful AAb-based screening tool for breast cancer, it is essential to have technologies capable of screening AAbs for thousands of antigens at the initial phase of malignancy. Since many single AAbs show poor performance as screening tools, it is critical to have high-throughput assays at the discovery phase to find complex panels of AAbs with suitable features to develop useful tools for early diagnosis (29, 30). High throughput approaches can process a large number of patient sera rapidly and identify many tumor-associated AAbs at the discovery phase which is important in the process of developing reliable assays. In recent years new technologies were developed facilitating the discovery of novel AAb markers, in high-throughput (31–34).

Phage display-based methods

Phage display-based microarray approaches have emerged as a powerful method to identify AAb profiles in cancers (33, 35, 36). Phage display evolved as a high throughput modification of the SEREX (Serological Screening of cDNA Expression Library) method which resulted in the identification of over 2000 tumor-associated antigens (37–39). In SEREX, total RNA is isolated from tumor cells or tissues to construct a cDNA library. The cDNA library is inserted into the ƛ-phage vectors and proteins are expressed in E.coli. The primary discovery is performed by transferring recombinant proteins into nitrocellulose membranes and probing with patient samples. The current phage display strategies avoid the immunoblotting step and instead subject the library to several cycles of affinity selection to enrich phages for specific clones. The enriched clones can then be eluted and propagated and lysates can be printed onto glass slides to develop a phage-protein microarray (33, 40). The array can be used to incubate serum samples from patients to discover novel AAbs (33). The method does not require large volumes of serum and allows screening thousands of antigens. However, frameshifts, truncated protein expression, lack of mammalian posttranslational modifications, bias towards high abundant transcripts, and labor-intensive procedures are drawbacks of this method. Several groups have implemented this technology for AAb discovery in numerous cancers including breast cancer (33, 35, 36, 41–43). Zhong et al. used a phage display strategy to report an AAb panel (ASB-9, SERAC1, and RELT) for early detection of breast cancer (AUC=0.86) (44). In this study, the authors utilized a breast cancer cDNA T7 phage library to screen 87 breast cancer patients and 87 normal serum samples and showed that the combined panel has a sensitivity of 80% at 100% specificity in predicting breast cancer. However, further validation studies are needed to evaluate its potential for early screening.

Serological proteome analysis (SERPA)

SERPA is a technology that combines two-dimensional electrophoresis, western blotting, and mass spectrometry to identify tumor-associated antigens and autoantibodies (34, 45, 46). In this approach, the proteome from tumor tissues or cancer cell lines is first separated with 2D electrophoresis. Separated proteins are transferred onto a membrane and probed with sera from healthy individuals and cancer patients. The protein spots that specifically react with cancer patient sera are located by superimposing the silver-stained 2D gels with the western blot. Proteins of interest are extracted from the gel and analyzed by mass spectrometry. SERPA utilizes in vivo derived tumor-associated antigens to identify AAb profiles. It avoids time-consuming construction of cDNA libraries and identifies tumor-specific post-translational modifications and various isoforms. However, many proteins are too low in abundance and only a fraction of proteins are detectable when extracted from cells or tumors. It is also challenging to detect membrane-associated antigen. In general, this method is biased toward abundant proteins. SERPA can only recognize responses against linear epitopes and could be labor-intensive to profile large cohorts of serum samples. SERPA has been used as an AAb discovery platform in several cancer types including breast cancer (30, 47–49). Desmetz et al. used SERPA to develop an AAb panel with five candidates to discriminate early-stage breast cancer and healthy controls (AUC=0.73)(30). The authors used both a discovery and validation cohort to develop this panel with 55.2% sensitivity at 87.9% specificity by combining three markers (PPIA, PRDX2, FKB52) they discovered by SERPA with two other previously reported markers (HSP60, MUC1) in the literature. However, it needs further validation in larger cohorts of retrospective and prospective studies before clinical development.

Protein microarrays

Protein microarray is an alternate approach to discover AAbs in high-throughput. Protein microarrays enable the screening of a large number of antigens with low sample consumption. Several types of protein microarrays can be used for this purpose (50).

a. Purified Recombinant Protein Arrays

Proteins can be expressed in heterologous systems (insect cells, E.coli, etc.), purified, and then printed on a surface (51, 52). They allow proteome scale screening (~19,000 human proteins with various isoforms) for AAbs with low sample consumption, albeit requiring proteins to be immobilized on the array substrate. Both known and predicted cancer-related antigens can be immobilized on a single microscopic slide to generate a comprehensive screening array to be assayed with serum and control samples. When protein arrays display consistent levels of protein at each spot, they avoid some of the biases of cDNA libraries. However, purified protein microarrays are costly, labor-intensive, and need significant quality control measures to ensure proper functionality and maintain stability. Based on the type of protein expression system used, some proteins immobilized on these arrays may lack post-translational modifications. E.coli- based protein expression systems are capable of producing large quantities of antigens in a cost-effective manner but fail to incorporate post-translational modifications. This can be overcome by expressing antigens in human cells.

b. Native Protein Arrays

Some AAb discovery studies utilize native protein arrays where human cell or tissue lysates with naturally expressed proteins are captured on a surface or fractionated with separation methods (53, 54). Once probed with patient sera, targets can be identified by mass spectrometry. This method closely mimics the in vivo environment by printing posttranslationally modified antigens. However, it is difficult to control the proper orientation of the proteins during immobilization, and may sterically block protein surfaces. Ladd et al. used this method to discover glycolysis and spliceosome AAb signatures from patients recently diagnosed with breast cancer (53). In this study, native protein arrays generated from fractionated MMTV-neu and MCF7 cell lysates were used for discovering immunogenic pathways and autoantibody signatures in breast cancer plasmas. This is an interesting study where they used pre-diagnostic plasmas from 48 women with ER+/PR+ breast cancer and 65 healthy controls and discovered significant enrichment of proteins in the glycolysis and spliceosome biological pathways. The ROC analysis on the glycolysis gene set (9 proteins) and spliceosome gene set (14 proteins) signatures gave AUCs of 0.68 and 0.73 respectively. They also reported an AUC of 0.77 with 35% sensitivity at 95% specificity for combined signatures. However, this study was limited due to a small sample cohort and requires more validation studies. In a follow-up study native protein arrays were also used to report autoimmune response signatures associated with the development of triple-negative breast cancer (TNBC)(55). Katayama et al. used a high-density protein array developed from MDA-MB-231 cell lysates to probe serum samples collected before clinical diagnosis of TNBC along with samples collected at the time of diagnosis from participants in the women’s Health Initiative cohort (n=13 for cases and control). The proteins that exhibited immunoreactivity in pre-diagnostic TNBC samples represented major nodes of TP53 and PI3K genes which were commonly mutated in TNBC. The study also reported AAb signatures for cytokeratin proteins associated with a mesenchymal/basal phenotype in pre-diagnostic human TNBC samples.

c. Programmable Protein Arrays

On the other hand programmable protein microarrays like Nucleic Acid Protein Programmable Arrays (NAPPA) print cDNAs encoding the target genes on the matrix instead of purified proteins (31, 32). The slides can then be incubated with a cell-free protein expression system to transcribe and translate the genes to produce proteins within a few hours and can be captured on to the surface via the aid of fusion tags. This method avoids protein purification and proteins can be expressed just in time, just before probing the array with patient sera, minimizing protein degradation, and maximizing the likelihood of natural folding. Cell-free systems with chaperone proteins assist in producing well-folded functional proteins and proteome scale discovery can be performed for discovering AAbs (29, 56). The proteins are displayed at the same level so the likelihood of measuring AAb against all types of antigen targets is high. It also facilitates the incorporation of post-translational modifications and allows to monitor AAb responses against both modified and unmodified targets. Anderson et al. utilized NAPPA arrays to develop an AAb panel (28 antigens) for early detection of breast cancer with 80.8% sensitivity and 61.6 % specificity (AUC=0.756) (56). This study was the first study that used a programmable protein microarray platform for the detection of novel AAb markers with nearly 5000 proteins displayed on the array. This was also the first serum biomarker panel developed for the discrimination of invasive breast cancer from benign breast disease. This study (discussed in detail under AAb panels) aided in the development of the first CLIA certified blood-based assay (Videssa Breast) for breast cancer detection (57–59). In 2015, Wang et al. used NAPPA arrays to build a 13 AAb panel for the detection of basal-like breast cancers with 33% sensitivity and 98 % specificity (AUC=0.68)(29). The programmable array was constructed with 10,000 antigens, approximately 50% of the human genome. This study also identified AAb markers reported in other studies (TP53, NY-ESO-1).

Glycan Arrays

Glycan arrays are high-throughput devices capable of detecting autoantibodies against aberrant glycans (60, 61). Around 1% of human genes undergo glycosylation, a posttranslational modification where carbohydrates are linked to proteins via glycosidic bonds with the aid of enzymes (glycotransferases)(62). When the activity of these enzymes is compromised, it results in the synthesis of aberrant glycans responsible for many diseases including cancer. These unusual glycan structures can trigger an immune response to produce anti-glycan antibodies long before disease onset (63). Some groups have fabricated high throughput devices where glycan structures are immobilized on glass surfaces to screen for anti-glycan antibodies in patient samples (60, 63, 64). Blixt et al. used a glycan array to look for anti-glycan antibodies against Mucin 1(MUC1) glycopeptides and found cancer-associated glycoforms of MUC1 at higher levels in early-stage breast cancer patients(64).

Validation assays

The AAbs discovered in the discovery phase need to be validated with clinically acceptable assay platforms to determine performance measures. Traditionally, singleplex-Enzyme Linked Immunosorbent Assays (ELISA) are the most commonly used platform for validation. Various formats of ELISAs exist and for AAb validation studies, different groups use different antigenic sources, variable attachment chemistries, and detection methods which causes difficulty when comparing results. Most ELISA assays use purified recombinant proteins from heterologous expression systems (E.coli, insects) as the antigenic source which is either adsorbed or captured on to the ELISA plate(30, 44). Antigens expressed from different systems may lack or may show variations in post-translation modifications. Changes may also occur to the conformational structure of the antigen, affecting the reproducibility of the assay. Some labs use antigen-specific antibodies to capture the antigen of interest from human tumor cells and thereby bypass the need for producing purified antigens (65, 66). RAPID ELISA (Rapid Antigenic Protein In Situ Display) is another assay adapted from the NAPPA technology that processes a large number of clinical samples against a limited number of antigens during AAb validation(31). Here, cDNAs for the gene of interest can be readily added and proteins can be produced just in time for testing. Antibodies or ligands against a fusion tag can be used to capture the protein on to the ELISA plate obviating protein purification. Bead-based methods like Luminex-xMAP technology are increasingly popular for AAb validation studies due to multiplexing capabilities (67–69). In these bead-based assays, the fluorophore-labeled beads can be coated with antigen-specific capture antibodies to immobilize the antigens on to the surface. It allows simultaneous analysis of serum antibodies against 100 different antigens saving sample consumption, cost, and time although absorption of antibodies in human sera on beads leads to non-specific background.

Autoantibodies to Individual Tumor Antigens in Breast Cancer

Tumor-specific AAbs which have the potential to be used for early screening have been reported in the sera of breast cancer patients (Summarized in Table 1 and Table 2). However, only very few have been studied in detail to understand the diagnostic utility in early cancer detection.

Table 1:

List of individual autoantibodies discussed in this review

Gene name	Patient cohort		% Positive	Sensitivity (%)	Specificity (%)	AUC	Methods	Reference	Year
MUC1	24 BC		8.3				ELISA	Kotera et al.	1994
TP53	176 BC, 76HC		25.5				ELISA	Green et al.	1994
TP53	182 BC, 76 HC		26				ELISA	Mudenda et al.	1994
MUC1	40 BBD, 140 BCP, 96 HC			26	88		ELISA	Von Mensdorff-Pouilly et al.	1996
HER2	107 BC, 200 HC		11				ELISA, Western Blotting	Disis et al.	1997
SURVIVIN	46 BC, 10 HC		23.9				ELISA	Yagihashi et al.	2005
LIVIN			32.6
CDKN2A (p16)	41 BC and 82 HC		7				ELISA, Western Blotting	Looi et al.	2006
c-MYC			9
TP53			5
c-MYC	97 PBC, 40 DCIS, 94 HC	PBC vs HC		13	97		ELISA	Chapman et al.	2007
		DCIS vs HC		8	97
TP53		PBC vs HC		24	96
		DCIS vs HC		15	96
NY-ESO-1		PBC vs HC		26	94
		DCIS vs HC		8	94
BRCA1		PBC vs HC		8	91
		DCIS vs HC		3	91
BRCA2		PBC vs HC		34	92
		DCIS vs HC		23	92
HER2		PBC vs HC		18	94
		DCIS vs HC		13	94
MUC1		PBC vs HC		20	98
		DCIS vs HC		23	98
AHSG	81 BC and 73 HC		79.1				2DE, Immunoblot, Mass spectrometry	Yi et al.	2009
HSP60	Discovery						SERPA, ELISA	Desmetz et al.	2008
	20 BC, 20 other cancers, 10 AID, 20 HC
	Validation	BC vs HC		31.8	95.7	0.637
	49 DCIS, 58 T1N0, 93 HC	T1N0 vs HC		31	95.7	0.634
		DCIS vs HC		32.7	97.5	0.642
Globo H	58 BC, 47 HC						Glycan arrays	Wang et al.	2008
MUC1 (comb)	395 BC, 108 BBD, 99 HC	BC vs HC		10.6	95.9	0.72	Glycopeptide arrays	Blixt et al.	2011
		BC vs BBD		10.6	97.2	0.77
SERPINA1 (Alpha 1-antitrypsin)	25 BC, 20 HC		96				2D-gel, Mass spectroscopy	Lopez-Arias et al.	2012
GAL3	Discovery 10 PBC, 10 DCIS, 20 BBL, 20 AID and 20 HC Validation 59 PBC, 55 DCIS and 68 HC	HC vs PBC		47	88	0.67	SERPA, ELISA	Lancombe et al.	2013
		HC vs DCIS		24	97	0.56
PAK2		HC vs PBC		31	94	0.59
		HC vs DCIS		27	91	0.52
PHB2		HC vs PBC		32	94	0.62
		HC vs DCIS		18	97	0.50
RACK1		HC vs PBC		31	97	0.61
		HC vs DCIS		29	94	0.57
RUVBL1		HC vs PBC		31	93	0.59
		HC vs DCIS		18	94	0.50
HNRNPF	155 BC, 40 other cancers, 155 HC	HC vs BC		84.2	60.8	0.72	SEREX, ELISA	Dong et al.	2013
FTH1		HC vs BC		81.2	56.1	0.68
BRCA1, TP53	13 pre-diagnostic (TNBC), 13 HC						Microarray, Mass spectrometry	Katayama et al.	2015
Cytokeratin
Network proteins
Plasminogen	29 BC, 43 HC		69				2D-gel, Mass spectrometry, ELISA	Goufman et al.	2015

AUC: area under curve; BC: breast cancer; HC: healthy Control; BBD: benign breast disease; BCP: breast cancer pre-treatment; PBC: primary breast cancer; DCIS: ductal carcinoma in situ; BLBC: basal-like breast cancer; TNBC: triple-negative breast cancer

Table 2:

List of autoantibody panels discussed in this review

Gene name	Sample cohort		Sensitivity (%)	Specificity (%)	AUC	Methods	Reference	Year
CDKN2A (p16), c-MYC, P53	41 BC, 82 HC		43.9	97.6		ELISA, Western Blotting	Looi et al.	2006
P53, c-MYC, NY-ESO-1, BRCA2, HER2, MUC1	97 PBC, 40 DCIS, 94 HC	HC vs PBC	64	85		ELISA	Chapman et al.	2007
		HC vs DCIS	45	85
ASB-9, SERAC1,RELT	87 BC, 87 HC		77	82.8		Phage Display, ELISA	Zhong et al.	2008
PPIA, PRDX,FKBP52, MUC1,HSP60	Discovery 20 BC, 10 other cancers, 10 AID, 20 HC Validation 82 DCIS, 60 PBS and 93 HC	HC vs Cancer	60.5	77.2	0.74	SERPA, ELISA	Desmetz et al.	2009
		HC vs PBC	55.2	87.9	0.73
		HC vs DCIS	72.2	72.6	0.80
ATP6AP1,PDCD6IP,DBT, CSNK1E,FRS3,RAC3, HOXD1,SF3A1,CTBP1, C15ORF48,MYOZ2,EIF3E, BAT4,ATF3,BMX,RAB5A,UBAP1, SOX2,GPR157, BDNF,ZMYM6,SLC33A1, TRIM32,ALG10,TFCP2, SERPINH1,SELL,ZNF510	Phase 1-Discovery 53 BC, 53 HC	BC vs BBD	9–40	91		NAPPA protein array, ELISA	Anderson et al.	2011
	Phase2-Training 51 BC, 39 BBD
		BC vs HC	80.8	61.6	0.756
	Phase3-Validation 51 BC, 38 HC
HER2, P53, CEA, CCNB1 (cyclin B1) HER2, P53, CCNB1(cyclin B1)	Initial Triage set 98 BC time of treatment, 98 HC					ELISA	Lu H et al.	2012
	Primary Validation 20 BC time of diagnosis, 20 HC				0.73
	Secondary Validation 33 BC before diagnosis, 45 HC				0.60
GAL3,PAK2, PHB2, RACK1, RUVBL1	Discovery 10 PBC, 10 DCIS, 20 BBL, 20 AID and 20 HC Validation 59 PBC, 55 DCIS and 68 HC	HC vs PBS	66	84	0.81	2D-gel-Mass spectrometry, ELISA	Lacombe et al.	2013
		HC vs DCIS	82	74	0.85
IMP1, P62, KOC1, TP53, c-MYCc, SURVIVIN,CDKN2A (p16),CCNB1 (cyclin B1) CCND1 (cyclin D1), CDK2	41 BC, 82 HC	HC vs BC	61	86.6		Mini array, ELISA	Ye et al.	2013
Glycolysis signature (9 proteins) Spliceosome signature (14 proteins) Glycolysis signature + Spliceosome signature	Discovery 48 BC (pre-diagnosed), 65 HC Validation 61 BC (newly diagnosed), 61 C 118 BC (pre-diagnosed), 120 HC				0.68	Native Microarrays, ELISA	Ladd et al.	2013
					0.73
			35	95	0.77
CCNB1,FKBP52,GAL3,PAK2, PRDX2,PPIA,P53,MUC1	Discovery and Validation 87 DCIS 153 PBS 156 HC	BC vs HC PBC vs HC DCIS vs HC				ELISA	Lancombe et al.	2014
			90	42
			90	51
			90	32
CTAG1B,CTAG2,P53,RNF216, PPHLN1,PIP4K2C, ZBTB16,TAS2R8,WBP2NL, DOK2,PSRC1,MN1,TRIM21	Discovery 45 BLBC,45 HC Validation 145 BLBC,145 HC		33	98	0.68	NAPPA Protein array, ELISA	Wang et al.	2015
LGALS3, PHB2,MUC1, GK2, and (CA15–3)	Discovery 10 BC, 5 HC Validation 100 BC, 50 HC		87	76	0.872	SEREX, ELISA	Zuo et al.	2016
CDKN2A (p16),c-MYC,P53, ANXA	102 BC, 146 HC		33.3	90	0.725	ELISA	Liu et al.	2017

AUC: area under curve; BC: breast cancer; HC: healthy control; BBD: benign breast disease; BCP: breast cancer pre-treatment; PBC: primary breast cancer; DCIS: ductal carcinoma in situ; BLBC: basal-like breast cancer

p53 AAbs

p53 is one of the most studied tumor-associated antigens in cancer(70). In healthy cells, wild type p53 is predominantly present in the nuclei in low concentrations. It plays a key role as a mediator in cell cycle arrest and apoptosis and is crucial for suppressing uncontrolled cell growth(71). p53 is often mutated in many solid tumors and various reports have shown these mutations can occur during the early stages of cancer development(72). Mutant p53 proteins are more stable with a half-life of several hours compared to wild type p53 which lasts only a couple of minutes. This causes mutant p53 to accumulate in the nucleus and escape into the cytoplasm, eventually inducing the immune system to generate autoantibodies (73, 74). The earliest report providing evidence for p53 AAbs goes back to 1979 when DeLeo et al. reported that the humoral response of mice to some chemically-induced tumor cells was directed against the protein p53(75). A few years later Crawford et al. demonstrated that around 9% of stage 1 and 2 breast cancer patients had p53 autoantibodies in their sera (76). Since then numerous studies have reported the presence of p53 AAbs in various cancers including breast cancer (77–80). Around 10–15% of early-stage breast cancers have detected p53 AAbs (81–84). Several studies have reported a positive correlation between the presence of p53 AAbs and p53 missense mutations and/or accumulation (70, 74). However, as a standalone marker p53, AAbs have low sensitivity for screening(85). Although there is a significant correlation between the anti p53 AAb levels among healthy controls and cancer patients, the AAb is not specific enough to distinguish one cancer from another. In addition, only around 20–40% of patients harboring p53 mutations develop AAbs(70). Patients with similar mutations in similar cancer types could either be positive or negative for p53 AAbs indicating the influence of other factors in antibody response (70, 86, 87). Therefore, p53 AAbs alone will not be sufficient for early disease screening of breast cancer. Moreover, the association of p53 AAbs has shown conflicting results for different tumor stages of breast cancer. Several studies have reported that the p53 AAb levels do not correlate with the disease stage (88, 89). Others have shown a higher frequency of AAbs in late-stage breast cancers (83, 84). Although p53 alone is less useful as an early screening marker its discovery has aided in developing AAb panels with better diagnostic characteristics (25, 78)

MUC1 AAbs

Mucin 1(MUC1) is a single-pass type 1 transmembrane protein with a heavily glycosylated extracellular domain (90, 91). MUC1 is normally expressed in the cell surface of secretory epithelia including the mammary gland, respiratory, urinary, gastrointestinal, and reproductive tract (92). Mucins are a family of glycoproteins with a high molecular weight with extracellular domains extending up to 200–500 nm from the cell surface (90, 91). In healthy tissues, MUC1 protects the epithelia and acts as a barrier against pathogen colonization(90). MUC1 overexpression is observed in more than 90% of breast cancers and frequently appears in other cancers including pancreatic, ovarian, colon, and lung cancers (92, 93). The MUC1 protein present in tumor cells shows aberrant glycosylation patterns and changes in cellular distribution (94, 95). High expression levels of MUC1 with altered glycan patterns can induce an immune response which leads to the production of glycoprotein specific AAbs. In early 1990s many groups reported the presence of humoral immune response to MUC1 in patients with benign and malignant breast tumors (96–98). Since then a number of studies have implicated the usefulness of anti-MUC1 antibodies for early detection of breast cancer (64, 78, 99). By using glycopeptide micro-arrays with 60mer MUC1 glycopeptides, Blixt et al reported significantly higher levels of MUC1 AAbs in early-stage breast cancer patients (n=365) than in women with benign breast disease (n=108) or healthy controls (n=99) (64). The data reported a sensitivity of 10.6% for MUC1 glycan combinations with 95% specificity. However, a large scale follow-up study performed by the same group with both discovery (Breast cancer patients n=240, controls n=273) and validation samples (Breast cancer patients n=431, controls n=431) showed no difference between the cases and controls(100). This study emphasized the importance of performing independent validation on diagnostic markers. Another study conducted with a population of women with BRCA1 and BRCA2 mutations (n=127) reported lower levels of MUC1 AAbs among the mutation carriers than the healthy controls(101).

HER-2/neu AAbs

HER-2/neu belongs to the family of epidermal growth factor receptors and plays an important role in cell proliferation(102). Around 20% of newly diagnosed breast cancers have amplification or overexpression of HER2 and show more aggressive disease with worse prognosis(103, 104). In 1997, Disis et al reported antibody titers of > 1:100 of HER-2/neu antibodies in 11% of breast cancer patients demonstrating a correlation with HER-2/neu protein overexpression in the primary tumor (105, 106). In a study reported with patients newly diagnosed with primary invasive breast cancer (PBC) and ductal carcinoma in situ (DCIS), AAbs for HER-2 reported a sensitivity of 18% for PBC and 13% for DCIS with 94% specificity(78). In a more recent study, Lu et al. used an initial triage set of breast cancer samples collected at the time of treatment (n=98) with matched controls (n=98) to measure the AAb response against eight known tumor-associated antigens which also included Her-2(25). Her2 demonstrated a significant increase in AAb response in cancer patients. When subjected to primary validation (20 breast cancer samples collected at the time of diagnosis along with matched controls) followed with secondary validation (breast cancer samples collected before the time of diagnosis n=33 with matched controls n=45), they observed a significantly high serum antibody response for Her-2 (AUC=0.63, p=0.026) in pre-diagnostic sera. About 15% of the pre-diagnostic breast cancer patients were positive for HER-2 AAbs. This study was one of the first studies that reported the occurrence of serum AAbs in pre-diagnostic sera from patients with breast cancer using samples collected using the PRoBE guidelines. However, the study was conducted with a small patient cohort and needs to be validated in a larger sample size.

Other Markers

Numerous other tumor-associated AAbs (Table 1 and Table 2) have been reported as potential markers for early diagnosis of breast cancer. More investigation and validation are needed to evaluate the true clinical potential of these markers (55, 107–116).

Autoantibody Panels for Early Detection of Breast Cancers

Most of the individual autoantibodies identified to date suffer from low clinical sensitivity, hence cannot be used for early disease screening. Only a fraction of patients respond to tumor antigens and no single serum marker exists that can be used for early breast cancer screening. Therefore, to increase the sensitivity for early diagnosis, several groups have developed tailor-made autoantibody panels (Table 2) (29, 30, 56, 85, 117–119). When Chapman et al. used six tumor antigens (p53, c-myc, HER2, NY-ESO-1, BRCA1, BRCA2, and MUC1) to investigate AAbs in PBC and DCIS patients, sensitivities for individual AAbs varied between 8–34% (PBC) and 3–23% (DCIS) compared to healthy controls for 91–98% specificity(78). However, when used as a panel, 64% of patients with PBC and 45% from DCIS showed elevation of at least one of the six autoantibodies at 85% specificity(78). In another study serum, AAbs detected against a combined panel of five tumor antigens (FKBP52, PPIA, PRDX2, HSP60, and MUC1) accurately discriminated between early-stage breast cancer (AUC=0.73; 55.2% sensitivity and 87.9% specificity) and carcinoma in situ (AUC=0.80; 72% sensitivity, 72.6% specificity) from healthy individuals(30). These initial AAb panels demonstrated a promising trend for early breast cancer screening. However, these studies are in phase 1/2 of the ProBE guidelines and need large-scale retrospective and prospective studies to understand their usefulness as early diagnostic markers.

In 2011, Anderson et al. used a three-phase screening approach to detect AAbs for early-stage breast cancers (IBC). In the first stage, sera from IBC (n=53) and healthy control (n=53) were screened against 4988 antigens via a high-density protein array, NAPPA(56). After eliminating uninformative antigens 761 antigens with high responses were screened in the second stage using an independent set of IBC sera (n=51) and sera from women with benign breast disease (n=39). 119 antigens were selected from the second phase (sensitivities from 9–40% at 91% specificity) to conduct the phase three validation study. In the third phase, with an independent serum cohort (n=51 cases/38 controls, also benign disease), 28 of these antigens were confirmed with an ELISA assay under blinded conditions. The 28 AAb panel had a sensitivity of 80.8% and a specificity of 61.6% (AUC=0.756) for early detection of breast cancer. This discovery and validation study (Phase1/2), later led to the development of Videssa Breast a blood-based combinatorial proteomic biomarker assay (57). In 2017, two prospective clinical trials were conducted to evaluate the potential of Videssa Breast, which combined 10 AAbs with 8 serum protein biomarkers to detect breast cancer in women under the age of 50 years(59). The validation cohort reported a sensitivity and specificity of 66.7% and 81.5%, for Videssa Breast, demonstrating its potential to effectively detect breast cancer and indicating it would be useful to combine this assay with image-based screening modalities. In other studies, Videssa Breast has further demonstrated that breast density does not impact the ability of the assay to detect breast cancer and it may provide clinicians extra information which potentially would aid in reducing false positives in breast cancer imaging (58, 120).

In a subsequent study, Wang et al. reported plasma autoantibodies associated with basal-like breast cancer (BLBC), a rare aggressive subtype less likely to be detected via mammography(29). This study used BLBC patients (n=45) and controls (n=45) from the Polish Breast Cancer study to screen 10,000 antigens on high-density NAPPA protein arrays in the discovery phase. From the initial screen, 748 promising AAbs were identified and subjected to further validation using a cohort of BLBC (n=145) and age-matched controls (n=145). This study reported a 13-AAb panel to distinguish BLBC from controls with 33% sensitivity and 98% specificity. This study (Phase1/2) was mainly focused on basal-like breast cancer subtype. They used a large number of BLBC patient samples and age-matched controls with detailed data on tumor characteristics, demographics, treatment information. However, it is unclear from this study how early these markers are present and require further validation in prospective cohorts.

Lacombe et al. reported a panel of five AAbs (GAL3, PAK2, PHB2, RACK1, and RUVBL1) as a diagnostic tool for screening early-stage and pre-invasive breast cancer(117). To discover new AAbs they used 2D gel analysis and Mass spectrometry on a discovery cohort (n=80) and identified 67 interesting targets that elicited a humoral response. Five of the targets were selected and validated with an independent sample set (n=182) with ELISA. As a panel, the five markers discriminated early-stage cancer from healthy controls (AUC=0.81; 95%CI) and reported a sensitivity of 66% and a specificity of 84% for early-stage breast cancer patients. Here, they used two independent serum cohorts to identify and validate the five protein panel (Phase1/2). However, a systematic, prospective trial is needed to further investigate the clinical effectiveness of this panel for early diagnosis. A follow-up study reported a multi-parametric serum marker panel for screening early-stage breast cancer that could be used along with mammography to improve early diagnosis (85). Here, the authors explored the AAb response against 13 antigens (HSP60, FKBP52, PRDX2, PPIA, MUC1, GAL3, PAK2, p53, CCNB1, PHB2, RACK1, RUVBL1, and HER2) already identified in the literature in a large prospective cohort of 240 patients with node-negative early-stage disease or DCIS with 156 healthy controls. Single AAbs demonstrated a weak performance in discriminating breast cancer from healthy controls (AUC ranging from 0.52–0.65.). When used as an AAb panel the discrimination power was improved (AUC=0.82; 95% CI) between the breast cancer and the healthy cohort. The screening test showed 90% sensitivity with 42% specificity. For a different subtypes, the panel discriminated node-negative early disease with 51% and DCIS with 32% specificities at 90% sensitivity. Patients younger than 50 years with node-negative early stage breast cancers showed 59% specificity with 90% fixed sensitivity. However, large-scale trials are needed to further evaluate its potential for early screening and clinical management.

Conclusions and Future Directions

Multiple breast cancer-specific AAbs have been identified for early diagnosis of breast cancer. Although individual AAbs have shown poor performance for population-based screening autoantibody panels have shown encouraging results. Modern screening digital mammography has a sensitivity of 86.9% for breast cancer screening (9). None of the AAb panels reported so far for breast cancer qualify as standalone screening assays but could be useful in combination with routine mammography screening(58). Moving these promising AAb candidates into clinical use necessitates a rigorous systematic approach. Proper study design, statistical models, extensive analytical and clinical validation with well-defined quantitative parameters are necessary attributes to develop a useful AAb-based diagnostic screening tool for breast cancer. Future studies should follow the recommended five-phase schema and the Prospective sample Collection Retrospective Evaluation (PRoBE) guidelines suggested for all biomarker studies before any clinical evaluation (27, 28). Many AAbs reported for breast cancer early screening have not gone beyond the discovery phase and most lack blinded validation studies. Most of the studies discussed here belong to Phase 1, preclinical exploratory, or Phase 2, clinical assay, and validation phases in biomarker development (29, 64, 78, 117). Only a few studies have conducted retrospective longitudinal studies (Phase 3) where they have attempted to detect preclinical disease (25). It is vital to have newly diagnosed patient sera and samples collected before diagnosis when validating AAb panels to determine the true potential of the markers for early detection. The National Cancer Institute Early Detection Research Network (NCI-EDRN) has supported early screening studies by developing a multi-center breast cancer reference set of plasma and sera, a precious resource for validation studies. Despite many challenges, AAbs have great potential for early screening of breast cancers and would be useful when used in conjunction with mammography.