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| Version | Summary | Created by | Modification | Content Size | Created at | Operation |
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| 1 | Claudia Scotti | + 5680 word(s) | 5680 | 2021-03-15 03:52:34 | | | |
| 2 | Peter Tang | Meta information modification | 5680 | 2021-03-30 07:20:00 | | |
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Auto-antibodies are classically associated with autoimmune diseases, where they are an integral part of diagnostic panels. However, recent evidence is accumulating on the presence of auto-antibodies against single or selected panels of auto-antigens in many types of cancer. Auto-antibodies might initially represent an epiphenomenon derived from the inflammatory environment induced by the tumor.
1. Introduction
Cancer is one of the leading causes of death worldwide along with cardiovascular diseases. Huge progress has been made during the last few decades towards early detection, effective treatment, and follow-up, giving oncologists powerful tools in the fight against cancer. However, more research is needed to reduce mortality and increase quality of life and disease-free survival for patients. In this perspective, the search for new markers as indicators for the presence of tumors and as tools to monitor disease progression is essential. Moreover, the discovery of each new marker opens the possibility to shed light on potentially new pathophysiological mechanisms. This is particularly true when antibodies are considered, because of the central role they have in the immune response. Physiologically, they represent a key defense mechanism against infectious diseases, recognizing molecules of exogenous microorganisms as non-self. Their effect is instead detrimental when they target the host tissues in autoimmune diseases. In these pathological conditions, auto-antibodies cause inflammation in joints, such as in rheumatoid arthritis (RA), or can affect the lungs, blood cells, nerves, and kidneys in systemic lupus erythematosus (SLE), or intestines, such as in inflammatory bowel disease (IBD). Worth noting is that patients affected by these diseases have a significantly modified risk to develop cancer [1]. This risk is often increased in SLE, RA, Sjögren syndrome, IBD, and systemic sclerosis, resulting in frequent occurrence of several types of cancers, as reviewed in [2], but is sometimes decreased, such as for breast cancer in SLE, RA, and psoriatic arthritis (PA) [3][4]. It is possible that pre-existing auto-antibodies could directly contribute to promote or suppress cancer progression, but they could also represent the indirect beacons of underlying immunological phenomena closely conditioning cancer development.
The specific pathogenic role of auto-antigens and auto-antibodies is still quite unclear in the realm of autoimmune disorders. Even more intriguing is the fact that auto-antigens and auto-antibodies are being detected in an increasing number of oncological conditions in the absence of overt autoimmune diseases and involving antigens totally unrelated to classical autoimmune conditions. This has raised questions about their role in the progression of cancer and about their potential applications in diagnosis, therapy, and prognosis, as well as about the connection between cancer and autoimmune diseases.
Tumors consist of a complex mixture of both germline-encoded and novel somatically generated antigens. The first class typically derive from proteins that are not antigenic in normal cells. However, when tumor cells start expressing them well above normal levels, or in places where they become exposed to the immune system, while normally they are not, they can become so called tumor-associated antigens (TAAs). The second class of antigens are derived from normal genes by somatic mutation, deletion, or epigenetic modifications and are called tumor-specific antigens (TSAs). On one side, it is not surprising that newly generated cancer antigens can induce an immune response; being novel sequences, they can be recognized as non-self by the immune system, as if they were exogenous molecules. It is more challenging to explain how unmodified cancer TAAs can induce an immune response and how both these phenomena occur only in subgroups of patients. In this respect, the current views concerning the pathogenic mechanisms triggering autoimmune diseases can be useful. Like cancer, even autoimmune diseases are derived from a combination of environmental and genetic factors. Polymorphisms in various genes can result in defective regulation or reduced threshold for lymphocyte activation, and environmental factors (e.g., infections, traumas) initiate or augment activation of self-reactive lymphocytes that have escaped control and that can react against auto-antigens [5]. A role for extracellular vesicles in the process of auto-antibody production has also been proposed [6].
Independently from the mechanism, the capacity of the immune system to detect TSAs and TAAs enables immunosurveillance, the process whereby the body can normally remove newly formed tumor cells well before their growth becomes uncontrolled. The escape from immunosurveillance is, in fact, one of the major mechanisms leading to tumor growth and, in recent years, has been tackled by some innovative immunotherapeutic approaches, among which are anti-PD-1 antibodies [7].
Some tumor antigens are expressed in such a high percentage of tumors that they are called “universal antigens”. Examples of antigens expressed in more than 50% of tumor types and able to induce an immune response are p53, NY-ESO-1, survivin, and MART-1 [8][9][10][11].
The concept of the host immune system generating anti-cancer immune cells and antibodies [12] is one of the pillars of immune-based targeted therapies [13][14], as cell- and antibody-based drugs have been shown to inhibit cancer growth through different complementary mechanisms. However, there is growing evidence suggesting that B cells and antibodies can also be involved in tumor promotion and resistance to cancer therapy [15][16][17], with the observation that B cell depletion can suppress tumor growth in mice [18]. In ovarian cancer, the presence of CD20+ B cells has been associated with an increased survival, while in contrast, the presence of regulatory B cells (Bregs) induces immunosuppressive effects, supporting tumor growth [19]. The B-cell response to cancer antigens, therefore, looks to have complex functional features, likely dependent on the specific antigen, or antigens, involved. One useful consequence of the engagement of B cells in the anti-tumor response is that, independently from their positive or negative role, new anti-tumor-specific antibodies are typically generated in the host organism. In cases where they can be detected in the bloodstream, they can herald tumor presence, facilitating early detection or aiding in the identification of an ongoing change within the tumor, for example, towards a metastatic phenotype. Treatment can also influence anti-cancer auto-antibody levels, as demonstrated by Evans et al. [20], where the treatment modality was shown to have a different effect.
2. Cancer Auto-Antibodies
Many different cancer types have been reported to induce the production of auto-antibodies. In the following paragraphs, a survey of the relevant literature regarding the most common cancer types is proposed in anatomical order from head to foot.
2.1. Head and Neck Cancer
Head and neck cancers (HNCs) are neoplasms that most commonly originate from squamous cells that make up the epidermis that line the mucosal surfaces inside the head and neck such as the oral cavity, the larynx and pharynx, as well as the nasal and paranasal spaces. Since these tissues are exposed to external factors, the main risk factors have been identified to be tobacco smoke, alcohol, and environmental pollution. In addition, pathogens such as human papillomavirus (HPA) are often causative for this type of cancer as well [21]. The main treatment of HNC remains one that is based on traditional approaches such as surgery, radiation therapy, and chemotherapy, often combined. However, in the last decade, several specific genetic targets have been identified that allow for targeted immunotherapeutic approaches [22]. This search for specific biomarkers has led to the identification of circulating antibodies specifically recognizing the cancer-testis antigen SP17, which was found to be present in 31% of serum samples taken from patients and not present in serum from the healthy control group [23]. The authors compared it to previously reported anti-p53 auto-antibodies, which were found in only 25% of HNC patients [24]. Most importantly, the SP17 antibodies were detected in patients with regionally confined as well as metastasized disease, making it a promising candidate for diagnostic application, especially when combined with additional biomarkers such as the “classical” cytokines and chemokines (IFN-γ, IL-13, MIP-1β, IP-10) [25].
2.2. Central Nervous System Cancers
Central nervous system (CNS) tumors are not easily diagnosed because of the reduced symptomatic manifestations. Moreover, the procedure to obtain a biopsy of suspect masses is a cumbersome, risky, and sometimes impossible procedure. Glioma and meningioma are the most common forms of CNS tumors [26]. O-6-methylguanine-DNA methylase (MGMT) is a biomarker of resistance to chemotherapy in glioma patients. MGMT is involved in DNA repair, and its overexpression and methylation state are routinely investigated in glioma patients to predict drug resistance. A peptide microarray study was conducted to evaluate the production of auto-antibodies against MGMT, and the authors found a good correlation between the presence of the MGMT-2 peptide auto-antibodies and the risk of chemotherapy resistance and disease recurrence [27]. Another study evaluated the proteome signature of meningiomas using human proteome arrays. The study results showed that several proteins overexpressed in grade I and grade II meningiomas are known targets of auto-antibodies, such as IGHG4, CRYM, EFCAB2, STAT6, HDAC7A, and CCNB1 [28]. In a different study, circulating anti-pituitary (APA) and anti-hypothalamus (AHA) antibodies were evaluated in children with CNS tumors. Detectable levels of APA and AHA antibodies were found in patients, but not in healthy controls. In particular, the presence of APA and/or AHA was elevated in patients diagnosed with germinomas, gliomas, and craniopharyngiomas. A similar increase in APA and/or AHA was described in autoimmune pituitary conditions [29].
2.3. Gastrointestinal Tumors
Gastrointestinal cancers are among the most frequent cancers, and they are the third leading cause of cancer-related death worldwide, with an estimation of growing incidence in the next few years. Diffuse gastrointestinal cancers are mainly related to abnormal expression or mutations of E-cadherin, a cell–cell adhesion protein. Several studies have analyzed auto-antibodies’ correlation with gastrointestinal cancer. The most frequently tested and detected are anti-p53 auto-antibodies, but their abundance does not always correlate with gastrointestinal cancer prognosis, stage, or grade, with some exceptions [30]. Adding anti-p53 antibodies to conventional markers significantly improved the overall detection rates of esophageal and colorectal cancers [31]. Stage-related auto-antibody abundance was described for the antigen NY-ESO-1, detectable in significant concentrations in late-stage patients for which the persistence of auto-antibodies is related to poor prognosis. Similarly, anti-AEG-1 auto-antibodies were also mostly found in late-stage patients [32]. Good sensitivity but poor correlation with tumor stage was described for anti-CTAG2, anti-DDX53, anti-MAGEC1, anti-MAGEA3, and GPR78 [32].
2.4. Thyroid Cancer
Differentiated thyroid carcinoma (DTC) is the most frequent form of endocrine tumor with a good survival rate and low recurrence in previously treated patients. DTC treatment often consists of total or partial surgical removal of the thyroid, followed by iodine-ablation [33]. Cancer clearance and recurrence in DTC is usually monitored by measurement of thyroglobulin (Tg) levels; complete ablation of cancer cells after thyroid removal results in an undetectable level of Tg, and therefore an increase in Tg level indicates only partial cancer removal or cancer recurrence [33][34]. An important aspect in the measurement of Tg levels in DTC patients is the presence of auto-antibodies produced against Tg. Anti-Tg antibodies appear in 10% of normal population and in 15–30% of DTC patients [35]. Patients with a high level of anti-Tg at diagnosis have a higher probability of disease recurrence. Moreover, anti-Tg levels can be monitored to evaluate disease relapse risk; in particular, stable or increasing concentrations of anti-Tg during DTC follow-up is significantly related with disease persistence and recurrence. Conversely, a decrease in anti-Tg after surgery is a sign of good prognosis [34][35].
2.5. Lung Cancer
The majority of lung cancer (LC) cases are diagnosed at advanced stages, primarily because earlier stages of the disease are either asymptomatic or symptoms may be attributed to other causes such as infection or long-term effects induced by smoking. Auto-antibodies are proving to be a useful tool for early detection. Most studies in this field focus on two classes: either general LC or non-small cell lung cancer (NSCLC).
2.6. Breast Cancer
Since very early lesions of the breast are undetectable by mammography or ultrasound scan, auto-antibodies represent a promising tool to allow for early diagnosis and monitoring of tumor progression from an in situ to an invasive or metastatic phenotype [36].
HSP60 is one of the breast cancer antigens that stimulate auto-antibody production, and in one study was detected in 31% cases of early stage breast cancer and in 32.6% cases of ductal carcinoma in situ (DCIS) compared to only 4.5% detection in healthy control patients [37]. In a cohort of women diagnosed with triple-negative breast cancer, a panel of antigens was detected after panning the patient sera with MDA-MB-231 cell line lysate. Out of the detected proteins, the highest score was for PI3K and p53 [38]. On the basis of similar evidence, researchers proposed an immunodiagnostic model for the prediction of breast cancer versus benign lesions and control [39]. Another breast cancer auto-antigen is mitochondrial nuclear retrograde regulator 1 (MNRR1), a mitochondrial protein that regulates multiple genes that function in cell migration and cancer metastasis and that is more highly expressed in cell lines derived from aggressive tumors [40]. Serum p53 auto-antibodies have also been shown to be associated with aggressiveness of breast cancer [41].
Gu et al. [42] have shown that anti-cancer antibodies produced by B cells can mediate recruitment of cancer cells derived from the primary breast cancer tumor into draining lymph nodes, thus contributing to the dissemination of the disease. In particular, this pro-metastatic effect was mediated by binding of pathogenic IgGs to glycosylated heat shock protein family A member 4 (HSPA4), a candidate tumor antigen of the HSP70 family. This cell-surface receptor can activate a signaling cascade that is involved in the expression of the CXCR4 ligand stromal-derived factor 1α (SDF1α) in lymph node stromal cells. In patients with breast cancer, high tumor expression of HSPA4 and elevated serum levels of anti-HSPA4 IgG were found to be associated with lymph node metastasis and poor prognosis [42].
2.7. Adrenocortical Carcinoma
Adrenocortical carcinoma (ACC) is a rare, but aggressive endocrine malignancy, which often has a negative prognosis especially when diagnosed at an advanced stage. Among the determinants of malignant behavior of this tumor, a critical role is played by molecules modulating cell death and resistance to chemotherapeutic agents. One of these molecules is the steroidogenic factor-1 (SF-1) target gene FATE1, encoding for a protein localized at the interface between mitochondria and endoplasmic reticulum, where it regulates Ca2+-dependent and mitotane-induced apoptosis in ACC cells by modulating the distance between the two organelles [43]. FATE1 is expressed at high levels in about 40% of adult ACC and its expression is significantly and inversely correlated with patients’ overall survival. Additionally, FATE1 could be relevant also in other tumors as it has been reported that its silencing increased sensitivity of the NCI-H1155 NSLC cell line to paclitaxel and reduced the viability of a variety of other cancer cell lines. Moreover, circulating antibodies directed against this protein were detected in 3 out of 41 (7.3%) and 4 out of 52 (7.7%) patients with hepatocellular carcinoma in two different studies. Patients with adrenocortical tumors had high tumor FATE1 mRNA expression levels and could mount an immune response against FATE1, as shown by the widespread presence of circulating antibodies directed against this cancer-testis antigen. This is associated with high steroidogenic gene expression, immune cell depletion, and a worse prognosis. High steroid production by FATE1-expressing tumors is likely to create an unfavorable environment for immune cell infiltration and local response against this antigen. On the other hand, FATE1 expression in the most aggressive group of ACC could open new perspectives for immunotherapy using vaccination against this and other cancer neoantigens [43].
2.8. Ovarian Cancer
The overall five-year survival rate for ovarian cancer is below 30%, as over 70% of patients are diagnosed with stages III or IV disease [44]. However, subjects diagnosed with localized disease have a survival rate of 75–90%. There is therefore a need to identify biomarkers that have utility for ovarian cancer early detection. At present, cancer antigen 125 (CA125) is the most investigated early detection marker for ovarian cancer and, because the protein detection in circulation has limited sensitivity, tumor-associated auto-antibodies may improve on the performance of CA125 alone. A study by Fortner et al. [44] demonstrated that, when circulating CA125 levels are examined in the context of anti-CA125 antibodies, their ability for the early detection of ovarian cancer may be improved, especially among women with higher anti-CA125 antibody levels. In these early detected cases, lower circulating CA125 levels were also observed among women with higher anti-CA125 antibody levels, consistent with the hypothesis that higher antibody levels may mask detection of circulating antigen. Circulating immune complexes (CIC) to CA125 have been identified, and potential interference with conventional assays for CA125 has been demonstrated.
2.9. Cervical Cancer
Thus far, auto-antibodies recognizing two different antigens have been identified in cervical cancer—one is the tetra saccharide CA19-9, which strongly increases in patients with more advanced cervical cancer [45], and one is GAPDH [46]. Interestingly, in this latter study, Xu and colleagues [46] found a negative correlation between the level of circulating auto-antibodies and the severity of the cervical lesions. The authors showed that lower anti-GAPDH IgG levels could discriminate between normal cells and more progressive stages of cervical lesions that often lead to cervical cancer (e.g., normal vs. cervical intraepithelial neoplasia (CIN) II and III). This negative correlation of the GAPDH antibody level with different stages of cervical lesion can function as an important biomarker in cervical cancer, especially when combined with additional markers, such as the presence of antibodies to the human papillomavirus (HPV), a pathogen found in nearly all cervical cancer patients [47].
A combination of ELISAs for anti-CA15-3, anti-CEA, and anti-CA19-9 reliably discriminated CINs from normal cases, and cancer from normal cases, suggesting that this combination assay could be useful for primary screening of cervical cancer [45].
2.10. Bladder Cancer
While bladder cancer (BC) is often not perceived as such, it is ranked sixth in absolute incidence rate for men worldwide, while only seventeenth for women [48]. The USA and Europe see the highest incidence rates often related to exposure risk factors such as cigarette smoking, causative of up to 50% of all BC diagnosed as well as some environmental chemical carcinogens [49]. Most BC patients are initially diagnosed with a better-treatable form defined as non-muscle-invasive disease (NMIBC). Several cancer progression subclassifications (using three stages and two grades) have been introduced to define the chance of recurrence, which, in more progressive cases, is very high (50–70%) and will eventually lead to a muscle-invasive disease (MIBC) variant that is associated with a very poor prognosis [49]. For this reason, adequate biomarkers are also needed in bladder cancer in order to identify the aggressive high-grade MIBC. Minami and colleagues [50] found that the expression levels of an important serine/threonine-specific protein phosphatase, PPP1CA, involved in signal transduction, apoptosis, protein synthesis, and cell-cycle regulation, was associated with poorer prognosis, and the authors subsequently investigated IgG serum levels raised against this protein. Serum levels of anti-PPP1CA IgG were found to be higher in BC patients than in healthy individuals, with a specificity of 64.2% and a sensitivity of 65.7%, and were associated with muscular invasion, a higher tumor grade, and poorer prognosis, making it a potential valuable diagnostic and prognostic marker. Besides a role as diagnostic biomarker, understanding the role PP1Ca plays specifically in cancer progression could lead to new therapeutic interventions for this type of cancer [51].
2.11. Prostate Cancer
In a study by Tan et al. [52], the proteins SPARC and Fetuin-A were selected for analysis of auto-antibodies as they were shown to be highly expressed at late stages of prostate cancer. Sera from 117 Caucasian American (CA) and 111 African American (AA) prostate cancer patients with Gleason grades 6–10, and healthy controls (CA, n = 52; AA, n = 45) were analyzed in addition to sera from a biopsy cohort (n = 99). The specificity of auto-antibodies against the respective target proteins was confirmed by immunoblot analysis. Both SPARC and Fetuin-A antibodies were detected in the sera, with significantly lower levels in both CA and AA prostate cancer patients compared to healthy controls. The range of auto-antibodies reactivity to SPARC and Fetuin-A was similar in both CA and AA prostate cancer patients, indicating a similar behavior also across ethnic groups [52].
2.12. Testicular Seminoma
Testicular seminoma accounts for 40% of testicular cancers and originates from the germinative components of testicular epithelium. Testicular seminoma is highly treatable and has a good prognosis. Diagnosis is mainly made by ultrasound investigation. Often, the onset of a testicular disease is accompanied by paraneoplastic encephalitis and, indeed, almost 20% of patients with paraneoplastic limbic encephalitis have concomitant testicular seminoma [53]. Several studies have shown the presence of serum auto-antibodies in patients with paraneoplastic encephalitis, of which, presently, six are recognized as indicative of an ongoing paraneoplastic encephalitis [54]. In particular, high levels of anti-Ta antibodies in men are associated with testicular seminoma diagnosis. Anti-Ta antibodies react with the paraneoplastic protein PNMA2 (former antibody name: anti-Ma2) [55]. In many cases, anti-Ta antibodies can be detected several months before diagnosis [56]. Mandel-Brem [57] and, more recently, Maudes et al. [58] have described the association of detection of Kelch-like protein 11 auto-antibodies to testicular seminoma in patients with paraneoplastic encephalitis. The presence of elevated concentrations of serum anti-Kelch-like protein 11 auto-antibodies, alone or in combination with anti-Ma auto-antibodies, was associated with a poor response to treatment of testicular seminoma [58].
2.13. Lymphomas
Non-Hodgkin’s lymphoma (NHL) is the most common form of lymphoma, a cancer that affects the lymphatic system. A 2012 study investigating the role of the immune system in the progression of NHL found significant levels of circulating auto-antibodies in 150 patients, 50% of which had large B cell lymphoma. The patients were either newly diagnosed, received treatment (i.e., chemotherapy), or were disease-free during follow up. In total, 84% of the patients had one or more auto-antibodies. The antigens identified were Jo-1 and Jo-3, single strand DNA (ssDNA), perinuclear anti-neutrophil cytoplasmic (p-ANCA), antinuclear (ANA), and rheumatoid factor (RF). Patients with newly diagnosed NHL had significantly higher levels of anti Scl-70, anti Jo-1, and RF compared to other patients, indicating the auto-antibodies could not only be used for diagnosis, but could potentially also help with staging [59]. Interestingly, the detected levels of anti-double strand DNA (dsDNA) and anti-ssDNA were relatively low compared to another study by Swissa et al., who showed high levels in 23.8% NHL patients as well as anti-RNP and anti-SM antibodies that were not detected in most of the control group [60]. An extensive investigation into auto-antibodies and recognized neoantigens in human mantle cell lymphomas (MCLs), a rare type of non-Hodgkin lymphoma, was undertaken by Khodadoust et al. [61] using a very thorough genomic and proteomic approach combining MHC isolation, peptide identification, and exome sequencing [61]. However, since no control group of healthy patients was included, no information could be given on diagnostic sensitivity or specificity using these auto-antibody levels. Interestingly, all identified neoantigens were peptides exclusively derived from the lymphoma immunoglobulin heavy or light chain variable regions and, surprisingly, no neoantigens were identified from non-immunoglobulin somatically mutated genes. The study showed that many of the genes presented by MHC were shared between the 17 patients used for the study. However, the peptides were generally unique to each patient except for those with similar MHC-I and/or MHC-II alleles. The authors concluded that, although they could not completely exclude a certain experimental bias towards more abundant epitopes that might have not allowed identification of very rare epitopes, the possibility to identify lymphoma-specific CD4 neoantigens and their use to select and expand endogenous T cells could present an effective lymphoma immunotherapy.
2.14. Melanoma
Different from lymphomas and many other cancers, melanomas are often characterized by a very high mutational load, which increases the likelihood of this type of tumor generating neoantigens. In melanomas, the prognostic value of the mutational load has been investigated and was found to be associated with the clinical benefits of immunotherapy such as the adoptive T cell therapy (ACT), which has shown remarkable results in clinical trials in certain patient groups while failing in others [62]. A comprehensive genomic analysis of melanoma tumor samples by Lauss et al. [63] showed a strong positive correlation between a high mutational load and improved clinical outcome following ACT. This also aligns well with the observation that tumors with a high mutational load respond better to treatment with immune checkpoint inhibitors in melanoma and lung cancer [64][65], and is indicative of an increased production of neoantigens. From these and other studies it has also become clear that the presentation of tumor antigens is dependent on the MHC class I antigen processing pathway and subsequent recognition by CD8+ T cells. Loss of MHC I antigen presentation was found in many advanced-stage melanomas while high-level expression of MHC class I antigen processing machinery (APM) genes was associated with clinical benefits to immune therapies. When combined, the studies on predictive outcome from ACT seems to indicate that the neoantigen load, predicted by the measured mutational load, can function as an independent predictor for survival after ACT. Two more important melanoma characteristics that were found to be strongly associated with improved outcome from immune therapy were a low-proliferative status and the upregulation of genes responsible for antigen presentation by the cells [64][65].
Due to the high mutational load and consequential formation of neoantigens, circulating auto-antibodies could function as important biomarkers for early detection, incredibly crucial for treatment of this disease. Using protein microarrays, melanoma patient sera was compared with healthy control sera. Antibodies in the patient sera (124 patients) recognized a total of 748 antigens, of which 139 stood out, giving a mean specificity of 97% [66]. Of the 139 antigens, 20 were not reactive with healthy control serum. Interestingly, most of the seroreactive proteins were found to be intracellular (101/139), many of which were nuclear (88/139). Many of the top 139 identified reactive antigens belong to common cancer pathways involved in apoptosis, cell cycle, p53 signaling, MAPK signaling, and immune response. The authors concluded by stating that the detection of combinations of auto-antibodies gave superior sensitivity and specificity, and for their cohort, a set of 10 auto-antibodies gave 79% sensitivity and 84% specificity, much higher than can be accomplished with existing individual biomarkers. A further combination of 10 autoantibody biomarkers (ZBTB7B, PRKCH, p53TP53, PCTK1, PQBP1, UBE2V1, IRF4, MAPK8_tv2, MSN, and TPM1) displayed a sensitivity of 79% and specificity of 84% for primary melanoma detection [66].
With high-throughput screening technologies, it is easy to envision that screening for larger sets of auto-antibodies will further increase the accuracy of diagnostic testing.
2.15. Angiosarcoma
Angiosarcoma is a rare soft tissue sarcoma, accounting for less than 2% of total soft tissue sarcomas. Non-specific symptomatology and manifestation, together with the high aggressiveness and propensity of spreading of this type of cancer, make angiosarcomas challenging both for diagnosis and for treatment [67]. Angiosarcoma is often positive for over-expression of VEGF and its receptor (VEGFR), both of which can be the possible target for therapy based on anti-angiogenic agents [67]. Diagnosis of the symptoms and location of angiosarcoma is quite challenging, but recently an association between angiosarcomas and anti-p53 serum auto-antibody levels has been proved statistically relevant and can be exploited both for early diagnosis and for disease staging [68].
2.16. Antibody-Mediated Paraneoplastic Syndromes
There are several paraneoplastic syndromes associated to auto-antibodies. Many of them are due to onco-neural auto-antibodies [69], whose molecular and pathogenic relationship with the original cancer is often difficult to identify. Anti-Ri antibodies [70], breast cancer anti-phospholipids antibodies [71][72], cerebellar degeneration [73][74], glomerular diseases [75], and lung cancer [76] are all conditions which still require a proper pathogenic explanation.
To summarize, Table 1 presents the antigens mentioned in the above paragraphs in alphabetical order and Table 2 lists the main auto-antibody combinations that seem promising in early cancer detection or in the prediction of cancer prognosis. Where available, sensitivity and specificity were also included in dedicated columns.
|
Table 1. Non-comprehensive list of antigens involved in auto-antibodies production in Cancer |
||||
|
Antigen (Short Name/ Gene Name) |
Antigen Name |
Uniprot ID |
Cancer |
Notes |
|
14-3-3z |
14-3-3 protein zeta |
P29310 |
Gastric cancer |
|
|
ACTR3 |
Actin-related protein 3 |
P61158 |
Lung cancer |
Early-stage marker |
|
AEG-1 |
Protein Lyric |
Q86UE4
|
Gastrointestinal cancer |
Stage related (late-stage patients) |
|
AHSG |
Alpha-2-HS-glycoprotein (Fetuin-A) |
P02765 |
Prostate Cancer |
|
|
ALDH1B1 |
Aldehyde dehydrogenase X |
P30837
|
Colorectal cancer |
|
|
ALK |
Anaplastic lymphoma kinase |
Q9UM73
|
Lung cancer |
NSCLC Inversely correlated with stage of disease |
|
ALMS1 |
Alstrom syndrome protein 1 |
Q8TCU4 |
Lung cancer |
|
|
Annexin-1 |
Annexin A1 |
P04083 |
Lung cancer |
|
|
ATP1A4 |
Sodium/potassium-transporting ATPase subunit alpha-4 |
Q13733 |
Lung cancer |
|
|
BCL7A |
B-cell CLL/lymphoma 7 protein family member A |
Q4VC05 |
Lung cancer |
IgA autoantigen, early-stage marker |
|
BCOR |
BCL-6 corepressor |
Q6W2J9 |
Ovarian Cancer |
|
|
C1D |
Nuclear nucleic acid-binding protein C1D |
Q13901 |
Ovarian Cancer |
|
|
CA125 |
Mucin-16 |
Q8WXI7 |
Lung cancer |
|
|
CA125 |
Mucin-16 (Cancer Antigen 125) |
Q8WXI7 |
Ovarian Cancer |
|
|
CA19-9 |
Tetra saccharide CA19-9 |
N/A |
Cervical Cancer |
|
|
CAGE |
Cancer-associated gene 1 protein |
Q8TC20
|
Lung cancer |
|
|
CCDC155 |
Coiled-coil domain-containing protein 155 |
Q8N6L0 |
Ovarian Cancer |
|
|
CCL18 |
C-C motif chemokine 18 |
P55774 |
Ovarian Cancer |
|
|
CCNB1 |
G2/mitotic-specific cyclin-B1 |
P14635 |
Central Nervous system (CNS) tumors |
|
|
CD25 |
Interleukin-2 receptor subunit alpha |
P01589
|
Lung cancer |
|
|
CEA |
Carcinoembryonic antigen-related cell adhesion molecule 5 |
P06731 |
Lung cancer |
|
|
CENPF |
Centromere protein F |
P49454 |
Colorectal cancer |
|
|
CREB3 |
Cyclic AMP-responsive element-binding protein 3 |
O43889 |
Ovarian Cancer |
|
|
CRYM |
Ketimine reductase mu-crystallin |
Q14894 |
Central Nervous system (CNS) tumors |
|
|
CTAG1 |
Cancer/testis antigen 1 |
P78358 |
Colorectal cancer |
|
|
CTAG1A |
Cancer/testis antigen 1 |
P78358 |
Lung cancer |
IgG autoantigen, early-stage marker |
|
CTAG2 |
Cancer/testis antigen 2
|
O75638
|
Gastrointestinal cancer |
not stage-related |
|
CXCL1 |
Growth-regulated alpha protein |
P09341 |
Ovarian Cancer |
|
|
cyclin B1 |
G2/mitotic-specific cyclin-B1 |
P14635
|
Colorectal cancer |
Hepatocarcinoma |
|
Cytokeratin 20 |
Keratin, type I cytoskeletal 20 |
P35900 |
Lung cancer |
|
|
DDX4 |
Probable ATP-dependent RNA helicase DDX4 |
Q9NQI0 |
Lung cancer |
IgG autoantigen, early-stage marker |
|
DDX53 |
Probable ATP-dependent RNA helicase DDX53 |
Q86TM3
|
Gastrointestinal cancer |
not stage-related |
|
EFCAB2 |
Dynein regulatory complex protein 8 |
Q5VUJ9 |
Central Nervous system (CNS) tumors |
|
|
ERP44 |
Endoplasmic reticulum resident protein 44 |
Q9BS26
|
Colorectal cancer |
|
|
FATE1 |
Fetal and adult testis-expressed transcript protein
|
Q969F0
|
Adrenocortical carcinoma |
|
|
FXR1 |
Fragile X mental retardation syndrome-related protein 1 |
P51114 |
Ovarian Cancer |
|
|
GAGE7 |
G antigen 7 |
O76087 |
Lung cancer |
|
|
Galectin1 |
Galectin1 |
P09382 |
Colorectal cancer |
|
|
GAPDH |
Glyceraldehyde-3-phosphate dehydrogenase |
P16858 |
Cervical Cancer |
|
|
GBU4-5 |
Putative ATP-dependent RNA helicase TDRD12 |
Q587J7 |
Lung cancer |
Also known as FLI3072 |
|
GCC2 |
GRIP and coiled-coil domain-containing protein 2 |
Q8IWJ2 |
Lung cancer |
|
|
GNA11 |
Guanine nucleotide-binding protein subunit alpha-11 |
P29992
|
Liver cancer |
Hepatocarcinoma |
|
GNAS |
Guanine nucleotide-binding protein G(s) subunit alpha isoforms short |
P63092
|
Liver cancer |
Hepatocarcinoma |
|
GPR78 |
G-protein coupled receptor 78 |
Q96P69
|
Gastrointestinal cancer |
Non stage-related |
|
GREM1 |
Gremlin-1 |
O60565 |
Lung cancer |
|
|
GRP78 |
Glucose regulated protein 78 |
P11021 |
Ovarian cancer |
|
|
HCA25a |
Hepatocellular carcinoma-associated antigen HCA25a |
Q8NHH4 |
Colorectal cancer |
|
|
HCC-22-5 (SMP30) |
Senescence Marker protein-30 (Regucalcin) |
Q15493 |
Colorectal cancer |
Homogeneous to COOH-terminal of 165 amino acids of senescence marker protein-30 (SMP30) PMID: 16356486 |
|
HCC1 |
Protein SCO1 homolog 1 |
Q8VYP0
|
Liver cancer |
Hepatocarcinoma |
|
HDAC7A |
Histone deacetylase 7 |
Q8WUI4 |
Central Nervous system (CNS) tumors |
|
|
HE4 |
Human epididymis secretory protein 4 |
Q14508 |
Lung cancer |
|
|
HMGB3 |
High mobility group protein B3 |
O15347 |
Lung cancer |
|
|
HRNR |
Hornerin |
Q86YZ3 |
Lung cancer |
|
|
HSF1 |
Heat shock factor protein 1 |
Q00613 |
Ovarian Cancer |
|
|
Hsp40 |
DnaJ homolog subfamily B member 1 |
P25685 |
Colorectal cancer |
|
|
Hsp60 |
Heat shock protein 60 |
P10809
|
Breast cancer |
31% cases of early breast cancer 32.6% ductal carcinoma in situ |
|
Hsp70 |
Heat shock 70 kDa protein |
P0DMV8
|
Colorectal cancer |
|
|
Esophagus cancer |
|
|||
|
HuD |
ELAV-like protein 4 |
P26378 |
Lung cancer |
|
|
IGHG4 |
Immunoglobulin heavy constant gamma 4 |
P01861 |
Central Nervous system (CNS) tumors |
|
|
IMP1 |
Insulin-like growth factor 2 mRNA-binding protein 1 |
Q9NZI8
|
Colorectal cancer |
|
|
Kelch-like protein 11 |
Kelch-like protein 11 |
Q9NVR0
|
Testicular Seminoma |
Patients with paraneoplastic encephalitics Associated to poor response to treatment |
|
KM-HN-1 |
Coiled-coil domain-containing protein 110 |
Q8TBZ0 |
Colorectal cancer |
|
|
Koc |
Insulin-like growth factor 2 mRNA-binding protein 3 |
O00425
|
Colorectal cancer |
|
|
Lmyc2 |
Protein L-Myc |
P12524 |
Lung cancer |
|
|
Ma |
Paraneoplastic antigen Ma1 |
Q8ND90
|
Testicular Seminoma |
|
|
Paraneoplastic antigen Ma2 |
Q9UL42 |
Testicular Seminoma |
|
|
|
MAGE A1 |
Melanoma-associated antigen 1 |
P43355
|
Lung cancer |
|
|
MAGE A4 |
Melanoma-associated antigen 4 |
P43358 |
Lung cancer |
|
|
MAGEA3 |
Melanoma-associated antigen 3 |
P43357
|
Gastrointestinal cancer |
Non stage-related |
|
MAGEC1 |
Melanoma-associated antigen C1 |
O60732
|
Gastrointestinal cancer |
Non stage-related |
|
MAGEC2 |
Melanoma-associated antigen C2 |
Q9UBF1 |
Lung cancer |
IgG autoantigen, early-stage marker |
|
MGMT |
Methylated-DNA-protein-cysteine methyltransferase |
P16455 |
Central Nervous system (CNS) tumors |
Associated to higher risk of chemotherapy resistance and disease recurrence |
|
MMP-7 |
Matrilysin |
P09237 |
Esophageal cancer |
|
|
MNRR1 (CHCHD2) |
Mitochondrial-Nuclear Retrograde Regulator 1 |
Q9Y6H1
|
Breast cancer |
Metastasis and aggressive tumors |
|
MPB-1 |
Alpha-enolase |
P06733 |
Lung cancer |
|
|
MRPL46 |
39S ribosomal protein L46, mitochondrial |
Q9H2W6 |
Ovarian Cancer |
|
|
MSH2 |
DNA mismatch repair protein Msh2 |
P43246
|
Liver cancer |
Hepatocarcinoma |
|
MTERF4 |
Transcription termination factor 4 |
Q7Z6M4 |
Lung cancer |
IgA autoantigen, early-stage marker |
|
MUC1 |
Mucin 1 |
P15941 |
Lung cancer |
|
|
Myc |
Myc proto-oncogene protein |
P01106
|
Colorectal cancer |
|
|
Liver cancer |
|
|||
|
Ovarian Cancer |
|
|||
|
NUDT11 |
Diphosphoinositol polyphosphate phosphohydrolase 3-beta |
Q96G61 |
Ovarian Cancer |
|
|
NY-ESO-1 |
Cancer/testis antigen 1
|
P78358
|
Gastrointestinal cancer
|
Stage related (late-stage patients)
|
|
Liver cancer
|
Hepatocarcinoma Intrahepatic cholangiocarcinoma |
|||
|
Lung cancer |
|
|||
|
Esophagus cancer |
|
|||
|
p16 |
Cyclin-dependent kinase inhibitor 2A |
P42771
|
Liver cancer |
Hepatocarcinoma |
|
Esophageal cancer |
|
|||
|
p53 |
Cellular tumor antigen p53 |
P04637
|
Breast cancer |
Triple negative |
|
Gastric cancer
|
in combination with CEA and/or CA19-9, associated with lymphatic node and distant metastasis |
|||
|
Colorectal cancer |
|
|||
|
Liver cancer |
Hepatocarcinoma |
|||
|
Lung cancer
|
EarlyCTD Lung panel Non-small-cell lung cancer (NSCLC) |
|||
|
Angiosarcoma |
|
|||
|
Ovarian Cancer |
|
|||
|
p62 |
Sequestosome-1 |
Q13501 |
Colorectal cancer |
Hepatocarcinoma |
|
Liver Cancer |
|
|||
|
p90 |
/ |
G8IFA7 |
Liver cancer |
Hepatocarcinoma |
|
PAX5 |
Paired box protein Pax-5 |
Q02548
|
Liver cancer |
Hepatocarcinoma |
|
PAX8 |
Paired box protein Pax-8 |
Q06710 |
Ovarian Cancer |
|
|
PD-1 |
Programmed cell death protein 1 |
Q15116 |
Lung cancer |
|
|
PI3K |
Phosphatidylinositol 4,5-bisphosphate 3-kinase |
P42336
|
Breast cancer |
Triple negative |
|
PPP1CA |
Serine/threonine-protein phosphatase PP1-alpha catalytic subunit |
P62136 |
Bladder cancer |
|
|
PrxVI |
Peroxiredoxin-6 |
P30041 |
Colorectal cancer |
|
|
PSIP1 |
PC4 and SFRS1-interacting protein |
O75475 |
Lung cancer |
Early-stage marker |
|
PTCH1 |
Protein patched homolog 1 |
Q13635
|
Liver cancer |
Hepatocarcinoma |
|
PVR |
Poliovirus receptor |
P15151 |
Ovarian Cancer |
|
|
RalA |
Ras-related protein Ral-A |
P11233
|
Gastric cancer
|
in combination with CEA and/or CA19-9 |
|
Colorectal cancer |
|
|||
|
Liver cancer |
Hepatocarcinoma |
|||
|
RPS6KA5 |
Ribosomal protein S6 kinase alpha-5 |
O75582 |
Lung cancer |
Early-stage marker |
|
SOX2 |
Transcription factor SOX-2 |
P48431 |
Lung cancer |
EarlyCTD Lung panel Non-small-cell lung cancer (NSCLC) |
|
SP17 |
Sperm surface protein Sp17 |
Q15506 |
Head and neck cancers (HCN) |
|
|
SPAG9 |
Sperm associated antigen |
|
Liver cancer |
Hepatocarcinoma |
|
SPARC |
SPARC |
P09486 |
Prostate Cancer |
|
|
STAT6 |
Signal transducer and activator of transcription 6
|
P42226 |
Central Nervous system (CNS) tumors |
|
|
Sui1 |
Eukaryotic translation initiation factor 1 |
P41567
|
Liver cancer |
Hepatocarcinoma |
|
Survivin |
Baculoviral IAP repeat-containing protein 5 |
O15392
|
Colorectal cancer |
|
|
Liver cancer |
Hepatocarcinoma |
|||
|
Ta proteins |
Tail-anchored proteins |
/ |
Testicular Seminoma |
Integral membrane proteins, known as C-tail anchored, is defined by the presence of a cytosolic NH2-terminal domain that is anchored to the phospholipid bilayer by a single segment of hydrophobic amino acids close to the COOH terminus. PMID: 12821639 |
|
TALDO1 |
Transaldolase |
P37837 |
Colorectal cancer |
|
|
Tg |
Thyroglobulin |
P01266 |
Differentiated Thyroid Carcinoma (DTC) |
|
|
TIMELESS |
Protein timeless homolog |
Q9UNS1
|
Lung cancer |
|
|
TIZ (ZNF675) |
Zinc finger protein 675 |
Q8TD23 |
Ovarian Cancer |
|
|
TM4SF1 |
Transmembrane 4 L6 family member 1 |
P30408 |
Ovarian Cancer |
|
|
TNS1 |
Tensin-1 |
Q9HBL0 |
Lung cancer |
|
|
TOP1
|
DNA topoisomerase 1 fragment |
P11387 |
Esophageal cancer |
TOP1 fragment in the amino acid sequence 329–765
|
|
TOP2A |
DNA topoisomerase 2-alpha |
P11388
|
Lung cancer |
Early-stage marker
|
|
TRIM21 |
E3 ubiquitin-protein ligase TRIM21 |
P19474 |
Ovarian Cancer |
|
|
TRIM33 |
E3 ubiquitin-protein ligase TRIM33 |
Q9UPN9 |
Lung cancer |
IgA autoantigen, early-stage marker |
|
TRIM39 |
E3 ubiquitin-protein ligase TRIM39 |
Q9HCM9 |
Ovarian Cancer |
|
|
UQCRC1 |
Cytochrome b-c1 complex subunit 1 |
P31930
|
Colorectal cancer |
|
|
VEGFR1 |
Vascular endothelial growth factor receptor 1 |
P17948
|
Lung cancer |
|
|
Table 2. Autoantibody panels useful in cancer diagnosis and treatment |
||
|
Panel (antigens) |
Cancer |
Reference |
|
c-Myc, p16, HSPD1, PTEN, p53, NPM1, ENO1, p62, HCC1.4 |
Gastric cancer |
[77] |
|
p62, c-Myc, NPM1, 14-3-3ξ, MDM2, p16 |
Gastric cancer |
[78] |
|
CTAG1B/CTAG2, DDX53, IGF2BP2, TP53, MAGEA3 |
Gastric cancer |
[79] |
|
c-MYC, cyclin B1, p62, Koc, IMP1, surviving and other 196 antigens |
Colorectal cancer |
[80] |
|
ALDH1B1, UQCRC1, CTAG1, CENPF |
Colorectal cancer |
[81] |
|
p53, RalA, HSP70, Galectin1, KM-HN-1, NY-ESO-1, p90, Sui1, HSP40, Cyclin B1, HCC-22-5, c-myc, PrxVI, VEGF, HCA25a, p62, Annexin |
Colorectal cancer |
[82] |
|
Sui1, p62, RalA, p53, NY-ESO-1, c-myc |
Liver cancer |
[83] |
|
PTCH1, GNA11, PAX5, GNAS, MSH2, Survivin, TP53 |
Liver cancer |
[33] |
|
HCC1, P16, P53, P90, Survivin |
Liver cancer |
[35] |
|
p53, NY-ESO-1, CAGE, GBU4–5, Annexin 1, SOX2 |
Lung cancer (Early CTD-Lung) |
[24]
|
|
p53, NY-ESO-1, CAGE, GBU4–5, Annexin 1, and SOX2, Lmyc2 and cytokeratin 20 or alpha-enolase, cytokeratin 20 |
Lung cancer (Implemented Early CTD-Lung) |
[26]
|
|
p53, NY-ESO-1, CAGE, GBU4-5, SOX2, HuD, MAGE A4 |
Lung cancer |
[27] |
|
SOX2, GAGE 7, MAGE A1, P53 |
Non-small cell lung cancer |
[84] |
|
p53, PGP9.5, SOX2, GAGE7, GBU4-5, MAGE A1, CAGE |
Lung adenocarcinoma with ground-glass nodules (GGNs) |
[85] |
|
BCL7A, TRIM33, MTERF4, CTAG1A, DDX4, MAGEC2 |
Lung cancer |
[86] |
|
CEA, CA125, Annexin A1-Ab, Alpha enolase-Ab |
Lung cancer |
[87] |
|
GREM1, HMGB3, PSIP1 |
Lung cancer |
[88] |
|
MUC17, CAMSAP2, KIF13B, SMG1, MED14, ALMS1, GCC2, TIMELESS, TNS1, ATP1A4, HRNR |
Lung cancer |
[89] |
|
|
|
|
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