In silico design and performance of peptide microarrays for breast cancer tumour-auto-antibody testing

The simplicity and potential of minimally invasive testing using sera from patients makes auto-antibody based biomarkers a very promising tool for use in cancer diagnostics. Protein microarrays have been used for the identification of such auto-antibody signatures. Because high throughput protein expression and purification is laborious, synthetic peptides might be a good alternative for microarray generation and multiplexed analyses. In this study, we designed 1185 antigenic peptides, deduced from proteins expressed by 642 cDNA expression clones found to be sero-reactive in both breast tumour patients and controls. The sero-reactive proteins and the corresponding peptides were used for the production of protein and peptide microarrays. Serum samples from females with benign and malignant breast tumours and healthy control sera (n=16 per group) were then analysed. Correct classification of the serum samples on peptide microarrays were 78% for discrimination of ‘malignant versus healthy controls’, 72% for ‘benign versus malignant’ and 94% for ‘benign versus controls’. On protein arrays, correct classification for these contrasts was 69%, 59% and 59%, respectively. The over-representation analysis of the classifiers derived from class prediction showed enrichment of genes associated with ribosomes, spliceosomes, endocytosis and the pentose phosphate pathway. Sequence analyses of the peptides with the highest sero-reactivity demonstrated enrichment of the zinc-finger domain. Peptides’ sero-reactivities were found negatively correlated with hydrophobicity and positively correlated with positive charge, high inter-residue protein contact energies and a secondary structure propensity bias. This study hints at the possibility of using in silico designed antigenic peptide microarrays as an alternative to protein microarrays for the improvement of tumour auto-antibody based diagnostics

Evaluation of auto-antibody serum biomarkers for breast cancer screening and in silico analysis of sero-reactive proteins

Aberrantly expressed proteins in tumours evoke an immunological response. These immunogenic proteins can serve as potential biomarkers for the early diagnosis of cancers. In this study, we performed a candidate marker screen on macroarrays containing 38,016 human proteins, derived from a human fetal-brain expression library, with the pools of sera from breast cancer patients (1 pool of benign samples, 3 pools of ductal carcinoma and 2 pools of lobular carcinoma) and 1 pool of sera from healthy women. A panel of 642 sero-reactive clones were deduced from these macroarray experiments which include 284 in-frame clones. Over-representation analyses of the sero-reactive in-frame clones enabled the identification of the sets of genes over-expressed in various pathways of the functional categories (KEGG, Transpath, Pfam and GO). Protein microarrays, generated using the His-tag proteins derived from the macroarray experiments, were used to evaluate the sera from breast cancer patients (24 malignant, 16 benign) and 20 control individuals. Using the PAM algorithm we elucidated a panel of 50 clones which enabled the correct classification prediction of 93% of the breast-nodule positive group (benign & malignant) sera from healthy individuals’ sera with 100% sensitivity and 85% specificity. This was followed by over-representation analysis of the significant clones derived from the class prediction

Sensitivity and specificity.

<p>Sensitivity and specificity of the combination of five biomarkers (CA125, MIF, Leptin, HE4, OPN) in differentiating between healthy women and OC patients (A), healthy wildtype women and wildtype OC patients (B), healthy <i>BRCA1</i> mutation carriers and <i>BRCA1</i> mutation carriers with OC (C), healthy wildtype women and healthy <i>BRCA1</i> mutation carriers (D) and wildtype OC patients and <i>BRCA1</i> mutation carriers with OC (E).</p

Diagnostic markers for the detection of ovarian cancer in <i>BRCA1</i> mutation carriers

<div><p>Background</p><p>Screening for ovarian cancer (OC) in women at high risk consists of a combination of carbohydrate antigen 125 (CA125) and transvaginal ultrasound, despite their low sensitivity and specificity. This could be improved by the combination of several biomarkers, which has been shown in average risk patients but has not been investigated until now in female <i>BRCA</i> mutation carriers.</p><p>Methods</p><p>Using a multiplex, bead-based, immunoassay system, we analyzed the concentrations of leptin, prolactin, osteopontin, insulin-like growth factor II, macrophage inhibitory factor, CA125 and human epididymis antigen 4 in 26 healthy wild type women, 26 healthy <i>BRCA1</i> mutation carriers, 28 wildtype OC patients and 26 OC patients with <i>BRCA1</i> mutation.</p><p>Results</p><p>Using the ROC analysis, we found a high overall sensitivity of 94.3% in differentiating healthy controls from OC patients with comparable results in the wildtype subgroup (sensitivity 92.8%, AUC = 0.988; p = 5.2e-14) as well as in <i>BRCA1</i> mutation carriers (sensitivity 95.2%, AUC = 0.978; p = 1.7e-15) at an overall specificity of 92.3%.</p><p>The used algorithm also allowed to identify healthy <i>BRCA1</i> mutation carriers when compared to healthy wildtype women (sensitivity 88.4%, specificity 80.7%, AUC = 0.895; p = 6e-08), while this was less pronounced in patients with OC (sensitivity 66.7%, specificity 67.8%, AUC = 0.724; p = 0.00065).</p><p>Conclusion</p><p>We have developed an algorithm, which can differentiate between healthy women and OC patients and have for the first time shown, that such an algorithm can also be used in <i>BRCA</i> mutation carriers. To clarify a suggested benefit to the existing early detection program, large prospective trials with mainly early stage OC cases are warranted.</p></div

Correlations of the levels of the investigated biomarkers with age in the four study groups.

<p>Correlations of the levels of the investigated biomarkers with age in the four study groups.</p

Patients characteristics of the healthy control group (wildtype and <i>BRCA1</i>) and patients tumor characteristics of the group of ovarian cancer patients (wildtype and <i>BRCA1</i>).

<p>Patients characteristics of the healthy control group (wildtype and <i>BRCA1</i>) and patients tumor characteristics of the group of ovarian cancer patients (wildtype and <i>BRCA1</i>).</p

Serum levels of the six biomarkers.

<p>Different serum levels of CA125 (A), MIF (B), Leptin (C), HE4 (D), IGF2 (E), OPN (F) in the four groups (Co WT = healthy wildtype, Co BRCA = healthy <i>BRCA1</i> mutation carriers, CaWT = wildtype ovarian cancer patients, CaBRCA = <i>BRCA1</i> mutation carriers with ovarian cancer).</p