O'Donnell Brian, Maurer Alexander, Papandreou-Suppappola Antonia, Stafford Phillip
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA.
Center for Innovations in Medicine, The Biodesign Institute, Arizona State University, Tempe, AZ, USA.
Cancer Inform. 2015 Jun 18;14(Suppl 2):219-33. doi: 10.4137/CIn.s17285. eCollection 2015.
One of the gravest dangers facing cancer patients is an extended symptom-free lull between tumor initiation and the first diagnosis. Detection of tumors is critical for effective intervention. Using the body's immune system to detect and amplify tumor-specific signals may enable detection of cancer using an inexpensive immunoassay. Immunosignatures are one such assay: they provide a map of antibody interactions with random-sequence peptides. They enable detection of disease-specific patterns using classic train/test methods. However, to date, very little effort has gone into extracting information from the sequence of peptides that interact with disease-specific antibodies. Because it is difficult to represent all possible antigen peptides in a microarray format, we chose to synthesize only 330,000 peptides on a single immunosignature microarray. The 330,000 random-sequence peptides on the microarray represent 83% of all tetramers and 27% of all pentamers, creating an unbiased but substantial gap in the coverage of total sequence space. We therefore chose to examine many relatively short motifs from these random-sequence peptides. Time-variant analysis of recurrent subsequences provided a means to dissect amino acid sequences from the peptides while simultaneously retaining the antibody-peptide binding intensities. We first used a simple experiment in which monoclonal antibodies with known linear epitopes were exposed to these random-sequence peptides, and their binding intensities were used to create our algorithm. We then demonstrated the performance of the proposed algorithm by examining immunosignatures from patients with Glioblastoma multiformae (GBM), an aggressive form of brain cancer. Eight different frameshift targets were identified from the random-sequence peptides using this technique. If immune-reactive antigens can be identified using a relatively simple immune assay, it might enable a diagnostic test with sufficient sensitivity to detect tumors in a clinically useful way.
癌症患者面临的最严重危险之一是从肿瘤发生到首次诊断之间出现较长时间的无症状间歇期。肿瘤的检测对于有效干预至关重要。利用人体免疫系统检测并放大肿瘤特异性信号，可能会通过一种低成本的免疫测定法实现癌症检测。免疫特征就是这样一种测定法：它提供了抗体与随机序列肽相互作用的图谱。它们能够使用经典的训练/测试方法检测疾病特异性模式。然而，迄今为止，从与疾病特异性抗体相互作用的肽序列中提取信息的工作做得很少。由于难以以微阵列形式呈现所有可能的抗原肽，我们选择在单个免疫特征微阵列上仅合成330,000种肽。微阵列上的330,000种随机序列肽代表了所有四聚体的83%和所有五聚体的27%，在总序列空间的覆盖上造成了一个无偏差但相当大的差距。因此，我们选择检查这些随机序列肽中的许多相对较短的基序。对重复子序列的时变分析提供了一种方法，可从肽中剖析氨基酸序列，同时保留抗体 - 肽结合强度。我们首先进行了一个简单的实验，将具有已知线性表位的单克隆抗体与这些随机序列肽接触，并利用它们的结合强度创建我们的算法。然后，我们通过检查多形性胶质母细胞瘤（GBM，一种侵袭性脑癌）患者的免疫特征，展示了所提出算法的性能。使用该技术从随机序列肽中鉴定出了八个不同的移码靶点。如果能够通过一种相对简单的免疫测定法鉴定出免疫反应性抗原，那么可能会实现一种具有足够灵敏度的诊断测试，以临床上有用的方式检测肿瘤。
