Large-scale delineation of secreted protein biomarkers overexpressed in cancer tissue and serum

Abstract

Genetic alterations in tumor cells often lead to the emergence of growth-stimulatory autocrine and paracrine signals, involving overexpression of secreted peptide growth factors, cytokines, and hormones. Increased levels of these soluble proteins may be exploited for cancer diagnosis and management or as points of therapeutic intervention. Here, we combined the use of controlled vocabulary terms and sequence-based algorithms to predict genes encoding secreted proteins from among approximately 12,500 sequences represented on oligonucleotide microarrays. Expression of these genes was queried in 150 carcinomas from 10 anatomic sites of origin and compared with 46 normal tissues derived from the corresponding sites of tumor origin and other body tissues and organs. Of 74 different genes identified as overexpressed in cancer tissues, several encode proteins with demonstrated clinical diagnostic application, such as alpha-fetoprotein in liver carcinoma, and kallikreins 6 and 10 in ovarian cancer, or therapeutic utility, such as gastrin-releasing peptide/bombesin in lung carcinomas. We show that several of the other candidate genes encode proteins with high levels of tumor-associated expression by immunohistochemistry on tissue microarrays and further demonstrate significantly elevated levels of another novel candidate protein, macrophage inhibitory cytokine 1, a distant member of the transforming growth factor-beta superfamily, in the serum of patients with metastatic prostate, breast, and colorectal carcinomas. Our results suggest that the combination of annotation/protein sequence analysis, transcript profiling, immunohistochemistry, and immunoassay is a powerful approach for delineating candidate biomarkers with potential clinical significance and may be broadly applicable to other human diseases.

Figure 1

Mining for genes that encode secreted proteins. Oligonucleotide probe-sets were filtered for candidate genes encoding secreted proteins by two distinct approaches (step 2, filtering). Affymetrix gene probe-sets were mapped to GO Consortium annotations, and those with evidence suggesting secretion of the encoded protein were identified (1,160 total). Protein sequences of the genes represented on the oligonucleotide microarray were interrogated by using two sequence-based algorithms, sigcleave, which estimates the likelihood of an authentic signal peptide cleavage site in arbitrary amino acid sequence data, and tmap (TM), which predicts transmembrane regions in proteins. A series of 1,724 probe-sets (genes) met the criteria imposed by both sequence algorithms. Probe sets selected by each method were then compared for the degree of overlap (step 3, method comparison), and, finally, their overexpression in carcinomas of different anatomic origin (step 4, tumor differential expression).

Figure 2

Candidate secreted biomarkers elevated in multiple cancer types. (A) Thirty-two genes encoding secreted proteins selected by annotation- and sequence-based analyses had significant overexpression in at least one tumor-normal counterpart tissue pair (>3-fold), and significant overexpression in tumors compared with any other normal tissue (>2-fold). Counterpart tissues are tissues at the sites of tumor origin; GeneAtlas tissues are those described in ref. . BR, breast (ER+, ER−); CO, colorectal; GA, gastric/esophagus adenocarcinoma; KI, kidney; LI, liver; LUA, adenocarcinoma of the lung; LUS, squamous carcinoma of the lung; LUO, lung other, small cell lung carcinomas, large cell undifferentiated carcinomas of the lung; OV, ovary; PA, pancreas; PR, prostate. Gene symbols are depicted to the right. (B) An expanded view of genes preferentially up-regulated in carcinomas of the prostate. Numbers of tissue samples from each counterpart site are given in parentheses. GeneAtlas tissues are: Te, testis; Th, thyroid; Ut, uterus; SG, salivary gland; Tr, trachea; AG, adrenal gland; He, heart; Pi, pituitary gland; Sp, spinal cord; CC, cerebral cortex; Nor. Pr, normal prostate. Transcript levels were normalized in cluster and visualized in treeview (20).

Figure 3

Validation of microarray gene expression by RT-PCR, IHC, and ELISA. (A) RNAs from multiple different human tissues. Three normal and six primary prostate carcinomas were reverse-transcribed and amplified under standard conditions by using primers directed toward relaxin-1 (4). (Upper) The primary microarray data are shown (hybridization intensity on the y axis; samples on the x axis). (Lower) A representative PCR is shown. Primers specific for 18S were used to control for the amount of amplified cDNA. (B) IHC was performed with an anti-NPY antibody on whole tissue sections. (Upper) Primary microarray data are shown. (Lower) Examples of IHC staining in normal, microarray-positive, and microarray-negative prostate cancers. (C) Box plot showing MIC-1 serum levels in normal subjects (n = 260) and patients with breast (n = 10), colon (n = 8), and prostate (n = 9) carcinoma. P values were determined by comparison with normal by ANOVA. The prostate serum MIC-1 levels were divided by 10 (1/10) for graphical representation.