Shotgun and targeted proteomics reveal that pre-surgery serum levels of LRG1, SAA, and C4BP may refine prognosis of resected squamous cell lung cancer

Dear Editor, Lung cancer—predominantly non-small cell lung cancer (NSCLC)—is the most prevalent cancer in the world, in terms of both incidence and mortality. Squamous cell lung cancer (SCLC) is the second most common type of NSCLC, making up about 30% of all cases. The average 5-year survival rate among NSCLC patients is barely 15% (Herbst et al., 2008). Among post-surgery patients, prognosis varies widely, even among patients with similar clinicopathological characteristics, demonstrating the need for improved ways to predict treatment outcomes (Chen et al., 2007). Previous global gene-expression studies have identified genes that could be referred to as signatures for classifying patients with significantly different prognostic outcomes. However, these studies generally analyzed NSCLC patients with mixed histopathologic subsets and some of them only focused on adenocarcinomas (ADC) or stage I disease (Potti et al., 2006; Chen et al., 2007; Guo et al., 2008; Shedden et al., 2008; Tomida et al., 2009; Jeong et al., 2010; Kadara et al., 2011). Interestingly, each study identified different sets of gene signatures. This could reflect the genetic heterogeneity of NSCLC and thus suggests the necessity of stratifying the prognosis of lung cancer with respect to more accurate grouping information, such as stages, histopathologic subsets, or other demographic factors. Correspondingly, currently known NSCLC blood biomarkers are rare and of little use in the prediction of prognosis. For the purpose of patient-tailored therapeutics, we herein attempted to identify relapse-related signatures that allow the selection of late stage SCLC with a high probability of rapid relapse. By harnessing current powerful approaches in shotgun and targeted proteomics (based on selected reaction monitoring, SRM) (Huttenhain et al., 2009), we have succeeded in profiling the pre-therapy serum proteome of patients with an improved analysis depth. Moreover, we elaborated a directed approach to avoid possible confounding factors, such as clinical and pathological features that might complicate efforts to delineate relapse discrepancies. The triple non-invasive blood signatures discovered here may be promising for refining the prognosis of stage IIb and IIIa SCLC. Fifty patients with SCLC who had disease recurrence within 10 months (7.44 + 2.08 months) after surgery were selected and defined as the ‘rapid recurrence’ group (RR group). Fifty-six patients who showed no evidence of recurrent disease (ND) .20 months (.27.04 + 5.73 months) post-surgery were defined as the ND group. To identify relapse-related blood proteins, all RR patients were selected with different genders, smoking history, metastasis symptoms, varied ages (from 41 to 73) and pathological features determined TNM stages and, therefore, may present fast relapse SCLC diseases with varied clinical and pathological features. Both RR and ND patients had either stage IIb or IIIa disease. All the ND patients under the same adjuvant-therapy and follow-up surveillance post-surgery were accordingly selected to match RR cases by gender, age, smoking habits and the exact pathological features determined TNM stage, except for a much better prognostic outcome (Figure 1A). A total of 106 patients were separated into two cohorts for this study: Cohort I, 30 pairs of RR/ND and Cohort II, 20RR/26ND. Patient demographics are summarized in Supplementary Table S1, with all clinicopathologic details listed in Supplementary Table S2. Five pairs of RR/ND samples in Cohort I were used and compared in the discovery phase (Figure 1B). Collectively, 711012 peptide hits from 4139 unique peptides, assigned to 595 protein groups, were credibly identified by shotgun proteomics with the final protein-level false discovery rate (FDR) ,1% in each pair. And 386–439 serum proteins were identified from each patient. The final peptide FDR for the whole data set was only 0.016%. By applying a label-free method termed localized statistics of protein abundance distribution (LSPAD) (Li et al., 2008; Luo et al., 2011), we identified 64–117 significantly up-regulated proteins and 70–108 downregulated proteins in RR cases compared with ND cases (P , 0.05, Figure 1C and Supplementary Figure S1). To distinguish the true protein abundance changes from random fluctuations, we used the following stringent criteria: (i) an LSPAD, P , 0.05 in all the five pairs; (ii) an average of P , 0.01; (iii) a fold-change of .1.3 between the RR and ND groups; and (iv) at least 20 MS/MS spectra had to be assigned to the candidate due to the sensitivity issue of spectral counting. Twenty-five proteins were thus identified as relapse-related signatures. Literature mining and biological interests then led us to focus on three proteins—C4b-binding protein alpha (C4BP), leucine-rich alpha-2glycoprotein (LRG1), and serum amyloid A protein (SAA) for the next phase of 344 | Journal of Molecular Cell Biology (2012), 4, 344–347 doi:10.1093/jmcb/mjs050