One limitation of nearly all systemic cancer therapies is that most exhibits clinical activity in only a subset of patients. As the field of targeted therapy evolves, it is becoming apparent that predictive biomarkers are integral to the success of these therapies. The successful development of any drug should be linked to predictors of its efficacy, as these markers would considerably increase the likelihood that an individual patient will benefit. Given the morbidity and economic burden of treating cancer patients with expensive and ineffective agents, it is imperative that endeavors to identify biomarkers predictive of treatment benefit are undertaken.
Modest response and disease stabilization rates are observed when EGFRIs are administered as monotherapy in unselected HNSCC, NSCLC, and CRC patients, and a phase III trial comparing gefitinib to cytotoxic chemotherapy did not show a difference in OS (1
). Nevertheless, there is compelling evidence that some patients significantly benefit from these agents, even when objective responses are not observed. This study found that a proteomic profile-based classification can be used to predict which patients, with a variety of malignancies, will benefit when treated with either EGFR-TKIs or cetuximab. By using survival as the end point and by testing the classification based on the proteomic profile in a control cohort of patients never treated with EGFRIs, it seems that this biomarker is associated with survival benefits from EGFRIs, which is of paramount importance to patients. Furthermore, the fact that the same signature was predictive in three different cancers and two different classes of EGFR pathway inhibitors suggests that this proteomic profile might detect tumor EGFR signal dependence and thus be useful in other populations in which EGFRI therapy might be appropriate, including almost all epithelial derived malignancies.
MALDI MS serum profiling has garnered criticism for being difficult to understand on a molecular basis. It seems that the information within the proteomic pattern is generated by specific, stable, and reproducible protein cleavages in the serum or plasma (32
). This is underscored by the ability to analyze at multiple facilities either serum or plasma, from routinely processed, multisite collected samples with equivalent results. We are undertaking a study to identify the nature of peptides constituting the MS signature. Notwithstanding the precise mechanism underlying these observations, the aim of this study was to evaluate a standardized and commercialized MS profile classification (VeriStrat) in association with PFS and OS in HNSCC and CRC patients treated with EGFRIs. We specifically chose OS and/or PFS benefits, not response to treatment, as end points of our classifier and its application to patients in this study. There are numerous studies that show a correlation of a biomarker with response, but not with survival. However, in case of advanced diseases, survival prolongation is a more important indicator of a benefit for the patient, and is the strength of this study. In our cohorts, the response rates were very low; however, we showed that good patients have a statistically significant lack of clinical progression and survival benefit over poor patients, even in the absence of clinical response. If we examine all of the patients that we have studied with NSCLC, we find that matched NSCLC patients classified as poor actually had a significantly worse survival when treated with EGFR TKIs than with chemotherapy.9
Thus, potential negative effects of EGFRIs in a subset of patients make pretreatment patient selection for these therapies especially important.
There are several advantages to the serum MALDI MS profiling described here, especially compared with more labor intensive and technically challenging as-says, such as immunohistochemistry, fluorescence in situ
hybridization, and PCR, due to the unavailability of high-quality tumor specimens and intratumoral heterogeneity (9
). For recurrent/metastatic patients in community-based clinical settings, obtaining an adequate amount of tumor tissue for analysis can be challenging, especially because the tumor's characteristics may have changed after the initial diagnosis and staging and multiple lines of treatments. Getting a new biopsy immediately before a therapeutic decision point may be impractical and/or delay treatment. Recent data about the presence of KRAS
mutations and treatment resistance to EGFRIs in NSCLC and CRC have been validated in several studies (15
). However, the latest large randomized studies provided conflicting evidence on the role of these genetic biomarkers (13
). Besides, the frequency of mutations varies depending on the organ of origin: they are 3.5% in HNSCC and ~12% in NSCLC (15
). Thus, there is no reliable way to select the majority of patients for therapy, and the burden and morbidity of inadequate treatment are very high. Our data suggest that MALDI MS profiling may provide an independent predictive marker that is reproducible, uses easily obtainable small volumes of pretreatment sera or plasma, does not require elaborate sample processing, and can provide results within days with an assay failure rate of only 2%. In the case when there are other biomarker measurements, proteomic tests can provide valuable additional information. Combining KRAS
mutation status and this simple blood test may improve the stratification of patients if adequate tumor material is available for KRAS
testing. The correlation found between the RNA expression levels of AREG and EREG in tumors and our serum-MS classification suggests that this proteomic profiling test may serve as a surrogate to identify high-AREG– and EREG-expressing CRC tumors.
Although most cancer therapies have been studied independently in each tumor type after phase I trials, highly specific targeted therapies should, in theory, work in any tumor that is driven by that pathway. There are few, if any, clinically relevant “pathway-specific” biomarkers available for the selection of therapy independent of tumor type. In this study, we propose that this biomarker, defined in lung cancer patients, seems to identify patients who will benefit from EGFRI therapy in two additional major tumor types treated with inhibitors of the EGFR pathway, regardless of the class of the inhibitor. Although the data generated in this study require further validation in prospective studies, we have shown that the classification of patients based on the standardized MALDI MS–based proteomic analysis VeriStrat predicts survival benefit in HNSCC, NSCLC, and CRC patients treated with both EGFR-TKIs or cetuximab, and the patients predicted to have poor outcome are unlikely to benefit from these treatments at the current doses and schedules administered. This predictive biomarker could be tremendously valuable given the financial and societal costs of treating unselected patients with EGFRIs.