Classification by mass spectrometry can accurately and reliably predict outcome in patients with non-small cell lung cancer treated with erlotinib-containing regimen.
OBJECTIVE
Although many lung cancers express the epidermal growth factor receptor and the vascular endothelial growth factor, only a small fraction of patients will respond to inhibitors of these pathways. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MS) has shown promise in biomarker discovery, potentially allowing the selection of patients who may benefit from such therapies. Here, we use a matrix-assisted laser desorption/ionization MS proteomic algorithm developed from a small dataset of erlotinib-bevacizumab treated patients to predict the clinical outcome of patients treated with erlotinib alone.


METHODS
Pretreatment serum collected from patients in a phase I/II study of erlotinib in combination with bevacizumab for recurrent or refractory non-small cell lung cancer was used to develop a proteomic classifier. This classifier was validated using an independent treatment cohort and a control population.


RESULTS
A proteomic profile based on 11 distinct m/z features was developed. This predictive algorithm was associated with outcome using the univariate Cox proportional hazard model in the training set (p = 0.0006 for overall survival; p = 0.0012 for progression-free survival). The signature also predicted overall survival and progression-free survival outcome when applied to a blinded test set of patients treated with erlotinib alone on Eastern Cooperative Oncology Group 3503 (n = 82, p < 0.0001 and p = 0.0018, respectively) but not when applied to a cohort of patients treated with chemotherapy alone (n = 61, p = 0.128).


CONCLUSIONS
The independently derived classifier supports the hypothesis that MS can reliably predict the outcome of patients treated with epidermal growth factor receptor kinase inhibitors.
Salmon
Stuart
S
Carolinas Hematology/Oncology Association, Charlotte, NC, USA.
Chen
Heidi
H
Chen
Shuo
S
Herbst
Roy
R
Tsao
Anne
A
Tran
Hai
H
Sandler
Alan
A
Billheimer
Dean
D
Shyr
Yu
Y
Lee
Ju-Whei
JW
Massion
Pierre
P
Brahmer
Julie
J
Schiller
Joan
J
Carbone
David
D
Dang
Thao P
TP
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000911:Antibodies, Monoclonal / Q000008:administration & dosage; D061067:Antibodies, Monoclonal, Humanized; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use*; D000068258:Bevacizumab; D014408:Biomarkers, Tumor / Q000097:blood*; D002289:Carcinoma, Non-Small-Cell Lung / Q000097:blood / Q000145:classification* / Q000188:drug therapy; D016022:Case-Control Studies; D015331:Cohort Studies; D000069347:Erlotinib Hydrochloride; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000097:blood / Q000145:classification* / Q000188:drug therapy; D008297:Male; D008875:Middle Aged; D009364:Neoplasm Recurrence, Local / Q000097:blood / Q000145:classification / Q000188:drug therapy; D016066:Pleural Effusion, Malignant / Q000097:blood / Q000145:classification / Q000188:drug therapy; D011379:Prognosis; D011799:Quinazolines / Q000008:administration & dosage; D015203:Reproducibility of Results; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D015996:Survival Rate; D016896:Treatment Outcome
D000911:Antibodies, Monoclonal; D061067:Antibodies, Monoclonal, Humanized; D014408:Biomarkers, Tumor; D011799:Quinazolines; D000068258:Bevacizumab; D000069347:Erlotinib Hydrochloride