Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: a multicohort cross-institutional study.
BACKGROUND
Some but not all patients with non-small-cell lung cancer (NSCLC) respond to treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). We developed and tested the ability of a predictive algorithm based on matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) analysis of pretreatment serum to identify patients who are likely to benefit from treatment with EGFR TKIs.


METHODS
Serum collected from NSCLC patients before treatment with gefitinib or erlotinib were analyzed by MALDI MS. Spectra were acquired independently at two institutions. An algorithm to predict outcomes after treatment with EGFR TKIs was developed from a training set of 139 patients from three cohorts. The algorithm was then tested in two independent validation cohorts of 67 and 96 patients who were treated with gefitinib and erlotinib, respectively, and in three control cohorts of patients who were not treated with EGFR TKIs. The clinical outcomes of survival and time to progression were analyzed.


RESULTS
An algorithm based on eight distinct m/z features was developed based on outcomes after EGFR TKI therapy in training set patients. Classifications based on spectra acquired at the two institutions had a concordance of 97.1%. For both validation cohorts, the classifier identified patients who showed improved outcomes after EGFR TKI treatment. In one cohort, median survival of patients in the predicted "good" and "poor" groups was 207 and 92 days, respectively (hazard ratio [HR] of death in the good versus poor groups = 0.50, 95% confidence interval [CI] = 0.24 to 0.78). In the other cohort, median survivals were 306 versus 107 days (HR = 0.41, 95% CI = 0.17 to 0.63). The classifier did not predict outcomes in patients who did not receive EGFR TKI treatment.


CONCLUSIONS
This MALDI MS algorithm was not merely prognostic but could classify NSCLC patients for good or poor outcomes after treatment with EGFR TKIs. This algorithm may thus assist in the pretreatment selection of appropriate subgroups of NSCLC patients for treatment with EGFR TKIs.
Taguchi
Fumiko
F
Department of Medicine, Vanderbilt-Ingram Cancer Center, Nashville, TN 37232-6838, USA.
Solomon
Benjamin
B
Gregorc
Vanesa
V
Roder
Heinrich
H
Gray
Robert
R
Kasahara
Kazuo
K
Nishio
Makoto
M
Brahmer
Julie
J
Spreafico
Anna
A
Ludovini
Vienna
V
Massion
Pierre P
PP
Dziadziuszko
Rafal
R
Schiller
Joan
J
Grigorieva
Julia
J
Tsypin
Maxim
M
Hunsucker
Stephen W
SW
Caprioli
Richard
R
Duncan
Mark W
MW
Hirsch
Fred R
FR
Bunn
Paul A
PA
Carbone
David P
DP
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000097:blood*; D002289:Carcinoma, Non-Small-Cell Lung / Q000145:classification* / Q000188:drug therapy; D066246:ErbB Receptors / Q000037:antagonists & inhibitors*; D005260:Female; D000077156:Gefitinib; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification* / Q000188:drug therapy; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D047428:Protein Kinase Inhibitors / Q000627:therapeutic use*; D040901:Proteomics; D011799:Quinazolines / Q000627:therapeutic use; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; D015996:Survival Rate; D016896:Treatment Outcome
D014408:Biomarkers, Tumor; D047428:Protein Kinase Inhibitors; D011799:Quinazolines; D066246:ErbB Receptors; D000077156:Gefitinib