Selection of head and neck cancer patients for treatment with drugs targeting EGFR pathway
Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a head and neck squamous cell carcinoma (HNSCC) patient is likely to benefit from a drug targeting an epidermal growth factor receptor pathway, including small molecule epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) and monoclonal antibody EGFR inhibitors.
1. A method of determining whether a head and neck squamous cell carcinoma (HNSCC) patient is likely to benefit from treatment with a drug targeting the EGFR pathway, comprising the steps of:
a) obtaining a mass spectrum from a blood-based sample from the HNSCC patient;
b) performing one or more predefined pre-processing steps on the mass spectrum obtained in step a);
c) obtaining integrated intensity values of selected features in said spectrum at one or more predefined m/z ranges after the pre-processing steps on the mass spectrum in step b) have been performed; and
d) using the values obtained in step c) in a classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from other cancer patients to identify the HNSCC patient as being either likely or not likely to benefit from treatment with the said drug;
wherein the drug targeting the EGFR pathway is selected from the group of drugs consisting of a small molecule epidermal growth factor receptor tyrosine kinase inhibitor and a monoclonal antibody epidermal growth factor receptor inhibitor.
2. The method of claim 1 , wherein the one or more m/z ranges comprises one or more m/z ranges selected from the group of m/z ranges consisting of:
5732 to 5795
5811 to 5875
6398 to 6469
11376 to 11515
11459 to 11599
11614 to 11756
11687 to 11831
11830 to 11976
12375 to 12529
23183 to 23525
23279 to 23622 and
65902 to 67502.
3. The method of claim 1 , wherein the mass spectrum is obtained from a MALDI mass spectrometer.
4. The method of claim 1 , wherein the predefined pre-processing steps comprise a background subtraction step producing a background-subtracted spectrum, and a normalization step performing a normalization of the background-subtracted spectrum.
5. The method of claim 1 , wherein the training set comprises class-labeled spectra produced from blood-based samples obtained from non-small cell lung cancer patients.