Abstract
Leptomeningeal metastasis (LM) is a devastating complication that occurs in 5% of patients with breast cancer. Early diagnosis and initiation of treatment are essential to prevent neurological deterioration. However, early diagnosis of LM remains challenging because 25% of cerebrospinal fluid (CSF) samples produce false-negative results at first cytological examination. We developed a new, MS-based method to investigate the protein expression patterns present in the CSF from patients with breast cancer with and without LM. CSF samples from 106 patients with active breast cancer (54 with LM and 52 without LM) and 45 control subjects were digested with trypsin. The resulting peptides were measured by MALDI-TOF MS. Then, the mass spectra were analyzed and compared between patient groups using newly developed bioinformatics tools. A total of 895 possible peak positions was detected, and 164 of these peaks discriminated between the patient groups (Kruskal-Wallis, p<0.01). The discriminatory masses were clustered, and a classifier was built to distinguish patients with breast cancer with and without LM. After bootstrap validation, the classifier had a maximum accuracy of 77% with a sensitivity of 79% and a specificity of 76%. Direct MALDI-TOF analysis of tryptic digests of CSF gives reproducible peptide profiles that can assist in diagnosing LM in patients with breast cancer. The same method can be used to develop diagnostic assays for other neurological disorders.
