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.
Highlights
Leptomeningeal metastasis (LM) is a devastating complication that occurs in 5% of patients with breast cancer
Forty-six percent of patients with breast cancer with LM presented with more than one neurological symptom, whereas the majority of patients with breast cancer (73%) in group II presented with a single symptom at the time of lumbar puncture
We studied the value of proteomic profiling in the diagnosis of LM in patients with breast cancer
Summary
Patient Selection—Using clinical databases and CSF banks, we retrospectively identified all patients with breast cancer with available CSF samples collected in the last 7 years in four participating institutions (Erasmus MC, Netherlands Cancer Institute, UMC Nijmegen, and Innsbruck Medical University). The samples were eluted in a new 96-well plate with an elution volume of 15 l of 50% acetonitrile/water HPLC grade 0.1% TFA; a pressure differential of 5 inches of Hg vacuum was used. The combined peak list was compared with a new spectrum until all peak lists had been combined The latter peak list was used to create a matrix displaying the frequency of each peak position for each sample. Building a Predictive Model—A supervised multivariate analysis method was used to determine whether sample groups I and II could be separated on the basis of their peak positions. A minimum of two peaks was required to determine whether a peak was present (Ն2, 1) in a sample or not (Ͻ2, 0) allowing the formation of a binary data matrix. We developed a model on the original data and corrected its AUC with the correction factor, producing a conservative estimate of the performance of the model
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