Biomarkers that discriminate multiple myeloma patients with or
without skeletal involvement detected using SELDI-TOF mass spectrometry and
statistical and machine learning tools
Affiliations: Department of Orthopaedic Surgery, Center for
Orthopaedic Research, University of Arkansas for Medical Sciences, Little Rock,
AR 72205, USA | Myeloma Institute for Research and Treatment,
University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Note: [] Corresponding author: Larry J. Suva, Ph.D., Department of
Orthopedic Surgery, Center for Orthopedic Research, University of Arkansas for
Medical Sciences, 4301 W. Markham St, #644, Little Rock, AR 72205, USA. Tel.:
+1 501 526 6110; Fax: +1 501 686 8987; E-mail: [email protected]
Abstract: Multiple Myeloma (MM) is a severely debilitating neoplastic disease
of B cell origin, with the primary source of morbidity and mortality associated
with unrestrained bone destruction. Surface enhanced laser
desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used
to screen for potential biomarkers indicative of skeletal involvement in
patients with MM. Serum samples from 48 MM patients, 24 with more than three
bone lesions and 24 with no evidence of bone lesions were fractionated and
analyzed in duplicate using copper ion loaded immobilized metal affinity SELDI
chip arrays. The spectra obtained were compiled, normalized, and mass peaks
with mass-to-charge ratios (m/z) between 2000 and 20,000 Da identified. Peak
information from all fractions was combined together and analyzed using
univariate statistics, as well as a linear, partial least squares discriminant
analysis (PLS-DA), and a non-linear, random forest (RF), classification
algorithm. The PLS-DA model resulted in prediction accuracy between 96–100%,
while the RF model was able to achieve a specificity and sensitivity of 87.5%
each. Both models as well as multiple comparison adjusted univariate analysis
identified a set of four peaks that were the most discriminating between the
two groups of patients and hold promise as potential biomarkers for future
diagnostic and/or therapeutic purposes.
