The success of many investigational drugs is dependent on matching treatments with the appropriate target populations, noted Daniel Chelsky, CSO at Caprion Proteomics, in his presentation. “Variability in response to therapy, both with regard to efficacy and to adverse events, is leading the pharmaceutical industry down the path of personalized medicine. Further pushing the process along are government and private insurance payers who are faced with expensive treatments that can help some but provide little benefit and possible harm to others.”
One promising solution to the problem, he observed, is to identify predictive biomarkers of drug efficacy; circulating proteins that stratify patients into populations of likely responders and nonresponders to a proposed therapy. Finding such biomarkers has been challenging due to the complexity of human plasma—the sample of choice—and to the available technologies for detection and quantification of thousands of proteins.
“Based on the experience of over two dozen preclinical and clinical proteomic studies with pharmaceutical partners, an industrialized and productive approach to biomarker discovery and validation has been developed by Caprion.” Dr. Chelsky stated.
On the path to personalized medicine, discovering biomarker candidates is not enough. The Caprion approach identifies proteins that predict drug efficacy or that stratify patients by stage and severity of disease through a well-controlled and industrialized mass spectrometry analysis of plasma samples, Dr. Chelsky added.
The process begins with uniform blood sample collection into tubes containing protease inhibitors. Plasma is depleted by antibody affinity of the high- and medium-abundance proteins that typically obscure biomarkers of interest. Isolated proteins from each sample are digested to peptides that are more accurately identified and quantified by a quadrupole time of flight mass spectrometer. Peptides are matched across all samples and compared for peak intensity in each cohort of patients.
Those peptides, which are differentially expressed, are targeted for fragmentation to generate the amino acid sequence that is matched to the parent protein. Peptides identifying the same protein are clustered and subjected to a consistency filter that requires all peptides from the same protein to show similar behavior in each patient.
In a study performed by Caprion for a pharmaceutical company, plasma samples were compared between patients with ovarian cancer and breast cancer, as well as with healthy control subjects. Of approximately 50,000 peptide ions tracked across the samples, 4,000 were found to be differentially expressed.
These peptides were used to determine the proteomic relationship between the patients by multidimensional scaling (MDS). Each cohort was found to be distinct from the perspective of plasma protein profiles. “Thus, not only can patients with disease be distinguished, but those with two related diseases can be separated as well,” Dr. Chelsky observed.
A similar study involved the identification of circulating prostate cancer biomarkers. To determine whether markers could be found in spite of significant differences in sample acquisition, three groups of samples were compared—healthy control samples from a commercial supplier and two sample sets from different suppliers of patients with prostate cancer.
The three groups were analyzed and compared to find 3,569 peptides out of approximately 43,000 detected across the sample population that distinguished the two cancer groups from the healthy controls. These peptides were used to compare the patients by MDS analysis. The peptides that distinguished the two prostate cancer sample sets from the controls were found to significantly overlap and the combined cancer group separated well from the healthy controls in the MDS plot.
Peptides that separated each cancer group from the controls were sequenced to identify approximately 200 proteins in each comparison. “The interesting finding,” Dr. Chelsky noted, “is that 141 of the proteins in each group were shared, demonstrating that the impact of collecting samples from different sources was relatively minor compared with the effect of the disease on the plasma proteome.”
Each of the prostate cancer patient sample groups contained individuals with stage T2 (n=14) and stage T3 (n=10) prostate cancer. A bioinformatics software similar to MDS was used to compare the patients. Most patients at the two stages could be separated based on their plasma proteomic profiles.
“This indicates that protein expression in plasma is not limited to distinguishing disease, but can be applied to measuring the stage or severity of the disease. The implications of this finding are important to diagnosis and treatment, as well as for monitoring response to therapy,” Dr. Chelsky concluded.