br Fig A Bar graph showing
Fig. 3. A. Bar graph showing the contribution of both BDNF and PDGF.ABBB to determining PFS in patients with greater than stage 3 disease and an optimal debulking (n = 51). The corresponding Kaplan Meyer curve is made by dividing patients based on the score generated for each patient based on the relative contribution bar graph. The score is determined specifically by summing the values generated post multiplying the serum level of BDNF and PDGF.ABBB by their respective relative contribution. B. Finally, ROC (receiver operator characteristic) curves were generated based on the ability of this combination to predict recurrence within 1.5 years and 3 years respectively.
four had stage 3 disease. Validation is needed to see on a larger scale how the SHOC score would consistently perform when evaluating early stage patients. Despite the concerns about early stage patients, this score was remarkably successful in predicting recurrence among advanced stage patients. Because of this score's success in identifying patients at the highest risk of recurrence, in the future the SHOC score may aide in determining which remission patients need early treatment and intervention.
Although these results are exciting, our study has a number of limi-tations that need to be addressed with future studies. One of the largest limitations is the lack of samples obtained from patients in the pretreat-ment setting. A pretreatment serum value for all Vorinostat (SAHA, MK0683) measured in this paper would provide a more complete picture of each protein's role, change over time, and allow better comparison to other papers discussing biomarkers in ovarian cancer. Another limitation is that this paper only consisted of 71 total remission patients with half of the pa-tients being used in the discovery data set. We suspect this small sample number of patients in the SOMAscan array and the overall small number of patients in the entire study most likely contributed to the large de-crease in hazard ratio when comparing SOMAscan results (35 patients), to the Luminex study (71 patients). However, the number of analyzed patients is similar to other published data sets of ovarian cancer patients [19–22]. Further studies should also focus on using assays capable of
analyzing a wider variety of serum proteins compared to Luminex. Ex-ample assays include mass spectrometry or using the SOMAscan array exclusively; both offer accurate and efficient detection of a large number of proteins. By analyzing concentrations of molecules part of numerous physiologic pathways, such as those involved in anti-tumor immunity or general inflammation, in a larger patient population, we would hope-fully be able to provide more evidence proteomic biopsies in remission can predict prognosis. Perhaps, we may eventually be able to make in-ferences into possible effective intervention. Despite these pitfalls, there are also a number of positives to this study.
One of the major strengths of this study is its longitudinal nature. Another positive is that the combined SHOC score consists of two clini-cal components, stage and optimal cytoreduction, which are already used by gynecologic oncologists . Furthermore, the eight proteins can be tested using a platform that has been previously validated and is high throughput. Finally, the SHOC score is used to determine risk of recurrence at a unique time point, remission.
Remission, since the development of targeted biologic therapy, im-munotherapy, and the SOLO-1 trial, has become of increasing interest as a time of intervention . Currently, gynecologic oncologists have no way to accurately monitor microscopic cancer and its molecular workings in a remission patient. The SHOC score demonstrates the abil-ity to predict prognosis during the remission time period, when only