Archives

  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • br It is not surprising that suboptimal cytoreduction

    2019-09-26


    It is not surprising that suboptimal cytoreduction is a predictor of PFS (HR 2.73, CI: 0.14–0.94, p-value: 0.037); however, only six patients had suboptimal cytoreduction (Fig. 2A); and therefore, its utility in predicting PFS is limited. Stage is a better predictor of PFS in terms of HR (HR = 8.49, CI: 2.05–35.24, p-value: 4 × 10−4) than the best individ-ual FLAG tag Peptide or in combination (PDGF.AA and BDNF) (Fig. 2A). Because of the known role of stage and optimal cytoreduction in predicting pa-tient prognosis, multivariate analysis was performed using stage, opti-mal cytoreduction, and having received maintenance chemotherapy
    Table 2
    Four prognostic protein candidates identified by SomaArray. Disease specific survival was calculated as time from blood draw to death.
    Select SOMA proteins predictive of DSS
    Molecule Cut off HR (95% confidence interval) Log rank-p
    Fig. 1. A. Progression free survival predicted by BDNF, PDGF.AA, and PDGF.ABBB. The percentile with the best combination of hazard ratio and p-value were chosen for display. B. The bar graph shows both BDNF's and PDGF.AA's relative contribution to generating a score, which predicts PFS for each patient. The scores were determined by summing the values given post multiplying the serum level of BDNF and PDGF.AA by their respective relative contribution. These scores determined which patients were in the low and high group for the Kaplan Meyer survival prediction. 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.
    PDGF.ABBB (p-value: 0.027) all remained significant for predicting PFS after correcting for these covariables.
    Most importantly, BDNF, PDGF.ABBB, and PDGF.AA can predict PFS in the subset of patients who have advanced stage disease (stage III and IV) and have had an optimal cytoreduction (n = 51) (Fig. 2B). When the glmnet algorithm was reapplied to this subset of patients, BDNF remained the main contributor to patient prognosis. However, the combination of BDNF and PDGF.ABBB resulted in better prediction than if BDNF was combined with PDGF.AA (Fig. 3A). The combination of BDNF and PDGF.ABBB was also capable of predicting recurrence within 1.5 years and 3 years of blood sampling date (Fig. 3B). These re-sults strongly suggest that BDNF, PDGF.AA, and PDGF.ABBB have prog-nostic value independent of the known clinical variables.
    Finally, we used the glmnet algorithm to create a composite serous high grade ovarian cancer score (SHOCS) consisting of serum proteins in combination with clinical variables (Fig. 4A). The best combination was at an alpha of 0.03 and consisted of two clinical factors (stage and optimal debulking) along with 8 different serum proteins (HR 6.55, CI: 2.57–16.71, p-value: 1.12 × 10−6). The three most important factors for determining time to recurrence are stage, serum BDNF level and then cytoreduction status. The SHOC score provides the best prediction of time from blood draw to recurrence within 1.5 and 3 years, with AUC values of 0.78 and 0.83, respectively (Fig. 4B). Notably, these AUC values were much better than the AUC values of stage alone when predicting 
    4. Discussion
    Here we present data on multiple serum proteins which were pre-dictive of PFS in high grade serous ovarian neoplasms. BDNF and PDGF.AA were validated by two different proteomic technologies (SOMAscan and Luminex). Interestingly, higher levels of both of these proteins, as well as, PDGF.ABBB were associated with better PFS, sug-gesting that these proteins most likely have protective roles in anti-tumor immunity. Indeed, this is supported by BDNF, PDGF.AA, and PDGF.ABBB are known to play major roles in immune functions related to anti-tumor immunity [11,15]. It is possible that patients with low protein levels do not efficiently destroy residual microscopic cancer and are therefore more likely to have recurrent disease.