	US 12,325,879 B2
	Methods for predicting a response to bevacizumab or platinum-based chemotherapy or both in patients with ovarian cancer
Konstantinos Aliferis, Minneapolis, MN (US); Boris Jan Nils Winterhoff, Minneapolis, MN (US); Sisi Ma, Minneapolis, MN (US); and Jinhua Wang, Edina, MN (US)
Assigned to REGENTS OF THE UNIVERSITY OF MINNESOTA, Minneapolis, MN (US)
Appl. No. 17/290,172
Filed by REGENTS OF THE UNIVERSITY OF MINNESOTA, Minneapolis, MN (US)
PCT Filed Oct. 31, 2019, PCT No. PCT/US2019/059218 § 371(c)(1), (2) Date Apr. 29, 2021, PCT Pub. No. WO2020/092808, PCT Pub. Date May 7, 2020.
Claims priority of provisional application 62/753,274, filed on Oct. 31, 2018.
Prior Publication US 2022/0017965 A1, Jan. 20, 2022
Int. Cl. C12Q 1/6886 (2018.01); G16H 10/40 (2018.01); G16H 20/10 (2018.01); G16H 50/30 (2018.01)

CPC C12Q 1/6886 (2013.01) [G16H 10/40 (2018.01); G16H 20/10 (2018.01); G16H 50/30 (2018.01); C12Q 2600/118 (2013.01); C12Q 2600/158 (2013.01)]

18 Claims

1. A method for treating a patient suffering from ovarian cancer following removal of a tumor, the method comprising:

determining whether the patient is predicted to benefit from the administration of bevacizumab, wherein such determination comprises:

determining the patient's gene expression level of microfibril associated protein 2 (MFAP2);

determining the patient's gene expression level of vascular endothelial growth factor A (VEGFA);

determining the size of the tumor tissue remaining post-removal of the tumor;

calculating a recurrence score as follows:

recurrence score=−3.5 surg_outcome+0.23xMFAP2+0.19×VEGFA/bevacizumab-0.15×MFAP2/bevacizumab,

wherein surg_outcome is −1 if the surgical outcome was suboptimal; 0 if the surgical outcome was optimal but tumor tissue smaller than 1 cm remained; or +1 if the surgical outcome was optimal and no visible macroscopic tumor tissue remained,

wherein MFAP2=gene expression level of MFAP2,

MFAP2/bevacizumab=interaction effect between MFAP2 and bevacizumab, and

VEGFA/bevacizumab=interaction effect between VEGFA and bevacizumab;

calculating the patient's risk of recurrence at time t (λ(t)) wherein

λ(t)=λ₀(t)e^{recurrence_score}

wherein λ₀(t) is the baseline hazard function estimated with a non-parametric strategy;

and

administering bevacizumab to a patient having a lower risk of recurrence with administration of bevacizumab than the risk of recurrence score without administration of bevacizumab.