Methods for predicting a response to bevacizumab or platinum-based chemotherapy or both in patients with ovarian cancer
Inventors
Aliferis, Konstantinos • Winterhoff, Boris Jan Nils • Ma, Sisi • Wang, Jinhua
Assignees
University of Minnesota System
Publication Number
US-12325879-B2
Publication Date
2025-06-10
Expiration Date
2039-10-31
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Abstract
This disclosure describes methods of predicting the response of a patient with ovarian cancer to platinum-based chemotherapy and/or treatment with bevacizumab using clinical and molecular tumor characteristics in patients, methods of treating patients with ovarian cancer, and kits for performing all or part of the methods described herein. This disclosure also describes methods that include determining a prediction of an outcome for a patient having ovarian cancer based on one or more signatures and patient test data comprising clinical data, gene expression data, or both.
Core Innovation
The invention provides methods for predicting the response of a patient with ovarian cancer to platinum-based chemotherapy and/or treatment with bevacizumab using clinical and molecular tumor characteristics. Clinical data, gene expression data (including microfibril associated protein 2 (MFAP2) and vascular endothelial growth factor A (VEGFA)), or both, are determined from a patient's biological sample and used to generate a recurrence score and risk prediction through the application of mathematical or computational models, such as a Cox model.
A central aspect of the invention is using these predictive methods to guide personalized treatment decisions for ovarian cancer patients, specifically following removal of a tumor. If the model predicts a patient will benefit from bevacizumab (alone or in addition to platinum-based chemotherapy) based on a lower risk of recurrence with the therapy, then bevacizumab is administered. The recurrence score integrates variables such as gene expression levels, FIGO stage, ECOG performance status, and surgical outcomes.
The invention aims to address the problem that not all ovarian cancer patients benefit from standard platinum-based therapy or the addition of bevacizumab. Traditional selection methods for therapy can be economically inefficient and may expose patients to unnecessary costs and adverse effects. The invention provides a statistically validated clinico-molecular stratification model to individualize therapy decisions, maximizing clinical benefit and health economic efficiency.
Claims Coverage
The patent claims cover multiple inventive features related to methods for predicting benefit from, and guiding administration of, bevacizumab and platinum-based chemotherapy in ovarian cancer using clinico-molecular data and predictive modeling.
Method for treating ovarian cancer using recurrence score and benefit prediction for bevacizumab
A method that includes: - Determining the patient's gene expression level of MFAP2 and VEGFA - Determining the size of the tumor tissue remaining after tumor removal - Calculating a recurrence score using a defined formula integrating surg_outcome, MFAP2, interactions with bevacizumab, and VEGFA/bevacizumab - Calculating the patient's risk of recurrence at time t using this score - Administering bevacizumab if the risk of recurrence is lower with bevacizumab than without
Incorporation of FIGO stage and ECOG performance status into prediction
Methods further comprising determining the patient's International Federation of Gynecology and Obstetrics (FIGO) stage and/or Eastern Cooperative Oncology Group (ECOG) performance status as additional inputs to the recurrence score and risk prediction.
Threshold-based and variable-based interpretation of benefit prediction
Utilization of threshold values for MFAP2 and VEGFA gene expression levels, FIGO stage, ECOG performance status, and tumor size to interpret likelihood of benefit from bevacizumab or platinum-based chemotherapy. Definitions specify, for example, higher MFAP2 indicates decreased likelihood of benefit from platinum, higher VEGFA indicates increased likelihood of benefit from platinum, and certain cutoffs for ECOG, FIGO, and tumor size are set.
Comparative modeling of progression-free survival with and without therapies
Methods comprising predicting the patient's progression-free survival time with and without bevacizumab and/or platinum-based chemotherapy, and using the difference in predicted times (benefit threshold) to guide administration decisions.
Use of ensemble prediction and signature generation based on multiple biomarker sets
Methods including: - Receiving an identified set of biomarkers (including at least MFAP2 and VEGFA) - Identifying other equivalent biomarker sets - Generating predictive models (signatures) for each set - Using ensemble prediction based on signatures and patient data for outcome prediction
In summary, the claims establish inventive methods for predicting and guiding the use of bevacizumab and platinum-based chemotherapy in ovarian cancer patients through the integration of molecular and clinical data, calculation of risk and recurrence scores, application of defined predictive models (including ensemble approaches), and actionable decision thresholds.
Stated Advantages
The methods enable individualization of ovarian cancer treatment, ensuring only patients predicted to benefit from bevacizumab receive it.
Significant health economic savings can be achieved by avoiding unnecessary administration of expensive therapies to non-benefitting patients.
Patients who are unlikely to benefit from standard or bevacizumab therapies may be routed to alternative experimental treatments, potentially improving survival outcomes.
The methods provide a statistically validated, unbiased prediction model, reducing confounding effects and improving clinical decision-making.
Predictive tests based on these models can support viable clinical strategies that incorporate health economics constraints.
Documented Applications
Predicting the response of ovarian cancer patients to platinum-based chemotherapy and/or bevacizumab based on clinical and/or gene expression data.
Calculating risk of recurrence and progression-free survival to inform individualized ovarian cancer treatment decisions after tumor removal.
Personalizing administration of bevacizumab and/or platinum-based chemotherapy based on predicted benefit.
Identifying ovarian cancer patients who should be routed to alternative or experimental therapies if not predicted to benefit from standard treatments.
Providing clinical companion diagnostics, kits, and systems for executing the predictive and treatment-guidance methods.
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