Deliverable

3.1Breast cancer modelling and going beyond the state-of the art

The main modeling efforts related to breast cancer concern biostatistical models of risk of cancer, prognosis and relapse [12]. In the context of large scale clinical trials, prediction of outcome and individualization of therapeutic strategies are crucial when trying to improve prognosis and reducing patient suffering due to unnecessary treatment [13]. Therefore, a more realistic effort adopted within INTEGRATE is to exploit the unique opportunity of its NeoBIG empowered collaborative environment and combine multi-scale biomarkers (from genetic level to tissue level including imaging biomarkers) in order to define a methodology for improving the prognostic power of currently used practices for assessing neoadjuvant therapies. Figure depicts the synergy between the BIG and NeoBIG research and Figure shows the envisioned workflow of development and validation of predictive biomarkers in NeoBIG trials. This will eventually empower the clinician to predict/define early the responsiveness of the chosen chemotherapy regimens.

Figure The synergy between BIG and NeoBIG

Figure The Development and Validation of Predictive Biomarkers

The neoadjuvant setting, where therapy is administered prior to surgery, is a promising new arena for addressing many of the challenges in both clinical and translational research faced by clinicians today. There are a number of reasons and advantages for employing the neoadjuvant approach:

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  Neoadjuvant systemic therapy produces outcomes equivalent to adjuvant systemic therapy, with an increased likelihood of breast conserving surgery and hence is a safe and viable option for breast cancer patients [14].
  
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  Breast cancer is a common disease usually diagnosed in healthy women who do not have other co-morbidities that might preclude participation in clinical trials;
  
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  The primary tumor is readily accessible for serial biopsies during treatment;
  
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  Surrogate short-term endpoints such as pathological complete response rate (pCR) have been proven to be strongly predictive of long-term survival for treatment modalities such as chemotherapy and are rapidly available within a short time frame;
  

This allows for obtaining multiple serial biopsies and images, to characterize at biological multiple levels response to new agents. Furthermore, the existence of a surrogate clinical endpoint allows clinicians to rapidly evaluate if the new drug is more efficacious than the currently used standard of care ones.

This will take the form of a ‘use-case’ VPH scenario emanating from and being deployed within the INTEGRATE environment. The goal is to demonstrate that the predictive power of responsiveness can be enhanced by using multi-scale biomarker signatures.