Classic dose escalation methods that aim to establish the safety profile of a new drug and determine the maximum tolerated dose(MTD) rely on the monotonic increasing assumption for dose toxicity relation. However, in the immuno-oncology trials, molecularly targeted agents may present different dose-toxicity and dose-efficacy relations from those for cytotoxic agents. In such cases, the traditional designs that aim to identify the MTD by relying on toxicity alone will not be suitable. In real studies, it is practically meaningful to determine dose with optimal risk-benefit tradeoff by simultaneously considering toxicity and efficacy information. In this research topic, we adapted the idea from EBE-CRO (Colin, et al., 2017) and proposed a simple and flexible model that uses latent Probit regression to integrate toxicity endpoint and efficacy biomarker with correct usage of over-toxicity control. The proposed model can be easily extended to incorporate additional biomarkers or adjusted to fit study-specific biomarker usage. Simulation studies showed that the proposed method has desirable operating characteristics by determining the target dose with the optimal risk-benefit tradeoff.