Clinical trial with multiple rare tumor type could be challenging. Complete pooling across all tumor types may lead to inaccurate estimates. On the other hand, stratified analyses ignore potential similarities across different tumor type due to pathway effect and can lead to less efficient estimate. We proposed a Bayesian Hierarchical model to borrow information across tumor types in multi-arm Phase 2 Oncology trial with rare indications. Due to robust nature the proposed model allows dynamic borrowing of information between groups. This implies more borrowing when the groups are consistent and less borrowing when the groups differ. In this way, the model is a compromise between the two alternate extremes of either a completely pooled analysis or a separate analysis in each group and provide reasonable strata specific and overall estimate. Design characteristics will be illustrated by data scenarios and simulation.