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Dynamic Contrast-enhanced MRI (DCE-MRI) synthesis is crucial for improving prostate cancer diagnosis by reducing reliance on contrast agents. Previous deep learning approaches use single-stage architectures, limiting the progressive refinement of perfusion features essential for preserving diagnostically relevant information. We propose MPM-DCE, a multi-stage GAN-based framework that synthesizes early and late response DCE-MRI images using multimodal inputs. The proposed encoder-decoder generator progressively refines features by expanding spatial context and exchanging information between stages sequentially and laterally via an Adaptive Inter-Cascade Feature fusion (AICF) blocks to learn low-level perfusion details within the anatomy. Extensive evaluations on the ProstateX dataset demonstrate that MPM-DCE (i) achieves state-of-the-art performance, surpassing existing approaches such as DCE-Former with +0.31 dB and +0.65 dB PSNR, +0.01 and +0.03 SSIM improvements, and -0.02 and -0.02 MAE reduction for early and late responses respectively and (ii) highlights the significance of multistage progressive feature refinement and inter-stage attention mechanisms for improved DCE-MRI synthesis.Clinical relevance - The proposed method achieves statistically significant improvement (Wilcoxon signed-rank test, p < 0.05) in DCE-MRI synthesis, preserving perfusion details while reducing reliance on gadolinium contrast agent, potentially improving prostate cancer diagnostics and minimizing unnecessary biopsies.