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Manifold-ranking has been recently exploited for query-focused summarization. It propagates query relevance from the given query to the document sentences by making use of both the relationships among the sentences and the relationships between the given query and the sentences. The sentences in a document set can be grouped into several topic themes with each theme represented by a cluster of highly related sentences. However, it is a well-recognized fact that a document set often covers a number of such topic themes. In this paper, we present a novel model to enhance manifold-ranking based relevance propagation via mutual reinforcement between sentences and theme clusters. Based on the proposed model, we develop two new sentence ranking algorithms, namely the reinforcement after relevance propagation (RARP) algorithm and the reinforcement during relevance propagation (RDRP) algorithm. The convergence issues of the two algorithms are examined. When evaluated on the DUC2005-2007 datasets and TAC2008 dataset, the performance of the two proposed algorithms is comparable with that of the top three systems. The results also demonstrate that the RDRP algorithm is more effective than the RARP algorithm.