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Metastatic breast cancer is still incurable so far; new specifically targeted and more effective therapies for triple-negative breast cancer (TNBC) are required in the clinic. In this study, our clinical data have established that basal and claudin-low subtypes of breast cancer (TNBC types) express significantly higher levels of Annexin A1 (ANXA1) with poor survival outcomes. Using human cancer cell lines that model the TNBC subtype, we observed a strong positive correlation between expression of ANXA1 and PPARγ. A similar correlation between these two markers was also established in our clinical breast cancer patients' specimens. To establish a link between these two markers in TNBC, we show de novo expression of ANXA1 is induced by activation of PPARγ both in vitro and in vivo and it has a predictive value in determining chemosensitivity to PPARγ ligands. Mechanistically, we show for the first time PPARγ-induced ANXA1 protein directly interacts with receptor interacting protein-1 (RIP1), promoting its deubiquitination and thereby activating the caspase-8–dependent death pathway. We further identified this underlying mechanism also involved a PPARγ-induced ANXA1-dependent autoubiquitination of cIAP1, the direct E3 ligase of RIP1, shifting cIAP1 toward proteosomal degradation. Collectively, our study provides first insight for the suitability of using drug-induced expression of ANXA1 as a new player in RIP1-induced death machinery in TNBCs, presenting itself both as an inclusion criterion for patient selection and surrogate marker for drug response in future PPARγ chemotherapy trials. Mol Cancer Ther; 16(11); 2528–42. ©2017 AACR.

Supplementary Figure 1: High ANXA1 and PPARγ expression levels positively correlates with the invasive, high-grade phenotype

Supplementary Figure 2: ANXA1 expressing TNBC cell lines display greater sensitivity to PPARγ ligands compared to ANXA1 depleted cells.

Supplementary methods and supplementary figure legends

Supplementary Figure 3: PPARγ induced expression of ANXA1 and cell death is abrogated in TNBC cells with pre-incubation of a PPARγ-specific antagonist or use of a dominant negative derivative of PPARγ.

Supplementary Figure 4: PPARγ ligand-induced changes in expression of RIP1 and NEMO and therefore NFκB activity are receptor dependent.

Metastatic breast cancer is still incurable so far; new specifically targeted and more effective therapies for triple-negative breast cancer (TNBC) are required in the clinic. In this study, our clinical data have established that basal and claudin-low subtypes of breast cancer (TNBC types) express significantly higher levels of Annexin A1 (ANXA1) with poor survival outcomes. Using human cancer cell lines that model the TNBC subtype, we observed a strong positive correlation between expression of ANXA1 and PPARγ. A similar correlation between these two markers was also established in our clinical breast cancer patients' specimens. To establish a link between these two markers in TNBC, we show de novo expression of ANXA1 is induced by activation of PPARγ both in vitro and in vivo and it has a predictive value in determining chemosensitivity to PPARγ ligands. Mechanistically, we show for the first time PPARγ-induced ANXA1 protein directly interacts with receptor interacting protein-1 (RIP1), promoting its deubiquitination and thereby activating the caspase-8–dependent death pathway. We further identified this underlying mechanism also involved a PPARγ-induced ANXA1-dependent autoubiquitination of cIAP1, the direct E3 ligase of RIP1, shifting cIAP1 toward proteosomal degradation. Collectively, our study provides first insight for the suitability of using drug-induced expression of ANXA1 as a new player in RIP1-induced death machinery in TNBCs, presenting itself both as an inclusion criterion for patient selection and surrogate marker for drug response in future PPARγ chemotherapy trials. Mol Cancer Ther; 16(11); 2528–42. ©2017 AACR.

Motivation: Pair-wise alignment of protein sequences and local similarity searches produce many false positives because of compositionally biased regions, also called low-complexity regions (LCRs), of amino acid residues. Masking and filtering such regions significantly improves the reliability of homology searches and, consequently, functional predictions. Most of the available algorithms are based on a statistical approach. We wished to investigate the structural properties of LCRs in biological sequences and develop an algorithm for filtering them. Results: We present an algorithm for detecting and masking LCRs in protein sequences to improve the quality of database searches. We developed the algorithm based on the complexity analysis of subsequences delimited by a pair of identical, repeating subsequences. Given a protein sequence, the algorithm first computes the suffix tree of the sequence. It then collects repeating subsequences from the tree. Finally, the algorithm iteratively tests whether each subsequence delimited by a pair of repeating subsequences meets a given criteria. Test results with 1000 proteins from 20 families in Pfam show that the repeating subsequences are a good indicator for the low-complexity regions, and the algorithm based on such structural information strongly compete with others. Availability: http://bioinfo.knu.ac.kr/research/CARD/ Contact: swshin@bioinfo.knu.ac.kr