CrossLink: a novel method for cross-condition classification of cancer subtypes.

BACKGROUND: We considered the prediction of cancer classes (e.g. subtypes) using patient gene expression profiles that contain both systematic and condition-specific biases when compared with the training reference dataset. The conventional normalization-based approaches cannot guarantee that the gene signatures in the reference and prediction datasets always have the same distribution for all different conditions as the class-specific gene signatures change with the condition. Therefore, the trained classifier would work well under one condition but not under another. METHODS: To address the problem of current normalization approaches, we propose a novel algorithm called CrossLink (CL). CL recognizes that there is no universal, condition-independent normalization mapping of signatures. In contrast, it exploits the fact that the signature is unique to its associated class under any condition and thus employs an unsupervised clustering algorithm to discover this unique signature. RESULTS: We assessed the performance of CL for cross-condition predictions of PAM50 subtypes of breast cancer by using a simulated dataset modeled after TCGA BRCA tumor samples with a cross-validation scheme, and datasets with known and unknown PAM50 classification. CL achieved prediction accuracy >73 %, highest among other methods we evaluated. We also applied the algorithm to a set of breast cancer tumors derived from Arabic population to assign a PAM50 classification to each tumor based on their gene expression profiles. CONCLUSIONS: A novel algorithm CrossLink for cross-condition prediction of cancer classes was proposed. In all test datasets, CL showed robust and consistent improvement in prediction performance over other state-of-the-art normalization and classification algorithms.

TNRC9 downregulates BRCA1 expression and promotes breast cancer aggressiveness.

Although the linkage between germline mutations of BRCA1 and hereditary breast/ovarian cancers is well established, recent evidence suggests that altered expression of wild-type BRCA1 might contribute to the sporadic forms of breast cancer. The breast cancer gene trinucleotide-repeat-containing 9 (TNRC9; TOX3) has been associated with disease susceptibility but its function is undetermined. Here, we report that TNRC9 is often amplified and overexpressed in breast cancer, particularly in advanced breast cancer. Gene amplification was associated with reduced disease-free and metastasis-free survival rates. Ectopic expression of TNRC9 increased breast cancer cell proliferation, migration, and survival after exposure to apoptotic stimuli. These phenotypes were associated with tumor progression in a mouse model of breast cancer. Gene expression profiling, protein analysis, and in silico assays of large datasets of breast and ovarian cancer samples suggested that TNRC9 and BRCA1 expression were inversely correlated. Notably, we found that TNRC9 bound to both the BRCA1 promoter and the cAMP-responsive element-binding protein (CREB) complex, a regulator of BRCA1 transcription. In support of this connection, expression of TNRC9 downregulated expression of BRCA1 by altering the methylation status of its promoter. Our studies unveil a function for TNRC9 in breast cancer that highlights a new paradigm in BRCA1 regulation.