ChIP-on-chip has emerged as a powerful tool to dissect the complex network of regulatory interactions between transcription factors and their targets. However, most ChIP-on-chip analysis methods use conservative approaches aimed at minimizing false-positive transcription factor targets. We present a model with improved sensitivity in detecting binding events from ChIP-on-chip data. Its application to human T cells, followed by extensive biochemical validation, reveals that 3 oncogenic transcription factors, NOTCH1, MYC, and HES1, bind to several thousand target gene promoters, up to an order of magnitude increase over conventional analysis methods. Gene expression profiling upon NOTCH1 inhibition shows broad-scale functional regulation across the entire range of predicted target genes, establishing a closer link between occupancy and regulation. Finally, the increased sensitivity reveals a combinatorial regulatory program in which MYC cobinds to virtually all NOTCH1-bound promoters. Overall, these results suggest an unappreciated complexity of transcriptional regulatory networks and highlight the fundamental importance of genome-scale analysis to represent transcriptional programs.