An Effective Model of Retinoic Acid and Vitamin D3 Induced myeloblastic Differentiation

Differentiation induction chemotherapy (DIC), using agents such as the vitamin A derivative all-trans retinoic acid (ATRA), is a promising approach for the treatment of many cancers. For example, ATRA treatment induces remission in 80–90% of promyelocytic leukemia (APL) PML-RAR-positive patients, thereby transforming a fatal diagnosis into a manageable disease. However, remission is often not durable and relapsed cases exhibit emergent ATRA resistance. To understand the basis of this resistance, we must first understand the ATRA-induced differentiation program. Toward this challenge, lessons learned in model systems, such as the lineage-uncommitted human myeloblastic cell line HL-60 reported to closely resemble patient derived cells, could inform our analysis of the differentiation programs occurring in patients. In previous studies, we have explored which signaling cascades are initiated following ATRA exposure in both wild-type and ATRA resistant HL-60 cells. In this study, we present an effective model All-Trans Retinoic Acid (ATRA)-induced differentiation of HL-60 cells. The model describes a key architectural feature of ATRA-induced differentiation, reinforcing feedback between an ATRA-inducible signalsome complex involving many proteins including Vav1, a guanine nucleotide exchange factor, and the activation of the mitogen activated protein kinase (MAPK) cascade. We decomposed the effective model into three modules; a signal initiation module that sensed and transformed an ATRA signal into program activation signals; a signal integration module that controlled the expression of upstream transcription factors; and a phenotype module which encoded the expression of functional differentiation markers from the ATRA-inducible transcription factors. The effective model was trained using time series and steady-state experimental data sets for ATRA and Vitamin D3 treated HL-60 cells. The model, which was developed by integrating logical rules with kinetic modeling, was significantly smaller than previous models. However, despite its simplicity, it captured key features of ATRA and D3 induced differentiation of HL-60 cells. Specifically, the model captured the expression of myelocytic markers, CD38 and CD11b in the presence of ATRA and D3, and the monocyctic marker, CD14 in the presence of D3 alone. Further, model analysis suggested which transcription factors that controlled the system response to ATRA and D3 stimulation. Taken together, these findings, combined with literature evidence, suggested that reinforcing feedback is central to hyperactive signaling in a diversity of cell fate programs. This study also identified controlling nodes in the ATRA response that could be implicated in ATRA resistance.