A Mediating Effect of AKT in EGFR Signal to Osteosarcomas in a Clinical Study Huiyun Wu, Adriana Gonzalez, and Yu Shyr Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN A National Cancer Institute-Designated Cancer Center ABSTRACT A mediation model was created with structural equation model (SEM) to analyze the function pattern of EGFR and AKT signaling in a osteosarcomas clinical study. The results suggested a mediating effect for AKT by showing significant associations for EGFR to Ki67 (p=0.0266), EGFR to AKT (p=0.0016), and AKT to Ki67 controlling EGFR (p=0.0035). After the impact of EGFR on Ki67 was carried by AKT, the relation between EGFR and Ki67 was no longer significant (p=0.4425). The mediating effect was verified with Sobel test (p<0.001). The study indicated that mediation model could be an interesting procedure in testing a biologically identified signal pathway with clinical data. INTRODUCTION Mediation model A mediation effect occurs when the third variable (mediator) carries the influence of a given independent variable (X) to a given dependent variable (Y). Concept and application were mainly developed in social science, behavioral science, and preventive medicine. Mediation models explain “how” an effect occurred by hypothesizing a causal sequence. The intervention program (X) is designed to change mediating variables (M) hypothesized to be causally related to the outcome (Y). Three models Poster Session: 3 Poster Number: 328 RESULTS STUDY DESIGN AND METHODS Causal sequence The p values from the above 3 models indicated that criteria for mediating effects were satisfied. Statistical tests The three significant tests suggested a mediating effect for AKT. Effects table Path Total effectsDirect effectsIndirect effects EGFR → AKT0.850 (0.0016) EGFR → Ki (0.0266)0.009 (0.4425)0.029 AKT → Ki (0.0035) Data are expressed as coefficients and p values. Total effect of EGFR → Ki67 =  x  +  ’ =0.85 x = Indirect effects of EGFR → Ki67 = x = Direct effects of EGFR → Ki67 = which is the estimate after adjusting for AKT. Statistical tests Testp value Sobel Goodman (I) Goodman (II) X Mediator Y Y =  0(1) +  X +  (1) (1) Y =  0(2) +  ’ X +  M +  (2) (2) M =  0(3) +  X +  (3) (3) 69 patients of primary OS were reviewed. 54 samples were collected. All patients completed one course of chemotherapy procedure. Initial variable EGFR and mediating variable AKT were immunostaining index. Outcome variable was cancer development expressed as Ki67 which was also immunostaining index. Statistical analysis was performed by creating SEM with AMOS. Structural Equation Model Significance tests with products of coefficients of the models a.Sobel test z-value = b. Goodman (I) test z-value = c. Goodman (II) test z-value = Sources: MacKinnon & Dwyer (1994) and MacKinnon, Warsi, & Dwyer (1995) MEK Gene transcription Cell cycle progression PI3-K RAS RAF SOS GRB2 PTEN AKT STAT R K R K Proliferation/ maturation Survival (anti-apoptosis) Angiogenesis Metastasis DNA myc Myc cyclin D1 Cyclin D1 JunFos PP pY pY pY MAPK EGFR Signal Pathway Signaling events are ordered both spatially and temporally CONCLUSIONS Statistical modeling could be another approach to elucidating a mechanistic causal relation between events observed in biology. Mediation model might be a useful tool to identify the EGFR-Akt or other pathway leading to cancer development.