Local Recurrence Growth Rate Predicts Outcome In Locally Recurrent Retroperitoneal Liposarcoma

1. Local Recurrence Growth Rate Predicts OutcomeIn Locally Recurrent Retroperitoneal Liposarcoma James Park, MD, Li-Xuan Qin, PhD, Francesco Prete, MDMurray Brennan, MD, Samuel Singer, MD

2. Background: Retroperitoneal Liposarcoma • Retroperitoneal sarcoma (RPS) 15% of soft tissue sarcomas (STS)1 • Liposarcoma (LS) most common; 20% of all STS, up to 50% of RPS2 • Complete resection feasible in 80% of primary RPLS3 • Local recurrence 40~80%; local effects cause of death in 75%1,2 1. Stoeckle. Cancer 2001 2. Lewis. Ann Surg 1998 3. Singer. Ann Surg 2003

3. Background: Retroperitoneal Liposarcoma • Gross margin, grade, and histologic subtype predict survival1,2 • Subtype and contiguous organ resection, predict local recurrence1 • No objective consensus to guide re-resection of local recurrence following complete resection 1. Singer. Ann Surg 2003 2. van Dalen. EJSO 2006

4. Histologic subtype defines grade and • predicts local recurrence and survival in RPLS 1. Singer. Ann Surg 2003

5. Purpose • Determine prognostic factors for survival and recurrence in patients with locally recurrent retroperitoneal liposarcoma • Use these factors to guide therapy and define subset of patients with locally recurrent retroperitoneal liposarcoma most likely to benefit from surgical resection

6. Methods • Prospective sarcoma database reviewed 7/82~10/05 All STS treated N=6682 All RPS treated N=607 All RPLS treated N=355 Primary RPLS treated N=207 Complete resection N=180 (180/207 87%) Local recurrence (LR) N=105 (105/180 58%) Complete resection of LR N=61 (61/105 58%)

7. Methods Endpoints: Disease-specific survival (from time of first local recurrence) for all 105 patients Local recurrence-free survival for 61 patients re-resected Statistics:Univariate analysis- Kaplan Meier curve and Log-rank test Multivariate analysis- Cox’s PH model and Score test Cut-point finding- Minimum P value method

8. Results: Patient/Tumor Characteristics

9. Results: Treatment characteristics

10. Tumor size (sum of max dimensions on imaging) Time from primary resection to LR • Univariate Analysis of Disease-Specific Survival • for First LR (N=105) Start time: First LR End point: Dead of disease LR Growth Rate =

11. Multivariate Analysis of Disease-Specific Survival • for First LR (N=105)

12. Tumor size (sum of max dimensions on pathology) Time from primary resection to LR • Univariate Analysis of Disease-Specific Survival • for Complete Resection of First LR (N=61) Start time: LR resection End point: Dead of disease Second recurrence LR Growth Rate =

13. Multivariate Analysis of Disease-Specific Survival • for Complete Resection of First LR (N=61)

14. Univariate Analysis of Disease-Free Survival • for Complete Resection of First LR (N=61)

15. Multivariate Analysis of Disease-Free Survival • for Complete Resection of First LR (N=61)

16. Finding a cutoff for LR growth rate • using the Minimum p value method 0.9 1. Mazumdar. Statist Med 2003

17. Disease-Specific Survival by LR Growth Rate All 105 Patients 61 Re-resected

18. Resection does not improve Disease-specific survival • for LR Growth Rate ≥ 0.9 (N=105)

19. Summary • LR growth rate and primary grade are independent predictors of disease-specific survival in locally recurrent RPLS  Patients with LR growth rate ≥ 0.9 cm/month had significantly worse disease-specific survival • Re-resection of the recurrence did not alter the poor outcome for patients with LR growth rate ≥ 0.9 cm/month

20. Conclusion LR growth rate predicts disease-specific survival and local control following complete resection of locally recurrent RPLS  Patients with LR growth rate ≥ 0.9cm/month did not benefit from aggressive operative management and should be considered for trials of novel targeted therapies