Multimodality therapy for locally advanced thymomas: a cohort study of prognostic factors from a European multicentric database Dr. GIOVANNI LEUZZI Department.

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Multimodality therapy for locally advanced thymomas: a cohort study of prognostic factors from a European multicentric database Dr. GIOVANNI LEUZZI Department of Surgical Oncology Thoracic Surgery Unit “Regina Elena” National Cancer Institute, Rome, Italy

Relevant Financial Relationship Disclosure Statement 95th ANNUAL MEETING OF AMERICAN ASSOCIATION FOR THORACIC SURGERY Seattle (U.S.A.), April 25-29, 2015 Multimodality therapy for locally advanced thymomas: a cohort study of prognostic factors from a European multicentric database Giovanni Leuzzi, M.D. In Compliance with UEMS/EACCME Guidelines, potential conflicts of interest or support relevant to the above presentation that might cause a bias are declared as follows:  No potential conflicts of interest to report  Conflicts of interest to report: (company name, type of relationship) x

Background Locally-advanced Thymomas (LATs): % Heterogeneous entity o Different tumor size o Different organ involvements Radical resection not usually feasible (50-78%) ~ 50 % LATs experience recurrence after surgery Higher stage (III-IV) and R+ resection decrease survival

Multimodality therapies Induction Therapy (IT) Chemo and radio-responsivity Few studies (retrospective or clinical trials, small samples) R0 resection: % * 7-y OS and RFS: ~70 % * AuthorYearsPatients(Stage)IT% R0OS Macchiarini et al.1988–19907 (7-III) PEV10 – 57 %80% (2 years) Rea et al.1985– (13-III, 3-IVA)ADOC69 – 100 %70% (3 years) Venuta et al.1989– (15-III)PAC67 – 100 %90% (10 years) Kim et al.1990– (11-III, 11-IV)ADOC76 – 95 %95% (5 years) Lucchi et al.1976– (III + IVA)PEV75 – 100 %78% (5 years) Wright et al.1997– (6-III, 3-IVA)PE + RT80 – 100 %69% (5 years) * Kondo K. Therapy for thymic epithelial tumors. Gen Thorac Cardiovasc Surg Aug;62(8):

Multimodality therapies Adjuvant Therapy (AT) Retrospective studies or clinical trials Heterogeneous samples Survival advantage controversial AuthorYearsPatients(Stage)ATSurvival Benefit Kondo et al (all) RTNo Korst et al.* (II-III)RTNo Omasa et al.1991– (III)RTNo Ruffini et al.1990– (all)RT/CTYes (OS) Weksler et al.? (SEER)476 (III)RTYes (RFS) * Meta-analysis

Multimodality therapies Surgery alone Surgery + AT IT + Surgery IT + Surgery + AT

AIM To explore:  the factors affecting the outcome  the impact of multimodality treatments in the subset of LATs (Masaoka-Koga stage III thymomas) To explore:  the factors affecting the outcome  the impact of multimodality treatments in the subset of LATs (Masaoka-Koga stage III thymomas)

Our experience European Society of Thoracic Surgeons (ESTS) thymic database (38 Institutions) 2317 surgically-treated Thymic Tumors (01/1990 – 01/2010) 370 Masaoka-Koga stage III Thymomas (WHO Histology A to B3) 370 Masaoka-Koga stage III Thymomas (WHO Histology A to B3) Stage I, II and IV excluded Thymic carcinoma and NETT excluded

Our experience 370 Masaoka-Koga stage III Thymomas (WHO Histology A to B3) 370 Masaoka-Koga stage III Thymomas (WHO Histology A to B3) * according to the IASLC/ITMIG TNM staging proposal EXAMINED FEATURES Demographics Paraneoplastic syndromes WHO histology Tumor size Type and extension of surgery Completeness of resection T classification * Kind of IT and AT Cause of death Recurrence OUTCOMES Overall Survival (OS) Cancer-specific Survival (CSS) Recurrence-free Survival (RFS) Cumulative Incidence of Recurrence (CIR)

Clinical, surgical and pathological features Age (years) Median (range)54(10-93) Gender Female195(52.7%) Male175(47.3%) Paraneoplastic syndromes Myasthenia Gravis120(36.5%) Others10 (3.0%) WHO Histology A25 (6.8%) AB37(10.1%) B147(12.8%) B2131(35.8%) B3126(34.5%) Surgical approach Sternotomy274(85.6%) Thoracotomy36(11.3%) VATS/Robotic10 (3.1%) Kind of thymectomy Radical254(96.2%) Partial10(3.8%) Tumor Size (cm) Median (range)5 (1-21) Extent of resection Pleura62(25.6%) Lung130(53.7%) Pericardium97(40.1%) Diaphragm3(1.2%) Phrenic nerve29(12.0%) Vessel33(13.6%) Pathological resection status R0258(74.1%) R153(15.2%) R237(10.7%) Pathologic invasion Pleura66(26.9%) Lung121(49.4%) Pericardium116(47.3%) Diaphragm3 (1.2%) Phrenic nerve26(10.6%) Vessel37(15.1%) pT 117(6.9%) 263(25.6%) 3166(67.5%)

Induction therapy IT group No IT groupp-value Age (years) < 5456 (63.6%)110 (45.3%)0.004 Gender Female41(46.6%)115(47.3%)0.91 Male47(53.4%)128(52.7%) Paraneoplastic syndrome17(21.2%)111(46.4%)< WHO Histology A1 (1.1%)18(7.5%)0.05 AB4(4.6%)27(11.2%) B110(11.5%)33(13.7%) B238(43.7%)83(34.4%) B334(39.1%) Radical thymectomy 64(91.4%)180(97.8%)0.03 Pathological resection status R053(65.4%)187(79.9%)0.01 R115(18.5%)25(10.7%) R213(16.1%)22(9.4%) pT 11 (1.6%)15(10.3%) (23.4%)34(23.3%) 348(75.0%)97(66.4%) Tumor size (cm) Median5 (2-17)5 (1-21) pts * 88 pts (24.9%) 88 pts (24.9%) CT 76.1% CT-RT 19.4% RT 4.5% *patients with available data on oncological therapies

Adjuvant therapy AT group No AT groupp-value Age (years) < (55.9%)43 (40.6%)0.008 Gender Female106 (43.3%)57 (53.8%)0.07 Male139 (56.7%)49 (46.2%) Paraneoplastic syndrome94 (42.0%)33 (35.5%)0.28 WHO Histology A10 (4.1%)11(10.6%)0.008 AB23 (9.4%)12(11.5%) B132(13.1%)13(12.5%) B285(34.8%)42(40.4%) B394(38.5%)26(25.0%) Radical thymectomy 179(96.2%)72(96.0%)0.93 Pathological resection status R0182(76.8%)71(71.7%)0.61 R132(13.5%)16(16.2%) R223(9.7%)12(12.1%) pT 115 (9.4%)2(2.9%) (28.9%)11(15.7%) 398(61.6%)57(81.4%) Tumor size (cm) Median5 (2-21)6 (1-17) pts * 245 pts (69.4%) 245 pts (69.4%) RT 64.1% CT-RT 31.0% CT 4.9% *patients with available data on oncological therapies

Outcome 5-year10-year OS82.8%68.9% CSS88.4%83.3% RFS80.0%71.5% 5-year10-year CIR20%28.5% Median Follow-up: 60 months (1-248)

Outcome analysis Variables OS HR [95%CI] p–value CSS HR [95%CI] p–value RFS HR [95%CI] p–value Age R0 resection Adjuvant therapy pT 1.80[ ] 2.38[ ] 2.02[ ] [ ] 2.44[ ] [ ] Multivariate Cox Regression analysis

Outcome analysis Variables OS HR [95%CI] p–value CSS HR [95%CI] p–value Adjuvant therapy2.68[ ] [ ]0.003 Propensity Score Match analysis Confounding variables Age Gender WHO Histology Exact match (1:2) for Pathological Resection Status

Outcome & Treatment Strategy Treatment strategy (n=353) Pts (%) Surgery alone66(18.7%) IT + Surgery + AT46(13.0%) IT + Surgery42(11.9%) Surgery + AT199(56.4%) Surgery alone IT + Surgery + AT IT + Surgery Surgery + AT p=0.006 CSS (months) Probability of Survival IT Group Primary Surgery Group p-value CSS 85.0%88.3%0.82 RFS 77.9%84.0%0.31

Outcome & Adjuvant Therapy Probability of Survival p= AT GroupNo AT Groupp-value CSS 91.1%81.5% RFS 85.5%79.3%0.19

Outcome & Adjuvant Therapy Type of AT: } p=0.06

Stage III & Postop Therapy Masaoka Stage III Thymomas Different Tumor Size Different p-Tumor invasion* Adjuvant therapy Surveillance Adjuvant therapy Surveillance Tumor heterogeneity * according to the IASLC/ITMIG TNM staging proposal

Probability of Survival p=0.08 pT2 pT & Adjuvant Therapy Adjuvant therapy? Surveillance? Adjuvant therapy p=0.04 Probability of Survival pT3

T-size & Adjuvant Therapy Adjuvant therapy Surveillance ? Adjuvant therapy? Tailored therapy ?

Limitations of the study Retrospective study Data collected from centers of different volume activity, expertise and geographic areas No central pathologic review IT and AT modalities not standardized Indications for IT and radiological response to IT not properly elucidated Pathological nodal status not available

Conclusions The assessment of the optimal multimodality strategy in LATs is still controversial Our analysis indicates that Induction Therapy is not associated with a survival advantage. Administration of Adjuvant Therapy and Completeness of Resection represent the most significant outcome predictors. Adjuvant Therapy should be administered whenever possible, especially in those patients with specific pathological features (pT2/3 or tumor size smaller than 5 cm) who may benefit the most from multimodality treatment. Further studies are needed to confirm these data and to define optimal postoperative therapeutic regimens according to different pathological cancer characteristics.

Thank you for your attention ….