1. Controversie nella determinazione della prognosi Nicola D’Ostilio
Carcinoma del rene: controversie
Chieti, 27 Maggio 2016

2. Fattori prognostici e predittivi
Caratteristiche anatomiche
Caratteristiche istologiche
Caratteristiche cliniche
Caratteristiche molecolari

3. Caratteristiche anatomiche
TNM
PADUA
RENAL
C-INDEX

4. PADUA
Preoperative Aspects and Dimension Used for an Anatomical Classification System Preoperative Aspect…. Eur Urol 2009 Ficarra V., et al

5. RENAL
Radium, Exophytic/endophytic properties, Nearness of the tumor to the collecting system of sinus, Anterior/posterior, Location relative to the polar line The R.E.N.A.L. nefhrometry score: a comprehensive standardized system for quantitating renal tumor size, location and depth. J Urol 2009 Kutikov A., et al

6. C-INDEX
Anterior/posterior position Kydney tumor location measurement usingthe c-index method. J Utol 2010 Simmons MN, et al

7. TNM Sopravvivenza a 5 anni T1:88-99% T2:70-82% T3: 10-60% T4: 20%

8. TNM
TNM staging system for renal cell carcinoma: current status and future prospectives Ficarra V, Galfano A et al. Lancet Oncol. 2007

9. Controversie TNM pT2 (malattia locale-localmente avanzata)
Cut-off
<7-10 cm
T2 classification for RCC. Can its accuracy be improved? J Urolol 2005
Frank I, Blute ML et al
=11 cm
Prognostic impact of tumor size on pT2 renal cell carcinoma: an international multicenter experience. J Urol 2007
Klatte T, Patard J,…Cindolo L, Ships L et al

10. TNM controversie T3
Prognostic rilevance of tumor size in T3a renal cell carcinoma: a multicentre experience. Eur Urol 2007
Lam JS, Klatte T,… Cindolo L, Ships L, et al
T3a con cut-off: 7 cm
A new staging system for locally advanced (T3-T4) renal cell carcinoma: a multicenter European study including patients. J Urol 2007
Ficarra V., Galfano A., Schips L., Cindolo L., et al

11. TMN controversie Infiltrazione delle via escretrice
Prognostic rilevance of capsular involvement and collecting system invasion in stage I and II renal cell carcinoma. BJU Int 2007
Prognostic role of urinary collecting system invasion in renal cell carcinoma: a sistematic review and meta-analysis. Article open

12. TNM controversie
T3b-c
Prognosi uguale per invasione v. renale e v. cava sottodiaframmatica
Prognosi peggiore per invasione v. cava sovradiaframmatica
Il parametro più importante: infiltrazione grasso perirenale + invasione per contiguità del surrene

13. Caratteristiche istologiche
Istotipo
Grado nucleare di Fuhrman
Componente sarcomatoide
Invasione microvascolare
Necrosi tumorale e interessamento sistema collettore

14. Istotipo Istotipi più frequenti
- ca. a cellule chiare (prognosi peggiore, 43-83%)
- ca. papillare (61-90%)
- ca. cromofobo (80-100%)
Comparisions of outcome and prognostic factures among histologic sutypes of renal cell carcinoma. AM J Surg Pathol 2003
(analisi univariata)

15. Ma…
In analisi multivariata la significatività prognostica dell’istotipo viene persa suggerendo che lo stadio della malattia e grading del tumore abbiano un maggior impatto sulla prognosi rispetto alle caratteristiche istotipiche
Prognostic value of histologic subtypes in renal cell carcinoma: a multicenter experience. J Clin Oncol 2005
Patard IJ, Leroy E., Cindolo L., et al

16. Caratteristiche clinichePS
Chirurgia
Anemia, Neutropenia, trombocitosi

17. Sistemi prognostici
UISS (University of California at Los Angeles Integrated Staging System
IMDC (International Metastatic Renal-Cell Carcinoma Database Consortium)

18. UISS Modello che integra il TNM, l’ECOG PS ed il grado di Fuhrman
Risk group assessment and clinical outcome algorithm to predict the natural hystory of patient with surgicaly resected renal carcinoma. J Clin Oncol 2002
Zisman A, et al

19. UISS
Conserva il suo valore prognostico solo nella malattia localizzata Patard JJ et al. Use of the UISS to predict survival in renal cell carcinoma. J Clin Oncol 2004

20. IMDC PS Hb Ca
Intervallo
Conta neutrofili
Conta piastrinica

21. IMDC Categorie di rischio:
Favorevole (0 numero fattori, sopravvivenza a 2 anni 75%)
Intermedia (1-2 fattori, sopravvivenza mediana 27 mesi, sopravvivenza a 2 anni 53%)
Sfavorevole (3-6 fattori, sopravvivenza mediana 8,8 mesi, sopravvivenza a 2 anni 7%)

22. Predictive Biomarkers – Myth or Reality?

23. Prognostic versus predictive Biomarkers
Prognostic Biomarkers
Identify patients likely to have a good/poor disease outcome (OS) independent of treatment
Predictive Biomarkers
Identify patients who are likely to benefit from a particular treatment

24. «Ideal» Prognostic Score
Easy to use
Accurately distinguishes between patient groups with different outcomes
Useful for informing patients
Helpful for treatment decisions (e.g. cytoreductive nephrectomy,
“active surveillance”)

25. Prognostic Scores: MSKCC
Time from Start of IFN-α, years 2 16 14 4 10 8 6
Proportion Surviving
0.0
0.2
0.4
0.6
0.8
1.0
5 risk factors
KPS <80
Time from diagnosis to IFN-α <1 year
Low serum haemoglobin
High corrected calcium (>2.5 mmol/L)
High LDH (>1.5× ULN)
Motzer et al. J Clin Oncol. 2002
Motzer et al. J Clin Oncol. 1999

26. Prognostic Scores: Heng criteria (IMDC)
Heng et al. J Clin Oncol. 2009

27. Prognostic Scores: Heng criteria (IMDC)
0 risk factors
1 – 2 risk factors
3 – 6 risk factors
N= 645
Heng et al. J Clin Oncol. 2009

28. IMDC Prognostic Factors: Validation
0 risk factors: Favorable
43 mo
N= 849
1-2 risk factors:Intermediate
23 mo
3-6 risk factors: Poor
8 mo
Heng et al. Lancet Oncol. 2013

29. Parameters of different Prognostic Models
Clinical
Biological
Table modified from Heng et al. Lancet Oncol. 2013
Motzer RJ et al. J Clin Oncol. 2002;20: ;Negrier S et al. Ann Oncol. 2002;13: ; Mekhail T et al. J Clin Oncol. 2005;23: ; Manola J
et al. Clin Cancer Res. 2011;17: ; Choueiri TK et al. Cancer ;110: ; Heng DY et al. J Clin Oncol. 2009;27:

30. IMDC in non-clear cell RCC
P<0.0001
Kroeger N et al. Cancer. 2013;119:

31. Heng et al. Eur Urol 2014

32. Time to recurrence is a significant predictor of cancer- specific survival after recurrence in patients with recurrent renal cell carcinoma: result from a comprehensive multi-centre database (CORONA/SATURN-Project)
Brookman-May SD,…, Cindolo L, et al. BJU Int. 2013

33. Potential Predictive Biomarkers Under Investigation

34. PD-L1 Expression

35. PD-L1 Interacts with PD-1 to Suppress T-Cell Response
Antigen-experienced
T cell
PD-L1
PD-1
No Signal 2
Tissue
Inflammation
Signal 1
Costim.
Receptor
CD28
Traffic to periphery
Ligand
T-cell priming
Signal 1 DC
PD-L1
Signal 2
PD-L1 is overexpressed in a variety of solid tumours including RCC1
Interruption of Signal 2 leads to T-cell non-responsiveness

36. Problems with Biomarker Development Measurement Issues, eg, PD-L1
PD-L1 expression is measured and interpreted in a variety of ways, which limit cross-study comparisons and validation
PD-L1 antibodies1,2
Many different antibodies are used in clinical studies
Two commonly used antibodies had poor concordance in NSCLC biopsies1
PD-L1 assays1
IHC interpretation is subjective
Some clinical studies use proprietary assays, limiting validation/ comparisons
PD-L1 cutoff2
Percentage cut-off varies widely (1% - 50%)
H-score is sometimes used
Tissue type measured for PD-L1 varies across clinical studies1
Tumour epithelial cells
Tumour epithelial cell membrane
Immune cells of peritumoural stroma
McLaughlin J et al [Published online ahead of print]. JAMA Oncol Nov 12
Patel P et al. Mol Cancer Ther. 2015;14(4):

37. PD-L1 is a Prognostic Marker in aRCC Meta-Analysis
Risk of Death in PD-L1+ vs PD-L1- RCC patients
(various cut-offs for PD-L1)a
a PD-L1 cut-off using IHC was: ≥5% (2 studies), ≥10% (1 study), H-score >55 (1 study), not-reported (1 study), and the remaining study used continuous ELISA to assess PD-L1
Reprinted with permission from Iacovelli R et al. [Published online ahead of print]
Targ Oncol Oct 2

38. Association of OS with PD-L1 expression status on tumor cell membrane.
Toni K. Choueiri et al. Clin Cancer Res 2015;21:
©2015 by American Association for Cancer Research

39. Probability of Overall Survival Probability of Overall Survival
PD-L1 was not Predictive of Nivolumab Benefit in aRCC in CheckMate-025 (1% Cut-off)
PD-L1 <1% (n=76%)
PD-L1 ≥1% (n=24%)
Median OS, months (95% Cl)
Nivolumab
21.8 (16.5–28.1)
Everolimus
18.8 (11.9–19.9)
Median OS, months (95% Cl)
Nivolumab
27.4 (21.4 – NE)
Everolimus
21.2 (17.7 – 26.2) 3 6 9 12 15 18 21 24 27 30 33
0.0
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Probability of Overall Survival
Nivolumab
Everolimus
HR (95% CI): 0.77
(0.60–0.97) 3 6 9 12 15 18 21 24 27 30 33
0.0
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Probability of Overall Survival
Nivolumab
Everolimus
HR (95% CI): 0.79 (0.53−1.17)
No. at Risk
Months
No. at Risk
Months
Nivolumab 94 86 79 73 66 58 45 31 18 4 1
Everolimus 97 77 68 59 52 47 40 19 9
276
265
245
233
210
189
145 94 48 22 2
299
267
238
214
200
192
137 92 51 16 1
Reprinted with permission from Motzer RJ et al. N Engl J Med. 2015;373(19):

40. PD-L1 Expression did not Predict Benefit with Atezolizumab in a Phase 1 Trial in RCC
1-year OS 2-year OS
<1% PD-L1 (n=22) 81% 65%
≥1% PD-L1 (n=33) 80% 51%
PD-L1
Patients , N (%)
ORR, %
Median PFS, months
(95% CI)
Median OS, months
<1% 22
6 (18)
5.6
( ) NR
(20.0-NR)
≥1% 33
2 (9)
4.5
( )
28.8
( )
Reprinted with permission from McDermott DF et al. J Clin Oncol. 2016;34(8):

41. Validation of IMDC prognostic model for first-line pazopanib in metastatic renal carcinoma: the Spanish Oncologic Genitourinary Group (SOGUG) SPAZO study
Perez-Valderramma B et al. Ann Oncol 2016

42. Final results from the large sunitinib global expanded-access trial in metastatic renal cell carcinoma
Gore ME, Porta C, Bracarda S, et al. Br J Cancer 2015

43. The International Metastatic Renal Cell Carcinoma Database Consortium model as a prognostic tool in patients with metastatic renal cell carcinoma previously treated with first-line targeted therapy: a population-base study
Ko JJ et al. Lancet Oncol 2015

44. Summary Prognostic models: Predictive Biomarkers
Aid patient counselling and therapy planning
IMDC and MSKCC are the most widely used prognostic models, with different baseline factors but similar patient outcomes
Predictive Biomarkers
There are no validated predictive biomarkers to aid treatment selection in aRCC
Many candidate predictive biomarkers have been identified, and need to be validated
PD-L1 may not be predictive of PD-1/PD-L1 inhibitor benefit, and may in fact be prognostic
VEGFR-1 polymorphisms may predict bevacizumab benefit
PBRM1 and KDM5C mutation status may predict benefit with everolimus and sunitinib, respectively
IL-8 polymorphisms may predict benefit with VEGFR TKI therapy
High circulating IL-18 levels may predict benefit with sunitinib vs everolimus
Limitations include tumour heterogeneity, unreliable detection assays, and lack of patients numbers

45. Predictive Biomarkers – Myth or Reality?