Come cambierà la strategia terapeutica

(1)

How treatment strategies will change

Orazio Caffo

(2)

Relapsed NMIBC Primary

NMIBC

(3)

(4)

• NMIBC has benefited from the use of immune-based

treatments in the form of intravesical BCG for over four

decades

(5)

multifactorial mechanism

• immune modulation

• direct interactions with TAICs

• interactions with benign and malignant urothelial cells

Nature Reviews Urology 11, 153–162 (2014)

(6)

4 no granuloma

PDL1 exp 4- 12

granuloma

PDL1 exp 11+

1-

16 pts

PDL1 exp 3+

13-

(7)

• 39 pts

– 23 BCG – 16 no BCG

• First biopsy positive PDL1 staining 7/39

• Second biopsy positive PDL1 staining 12/39

• Third biopsy positive PDL1 staining 7/13 (p = 0.048 by Fisher’s Exact Test)

• PDL1 expression appears to increase as disease recurs

• BCG treatment did not correlate with PDL1 expression

(8)

NMIBC

Modified from J. Bellmunt et al. / Cancer Treat Rev 54 (2017) 58–67

(9)

NMIBC

(10)

NMIBC

Intravesical BCG Surgery Intravesical BCG Intravesical Chemo

(11)

MIBC Relapsed

NMIBC Primary

NMIBC

(12)

(13)

MIBC Relapsed

NMIBC Primary

NMIBC

Surgery (± chemo) Organ preservation

(14)

MIBC Relapsed

NMIBC Primary

NMIBC

Chemo→Surgery Organ preservation

(15)

MIBC Relapsed

NMIBC Primary

NMIBC

(16)

MIBC Relapsed

NMIBC Primary

NMIBC

Surgery→Chemo preservation

(17)

MIBC Relapsed

NMIBC Primary

NMIBC

(18)

MIBC Relapsed

NMIBC Primary

NMIBC

(19)

(20)

MIBC Relapsed

NMIBC Primary

NMIBC

(21)

MIBC Relapsed

NMIBC Primary

NMIBC

Surgery (± chemo) XRT+TURV+Chemo

(22)

XRT-related immune modulation

• XRT can induce immunogenic cell death (ICD) of cancer cells

• XRT induces upregulation of MHC class I

molecules, which are crucial for T-cell activation

• XRT upregulates tumor-associated antigens (TAA), which are expressed at the surface of cells in

association with MHC class I antigens

• XRT may alter the MHC class I associated peptide

profile

(23)

• XRT acts as an in-situ vaccination, to cause T-cell priming and to affect trafficking, infiltration and killing of cancer cells

• Unfortuntely, ‘abscopal’ responses are hardly observed due to efficient mechanisms aiming to prevent auto-immunity dampen the anticancer immune response

(24)

Buchwald & Efstathiou Bladder Cancer 2015

(25)

(26)

(27)

(28)

MIBC Relapsed

NMIBC Primary

NMIBC

Modified from Kang et al. J ImmunoTher Cancer (2016) 4:51

(29)

MIBC Relapsed

NMIBC Primary

NMIBC

Surgery (± chemo) XRT+TURV+Chemo

(30)

MBC 2^nd line

MBC 1^st line

MIBC Relapsed

NMIBC Primary

NMIBC

Surgery+Chemo XRT+TURV+Chemo

(31)

(32)

MBC 2^nd line

MBC 1^st line

MIBC Relapsed

NMIBC Primary

NMIBC

Cis-based Carbo-based

(uneligible)

Vinflunine

(33)

ATEZO: II line/ I line Cis-ineligible

NIVO: II line

PEMBRO: II line

(34)

from Oing et al. / J Urol 195 (2016) 254–63

(35)

(36)

Population Median OS 1-y Surv Rate

IC2/3 11.4 48%

IC1 6.7 30%

IC0 6.5 29%

≥ 1% 11.3 47%

< 1% 5.95 40%

Vinflunine 6.9 27%

PEMBRO 10.3 43.9%

(37)

IC2/3 11.4 48%

IC1 6.7 30%

IC0 6.5 29%

≥ 1% 11.3 47%

< 1% 5.95 40%

PEMBRO 10.3 43.9%

Vinflunine 6.9 27%

(38)

• Phase II

• 310 pts

• mFU 11.7 mos

• Phase II

• 270 pts

• mFU 7 mos

• Phase III

• 253 pts

• mFU 42 mos

• Phase III

• 270 pts

• mFU 14.1 mos

(39)

(40)

(41)

(42)

• Phase II

• 119 pts

• mFU 17.2 mos

• ORR 23%

• mOS 15.9 mos

• 1y Surv Rate 57%

• Phase III

• 118 pts

• mFU 4.5 yrs

• ORR 36%

• mOS 9.3 mos

• 1y Surv Rate 37%

(43)

MBC 2^nd line

MBC 1^st line

MIBC Relapsed

NMIBC Primary

NMIBC

(44)

MBC 2^nd line

MBC 1^st line

MIBC Relapsed

NMIBC Primary

NMIBC

(45)

MBC 2^nd line

MBC 1^st line

MIBC Relapsed

NMIBC Primary

NMIBC

(46)

MBC 2^nd line

MBC 1^st line

MIBC Relapsed

NMIBC Primary

NMIBC

(47)

MBC 2^nd line

MBC 1^st line

MIBC Relapsed

NMIBC Primary

NMIBC

(48)

MBC 2^nd line

MBC 1^st line

MIBC Relapsed

NMIBC Primary

NMIBC

Cis-based Carbo-based

(uneligible)

Vinflunine

(49)

MBC 2^nd line

MBC 1^st line

MIBC Relapsed

NMIBC Primary

NMIBC

Cis-based CPi (uneligible) Carbo-based (un)

CPi Vinflunine

(50)

Patients selection

(51)

Patients selection

(52)

Patients selection

(53)

Patients selection

(54)

(55)

(56)

(57)

Tumor is NEGATIVE

Addictive oncogenic mutation or fusion gene is ABSENT

The patient don’t benefit from therapy

Tumor is POSITIVE

Addictive oncogenic mutation or fusion gene is PRESENT

The patient benefits from therapy

Biomarker is ABSENT or at low level The patient is unlikely to benefit from therapy

Biomarker is PRESENT at intermediate level The patient may benefit

from therapy

Biomarker is PRESENT at a HIGH level The patient is likely to

benefit from therapy

Biological continuum of biomarker expression

Modified from Kerr

Assessment pitfalls: cut-off

(58)

• PDL1 epitopes detected by some antibodies are potentially unstable with prolonged specimen fixation or inadequate tissue handling before fixation

• Antibodies used for PDL1 detection have different affinities and specificities

Assessment pitfalls: how

(59)

Assessment pitfalls: where

• Focal programmed cell death 1 ligand 1 (PDL1) expression in some tumours may be missed in small biopsy specimens, such as needle biopsies

• PDL1 expression among multiple tumour lesions from individual patients can vary over time and by anatomical site

(60)

• PDL1 expression in tumour biopsies collected months or years earlier might not accurately reflect PDL1 status at the time of treatment initiation

• The potentially dynamic nature of tumor PD-L1 expression over time in response to both chemotherapy and radiotherapy administered after biopsy has the potential to confound associations with treatment response

Assessment pitfalls: when

(61)

Assessment pitfalls: where & when

(62)

• Is the drug targeted in this scenario a singular factor in our target system? 

• Is the biomarker present or absent? 

• Is the biomarker stable and functionally unique? 

• Is the biomarker easily measured? 

• Is it 100% predictive? 

Ideal biomarker characteristics

(63)

Biomarkers of response to

anti-PD1 (PD-L1) therapies

(64)

Conclusions

(65)

Conclusions

• Immune checkpoint inhibitors have started to alter the therapeutic landscape for bladder cancer

• Clinical outcomes data need to be confirmed in larger phase III trials

• Many efforts should be made to identify biomarkers

able to select responders pts

(66)

Conclusions

• Immune checkpoint inhibitors have started to alter the therapeutic landscape for bladder cancer

• Clinical outcomes should be confirmed in phase III trials (only PEMBRO has this data)

• Longer follow-up data is needed

• Many efforts should be made to identify biomarkers

able to select responders pts

(67)