Session 490003

Principles of immunotherapy
Dragana Jovanovic
University Hospital of Pulmonology
Clinical Centre of Serbia
Belgrade
Dragana Jovanovic
University Hospital of Pulmonology
Clinical Centre of Serbia
Belgrade
Baltic and Eurasia Masterclass in Clinical Oncology
11-14 July , 2019
Minsk, Belarus
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rom
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Imunotherapy in oncology nowadays –
the change of paradigm
Do not duplicate or distribute without
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Cancer-immunity cycle, Tumour evasion mechanisms
Basic principles of immunotherapy
Immunotherapy Efficacy, evaluation of response
Biomarkers for immunotherapy
Combination immunotherapy
Immune related Adverse Events
Principles of immunotherapy
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Adaptive anticancer immunity
The adaptive anticancer immune response is
initiated by immature DCs, which capture and
process tumor antigens.
DCs migrate to tumor-draining lymph nodes,
where they present tumor antigens within
MHC molecules to naïve T cells, triggering T-
cell activation which requires interaction not
only between the antigen–MHC complex on
DCs and TCRs but also among an array of co-
stimulatory molecules, including CD80/86 on
DCs and the CD28 receptor on T cells.
Carbone et al. J Thorac Oncol. 2015
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Naïve T cell activation requires signals from the T cell receptor complex,
co-receptors and co-stimulatory molecules - immune checkpoints
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Immune-mediated mechanisms of antitumor
activity: immunogenic cell death, antigen
release and presentation, activation of T-cell
responses, lymphocytic infiltration into
tumors and depletion of immunosuppression.
Naïve T cell activation requires signals from the T cell receptor complex,
co-receptors and co-stimulatory molecules - immune checkpoints

Tumour
Lymph node
Blood vessel
CANCER - IMMUNITY CYCLE
Steps leading to anti-tumour immune response
Chen and Mellman, 2013
Release of cancer
cell antigens
(cancer cell death)
1
Cancer antigen
presentation
(dendritic cells/APCs)
2
Priming and activation
(APCs and T cells)
3
Infiltration of T cells
into tumours
(CTLs, endothelial cells)
5
Recognition of cancer
cells by T cells
(CTLs, cancer cells)
6
Killing of cancer cells
(immune and cancer cells)
7
Trafficking of T cells
to tumours (CTLs)
4
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Lebbe et al, 769O ESMO 2008
Tumor immune escape
A variety of mechanisms can facilitate tumor immune escape – tumor evasion
Deregulation of immune checkpoint signaling noted in multiple malignancies
Wolchock et al. Nature 2014; Chen and Mellman, Nature 2017.

Mechanisms of Immune Escape in the Tumor Microenvironment
Roman M. Chabanon et al. Clin Cancer Res 2016
PD-1 acts predominantly in the tumor
microenvironment (contrary to CTLA-4),
where PD-L1 is overexpressed by multiple
cell types, including dendritic cells, M2
macrophages, and tumor-associated
fibroblasts.
Lung cancer as an example is
characterized by such strongly
f

Wolchock et al. Nature 2014; Chen and Mellman, Nature 2017.
Evading immune detection by expression of inhibitory receptors, e.g. CTLA-4, PD-L1 and PD-L2, leading to
T-cell inhibition - Blocking inhibitory pathways to unleash tumor immune response!
Targeting CTLA-4 and PD-1 signals with mAbs - Checkpoint Inhibitors
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X
X
Checkpoint Blockade activates Antitumor Immunity
or
f

Targeting CTLA-4 and PD-1 signals with mAbs - Checkpoint Inhibitors
Avelumab
Checkpoint Blockade activates Antitumor Immunity
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Immunotherapy to boost anti-cancer immune responses:
through passive and active strategies
Active immunotherapy
Passive immunotherapy
Peptide vaccine
DC vaccine
Genetic vaccine
TCR or CAR
genetic engineering
T cell cloning
T cell recruitment
via T cell bispecifics
NK cell recruitment
by antibodies
PD-L1
CTLA-4
CD40
CD137
OX40
IL-2
IFN
IL-15
IL-21
Tumour cell
Baeuerle and Reinhardt, 2009; Chen and Mellman, 2013
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Efficacy of Immunotherapy (2015 – 2016) – ICIs in pretreated NSCLC patients
Nivolumab – CheckMate 017 (Ph III)1
2nd Line, squamous, PD-L1 All-Comer
Nivolumab – CheckMate 057 (Ph III)2
2nd Line, non-squamous, PD-L1 All-Comer
Pembrolizumab - Keynote 010 (Ph II/III)3
2nd+ Line, PD-L1 TPS ≥1%
Atezolizumab – OAK (Ph III)4
2nd+ Line, PD-L1 All-Comers
1. Borghaei H et al. Poster presentation at ASCO 2016. 9025. 2. Brahmer JR et al. Oral presentation at AACR 2017. CT077. 3. Herbst RS et al. Poster presentation at ASCO 2017.
9090. 4. Rittmeyer A et al. Lancet. 2017;389(10066):255-265.
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Immunotherapy in pretreated NSCLC - Consistent Benefit in OS
Borghaei et al., 2016, ASCO.1
Time (Months)
100
80
60
40
20
0
0 6 30
OS
(%)
18
12 24 36
Nivolumab
Docetaxel
Checkmate 017 (SQ)1
Time (Months)
2-yr OS = 23%
2-yr OS = 8%
Nivolumab
Docetaxel
100
80
60
40
20
0
0 6 30
OS
(%)
18
12 24 36
Checkmate 057 (NSQ)1
2-yr OS = 29%
2-yr OS = 16%
Herbst et al., 2017, ASCO.3
OS
(%)
Rittmeyer et al., 2017, Lancet.4
Time (Months)
100
80
60
40
20
0
0 3 6 9 12 15 18 21 24 27
Atezolizumab
Docetaxel
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OAK4
18-mo OS = 40%
18-mo OS = 27%
Time (Months)
100
80
60
40
20
0
0 5 10 15 20 25 30 35
OS
(%)
Pembro 2 mg/kg
Pembro 10 mg/kg
Docetaxel
KEYNOTE-010 (≥1% PD-L1)3
30-mo OS = 29.5%
30-mo OS = 22.1%
30-mo OS = 12.3%
1. Borghaei H et al. Poster presentation at ASCO 2016. 9025. 2. Brahmer JR et al. Oral presentation at AACR 2017. CT077. 3. Herbst RS et al. Poster presentation at ASCO 2017.
9090. 4. Rittmeyer A et al. Lancet. 2017;389(10066):255-265.
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1st Line Immunotherapy
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13 patients treated for
colorectal MSI–high
genotype (n=5),
urothelial carcinoma(n=3),
melanoma(n=2),
NSCLC (n=2) and
TNB (n=1)
for a median of12 months
(range10.6–12)
Efficacy even with Rechallenging with the same ICI at progression –
Outcomes of long-term responders
2 PR, 6 SD
Bernard-Tessier, EJC 2018.
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How to evaluate tumour response to immunotherapy
▪ Immune-related RECIST
▪ Specific patterns of response under immunotherapy
Pseudoprogression
Hyperprogression
Immune unconfirmed progressive disease (iUPD)
➢ Durable clinical benefit/response
➢ Major pathological response
➢ Molecular Response (ctDNA)
➢ Response by Cancer-immune phenotypes
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Responses of 41 patients were analyzed:
• Response rate:
•29.2% assessed by RECIST v1.1
•34.1% by irRC
• PD: 4.9% defined by RECIST but not by irRC.
The patients eventually experienced tumor
regression, suggesting delayed
pseudoprogression.
Comparison of RECIST to Immune-related Response Criteria
in NSCLC patients treated with immune-check point inhibitors
Kim, Cancer ChemotherPharmacol, 2017
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Pseudoprogression
Topalian SL, et al. N Engl J Med 2012
Initial progression
Nivolumab
Followed by regression
Metastatic
nonsquamous
NSCLC
• Radiologic pseudoprogression
- tumours initially exhibit features of progression - tumour enlargement and/or
development of new lesions
- a subsequent radiologic tumour response (shrinkage) on serial imaging with sustained
therapy Wolchok JD, et al. Clin Cancer Res, Hodi FS, et al. J Clin Oncol 2016
Incidence up to 5%
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Ribas A, et al. Clin Cancer Res 2009; 15:7116-7118
Cancer lesions – made
up mainly of cancer
cells and stromal cells
Antoni Ribas, et al. Clin Cancer Res. 2009
Pseudoprogression
Size of tumour lesions decreases
in patients with an objective
response (RECIST)
Tumour lesions increase in size in
cases of disease progression
In some cases, the tumour lesions
may become heavily infiltrated by
by immunotherapy-recruited
immune and inflammatory cells
resulting in an apparent increase
in size of lesions and sometimes
new lesions (PD by RECIST)
With immune checkpoint
inhibitor therapy
Pseudo-progression
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• Can be difficult to differentiate pseudoprogression from true disease progression
Wolchok JD, et al. Clin Cancer Res 2009, Liam WCLC 2018.
TRUE PROGRESSION PSEUDO-PROGRESSION
Performance status, PS Deterioration Remains stable or improves
Systemic symptoms Worsen May or may not improve
Symptoms of tumour
enlargement
 May or may not be present
Tumour burden
Baseline  Initial  followed by a response
New lesions Appear and increase in size Appear then remain stable and/or
subsequently respond
Biopsy may reveal Evidence of tumour growth Evidence of immune cell
infiltration
True or pseudo-progression ?
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HPD: described as an
increase ≥ 50% in tumour
volume
Hyperprogression
Hyperprogressive disease
Acceleration in tumor growth rate (TGR), with varying numerical
definitions, in the context of cancer immunotherapy.
Ferrara et al JTO 2017, JAMA Oncol 2018. Champiat et al. Nat Rev ClinOncol 2018
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Hyperprogressive disease in advanced NSCLC treated with
PD-1/PD-L1 inhibitors or with single-agent chemotherapy
• 406 pts treated with ICPIs in 2nd line:
• 13.8% Hyperprogression > 2 metast. Sites
• 4.7% Pseudoprogression
• 59 pts treated with chemotherapy in 2nd line
• 5% Hyperprogression (3 vs 18 PD)
• 0% Pseudoprogression
IMMUNOTHERAPY COHORT CHEMOTHERAPY COHORT
Ferrara JAMA Oncol 2018
▪ Supported by preclinical evidence, highlighting the role of innate immune cells in driving HPD.
Hyperprogressive correlates with > 2 metastatic sites and predicts poor OS
16% HPD
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Borcoman et al, Ann Oncol 2019
Novel patterns of response under immunotherapy
Immune unconfirmed progressive disease (iUPD) in iRECIST
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RECIST 1.1 iRECIST
Definitions of measurable
and non-measurable
disease; numbers and site of
target disease
Measurable lesions are ≥10 mm in diameter
(≥15 mm for nodal lesions); maximum of five
lesions (two per organ); all other disease is
considered non-target (must be ≥10 mm in
short axis for nodal disease)
No change from RECIST 1.1; however, new lesions are
assessed as per RECIST 1.1 but are recorded separately
on the case report form (but not included in the sum of
lesions for target lesions identified at baseline)
Complete response, partial
response, or stable disease
Cannot have met criteria for progression
before complete response, partial response,
or stable disease
Can have had iUPD (one or more instances), but not iCPD,
before iCR, iPR, or iSD
Confirmation of complete
response or partial response
Only required for non-randomised trials As per RECIST 1.1
Confirmation of stable
disease
Not required As per RECIST 1.1
New lesions Result in progression; recorded but not
measured
Results in iUPD but iCPD is only assigned on the basis of
this category if at next assessment additional new lesions
appear or an increase in size of new lesions is seen (≥5
mm for sum of new lesion target or any increase in new
lesion non-target); the appearance of new lesions when
none have previously been recorded, can also confirm
iCPD
Independent blinded review
and central collection of
scans
Recommended in some circumstances—eg,
in some trials with progression-based
endpoints planned for marketing approval
Collection of scans (but not independent review)
recommended for all trials
Confirmation of progression Not required (unless equivocal) Required
Consideration of clinical
status
Not included in assessment Clinical stability is considered when deciding whether
treatment is continued after iUPD
Comparison of RECIST 1.1 and IRECIST Seymour et al 2017.
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Durable clinical response
Brahmer, AACR 2017
Felip, ASCO 2018
Rizvi, Science 2015
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Forde, ESMO 2016 and NEJM 2018
Neoadjuvant PD-1 blockade in resectable
lung cancer with two doses of nivolumab 2
weeks apart
% pathological regression:
Pathological response after Neoadjuv. Chemoth
in resectable NSCLC: proposal for the major
pathological response as a surrogate endpoint
Hellmann, Lancet Oncol 2014
Major pathological response
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Cancer-immune phenotypes
1. Immune-desert tumour
2. Immune-excluded tumour
3. Inflamed tumour
Chen and Melman, Nature 2017
Tumor Microenvironment
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Pennock G and Chow L, Oncologist 2015
1. Immune-desert tumour
Quite rare/No response
2. Immune-excluded tumour
Possible Response
3. Inflamed tumour
More Frequent Response
Response to Immune Checkpoint blockade by Cancer-immune
phenotypes
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Survival Curves by
Tumor Lymphocytic Infiltration
OS
DFS
Inflamed tumour
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Mutational Burden in various tumor types
Somatic mutations in individual cancers range from 0.01 to >400 mutations per mB
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•Biomarkers indicative of
hypermutation & neoantigens
may predict response to
immuno-oncology therapies
Examples:
‒TMB, MSI-high, neoantigens
Tumor antigens
•Biomarkers that identify tumor
immune system evasion
beyond PD-1/CTLA-4 to inform
new immuno-oncology targets
and rational combinations
Examples:
‒Tregs, MDSCs, IDO, LAG-3
Tumor immune
suppression/evasion
•Biomarkers (intra- or peri-
tumoral) indicative of an
inflamed phenotype may predict
response to immuno-oncology
therapies
Examples:
‒PD-L1, inflammatory
signatures
Tumor
microenvironment
(inflammation)
•Biomarkers that characterize the
host environment, beyond tumor
microenvironment, may predict
response to immuno-oncology
therapies
Examples:
‒Microbiome, germline genetics
Host environment
Tumor
antigens
Tumor immune
suppression
Inflamed
tumor
Adapted from Blank CU, et al. Science 2016;352:658–660
Tumor & Immune Microenvironment Factors as potential
Predictive Biomarkers for benefit from Immunotherapy
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Checkpoint Immunotherapies as “Targeted therapy”
- PD-L1 as a Druggable Target?
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PD-L1 Expression and ORR to Immunotherapy
Callahan. ASCO 2014.
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n= 42 44 34 94 30 53 113 129 65 55 411
Response Rates
Unselected 21% 32% 29% 22% 23% 23% 40% 19% 26% 18% 40%
PD-L1 + 36% 67% 44% 39% 27% 46% 49% 37% 43% 46% 49%
PD-L1 − 0% 19% 17% 13% 20% 15%* 13% 11% 11% 11% 13%
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Reck et al. 2016.
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Correlation of PD-L1 Expression and efficacy
Borghaei H, ASCO 2016; Rittmeyer A, Lancet 2016; Herbst R, Lancet 2015
KN -010 CM – 057 Non-Squam.
PEMBROLIZUMAB Indication PD-L1 Cutoff
▪ First-line metastatic NSCLC ▪ TPS ≥ 50%
▪ Second-line metastatic NSCLC ▪ TPS ≥ 1%
▪ Recurrent locally advanced/metast.
gastric/gastroesophageal junction adenoc.
▪ CPS ≥ 1
▪ Recurrent/metastatic cervical cancer ▪ CPS ≥ 1
▪ Locally advanced/metast. urothelial carc. ▪ CPS ≥ 10
PD-L1: Multiple Interpretive Criteria
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OAK Study – 2nd Line Atezolizumab vs Docetaxel
Barlesi et al. 2016
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▪ Despite all inadequacies of PD-L1 testing, clinical data collectively strongly suggest that
higher levels of PD-L1 expression are associated with better clinical efficacy.
▪ Testing platforms and antibodies do not seem to differ much
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Awad et al: Outcomes in NSCLC Patients Treated with First-Line Pembrolizumab and a PD-L1 TPS of 50-74%
vs 75-100% or 50-89% vs 90-100%
• 150 patients with PD-L1 ≥50% and treated with 1st line anti-PD-1 across 3 US-based academic institutions
N
PD-L1 TPS score %
Patrick M. Forde, WCLC 218
Novel Approaches with IO - Choosing wisely: what, when and why?
Implications for clin. trial stratification - very high PD-L1 tumors can be a major driver of benefit to anti-PD-1
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induced by
▪IFN-gamma, IL-4
▪MAPK
▪PI3K-AKT
▪HIF-1alpha
▪STAT3
The Issue of an Appropriate Biomarker?
J. Chen et al. Ann Oncol 2016
Induction of PD-L1 by IFNγ
Mandai et al. Clin Cancer Res 2016
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Looking for better biomarkers
IFNγ-signature for atezolizumab in NSCLC
Teff /IFN-γ gene signature subgroups
Teff /IFN-γ high
HR 0.43
(95% CI
0.24–0.77)
Teff /IFN-γ low
HR 1.10
(95% CI
0.68–1.76)
0 2 4 6 8 10 12 14 16 18 20
0
20
40
60
80
100
Atezolizumab (Teff /IFN-γ high)
Atezolizumab (Teff /IFN-γ low)
Docetaxel (Teff /IFN-γ high)
Docetaxel (Teff /IFN-γ low)
Follow-up (months)
OS
(%)
Fehrenbacher L et al. Lancet. 2016;387(10030): 1837-1846
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Overall Survival
• Although Teff signature enriched for improved OS benefit at all expression cut-offs, the OS benefit was similar to
BEP in all subgroups, and a trend toward OS benefit was observed in patients with Teff < median
•BEP, biomarker-evaluable population. Data cutoff: July 7, 2016 Kowanetz et al. OAK Teff biomarker. WCLC 2017.
0,25
0.25 1.0 1.5
OS HR
Favors atezolizumab Favors docetaxel
0.71
0.87
0.67
0.87
0.59
0.76
0.60
OS HR (95% CI)
0.67 (0.54, 0.83)
0.87 (0.63, 1.21)
0.71 (0.59, 0.85)
Population
Teff ≥ 25%
Teff < 25%
BEP
0.59 (0.46, 0.76)
0.87 (0.68, 1.11)
Teff ≥ 50%
Teff < 50%
0.60 (0.42, 0.87)
0.76 (0.62, 0.92)
Teff ≥ 75%
Teff < 75%
Atezolizumab, ≥ median
Atezolizumab, < median
Docetaxel, ≥ median
Docetaxel, < median
Teff ≥ median, HR = 0.59 (0.46, 0.76)
Teff < median, HR = 0.87 (0.68, 1.11)
Overall
Survival
(%)
Months
Teff
expression
n (%)
189 (25%)
564 (75%)
382 (51%)
371 (49%)
566 (75%)
187 (25%)
753 (100%)
Association Between Teff Gene Signature and OS in OAK
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Tumor Mutational Burden (TMB) predicts outcomes to Immunotherapy
Rizvi et al. Science 2015
TMB can be estimated by a variety of
methodologies, from Whole Exome
Sequencing (WES) to Comprehensive
Genomic Profiling (CGP), as in
Foundation1 panel)
• The more “antigenic” the tumor, the
more efficacious the therapy
• Proxy for direct quantification of
neoantigenic proteins is TMB
Guha. The Pharmaceutical Journal. 2014. Chan. Ann Oncol. 2019
High TMB increases the
immunogenicity of tumors making
them a good target for treatment with
I-O therapies
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Carbone D. NEJM 2017.
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Checkmate 227: The role of Tumor Mutational Burden (TMB)
High TMB (≥ 10m/Mb) a positive predictive factor
for improved PFS with Ipi/Nivo regardless od PD-L1
Expression and histologic type
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Role of TMB across tumor types and lines of Therapy:
Tissue TMB ≥ 16 mut/Mb identifies a Patient Population distinct from PD-L1 IHC
▪ TMB is providing separate information from PD-L1 expression
▪ TMB ≥ 16 mut/Mb is being studied prospectively in multiple Phase III trials
Gandara D. ASCO 2018.
Pooled analysis
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TMB: Relationship between Mutational Load and OS
after Checkpoint Inhibitor Therapy
Samstein. Nat Genet. 2019.
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Tumor mutational burden in blood (bTMB) is associated with
Atezolizumab efficacy in 2nd-Line+ NSCLC (POPLAR & OAK Trials)
Gandara DR, et al. ESMO 2017.
OAK Study
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Gandara DR, et al. bTMB in POPLAR & OAK
LIMITED OVERLAP BETWEEN bTMB ≥16 AND PD-L1
EXPRESSIONa (OAK BEP)
a PD-L1 expression was evaluated by immunohistochemistry (IHC) using the VENTANA SP142 assay;
TC3 or IC3, ≥50% of TC or ≥10% of IC express PD-L1.
BEP, biomarker-evaluable population; IC, tumor-infiltrating immune cell; TC, tumor cell.
54
• Non-significant overlap between the
bTMB ≥16 and TC3 or IC3 subgroups
(Fisher exact test, P = 0.62)
• 19.2% of tumors with bTMB ≥16
were also TC3 or IC3
• 29.1% of tumors with TC3 or IC3
also had bTMB ≥16
PFS HR (95% CI) OS HR (95% CI)
bTMB ≥16 0.64 (0.46, 0.91) 0.64 (0.44, 0.93)
TC3 or IC3 0.62 (0.41, 0.93) 0.44 (0.27, 0.71)
bTMB ≥16 and
TC3 or IC3
0.38 (0.17, 0.85) 0.23 (0.09, 0.58)
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Assessment of genomic
alterations with hybrid capture-
based NGS
Foundation Medicine’s comprehensive
genomic profiling
Data aggregation
and analysis Scientific/clinical
expert review
A report connecting patients
to targeted therapies
FMI: Foundation Medicine, Inc.; NGS: next-generation sequencing.
Foundation Medicine, Inc. (2017) https://www.foundationmedicine.com/ Accessed Feb 2017; Foundation Medicine, Inc. Patient report.
permission from
author and ESO

Anagnostou, CR 2019
Dynamics of tumor and immune responses during immune
checkpoint blockade in NSCLC by ctDNA
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Why should we combine?
• Treatment benefit restricted to subgroup of
patients, app. 20%
• Significant proportion of patients, app. 80%,
among all tumor types included, still do not
respond to these drugs.
• Several preclinical data suggest synergistic
activity with several therapeutic approaches, that
might enhance efficacy of immunotherapy...
• Potential synergistic combinations: ICIs with
conventional (RT, Chemoth. and targeted
therapies), newer immunotherapies (cancer
vaccines, oncolytic viruses…).
•Reliable biomarkers are necessary to define
patients who will achieve best clinical benefit
with minimal toxicity in combination therapy.
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Anti PD-1/PD-L1 + anti-CTLA-4 combinations
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T cell activation –
a Multiple Signalling Process
Combo immunotherapy agents
Phase Ib Trial: Responses to Durvalumab
plus Tremelimumab by PD-L1 Overexpression
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Combo immunotherapy agents
Check Mate 012: 1st Line
Nivolumab plus Ipilimumab in NSCLC
Nayer Rizvi, WCLC 2015.
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Rationale for investigating Combination Immunotherapy
with other Treatment Modalities
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Interaction Between Treatment Modalities –
Rational combinations can improve immunotherapy activity
Melero et al, Nature Reviews, 2015
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Overall Survival
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Overall Survival
Paz Ares NEJM 2018, Jotte ASCO 2018.
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Rationale for immunotherapies in combination with radiotherapy
Chemoradiation has direct
cytotoxic effects and induces
immunomodulatory changes.1-3
• Increased antigen release
from dying tumor cells
(antigen storm)1,2
• Upregulation of PD-L1 and
immunogenic cell surface
proteins1
• Immunomodulatory changes
in the tumor
microenvironment1,3
Tumor-
associated
macrophage
MHC I
Tumor
cells
PD-
L1
Cytotoxic T
cell
Tumor
antigens
Primed
dendritic cell
Upregulation of
immunogenic cell
surface markers
Induction of
immunogenic cell
death: antigen release
Upregulation of
PD-L1
1. Daly ME, et al. J Thorac Oncol. 2015;10:1685-1693.
2. Kaur P, Asea A. Front Oncol. 2012;2:191.
3. Deng L, et al. J Clin Invest. 2014;124:687-695.
RT - characteristic changes of transcription
factors and signaling pathways.
Potentially modulates the immuno-phenotype
and immunogenicity of tumor cells
permission from
author and ESO

Randomized Phase II study of pembrolizumab after
stereotactic body radiotherapy (SBRT) vs pembrolizumab alone
in advanced NSCLC – The PEMBRO-RT study
Theelen, ASCO 2018
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Targeted therapy may modulate immune cell function
Agent Effect
Bevacizumab Reduces MDSC, Treg; increase DC maturation/ priming and T cell tumor
infiltration
EGFR-targeting mAb Promotes ICD
Erlotinib Upregulates NKG2D ligands
Ibrutinib Generation of T helper cells & IFNg
Imatinib Promotes expansion of circulating NK cells, tumor infiltration by CTLs
JAK2 inhibitors Increase DC maturation, decrease STAT3, decrease tumor PD-L1
Lapatinib Promotes tumor infiltration by CTLs
MAPK inhibitors Upregulates expression of MHC class I molecules
Sorafenib Depletes Treg
Sunitinib Increases CTL/Treg ratio
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Synergistic effect of immune checkpoint blockade and
anti-angiogenesis
Yi et al. Molecular Cancer2019
Reduces MDSC, Treg;
increase DC maturation/priming
and T cell tumor infiltration
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Time (months)
Median, 19.2 mo
(95% CI: 17.0, 23.8)
Median, 14.7 mo
(95% CI: 13.3, 16.9)
IMpower150: PFS and OS in ITT-WT population (Arm B vs Arm C)
aStratified HR. bFor descriptive purposes only. Data cutoff: January 22, 2018.
Socinski MA, et al. ASCO 2018. Abstract 9002.
Progression-Free
Survival
(%)
HRa, 0.59 (95% CI, 0.50–0.70)
P < 0.0001b
Median follow-up: ~20 mo
Time (months)
Median, 8.3 mo
(95% CI: 7.7, 9.8)
Median, 6.8 mo
(95% CI: 6.0, 7.1)
Updated PFS analysis in the ITT-WT (Arm B vs Arm C)
Landmark PFS, %
Arm B:
atezo + bev + CP
Arm C:
bev + CP
6-month 66% 56%
12-month 38% 20%
18-month 27% 8%
OS in the ITT-WT (Arm B vs Arm C)
Landmark OS, %
Arm B:
atezo + bev + CP
Arm C:
bev + CP
12-month 67% 61%
18-month 53% 41%
24-month 43% 34%
HRa, 0.78 (95% CI, 0.64–0.96)
P = 0.0164
Median follow-up: ~20 mo
Overall
Survival
(%)
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Nivolumab Pembrolizumab Durvalumab
Atezolizumab
• Chemotherapy
• Radiation/Ablation
• EGFR/ ALK TKI
• Anti-VEGF/VEGFR inhibitor
• Vasc Disrupt Agent
• Hypomethylating Agent
• HDAC inhibitor
• SPK Inhibitor
• C-Met inhibitor
• Glutaminase inhibitor
• Dasatinib
• Vaccine
• Gene therapy
• IL15 agonist
• PEG IL10
• TGFᵦR1 inhibitor
• Anti-CD27
• Ant-CXCR4
• Anti-CSF-1R
• IDO-1 inhibitor
• Anti-CTLA4
• Anti-LAG, Anti-TIM-3, Anti-KIR
• Chemotherapy
• Radiation
• EGFR/ALK TKI
• Anti-VEGF/VEGFR inhibitor
• Hyomethylating Agent
• HDAC inhibitor
• CDK Inhibitor
• BTK inhibitor
• PI3K Inhibitor
• KIT/CSF1R/FLT3 Inh
• FGFR inhibitor
• JAK1 Inhibitor
• CRM1 Inhibitor
• FAK Inhibitor
• Anti-EGFR
• Anti-CEACAM1
• PEG hyaluronidase
• Vaccine
• Oncolytic
• PEG IL10, Anti-CSF-1
• IDO1 Inhibitor
• Anti-CTLA4, Anti-B7-H3
• Chemotherapy
• Radiation
• EGFR/ALK TKI
• Anti-VEGF/Ang-2
• MEK Inhibitor
• Vaccine
• Adoptive Cell Therapy
• Anti-CEA/CD3
• Anti-CEA/ IL-2
• Anti-OX40, Anti-CD40
• Anti-CD27
• Anti-CSF-1
• Adenosine A2A Inhibitor
• IDO-1 Inhibitor
• Anti-CTLA4
• Anti-TIGIT
• Chemotherapy
• Radiation
• EGFR/ALK TKI
• VEGFR Inhibitor
• BTK Inhibitor
• MEK Inhibitor
• HAD Inhibitor
• PARP Inhibitor
• WEE1 Inhibitor
• ATR Inhibitor
• Anti-OX40
• CXCR4 Inhibitor
• CSF
• Anti-CD73
• Anti-CCR4
• Anti-CSF1R
• Anti-NKG2A
• Adenosine A2a
Inhibitor
• IDO1 Inhibitor
• Anti-CTLA4
• Anti-PD1
Avelumab
• ALK inhibitor (criz. and lorl.)
• Anti-41BB
• Anti-OX40
Combination immunotherapy
permission from
author and ESO

Zinngrebe J et al. EMBO reports 2014
Immune activation by ICIs can lead to autoimmunity or
inflammatory side effects
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Brahmer et al. JCO 2018, Postow et al. NEJM 2018.
permission from
author and ESO

Potential toxicities of immune checkpoint inhibitors
▪ Incidence of all-grade irAEs-58% with
anti CTLA-4, 35% with anti PD-(L)1
▪ Majority of irAEs: Grade 1-2 (most
common skin or GI tract)
▪ GI tract Grade 3–5: ≥10% of patients
receiving CTLA-4 inhibition
▪ Grade 3-5: <5% of patients in
monotherapy with anti PD-1/ PD-L1
Hodi FS et al. N Engl J Med 2010; Yamazaki N et al. Cancer Chemother Pharmacol. 2017; Topalian SL et al. N Engl J Med 2012; Michot JM et al. Eur J Cancer.
2016; Roberts et al, Asia-Pacific J of Clin Oncol 2017.
permission from
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Time to onset of Immune-related AEs associated with ICIs
Hepatitis
Colitis
Rash
Encephalitis
Endocrinopathies:
Pneumonitis
Nephritis and renal dysfunction
0 5 10 15 20 25 30
Hypophysitis
Adrenal Insufficiency
Hypothyroidism
Hyperthyroidism
Median Time to Onset, months (range)
CheckMate 057
CheckMate 069
CheckMate 025
NA
Type 1 Diabetes Mellitus
May occur even 1 year after
discontinunation of Th!
Most irAEs during
the first few doses
permission from
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Distribution of mild and severe immune-related adverse events (irAEs)
associated with immune checkpoint inhibitor therapy
Michot JM et al. Eur J Cancer 2016
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author and ESO

The odd ratio (OR) of different irAEs (all grades) comparing PD-1/PD-L1 vs CTLA-4 immune CPIs
CTLA-4 and PD-1 mAbs have distinct
irAE profiles. Different immune
microenvironments may drive
histology-specific irAE patterns
2017.
permission from
author and ESO

Comparison of the incidence of irAE
between tumor types
for patients receiving anti PD-1 agent
The odds ratio (OR) of different immune-related adverse events (all grades) comparing melanoma and NSCLC anti-PD-1
immune checkpoint inhibitor studies.
2017.
permission from
author and ESO

J Clin Oncol 33, 2015 (suppl; abstr 9018)
Kinetics of immune-related
AEs of CPIs: median time to
onset and to resolution of AEs
Anti CTLA4: Ipilimumab
permission from
author and ESO

irAEs associated with immune checkpoint blockade
▪ NCCN GL
▪ ASCO GL
S. Champiat et al. Ann Oncol 2016;27:559-574
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1. Yervoy Risk Evaluation and Mitigation Strategy. http://www.accessdata.fda.gov/drugsatfda_docs/rems/Yervoy_2012-02-16_IMMUNE%20MEDIATED%20ADVERSE%20REACTION%20MANAGEMENT%20GUIDE.pdf. Accessed January 2016.
2. http://www.opdivohcp.bmscustomerconnect.com accessed March 2016. 3. Ledezma B, et al. Cancer Manag Res. 2014;6:5-14.
Key Principles of imAEs Management
Early identification
Timely intervention
Stay alert: continuous monitoring
• Most imARs occur during treatment
• Monitor after the last dose; imARs can occur week to months later
Evaluate differential diagnoses per standard practice
• Consider noninflammatory etiologies
• Consider all signs and symptoms
Patients
must report new,
persistent or
worsening
symptoms
Robust, proven
management
guidelines available
Patient education essential
Individualized
patient follow-up
and counseling
Systemic high-dose corticosteroids and other
immunosuppressive medication for severe events
with antibiotic prophylaxis
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Univariate
Hazard Ratio
(95% CI)
P value
Multivariate
Hazard
Ratio* (95%
CI)
P value
PFS
Any 0.41 (0.3-0.57) P < 0.0001
0.48
(0.34-0.67)
P < 0.0001
Lung irAEs 0.54 (0.31-0.92) P = 0.024
0.56
(0.33-0.96)
P = 0.038
Gastrointestinal
irAEs
0.45 (0.26-0.77) P = 0.004
0.52
(0.3-0.9)
P = 0.021
Endocrine irAEs 0.5 (0.34-0.72) P < 0.0001
0.59
(0.4-0.89)
P = 0.011
Skin irAEs 0.51 (0.32-0.83) P = 0.007
0.57
(0.35-0.95)
P= 0.031
Hepatobiliary
irAEs
0.68 (0.4-1.16) P = 0.16
0.72
(0.41-1.24)
P = 0.23
OS
Any 0.33 (0.23-0.47) P < 0.0001
0.38
(0.26-0.56)
P < 0.0001
Lung irAEs 0.41 (0.21-0.78) P = 0.007
0.46
(0.24-0.89)
P = 0.022
Gastrointestinal
irAEs
0.38 (0.2-0.73) P = 0.004
0.5 (0.26-
0.98)
P = 0.045
Endocrine irAEs 0.49 (0.34-0.72) P < 0.0001
0.45
(0.28-0.72)
P = 0.001
Skin irAEs 0.6 (0.36-1.02) P = 0.06
0.8 (0.46-
1.39)
P = 0.43
Hepatobiliary
irAEs
0.84 (0.48-1.47) P = 0.55
0.94
(0.53-1.66)
P = 0.83
ORR: 51.4% vs. 20%, P < 0.01
DCR: 84.3% vs. 34%, P < 0.001
Impact of immune-related adverse events on survival in patients with
advanced NSCLC treated with nivolumab
irAEs significantly associated with a better treatment outcome.
Multiple irAEs - better outcome than 1 irAE - the long term impact of
early irAEs development on survival.
Ricciuti Biagio, WCLC 2018.
A B
C D
no-irAEs (n:110) 2.0 (1.69-2.31)
irAEs (n: 85) 5.7 (4.18-7.38)
P < 0.0001
HR: 0.41 (95%CI: 0.3-0.57)
P < 0.0001
HR: 0.33 (95%CI: 0.23-0.47)
no-irAEs (n:110) 1.9 (1.6-2.1)
1 irAEs (n: 48) 5.0 (2.6-7.5)
≥2 irAEs (n: 37) 8.4 (2.9-14.1)
P < 0.0001 P < 0.0001
mPFS, months (95%CI)
no-irAEs (n:110) 4.0 (3.42-7.46)
irAEs (n: 85) 17.8 (11.6-24.1)
mOS, months (95%CI)
mPFS, months (95%CI)
no-irAEs (n:110) 4.0 (3.2-4.8)
1 irAEs (n: 48) 11.9 (8.2-15.1)
≥2 irAEs (n: 37) 26.8 (18.2-31.2)
mOS, months (95%CI)
Median follow-up = 30.1 months
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Figure 1
Forest plot of PFS according the occurrence or nonoccurrence of immune-related
adverse events (irAEs)
Remon et al. JTO 2019
Immune-Related Adverse Events and Outcomes in Patients
with NSCLC: A Predictive Marker of Efficacy?
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▪ Immune Checkpoint Inhibitors (ICI) have revolutionized treatment of various malignant
diseases.
▪ Despite clear clinical advances, the biological mechanisms that underlie antitumor
immunity and determine sensitivity to ICIs agents are still poorly understood.
▪ Significant proportion of patients, app. 80%, among all tumor types included, still do not
respond to these drugs.
▪ Potential synergistic combinations include checkpoint blockade with conventional
(radiation, chemotherapy and targeted therapies) and newer immunotherapies (cancer
vaccines, oncolytic viruses, among others).
▪ Reliable biomarkers are necessary to define patients who will achieve best clinical
benefit with minimal toxicity in combination therapy.
▪ Regarding irAEs associated with ICIs, close patient monitoring is essential.
Conclusions
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Thank you for your attention!
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Session 490003

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Session 490003