Treatment decision making based on molecular phenotyping
Heinz Zwierzina, M.D.
Innsbruck Medical University
Frankfurt, June 13, 2017
Concepts of drug development have become very challenging
- (Treat „all“ patients with blockbuster drugs)
- Treatment of biomarker-defined subgroups
e.g. HER-2, K-ras wild type, immunotherapy
- Individualized therapy („molecular phenotyping“)- BUT: what if the „baskets“ get too small?
- Are all mutations druggable?
-Will we have drugs for all mutations?
Both concepts are critical to persue
1. Individualized therapy based on subgroup analysis
Evolving immunotherapy approaches
Immunosuppressivemicroenvironment
Immune priming
• Multiple vaccine approaches• Use chemotherapy/
radiotherapy to prime • Adoptive immunotherapy approaches• Toll-like receptors*
• IDO*• TGFβ*• IL-10
T-cell modulation
• CTLA-4*• PD1 pathway*• Lag 3*• CD137*• CD53/OX44*• OX40/L*• CD40/L• Tregs*• Adoptive immunotherapy approaches
Enhancing adaptiveimmunity
• NK-cell activation*• ADCC*• CD137*• IL-21• IL-15• CD40*• Toll-like receptors*• APC modulation
*Target for therapeutic modulation Finn OJ. N Engl J Med. 2008;358:2704-15Spagnoli GC et al. Curr Opin Drug Dev 2010;13:184-192
Galon J et al. Cancer Res 2007;67:1883-1886
Immune Control of Cancer T Cell Infiltration
Increase in TILs at Week 4 from Baseline Associated with Clinical Activity of Ipilimumab
Not all samples were evaluable for every parameter, and not all patients provided data for all time points P values uncorrected for multiple testing
Biomarker
# with TILS increased from baseline
(N=27)
P-value
Odds Ratio in favor of clinical
benefit
(95% CI)
Benefit group 4/7 (57%)
0.00513.27
(1.09, 161.43)
Non-benefit group 2/20 (10%)
Hamid et al, J. Trans Med, 2011
TILs at baseline were not correlated with benefit
Sources for biomarker development
Immunotherapy
Challenge:
- turn immune excluded tumors into T cell infiltrated tumors
- Reprogramming the tumor microenvironment
Biomarker development for subgroup definition:
- Collection of serum/plasma should be „mandatory“ for all patients
treated with immunotherapy
- Retrospective hypothesis generation, prospective analysis
For immunotherapy we can not get around liquid biopsies*
•Quandt et al.: Implementing liquid biopsies into clinical decision making
for cancer immunotherapy. Oncotarget, April 2017 (ahead of publication)
Case story: Anticancer vaccines
After some 25 years of clinical experience we only know:
• They can work
• They are non toxic
Did we put enough emphasis on what is happening at thetumour site and on subgroup analyses?
Just a thought!?
• Immunotherapy frequently is a bi-targeted approach
– Vaccines: expression of the TAA + detection byimmunocompetent cells
• Molecular phenotyping of immuno (-competent) cells (TCR etc)?
2. Individualized therapy –
„molecular phenotyping“
Integrating Molecular Profiling Into Cancer Treatment Decision Making: Experience With Over 35,000 Cases
Zoran Gatalica1, SZ Millis1, S. Chen1, G.D. Basu1, W. Wen1, L. Paul1, R.P. Bender1, Daniel D. Von Hoff1,2
1 Caris Life Sciences, Irving, TX / Phoenix, AZ 2 Translational Genomics Research Institute, Phoenix, AZ
J Clin Oncol. 2010 Nov 20;28(33):4877-83.
Methods - Multi-platform, technology independent analysis
• Mutational Analysis by Next Generation Sequencing of a predefined panel (n=44)
• Gene Copy Number by Fluorescent In Situ Hybridization / Chromogenic In Situ Hybridization (FISH / CISH) (n=7)
• MGMT Methylation by PCR (n=1)• Protein Expression Analysis by Immunohistochemistry
(IHC) (n=17 biomarkers)
Enables a robust analysis of the tumor’s biology and potential clinically actionable targets
Gatalica et al. ASCO 2013
Tumor Type Braf Kras EGFR PIK3CA NRAS cKit GNA11 GNAQ/GNAS
Lung & Bronchus 3 31 13 4 0 0 0 0
Colon 9 39 1 21 5 1 0 2
Pancreatic 1 82 1 2 0 0 1 2
Melanoma 34 2 0 1 20& 2 0 0
GE/Gastric 1 5 1 4 0 0 0 0
Kidney 2* 1* 0 4* 0 0 0 0
Leukemia & Lymphoma
1* 3* 4* 0 6* 0 0 0
Prostate 1 2 0 9* 2* 2* 0 0
Breast 0 1 1 27 0 0 0 0
Ovarian 1 8 1 6 1 0 0 0
Frequency (%) of Mutations In Common Cancers
Mutations determined by Cobas, Sanger, and /or Illumina NGS*n ≤ 100 &Ascierbo et al Lancet, 14:249-56, 2013
Gatalica et al. ASCO 2013
Tumor Type PTEN (loss) RRMI SPARC TLE3 Topo2A TOPO1 TS TUBB3
Lung & Bronchus
66 24 32 30 53 57 10 64
Colon 78 41 32 14 66 58 10 35
Pancreatic 75 21 35* 26 33 58 7 48
Melanoma 54 24 41 16 48 45 27 69
GE/Gastric 74& 37 32 26 69 65 15 27*
Kidney 72 14 31 13 20 52 6 50*
Leukemia & Lymphoma
67 36 46 16* 50 57 25 10*
Prostate 73 31 37 50 27 65 5 21*
Breast 64 30 52 54 53 73 11 42
Ovarian 47 28 26 19 59 49 30 47
Frequency (%) of IHC in Common Cancers
Gatalica et al. ASCO 2013
*n ≤ 100&Zheng et al Exp. Ther. Med. 5:57-64, 2013
ONCO-T-PROFILING Status: Jan 27, 2017*
• Collaborative project
• 131 patients with solid tumours within 22 months, ECOG status 0-2
• 87 patiuents treated according to profile
• We routinely type for HER-2, EGRFmut, K-ras, B-raf, EML4-alk, ros1
• Re-biopsy when possible
• Molecular profiling by Caris Life Sciences
• No clinical trial but „individual attempt to treat“ („Individueller Therapieversuch“)
*A. Seeber et al, Genes Cancer 2016;7:301-308
Rationale for evaluation
PFS1
PFS2
PFS3
PFS4
time
Bailey et al. (2012) Journal of Cancer
„Successful“ treatment is defined as >1,3 increase of PFS compared to previous treatment
Patient Tumor Type Age ECOGPrior Lines
of TherapyCMI-guided Therapy Biomarker
PFS (at time of
report)
1 CRC 44 2 8 nab-paclitaxel, gemcitabine SPARC, RRM1 56
33 Appendix 70 2 3 Ralitrexed TS 63
3 Adrenocortical 47 2 5 nab-paclitaxel SPARC, PgP 30
4 Spindle sarcoma 68 0 4 Nab-paclitaxel, gemcitabinePgP, TLE3, TUBB3,
RRM1237
29 NEC 50 1 2FOLFIRI + maint. AR-
BlockadeAR, TOPO1, TS 488
6 Endometrial 83 0 2 liposomal doxorubicin TOP2A, PgP 74
7 Pancreatic 46 2 2 regorafenib c-myc 56
8 SCLC 55 2 5 irinotecan TOPO1 68
30 CRC 45 2 5 Tamoxifen ER 77
36 Vulva 66 0 3 Tamoxifen ER, PR 560
31 CCC 54 1 2 Temozolomid MGMT 175
12 Gastric 49 1 3 Epirubicin, docetaxelPgP, TOP2A, TUBB3,
RRM174
13 CRC 57 1 3 regorafenib KRAS 176
34* Clearcell 46 2 3 crizotinib ALK 71
35* Thymus 56 1 6 irinotecan TOPO1 158
16 Endometrial 83 0 2 liposomal doxorubicin TOP2A 156
17 Ovarian 25 0 1 everolimus PIK3CA 156
18 Breast 64 0 5 carboplatin, gemcitabine ERCC1, RRM1 250
19 CCC 67 2 5 nab-paclitaxel SPARC, TUBB3 57
Examples for molecularly guided therapy
Seeber et al. (2016) Genes and Cancer
Potentially active agents
249 therapeutic options in 58
patients (4.29/patient):
- Chemotherapy: 213/249; 86%
- “Targeted” therapies: 17/249; 7%
-Hormone receptor blockers:
15/249; 6%
0
5
10
15
20
25
30
35
40
45
Zytostatika
Hormontherapie
Targeted Therapie
Seeber et al. (2016) Genes and
Cancer
• Male, 64a
• Soft tissue sarcoma (metastatic)
• Initial diagnosis 10/2009
• Previous therapies: doxorubicin, trabectidin, pazopanib, ifosfamide
• ONCO-T-Profiling: 04/16 TUBB3 +, RRM1 -
• Start paclitaxel + gemcitabine
• Interim analysis 01/17: stable disease (SD)
Patient 19
• Male, 71a
• NET „unknown primary“
• Initial diagnosis 01/13
• 1st line: Cisplatin/VP-16, 2nd line Carboplatin/Taxol
• ONCO-T-Profil: 09/15 TOPO1 pos.
• Start Topotecan April 4, 2015
• Stable disease in 01/16
Progression-free Survival data
22/40 (55%) PFS ratio ≥1.3Mediane PFS1 = 126d, Mediane PFS2 = 187d
0
100
200
300
400
500
600
700
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10P11P12P13P14P15P16P17P18P19P20P21P22P23P24P25P26P27P28P29P30P31P32P33P34P35P36P37P38P39P40
Pro
gre
ssio
n-f
ree
Su
rviv
al [i
n d
ay
s]
Seeber et al. (2016) Genes and Cancer
Many questions remain
• How can we run confirmatory trials in small but well-defined patient
populations?
• Do we always need another biopsy before targeted therapy?
• What if a patient has got two or more mutations that can each be
targeted with a drug?
• Combination of cytotoxic drugs with agents that have not been
determined by molecular typing?
• Etc.
A molecularly tailored approach only works
for a subgroup of patients
SHIVA study failed
25
• First randomized study of molecular guided vs. physician choice
• 200 patients evaluated– hormone receptor pathway (for ER, PR and AR overexpression)– PI3K/AKT/mTOR pathway– RAF/MEK pathway
• No difference in PFS between guided treatment and physicians choice
• Molecular guided arm strictly defined in protocol– Treating physician had no treatment choice – Most treatment algorithms were based on preclinical/scientific hypotheses
• Only patients with pre-defined biomarkers were included– 60% of patients excluded up front
• Main focus on “targeted drugs”, everolimus most frequently used
Key differences between Oncotyrol project and SHIVA trial
SHIVA Oncotyrol registry
• Predefined treatment had to be given
• Only one biomarker drives treatment
• Very limited number of targets
• Compares algorithm vs. unselected chemotherapy
• Only for those with certain biomarkers (40% of patients)
• Cross-over allowed
• No clinical trial
• Compares benefit vs. lack of benefit treatments
• For (almost) all patients
• Combination therapies allowed
• Oncologist fully responsible, patient preferences taken into account
The way ahead
- Molecular phenotyping has a role for well-defined patient population
- Biomarker development in the peripheral blood must be a
joint project of all stakeholders
„collaboration is key“
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