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“