Presentation at the CDAC 2018 Workshop and School on Cancer Development and Complexity http://cdac2018.lakecomoschool.org

1. A cancer precision medicine program
driven by multi-omic profiling, analytics
and modeling
Olivier Elemento, PhD
Director, Englander Institute for Precision
Medicine

2. Talk Overview
• The cancer precision medicine program at Weill
Cornell – what have we learnt ?
• Key challenges and how we can address them
- How do we identify what treatments are most effective
in individual patients? Patient-specific avatars
- Can we rationally identify effective combination
therapies ? Modeling complex pathways
- How do we improve our understanding of disease
biology ? Integrative and single cell biology
• The need to meld analytics and experimentation

3. Precision Medicine at Weill Cornell
Advanced
cancer
patient
Highly
personalized
treatment
recommendation
Clinical sample Profiling Interpretation Recommendation

4. Return of results requires clinical assays:
Cornell CLIA-approved whole-exome
sequencing test queries >21,000 genes
Rennert et al, 2016

5. How we report results matters – precision
medicine reports

6. Cataloguing Clinically Relevant Mutations
The Precision Medicine Knowledge Base (PMKB)
 Genes
 Variants
 Interpretations
 Tumor Types
 Tissue Types https://pmkb.weill.cornell.edu/ Huang et al, 2016

7. Beltran et al, 2015; Rennert et al, 2016; Pauli et al, 2017
>1,500 cancer patients sequenced so far

8. What have we learnt ?

9. Revealed: Divergent evolution of
metastatic tumors
Faltas et al, 2016

10. Sailer and Beltran, in preparation
Revealed: High prevalence of germline DNA
repair alterations in metastatic cancer patients

11. Revealed: Transcriptomic reports help
interpret genomic alterations

12. After Herceptin treatment
Unexpected HER2 amplification in
a bladder cancer patient leads to complete response

13. Pauli et al, 2017
Currently actionable mutations are
not as frequent as we would like

14. Key challenges
- How do we identify what treatments are most
effective in individual patients?
- Can we rationally identify effective
combination therapies ?
- How do we improve our understanding of
disease biology and improve actionability ?

15. Leveraging patient specific avatars to
identify what treatments are most
effective in individual patients

16. Patient Avatars for Clinical Drug Screens

17. Example of organoids utility in drug testing:
In vitro – Mini Sarcoma Production
Pauli et al. , 2017

18. Pauli et al, 2017
Tumor Organoid Biobank
Total Organoids in Biobank: 56
0
1
2
3
4
5
6
7
8
Cases
PDX Establishment from
Tumor Organoids
PDX
Established
PDX Failed
7/7
3/3
1/2
1/1
2/2
0/1
2/2
1/1
2/3
0
10
20
30
40
50
60
Biopsies
Tumor Organoid Establishment
from Fresh Tissue
Tumor Organoids
Established
Tumor submitted for
Organoids
10/52
8/24
5/7
1/1 2/21/2
2/3
6/10 8/10
4/6 1/6
5/9
3/6

19. Single Agent Screen Results

20. Secondary Screen Results
Pauli et al, 2017

21. Expected and new correlations between
genomics and high-throughput drug screening

22. Building “vascularized” tumor
organoids
With Shahin Rafii

23. Xenograft 2 PID3
Zebrafish tumor xenograft/organoids
Zebrafish embryos
With Yariv Houvras

24. Can we more
rationally identify
effective
combination
therapies ?
Individual molecules effective at killing some lymphoma cells
There are tens of millions of possible
combinations of 2, 3, 4, etc drugs !!!

25. What if we could create virtual disease models
of cancer cells to test combinations in silico ?
Proliferation
Lymphomas
are addicted
to the BCR
pathway

26. Du et al, 2017
Virtual disease model recapitulates
known signaling data well

27. Virtual disease model predicts synergistic
and antagonistic drug combinations
Predictions
Experiments

28. How do we improve our
understanding of disease biology
and improve actionability ?

29. Junttila et al, 2013
Complexity of the tumor micro-environment

30. Science May 2016
The cancer immune landscape – an example
of complex process

31. 0
5000
10000
15000
20000
TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9
Gene
Frequency
0
5000
10000
15000
20000
Freq
JURKAT
0
10
20
TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9
Gene
Frequency
0
5
10
15
20
25
Freq
PM1026_Glioblastoma
0
5
10
15
TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9
Gene
Frequency
0
5
10
15
Freq
PM1032_Low_grade_neuroepithelial_neoplasm
0
100
200
300
TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9
Gene
Frequency
0
100
200
Freq
PM1041_Clear_Cell_Renal_Carcinoma
0
200
400
600
TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9
Gene
Frequency
0
200
400
600
Freq
PM1042_Squamous_Cell_Carcinoma_Lung
0
5
10
15
20
TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9
Gene
Frequency
0
5
10
15
20
Freq
PM1058_Medulloblastoma
0
1
2
3
4
TRBV10−1TRBV10−2TRBV10−3TRBV11−1TRBV11−2TRBV11−3TRBV12−3TRBV12−4TRBV12−5TRBV13TRBV14TRBV15TRBV16TRBV18TRBV19TRBV2TRBV20−1TRBV21−1TRBV23−1TRBV24−1TRBV25−1TRBV27TRBV28TRBV29−1TRBV3−1TRBV30TRBV4−1TRBV4−2TRBV4−3TRBV5−1TRBV5−4TRBV5−5TRBV5−6TRBV5−8TRBV6−1TRBV6−2TRBV6−3TRBV6−4TRBV6−5TRBV6−6TRBV6−7TRBV7−1TRBV7−2TRBV7−3TRBV7−4TRBV7−6TRBV7−7TRBV7−8TRBV7−9TRBV9
Gene
Frequency
0
1
2
3
4
Freq
PM856_Malignant_neuroepithelial_neoplasm
trb V−usage by count
TCR V beta usage by count
We can quantify the diversity of
immune cells within tumors – example
of TCR-seq

32. Predicting MHC presented neo-
epitopes in tumors

33. New improved approaches for
immune deconvolution from bulk RNAseq
Also – CIBERSORT (Newman et al, 2016) Davide Risso

34. The Immune Response Index integrates the
immune landscape to predict immunotherapy
responders
Machine learning
(random forest)
using clinical
outcome data
Bhinder et al, 2017;
In preparation
Independent
test set

35. Junttila et al, 2013
Tumor Microenvironment,
Single cell analysis and imaging of tumors

36. What disease really looks like at
single cell resolution
B cell lymphoma
• How do cell
population
correlate with
outcomes ?
• How do cells
communicate
and can cross-
talks be
disrupted

37. Choi et al, 2015; Durrans et al, 2015
New paracrine crosstalk between
macrophage IL6 and tumor IL6R

38. Conclusions
• Patient-specific avatars enable mini n=1
clinical trials but also iterative learning
• New technologies especially single cell
technologies allow unprecedented
understanding of the disease
• Disease is complex – requires modeling and
integrative analysis
• Experimentation/measurements and
analytics need to be closely integrated