Making Sense of the Science of WM

Zachary Hunter Bing Center for WM Dana-Farber Cancer Institute Harvard Medical School

Bing Center for WM Harvard Research at the Medical Dana-Farber School Cancer Institute  e

Bing Center for WM Research at the Dana-Farber Cancer Institute Understanding Genetics If you only had four letters to work with, what kind of story could you tell? Genomics is easy…

➢ Deoxyribonucleic Acid (DNA) is made of complex molecules called nucleotides. There are 4 types abbreviated A,T,C,G. ➢ Nucleotide bases form stables pairs: A-T and C-G. Two complimentary strands of these bases form DNA. ➢ DNA is broken into 23 long strands known as chromosomes. ➢ The sections of the DNA that contain instructions on how to build proteins are called genes. ➢ Genes in the DNA are transcribed into a single strand of similar nucleotides called Ribonucleic Acid (RNA) and this “message” is processed by the cell and turned into protein. Something easy with ~ 3,000,000,000 pieces can be really complicated…

 For context, computers operate with just two “bases” 0 and 1.

 With enough 0s and 1s it turns out you can make computer’s do some pretty impressive and complicated stuff.

 DNA has 4 bases, x 3 Billion with a lot more chemical and spatial annotation.

 This makes IBM’s Watson look simple by comparison, even if it does beat us at Jeopardy DNA – RNA – Protein

• Transcribed • Stores Provides from DNA information structure, • Encodes signaling, • Used as a instructions and carries template to make out most for RNA protein cellular work

• Genes are regions of the DNA that are transcribed into RNA • RNA carries the DNA code out into the rest of the cell where it can be used as instructions to make protein How to make it all fit…

 3 billion base pairs strung end to end is about 40 inches in length.

 This quantity of DNA resides inside the nucleus of every cell in your body

 Needless to say, this is a tightly controlled process. Our apologies, the DNA you are looking for is temporarily unavailable…

Laura, B. (2008) Epigenomics: The new tools in studying complex disease. Nature Education Introducing the “omes”

The Genome The Transcriptome The Proteome

• Transcribed • Stores Provides from DNA information structure, • Encodes signaling, • Used as a instructions and carries template to make out most for RNA protein cellular work

The Epigenome The genome as the cell’s operating system The Environment

The operating system of your Protein computer (Windows/Max OSX/Linux) provides a set of tools to allow programs to be loaded and run. Every laptop may have the same operating system, but no one uses their RNA DNA laptop in exactly the same way DNA to Protein… translation please?

➢ Proteins are folded chains of amino acids ➢ Just like there are 4 bases that make of DNA, there are 20 different amino acids ➢ Groups of 3 DNA base pairs, called codons, code code for specific amino acids for example: DNA:CTC -> RNA:CUC -> Leucine ➢ T (thymidine) in the DNA is transcribed as U (uracil) in the RNA. DNA to Protein… translation please?

DNA Amino Acid Translation XGAT GAT TAC CTG DDYL

ATG ATT ACC TGC MITC Frame-shift The Genomics of WM

Understanding the WM “Operating System” Mutation and Cancer

 Cancer can be caused by accidental changes in the genome known as mutations

 As these accumulate, genes start to gain new functions, or lose old ones

 At the point that the cells start to multiply in an uncontrolled way, we call it cancer MYD88 Signaling MYD88 InhibitorMYD88 Treon et al NEJM NEJM Treon al et2012 This doesn’t look easy! Control - mutation the the mutation gene MYD88 in over found is 95% of WM A single base pair pair base single A NFKB Nuclear Translocation in Translocation NFKB Nuclear Cell Line BCWM.1 The

Wait… Didn’t I promise that be this would easy? <

R

1

L

5

I

6

β

p

K

K

I O

α 4

8 1 K 0

K B

M

8 A 5

A κ

E

I

R

D R p I

I N

α

6

Y

F

K

M

K

A

I

R

8

T

8 4 1 n D K K o Y A

A R 1

R I a

M B

I c

A 1

4 T

o

K

l

R

s

A

L

2

g

l

TIRAP

n

T T

B n

n

a

K i

A a

l o

T r

T

m

a

T

B

o

n

a

r

s

g

d

a

i

a

a

e

S

e

l

r

t

c

d

g

o

u

e

n

r

a

N

D P Don’t Sweat the details unless necessary… Simple Complex Ouch…

TLRs TLR4 IL1R

T

I

R

MYD88 A

P

MYD88 MYD88 IRAK1/4 BTK BTK I RA 4 K AK 4 IR I RA 1 K AK 1 IR IkBα

2 TA p50 p65 B B TRAF6 A 1 T TAK1 NEMO IKKα IKKβ growth survival Proteasomal IκBα NFKBp50 p65 Degrada on p50 p65 Nuclear Transloca on and Signaling MYD88 L265P in WM/IGM MGUS METHOD TISSUE WM IGM MGUS Treon WGS/Sanger BM CD19+ 91% 10% Xu AS-PCR BM CD19+ 93% 54% Gachard PCR BM 70% Varettoni AS-PCR BM 100% 47% Landgren Sanger BM 54% Jiminez AS-PCR BM 86% 87% Poulain PCR BM CD19+ 80% Argentou PCR-RFLP BM 92% 1/1 MGUS Willenbacher Sanger BM 86%

Mori AS-PCR/BSiE1 BM 80% Ondrejka AS-PCR BM 100% Ansell WES/AS-PCR BM 97% Patkar AS-PCR BM 85% Updated from Treon and Hunter. Blood. 2013;121(22):4434-6. 17 MYD88 Signaling MYD88 InhibitorMYD88 Guang Yang, PhD Guang Yang, Blood al.et Yang 2013 Control mutation the the mutation gene MYD88 in over found is 90% of WM A single base pair pair base single A

NFKB Nuclear Translocation in Translocation NFKB Nuclear Cell Line BCWM.1 The

R

1

L

5

I

6

β

p

K

K

I O

α 4

8 1 K 0

K B

M

8 A 5

A κ

E

I

R

D R p I

I N

α

6

Y

F

K

M

K

A

I

R

8

T

8 4 1 n D K K o Y A

A R 1

R I a

M B

I c

A 1

4 T

o

K

l

R

s

A

L

2

g

l

TIRAP

n

T T

B n

n

a

K i

A a

l o

T r

T

m

a

T

B

o

n

a

r

s

g

d

a

i

a

a

e

S

e

l

r

t

c

d

g

o

u

e

n

r

a

N D P Ibrutinib WHIM-like CXCR4 mutations in Waldenstrom’s Macroglobulinemia

• 30-40% of WM patients CXCL12/SDF-1a • Occur in the C-terminal domain (same site as WHIM patients) • Nonsense and frameshift mutations Most common • Associated with high mutation is S338X IGM, hyperviscosity. • Multiple CXCR4 352 S338X mutations can be present within an individual patient. Hunter et al, Blood 2013; Rocarro et al, Blood 2014: Poulain et al, Blood 2016; Poulain et al, CCR 2016; Xu et al, BJH 2016; Varettoni et al, Haematologica 2017. Hunter et al, Blood 2013 WHIM Syndrome Like Mutations in CXCR4

Wu et al, Science, 2010 Using Mutation Status to Understand Responses to Ibrutinib

Serum IgM Response Hemoglobin Response

5 5

. .

1 1

8 8

. .

s

l

1 1

s

l

e

e

v

v

e

e

L

L

e

e

n

i

n

l

i

l

e

e

s

s

a

6 6

a

. .

B

B

1 1

0 0

o

o

. .

t

t

1 1

e

e

v

v

a

a

l

l

e

e

R

R

4 4

. . e

e minor response

s

1 1

s

n

n

o

o

p

p

s

s

e

e

5 5

R

. .

R

0 0

par al response n

i

M

b

g

2 2

o

I

. .

l

1 1

g

m

o

u

m

r

e

e

S

H

t

t

s

s

e

e B

B very good

0 0

0 0

. . .

. par al response

1 1

0 0

L265P WT L265P WHIM WT WT MYD88L265PCXCR4 MYD88 WHIMCXCR4 MYD88 WTCXCR4 MYD88L265PL265PCXCR4WT MYD88L2WHIM65PCXCR4WHIM MYD88WWTTCXCR4WT

Treon et al, NEJM 2015 Hunter et al, Blood Advances 2018 Mutations in MYD88WT WM Hunter et al, Blood Advances 2018 Ibrutinib in MYD88WT WM

Serum IgM Response Somatic Deletions on Chromosome 6q

Whole Genome Sequencing

0

1 s

t PLEKHG1

9

n

e

i t

a HIVEP2

8

P

d

e HLA-DQA/B BCLAF1 ARID1B

t

7

c

e f

f COL19A1 BACH2 FOXO3

A

6

f

o

r CD83 EEF1A1 PRDM1 MAN1A1 TNFAIP3 CCR6

5

e b

m Detection Threshold IBTK

u

4

N

3

1 1 3

3 2 1 3 1 3 1 2 1 3 1 2 1 3 1 3 1 2 3 1 2 3 1 2 3

...... 3 ...... 3 3 ......

3 2 3 5 1 6 7

. . .

5 5 5 4 4 2 2 1 1 2 2 1 4 4 6 6 2 3 3 4 4 4 5 5 5

2 1 1 1 2 2 2

1 2 2

2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2

p q q q q q q

2 2 2

p p p p p p p p p p p p q q q q q q q q q q q q q

p q q ChroDeletionsmosome 6 on Chromosome 6q

Maria Luisa Guerrera: Chromosome 6q deletions are common in Waldenström’s Macroglobulinemia, and target regulatory genes for MYD88, CXCR4 and BCL2 signaling MYD88Mutant WM

MYD88WT WM Guererra et al, Haematologica 2018 Guererra et al, Haematologica 2018 Overlapping signatures between deletion 6q and CXCR4WHIM

Gene Chrom Start End LogFC Del6q LogFC CXCR4mut FAM110C chr2 38813 41627 -3.61 -3.29 WNK2 chr9 95947211 95947892 -1.88 -4.92 SGCD chr5 155135062 155135118 -1.87 -3.26 CCDC141 chr2 179694483 179699167 -1.67 -1.30 PKHD1L1 chr8 110374705 110374882 -1.56 -1.47 EML6 chr2 54952148 54952395 -1.49 -1.03 ZNF214 chr11 7020548 7022786 -1.39 -1.85 SYTL2 chr11 85405264 85406383 -1.32 -1.30 C11orf92 chr11 111164113 111169391 -1.31 -0.91 IL17RB chr3 53880576 53880675 -1.26 -2.04 ZNF215 chr11 6947653 6947916 -1.19 -1.29 ZNF804A chr2 185463092 185463797 -1.12 -0.69 LINC00271 chr6 135818938 135819138 -0.99 -0.69 CDK14 chr7 90095737 90095827 -0.76 -0.57 FOXO3 chr6 108881025 108881218 -0.75 -0.54 CYP4V2 chr4 187112673 187113191 -0.69 -0.53 IGF2R chr6 160390130 160390427 -0.53 -0.43 EPS15 chr1 51819934 51822518 -0.39 -0.37 HRK chr12 117299026 117299271 1.84 2.02

Putting it all together : Signaling Networks in WM

How we can use machine learning and bid data to better understand WM Negative Feedback Loops: Self Regulating Biological Networks

A centrifugal governor is a mechanical analog to biological negative feedback loops.

Not familiar with old steam steam engine mechanics? Don’t worry, the concept is simple: As the engine turns it spins a pair of heavy metal balls attach to a lever arm. As the balls spin faster, they start to lift due to centripetal force. This causes the lever arm to push down on the throttle valve Scan from Hawkins, Nehemiah: New By Globbet - Own work by uploader, with Catechism of the Steam Engine, Category: permission from the Mill Meece Pumping reducing power to the engine New York: Theo Audel, Public Domain, Station Preservation Trust, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php? https://commons.wikimedia.org/w/index. and preventing it from spinning curid=6989657 php?curid=7421535 too quickly. Gene Signaling Networks

Mutation

Mutation X

Negative Feedback X Loop Signaling Pathways Driven by Mutated MYD88 in Waldenström's Macroglobulinemia

IL-6R TLRs/IL-1R IL-6 IL-6 IL-6

gp-130 MYD88 Ibrutinib HCK Ibrutinib ACP196 BTK BTK PIK3R2 CC-292 IRAK4 PLCγ BGB-3111 IRAK1 PI3K

TRAF6

TAK1 PKC

AKT

NEMO ERK1/2

IKKα IKKβ HCK

mTOR

IL-6 Yang et al, Blood 2013 Yang et al, Blood 2016 growth Chen et al, Blood 2018 Degradation survival NFKB Machine Learning: How “deep learning” neural networks can help us understand the biology of WM

Thanks to the support in part from the IWMF and WMFC we have collected genomic and transcriptional sequencing data from 300 untreated WM patients. We have partnered with pathologists, epigenomic experts and machine learning experts to use cutting edge deep learning methods to explore the networks present in WM.

Glass, K., Huttenhower, C., Quackenbush, J., & Yuan, G.-C. (2013). Passing Sonawane, A. R., Weiss, S. T., Glass, K., & Sharma, A. (2019). Messages between Biological Networks to Refine Predicted Interactions. Network medicine in the age of biomedical big data. In PLoS ONE, 8(5), e64832. Frontiers in Genetics. New Targets, New Therapeutic Opportunities

M

d I d

H n e s t

n a a

n W inhibitors

t

t o o l u

n

e a y a M a o s l

n b t i r y

e r o u d

m o R e a

d s r

t h M i z e e a r p M i t x t s v l e - o l o W a

a t C e c h t n D i A P n p o r e e c m

t ulocuplumab

e P

e

n

n

I R

h

l

i

s C In Development: Inhibitors HCK MAPK/ERK a

e T

r

l 2

r P

a /

n

A l

i

1

β

4 m

K a

r

R e R

t v C -

E

i

C X α v - C r 1 F u

i D S

α K S

G 3 l I γ P l P

G e

T β K

C G A

inhibitors R

P 1 P

L 5 I

6 β p K

K P

P

P

I In Development: IRAK O

α 4

8 1 K 0

K B M

8 A 5

A κ E

I

R D R p I I N α P 6 venetoclax Y

F K P M K A I

R

8 T P

8 4 1 D K K Y A A R 1

R I

M B

I

A 1 P 4 P

T

K

R

A

A L

2 2 P 1 l

K TIRAP

T T B n P

P

a T 3

A E o B B T P m V I B I

o Y a A P

H s

d F P M a a N e r t

T g o e r D P

clinical trial clinical

new Drugs in Drugs new Ibrutinib & Ibrutinib Acknowledgements

Funding and Support The Bing Center • The Bing Fund for WM Bing Center for WM • International Waldenstrom’s Steven P. Treon, Lian Xu, Macroglobulinemia Foundation Guang Yang, Xia Liu, • Waldenstrom’s Macroglobulinemia Jorge Castillo, Robert Foundation Canada Manning, Christopher J • Orszag Family Fund for WM Genomics Patterson, Philip Broadsky, • Coyote Fund for WM Kirsten Mied, Jie Chen, • Bailey Family Fund for WM Nicholas Tsakmaklis, Jiaji • D’Amato Family Fund for Genomic Chen, Maria Demos, Discovery Joshua Gustine, Maria • Leukemia and Lymphoma Society Luisa Guererra • NIH Spore Development Award Jerome Lipper Myeloma Center • ASH Scholar Award Kenneth Anderson, Nikhil Munshi, • LLS/IWMF Strategic Roadmap grant Mehmet Samur Memorial Sloan Kettering Ari Melnick Yaoyu Wang And the support of all the John Quakenbush WM patients who made Derrick DeConti Brian Lawney this study possible!