HiTS Symp 2019 - Program...

Contents

Schedule .................................................................................................................. 2

Speaker Bios and Talk Abstracts ............................................................................. 5

Attendee List ......................................................................................................... 18

Poster Index .......................................................................................................... 27

Poster Abstracts .................................................................................................... 29

2 HiTS Symposium 2019

Schedule

8:30 – 9:00am Registration and Light Breakfast

9:00 – 9:15am Welcome

Peter Sorger, Head of Harvard Program in Therapeutic Science and Otto Krayer Professor of Systems Pharmacology, Harvard Medical School

9:15 – 10:00am Session 1: Single‐cell Dynamics in Cancer

Chair: Luca Gerosa, Postdoctoral Fellow, Harvard Program in Therapeutic Science, Harvard Medical School

9:15 – 9:45am Giorgio Gaglia, Postdoctoral Fellow, Department of Pathology, Brigham and Women’s Hospital Stress adaptation and cell fate decisions mediated through HSF1 phase transition

9:45 – 10:00am Mariya Atanasova, Postdoctoral Fellow, Harvard Program in Therapeutic Science, Harvard Medical School Characterization of short‐ and long‐term adaptive resistance to vemurafenib in melanoma

10:00 – 10:15am Coffee Break

10:15 – 11:00am Session 1 Continued: Single‐cell Dynamics in Cancer

10:15 – 10:45am Michael Tsabar, Postdoctoral Fellow, Department of Systems Biology, Harvard Medical School Caspase‐2‐PIDDosome switches p53 dynamics in cells that escape DNA damage‐induced arrest

10:45 – 11:00am Jia‐Yun Chen, Jane Coffin Childs Fellow, Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School Molecular dynamics of oncogenic BRaf induced senescence

11:00 – 11:30am Session 2: Student Lightning Talks

Moderator: Catherine Dubreuil, Director of Education and Training, Therapeutics Graduate Program, Harvard Medical School

Sergine Brutus High‐content screening of kinase inhibitors: implications for microtubule regulation and drug discovery

Chelsea Powell Chemically induced degradation of anaplastic lymphoma kinase (ALK)

Carmen Sivakumaren Targeting the PI5P4K and PIKfyve lipid kinases in cancer using novel covalent inhibitors

Shu Wang Spatial statistics of cell populations are governed by supercommutative mechanisms

October 21, 2019 3

11:30 – 1:00pm Poster Session and Lunch – Washington Ballroom

1:00 – 2:45pm Session 3: Informatics and Modeling

Chair: Artem Sokolov, Director of Modeling, Laboratory of Systems Pharmacology, Harvard Medical School

1:00 – 1:30pm Bree Aldridge, Assistant Professor, Molecular Biology and Microbiology, Tufts University Rational design of combination therapies for TB

1:30 – 2:00pm Aedin Culhane, Senior Research Scientist, Department of Biostatistics, Dana‐Farber Cancer Institute Matrix factorization for multi ‘omics data integration

2:00 – 2:15pm John Bachman, Fellow in Therapeutic Science, Laboratory of Systems Pharmacology, Harvard Medical Accelerating biomedical discovery: Machine‐assisted modeling

2:15 – 2:30pm Benjamin Gyori, Research Associate in Therapeutic Science, Laboratory of Systems Pharmacology, Harvard Medical School Accelerating biomedical discovery: human‐machine collaboration

2:30 – 2:45pm Deborah Plana, MD‐PhD Student, Harvard Systems Biology Graduate Program, Harvard Medical School Parametric fitting of clinical trial data to design novel treatment regimens in oncology

2:45 – 3:00pm Coffee Break

3:00 – 3:50pm Session 4: Drug Assessments in Regulatory Science

Chair: Florence Bourgeois, Co‐Director of the Harvard‐MIT Center for Regulatory Science and Associate Professor of Pediatrics, Harvard Medical School

3:00 – 3:25pm Qais Hatim, Computer Scientist, Center for Drug Evaluation and Research, US Food and Drug Administration Using systems pharmacology approach to determine potential pharmacodynamic drug‐drug interactions that may cause hepatotoxicity

3:25 – 3:50pm Brian Alexander, Chief Medical Officer, Foundation Medicine <Title TBD>


3:50 – 5:20 pm Session 5: Relevance to Human Disease

Chair: Jennifer Guerriero, Instructor, Harvard Medical School and Director, Breast Tumor Immunology Lab, Dana‐Farber Cancer Institute

3:50 – 4:20pm Kevin Wei, Instructor in Medicine, Brigham and Women’s Hospital Defining pathogenic cell states in rheumatoid arthritis by single cell profiling

4:20 – 4:50pm David Liu, Instructor in Medicine, Dana‐Farber Cancer Institute Dissecting evolution of immunotherapy resistance in a melanoma exceptional responder

4:50 – 5:05pm Rumana Rashid, Research Associate, Laboratory of Systems Pharmacology, Harvard Medical School Viewing and sharing high‐dimensional multiplexed data for studying human disease

5:05 – 5:20pm Jennifer Guerriero Relevance to human disease

5:30 – 6:30pm Poster Session, Poster Prizes and Refreshments – Washington Ballroom

October 21, 2019 5

Speaker Bios and Abstracts

Welcome

Peter Sorger, Harvard Medical School

Peter Sorger is the Otto Krayer Professor of Systems Pharmacology

at Harvard Medical School. He received his AB from Harvard College

and PhD from Trinity College, Cambridge University U.K., working

under the supervision of Hugh Pelham. He trained as a postdoctoral

fellow at the University of California, San Francisco with Harold

Varmus and Andrew Murray. Prior to coming to HMS Peter served

as a Professor of Biology and Biological Engineering at MIT. Sorger

was cofounder of Merrimack Pharmaceuticals and Glencoe

Software and is an advisor to multiple public and private companies

and research institutes in the US, Europe and Japan.

Peter’s research focuses on the signal transduction networks

controlling cell proliferation and death, dysregulation of these networks in cancer and

inflammatory diseases and mechanisms of action of therapeutic drugs targeting signaling

proteins. His group uses mathematical and experimental approaches to construct and test

computational models of signaling in human and murine cells as a means to understand and

predict responses to drugs applied individually and in combination. The Sorger group also

develops open‐source software for analyzing biological networks and drug mechanism of action

and it participates in multiple collaborative programs working to improve data access and

reproducibility. Recent research extends a systems pharmacology approach to analysis of

clinical samples and interpretation of clinical trials.


Session 1: Single‐cell Dynamics in Cancer

Chair: Luca Gerosa

Luca Gerosa is a postdoctoral fellow in the Sorger Lab and the

LSP. Luca uses computational and experimental systems biology

approaches to study adaptive drug resistance in BRAF‐mutant

cancers. His research goal is to quantitatively and

mechanistically explain the link among drugs, regulatory circuits

and cellular physiology to uncover basic principles of cancer

biology and assist the development of novel targeted drug

strategies.

Giorgio Gaglia

Giorgio Gaglia is a postdoctoral fellow in the Santagata lab at

LSP. He obtained an applied mathematics degree from Oxford

University and a PhD in Systems Biology from Harvard Medical

School. His research aims to combined single cells time lapse

imaging of protein dynamics with tissue level multiplexing to

investigate key cellular processes such as stress responses, cell

cycle dynamics and nuclear envelop integrity.

Mariya Atanasova

Mariya received her PhD in 2016 from Boston University

Department of Chemistry where her graduate work focused on

characterization of the activation mechanism and downstream

signaling pathways of the RET receptor tyrosine kinase.

Currently, she is a Postdoctoral Fellow in the LSP and Sorger lab.

Her interests include elucidating the mechanisms and time

evolution of resistance to targeted therapy in melanoma, as well

as finding emerging sensitivities in the drug‐adapted states,

which may present a clinical window for combination or

sequential therapies and result in elimination of residual

disease.

October 21, 2019 7

Michael Tsabar

Michael got his undergraduate degree in Israel studying marine

biotechnology. Later he moved to the US to pursue a PhD at Jim

Haber's lab at Brandeis university, studying homologous

recombination and the DNA damage response in buddying

yeast. Following that he joined Galit Lahav's lab and Aviv

Regev's lab to study p53 dynamics in response to different

treatments.

Jia‐Yun Chen

Jia‐Yun Chen obtained her M.S. in Molecular Medicine from

National Taiwan University where she worked on programmed

cell death in C. elegans. She then completed her Ph.D. in

Chemical and Systems Biology at Stanford University in the lab

of Tobias Meyer. During her Ph.D. study, Dr. Chen combined

single‐cell image analysis, multi‐parameter signal profiling, and

high‐content siRNA screening to understand how growth factor

signals are translated by individual cells into a decision to

proliferate or differentiate. In the Lab of Systems Pharmacology,

Dr. Chen combine time‐lapse imaging and single‐cell

quantification to study the process of oncogene‐induced

senescence with a focus on how cell‐to‐cell variability in

oncogenic activity is linked to different cellular fates.


Session 2: Student Nano Talks

Chair: Catherine Dubreuil

Catherine Dubreuil’s doctoral and postdoctoral research efforts

were focused on the bench/drug discovery interface, with the

aim to elucidate novel drugs and targets to promote

regeneration/repair in neurodegenerative diseases. She became

interested in mentoring and training during her postdoctoral

fellowship at HMS, and since 2010 has focused her professional

efforts on innovative training for PhD and postdoctoral trainees.

Dr. Dubreuil has worked on building effective training,

professional development activities, and curriculum for

graduate students in the area of therapeutics, pharmacology

and drug discovery/development at HMS. To this effect, she

helped spearhead the creation of the Therapeutics Graduate

Program (TGP) at HMS, which aims to provide graduate students

with much‐needed training in discovering and developing new therapeutics that progress to

clinical use. The TGP is the first program at HMS to require an external professional internship

experience within the PhD training.

Sergine Brutus

Sergine Brutus is a 5th‐year Ph.D. Candidate in Biological

Sciences in Public Health and the Therapeutics Graduate

Program. In the Mitchison lab, she studies the regulation of

microtubule dynamics, a process that is commonly targeted to

treat multiple cancer etiologies. The primary goal of her

research is to further our understanding of this process to

better inform the development of microtubule‐targeting agents

and targeted kinase inhibitors as cancer therapies. Upon

completion of her degree, Sergine plans to pursue a career in

translational research and drug development.

October 21, 2019 9

Chelsea Powell

Chelsea successfully defended her PhD in Chemical Biology at

Harvard University this September. After growing up in New

York City, she attended Stanford University where she received

a B.S. degree in Chemical Engineering with Honors. She

completed her dissertation work in Dr. Nathanael Gray's lab

(Harvard University/Dana‐Farber Cancer Institute). Her research

with Dr. Gray focuses on developing novel cancer therapeutics

by either inhibiting kinases or inducing their degradation. She

will be pursuing her postdoctoral research in Sloan Devlin's lab

at Harvard Medical School.

Carmen Sivakumaren

Carmen Sivakumaren is a research fellow and recent PhD from

Nathanael Gray's lab in the Dana‐Farber Cancer Institute. Hailing

from Malaysia, she majored in Chemistry and Psychology at the

Johns Hopkins University where she developed a keen interest

for the chemistry‐biology interface and pharmacology of cancer

therapeutics. In the Gray Lab, she is delving into lipid kinase

signaling inhibition, focusing on phosphatidylinositol‐5‐

phosphate‐4‐kinase (PI5P4K) in cancer and PIKfyve in cancer and

Ebola, with an interest in the phosphoinositide signaling‐

metabolism intersection in disease. Carmen was in the

Therapeutics Graduate Program where she was first exposed to the science‐business side of

drug discovery, and will continue on to a career in life sciences consulting. Outside of the lab,

she paddles for the Ohana New England Dragon Boat Team and writes for music magazine

PureGrainAudio.


Shu Wang

Shu received his B.A. from Cornell University in Biology,

Chemistry, Physics, and Math, where he researched

phospholipid membrane statistical physics using FRET, spin

resonance, and simulation. He is now pursuing a Biophysics

Ph.D. in the Sorger lab, where he has focused on mechanistic

analyses of plate CyCIF, as well as descriptive analyses of tissue

CyCIF. Broadly, he is interested in using mathematical models to

gain intuition about complex biological systems.

October 21, 2019 11

Session 3: Informatics and Modeling

Chair: Artem Sokolov

Artem completed his PhD in Computer Science and

Bioinformatics at Colorado State University under the

supervision of Asa Ben‐Hur, with whom he worked on

developing novel state‐of‐the‐art methods for accurate protein

function prediction. Artem's postdoctoral work at the University

of California Santa Cruz focused on building robust,

interpretable in silico models of human cancers and correlating

the output of these models with biological and clinical

outcomes. This work was a major part of his involvement in The

Cancer Genome Atlas (TCGA) and the West Coast Dream Team

(WCDT) consortia.

As Director of Informatics and Modeling at the Laboratory of

Systems Pharmacology (LSP), Artem leads a group of

computational biologists and software engineers who model pre‐clinical, translational and

clinical data using a wide range of machine learning and artificial intelligence approaches. He

plays a key role in training and mentoring a diverse group of students and postdocs and in

managing the lab’s collaborations with academic and industrial groups.

Bree Aldridge

Bree Aldridge is an Assistant Professor in the Department of

Molecular Biology and Microbiology and Department of

Biomedical Engineering at Tufts University. The Aldridge lab

seeks to bring a quantitative framework to understand

tuberculosis infection and to drive multi‐drug regimen design in

a data‐driven manner. She specializes in combining quantitative

experiments and mathematical modeling to create intuitive

descriptions of complex cell biology. Her lab website is:

https://sites.tufts.edu/aldridgelab/


Aedin Culhane

My lab and I develop and apply multivariate statistical methods

and machine learning to the analysis of high‐throughput whole

genome data arising from molecular and genomic studies of

cancer, with a particular focus on meta analysis and

development of models that integrate of multiple sources of

data. My research is applied to understanding;

the molecular heterogeneity of tumor subtypes

the role of the cancer microenvironment in disease

progression and drug resistance

John Bachman

Dr. John Bachman is a Fellow in Therapeutic Science at Harvard

Medical School's Laboratory of Systems Pharmacology. His

research focuses on the development of computational tools for

understanding the behavior of complex biological systems, and

the application of these tools to studying problems of cellular

decision‐making in health and disease. In his most recent work

he co‐developed the Integrated Network and Dynamical

Reasoning Assembler (INDRA) to automate the construction of

explanatory biological models from natural language and

scientifc literature. John received his Ph.D. in Systems Biology

from Harvard University working in the lab of Dr. Peter Sorger, where he combined wet‐lab

experimentation and computational modeling to address unresolved mechanistic questions in

programmed cell death. Before obtaining his Ph.D. John worked as a scientist for four years at

the Cambridge, MA, research software company Charles River Analytics. At CRA he worked on

several projects for the Army and Air Force Research Labs using simulation and knowledge

management tools to improve human decision making.

October 21, 2019 13

Benjamin Gyori

Benjamin M. Gyori, Ph.D. is a Research Associate in Therapeutic

Science at the Laboratory of Systems Pharmacology, Harvard

Medical School. His research is at the intersection of systems

biology and artificial intelligence, and aims to understand, using

computational approaches, how biological cells and other

complex systems function and react to interventions. Ben co‐

developed INDRA, a software tool which automatically

assembles biochemical mechanisms extracted from the

scientific literature into explanatory models. He is also working

on a human‐machine communication system which allows

scientists to interact with a computer partner to construct and test hypotheses about molecular

systems. Ben was selected as a DARPA Riser in 2018, and has been an active performer in

several DARPA programs aimed at developing artificial intelligence applications to accelerate

scientific discovery. Ben obtained his Ph.D. in computational systems biology from the National

University of Singapore, where he focused on the computation required to reason about

uncertainty in models of biological systems.

Deborah Plana

Deborah Plana is an MD‐PhD student in the Harvard‐MIT Health

Sciences and Technology program. She is currently pursuing her

PhD in the Harvard Systems Biology program, jointly supervised

by Peter Sorger and Adam Palmer from UNC School of Medicine.

She previously earned her undergraduate degree in Biological

Engineering at MIT, working under the supervision of Douglas

Lauffenburger. Her research focuses on using data‐driven

approaches to design treatment regimens in oncology.


Session 4: Drug Assessments in Regulatory Science

Chair: Florence Bourgeois

Dr. Bourgeois, MD, MPH is Associate Professor of Pediatrics at

Harvard Medical School and Co‐Director of the Harvard‐MIT

Center for Regulatory Science. She also directs the Initiative in

Pediatric Therapeutics and Regulatory Science in the

Computational Health Informatics Program at Boston Children’s

Hospital. Dr. Bourgeois’ research is focused on the regulation

and use of medications in children and the evaluation of gaps in

pediatric drug evidence at the point of care. She has led studies

inves‐tigating the development of drugs and devices in pediatric

populations, the quality of pre‐market pediatric safety and

efficacy assessments, and the development of standardized metrics to assess the impact of

FDA’s regulatory programs on pediatric product information. She is the recipient of an

Innovation in Regulatory Sci‐ence Award from the Burroughs Wellcome Fund to evaluate the

epidemiology of off‐label drug use in children and improve provider access to benefit‐risk

information on FDA‐regulated products. Most recently, Dr. Bourgeois served as an Expert

Visitor to the European Medicines Agency to analyze the EU’s pediatric drug legislation. Her

clinical training and experience are in pediatrics and pediatric emergency medicine.

Qais Hatim

Qais received dual Ph.D. degrees in operation research and

industrial engineering from Pennsylvania State

University/University Park in August 2015. In his role as a

computer scientist/statistician at the FDA he is conducting

research in statistical/operational modeling and computer

science at Center of Drug Evaluation and Research (CDER)/

Office of Translational Science (OTS)/ Office of Computational

Science (OCS) in U.S. Food and Drug Administration (FDA).

Specifically, he is applying advanced statistical modeling and

scientific computing techniques to computationally intensive

tasks that are encountered in regulatory and scientific

applications. For this purpose, I am utilizing various statistical and operation research

methodologies such as machine learning and data mining algorithms, natural language

processing (NLP) techniques, Neural Networks procedures, and test analytics to extract

meaning, patterns and hidden structures in structured and unstructured data; identifying the

most feasible approaches to software/networking system design and development problems;

consulting reviewers, fellow scientists, and regulations to analyze problems and recommend

October 21, 2019 15

technology based solutions. He is also preparing reports and manuscripts based on research

findings and will present at scientific meeting as necessary. Moreover, Qais is an active member

in several working groups across the FDA such as the Modeling and Simulation Workgroup,

INFORMED and HIVE.

Andrea Arfè

Andrea Arfè is a Fellow at Harvard Medical School under the

mentorship of Prof. Florence Bourgeois (Boston Children’s

hospital) and Prof. Giovanni Parmigiani (Dana‐Farber Cancer

Institute). He is a PhD candidate in Statistics from the Bocconi

University of Milan, Italy. His research interests include Bayesian

methods, decision theory, and survival analysis. In current work,

he uses tools from Statistics and Machine Learning to improve

how we test new therapies for children. In particular, he is

developing designs for pediatric clinical trials in oncology that

leverage external data, e.g. from adult populations.

Alejandra Avalos‐Pacheco

Alejandra is a postdoctoral fellow in Statistics at the Harvard‐

MIT Center for Regulatory Science (CRS). She is also part of Dr

Lorenzo Trippa's group at the Dana‐Farber Cancer Institute

(DFCI) in the Department of Data Sciences. She did her PhD in

Statistics on the joint CDT programme between the University of

Warwick and the University of Oxford (OxWASP). She worked on

statistical methods for genomic data analysis with Dr David

Rossell (UPF), Dr Richard Savage (Warwick) and Dr. Christopher

Yau (Birmingham). Her main research interests include

dimensionality reduction, data integration, high‐dimensional

inference, applied Bayesian statistical modelling and clinical trials. Currently she researches

Bayesian statistical models that incorporate real world data into controlled trials.


Session 5: Relevance to Human Disease

Chair: Jennifer Guerriero

Dr. Guerriero received her bachelor’s degree in BioChemistry

from Northeastern University and has a PhD in Molecular and

Cellular Biology and Immunology and Pathology from Stony

Brook University where she studied cell death pathways and

innate immunity during breast cancer therapy. Dr. Guerriero

completed her postdoctoral training in the laboratory of Dr.

Anthony Letai at Dana‐Farber Cancer Institute where she

investigated the role of tumor associated macrophages in breast

cancer and identified novel mechanisms to target pro‐tumor

macrophages to an anti‐tumor phenotype to induce tumor

regression. She is now an Instructor in Medicine at Harvard

Medical School and is the Director of the Breast Tumor

Immunology Laboratory in the Susan F. Smith Women’s Cancer

Program at Dana‐Farber Cancer Institute. Her research interests include harnessing the anti‐

tumor potential of tumor‐associated macrophages for cancer immunotherapy in triple negative

breast cancers, understanding how tumor cell intrinsic mutations regulate the tumor

microenvironment and understanding the biology, phenotype and ontogeny of tumor

macrophages.

Kevin Wei

Kevin Wei MD PhD is an Instructor of Medicine at Harvard

Medical School and an Associate Physician at Brigham and

Women’s Hospital. He received his MD and PhD at Stanford

University School of Medicine. He completed internal medicine

residency followed by a rheumatology fellowship at Brigham

and Women’s Hospital. There he received the 2017

Rheumatology Research Foundation Scientist Development

Award with the Tobe and Stephen E. Malawista, MD

Endowment in Academic Rheumatology designation. Dr. Wei’s

research focuses on using single‐cell transcriptomics to identify

novel cellular and molecular therapeutic targets in rheumatoid

arthritis.

October 21, 2019 17

David Liu

Dr. Liu is a medical oncologist who sees melanoma patients and

a computational biologist who has a lab at DFCI. He has an M.S.

in Computer Science, and worked at Amazon.com as a software

engineer and analyst before switching careers and going to

medical school, where he also got an M.P.H. with a

concentration in biostatistics and epidemiology. He did his post‐

doctoral fellowship work with Eliezer Van Allen in clinical

computational oncology before starting his own lab at DFCI. His

lab focuses in dissecting molecular and clinical predictors of

therapeutic response (including chemo, targeted, and immune‐

therapies) using computational approaches in molecularly

characterized patient samples, and building integrated

predictive models of therapeutic response.

Rumana Rashid

Rumana (Ru) Rashid is a Research Associate at Harvard Medical

School working with Sandro Santagata, MD, PhD and Peter

Sorger, PhD on the CyCIF Platform at the Laboratory for Systems

Pharmacology. She recently completed the Master of

Biomedical Informatics program at HMS and plans to go to

medical school next year. She is interested in oncology, clinical

trials, and data‐driven medicine.


Attendee List

Torrey Ah‐Tye

Meenta [email protected]

Mark Albers

MGH [email protected]

Bree Aldridge

Tufts University [email protected]

Mesky Alemu

CRG [email protected]

Nicole Anderson

HMS HiTS [email protected]

Patrik Andersson


Andrea Arfe

Center for Regulatory Science [email protected]

Mariya Atanasova

HMS [email protected]

Alejandra Avalos Pacheco

Harvard Medical School [email protected]

John Bachman

Laboratory of Systems Pharmacology,


Greg Baker

Harvard Program in Therapeutic Science [email protected]

Ankur Bamezai

Boston University [email protected]

Michael Baym


Matthew Berberich

Dana‐Farber Cancer Institute [email protected]

Shruti Bhatt


Patrick Bhola


Christopher Bird


Noah Bloch

HMS/DFCI [email protected]

Stepham Bohl


Sarah Boswell


Nazim Bouatta


Florence Bourgeois


October 21, 2019 19

Gary Bradshaw

Laboratory of Systems Pharmacology [email protected]

Karel Brinda


Raphael Bruckner

ICCB‐L [email protected]

Sergine Brutus


Jonathan Bushman


Heidie Cabanos

Massachusetts General Hospital ‐ Harvard

Medical School [email protected]

Hilda Castillo

HMS/ HiTS [email protected]

Liang Chang

Broad Institute [email protected]

Yu‐Fang Chang


Suyog Chavan


Alyce Chen

HiTS [email protected]

Chu‐Yen Chen

Dana‐Farber Cancer Institute Chu‐[email protected]

Jenny Chen

MIT [email protected]

Jia‐Yun Chen

HiTS at Harvard Medical School [email protected]

Yu‐An Chen

Laboratory of Systems Pharmacology yu‐[email protected]

Jenny Cheng


Christopher Chidley


Mirra Chung


Lily Chylek


Pau Creixell

Koch Institute for Integrative Cancer

Research at MIT [email protected]

Aedin Culhane

Dana‐Farber Cancer Institute, Harvard TH

Chan School of Public Health [email protected]

Joseph Cunningham

HITS/HMS [email protected]

Stephanie Davis

LSP [email protected]


Yonatan Degefu

Tufts Medical School [email protected]

India Dittemore


Phillip Dmitriev

Boston Children Hospital [email protected]

Laura Doherty

Harvard University [email protected]

Ziming Du

BWH [email protected]

William Duan

HMS/MGH [email protected]

Catherine Dubreuil


Robyn Eisert


Vlad Elgart


Diego Elliot

Hult International Business School [email protected]

Kyle Evans

Massachusetts General Hospital [email protected]

Geoffrey Fell

Dana Farber [email protected]

Stan Finkelstein

Harvard/MIT [email protected]

Jeffrey Flier


Patrick Flynn

Harvard [email protected]

Alexandra Franz


Fabian Froehlich

HITS [email protected]

Cynthia Frommit

IND [email protected]

Jingxin Fu


Giorgio Gaglia

Laboratory for Systems Pharmacology, and

Brigham and Women's Hospital [email protected]

Benjamin Gaudio


Luca Gerosa


Walter Goh


David Golan


October 21, 2019 21

Jonathan Goldberg


Patrick Greene


Stan Gu


Jennifer Guerriero


Xinzhou Guo

Harvard School of Public Health [email protected]

Benjamin Gyori


Emma Hathaway


Qais Hatim

U.S. Food and Drug Administration [email protected]

Raidhy Esther Herrera Jimenez

Hult [email protected]

Hon Ho

Harvard / Tufts / McLean [email protected]

John Hoffer

Laboratory of Systems Pharmacology [email protected]

Kai Hsu

NEG [email protected]

Clemens Hug


Cheryl Hutt


Robert Ietswaart


Connor Jacobson


Russell Jenkins

HMS/MGH [email protected]

Lauren Jiang


Hu Jin


Nathan Johnson


Elaine Joseph

Deepbiome Therapeutics [email protected]

Sheheryar Kabraji

Partners [email protected]

Marian Kalocsay

LSP [email protected]

Klas Karis

Harvard Program in Therapeutic Science,



Sefa Kilic

Suffolk University [email protected]

Ivy Ko

Baphiq [email protected]

Diana Kolusheva


Ilya Korsunsky


Milka Kostic


Galit Lahav


Jonah Larkins‐Ford

Tufts Universtiy [email protected]

Pinji Lei

Mass General Hospital [email protected]

Shawn Li

Shanghai Yiyi Infotech Company [email protected]

Anurag Limdi

Baym Lab [email protected]

Changchang Liu


David Liu

Dana Farber Cancer Institute [email protected]

Priscilla Louie

Boston University [email protected]

Zhixiang Lu


Carmen Lujan


Catherine Luria


Charles Ma [email protected]

Yan Ma [email protected]

Ousman Mahmud

IBM [email protected]

Zoltan Maliga

LSP, HMS [email protected]

Laura Maliszewski


Alyssa Masciarelli


Elie Massaad


Kate McDonnell‐Dowling

HMS kate_mcdonnell‐[email protected]

Bonnie McFarlane

Harvard Program in Therapeutic Science ‐

October 21, 2019 23


Kelley McQueeney


Anita Mehta


Jay Mettetal

AstraZeneca Oncology [email protected]

Eric Miller

NanoString [email protected]

Brittney Milligan


Nienke Moret


Scarlet Morillo

Microlabs [email protected]

Jeremy Muhlich


Ayaz Najafov


Kevin Nam

HMS Center for Regulatory Science / MIT [email protected]

Maulik Nariya

Harvard Program in Therapeutic Science [email protected]

Nisha Nepal

Boston Children's Hospital [email protected]

Dan Nguyen


Mario Niepel

Ribon Therapeutics [email protected]

Ajit Johnson Nirmal

Harvard/ DFCI [email protected]

Edward Novikov

Harvard Medical School/Harvard SEAS [email protected]

Synclaire Oglesby


Erika Olson


Michaela Olson

Tufts University [email protected]

Rongqing Pan


Todd Paporello

Bayer [email protected]

Ricardo Pastorello


Jayashri Pawr

MGH BWH Center for Clinical Data Science [email protected]


Tenzin Phulchung


Marissa Pioso


Deborah Plana

Sorger Lab [email protected]

Erik Pohl [email protected]

Alex Polanco

Microlab [email protected]

Sarah Potter


Chelsea Powell

Harvard University/DFCI [email protected]

Jason Qian


Natalia Quinones

Baym Lab [email protected]

Ravi Ramanathan

PropelAI [email protected]

Rumana Rashid


Carlos Rodarte

Health Catalyst, Inc. [email protected]

Meri Rogava

LSP/HiTS/HMS [email protected]

Robert Rosenberg

S M C [email protected]

Ari Roshko

CoreTech [email protected]

Brittainy Roth


Ifat Rubin‐Bejerano

Harvard Medical School Ifat_rubin‐[email protected]

Yeni Rubio

Microlab [email protected]

Andrea Ruf

UMass Medical School [email protected]

Monica Ruse

Harvard‐MIT Center for Regulatory Science [email protected]

Massimiliano Russo


Marina S

MKS [email protected]

Sam Sabrin

Takeda [email protected]

Sanjay Sahoo

Food and Drug Administration [email protected]

October 21, 2019 25

Sandro Santagata


Amy Schade

Brigham & Women's Hospital [email protected]

Denis Schapiro

Harvard Medical School and Broad Institute [email protected]

Subrata Shaw [email protected]

Kenichi Shimada


Michael Sinha


Carmen Sivakumaren


Jennifer Smith

ICCB‐L, HMS [email protected]

Stephan Smith

Meenta [email protected]

Artem Sokolov

Laboratory of Systems Pharmacology,


Peter Sorger


Brandon Spiegel [email protected]

Michael Springer


Venkat Sreekar

NEU [email protected]

Jane Staunton

Ludwig Center at Harvard [email protected]

Magdalena Taber

Campanaro Clinical Trial Consulting [email protected]

Nilesh Talele


Emily Thrash


Collin Tokheim


Michael Tsabar


Mark Tye


Rebecca Valentin


Tuulia Vallius


Jan Willem Van Wijnbergen

Massachusetts General Hospital [email protected]


Chiara Victor


Ajay Vishwakarma

LSP, HiTS, Harvard Medical School [email protected]

Raghuvir Viswanatha


Comanjen Wang

Laboratory of Binfoo [email protected]

Huan Wang

Peking University [email protected]

Shu Wang


Kevin Wei

Harvard Medical School, Brigham and

Women's Hospital [email protected]

Hung‐Yi Wu

Sorger lab [email protected]

Ming‐Ru Wu

Dana‐Farber Cancer Institute ming‐[email protected]

Sam Wu

Harvard [email protected]

Helen Yang


Clarence Yapp

Laboratory of Systems Pharmacology / Image

and Data Analysis Core [email protected]

Tanya Yeh

BCH [email protected]

Hang Yin


Pencho Yordanov


Inchul You


Bo Yuan

Harvard/DFCI/Broad [email protected]

Qing Zhang

3E Bio [email protected]

Wubing Zhang


Yi Zhang

Sunovion [email protected]

Zhaojie Zhang

H3 BIomedicine [email protected]

Zhe Zhang


October 21, 2019 27

Poster Index

# Title Presenter

1 Leveraging prior data to increase the power of pediatric trial designs Andrea Arfe

2 Systemic immune response profiling with SYLARAS implicates a role for CD45R/B220+ CD8+ T cells in glioblastoma immunology.

Greg Baker

3 Stereoselective organocatalyzed synthesis of 2‐Deoxyglycosides Gary Bradshaw

4 Rapid heuristic inference of antibiotic resistance and susceptibility by genomic neighbor typing

Karel Břinda

5 High‐content screening of kinase inhibitors: implications for microtubule regulation and drug discovery.

Sergine Brutus

6 Proteomics‐based interrogation of the ubiquitin proteasome system Jonathan Bushman

7 Under the hood: Improved processing of multiplexed tissue images to investigate tumor architecture

Yu‐An Chen

8 Functional characterization of amino acid import mechanisms in human cells using CRISPR‐based genetic screens

Chris Chidley

9 Genes of future past: Investigating the emergence of Vemurafenib resistance in melanoma

Lily Chylek

10 Widespread twin phosphotyrosine priming bifurcates signaling to control p27‐driven cell cycle progression

Pau Creixell

11 The use of in‐vivo models in the LSP Stephanie Davis

12 Characterizing the Inflammatory Consequences of Failed Mitoses Patrick Flynn

13 Allosteric Modeling of ERK and EGFR signaling explains inhibitor‐mediated rewiring between oncogenic and physiological signaling

Fabian Froehlich

14 Baseline omics and drug response profiling of breast cancer cell lines and models

Benjamin Gaudio

15 Sporadic ERK pulses drive non‐genetic resistance in drug‐adapted BRAF V600E melanoma cells

Luca Gerosa

16 Clonal tracing reveals the contribution of both cancer‐intrinsic and ‐extrinsic mechanisms to the heterogeneity of responses to immune checkpoint blockade

Stan Gu

17 Machine learning on drug signatures identifies repurposing candidates for Alzheimer’s diseases

Clemens Hug

18 Transcriptomic profile after a single‐dose of neoadjuvant dual‐HER2 blockade better predicts pathologic response than at baseline in HER2‐positive inflammatory breast cancer

Nathan Johnson

19 Mechanism of adrenergic CaV1.2 stimulation revealed by proximity proteomics

Marian Kalocsay

20 Chemical Probes – Re‐thinking our Ecosystem Milka Kostic

21 Cellular profiling of adverse reactions to immune checkpoint blockade in skin

Zoltan Maliga

22 Guiding treatment for ovarian cancer using high‐throughput dynamic BH3 profiling

Kelley McQueeney

23 Using polypharmacology to overcome functional genetic redundancy Nienke Moret

24 Stitching, registering and assembling multi‐field microscopy images using Ashlar

Jeremy Muhlich


# Title Presenter

25 Predicting drug response from baseline omics profiles of breast cancer cells

Maulik Nariya

26 Building a Deeper Understanding of the Virus‐Host Arms Race Inside the Cell

Erika Olson

27 Mitochondrial functional approach to predicting effective therapies in relapsed acute myeloid leukemia

Marissa Pioso

28 Chemically Induced Degradation of Anaplastic Lymphoma Kinase (ALK) Chelsea Powell

29 Inference for clinical trials when the patient population is subjected to changes over time

Massimiliano Russo

30 shinyDepMap: an interactive web‐tool to explore gene essentiality in Cancer Dependency Map

Kenichi Shimada

31 Targeting the PI5P4K and PIKfyve lipid kinases in cancer using novel covalent inhibitors

Carmen Sivakumaren

32 DeepDyeDrop: a high‐throughput microscopy platform for phenotyping the response of cancer cell lines to therapeutic agents

Chiara Victor

33 TANK‐Binding Kinase 1 (TBK1) As A Novel Cancer Immunotherapy Target

Ajay Vishwakarma

34 Mapping Immune Landscape in Clear Cell Renal Carcinoma by Single‐Cell RNA‐seq

Ajay Vishwakarma

35 Reaction networks and toric geometry in single‐cell, multiplex data Shu Wang

36 Synthetic gene circuits for cancer immunotherapy: Turning cancer cells against themselves.

Ming‐Ru Wu

37 Automated image acquisition and analysis tools for quantifying multiplexed high‐dimensional data

Clarence Yapp

38 Discovery of a Small Molecule Degrader that Induces Sustained AKT Degradation and Prolonged Inhibition of Downstream Signaling

Inchul You

October 21, 2019 29

Poster Abstracts

1. Leveraging prior data to increase the power of pediatric trial designs

Andrea Arfe

Use of data from prior randomized trials could improve the efficiency of pediatric late‐phase

trials, a setting where patient accrual is difficult. We developed a test for treatment effects that

leverages data from past trials in the final analysis of a randomized survival study. Our test

solves an optimization problem: conditional on prior data, it maximizes the probability of

detecting the treatment effect (predicted power) among those that control false‐positive errors

at a prespecified rate. We illustrate our test in a simulation study tailored to results of

Children's Oncology Group Trial AAML0531on acute myeloid leukemia.

2. Systemic immune response profiling with SYLARAS implicates a role for

CD45R/B220+ CD8+ T cells in glioblastoma immunology.

Greg Baker

Detailed characterization of the systemic immune response to cancer is essential for improving

immunotherapy. Here we describe SYLARAS (SYstemic Lymphoid Architecture Response

Assessment; www.sylaras.org), a generally useful tool for multi‐organ immuno‐phenotyping

data into a time‐resolved visual compendium. Leveraging SYLARAS against a syngeneic mouse

model of glioblastoma (GBM), we reveal brain cancer’s widespread perturbation in systemic

lymphoid architecture and identify CD45R/B220+ CD8+ T cells as a distinct subset of tumor

infiltrating lymphocytes. The potential for SYLARAS to not only integrate information across

tissues but also different models of disease and therapy may help to identify recurring,

network‐level motifs of systemic immune response and inform the design of future

immunotherapy and its evaluation in clinical trials. Resources generated in this study are freely‐

available at www.sylaras.org and include a 12‐color immunophenotyping panel for flow

cytometry, a 240‐tissue single‐cell dataset, and a computational tool for automated cell type

identification and tracking.


3. Stereoselective organocatalyzed synthesis of 2‐Deoxyglycosides

Gary Bradshaw

Following previous work by McGarrigle and Galan using thioureas as glycosylation catalysts for

the synthesis of 2‐deoxygalactosides, new cheaper organocatalysts have since been discovered

for this process. This organocatalytic method is a simple way to form disaccharides of which

one moiety is a 2‐deoxymonosaccharide.

This standard method uses mild conditions and very low catalyst loadings (1‐10 mol%) to obtain

high yields. The process is tolerant of many common protecting groups and is selective for α‐

glycosidic linkages. The catalytic system works with monosaccharide acceptors bearing free

primary/secondary alcohols and for the first time acceptors bearing an amine functional group

are tolerated.

Using water as an acceptor resulted in the formation of 1,1'‐linked galactal derivative, 2,2'‐

dideoxy‐galactotrehalose. This is an analogue of galactotrehalose which has interesting

chemical/biological properties. Although in this case the reaction gives a mix of α,α' and α,β'‐

2,2'‐dideoxy‐galactotrehaloses, these are separable after column chromatography.

The new catalyst structure, which will be described in this presentation, indicates the previously

proposed mechanism invoking double hydrogen‐bonding by a thiourea catalyst is not operative.

Mechanistic experiments and insights will also be presented.

4. Rapid heuristic inference of antibiotic resistance and susceptibility by

genomic neighbor typing

Karel Břinda

Surveillance of drug‐resistant bacteria is essential for healthcare providers to deliver effective

empiric antibiotic therapy. However, traditional molecular epidemiology does not typically

occur on a timescale that could impact patient treatment and outcomes. Here we present a

method called ‘genomic neighbor typing’ for inferring the phenotype of a bacterial sample by

identifying its closest relatives in a database of genomes with metadata. We show that this

technique can infer antibiotic susceptibility and resistance for both S. pneumoniae and N.

gonorrhoeae. We implemented this with rapid k‐mer matching, which, when used on Oxford

Nanopore MinION data, can run in real time. This resulted in determination of resistance within

ten minutes (sens/spec 91%/100% for S. pneumoniae and 81%/100% N. gonorrhoeae from

isolates with a representative database) of sequencing starting, and for clinical metagenomic

sputum samples (75%/100% for S. pneumoniae), within four hours of sample collection. This

flexible approach has wide application to pathogen surveillance and may be used to greatly

accelerate appropriate empirical antibiotic treatment.

October 21, 2019 31

5. High‐content screening of kinase inhibitors: implications for microtubule

regulation and drug discovery.

Sergine Brutus

Microtubules are dynamic polymers that facilitate several important cell functions, including

mitosis. The regulation of microtubule dynamics, a process that is commonly targeted to treat

multiple cancer etiologies, is not fully understood. Although kinase signaling has been

implicated in this process across various cell lines and cell states, few definitive mechanistic

studies to identify strong kinase regulators have been conducted. To address this knowledge

gap, we conducted a high‐content screen of 43 kinase inhibitors for effects on microtubule

growth. Automated counting of growing microtubules was scored in retinal pigmented

epithelial (RPE1) cells expressing an EB3‐GFP reporter. Given the numerous reports of kinase

signaling driving multiple parameters of microtubule dynamics, we expected kinase inhibition

to have substantial effects on microtubule growth. Surprisingly, we did not observe robust

effects due to kinase signaling in a baseline screen or in a sensitized screen, where cells were

co‐treated with a microtubule depolymerizing drug. However, we were able to identify a

compound that perturbs microtubule polymerization via direct interactions with tubulin using

this approach, which was validated using an in vitro imaged‐based microtubule polymerization

assay. There are multiple implications of this work: (1) We have helped contextualize previous

reports on the signaling inputs into microtubule function by determining that the role of kinase

signaling in the regulation of microtubule dynamics is context dependent. (2) We have

validated an approach that can be used to explore off‐target effects on microtubules, which can

be used to better understand the pharmacology that drives efficacy of targeted cancer

therapies.

6. Proteomics‐based interrogation of the ubiquitin proteasome system

Jonathan Bushman


7. Under the hood: Improved processing of multiplexed tissue images to

investigate tumor architecture

Yu‐An Chen

Multiplexed optical imaging of fixed tissue sections provides deep, single‐cell phenotypes at

tissue‐scale to guide discovery, diagnosis and therapeutic decisions. However, the physical

limitations of optical microscopy and the natural complexity of tissues impact the types and

quality of the resulting data sets. Here, we describe a suite of image pre‐processing and image

segmentation solutions to be incorporated in our current image workflow to improve cell

phenotyping and quantitative whole slide imaging analysis from data sets obtained by cyclic

immunofluorescence (CyCIF) imaging of FFPE tissues sections1. These include correcting

illumination to improve quantitative phenotyping, stitching and templated image registration of

large tissue section images, sample extraction from tissue microarrays and benchmarking cell

segmentation methods in biopsies and large data sets. With continuing improvements in image

processing and visualization at LSP, we aim to deliver on the potential of CyCIF imaging for

discovery, biomedical education, and clinical impact.

8. Functional characterization of amino acid import mechanisms in human cells

using CRISPR‐based genetic screens

Chris Chidley

The major classes of SLCs that import amino acids and other nutrients into human cells have

been identified and are well‐annotated. However, few accurate methods are available to

functionally characterize the contribution of each SLC to nutrient import in human cell lines. We

developed pooled libraries of knockdown and overexpression mutants using CRISPR‐derived

techniques, namely CRISPRi and CRISPRa, including all annotated transporters. These

techniques allow a specific and robust control of transporter expression levels and permit

identification of transporters whose perturbation alters growth at limiting extracellular

concentrations of amino acids. Specifically, increased transporter expression and thus nutrient

import would allow mutant cells to propagate at an increased rate compared to the parental

population. Conversely, decreased nutrient import would result in depletion of reduced

expression mutants from the library. After growing these pooled libraries over the course of 14

days in single amino acid limited medium, clones with altered growth phenotypes were

identified by bulk high‐throughput sequencing and enrichment analysis. Using this genetic

screening strategy, we determined the role of individual SLCs in import of all 14 amino acids

whose extracellular levels can limit growth rate in K562, a chronic myeloid leukemia cell line.

Further, such knockdown and overexpression screens provide complementary information:

transporter knockdown uncovers import mechanisms utilized by the tested cell line whereas

transporter overexpression identifies all viable transport mechanisms. For example, we were

October 21, 2019 33

able to confirm that SLC7A1/CAT1 is the major contributor to import of the cationic amino acids

arginine and lysine in K562. However, knockdown of SLC7A6/y+LAT2 decreased net lysine

import but increased arginine import. Overexpression of SLC7A7/y+LAT1 increased net import

of lysine but not that of arginine. These results highlight the role that y+LAT transporters play in

the discrimination between cationic amino acids and their contribution to the net flux of these

amino acids. In summary, we present our efforts towards building a comprehensive view of

amino acid import into human cells.

9. Genes of future past: Investigating the emergence of Vemurafenib resistance

in melanoma

Lily Chylek

10. Widespread twin phosphotyrosine priming bifurcates signaling to control

p27‐driven cell cycle progression

Pau Creixell

Protein tyrosine kinases activate cell cycle programs in response to pro‐mitotic external cues in

a process that needs to be tightly regulated to prevent cellular transformation. The precise

mechanisms by which such tight regulation is achieved remain unclear. Here, we discover a

widespread preference amongst tyrosine kinases to phosphorylate twin tyrosine residues

conditional on one of the sites being previously phosphorylated. More specifically, whereas Abl

has a preference for phosphorylating tyrosine residues directly following phosphotyrosines (pYY

motifs), Src prefers phosphorylating tyrosine residues directly preceding phosphotyrosine (YpY

motif). We identify the CDK inhibitor p27KIP1 as a twin tyrosine containing substrate that is

differentially phosphorylated by Abl and Src in a phosphotyrosine‐dependent manner. By mass

spectrometry, we observe that p27KIP1 is doubly phosphorylated in RPE1 cells and, by live cell

imaging, that depletion of Abl or Src drives cells into p27KIP1‐dependent quiescence. Data from

cell lines endogenously expressing p27KIP1 show that both tyrosine 88 and tyrosine 89 play

critical, non‐redundant roles in p27KIP1 stability and cell cycle progression. These observations

support a model in which doubly phosphorylating p27KIP1 de‐stabilizes this CDK inhibitor and

promotes cell cycle entry. We propose that "priming" provides tyrosine kinases with a

mechanism to conditionally regulate specific downstream cellular programs as a function of

upstream tyrosine kinase signaling events.


11. The use of in‐vivo models in the LSP

Stephanie Davis

It is common practice in translational research to use in vivo models to validate promising

results found using in vitro systems. This poster will outline ongoing animal work in the

Laboratory of Systems Pharmacology (LSP). Syngeneic models, standard xenografts established

from conventional cell lines, as well as patient derived xenograft (PDX) models are currently in

use for breast cancer and melanoma research in LSP. Standard xenografts established in nude

mice are grown to ~200 mm3 and then used for acute dose response studies or long term

tumor response experiments. PDX models are established in NSG mice, propagated from mouse

to mouse, and used for similar studies. Syngeneic models are established in C57BL/6 mice and

are used to explore tumor immunology and tumor biology, particularly to study resistance or

sensitivity to immune checkpoint blockade (ICB) as well as metastasis. Upon study completion,

tumors are resected and used for a variety of downstream analyses including RNA sequencing,

proteomics, and cyclic immunofluorescence. We have introduced fluorescent and/or

bioluminescent reporters into cells (PDX tumors can be dissociated ex vivo and cultured) prior

to engraftment to facilitate separating tumor cells from stroma, and to enable imaging for more

accurate measurement of tumor burden, respectively.

12. Characterizing the Inflammatory Consequences of Failed Mitoses

Patrick Flynn

Cytoplasmic self‐DNA (cyDNA) accumulation has been proposed to play a causative role in

multiple pathologies including autoimmune disorders, cancer and possibly aging by activating

innate immune pathways. Specifically, the cGAS‐STING‐Interferon (IFN) pathway has been

identified as playing a critical role in mediating cellular and tissue response to cyDNA. cyDNA is

thought to accumulate through multiple routes, including mechanical rupture of micronuclei,

nuclear budding and export of DNA fragments via nuclear pores. Anti‐microtubule drugs, such

as Vinca alkaloids and Taxanes, represent a cornerstone of cancer chemotherapy. They kill

cancer cells in culture via perturbation of mitosis, and are often termed “anti‐mitotics”.

However, other drugs that target mitosis‐specific proteins such as inhibitors of Eg5/Kif11,

Aurora kinases A and B, and Polo‐like kinase 1 have proven to be ineffective for cancer

treatment. I hypothesized that anti‐mitotic drugs might differ in their ability to trigger cGAS‐

STING signaling after a failed mitosis, and this might help to explain their observed differences

in clinical efficacy. To test this I performed a pharmacological screen of a diverse set of anti‐

mitotics and systematically measured their ability to induce micronuclei, cyDNA and IFN. To

measure IFN production in a physiologically relevant manner, I developed a high‐throughput

co‐culture assay in which engineered immune cells report on the concentration of extracellular

IFN secreted by drug treated cancer or stromal cells. I found that some anti‐mitotic drugs

October 21, 2019 35

generate more IFN than Taxanes, and others much less. My data point to a role for mitotic

kinases in regulating cGAS activation following a failed mitosis. Manipulation of mitotic kinases

also allowed me to distinguish alternative pathways by which DNA enters the cytoplasm in

response to anti‐mitotic vs. DNA‐damaging drugs.

13. Allosteric Modeling of ERK and EGFR signaling explains inhibitor‐mediated

rewiring between oncogenic and physiological signaling

Fabian Froehlich

Allosteric interactions are at the core of many signal transduction processes and provide

robustness and enable context dependency for the underlying molecular mechanisms. In the

context of RAF inhibitors allosteric interactions give rise to paradoxical activation, a clinically

observed phenomenon where RAF inhibitors effectively decrease tumor growth in BRAF mutant

cancers, but promote tumor growth in BRAF wild‐type cancers. Energy based formalisms to

describe such allosteric effects in kinetic models have been developed, but are so far rarely

applied in practice, which is likely to be a result from the high complexity of the generated

models.

Here we demonstrate the use of a thermodynamic, energy‐balanced rule‐based formalism to

describe allosteric interactions. We tackle the numerical challenges of large kinetic models by

using state of the art simulation tools and address the conceptual challenge of analyzing large

kinetic models by exploiting the strong structure imposed on the model by the rule‐based

formulation and the compliance with energy balance.

We apply these methods to an ordinary differential equation model of adaptive resistance in

melanoma (EGFR and ERK pathways, >1k state variables, >10k reactions). We trained the model

on absolute proteomic and phospho‐proteomic as well as time‐resolved immunofluorescence

data, both in dose‐response to small molecule inhibitors. We identify and explain a time‐scale

separation between transcriptional feedbacks and phospho‐signaling. Moreover, we illustrate

how kinetic models can extrapolate from single drug treatments to drug combinations.


14. Baseline omics and drug response profiling of breast cancer cell lines and

models

Benjamin Gaudio

Several publications have addressed concerns surrounding drug response screens by identifying

sources of variability and by providing recommendations for improved experimental methods

and more robust analytical approaches. In the first phase of the HMS LINCS breast cancer

profiling effort, we selected 73 breast cancer cell lines that include 27 triple negative (TNBC)

lines, 21 HER2‐amplified, 16 HR+ as well as 4 cell lines established from relevant patient‐derived

xenografts. We evaluated a panel of 68 clinically relevant agents, biased towards kinase

inhibitors that target CDK’s and components of the PI3K pathway, using the microscopy‐based

Deep Dye‐Drop dose response assay to measure drug potency, and to quantify drug efficacy in

terms of growth inhibition (GR metrics), cell death and cell cycle fractions. The use of GR

metrics to quantify drug sensitivity enabled us to identify and study differences between

cytostatic and cytotoxic responses. Quantification of cell cycle distributions reveal subtleties in

drug mechanism of action that are otherwise missed in cell viability assays. This systematic dose

response dataset is complemented by measurements of baseline transcript expression levels by

mRNAseq, quantification of absolute abundance of ~12,000 proteins, and relative

phosphoprotein levels by shotgun mass spectrometry across all cell lines. Normalized protein

expression did not show any batch effects, as evidenced by reproducibility in terms of the

proteins identified and correlation in relative abundance across technical replicates.

Additionally, the baseline activity of kinases and transcription factors were inferred from

phosphoprotein (using a custom kinase enrichment analysis) data and mRNA (using VIPER),

respectively. The three baseline expression datasets and the two inferred activity datasets were

used to build predictive models of drug response. Overall these datasets will be a valuable

resource for understanding drug response in breast cancer models, and the molecular

mechanisms that influence them.

15. Sporadic ERK pulses drive non‐genetic resistance in drug‐adapted BRAF

V600E melanoma cells

Luca Gerosa

Anti‐cancer drugs commonly target signal transduction proteins activated by mutation. In

patients with BRAF V600E melanoma, small molecule RAF and MEK kinase inhibitors cause

dramatic but often transient tumor regression. Emerging evidence suggests that cancer cells

adapting by non‐genetic mechanisms constitute a reservoir for the development of drug‐

resistant tumors. Here, we show that few hours after exposure to RAF/MEK inhibitors, BRAF

V600E melanomas undergo adaptive changes involving disruption of negative feedback and

sporadic pulsatile reactivation of the MAPK pathway, so that MAPK activity is transiently high

October 21, 2019 37

enough in some cells to drive proliferation. Quantitative proteomics and computational

modeling show that pulsatile MAPK reactivation is possible due to the co‐existence in cells of

two MAPK cascades: one driven by BRAF V600E that is drug‐sensitive and a second driven by

receptors that is drug‐resistant. Paradoxically, this may account both for the frequent

emergence of drug resistance and for the tolerability of RAF/MEK therapy in patients.

16. Clonal tracing reveals the contribution of both cancer‐intrinsic and ‐extrinsic

mechanisms to the heterogeneity of responses to immune checkpoint blockade

Stan Gu

Although multiple studies have investigated the biomarkers of response to immune checkpoint

blockade (ICB), the significance of each biomarker varies across clinical cohorts independent of

cancer type. It remains unclear whether primary ICB response and resistance is encoded in the

cancer cells (cancer‐intrinsic) or driven by the immune microenvironment unique to each host

(cancer‐extrinsic). To answer this question, we established a novel mouse system that

facilitates clonal tracing and mathematical modeling to uncouple the cancer‐intrinsic and ‐

extrinsic mechanisms of ICB resistance. We found that tumors with the same clonal constitution

show heterogeneous ICB response in different hosts. Primary resistance is associated with the

cancer‐extrinsic immune microenvironment rather than proliferation of intrinsically ICB‐

resistant cancer cells. Instead, pre‐existing cancer‐intrinsic ICB‐resistant clones with distinct

transcriptional and epigenetic profiles were enriched in responders. We identified two gene

expression signatures associated with cancer‐intrinsic resistance, including increased interferon

response genes and glucocorticoid response genes. Analyses of patient tumor data from

multiple ICB treatment cohorts recapitulate our mouse model results that cancer‐extrinsic

resistance signature correlates with primary resistance while cancer‐intrinsic signatures are

enriched on treatment in ICB responders. These findings demonstrate the importance of

immunotherapy biomarkers that account for both cancer‐intrinsic and ‐extrinsic mechanisms of

resistance, and implicate the value of on‐treatment cancer samples to determine the response

to ICB.


17. Machine learning on drug signatures identifies repurposing candidates for

Alzheimer’s diseases

Clemens Hug

Alzheimer’s Disease (AD) is a growing epidemic as longer life expectancy fuels its principal risk

factor ‐ aging. As understanding of AD grows in the setting of many failed clinical trials, the

concept of AD as a single disease is giving way to the hypothesis that it is a syndrome with

multiple disease pathways progressing towards a common end‐stage clinical presentation.

Here, we aim to identify FDA‐approved drugs that target these pathways and thus are

candidates for repurposing in AD.

Given an FDA‐approved drug, we asked if its mechanism of action is related to AD biology by

training a predictor of disease stage. The predictor was limited to genes known to be perturbed

by the drug, and its performance was compared to predictors constructed on randomly‐

selected gene sets of equal size. Top‐performing drugs were subsequently profiled on human

neuroprogenitor cell lines that differentiate into a mixed culture of neurons, glia and

oligodendroctyes to further refine their mechanisms of action in relevant cell types. Jak

inhibitors Tofacitinib and Ruxolitinib were among the top performers, and additional in vitro

experiments demonstrated that the two drugs can rescue inflammatory‐induced neuronal

death, suggesting their potential as repurposing candidates for AD.

18. Transcriptomic profile after a single‐dose of neoadjuvant dual‐HER2

blockade better predicts pathologic response than at baseline in HER2‐positive

inflammatory breast cancer

Nathan Johnson

Inflammatory breast cancer (IBC) is an understudied, aggressive, and rare form of breast

cancer. We present results from a phase II clinical trial (NCT01796197) that examined the effect

of two monoclonal antibodies targeting HER2, trastuzumab and pertuzumab (jointly, HP).

Twenty‐three HER2+ IBC patients were enrolled from Aug 2013 ‐ June 2017 with a 43% (10/23)

response rate. There are two analytical objectives from this study. The first is whether

multidimensional machine learning modeling provides a more informed framework than a

standard low‐dimensional RNA‐Seq analysis that compares single genes between biologically

relevant groups. The second objective is to determine whether a RNA‐Seq based gene

signature from the Day 8 (D8) biopsy, collected after treatment, is a better predictor than the

Day 1 (D1) baseline. A Random Forest model was trained to predict treatment response from

D1 or D8 mRNA expression and evaluated using leave‐pair‐out cross‐validation. Across all

metrics (Accuracy, MCC, AUC, Precision, Recall, Fscore), the D8 model significantly separates

responders from non‐responders than the D1 model (p‐value: 1.0X10‐15), showing

October 21, 2019 39

improvement with each additional gene. These findings may have implications for the

assessment of immunotherapy for patients with HER2‐positive IBC, but require further

validation in a larger IBC cohort.

19. Mechanism of adrenergic CaV1.2 stimulation revealed by proximity

proteomics

Marian Kalocsay

Increased cardiac contractility during fight‐or‐flight response is caused by b‐adrenergic

augmentation of CaV1.2 channels. In transgenic murine hearts expressing fully PKA

phosphorylation‐site‐deficient mutant CaV1.2 a1C and b subunits, this regulation persists,

implying involvement of extra‐channel factors. Here, we identified the mechanism by which b‐

adrenergic agonists stimulate voltage‐gated Ca2+ channels. We expressed ascorbate‐

peroxidase‐conjugated‐�1C or �2B subunits in mouse hearts and used multiplexed, quantitative

proteomics to track hundreds of proteins in proximity of CaV1.2. We observed that Rad is

enriched in the CaV1.2 micro‐environment but is depleted during �‐adrenergic stimulation. We

found that PKA‐catalyzed phosphorylation of Rad relieves its inhibition of CaV1.2 observed as

an increase in channel open probability that depended on specific Ser residues in Rad.

Expression of Rad or Rem, another member of this small G‐protein family, also imparted PKA‐

induced stimulation of CaV1.3 and CaV2.2. These results reveal an evolutionary conserved

mechanism that confers adrenergic‐modulation to voltage‐gated Ca2+ channels.

20. Chemical Probes – Re‐thinking our Ecosystem

Milka Kostic

Chemical tool compounds have been used to interrogate intricate functional and mechanistic

questions in biology for decades. These compounds represent one of the key contributions that

chemical biology as a field continues to make to the broader life science and biomedical

research communities. As such, chemical biologists have been investing resources and grass‐

roots efforts into defining what constitutes a high quality chemical tool compound (often

referred to as chemical probe), and developing guidelines for characterization and validation.

However, although standards have emerged, their wide adoption, implementation and

enforcement are lagging behind. More importantly, in practice, many biologists continue to use

“discredited” chemical tools, thus generating unreliable results and incorrect scientific

conclusions. If you are interested in providing comments or feedback on what can and should

be done, consider scanning the QR code below and making your opinions and suggestions

heard. Thank you!


21. Cellular profiling of adverse reactions to immune checkpoint blockade in

skin

Zoltan Maliga

22. Guiding treatment for ovarian cancer using high‐throughput dynamic BH3

profiling

Kelley McQueeney

The use of imperfect models and ex vivo culture systems to try to predict patient drug response

represents an enormous bottle neck in cancer treatment. Determining how effective an

approved drug will be for a given cancer patient, as well as identifying novel compounds that

may be beneficial to a specific population requires the use of primary tumor cells. High‐

throughput dynamic BH3 profiling (HT‐DBP) provides an assay platform that allows us to

maximize the amount of information obtained from a limited number of cells and requires only

24 hours of culture ex vivo to more accurately reflect the behavior and vulnerabilities of the

primary tumor. HT‐DBP is a microscopy‐based assay performed on adherent cells that visualizes

and quantifies mitochondrial outer membrane permeabilization to identify compounds that

induce mitochondrial apoptotic signaling.

Ovarian cancer is a devastating disease desperately in need of novel therapeutic interventions.

The standard‐of‐care cytotoxic agent regimen remains largely unchanged over the last 15 years.

Although there has been some success using molecular targeted agents and maintenance

therapies, the genetic complexity and lack of common molecular drivers in the disease have

made the selection of patient populations most likely to benefit from therapies difficult.

Ovarian cancer, however, frequently has a unique pathology wherein fluid, known as ascites

fluid, accumulates in the peritoneum of patients. This ascites fluid frequently contains tumor

cells and is routinely aspirated from patients throughout treatment. Therefore, ascites fluid

presents a potentially abundant and easily accessible source of tumor cells from ovarian cancer

patients.

We have optimized the use of tumor cells from primary ascites samples in the adherent HT‐DBP

assay. To evaluate how susceptible the tumor cells from individual patients will be to currently

available chemotherapeutic agents, we have treated the cells with a range of concentrations of

approved compounds. We observed varying sensitivities to these frequently used drugs. In an

effort to identify new drugs, or drug targets, that may benefit patients, we have also performed

an 880 compound screen at 3 concentrations per compound on primary ascites samples. This

screen identified novel chemical vulnerabilities to investigate in the future. In total, we have

demonstrated the potential utility of tumor cells isolated from ascites samples in the HT‐DBP

assay to both predict patient drug response and identify new therapies.

October 21, 2019 41

23. Using polypharmacology to overcome functional genetic redundancy

Nienke Moret

Polypharmacology, the tendency of drug‐like small molecule drugs to bind multiple targets,

challenges common assumptions about targeted drug discovery. In targeted drug discovery, the

goal is typically to identify a gene product primarily responsible for disease (a driver gene in

oncology) and then identify a drug that selectivity binds to the gene product to restore a

normal phenotype. High selectivity is typically sought, particularly in oncology, to minimize

toxicity. However, we find that FDA‐ approved drugs average 6‐20 high affinity targets. It is

poorly understood how this polypharmacology impacts efficacy; some drugs resulting from

targeted discovery are known achieve their effects by binding to multiple gene products but in

other cases, polypharmacology is thought to be unimportant or even counterproductive. For

example, development of small molecule CDK or AKT kinase inhibitors has focused on ever

more selective targeting of specific gene products/isoforms to increase efficacy and reduce

toxicity. In this paper we combine data on small molecules and RNAi (which also has off‐targets)

to ask whether polypharmacology is adventitious or whether it is generally advantageous

because it overcomes gene redundancy. We utilize target information of drug‐like small

molecules to infer which proteins would be redundant if compounds do overcome functional

gene redundancy. We subsequently use the property of siRNA to target multiple genes and

show that the redundant nodes inferred from small molecule data are also epistatic. Our results

support a model that drug discovery efforts do not have to revolve around finding specific small

molecules, but rather the right polypharmacology has to be found. We expect our results to

contribute to an alternative compound optimization process in drug discovery campaigns.

24. Stitching, registering and assembling multi‐field microscopy images using

Ashlar

Jeremy Muhlich

Multiple methods have been developed over the past five years to collect highly multiplexed

images of cells and tissues, including the biopsies and resections used to diagnose human

disease and guide therapy. In many cases, it is necessary to register a succession of images of

the same cells obtained under different staining conditions and stitch together successive fields

of view obtained by scanning the specimen. In this paper we describe a Python tool for image

registration and stitching, Ashlar (Alignment by Simultaneous Harmonization of

Layer/Adjacency Registration), that is more rapid and accurate than existing methods in

assembling subcellular‐resolution, multi‐channel images up to several square centimeters in

size. Ashlar is easy to use, reads and writes the OME‐TIFF standard and, using BioFormats

software, is compatible with virtually any microscope image file.


25. Predicting drug response from baseline omics profiles of breast cancer cells

Maulik Nariya

In this work we profiled 69 breast cancer cells across different subtypes with 68 clinically

relevant therapeutic compounds from various drug classes using a microscopy‐based dose

response assay and quantified the drug sensitivity of the cells in terms of growth rate inhibition

metrics. The dose response data was complemented by measurements of baseline transcript

expression levels using mRNA‐seq as well as the unperturbed protein and phosphoprotein

abundances obtained using shotgun mass spectroscopy. We used the baseline datasets to build

predictive models using a Random Forest Regressor with the leave‐pair‐out cross validation

scheme. The area over the growth rate curve (GRAOC) was used as the response metric for the

predictions, and we evaluated the model accuracy as the fraction of cell pairs that were

correctly ranked in cross‐validation folds. We observed high accuracies for the model across

different drug classes indicating that the baseline expression and inferred profiles were good

predictors of drug sensitivity in cancer cells. We found that the baseline transcriptomics profile

was superior for predicting drug responses across a large number of drugs. Furthermore, our

approach enabled us to gain insight on biological processes that responsible for differential

response across drugs and cell lines.

26. Building a Deeper Understanding of the Virus‐Host Arms Race Inside the Cell

Erika Olson

All mammalian cells have a cohort of protein sensors that detect and respond to virus‐

associated molecular patterns, which are in aggregate termed the intracellular innate immune

system. Viral pathogenicity strongly correlates with the potency and quantity of mechanisms by

which the virus subverts these sensors and signaling pathways; of the only eight genes that

Ebolavirus encodes, four genes (half!) inhibit the innate immune system, and two of the four

genes are solely devoted to inhibiting intracellular innate immunity. However, the mechanisms

by which viruses accomplish this are generally unknown, due to a combination of lack of study

and extreme biological variability between viruses. We are studying the mechanisms by which

viruses inhibit the human intracellular innate immune system by screening viral genes in high‐

throughput using fluorescence microscopy. Our aim is to enable computational detection of

viral pathogenicity and development of broad‐spectrum antivirals.

27. Mitochondrial functional approach to predicting effective therapies in

relapsed acute myeloid leukemia

Marissa Pioso

October 21, 2019 43

28. Chemically Induced Degradation of Anaplastic Lymphoma Kinase (ALK)

Chelsea Powell

We present the development of the first small molecule degraders that can induce anaplastic

lymphoma kinase (ALK) degradation, including in non‐small‐cell lung cancer (NSCLC), anaplastic

large‐cell lymphoma (ALCL), and neuroblastoma (NB) cell lines. These degraders were

developed through conjugation of known pyrimidine‐based ALK inhibitors, TAE684 or LDK378,

and the cereblon ligand pomalidomide. We demonstrate that in some cell types degrader

potency is compromised by expression of drug transporter ABCB1. In addition, proteomic

profiling demonstrated that these compounds also promote the degradation of additional

kinases including PTK2 (FAK), Aurora A, FER, and RPS6KA1 (RSK1).

29. Inference for clinical trials when the patient population is subjected to

changes over time

Massimiliano Russo

A common assumption of clinical trials is that patients' characteristics, as well as treatment

effects, do not vary during the course of the study. However, when trials enrolls patients over

several years, this hypothesis may not hold. Ignoring variations of the outcome distribution of

patients over time can lead to biased treatment effects estimates and inflated Type I error of

standard testing procedures. We propose two testing procedures that account for trends in

patients' outcomes over time applicable in adaptive clinical trials. The proposed methods

preserve targeted frequentist operating characteristics (error rates). The first testing procedure

models trends in the patient outcomes with splines in a semi‐parametric ANOVA testing

framework. A second strategy consists in quantifying the difference in enrollment time

between a treatment and the control groups via a suitable discrepancy measure, and use such

measure to adjust for the presence of trends. We investigate through simulations the proposed

methods and assess the consequences of time trends in Bayesian adaptive randomized designs

and in platform trials.


30. shinyDepMap: an interactive web‐tool to explore gene essentiality in Cancer

Dependency Map

Kenichi Shimada

Individual cancers rely on distinct essential genes for their survival. Towards achieving precision

medicine, it is crucial to understand relationship between cancers and essential and tumor

suppressor genes, which is still a daunting task. Cancer Dependency Map, or DepMap, is an

ongoing project to achieve this through genome‐wide CRISPR and shRNA screening in hundreds

of cell lines. While DepMap has been shown powerful, its interpretation is not straightforward

because of the complicated nature. Here, we showed that a unified ‘efficacy score’ combining

CRISPR and shRNA data allowed us to identify essential genes and tumor suppressor genes.

Further, cluster analysis of efficacy profiles of the essential genes revealed proteins working

together in the same pathways or complexes. The analysis has been made accessible by a web‐

based tool, called shinyDepMap, which allows broader researchers to explore functions of the

genes of their interest.

31. Targeting the PI5P4K and PIKfyve lipid kinases in cancer using novel covalent

inhibitors

Carmen Sivakumaren

The phosphatidylinositol 5‐phosphate 4‐kinases (PI5P4Ks) have been demonstrated to be

important for cancer cell proliferation and other diseases. However, the therapeutic potential

of targeting these kinases is understudied due to a lack of potent, specific small molecules

available. Here we present the discovery and characterization of a novel pan‐PI5P4K inhibitor,

THZ‐P1‐2, that covalently targets cysteines on a disordered loop in PI5P4K. THZ‐P1‐2

demonstrates cellular on‐target engagement with limited off‐targets across the kinome.

AML/ALL cell lines were sensitive to THZ‐P1‐2, consistent with PI5P4K’s reported role in

leukemogenesis. THZ‐P1‐2 causes autophagosome clearance defects and upregulation in TFEB

nuclear localization and target genes, disrupting autophagy in a covalent‐dependent manner

and phenocopying the effects of PI5P4K genetic deletion.

PIKfyve, a related understudied lipid kinase, has also been validated as a target in Non‐Hodgkin

lymphoma and Ebola viral infection. We observed that THZ‐P1‐2 was also capable of covalently

binding to PIKfyve, albeit at a much lower preference than PI5P4K. We reengineered compound

selectivity towards PIKfyve and identified a class of compounds from the THZ‐P1‐2 scaffold

exhibiting picomolar‐to‐nanomolar affinity. These inhibitors exhibit cellular on‐target

engagement and vacuolar enlargement, a well‐established PIKfyve inhibitory phenotype.

Docking/modeling studies and mass spectrometry revealed a similar distant cysteine, Cys1970,

to be covalently labeled by these compounds. Lastly, this THZ‐family of inhibitors, particularly

October 21, 2019 45

two top compounds MFH‐5‐3 and MFH‐5‐4, exhibit potent antiproliferative activity in

lymphoma cell lines and inhibition of Ebola viral infectivity.

Taken together, our studies demonstrate that the PI5P4Ks and PIKfyve are tractable targets,

with inhibitors serving as useful tools to further interrogate the therapeutic potential of these

noncanonical lipid kinases and inform drug discovery campaigns in the context of cancer, Ebola,

and potentially other autophagy‐dependent diseases.

32. DeepDyeDrop: a high‐throughput microscopy platform for phenotyping the

response of cancer cell lines to therapeutic agents

Chiara Victor

The accurate measurement of diverse phenotypic response in cells treated with drugs is an

integral step in the pre‐clinical development of new therapeutics, in addition to providing

insight into drug mechanisms of action. Dose‐response studies aimed at identifying sensitive

and resistant cell lines or differences among related compounds are increasingly performed on

a relatively large scale with panels of genetically diverse cancer cell lines and compound

libraries. To enable this level of throughput, these screens have typically been performed using

surrogate measurements of population level cell viability, such as CellTiterGlo. Hits from such

primary screens are then typically pursued using follow‐up studies for specific readouts that are

time consuming and expensive. Although there will always be trade‐offs to be made between

throughput, and resolution, an intermediate assay that enables sufficient throughput and

information output has the potential to be time‐saving and cost‐sparing in pre‐clinical research

thus justifying the use of a richer assay at an earlier stage of compound screening. We present

the Deep Dye Drop assay, a minimally disruptive, high‐throughput, customizable microscopy‐

based method that can serve as a primary screening platform and provide phenotypic insight at

the single cell level. In addition to cell viability, the Deep Dye Drop assay provides detailed cell

cycle information enabling the detection of phenotypic subtleties and fractional responses

otherwise missed if data are only considered at the population level. We apply these methods

to identify differences in phenotypic responses among 42 ovarian cancer cell lines upon

treatment with 40 small molecule kinase inhibitors


33. TANK‐Binding Kinase 1 (TBK1) As A Novel Cancer Immunotherapy Target

Ajay Vishwakarma

Despite the unprecedented success of immune checkpoint blockade (ICB) in melanoma and

other cancers, tackling innate (primary) resistance remains a major challenge and robust

biomarkers to guide treatment are lacking. Clinical trials are already underway evaluating novel

immune modulatory agents in combination with anti‐PD‐1/PD‐L1 therapies in an effort to

overcome innate resistance. Despite increasing reports of ‘rational’ combination strategies,

these therapies remain “one size fits all”, due the lack of robust biomarkers to guide clinical

decision‐making. There is an unmet need for novel approaches, tools, techniques, and methods

for pre‐clinical and clinical use to better understand mechanisms of response and resistance to

immune checkpoint inhibitors and next‐generation anti‐tumor immune modulatory drugs. We

have adapted a 3D microfluidic device to the short‐term culture of murine‐ and patient‐ derived

organotypic tumor spheroids (MDOTS/PDOTS)for functional ex vivo profiling of PD‐1 blockade

in a model tumor immune microenvironment (TIME) to facilitate identification of mediators of

response and resistance to ICB and to guide development of next‐generation immunotherapy

combinations. The MDOTS/PDOTS platform provides a window into the complex and dynamic

events of the TIME during response (and resistance) to ICB and is an ideal model system for

pre‐clinical evaluation of novel cancer immunotherapy targets. Focused evaluation of novel

treatment combinations using MDOTS identified TANK‐binding kinase 1 (TBK1) as a novel

cancer immunotherapy target, mirroring in vivo efficacy of this treatment approach. TBK1 is a

Ser/Thr kinase involved in innate immune signaling and is an emerging target for anti‐cancer

therapy. Importantly, independent orthogonal data from two different laboratories has also

identified TBK1 as a cancer immunotherapy target. We have confirmed that Tbk1 deletion

and/or TBK1 pharmacologic inhibition enhances response to in vivo anti‐PD‐1 therapy, and have

characterized the impact of deletion of Tbk1 (CRISPR) or pharmacologic inhibition of TBK1

(Cmpd1) on the tumor‐immune microenvironment.Lastly, we confirmed that resistance to PD‐1

blockade can be overcome with pharmacologic TBK1 inhibition using murine‐ and patient‐

derived organotypic tumor spheroids in 3D microfluidic culture. These findings confirm TBK1 as

a target to overcome resistance to PD‐1 blockade, further supporting the pre‐clinical and

clinical development of this novel combination strategy.

October 21, 2019 47

34. Mapping Immune Landscape in Clear Cell Renal Carcinoma by Single‐Cell

RNA‐seq

Ajay Vishwakarma

Human clear cell renal cell carcinoma (ccRCC) is one of the most immunologically distinct tumor

types due to high levels of tumor‐infiltrating immune cells including T cells, but ccRCC has

relative low mutational burden and neoantigens and not every patient responds to

immunotherapy. In contrast to other cancers infiltration with cytotoxic CD8+ T cells is

associated with poorer overall survival in ccRCC, suggesting that sub‐populations of CD8+ and

other immune cells may underlie this observation. To characterize the tumor immune

microenvironment of ccRCC, we applied single‐cell RNA sequencing along with T cell receptor

sequencing to map the transcriptomic heterogeneity of 24,904 individual CD45+ lymphoid and

myeloid cells in matched tumor and blood from patients with ccRCC. We identified multiple and

distinct immune cell phenotypes for B and T (CD4 and CD8) lymphocytes, natural kill (NK) cells,

and myeloid cells. Evaluation of T cell receptor (TCR) sequences revealed limited shared

clonotypes between patients, whereas tumor‐infiltrating T cell clonotypes were frequently

found in peripheral blood, albeit in lower abundance. Evaluation of myeloid subsets revealed

unique gene programs defining monocytes, dendritic cells and tumor‐associated macrophages.

In summary, here we have leveraged scRNA‐seq to refine our understanding of the relative

abundance, diversity and complexity of the immune landscape of ccRCC. This report represents

the first such characterization of the ccRCC immune landscape using scRNA‐seq. With further

characterization and functional validation, these findings may identify novel sub‐populations of

immune cells amenable to therapeutic intervention.

35. Reaction networks and toric geometry in single‐cell, multiplex data

Shu Wang

The goal of many single‐cell studies on eukaryotic cells is to gain insight into the biochemical

reactions that control cell fate and state. We introduce a new notion, which we call the

effective stoichiometric space (ESS), that elucidates network structure from the covariances of

single‐cell, multiplex data. The ESS approach differs from methods that are based on purely

statistical models of data: it allows a new, data‐driven translation of the theory of toric varieties

in geometry and specifically their role in chemical reaction networks. As illustrations of our

approach, we find stoichiometry in different single‐cell datasets, and pinpoint dose‐

dependence of network perturbations in drug‐treated cells.


36. Synthetic gene circuits for cancer immunotherapy: Turning cancer cells

against themselves.

Ming‐Ru Wu

Cancer immunotherapy has demonstrated robust efficacy in clinical trials, but challenges such

as the lack of ideal targetable tumor antigens, severe toxicity, and tumor‐mediated

immunosuppression still limit its success. To overcome these challenges, I have designed a

synthetic cancer‐targeting gene circuit platform that enables a localized and robust

combinatorial immunotherapy from within cancer cells: a Trojan horse‐like approach. Once the

circuits are introduced into cells, they will sense cancer‐specific transcription factor activities,

and trigger an effective combinatorial immunotherapy selectively from within cancer cells,

while keeping normal cells unharmed. The circuit cured disseminated ovarian cancer in vivo in a

mouse model. This platform can be adjusted to treat multiple cancer types and can potentially

trigger any genetically‐encodable immunomodulators as therapeutic outputs. Moreover, this

gene circuit platform can be adapted to treat additional diseases exhibiting aberrant

transcription factor activities, such as chronic metabolic diseases and autoimmune disorders.

37. Automated image acquisition and analysis tools for quantifying multiplexed

high‐dimensional data

Clarence Yapp

Automated microscopes (including plate‐based imagers and slide scanners) have resulted in a

significant increase in the rate of data generation. Vendors continue to offer accessories to

their instruments with direct benefits toward 1) throughput (scheduling software, robot plate

handlers, filter wheels, larger camera sensors), 2) resolution (water immersion lenses, spinning

disk confocal units), 3) sensitivity and deeper penetration (Class 4 lasers and higher QE

cameras), and 4) number of channels (5‐8 channels ranging from UV to near infrared excitation)

all in modular platforms. However, the increase in availability of data from clinical samples puts

a strain on image analytical techniques where there is an even higher demand for performance

and accuracy. Because of the inter‐ and intra‐ heterogeneity of patient‐derived tissue, we have

turned to machine learning and, more recently, deep learning models for cell detection and

object classification. These have been deployed on a high performance cluster for faster

turnaround and parallelization. Here, we highlight just some of these different image

acquisition instruments and image segmentation tools used at the Laboratory of Systems

Pharmacology, Harvard Program in Therapeutics Science.

October 21, 2019 49

38. Discovery of a Small Molecule Degrader that Induces Sustained AKT

Degradation and Prolonged Inhibition of Downstream Signaling

Inchul You

The PI3K/AKT signaling cascade is one of the most commonly dysregulated pathways in cancer,

with over half of tumors exhibiting aberrant AKT activation. Although potent small molecule

AKT inhibitors have entered clinical trials, robust and durable therapeutic responses have not

been observed. As an alternative strategy to target AKT, we report the development of INY‐03‐

041, a pan‐AKT degrader consisting of an AKT inhibitor conjugated to a recruiter of the E3

ubiquitin ligase. INY‐03‐041 induced potent degradation of all three AKT isoforms and displayed

enhanced anti‐proliferative effects relative to current AKT inhibitors. Notably, INY‐03‐041

promoted sustained AKT degradation and inhibition of downstream signaling effects for up to

96 hours, even after compound washout. Overall, our findings suggest that AKT degradation

may confer prolonged pharmacological effects compared to inhibition, and highlight the

potential advantages of AKT‐targeted degradation.

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