THANKS YOU SO MUCH FOR EVERYONE
WHO CAME IN PERSON AND OTHERS
WHO ARE ON THE ZOOM.
IT'S REALLY A PLEASURE TO HAVE
DR. PENG JIANG AS OUR CCR GRAND
ROUNDS SPEAKER TODAY.
SO, HE JOINED OUR NEW BRANCH IN
JULY 2019, SO HE'S AROUND FOUR
YEARS HERE.
AND HE WILL SHOW YOU THE STELLAR
WORK HE'S BEEN DOING IN THIS
RELATIVELY SHORT TIME.
SO HE AS YOU MAY KNOW WORKS ON
ARTIFICIAL INTELLIGENCE AND
COMPUTATIONAL BIOLOGY, AND
IMMUNOTHERAPY FROM BASIC AND
TRANSLATIONAL ASPECTS, AND HIS
LAB INTEGRATES BOTH
COMPUTATIONAL ANALYSIS AND
EXPERIMENTAL ANIMAL WORK.
SO, PENG COMPLETED UNDERGRAD IN
ONE OF THE BEST UNIVERSITIES IN
CHINA, IN THE WORLD, AND QUITE
AMAZINGLY HE HAS DONE THAT WITH
THE HIGHEST NATIONAL HONORS
POSSIBLE, AND IN A SMALL COUNTRY
LIKE CHINA IT'S NOT TRIVIAL
FEAT.
IT'S JUST AMAZING.
AND THEN LESS IMPRESSIVE THINGS,
HE WENT AND DID HIS Ph.D. IN
PRINCETON, POSTDOC AT
DANA-FARBER HARVARD, NOT AS
IMPRESSIVE AS, YOU KNOW, GETTING
THE NATIONAL HONOR IN CHINA, I
WOULD THINK.
I DIDN'T EVEN GET A NATIONAL
HONOR IN A SMALL REGIONAL PLACE
IN ISRAEL, SO, YOU KNOW, WHICH
IS INHERENTLY A VERY SMALL
PLACE.
AND OUR AND PATHS MET IN 2018,
BOTH OUR LABS, WE PUBLISHED
PAPERS IN NATURE MEDICINE, I
THINK SIDE BY SIDE.
>> YES.
>> SIDE BY SIDE EVEN.
AND ABOUT PREDICTING SIGNATURES
OF EXPRESSION FOR IMMUNOTHERAPY,
BUT THE SMALL DETAIL IS THAT
PENG'S WORK IS CITED FIVE TIMES
MORE THAN OUR WORK.
BY NOW I JUST CHECKED YESTERDAY,
2500 TIMES, IT WAS PUBLISHED
JUST IN 2018.
SO, JUST TO GIVE YOU AN IDEA.
SINCE THEN, SINCE HE JOINED HIS
OWN LAB, PUBLISHED PAPERS IN
NATURE MEDICINE, NATURE
COMPUTATION, DOING STELLAR WORK.
HE RECEIVED THE NCI K99 PATHWAY
TO INDEPENDENCE AWARD,
SCHOLAR-IN-TRAINING AWARD,
TECHNOLOGY AND INNOVATION AWARD,
CRI, AND MOSTLY SIMPLY NCI AWARD
FOR DATA SCIENCE.
WE'RE SO LUCKY TO HAVE HIM,
BEYOND BEING AN AMAZING
SCIENTIST HE'S A WONDERFUL
PERSON AND VERY KIND AND
GENEROUS COLLEAGUE, AS THOSE OF
YOU WHO WORK WITH HIM KNOW, AND
ANYTIME I TALK TO HIM I LEARN,
YOU KNOW, SO WHAT CAN SOMEONE
ASK FOR MORE?
THANK YOU SO MUCH FOR COMING TO
TALK TO US.
>> SO LUCKY FOR ME, LIKE DATA
SCIENCE BRANCH, FIRST YOUNG
FACULTY HAS RECRUITED.
TODAY I WOULD LIKE TO SHARE SOME
OF OUR RECENT RESEARCH WORK,
UTILIZING BIG DATA APPROACH TO
STUDY INTRACELLULAR SIGNATURE.
BEFORE STARTING I WOULD LIKE TO
RECOMMEND REVIEW OF PAPER THAT
WE AND OTHER COLLEAGUES WROTE
ABOUT APPLICATION DATA AND A.I.
IN TRANSLATIONAL RESEARCH.
CANCER RESEARCHERS HERE, ALSO
POTENTIAL PROBLEMS OF A.I.,
LIMITATIONS.
FOR EXAMPLE NOW WE SEE LOTS OF
PAPER SHOWING A.I. IS DOING
FANTASTIC JOB, SOME DIGITAL
PATHOLOGY, CLASSIFICATION TASKS.
REVIEW PAPER CITE LIKE REPORT.
BUT ALL OF THE EVALUATIONS ARE
BASED ON THE PERFORMANCE ON ONE
SINGLE IMAGE, CLASSIFICATION.
HOWEVER, CLINICAL DECISIONS FOR
EXAMPLE IF PATHOLOGISTS ARE NOT
SURE ABOUT THE CANCER OR
NON-CANCER DECISION, USE MARKERS
TO FIND DEFINITIVE ANSWER.
(INDISCERNIBLE) TO SEE WHETHER
IT'S TRUE AND NECESSARY.
ALSO SOME PAPER FROM OUR NCI
COLLEAGUES, PREDICTION PROBLEM
WHICH THEY CLAIM COLLEAGUES
SOMEHOW -- SINGLE POINT
MUTATIONS CAN STOP A LOT OF
STRUCTURES.
FOR EXAMPLE, NOTATION DAMAGE
HYDROPHOBIC POCKET IN THIS
PROTEIN, LOSS OF FUNCTION, OTHER
SIMPLY PREDICT SAME STRUCTURE,
BASICALLY SIMILARITY IN
MUTATION.
IN GENERAL THIS REVIEW PAPER
LOOKS TO BE CORRECT CITATION FOR
A.I. HOW SHOULD YOU APPLY A.I.
IN CANCER TRANSLATIONAL
RESEARCH.
MAJOR TOPIC FOR TODAY IS
INTRACELLULAR COMMUNICATION,
RECEDING SIGNAL IS BASIC NOTE
ALMOST ALL IMMUNOLOGIC PRESENCE
INCLUDING TUMOR EVASION.
WE'VE BEEN FOCUSED ON DEVELOPING
COMPUTATIONAL SOLUTIONS,
INTRACELLULAR COMMUNICATION WITH
APPLICATION IN CANCER
IMMUNOTHERAPIES.
FIRST WORK FOR STUDYING
SIGNALING ACTIVITIES, DEVELOPED
A COMPUTATIONAL MODEL IN TUMOR
MICROENVIRONMENT.
ALSO LIKE TO FIND
(INDISCERNIBLE) BASED ON THIS
MODEL.
FINALLY INTRODUCING THE
FRAMEWORK FOR ANALYZING DATA IN
CANCER RESEARCH.
FIRST OFF, CYTOKINE ACTIVITY
RESEARCH MOST COMMON APPROACH
IMMUNOLOGIST APPLY, CYTOKINE
RELEASE IS TRANSIENT PROCESS.
IT WOULD MISS THE ACTIVITY.
NOT EQUAL TO FUNCTION ACTIVITY.
CYTOKINES COULD HAVE FUNCTION
COMPLEXITY AND REDUNDANCY SO ALL
OF THIS MEANS MORE EFFECTIVE
APPROACH TO STUDY CYTOKINE
SIGNALING ACTIVITIES.
DEVELOPED THIS FRAMEWORK TO
ANSWER TWO QUESTIONS.
FIRST QUESTION IF YOU HAVE A
POSITIVE FOCUS WHAT ARE THE
CYTOKINES GENE FOCUSED, AND
SECOND IF YOU HAVE SAMPLE WITH
GENE EXPRESSION PROFILE
AVAILABLE WHAT ARE CYTOKINE
SIGNALING ACTIVITIES IN YOUR
SAMPLE?
SO THE FIRST STEP TO CREATE
FRAMEWORK IS ALWAYS DATA
COLLECTION.
WE HYPOTHESIZE THIS WAS
UTILIZED, COULD SERVE AS
KNOWLEDGE BASE TO MAKE KNOWLEDGE
DISCOVERIES FOR NEW PROBLEMS.
HOWEVER, KEY CHALLENGE IS THAT
LIKE THE DATA DEPOSIT IN
DIFFERENT DATABASE ARE
HETEROGENEOUS, ESPECIALLY
METADATA A FOR EXAMPLE LIKE THEY
GIVE YOU A FREE TOUCH TO
DESCRIBE EACH TIME THEY DO,
HOWEVER THIS FREE TOUCH COULD BE
SO DIFFERENT ACROSS, THERE'S NO
WAY YOU CAN EASILY KNOW
CONDITION, DURATION, CELL MODEL.
YOU HAVE TO READ THEM AND EXACT
THIS KEY INFORMATION.
DATA INTEGRATION PROBLEM, FIRST
DEVELOP A FRAMEWORK CALLED
FRAMEWORK FOR DATA CURATION,
AVAILABLE HERE.
SO UTILIZE FUNCTIONS TO HELP YOU
TO REANNOTATE DIFFERENT EDITIONS
TO MAKE THEM POSSIBLE FOR
ALGORITHMIC AUTOMATIC ANALYSIS.
WITH THE HELP OF FRAMEWORK
COLLECT MORE THAN 20,000 KITE
SIGN TREATMENT RESPONSE
PROFILES, EACH PROFILE HAVE AT
LEAST TWO BIOLOGICAL REPLICASE,
SO DATA PROFILE IS A LOT MORE
THAN THIS 20,000 NUMBER, SOME
CYTOKINES INTERFERENCE HAVE
LARGE DATASET COLLECTED, FOR
MOST CYTOKINES, THERE'S EITHER
ZERO OR VERY FEW DATASETS
COLLECTED.
AND HERE ARE SOME EXAMPLES OF
DATASET.
EACH POINT MEANS ONE TREATMENT
RESPONSE EXPERIMENT IN SOME CELL
MODEL.
AND Y-AXIS IS TARGETED GENE.
X-AXIS IS TREATMENT CYTOKINE.
YOU CAN SEE DATASET CAPTURED THE
WELL KNOWN LIKE CYTOKINE
RESPONSE CASCADE.
FOR EXAMPLE, IL-12 INDUCE
INTERFERON GAMMA, INDUCE BY
IL-12, MOST OF THIS EXPERIMENT,
AND THEN INTERFERON GAMMA WILL
INDUCE (INDISCERNIBLE) DATA
COLLECTION.
YOU CAN ALSO SEE OVERALL WHAT IS
THE CYTOKINE REGULATORY CASCADE.
X-AXIS.
Y-AXIS IS LEVEL TWO CYTOKINE.
WE CAN SEE SOME TNF ALPHA WORKS
THROUGH, INDUCE A BUNCH OF
CHEMOKINES, INTERFERENCE THROUGH
THE STAT1 PATHWAY INTRODUCE A
DIFFERENT SET OF CYTOKINES.
WITH THIS DATASET CAN CHECK
CYTOKINES CAN WORK TOGETHER OR
FIGHT AGAINST EACH OTHER.
MOST OF THE TIME CYTOKINES
(INDISCERNIBLE).
FOR EXAMPLE, INDUCE THE PATHWAY,
WE CAN SEE THE CO-INDUCE OR
CO-REPRESS A LOT OF TARGET.
CYTOKINES WILL FIGHT AGAINST
EACH OTHER.
HERE IS GOOD EXAMPLE.
IL-1 BETA INDUCE SOME
CHEMOKINES, THEY ALL SHARE THE
SAME RECEPTOR.
BUT CONSIDERED GROWTH FACTOR
BEFORE, TENDS TO DOWNREGULATE
THIS TARGET.
TO THE BEST OF OUR KNOWLEDGE
BEFORE THIS THERE WAS NO REPORT
ABOUT THE (INDISCERNIBLE) OF BMP
6 GROWTH FACTOR AGAINST IL-1
BETA.
WE PERFORM EXPERIMENTAL DATA.
COVID WAS HOT TOPIC DEALING WITH
THIS PRODUCT.
WE TREAT LUNG EPITHELIAL CELL
LINE WITH IL-1 BETA, AND YOU CAN
SEE INTRACELLULAR PROTEIN LEVEL
BY FLOW CYTOMETRY AND SECRETION
LEVEL BY ELISA, BMP6
SIGNIFICANTLY CONTROL INDUCTION.
SO THIS MOLECULE, AND KEY GOAL
OF THE FRAMEWORK TO ACHIEVE
CLINICAL PREDICTION OF FUNCTION.
SO, IN ORDER TO PREDICT CYTOKINE
ACTIVITY FROM CLINICAL SAMPLE WE
USE VERY SIMPLE LINEAR
REGRESSION MODEL.
THE MATRIX IS SIMPLY CYTOKINE
TREATMENT RESPONSE PROFILES WE
COLLECT, ON THE RESPONSE
EXPRESSION PROFILE, REGRESSION
COEFFICIENT, IF IT'S LOW
(INDISCERNIBLE).
HERE IS EXAMPLE.
THIS DATA IS FROM CLINICAL TRIAL
OF A PATIENT TREATED WITH IL-1
BETA NEUTRALIZING ANTIBODY.
IN THIS CLINICAL TRIAL THEY HAVE
PLACEBO GROUP.
THEY ALSO DEFINE DIFFERENT LEVEL
OF PATIENT RESPONSE.
DAY 15 AFTER IL-1 NEUTRALIZING
THERAPY.
THEY ALSO GENE EXPRESSION
PROFILE PATIENT SAMPLES, YOU CAN
SEE PREDICTION OF IL-1 BETA
ACTIVITY, HINGE ON THIS BECAUSE
IT'S Y-AXIS, IT'S REALLY
ACTUALLY CLINICAL RESPONSE ON
DAY 15 WHICH MEANS ALMOST
PREDICT WHAT WILL BE THE TRUE
RESPONDERS TO THIS NEUTRALIZING
ANTIBODY.
HERE ANOTHER CLINICAL STUDY,
CLINICAL ENDPOINT IS INTERFERON
ALPHA IN THE PATIENT BLOOD.
PBMC EXPRESSION PROFILE FOR THIS
PATIENT, PREDICTION, CLINICAL
ENDPOINT.
IN CANCER THE MOST STUDY IS EGF
THERAPY, SO TAKE TWO CLINICAL
STUDIES, KINASE INHIBITOR, AND
BEVACIZUMAB.
IF THEY HAVE HIGHER ACTIVITY
PREDICTED, YOU CAN SEE BETTER
CLINICAL OUTCOME.
REMARKABLY NCI COLLEAGUE
PERFORMED THIS LARGE SCALE
CLINICAL TRIAL OF BEVACIZUMAB
SENSE PLACEBO IN GLIOBLASTOMA,
AND CLINICAL TRIAL RESULT IS
SHOWN NO BENEFIT OF BEVACIZUMAB
COMPARED TO PLACEBO.
HOWEVER, RETROSPECTIVE ANALYSIS
OF CLINICAL TRIAL DATA WHICH
THEY HAVE PRE-TREATMENT
EXPRESSION PROFILES MEANS THERE
ARE SOME EFFICACY.
YOU HAVE TO FOCUS ON THE PATIENT
WITH HIGHER VEGF THERAPY, NEED
TO TARGET IN THE TUMOR FIRST.
POTENTIALLY MAY RESCUE CLINICAL
TRIAL IF YOU HAVE PROTEIN
EXPRESSION PROFILE AVAILABLE.
WE COLLECT MANY DATA CYCLE OF
CYTOKINE NEUTRALIZING THERAPY
FROM NCBIG, YOU CAN SEE
PREDICTION WHICH MEANS IF CELL
SIGNAL PREDICT REDUCTION OF
CYTOKINE ACTIVITY, THE PATIENT
WILL BE THE RESPONDER.
HERE IS OUR FRAMEWORK.
INTERACTIVE MODELS, RESEARCH
DATA, DEMONSTRATE DATA
COLLECTION CAN REVEAL NEW
KNOWLEDGE SUCH AS
ANTI-INFLAMMATORY MOLECULE
FIGHTING AGAINST IL-1 BETA.
FURTHER CREATE ANOTHER
COMPUTATIONAL MODEL T RAS TO
ADDRESS IMPORTANT QUESTION IN
IMMUNOLOGY.
(INDISCERNIBLE) THE OTHER ARE
NON-RESPONDERS.
WORKS IN ONE PART OF THE MODEL,
NO TREATMENT.
THIS ADOPTIVE TIL TRANSFER DOES
NOT WORK ANYMORE, CAR-T WORKS IN
SOME.
ONE REASON FOR LIMITED
THERAPEUTIC EFFICACY FOR SOLID
TUMORS IS IT'S SUCH AN
IMMUNOSUPPRESSIVE ENVIRONMENT.
CANCER CELLS, TUMOR CONSIDER TO
FIGHT BACK, ONLY ONE OF MY
REASONS.
IT'S SUCH A SUPPRESSIVE
ENVIRONMENT.
SO, TO THINK OUT A SOLUTION
STILL FOCUS ON CYTOKINE, LOOK AT
T CELL.
WE ANALYZE WHICH T CELL CAN DO
SO WELL IN SUPPRESSIVE
ENVIRONMENT.
THIS IS OUR MODEL.
IN THIS EXAMPLE UTILIZE SINGLE
CELL STUDY OF T CELL FROM
MELANOMA TUMOR.
THIS WAS PUBLISHED PREVIOUS
GRADUATE STUDENT.
T CELLS, X-AXIS IS THE TGF-BETA
ACTIVITY PREDICTED.
INDICATOR OF IMMUNOSUPPRESSIVE
IS RECEDING.
ON THE Y-AXIS THE CELL CYCLE
ACTIVITY PREDICTED USING KEGG
CELL CYCLE PATHWAY.
THE CORRELATION IS NOT TRUE FOR
ALL THE T CELLS.
ACCORDING TO EXPRESSION MARKER,
OBSERVE DIFFERENT LOCALIZATIONS.
THIS GRAPH SHOW BASIC IDEA OF
MODEL SEARCHING FOR MOLECULAR
MARKERS, MODULATE
IMMUNOSUPPRESSION AND T CELL
PROLIFERATION, UTILIZE TWO, TO
IDENTIFY FROM MARKER GENES OF
TUMOR RESILIENT T CELLS, FIRST
STAGE, QUANTIFIES SIGNAL EACH T
CELL RECEIVE FROM TUMOR
MICROENVIRONMENT USING SINGLE
CELL STUDY.
IN SECOND STATE YOU COULD HAVE T
CELL WHO RECEIVED LOTS OF
SUPPRESSIVE SIGNAL BUT ARE STILL
PROLIFERATIVE.
TUMOR RESILIENT T CELLS, THE WAY
TO FIND MARKER IS THROUGH VERY
SIMPLE INTERACTION REGRESSION.
YOU PUT LIKE IMMUNOSUPPRESSION
AND MOLECULAR MARKER
INTERACTION, COEFFICIENT, THAT
IS SCORE FOR TUMOR RESILIENT T
CELL MARKERS.
THEIR ARE EXAMPLES.
THIS IS A PAPER FROM OUR NCI
COLLEAGUES, TRYING TO FIND SOME
MARKERS OF POSITION T CELLS.
AND HERE ARE GENE SCORE.
X-AXIS.
ON Y-AXIS IS TRES SCORE.
PREDICTION FROM OUR NCI
COLLEAGUES PAPER, FROM MODEL,
PATIENT SAMPLE.
AND THEN WE TEST WHETHER SIGNAL
COULD ACHIEVE SOME PREDICTION
PERFORMANCE, HERE IS OUR
PREDICTION.
FIRST COLLECT SINGLE CELL
RNAseq DATASET FROM 168
TUMORS, 19 CANCER TYPES.
WE HAVE PROFILE FOR EACH
SIGNATURE, MEDIA SIGNATURE.
AND THEN VALIDATION IS TOTALLY
INDEPENDENT, T CELL PROFILE,
INHIBITORS, INFUSION FOR CAR
Ts, PRE-MANUFACTURE SAMPLE FOR
CAR T OR TIL TRANSFER THERAPIES.
MANUFACTURE SAMPLES PBMC BEFORE
THE CONSTRUCTION FOR CAR T,
TUMOR SAMPLES BEFORE T CELLS.
AND THEN WE LOOK AT CORRELATION.
IT'S POSITIVE, PREDICT T CELL TO
BE AFFECTED T CELL.
OTHERWISE PREDICT WITH 95,
EXAMPLES OF PREDICTION.
COHORT HERE, Y-AXIS IS
CORRELATION SCORE I SHOW HERE.
THE COLOR IS ACTUAL CLINICAL
OUTCOME, IN EACH CLINICAL STUDY.
ARE YOU CAN SEE OUR PREDICTION
PRETTY WELL WITH THE REAL
CLINICAL OUTCOME LABEL.
AND SO MANY T CELL EFFICACY
SIGNATURE, FOR EXAMPLE SIGNATURE
FROM OUR NCI COLLEAGUES, T CELL
PRODUCE SIGNATURE, PD-1
SIGNATURE, CAR T DYSFUNCTION
SIGNATURE, WE DID PERFORMANCE
COMPARISON AND OVERALL TRES BEST
PREDICTION PERFORMANCE ACROSS
SIGNATURE COMPARED.
AND THEN FURTHER LOOK AT SOME
BULK RNA SEQUENCING DATA FOR
PRE-MANUFACTURE SAMPLES.
IT'S VERY EXPENSIVE, AND IF WE
CAN PREDICT BASED ON
PRE-MANUFACTURED SAMPLES COULD
BE LIKE STOP THE EXPENSIVE
PROCESS FOR OBVIOUS
NON-RESPONDERS.
FOR BOTH, THE EXPRESSION WITH
SIGNATURES IS QUITE PREDICTIVE
OF THE OUTCOME IN THE PATIENT.
PATIENT IS POTENTIALLY FOR CELL
THERAPY, CAN FIND OBVIOUS
NON-RESPONDERS AND STOP THE
EXPENSIVE PROCESS AT THE VERY
BEGINNING.
AND ALSO COMPARISON PREDICTION
COMPARISON, ONLY TWO SIGNATURES
PREDICTION OF PRE-MANUFACTURED
SAMPLES TRES SIGNATURE ON THE
SIGNATURE FROM OUR COLLEAGUES,
TRES FOR THE BEST.
STEM CELL SIGNATURE ALL OTHER
SIGNATURES DIRECTION PREDICTION.
SO, ENCOURAGED BY RELIABLE
PREDICTION PERFORMANCE ON
CLINICAL OUTCOME FURTHER LOOK AT
OUR SIGNATURE TO SEE WHETHER WE
CAN FIND MOLECULAR REGULATORS OF
APPROACH.
SO, WE FOCUS ON THE MODELS FROM
OUR TR ERICS MODEL.
MOST THERAPIES CURRENTLY BY
SMALL MOLECULE COMPOUND, BY
NEUTRALIZING, MOSTLY INHIBIT A
TARGET.
WE FOCUS ON MARKERS.
THE NUMBER ONE MARKER IS GENE
CALLED FIBP.
THE FULL NAME IS
(INDISCERNIBLE).
VERY FEW LITERATURE ABOUT THIS
GENE EXCEPT THIS GENE INTERACT.
CANCER CELL LINES CAN STOP
CANCER CELL LINE GROWTH HOWEVER
CANNOT REPRODUCE THE RESULT,
PRETTY MUCH UNKNOWN GENE.
NUMBER ONE MARKER IN MODEL.
LET'S DO SOME VALIDATION.
FIRST INITIAL COHORT OF MOUSE,
CANCER CELL LINES, YOU CAN SEE
IN THIS HUMAN, TARGET CANCER
CELL LINES.
T CELLS WITH KNOCKOUT,
SIGNIFICANT CANCER CELLS, RED
FLUORESCENT MARKERS, COMPARED TO
T CELLS WITH KNOCKOUT.
AND LIKE THE REPEAT EXPERIMENT,
PRODUCE FROM TWO DONORS, TWO
DIFFERENT CANCER CELL LINES.
WE THOUGHT VERY CONVINCING.
COMPARED TO AAVS KNOCKOUT,
OVERALL SIMILAR TO OUR CONTROL,
KNOCKOUT IS A MODIFICATION FOR
EFFICACY, AND THIS CBLB, FIBP IS
AT THIS LEVEL SIMILAR.
PERFORM IN VIVO MOUSE
EXPERIMENT, ISOLATE THE MOUSE T
CELLS, KNOCK OUT, CONTROL
KNOCKOUT IS VERY IMPORTANT
BECAUSE CRISPR GENE WILL CAUSE
LOT OF ARTIFACT YOU WOULD NOT
EXPECT.
SO COMPARED TO THE CONTROL
KNOCKOUT, REALLY ACHIEVE MUCH
BETTER CONTROLLING OF THE TUMOR
GROWTH IN THE EXPERIMENT.
OVERALL THE TUMOR CONTROL
EFFICACY IS SIMILAR TO OUR PATH
OF CONTROL, GENE KNOCKOUT.
SO, THIS SIGNIFICANT PATH FROM
OUR STUDY WONDERING WHY KNOCKOUT
CAN ENHANCE T CELL EFFICACY ON
TUMORS.
SO PERFORMED SOME RNA SEQUENCING
EXPERIMENT, FIBP KNOCKOUT, SEE
WHAT THE EXPRESSED GENES.
SURPRISINGLY AFTER WE GOT
RNAseq PERFORMED, GENE
ANALYSIS, ALL OF THE PATHWAY IS
VERY SIMPLE, ALL OF THEM ARE
DOWNREGULATION OF CHOLESTEROL
METABOLISM, LIKE ENZYME FOR
HUMAN CHOLESTEROL METABOLISM
PATHWAYS, ALL DOWNREGULATION
AFTER KNOCKOUT, TO REMOVE
CHOLESTEROL FROM THE CELL, IL-6
GIVE US SIMPLE CHOLESTEROL
METABOLISM DOWNREGULATION,
FURTHER VALIDATE USING
MULTI-PANEL FLOW ANALYSIS, T
CELLS WITH KNOCKOUT ISOLATE FROM
MOUSE TUMOR.
YOU CAN SEE CHOLESTEROL LEVEL IS
SIGNIFICANTLY LOWER THAN
POSITIVE CONTROL.
IF YOU OVEREXPRESS THE
CHOLESTEROL LEVEL IS GOING TO BE
ELEVATED.
REVERSE DIRECTION.
SO, KNOWN TO INDUCE T CELL
STRESS AND T CELL EXHAUSTION.
IF YOU EXPOSE T CELL, HIGH
CHOLESTEROL MEDIUM FOR FIVE
DAYS, YOU CAN SEE THAT T CELL ON
TUMOR CELLS IS GOING TO BE
SIGNIFICANTLY WORSE.
HOWEVER, FOR KNOCKOUT T CELLS IF
YOU SOAK THEM IN HIGH
CHOLESTEROL MEDIUM FOR FIVE DAYS
THEY DON'T CARE.
THEY ACHIEVE THE SAME LEVEL OF
CANCER TREATMENT EFFICACY
COMPARED TO T CELL FROM
CHOLESTEROL NORMAL MEDIUM.
WHICH MEANS KNOCKOUT IS MAKING T
CELLS LIKE REALLY RESILIENT TO
HIGH CHOLESTEROL LEVEL, WHICH IS
COMMON FEATURE OF SOLID TUMOR
MICROENVIRONMENT BECAUSE TUMOR
TAKE CHOLESTEROL THEIR FOOD TO
GROW.
PRESENTLY, STARTING TREATMENT
DOES NOT BRING DOWN CHOLESTEROL
LEVELS IN T CELLS.
FOR T CELLS BEFORE, STARTING
TREATMENT, BRING DOWN THE
CHOLESTEROL LEVELS, AS YOU
EXPECT.
BUT REPEAT THIS PATTERN MANY
TIMES.
YOU WILL SEE A SIGNIFICANTLY
ELEVATED LEVEL OF CHOLESTEROL.
WE DON'T KNOW THE REASON WHY.
THIS IS SO CONSISTENT.
MEASUREMENT FOR EFFECTOR T CELL
STATIN IS NOT GOOD
ANTI-CHOLESTEROL THERAPY.
AND THEN WE HAVE CREATED MANY
FUNCTION MODULES, CAN EVALUATE T
CELL MARKERS, BASED ON
PREDICTION ON MARKERS YOU WILL
FIND USING THE PLATFORM, ALSO
DEMONSTRATE TRES MODEL APPROACH
IN SOLID TUMORS.
AFTER THIS STUDY WE FOUND THAT
THE FOCUS ON MARKERS OF T CELL
TRES MODELS IS WRONG BECAUSE
MANY OF THE MARKERS EVEN TELL US
SOMETHING SURPRISING.
FOR EXAMPLE MY POSTDOC FOCUS ON
MARKERS WHO ARE SECRETED
PROTEINS.
ONCE WE INJECT SECRETED PROTEINS
INTO MOUSE TUMOR, TUMOR
COMPLETELY GOT (INDISCERNIBLE),
DRAMATIC CELL TYPE IN MOUSE
PARTLY BECAUSE SO AGGRESSIVE.
RIGHT NOW WE'RE FOLLOWING UP THE
POSITIVE TRES MARKERS RIGHT NOW.
AND THEN LIKE THE LAST TO
INTRODUCE THE FRAMEWORK FOR
SPECIAL ANALYSIS FOR TUMOR.
SO, HERE IS OVERALL WORK FLOW,
TYPICAL WORKFLOW ANALYZE DATA
FROM THE PLATFORM FOR TUMOR.
TRANSCRIPTOMIC DATA, THE SIGNAL,
THE FIRST STEP, KNOW THE
DIFFERENT LOCATIONS.
THEN YOU CAN DO ANALYSIS LIKE
CELL-CELL INTERACTION ANALYSIS,
RECEPTOR INTERACTION ANALYSIS,
MANY COMPUTATIONAL PACKAGE DOING
SIMILAR STUFF, DO WE WANT TO
CREATE ANOTHER COMPUTATIONAL
PACKAGE, HERE IS REASON.
BECAUSE CURRENT ARE NOT --
(INDISCERNIBLE) FOR SOME UNIQUE
CHALLENGE.
FIRST OF ALL, SINGLE CELL
(INDISCERNIBLE) TYPICAL WAY USED
TO FIND CANCER CELLS IS THROUGH
OF (INDISCERNIBLE).
THERE'S NO MOLECULAR MARKERS OF
CANCER CELLS TYPICALLY LOOK AT
SEGMENTAL OPERATION PATTERNS IN
SINGLE CELL TRANSCRIPTOMETRY
FIRST.
THIS IS WRONG.
HERE IS EXAMPLE.
IF YOU PUT LOTS OF MICRO PHAGE
CELL AT THE NORMAL REFERENCE,
YOU'RE GOING TO GET DELETION THE
CHROMOSOME 6 ARM, PROFILE AT
CANCER CELL.
OTHERWISE, IF YOU HAVE ENOUGH
NORMAL CELLS, YOU'RE GOING TO
SEE AMPLIFICATION, POTENTIAL
CANCER CELLS.
SECOND, MANY CANCER CELLS JUST
DON'T HAVE ALTERATIONS BECAUSE
IN THIS STUDY CANCER TUMORS WITH
HIGH MUTATION LEVELS, RELATIVELY
LOW COPY ALTERATIONS, NOT COPY
ENOUGH ALTERATIONS.
UNFORTUNATELY, WE HAVE STUDIED
THIS PROBLEM, IN 2013 USED NIP
-- SNP ARRAY, PATTERNS ARE
DISTANT.
BREAST CANCER, SKIN CANCER,
COLORECTAL, THEY ARE ALL ROUGHLY
THE SAME PATTERNS.
SO, WE FIRST TALKING ABOUT RATE
FROM COHORT FOR DIFFERENT SOLID
TUMOR TYPES.
IF YOU SEE A SPOT SIMILAR,
PROFILE AS CANCER.
OTHERWISE WE GO TO SECOND TEST
TO CHECK IF THE EXPRESSION
PROFILE IS SIMILAR TO SOME TYPE
OF TUMOR NORMAL.
SIMILAR PROFILE HAVE CANCER
SPOT.
OTHERWISE WE CLASSIFY AS
NON-CANCER DECOMPOSITION.
AND THEN THE SECOND TYPE OF REAL
CHALLENGE, IN THIS EXAMPLE YOU
HAVE SOME REGIONS WITH A LOT OF
CELLS.
SOME STROMAL REGIONS WITH VERY
FEW CELLS.
IF YOU DO A CELL DECOMPOSITION
ON MODEL, CELL FRACTION, ONE,
FUNDAMENTALLY WRONG.
CELL FRACTION IN HIGH DENSITY
AND LOW DENSITY REGIONS ARE NOT
COMPARATIVE WITH EACH OTHER.
FORTUNATELY, WE FIND A SOLUTION,
REGRESSION MODEL WE CAN SIMPLY
PUT UNKNOWN COMPONENT IN THE
REGRESSION MODEL, UNLESS
REGRESSION PANELLIZE OVERALL
TYPE, LOTS OF EXPRESSION INDUCED
BY LOW DENSITY AND THEN THIS
AUTOMATIC ADJUST TO OVERALL CELL
FRACTION FOR LOW DENSITY TISSUE
REGION.
ALSO PERFORM CONVOLUTION TO MAKE
SURE THE CO-LINEARITY WILL NOT
AFFECT YOUR LIABILITY, TWO
SIGNAL WITH EACH OTHER.
WITH ALL OF THESE TRICKS,
PERFORM (INDISCERNIBLE) OUR
PATHOLOGY COLLABORATIVE,
PATHOLOGY OF A PATIENT, TUMOR
REGIONS, STROMAL REGIONS,
LYMPHOCYTE AND MACROPHAGE
REGIONS.
FOUR REGIONS, ONE TRUTH, INDEED
OUR STATED FRAMEWORK ACHIEVES
BEST PREDICTION PERFORMANCE
AMONG ALL METHOD COMPARED.
LAST YOU CAN ALSO DO SOME
CELL-CELL INTERACTION ANALYSIS
TO SEE WHERE DOES THE CELL
INTERACTION LIGAND RECEPTION
FACTOR HAPPEN IN THE PROFILE.
HERE IS EXAMPLE.
IN THIS YOU CAN SEE THE CANCER
CYTO BLASTS, TENDS TO REALLY
CO-LOCALIZE WITH EACH OTHER,
RECEPTION REGION.
OVER HERE, REALLY MIXED WITH
EACH OTHER.
IN THE REGION THAT THEY INTERMIX
WITH EACH OTHER, YOU SEE LOTS OF
RECEPTOR PAIRS CO-EXPRESSED OVER
THERE.
AND THEN LIKE REVERSELY IF YOU
LOOK AT THE TUMOR CELLS, THAT
ARE CLOSE, REALLY FAR AWAY FROM
THE CUFF ON MACROPHAGE
INTERACTION LOCATIONS YOU CAN
SEE WE HAVE A PRETTY DIFFERENT
EXPRESSION PROFILE.
FOR TUMOR CELLS, CLOSE
THEY GO SLOWER.
HOWEVER, IF THEY HAVE HIGHER
EXPRESSION LEVEL FOR THE
MESENCHYMAL TRANSITION GENE
SITE, WHICH MEANS TUMOR CELLS
THAT ARE FAR AWAY FROM THE
STROMAL CELL REACTION ARE FASTER
BUT LESS AGGRESSIVE.
TUMOR CELL MUCH MORE AGGRESSIVE
EVEN THROW THEY GO SLOWER.
THIS IS EXAMPLE OF SPATIAL
TRANSCRIPTOMICS WITH FRAMEWORK
TIES BEHAVIOR DIFFERENCE OF
TUMOR CELLS IN SPATIAL
GEOGRAPHIC SITE.
AND HERE IS OUR -- OUR PACKAGE
HAS INTERACTIVE FUNCTION MODELS
FOR YOU TO INTERACTIVELY FOLLOW
DATASET, OUTCOME TO CELL
PACKAGE.
AND FINALLY THIS IS THE OVERALL
FRAMEWORK HAVE DEVELOPED LIKE IN
PAST FOUR YEARS TO STUDY THE
INTRACELLULAR SIGNALINGS
HOPEFULLY LIKE RESEPTEMBER
GALORE YOUR PRODUCT.
LAST THE SPECIAL THANKS TO MY
BRILLIANT TEAM MEMBERS, I THINK
I'M LIKELY TO GET SOME STELLAR
(INDISCERNIBLE) OF MY CAREER,
ESPECIALLY FIRST START AT NCI
COVID PANDEMIC BREAK OUT, AND
THEY WERE REMARKABLY RESILIENT
AT THAT TIME.
A SHOUT OUT, STILL MANAGE THE
PRODUCT MOVING FORWARD DURING
HARD TIME.
SPECIAL THANKS TO MY NCI
COLLEAGUES AND MENTORS.
ALL OF THEM REALLY GIVING ME
REMARKABLE HELP AND MENTORSHIP
AS A YOUNG FACULTY.
OTHERWISE I WOULD MAKE SO MANY
MISTAKES.
REALLY HELPED PRODUCT MOVING
FORWARD DURING HARD TIME.
THANKS TO MY NCI COLLEAGUES AND
MENTORS.
[APPLAUSE]
>> OKAY.
THANK YOU SO MUCH.
THIS IS SUCH A TOUR DU FORCE.
I THINK YOU MANAGED TO PRESENT
70 SLIDES IN 35 MINUTES.
I THINK IT'S A NEW NCI RECORD.
MY HEAD STILL SPINS.
IT'S TIME FOR DISCUSSION.
>> YES, VERY INTERESTING.
A QUESTION ABOUT THE TRES
PIPELINE, HOW IT CAN BE EXTENDED
TO CAPTURE LIKE SYNERGISTIC
EFFECT BETWEEN MULTIPLE GENES.
>> OH, SO LET ME BACKTRACK,
TRES?
>> YES.
>> YOU ARE ASKING WHETHER TRES
FRAMEWORK COULD BE REPURPOSED TO
STUDY SYNERGIES BETWEEN
PROTEINS.
>> BETWEEN THE GENES EXPRESSION
RATIO.
>> OH, I SEE.
I SEE.
YES, LIKE -- I SEE.
I THINK OF COURSE, LIKE YOU
CAN -- WITHOUT TRES BASICALLY
INPUT, VECTORS CAUSE DIFFERENT
SINGLE CELLS.
YOU CAN SIMPLY CREATE DIFFERENT
GENES AND INTO A DATA MATRIX,
AND SEE WHETHER OR NOT YOU SEE
SIGNIFICANT (INDISCERNIBLE).
VERY GOOD POINT TO REPURPOSE THE
FRAMEWORK.
WHICH IS PROBABLY VERY RELEVANT
TO YOUR CURRENT PROJECT.
>> THANK YOU.
>> OTHERS QUESTIONS?
SURELY YOU HAVE THOUGHTS TO
SHARE.
I SEE IT ON YOU.
>> WE HAVE ALL THESE PAPERS
IDENTIFYING SIGNATURES, FROM ONE
PAPER TO ANOTHER DON'T
NECESSARILY OVERLAP.
WHAT DO YOU THINK ABOUT THAT?
WE'RE KIND OF LOOKING AT SAME
DATASETS WITH VERY DIFFERENT
SIGNATURE AT THE END.
>> YOU RAISE A VERY IMPORTANT
POINT, THERE ARE SO MANY
SIGNATURES.
THEY OVERLAP WITH EACH OTHER.
FIRST OF ALL, I CAN ONLY COMMENT
ABOUT TRES SIGNATURES.
THE TRES SIGNATURE HAS QUITE
SOME OVERLAP WITH THE CENTRAL
(INDISCERNIBLE) SIGNATURE.
MEANS QUIESCENT T CELLS, LIKE
BOTH MARKERS ARE ALL VERY HIGH,
TRES SIGNATURE.
MEANWHILE TRES HAVE A FEW
MARKERS, SOME SECRETION FACTORS
NOT PROVEN IN THE SIGNATURES,
AND (INDISCERNIBLE) ADDRESS THAT
TO PROVE THAT.
I GUESS LIKE -- AGAIN TO MAKE T
CELL WORK BETTER, OR WORK WORSE.
LIKE EACH SIGNATURES PROBABLY
CAPTURE ONE PART OF THAT.
AND TUMOR ARE SO HETEROGENEOUS.
I GUESS DIFFERENT PATIENTS AND
DIFFERENT TUMORS UTILIZE VERY
DIFFERENT STRATEGIES TO MAKE T
CELLS.
AND YOU PROBABLY NEED VERY
DIFFERENT STRATEGIES TO UTILIZE
T CELLS ACCORDING TO THE
SPECIFICITY, THE PROGRAM OF
DIFFERENT T CELLS.
SIMPLY REFLECTION OF MOLECULAR
MECHANISM THAT COULD HAVE LIKE
OTHERWISE I DON'T THINK FROM ONE
OR TWO UNIVERSAL FRAMEWORK FOR
THIS.
>> I GUESS WE DON'T DEAL SO WELL
WITH THE TUMOR SIDE, RIGHT?
IF WE HAD MORE CONTEXT, MAYBE
DIFFERENT SIGNATURE WOULD MAKE
MORE SENSE, RIGHT?
>> YEAH, IDEALLY, TUMOR SPECIFIC
CONTEXT, YEAH.
>> HI.
VERY NICE WORK.
THANKS SO MUCH.
A WHILE AGO A LOT OF PEOPLE
TRIED TO ANALYZE ASSOCIATION
THAT FOLLOWED JUST HISTOLOGY
ANALYSIS JUST LIKE WITH GENE
EXPRESSION.
COULD YOU COMMENT ON THAT?
>> OH, OKAY.
TRICKY.
HIV GENE EXPRESSION FIRST OF ALL
LIKE DEVELOPMENT FRAMEWORK BY
PREDICTION, LIKE MY
UNDERSTANDING CAN ONLY PREDICT
CERTAIN GENES WITH GOOD
PERFORMANCE.
EYTAN, DO YOU HAVE SOMETHING TO
ADD UP HERE?
>> YEAH, SO WE NOW BELIEVE THAT
WITH THE RECENT ADVANCES IN DEEP
LEARNING A.I., ALL THESE
BUZZWORDS, YOU CAN PREDICT QUITE
WELL.
IT'S REALLY SURPRISING
EXPRESSION OF GENES AND
METHYLATION OF GENES DIRECTLY.
AND PEOPLE HAVE SHOWN IT BEFORE
US, AND WE ALSO STUDY THAT.
ACTUALLY, OUR NEXT BIG CHALLENGE
IS TO PREDICT SPATIAL
TRANSCRIPTOMICS, REMAINS TO BE
SEEN.
THERE HAVE BEEN, FOR EXAMPLE, ZU
PUBLISHED A PAPER A FEW YEARS
AGO, AND OTHERS, AND SO ON, FOR
A FEW HUNDRED GENES.
WE BELIEVE WE CAN DO IT FOR
THOUSANDS OF GENES.
SO IS ANYONE IS INTERESTED, WE
HAVE A bioRxiv PAPER NOW
UNDER REVIEW WHETHER WE SHOW THE
PREDICTIVE ABILITY OF
TRANSCRIPTOMICS FROM THE SLIDES.
VERY INTERESTING.
I WOULD LIKE TO ASK THE
AUDIENCE, WHAT PATHWAYS DO YOU
THINK ARE THE MOST PREDICTABLE,
THE EXPRESSION OF WHICH PATHWAYS
ARE OF MOST PREDICTABLE FROM THE
HME SLIDES AT LEAST IN OUR
HANDS?
YEAH?
WHAT?
>> EMT.
WELL, NOT ACCORDING TO OUR
ANALYSIS.
WE FIND CELL PROLIFERATION AND
SOME IMMUNE INTERACTION PATHWAYS
ARE PREDICTED ACROSS ALMOST ALL
CELL TYPES IN TCGA FROM HNE
IMAGES.
PREDICTIVE ABILITY IS FAR FROM
PERFECT, AND SO ON.
BUT I HAVE TO SAY THE TOOLS
UNDERLYING ARCHITECTURES
EVOLVING SO FAST.
I'M SURE PEOPLE CAN DO BETTER
THAN WE DID.
WE'RE ALSO TRYING TO DO BETTER
THAN WHAT CAN'T BE PUBLISHED.
I'M STRUCK BY SO MUCH
INFORMATION.
AS YOU KNOW, A LOT OF YOU KNOW
THERE'S MULTISPECTRAL IMAGING
COMING UP, BIG PHARMA IS BIG
INTO MULTISPECTRAL IMAGING, AND
I JUST IMAGINE WITH DEEP
LEARNING, MULTISPECTRAL IMAGING
YOU'LL BE ABLE TO DO WAY BETTER
THAN WHAT WE DO FROM THE H AND
THE E.
RIGHT?
>> YOU SHOWED KIND OF THE BENCH
TO BEDSIDE AND BACK, STARTING
WITH THE HUMAN DATASETS AND THEN
T CELL RESILIENT STUDIES LOOKING
AT SPECIFICALLY AT MOUSE MODELS,
BACK TO HUMAN STUDIES.
WONDERING FOR THOSE WHO HAVE
MOUSE MODELS THAT WE HAVE TO USE
BECAUSE OF THE DIFFICULTY AND
RARITY OF SEEING SOME OF THE
THINGS WE'RE STUDYING
CLINICALLY, TO WHAT EXTENT ARE
THE TOOLS THAT YOU DEVELOPED
APPLICABLE YET POOR MOUSE
STUDIES WHERE PERHAPS MOUSE DATA
JUST DOESN'T EXIST IN THIS
CONTEXT?
>> EXCUSE ME, I WANT TO MAKE
SURE I CAPTURE THE QUESTION.
ASKING FOR THE TOOLS AND MOUSE
MODEL RESULT, WHAT ARE THE
FRACTION COULD BE SOMEHOW
REFLECTING CLINICAL SETTINGS, IS
YOUR QUESTION?
DO YOU MIND --
>> FOR EXAMPLE, TRES, AND YOUR
SPATIAL TRANSCRIPTOMICS
APPROACHES, ALL USE BIG DATA.
PRIMARILY FROM HUMAN DATA
SOURCES.
IN THE CASES WHERE YOU DID LOOK
BACK AT THE MOUSE MODELS, THOSE
WERE FOCUSED MOUSE EXPERIMENTS,
WONDERING ARE WE THERE YET WITH
MOUSE DATA TO BE ABLE TO APPLY
SIMILAR TOOLS FOR THOSE TOOLS
SPECIFICALLY YOU DEVELOPED TO
MOUSE DATASETS OR DO WE NOT HAVE
THE DATA THERE?
>> I SEE.
FIRST I WANT TO CLARIFY FOR
PREDICTION EVALUATION HUMAN
CLINICAL DATA, FOR EXAMPLE DATA
FROM CLINICAL TRIALS, SOME
SMALLER SCALE CLINICAL STUDY
FROM PRE-MANUFACTURED SAMPLES OF
CELL THERAPIES, SO LIKE AT LEAST
ON TRES USE HUMAN CLINICAL DATA
TO DEMONSTRATE THE POSSIBILITY
TO FIND SOME PATIENT FROM THE
THERAPIES.
ON THE MOUSE DATA I TALK ABOUT,
VALIDATION PART.
>> RIGHT.
>> THE TARGET.
SO CURRENTLY TRES NOT APPLY FOR
MOUSE DATA LIKE CLINICAL DATA IS
MORE IMPORTANT.
AND THE MOUSE, ALL OF OUR IS
MORE FROM THERAPEUTIC VALIDATION
PART, YEAH.
>> OKAY.
>> THE QUESTION IS CAN YOU?
>> IF WE HAVE MOUSE MODELS FOR
DISCOVERY.
>> TO GET TO THE HUMAN.
>> I THINK THE ANSWER IS YOU
COULD.
LIKE REPURPOSE FOR SOME MOUSE
DATA, SOME CONFUSION, RIGHT?
(INDISCERNIBLE) PEOPLE USE FOR
MOUSE.
>> RIGHT.
BRIEFLY TO RESPOND TO THAT, SO,
THE MOUSE MODEL DESIGNED TO
MIMIC DATA PATHOGENESIS OF A
HUMAN, HUMAN DISEASE, IN THAT
WAY YOU CAN -- SO BY COMPARATIVE
STUDY OF GENOMIC STUDY, OTHER
STUDY, SHOW YOU CAN DEFINE WHAT
EXACTLY THE HUMAN DISEASE
PROCESS MOUSE MODEL FOR SO THE
EXAMPLE SO WE INDUCE IN OUR
MOUSE MODEL WE INDUCE BY UV, SO
WE MIMIC THIS PROCESS.
SO MORE CONFIDENCE TO USE TO
STUDY THE RESPONSE OF THE UV
INDUCE HUMAN, THAT'S A QUICK
ANSWER, SO THAT'S -- BUT BY
DEFINE MODELING I THINK THIS CAN
BE ACHIEVED.
>> ALSO LIKE CURRENTLY WHAT
WE'RE ALSO DOING IS USING MOUSE
DATA TO SEE WHETHER THEY CAN
MODEL THE CLINICAL OUTCOME OF
HUMAN, FOR EXAMPLE, IN
COLLABORATION WITH WAKEFIELD,
HISTOPATHOLOGY FEATURES FROM
MOUSE RESPONDERS AND
NON-RESPONDERS, CERTAIN THOSE
HUMAN CLINICAL DATA, MORE WHAT
WE'RE DOING IS STILL FROM MOUSE
TO HUMAN, YOU KNOW, PRAGMATIC
WAY LIKE THIS DIRECTION BUT IF
YOU COULD REVERSE DIRECTION,
CRITICISM (INDISCERNIBLE).
>> HI.
THANK YOU FOR A VERY INTERESTING
TALK.
SO I THOUGHT I WOULD TAKE THE
OPPORTUNITY TO ASK A SELFISH
QUESTION THAT'S RELATED.
IN THE ERA OF MULTIOMICS WHERE
WE LOOK AT PROTEOMICS AND
TRANSCRIPTOMICS, THE TWO
APPROACHES DON'T ALWAYS ALIGN.
WE MIGHT SEE, YOU KNOW, SOME
FINDINGS FROM OMIC APPROACHES WE
DON'T SEE FROM TRANSCRIPTOMIC
APPROACHES, SORT OF DO YOU
CONSIDER THAT THERE ARE
LIMITATIONS TO SOME OF THESE
TRANSCRIPTOMIC APPROACHES AT
LEAST IN MY DATA I MIGHT FIND
SOMETHING THAT'S REALLY RELEVANT
FROM FLOW OR IMAGING DATA THAT I
JUST CAN'T FIND IN MY
TRANSCRIPTOMIC DATA, HOW DO
YOU -- WHAT DO YOU THINK ABOUT
THAT?
>> SO THIS IS ACTUALLY A GREAT
QUESTION.
EXAMPLE, THE ANTI-INFLAMMATORY
FUNCTION VALIDATE FLOW CYTOMETRY
AND ELISA.
(INDISCERNIBLE) EVEN UNTIL NOW
DO NOT -- LIKE WITH REPEAT THE
FLOW, WE SEE PROTEIN CHANGE
COLOR ON TRANSCRIPTOMIC LEVEL,
OTHER CHANGE BY PCR, STILL DON'T
UNDERSTAND WHY.
ALSO FOR SOME OF MY ONGOING
PROJECT WE LOOK AT MULTIPLY
PROTEOMICS IMAGING DATA.
LIKE DIFFERENT PATTERNS,
COMPARED TO TRANSCRIPTOMIC
STUDIES.
ONE REASON IS THE QUALITY.
FOR MANY PROTEOMICS DATA BASED
ON EITHER RPTA ARRAYS OR
MULTIPLEXING LIKE COVAX, NOT
VALIDATION.
IF YOU RUN A TRIAL, A LOT OF
SMEARS, GERM KNOCKOUT YOU DON'T
SEE TARGET OR DOWNREGULATION.
SO I THINK THE QUALITY OF
MULTIPLEX PROTEOMICS IMAGING,
FOR EXAMPLE, IS QUITE LIMITED
UNTIL NOW.
AND HARDER WORK LIKE
TRANSCRIPTOMIC DATA DOES NOT
REFLECT PROTEIN CHANGE WHICH IS
ANOTHER STEP.
I THINK THAT THE ANSWER IS EACH
TECHNOLOGY HAVE LIMITATIONS.
IDEALLY SHOULD USE BOTH TO DRAW
CONCLUSION.
THERE'S NO WAY YOU CAN DRAW
RELIABLE CONCLUSION BASED ON
ONLY ONE TECHNOLOGY, RIGHT?
>> HI.
SO, MY QUESTION IS ABOUT DATA
INTEGRATION AND CYTO Seq.
YOU TRAIN ON A DATASET THAT
CONTAINS A LOT OF TUMOR TYPES.
AND THEN YOU USE IT TO PREDICT A
SINGLE CLINICAL TRIAL.
HOW DOES THIS MODEL PERFORM
ACROSS DIFFERENT CLINICAL TRIALS
OR TUMOR TYPES FOR LIKE OTHER
ONES THAT IT'S BETTER AT
PREDICTING AND ONES THAT ARE
WORSE?
>> YEAH, SO I GUESS PROBABLY CAN
SPLIT YOUR QUESTION IN TWO
DIFFERENT PART.
FIRST DATA COLLECTION FROM MANY
DIFFERENT MODELS, RIGHT?
FOR EXAMPLE TGF-BETA, WE FIND
TREATMENT RESPONSE SIGNATURE SO
DIFFERENT BETWEEN FIBROBLASTS ON
LYMPHOCYTE, THEY ALMOST INDUCE A
DISTINCT SET OF DOWNSTREAM
TARGET.
CYTO Seq USE SIGNATURE IS
WRONG IDEA.
HOWEVER WE CANNOT ACHIEVE
SPECIFIC PREDICTION BECAUSE MANY
OTHER CYTOKINES ONLY HAVE THREE
OR TWO PROFILES AVAILABLE.
THERE'S NO LUXURY TO DO THE
LINUX PREDICTION IN GENERAL,
WHICH MEANS CYTO Seq BY DOING
LINEAGE PREDICTION IS WRONG
IDEA, THE ONLY WAY WE COULD DO
RIGHT NOW.
AND FOR THE CLINICAL TRIAL
PREDICTION PERFORMANCE,
ACCORDING TO OUR SYSTEMATIC
ANALYSIS IF THE CYTOKINE
PREDICTION IS ACCURATE, PB 4 IS
ACCURATE IN CLINICAL TRIALS
WE'RE EVALUATING.
THE PROBLEM IS NOT REALLY GOOD
FOR CERTAIN FAMILY OF CYTOKINES.
FOR EXAMPLE IL-4 FAMILY DOES NOT
WORK WELL.
IN TERMS OF CLINICAL DIVERSITY,
(INDISCERNIBLE) IN GENERAL, BUT
MAJOR PROBLEM IS LIMITATION OF
IL-4 FAMILIES.
DOES THAT ANSWER THE QUESTION?
>> YEAH, THE PROBLEM IS LACK OF
DATA?
>> NO, PLENTY OF DATA.
YOU SEE LOTS OF THEM.
REALLY CAPTURING THE -- LIKE
YESTERDAY, YOU KNOW, CLINICAL
FUNCTION SYMPOSIUM, LIKE CRUCIAL
TALK ABOUT STUDY OF CELL
CYTOKINES INDUCE IN NEW GENES, I
THINK (INDISCERNIBLE) INSTEAD OF
DOING LOTS OF DIFFERENT
EXPERIMENT WHICH MEANS LOTS OF
DATA FOR IL-14.
CELLS CAPTURE THE KNOWLEDGE.
HOWEVER, DON'T KNOW WHY IF YOU
APPLY THE PREDICTION MODEL TO DO
A VERY SIMPLE LINEAR REGRESSION
IT JUST DOES NOT PREDICT IL-4
BLOCKINGOUT OF OUTCOME.
LOGICALLY THERE SHOULDN'T BE.
THIS IS SOMETHING THAT'S STILL
(INDISCERNIBLE) US BUT INTEREST
IN PASSING FORWARD, WILLING TO
COLLABORATE AND FIGURE OUT THE
REASON.
NOT BY LACK OF DATA, BY SOME
STILL UNKNOWN WHICH CANNOT BE
EXPLAINED RIGHT NOW.
>> THANK YOU.
>> IF THERE ARE NO MORE
QUESTIONS, WE'RE ALREADY BEYOND
TIME.
SO THANK YOU AGAIN SO MUCH FOR
THIS WONDERFUL TALK.
[APPLAUSE]
AND THANK YOU, EVERYONE,