GOOD MORNING.
YOU'LL VERY HAPPY TO INTRODUCE
DR. ALTAN-BONNET.
HEAD OF THE IMMUNODYNAMIC GROUP
IN THE LABORATORY OF CANCER AND
IMMUNOLOGY HERE IN BUILDING 37.
GREGOIRE TRAINED AS A PHYSICIST
IN PARIS AND Ph.D. IN
STATISTICAL DYNAMICS AND HIS
FILED OF EXPERTISE IS IMMUNE OLY
FIRST AT SLOAN KETTERING AND IN
THE LAST SEVERAL YEARS NOW AT
NCI.
AND WHAT DISTINGUISHES THE WORK
AND YOU'LL UNDERSTAND THE SYSTEM
IMMUNOLOGIES WE NEED GOOD DATA
FOR WHAT HE'S DOING AND HE'S NOT
TRUSTING ANYBODY'S DATA.
HE WANTS TO HAVE THIS OWN DATA
THAT'S LONGITUDINAL AND
CONNECTED AND THEN TRUST I'LL
INTRODUCE HIM.
>> WE DO CLINICAL TRIAL WITH
PEOPLE ON A STORY DAY.
THERE'LL BE A QUIZ AT THE END.
AND THERE'S COMPLEXITY OF THE
LYMPH NODES AND WE TRIED TO
BUILD A MODEL TO LEARN THE
BREAKING POINT AND HOPEFULLY BE
ABLE TO DESIGN.
TO DO THAT WE HAVE TWO
APPROACHES.
YOU GET RID OF TISSUES AND TRY
TO PROFILE THEM IN A HIGH
DIMENSIONAL SPACE AND WHAT'S NEW
IN THE FIELD YOU CAN GET A LOT
OF CELLS IN A FAIRLY HIGH
DIMENSIONAL SPACE AND DO
CLASSICALLY WHAT WE'VE BEEN
DOING IS DEFINING NEW BIOMARKERS
AND IDENTIFY NEW SIGNATURES OF
THE IMMUNE RESPONSE.
WE START FROM INTERACTIONS OF
INDIVIDUAL CELLS AND HOW THEY
ARE GET ACTIVATED AND BUILD THE
COMPLEXITY FROM THE GROUND UP
LOOKING AT TUMORS AND LOOK AT
THE SYSTEM AND THE SPACE WE'RE
IN IS SOMETHING IN BETWEEN WHERE
WE'LL TWRI TO LEARN WHAT WE'RE
LOOKING AT IN TERMS OF TARGET
AND MARKERS TO MODEL AND USE
ROBOTICS TO CHARACTERIZE IN A
COMPLEX WAY AND BUILD A MODEL
AND ASK QUESTIONS AND IT'S ALL
BASED ON THAT.
THIS IS FROM THE INTERNET AND
THE KIND OF METHOD WE'RE HAS
REALLY BEEN DEVELOPED TODAY AND
WHAT I'LL SHOW YOU TODAY.
FIRST I'LL TALK TO ABOUT THE
APPROACH AND HOW TO CHARACTERIZE
THE RESPONSE HAPPENING AND WHEN
WE START TO PUT NUMBERS WE CAN
LEARN NEW ASPECTS OF CANCER
IMMUNOTHERAPIES AND TRY TO BUILD
MODELS AND REDESIGNING BETTER
CAR T CELLS AND NCI IS ONE OF
THE LANDMARK INSTITUTES AND
COLLECT LEUKOCYTES FROM
METASTASIS AND YOU HOPE THERE'S
ENOUGH SPECIFICITY IN THE CELLS
THAT CAN GROW TO TUMORS.
THERE'S MANY EXAMPLES FROM LABS
THAT SHOW IT CAN BE SUCCESSFUL.
AND WE WOULD LIKE TO BE ABLE TO
UNDERSTAND HOW WE GO FROM THE
BAG OF CELLS AND CONNECT THE
DOTS TO DESIGN AND DESIGN BETTER
THE NEW THERAPY.
AGAIN, ON ALL KALE WE'LL KNOW
MORE OF THE BASIC APPROACH.
WHEN WE STARTED TO DO HIGH
DIMENSIONAL CYTOMETRY AND AU LEB
SAID LET'S TRY TO CHARACTERIZE
THE BAGS AND SEE HOW MUCH
DIVERSITY THERE IS AND LEARN
WHAT MATTERS.
WE DEVELOPED THIS PANEL WITH
MARKERS AND WHEN YOU
CHARACTERIZE THE CELLS THEY'RE
SIMPLE WHERE THEY'VE BEEN
EXPANDED AND AFTER ONE OR TWO
WEEKS OF EXPANSION YOU GET A LOT
OF DIVERSITY IN TERMS OF THE
TYPE OF T CELL AND IT'S NOT JUST
T CELL IT'S NOT REALLY A PURE
PRODUCT BUT IT'S VERY USEFUL IN
TERMS OF TELLING US THE
DIVERSITY AND WE CAN START TO
PARS THE SPACE AN LEARN NEW
BIOMARKERS AND WHICH PATIENT CAN
BENEFIT.
THE IDEA WHERE WE HELPED IN
MAKING THE MEASUREMENTS AND
QUICKLY WE DEVELOPED A PIPELINE
TO DO MORE MACHINE LEARNING
APPROACH AND THIS WAS VERY
SUCCESSFUL.
THEY WENT BACK TO THE BENCH AND
CAME UP WITH A SIMPLE SIGNATURE
AND FORMALIZE IT WITH MACHINE
LEARNING AND TO TELL YOU AT SOME
LEVEL WHEN YOU LOOK AT THE
DIVERSITY OF WHAT IS PREPARED IN
THE CLINIC YOU CAN IDENTIFY A
SUB POPULATION OF CD CELLS AND
VICE VERSA SOME DON'T DO WELL IF
THEY'RE LOW RESPONDERS.
WITH HAD' MOUSE MODEL TO PREPARE
THE WAY YOU PREPARE THEM FOR
HUMAN PATIENTS AND IT'S A SIMPLE
SIGNATURE AND COMPARE IT AND
THERE'S IMMUNOFUNCTION FOR TIME
AND INDEED IN THE MOUSE MODELS
THE SAME WAY WE EXPECT WE SEE
THAT IN THE HUMAN PATIENTS.
THESE ARE THE T CELL YOU WANT TO
PREPARE FOR TUMORS.
THIS IS QUICK SNIP TO SHOW WE
CAN DO HIGH DIMENSIONAL PROFILES
TO LOOK AT WHAT MATTERS.
IN THIS CASE IT PUSHED US TO
QUANTIFY THINGS EFFICIENTLY.
THE SECOND PART IS THE FREQUENCY
OF THE CELLS IN THE BAGS ARE
VERY LOW WITHIN LIKE .1 TO 1%.
IT'S NOT JUST A BIOMARKER THESE
ARE THE ONLY T CELLS WHICH
MATTER TO GO AFTER THE TUMOR.
THE QUESTION WE HAD IN THE LAB
IS HOW MANY T CELLS DO WE NEED
AND WE SHOWED A PROJECT FROM THE
LAB TO TAKE THE INSIGHT AND TRY
TO DEVELOP A SIMPLE ASSAY AND
TRY TO UNDERSTAND THE MATH OF
THE WAY T CELLS CAN GO AFTER
TUMORS.
AND GOING BACK TO A CLASSICAL
MODEL AND THE TUMOR LINE
EXPRESSING THIS AND WE CAN DO IT
IN A FLEX MANNER.
WE HAVE A ROBOTICS APPROACH
WHERE IT REPLICATES AND TEST
MANY TYPES OF CONFIGURATIONS AND
CAN RECHARACTERIZE THESE IN A
VERY NICE WAY AND FIRST QUESTION
IN THE LAB IN A WAY WAS TO CHECK
WE'RE TRYING TO COUNT THE CELLS
IN THE LAB AND THERE'S A SIMPLE
EXPERIMENT.
WE TOOK CELLS FROM 100,000 T
CELLS TO ONE T CELL AND YOU SEE
THE CRUX FOR UNDERSTANDING THE
WAY IT BEHAVES AND SEE IT
CORRELATES VERY NICELY BUT
MEASURE THE NOISE AND
COEFFICIENT OF EVALUATION AND IF
THE SIMPLE ASSAY WHERE WE
CALIBRATE INSTRUMENTS AND SHOW
IT'S ESSENTIALLY AND WHEN YOU
COUNT CELLS IF YOU'RE MEASURING
THE CELLS YOU'LL BE 10 OR PLUS
MINUS 3 AND 100 THIS IS DOWN TO
A LOW LEVEL OF NOISE AND THEY'RE
QUANTITATIVE.
AND WE MIXED THE TUMORS WITH THE
T CELLS AND SEE A NICE DOSE
RESPONSE.
EACH DOT IS A DIFFERENT ONE AND
EACH SHOW YOU WHEN YOU HAVE A
LOT OF T CELLS YOU GET NICE
KILLING.
IF WE TAKE 3200 BLAST CELLS AN
MIX THEM WITH TUMORS SOMETIMES
YOU'LL KILL ALL THE TUMORS
INSIDE THE DISH WE'LL EMULATE
WHAT'S SEEN IN THE SYSTEM AND
PREFER THE VIABILITY WE CAN GET
WHEN WE EVALUATE AGAINST TUMORS.
YOU CAN SEE ONE THE T CELL BLAST
AND QUANTIFY IT NICELY.
WE GET THIS HONEYCOMB IS THE
NUMBER OF WELLS WE MEASURED.
BE TUMORS SURVIVING AND OTHER
WELLS HAVE AND THINGS GET NOISY
AND FROM ONE WELL TO ANOTHER YOU
CAN GO TO 100% OF TUMORS
ATTACKED AND KILLED IN THE
RESPONSE.
THIS COULD BE DUE TO THE
VIABILITY OF THE TUMORS.
AND USE DRUGS CAN HAVE NO NOISE
AND HAVE 60% WILLING AND FROM
WELL TO WELL IS FAIRLY
CONSISTENT.
IF YOU'RE KILLING THEM WITH THE
DRUG YOU HAVE A CONSISTENT
TREATMENT.
AND I COMBELT -- GET TO A NOISY
SITUATION AND AS PUREE SAMPLE WE
CAN GET WE TRACED IT BACK WITH
REGULATION UP TO 48 HOURS AND
GET THE MONEY COMB AND SOMETIMES
WE CAN GET THE DOSE RESPONSE AND
YOU END UP WITH SOMETHING WHICH
LOOKS LIKE MEASURING THE
COEFFICIENT VARIATION FROM WELL
TO WELL.
WE REPEAT TO UNDERSTAND
INTERACTIONS AND LOOK AT THE T
CELL ACTIVATING IN THE CELLS AND
MEASURE STANDARD DEVIATION BY
THE MEAN CELL ACTIVATED AND GET
THE LIGHT BLUE CURVE.
THE MEASUREMENT LOOKS LIKE THAT.
IT'S ESSENTIALLY THE SAME ONE
BUT SLIGHTLY SHIFT ED AT SOME
LEVEL THERE'S A NUMBER OF CELLS
BEING ACTIVATED AND IT SEEMS TO
BE SHIFTED.
AND THE MODEL STATES AS THE CORE
OF INTERACTIONS YOU'RE DEALING
WITH A SMALL NUMBER OF CELLS AND
BECAUSE IT WILL BE NOISY AND YET
THE NUMBER OF CELL ACTIVATED
SEEMS TO BE HIGH BECAUSE THERE'S
SOME PROPAGATION WHICH EXPANDED
THE NOISE AND YOU END UP WITH
THIS.
SO A LITTLE BIT OF MATH AND
BASICALLY WHEN LIEU YOU LOOK AT
THE DATA WE THINK WE'LL SEE THE
HALLMARK AND THE CORE OF
WHATEVER PROCESS YOU'RE LOOKING
AT IT'S LIKELY A SMALL NUMBER OF
CELLS WHICH DECIDE EVERYTHING.
SO THE MODEL WE SPEND TIME
LOOKING AT THE MECHANISM AND
SHOW WHAT YOU END UP WITH THE
CELLS THERE SEEMS TO BE SPECIAL
ONES.
WE CALL THEM SPARKING CELLS AND
WHEN THEY ARE GET ACTIVATED THE
TUMORS WILL LIFT UP THE
BYSTANDERS AND LOWER THE
ACTIVATION AND GIVE MASSIVE
IMMUNE INTERACTION.
THE KEY IDEA IS THERE WILL BE A
SUBCLUSTER OF CELLS AND CONTROL
THE WAY THE INTERACTION CAN GET
TRIGGERED.
AND AGAIN THE IDEA IS BECAUSE
IT'S A LOW NUMBER YOU'LL GET
SIGNATURES AND GET THE DEF
INANTS AND WE MEASURED ALL THE
PARAMETERS AND THE BLACK LINE
WHERE YOU MEASURE AS A FUNCTION
OF THE MEAN NUMBER OF ACTIVATED
CELLS CAN BE FITTED WITH FEW
PARAMETERS AND CAN EXPLAIN
EVERYTHING WITH THE SIMPLE
MODEL.
AND THEY RESPOND AND THEY CAN
ENTRAIN ALL THE T CELLS AND YOU
END UP WITH THE NOISY
INTERACTIONS AN CAN ALONG AT THE
TUMOR WITH UP REGULATE THE
SIGNAL TO BE NOISY BUT WE CAN
ACCOUNT FOR ALL THESE THINGS AND
AGAIN AT THE END VERY SIMPLE
MODEL THE SUB CLUSTER OF T CELLS
SPOT DIRECTIONS AND MAKE ENOUGH
CYTOKINE AND BE ABLE TO REALLY
CREATE THIS MASSIVE KILLING OF
TUMORS WHERE EVERYBODY GETS
KILLED AT THE END.
IN THIS EXPERIMENT WE TITRATED
THE NUMBER OF T CELLS AND EACH
DOT IS A WELL.
WE'RE DEALING WITH MASSIVE
AMOUNT OF INTERACTIONS AN
LOOKING AT A FRACTION OF THE
WELL ACTIVATED TO SEE THE T
CELLS WERE ACTIVATED WITH THE
SIGNATURE OF INFLAMMATION
HAPPENING.
IF YOU HAVE NO T CELLS IN THE
LEFT YOU HAVE ZERO WELLS BEING
ACTIVATED.
IF YOU HAVE A LOT OF T CELLS
100% WILL BE ACTIVATED WITH
NOISE IN THE ACTIVATION AND 100%
PASSING THE THRESHOLD.
IF WE MEASURE THE FRACTION OF
WELLS WHICH HAVE BEEN SPARK AS A
FUNCTION OF INPUT T CELLS WE GET
THE DATA IN THE BLACK MARK AND
IF IT'S A PROCESS WHERE WE HAVE
THE CURVE IN BLACK WHERE THE
FRACTION OF WELLS BEING
ACTIVATED WILL BE ONE OF THE
SQUARE ROOT OF N BUT WE ARE
SHIFTED BECAUSE IT LOOKS LIKE
THE REACTIONS AND ONLY ONE OF
2500 WILL BE ABLE TO SPOT AND
FROM THIS ANALYSIS WE'LL BE ABLE
TO REALLY ESTIMATE THE FREQUENCY
OF THE T CELL.
IT'S IN THE A SIMPLE DOSE
RESPONSE BECAUSE EACH CORRESPOND
TO THE WELL AND LOOK AT THE
MICRO DOMAIN AND YOU'LL GET
INTERACTIONS OR NONE AT ALL AND
FROM THIS ANALYSIS WE KNOW WE'RE
LOOKING FOR 0.04% OF THE CELL I
WANT TO ACTIVATE.
SO THIS IS JUST THE BEGINNING OF
THE START.WE KNOW HOW TO LOOK AT
THE DATA.
IF YOU LOOK AT THE POPULATION OF
T CELLS YOU CAN DO SINGLE CELL
RNA SEQ AND FLOW CYTOMETRY AND
SEE DIVERSITY.
WHAT DIVERSITY MEANS CAN BE LARD
TO ANALYZE.
HERE WE HAVE A CHANCE TO SET UP
THE NUMBERS AND WE'RE LOOKING AT
SOMETHING WHICH IS INFREQUENT
AND USE THAT TO IDENTIFY WHAT
CLUSTER OF T CELLS SEEM TO
MATTER.
THIS IS A PIPELINE DEVELOPED AND
LOOK AT IDENTIFICATION OF CELL
SUBSETS AND BE ABLE TO GO IN OUR
DATA AND TRY TO IDENTIFY THE T
CELL WHICH REALLY MATTER TRYING
TO SPARK THE IMMUNE INTERACTS
AGAINST THE TUMOR.
THE PIPELINE GOES LIKE THAT AND
HAVE EX VIVO REACTIONS AND GIVE
ME A TUMOR AND POPULATION OF T
CELL IF WE HAVE ENOUGH WE CAN
REPLICATE BLOCKING DIFFERENT
PATHWAYS AND MEASURE THE
FRACTION OF TIME IT GOES RIGHT
OR DOESN'T AND START TO BUILD A
BIG MATRIX AND CHANGING THE
NUMBER OF T CELLS AND YOU'LL
REALLY NAVIGATE ALL THE
POSSIBILITIES HOW THE T CELLS GO
AFTER TUMORS.
AND THE DIFFERENT TIME POINTS
AND THE 12 HOURS AND THE CD T
CELLS OR THE DIVERSITY SOME
CELLS ACTIVATED AND SOME NOT.
.
AND WE KNOW FROM THE DATA WHAT
FREQUENCY OF T CELLS WE'RE
EXPECTING DEPENDING ON THE
CONDITION.
IF WE HAVE A LOT OF T CELLS WE
KNOW THE NUMBER WHAT WE SHOULD
GET AND NOW WE HAVE THE T CELL
DIFFERENTIATION AND MATCH THE
MEASUREMENT IN TERMS OF T CELL
DIFFERENTIATION WITH WHAT WE
PREDICT ARE THE INTERACTIONS.
THIS IS A GAME OF MATCHING
PATTERN BECAUSE YOU'RE IN SUCH A
HIGH DIMENSIONAL SPACE YOU'LL
HAVE ONE PROCESS AND BE ABLE TO
LINE UP THE DATA FROM WHAT WE
PREDICTED IN THE LONG TIME
SCALES.
IF YOU LOOK AT THE SMALL CLUSTER
IT LOOKS LIKE WHEN YOU MEASURE
THE FREQUENT FOR A NUMBER OF T
CELLS AND ANTIGENS IT MATCHES
THE PREDICTIONS WHEN WE LOOK AT
THE PROCESS AND USE A MACHINE
LEARNING APPROACH AND IDENTIFY
THE BAGS OF T CELLS AND WHERE
THE CELLS ARE COMING FROM AND WE
END UP WITH SOMETHING SIMPLE ALL
THE RAUR CELL -- RARE CELLS
MAKES A STOCHASTIC SIGNATURE AND
TRAINS THE OTHER CELL
INTERACTIONS.
IT TURNS UP IN SIDE THE NAIVE
CAR T CELLS ARE HAVE THE
PHENOTYPE AND 1% TO 2% IS A
FAIRLY RARE POPULATION.
AN YOU CAN GO BACK AND SOLVE THE
SELVES AND TEST THEIR POTENTIAL
AND WE'RE MEASURING TUMOR
KILLING AS A MEASURE OF T CELLS
AND SORTED FOR THE SIGNATURE OR
SORTED FROM THE CD 5 HIGH AND
YOU SEE THE POTENCY CHANGES BY A
FACTOR OF ALMOST 1,000.
THIS IS VERY STRIKING.
WE STARTED WITH A POPULATION OF
T CELLS AND YET A RAURL
POPULATION AND YOU CAN BOOST THE
PROBABILITY OF ACTIVATION AND
THE FRENZY OF TUMORS BY SORTING
THEM OUT AND INCREASING THEIR
FREQUENCY.
WE STARTED FROM A BASIC APPROACH
OF MEASURING THE NOISE AND
SEEING IT CAN BE NOISY AND
QUANTIFY THE DIVERSITY IN SINGLE
CELL AND PUSH THOSE TO GET
ACTIVATED.
THIS WAS ALL DONE IN THE CONTEXT
OF THE T CELLS BECAUSE WE CAN
SORT THEM OUT AND DO SINGLE CELL
SEQ AND SIGNATURE WE EXPECT TO
CONTAIN MOST THE CAR T CELLS AND
WHEN YOU DO THE TAC SEQ THERE'S
A CLUSTER WHICH HAS IT MAKING
SENSE AND AGAIN CELLS ARE IN A
QUIESCENT STATE AND THERE'S A
SUBSET OF CELLS MAYBE LESS THAN
1% WHICH SEEMS TO HAVE CHROMATIN
IN THE LOCUS IT WILL BE EASY FOR
THEM TO GET GOING AND MAKE THE
CYTOKINE AND TRAIN EVERYBODY
ELSE.
INDEED WHEN WE TEST THAT
PHYSIOLOGICALLY AND COMPARE THE
QUICK ACTIVATION AND ACTIVATION
THIS POPULATION WE'RE ABLE TO
TEST IT WHERE TO THE OTHER CELLS
THE CELLS WILL TAKE 24, 48 HOURS
TO GET GOING.
WE LOOKED AT THE DIVERSITY OF
THE T CELLS AND EXPLAIN THE
IMMUNOACTION OF THE TUMORS AND
THEN IDENTIFY THE SUB POPULATION
WITH BASAL MARKERS AN CHROMATIN
STATE AND GET GOING.
THIS WAS DONE IN A MOUSE MODEL.
YOU CAN CHARACTERIZE THESE
THINGS.
WE LOOKED AT THE KIND OF
PREPARED TRANSGENICS WHICH
EXPRESSED THE TCR AGAINST THE
ANTIGENS AND DID THE SAME
INTERACTS AND MULTIPLIED THE
ASSAY AND TESTED THE TYPE OF
TUMORS AND TRIED TO BUILD A MAP
OF THE RESPONSE.
FIRST, WHAT WE OBSERVED IS
THOUGH THEY'RE PREPARED IN A
FAIRLY HOME -- HOMOGENEOUS CELLS
THEY'LL GIVE DIVERSITY IN THE
WAY THEY CAN KILL AN GET
ACTIVITY AGAINST TUMORS.
THIS IS WHAT WE SIMULATE IN THE
CENTERS.
THEY COULD BE DIVERSE WHEN YOU
GO DOWN THE DOSE RESPONSE AND
APPLY AND CAN IDENTIFY A
SUBCLUSTER OF THE MATCHING
PREDICTION IN TERMS OF
STOCHASTICALLY AND HAVE CD 45
PLUS AND WE CAN DO THIS MORE
SYSTEM
SYSTEMATICCALLY AND TRIGGER THE
REACTION INSIDE THE SAMPLE SO
COMPLEX TYPE AND IT'S VERY
DIFFERENT WITH THE CD 2 HIGH AND
WE CAN SORT THE CELLS AND CHECK
AND WHEN WE ACTIVATE THEM THE
CELLS WILL MAKE THE GAMMA
QUICKLY AND IF YOU LOOK AT THE
OTHER CELLS THEY'LL BE DELAYED
AND LESS POTENT IN MAKING GAMMA.
THERE'S WHEN WE LOC AT THIS IT
VARIES A LOT AND EXPRESSING AND
BY THE TIME WE TEST THEM IN THE
ASSAY SOME CELLS MAY BE AROUND
50%.
SOME ARE AROUND 30%.
THERE'S DIFFERENT WAYS THE
DONORS WILL GENERATE AND MAKING
A DIFFERENT GAMMA AFTER ONE
HOUR.
SO IN A SENSE WHAT IT'S SAYING
THE WAY THE CELLS ARE BEING
PREPARED PUTS THEM IN MANY
STATES AND BEING ABLE TO PARSE
WILL BE A CHALLENGE AND WE CARE
ABOUT THE GAMMA AND DRIVE THE
OTHER CELLS DEPENDING ON WHAT WE
CARE ABOUT, T CELL RESISTANCE
AND GOING TO DIFFERENT TISSUE
AND SO ON AND SO FORM AND
THERE'S A LOT OF DIVERSITY AND
WILL ALLOW US TO REQUANTIFY THE
VIABILITY.
THE LAST THING IS TO IMPRESS HE
HAD TOWARDS HOPEFULLY WHAT WE
WENT BACK TO SOME CLINICAL DATA
AND LOOKED AT THE SIGNATURE AND
SEE IF IT COALESCES WITH READOUT
AND IF YOU LOOK AT THE
CHECKPOINT INHIBITORS AND PASS
THEM FOR IMAGING LOW TUMORS
COMPARED TO THE IMAGE AND
SEPARATE THE PATIENT FOR LOW
SIGNATURE VERSUS HIGH SIGNATURE
AND INDEED THE PATIENTS WITH LOW
LEVEL OF IMAGING AND HIGH
SIGNATURE OF THE CD 39 HIGH
THESE ARE THE PATIENT WHICH CAN
DO WELL AND SAME COHORT THE
PATIENT DON'T HAVE MANY AND NOT
PRESENTING A LOT OF THE CAR T
CELLS.
WHAT IT SAYS IN THE CARTOON IS
ALL THE PATIENTS WHICH ARE HIGH
LEVEL IMAGERY YOU DO WELL WHEN
YOU DO THE THERAPY BUT THE
PATIENTS WHICH HAS AN IMAGE VERY
LOW AND HAVE A CELL EXPRESSING A
LOT OF THE SIGNATURE SEEMS TO BE
RESCUED AND BENEFIT.
SO THIS IS LIKE 50% OF THE
PATIENTS AND GET INTO A REGIME
TO EXPLAIN WHY WE DO SO WELL IN
TERMS OF THE COMBINED
IMMUNOTHERAPY.
THE CONCLUSION IS THERE'S REALLY
WE CALL INTRINSIC VARIABILITY IN
THE WAY THEY CAN TAKE PLACE.
AND WITH HELP WITH THE MACHINE
LEARNING AND PROCESSING OF THE
SINGLE CELL AND A SIMPLE MODEL
DEALING WITH A SMALL FRACTION OF
T CELLS AND EXPRESS GAMMA
QUICKLY.
THIS IS CRITICAL BECAUSE WE ARE
DEALING WITH TUMORS WHICH
EXPRESS LOW LEVEL OF IMAGING.
IF THE CELLS ARE PRESENT, THEY
GET ACTIVITY QUICKLY AND MAKING
A DIFFERENT GAMMA AND UPREGULATE
AND WHY EN TRAIN THE BYSTANDERS
AND THESE ARE RARE AND DEALING
WITH A SMALL NUMBER OF CELLS
FROM ONE TO 10 CELLS AN GET INTO
A REGIME WHERE SUDDENLY IT
BECOMES RARE AND NOISY.
YOU HAVE A PIPELINE YOU CAN
APPLY TO LOOK AT THE STOCHASTIC
ITY AND -- AND THIS IS THE
PIPELINE YOU WANT TO USE TO
MRACH THE NUMBER OF CELLS IN
YOUR REACTION WITH THE DIVERSITY
OF THE IMMUNE FUNCTION AND
IDENTIFY THE CAR T CELLS AND CD
5 LOW IN HUMAN T CELLS AND MAKE
THE DPA MA QUICKLY AND THE CELLS
YOU CARE ABOUT IF YOU WANT TO GO
AFTER TUMORS AND WE HOPE TO
REALLY GET PROTOCOLS TO BOOST
THE COMPARTMENT AND INCREASE THE
SUCCESS.
AND THIS IS UNDER REVIEW BUT YOU
CAN DOWNLOAD IT IF YOU WANT TO
SEE MORE DETAILS AND WANT TO
DISCUSS MORE OF THIS.
NOW LET'S MAKE A TRANSITION.
I'LL DO MORE MATH.
YOU COULD BE SHOCKED AND WE THIS
GIVES YOU THE INTERACTIONS.
IS IT POSSIBLE FOR A SINGLE
LYMPHOCYTE TO DECIDE THE FATE OF
THE REACTION?
ARE WE FOOLING OURSELVES?
IF YOU LOOK AT THE MATH IT'S NOT
CRAZY.
THE SINGLE T CELL CAN SECRETE AT
THE RATE OF 1,000 MOLECULES PER
SECOND.
SO BECAUSE YOU'RE ALL VERY GOOD
AT MATH AND IT'S EARLY IN THE
WEEK YOU CAN GO FROM ONE TL SO
ONE LYMPH NODE OR THE CRITICAL
LEVEL IS A SMALL LYMPH NODE DO
YOU THINK IT TAKES ONE HOUR, ONE
DAY OR 100 DAYS TO GET TO SINGLE
CELL?
IT TAKES 17 HOURS.
WE CAN DO THE MATH AND SEE THE
SINGLE T CELLS AND SECRETING AT
1,000 PER SECOND WITHIN 17 HOURS
WILL MAKE ENOUGH CYTOKINE AND
FLOOD IT WITH THE GAMMA FOR
EVERYBODY TO RESPOND.
SINGLE CELL IS ENOUGH TO GET
THERE AND ON TOP OF THAT IF YOU
LOOK AT THE SINGLE T CELL WHICH
STARTS TO PROLIFERATE IT'S ONE
EVERY SIX HOURS.
AND IT TAKES ONE HOUR, ONE DAY,
100 DAYS.
IT'S VERY FAST.
WITHIN FOUR DAYS YOU GET 50% TO
10% OF THE LYMPH NODE IS MADE OF
THIS AND IF THEY ARE MAKE THE
RIGHT CYTOKINE HU THE SYSTEM
BEHAVES AND I HOPE I'VE GIVEN
YOU ENOUGH MATH TO GET GOING AND
LOVE DOING THE CALCULATION TO
SEE WHERE WE ARE IN TERMS OF
TRIGGERING AN IMMUNE RESPONSE.
[OFF-MIC]
>> THEY'RE ASKING ABOUT WHAT
WOULD HAPPEN IF WE DELIVER
DIFFERENT GAMMA.
YOU GET PD 1 VERY QUICKLY AS
WELL.
THE TIME GOING FROM LOCAL
DELIVERY TO THE INTERFERON GAMMA
AND SECRETION OF THE CYTOKINES
THEY'RE NOT ADDRESSED YET BUT
KNOW SYSTEMIC DELIVERY HAS BEEN
A BIT OF A FAILURE IN TERMS
OF -- WE'RE IN THE IDEA THAT.
SO DIFFERENT GAMMA TO BE ABLE
AND WE HAVE NOT EXPLORED IT BUT
CAN ADD THAT TO THE MODEL.
VERY GOOD IDEA.
WE'LL SWITCH TO THE LAST PART
AND HOW TO PROFILE THE T CELLS
AND IN A REGIME TO UNDERSTAND
IT'S EASY TO SEE THE DIVERSE
TYPE OF T CELLS AND WHERE THATRY
COMING FROM AND BUILD A MODEL
WHERE YOU'LL BE ABLE TO EXPLAIN
AND MARK WHERE THE T CELLS ARE
ACTIVATED.
WE'RE DOING THAT IN CONTEXT OF T
CELLS AND IN THE CONTEXT OF CAR
T CELLS AND THEN I'LL SHOW YOU
WHAT WE DO IN THE CONTEXT.
SO THIS COMES FROM AN OLD IDEA
WHICH IS IN THE FIELD IS AN OLD
PAPER FROM MY POSTDOC ADVISER
AND WE WERE TESTING THE
ACTIVATION OF T CELLS AGAINST
DIFFERENT PEPTIDES AND THE
SIMPLE DOSE RESPONSE AND THE
POINT OF THE PAPER WAS TO SHOW
THE DIFFERENT READOUT SEEM TO
RESPOND TO DIFFERENT DOSES OF
ANTIGENS.
SO IF YOU CARE ABOUT MAKING
GAMMA YOU NEED A LOW DOSE OF
PEPTIDES AND SHOULD CARE OF
MAKING IL3.
AND WHEN WE LOOK AT THE
DIVERSITY OF THE RESPONSE OF THE
IMMUNE SYSTEM IT MIGHT INFER
SOMETHING ABOUT THE QUANTITY OF
ANTIGEN IN THE SYSTEM.
THIS GOES BACK TO WORK WE'VE
BEEN DOING SINCE MY POSTDOC HERE
AT NIH AND OVER THE YEARS WE
HAVE A MODEL SO GOOD IT HAS BEEN
STANDING FOR 20 YEARS TO REVISE
IT WHEN WORK ON CAR T CELL
CELLS.
WE'RE SEEING WHEN WE LOOK AT THE
LIGAND INTERACTING WITH PCR WITH
THE RECEPTORS, YOU GO FROM THE
CHAIN OF SIGNALLING WHERE YOU'LL
PHOSPHORYLATE ONCE, TWICE, 10
TIMES AND THE DIFFERENT STAGES
MIGHT CONNECT TO DIFFERENT
READOUT.
SO THE IDEA IS AGAIN THE TCR IS
COMPLEX BECAUSE TEA VERY GOOD AT
DISTINGUISH DIFFERENT PEPTIDES
AND MAKE THE GAMMA AND SO ON AND
SO FORTH.
COMING TO NIH WE WANTED TO STUDY
THIS IN MORE DETAILS AND TO
BUILD A MODEL OF THE WAY T CELLS
ARE ABLE TO INTERACT WITH
IMAGING AND GO TO THE COMPLEX
SPACE.
NOW, TO DO THAT WE BUILT A
PIPELINE.
I'M VERY THANKFUL TO THE
LEADERSHIP AND WAS ENCOURAGING
TO BUILD THE ROBOT.
WE'RE DOING ESSENTIALLY WHAT YOU
WOULD DO EXCEPT BUILT IN SUCH A
WAY TO BUILD ACCESSORY QUICKLY
AND YOU GET T CELLS FROM
PATIENTS AND FROM MOUSE MODEL
AND TOMB TUMORS AND MIX THAT
WITH PLAMACROPHAGES AND PUT THEN
THE WELL AND IF IT WORKS WELL
YOU CAN COLLECT ALL THE CELLS
AND COLLECT THE COLLECT THE
CELLS IN TERMS OF
DIFFERENTIATION AND MARKERS AND
YOU'LL SEE ON THE GRAPH WE HAVE
THE B CELLS AND THE BYSTANDERS
BEING ACTIVATED.
THE T CELLS ON THE RIGHT AND YOU
SEE THERE'S A LOT OF DIVERSITY
IN TERMS OF CLUSTER OR CELLS
BEING ACTIVATE AND COMPLEXITY IN
THE SIMPLE MAP IN TERMS OF THE
WAY THE CELL IS ACTIVATED.
WHAT IT LOOKS LIKE IN THE LAB
AND COME TO TOP BY AND PEOPLE
HAVE BEEN USING THE PLATFORM TO
DO THAT.
SO I HOPE THE MOVIE WORKS.
IT'S A SIMPLE PLATFORM WHICH
ENABLES TO DO WHAT YOU'D DO AT
THE BENCH AND THE INCUBATOR ON
THE RIGHT AND WE MOVE THE COVER
AND STOP IT'S ROBUST ENOUGH MANY
PEOPLE CAN HAVE THE PLATFORM IN
THE LAB.
HERE ON THE RIGHT I'M SHOWING A
PIPELINE INTERFACE AND ENCODE
THE DATA.
ONCE THE ROBOT IS DONE YOU'LL
GET A BIG MATRIX OF CYTOKINES
AND ENCODE THINGS QUICKLY.
AND THANKS TO ALL THE TOOLS
WE'RE ONE GIGANTIC TO ENCODE
EVERY CYTOKINE AND MARKER
MEASURED FOR THE LAST TWO YEARS.
AND THIS IS BILLIONS AND
BILLIONS OF CELLS WHERE FOR EACH
CELL WE KNOW HOW LONG THEY'VE
BEEN ACTIVATED AND THE TYPE OF
ACTIVATION AND WHAT RESPONSE WE
HAVE AND NOW WE'RE IN THE SYSTEM
TO START TO DO A BIT OF MACHINE
LEARNING TO REBUILD MODELS OF
THESE THINGS.
FIRST ORDER OF BUSINESS IS THE
TRAINING SET WHERE WE TAKE T1 T
CELLS AND MEASURE DIFFERENT TIME
POINTS ON THE Y AXIS AND
DIFFERENT CYTOKINES AND IF WE
DON'T HAVE THE RIGHT MAP OR HOW
TO LOOK AT THE DATA, IT'S KIND
OF A MESS.
AND THE IDEA IS THAT THE DATA WE
ARE COLLECTING AND I THINK WE'RE
IN THE STATE OF LOOKING AT T
CELLS THE WAY ASTROLOGISTS AND
GREEK ASTRONOMERS LOOK AT THIS
AND IF YOU BELIEF THE EARTH IS
THE CENTER OF THE UNIVERSE AND
MAYBE THINGS WILL CHANGE WE HAVE
A MODEL OF THE SOLAR SYSTEM AND
IT'S BEAUTIFUL AND OF COURSE IF
YOU START TO LOOK AT THE DATA
AND PUT THE VARIABLES AND LOOK
AT THE DATA IN THE PROPER WAY
THINGS LOOK WAY MORE
ORGANIZED -- COMMENTS?
>>
[OFF-MIC]
.
>> WHAT YOU STAND FOR BUT
SOMETIMES YOU WANT TO FLY, I
KNOW.
I AGREE.
SO THE BEAUTY IS THESE DAYS IN
MACHINE LEARNING THERE'S AN
ALGORITHM THAT CAN TAKE A HUGE
AMOUNT OF DATA AND LEARN THE
MODELS AUTOMATICALLY.
THE IDEA IS NOT LOOK FOR
SOMETHING PRETTY BUT SOMETHING
SIMPLE.
SIMPLE MEANS YOU CAN GO TO A LOW
DIMENSIONAL SPACE AND LOOKED AT
THE SOLAR SYSTEM AND FED IT INTO
THE NETWORK AND FOUND THE BEST
WAY TO CAPTURE THE DYNAMICS WAS
TO PUT THE SUN IN THE MIDDLE OF
THE UNIVERSE.
THIS IS A START IN TERMS OF THE
PHYSICS AND WE'RE NOT LEARNING
ANYTHING BUT PROOF OF PRINCIPLE
TO SAY IF START TO TRY TO
SQUEEZE THE DATA INTO LOW
DIMENSIONAL SPACE YOU MAY BE
ABLE TO LEARN NEW VARIABLES.
WE DID EXACTLY THAT IN THE
CONTEXT OF THE CYTOKINE DIYNAMIS
AND THE DIMENSIONAL SPACE AND
DEPENDING ON THE SYSTEM YOU MAY
NOT NEED A LOT OF CYTOKINE AND
DO PROCESSING OF THE DATA AND
FEED IT INTO A SIMPLE NEURAL
NET.
THE IDEA IS THE DATA LOOK
COMPLICATED AND MESSY.
THE COLOR CODE CORRESPONDS TO
DIFFERENT RESPONSE AND WE HAVE A
LOT OF CONSIDERATION.
ONCE YOU PUT THEM INSIDE THE
NEURAL NET AND DESIGN IT AND
DESIGN THE ARCHITECTURE AND
THERE'S LOW DIMENSIONAL SPACE
AND TAKING A COMPLEX OR HIGH
DIMENSIONAL SPACE AND SQUEEZE
THE INFORMATION AND DO THAT WITH
THIS KIND OF STUFF AND THESE
TOOLS ARE NOT GOOD ENOUGH
BECAUSE WE'RE DOING DIMENSIONAL
REDUCTION WITHOUT LINING IT UP
WITH THE DATA.
WE TAKE OUR VERY COMPLEX DATA
SETS AND SQUEEZE IT INTO A LOW
DIMENSIONAL SPACE WITH AN EYE TO
A FUNCTION AND WITH THE TYPE OF
ANTIGEN WE FEED INTO THE CENTER.
IT GOES BACK AND FORM BY
SQUEEZING THE DATA INTO LOW
DIMENSIONAL SPACE AND MAKING
SURE IT CAN WE PREDICT THE
ANTIGENS.
WE'RE REVERSE ENGINEERING AND
TAKE THE PATTERN AND GO THE
BINDING RECEPTOR AND GO TO SOME
SIGNALLING AND TRANSCRIPTIONAL
NETWORK AND THEN GIVE YOU A
RESPONSE.
WHAT YOU CAN DO IN THE LAB IS
GENERATE ALL THE OUTPUT DATA AND
CALCULATE WHAT THE T CELLS DO
AND YOU END UP WITH THIS MODEL.
IT'S A LOW DIMENSIONAL SPACE YOU
CAN SHOW ON THE SCREEN.
IT'S MADE OF ABSTRACT LATENT
VARIABLES AND COMBINATION OF
MANY THINGS AND HERE WHAT YOU
SEE IS THAT FOR THE LE
DIMENSIONAL SPACE YOU CORRESPOND
TO THE TIME ASPECT OF T CELL
ACTIVATION AND THE COLOR CODING
CORRESPONDS TO DIFFERENT
ANTIGENS AND THAT WAS NOT THE
CASE IN THE OTHER DATA.
WE CAN LOOK AT THE VECTORS AND
YOU'LL SEE TE SPEED AND IF YOU
GIVE ME SOME T CELLS AND ATCs
AND TUMOR CELL AND MIX THEM IN
THE ROBOT YOU'LL GET A COMPLEX
CYTOKINE MARKERS AND PROJECT IT
INTO A LOW DIMENSIONAL SPACE AND
THIS RESPONSE TO THE QUALITY.
WHAT I'M SHOWING THE OUTPUT OF
THE ROBOT AND MACHINE LEARNING
ON THE Y AXIS AND WE CAN WE CAN
CHARACTERIZE THEM WITH A SIMPLE
RESPONSE AND THEY'VE BEEN DONE
WITH T CELLS AND QUANTITY OF
ANTIGENS AND THE MODEL IS A HIGH
DIMENSIONAL SPACE AND ALL THE
COMPLEXITY LOOKING AT THE T CELL
BEING ACTIVATED CAN BE SQUEEZED
AND LINED UP AGAINST THE
ANTIGENS.
MAYBE THEY'RE NOT AS BEAUTIFUL
AS YOU WERE THINK BUT USEFUL
BECAUSE BY LOOKING AT THE T
CELLS AND WE CAN TELL YOU IT'S A
TUMOR INDEPENDENT OF THE CONTEXT
OF THE TYPE OF TISSUE.
THE CRAZY PART IS WE TRAIN THE
MODEL WITH A MOUSE NAIVE CELLS
WELL CHARACTERIZED AND APPLY IT
TO THE CELLS THAT WE WERE
DISCUSSING EARLIER AND YOU SEE
THE DETAILS.
THE SAME PARAMETER AND MODEL
WHICH MIX ALL THE CYTOKINES AND
SEEMS TO TRANSLATE WELL TO T
CELL BLASTS LIKE WE DO AND IT'S
DEALING WITH THIS SO ON AND SO
FORTH.
WE BUILT A MODEL OF THE WAY
CELLS RECOGNIZE ANTIGEN AND CAN
MOVE FORWARD.
I'LL MOVE TO CAR T CELLS AND
ONCE WE BUILD THE MODEL WE
WANTED TO CHARACTERIZE THE CAR T
CELLS AND WE HAVE AN ANTIBODY
WHICH BINDS WELL TO THE TARGET
AND THE DOMAIN OF THE TCR THAT'S
VERY DIFFERENT.
IT COULD BE A TOTALLY DIFFERENT
MODEL AND LOOK VERY ABSTRACT.
WE TRAIN OUR MODELS AND NORMAL T
CELLS AND THE THEY PREPARE THE
MOUSE CAR T CELLS AND CAN LINE
THEM UP AND REALLY HAVE THE SAME
KIND OF MATH OF A NORMAL T CELL
EXCEPT WHEN WE COMPARE THE CAR T
CELLS WE NOTICE IT COMPARES TO
ONE OF THE WEAKEST ANTIGENS WE
CAN EXPECT.
IT'S VERY SURPRISING BECAUSE
THEY ARE WERE DESIGN WITH
ANTIBODIES SO YOU WOULD EXPECT
THEM TO BIND WELL TO THE TARGETS
AND BE GOOD AT BEING ACTIVATED
AND BECAUSE IT'S SUB OPTIMAL YOU
WIND UP WITH CAR T CELLS WHICH
WINDS UP BEING THE WEAKEST
ACTIVATION YOU CAN GET AND IF
YOU GO TO MOUSE CAR T CELLS AND
EXPOSE THEM TO A DIFFERENT
PEPTIDE SUDDENLY YOU GET LESS OF
A RESPONSE.
AND THIS IS SOMETHING THAT WE
CARE ABOUT IN MODELLING AND WE
NOTICED THE LATENT SPACE AND IT
GOES TO AN ANTAGONISM IN THE
SYSTEM BY MEASURING THE VELOCITY
OF THE CAR T CELLS AND SO WE RAN
A DIFFERENT STORY AND
COLLABORATION WHERE WE ARE
LOOKING AT THE CROSS TALK
BETWEEN THE TCR AND CAR AND YOU
CAN SEE WE CAN DO NEED THINGS
WITH THAT.
WE PREPARED CAR T CELL AND THE
TCR AND PREPARED TUMORS TO
PREVENT THE IMAGING AND THE
FIRST SURPRISE WHEN WE DO THAT
IN THE MOUSE MODEL WHERE WE
EXPOSED TRANSFER AND TREATED THE
NICE WITH THE PROJECT IT CAN
PROTECT THE MOUSE A LITTLE BIT.
IF YOU HAVE A CAR T CELL WHICH
EXPRESSES AND IS EXPOSED TO
ANTIGEN YOU END UP WITH GOOD
PROTECTION THEIR MOUSE BUT
SURPRISINGLY WHEN YOU TALK THE
CAR AND WEAK ANTIGENS YOU END UP
WITH SOMETHING WHICH IS WORSE.
WHEN YOU HAVE TWO SIGNALS COMING
OUT OF THE T CELLS YOU MIX THE
CAR SIGNAL AND TCR SIGNAL, IT
COULD BE LESS.
AND THAT'S A CAUTIONARY TALE
BECAUSE DEPENDING ON THE TYPE OF
SIGNAL YOUR CAR IS LOOKING AT
YOU COULD BE IN A REGIME WHERE
THE TUMORS ANTAGONIZE THE CAR
RESPONSE AND END UP WITH WORSE
TREATMENT THAN THE NORMAL CAR.
WE LEARN FROM MACHINE LEARNING
FROM THE LATENT SPACE AND
THERE'S A PATHWAY THERE TO
ANTAGONIZE AND BLOCK THE
RESPONSE.
THIS IS GOOD IN NORMAL T CELL
AND USE THAT TO BLOCK THE
ACTIVATION IN THE CONTEXT OF THE
CAR YOU SHOULD END UP WITH THE
TCR AND SHUT DOWN THE ACTIVATION
OF THE CAR.
IN A SENSE T CELLS ARE BAD AT
MATH AND WOULD THINK YOU MIX CAR
WITH SIGNALS YOU GET TWO AND WIN
WEAK SIGNAL YOU GET A DIFFERENT
SIGNAL AND THE MATH WILL BE VERY
COMPLICATED.
AND T CELLS ARE VERY BAD AT
MATH.
SO WE DID THIS IN A ROBOT AND
CHARACTERIZE THE CONDITION.
WE TESTED DIFFERENT
CONFIGURATION OF THE CAR AND
LEVEL OF ANTIGENS AND WE STARTED
TO BUILD A VERY COMPLEX VIEW OF
MANY DIFFERENT READOUTS AND SO
ON AND DEPENDING ON THE READ OUT
AND TYPE OF SETTINGS YOU'LL GET
A BOOST OF ACTIVATION WHEN YOU
HAVE A TCR SIGNAL OR GET
ANTAGONISM.
WE CAN DO A LOT OF MATH AND I'LL
SKIP THAT FOR THE SAKE OF TIME
AND THE MODEL WE HAD IN 2005 TO
BE UPDATED FINALLY.
WE HAVE A DIFFERENT PATHWAY AND
WE CAN FIT ALL THE DATA.
AND MOVE VERY QUICKLY BECAUSE WE
USE THAT TO BE ABLE TO MODULATE
CAR T CELLS.
NORMAL CAR T CELLS IS EXCELLENT
TO GO AFTER LIQUID LEUKEMIA AND
THE ATTEMPT TO KILL ALL THE
NORMAL B CELLS AND HAVE THE HER
T CAR T CELLS AND CAN BE VERY
GOOD AT KILLING AND THE LEVELS
YOU END UP WITH A LOT OF
TOXICITY.
SO THE IDEA WE HAVE IS NOW THAT
WE KNOW THE TCR CAN MODULATE CAR
ACTIVATION WE WANTED TO SEE IF
WE CAN USE THE MODEL TO DESIGN
THE SYSTEM TO PROTECT THE TISSUE
AND ATTACK THE TUMOR VERY WELL.
THIS IS A CONCEPT OF CAR T CELLS
ANDAA THE ABS SYSTEM AND WE
WANTED TO MAKE SOMETHING AND THE
IDEA THE CAR WHICH HAS BEEN
ENGINEERED IN A WAY IT CAN
RECOGNIZE THE TUMOR IN A
POSITIVE WAY AND BOOST THE
ACTIVATION OF THE CAR AND THE
TCR IS ONE MUTATION FROM
ANTIGENS FROM WHICH WILL BE AN
ANTAGONIST AND WE KNOW IT CAN BE
POSITIVE OR NEGATIVE AND IF WE
DESIGN IT PROPERLY WE SHOULD BE
ABLE TO SHUT DOWN CAR ACTIVATION
AND BOOST IT IN THE TOUMORS.
WE DESIGN THE TCR AND CAN DO
TESTING AND THE TIME OF TCR WILL
RECOGNIZE THE ANTIGENS VERY WELL
AND RECOGNIZE TE THE COUNTERPART
WHEN YOU EXPOSE THE T CELL YOU
GET A RESPONSE.
IT TELLS US THIS IS HEAVILY
NEGATIVE FEEDBACK AND YOU CAN
USE THAT TO BLOCK ACTIVATION I'M
RUSHING THROUGH THIS PART BUT
IT'S A COOL MODEL WE DEVELOPED
AND THE IDEA IS WE HAVE TWO
TISSUE WE'RE TRYING TO TEST.
A HEALTHY TISSUE AND TRANSFER
THE LONG CELL AND BE ABLE TO SEE
THE SYSTEM AND EXPRESS AND
MEASURE TUMOR GROWTH AND NOW
YOU'LL BE ABLE TO COMPARE THE
WAY THE CAR T CELLS COMPARE FROM
HEALTHY TISSUE TO TUMORS AND YOU
MEASURE AND SEE IF YOU CAN
PROTECT THE CELLS AT THE END.
AND THE KIND OF DATA WE HAVE IS
MEASURE ON THE Y AXIS A MEASURE
OF HOW WELL WE CAN PROTECT THE
LONG CELLS ON THE Y AXIS THE
BIGGER IT GETS THE SMALLER THE
KILLING AND YOU CAN KILL THE
TUMOR WELL BUT CAN KILL A LOT OF
CELL INSIDE THE LUNG AND IF YOU
TAKE A LOT OF ANTIGENS IT DIDN'T
KILL THE TUMOR VERY WELL AND
ONLY ABS CAR T CELLS CAN DO BOTH
HEAL THE TUMORS AND PROTECT THE
HEALTHY CELL INSIDE THE LUNG.
SO THIS IS A COOL SYSTEM AND
MOVE FORWARD TO REALLY FINE TUNE
THE WAY THAT YOU CAN GO AFTER
TUMORS AND PROTECT THE TISSUES.
THIS IS THE WORK OF MANY YEARS.
AND THAT'S THE WORK OF FRANCOISE
AND THOMAS TO MAKE THE CELLS AND
COLLABORATION BETWEEN LABS AND
MYSELF AND WITH CAN DRIVE THE
CAR BY ADJUSTING IT WITH THE
TCR.
THERE'S THE IDEA WE CAN LEARN
ABOUT THE WAY TCR INTERACT WITH
EACH OTHER AND USE THAT TO BLOCK
THE TISSUES AND FINE TUNE THE
CARS AND GET BETTER PROTECTION
TISSUES AND BETTER KILLING OF
TISSUES.
THIS IS THE ROBOT IS IN THE BACK
AND WE'RE PROUD OF THE ROBOT AND
ALL THE OTHER MEMBERS.
I DIDN'T HAVE TIME TO SPEAK
ABOUT EVERYTHING ELSE AND A LOT
OF A BIG PARTNER IN CRIME IS NOW
ME AND DOING ALL THE MEMBERS OF
THE LAB AND A LOT OF WORK IN
MACHINE MODELLING AND OTHERS ON
THE CAMPUS AND WE THANK THE
ROBOT AS WELL.
THEY GIVE US TROUBLE BUT HELP A
LOT AND THIS IS ALL THE LABS
USING THE ROBOT DOING
COLLABORATIONS WE'RE GOING VERY
FAR AND GETTING FUNDING FROM
DIFFERENT SOURCES AND IT'S BEEN
FANTASTIC AND THANKS FOL FOR
THOSE WHO SAVED ME FROM WRITING
GRANTS ALL MY LIFE AND THANK
YOU.
>> THAT WAS REALLY TERRIFIC.
THANKS, I WAS FASCINATING BY
YOUR ANALYSIS AND IDENTIFICATION
OF THE SPARK T CELLS.
AND I WAS JUST -- I HAVE TWO
QUESTIONS.
ONE IS HOW DOES FROM YOUR
INITIAL EVALUATION USING THE
MELANOMA CELLS, WE MAKE THE
ASSUMPTION THEY'RE CLONAL AND
THEY'RE NOT HETEROGENEOUS BUT WE
KNOW REALLY THEY ARE ARE
HETEROGENEOUS.
HOW DO THINK THAT AFFECTS --
>> I CAN TELL YOU WE SPEND A LOT
OF TIME ON THE DISEASE ALL CAME
FROM THE TUMORS.
AND WE DID A MYRIAD OF
EXPERIMENTS AND LOOK BETWEEN THE
TWO REACTIONS.
WE FIND BASICALLY THE VIABILITY
DOESN'T COME FROM THE TUMORS.
THERE'S NO CLONES WHICH SEEMS TO
CONTROL THINGS.
IN ALL SYSTEMS WE REDUCE THE
DIVERSITY OF THE TUMORS AND
MOVING FORWARD WE HAVE PROJECTS
IN THE LAB WHERE WE LOOK AT
TUMORS AND YOU'RE RIGHT, IT
BUILDS COMPLEXITY ON TOP OF
THAT.
IF YOU HAVE GAMMA RECEPTOR MIXED
WITH DIFFERENT GAMMA RECEPTORS
THE WAY THE TWO CELLS ACTIVATE
THE IMMUNE INTERACTION IS
COMPLICATED AND YOU BUILD ON TOP
OF WHAT WE HAVE.
>> LOOKING AT THE SPARK CELLS
AND THE CHROMATIN IS OPEN ARE
THEY ARE IN THEIR NATURAL STATE
SECRETING INTERFERON GAMMA.
>> THEY ARE DON'T SECRETE BUT
YOU NEED THE SIGNAL.
>> EITHER THEY REACT WITH THE
LIGAND.
>> THE CRAN -- TRANSCRIPTOMICS
AND WE THEY ARE LOOK SIMILAR AND
THEY'RE A LITTLE BIT MORE OF ANA
AND MOST LIKELY THE WAY WAY
THEY'RE EXPOSED TO INFLAMMATION
IN THE TISSUES PUTS THEM IN THE
STATE WHICH IS RARE.
BUT AGAIN AS FAR AS YOU CAN TELL
THAT'S WHY THEY'RE HARD TO
IDENTIFY.
WE COULD NOT IDENTIFY THEM ONLY
BY DOING ALL THE BIO
AVAILABILITY OF LONG-TERM SCALES
WE WERE ABLE TO SORT THEM OUT.
HOPEFULLY WE CAN CREATE THAT IN
THE DISH AND MAKE BIG BUCKETS OF
CELLS.
>> THAT'S THE NEXT QUESTION I
HAD.
WHAT WOULD YOU IMAGINE IF YOU
PUT THE PURIFIED SPARK CELLS
INTO A REACTION WITH NO SPARK
CELLS?
>> WE CAN BOOST FOR THE
PROBABILITY OF GOING THE RIGHT
WAY.
YOU DON'T WANT TO DO THAT ALL
THE TIME BECAUSE LIKELY IT SEEMS
THE PARTICULAR CONTEXT OF GOING
AFTER THE TUMOR IF WE CAN MAKE
THE CELLS ON DEMAND.
>> THAT WAS TERRIFIC.
MY QUESTION IS RELATED ABOUT THE
SPARK CELLS.
ARE THEY A COMPLETELY STABLE
POPULATION OR DO THEY
INTERCONVERT STOCHASTICALLY OR
OTHERWISE?
>> WHEN WE PUT THEM BACK IN THE
DISH I THINK IN ONE WEEK, FIVE
DAYS WE ARE STILL SPARK T CELLS.
WE WE NEED TO DO TRANSFERS AND
GO BACK AT DIFFERENT TIME POINT
TO SEE IF NORMAL SETTINGS WILL
CONVERT AND BECAUSE OF THE CR
CHROMATIN LEVEL THEY'RE STABLE.
>> THEY CONDITION CONVERT
QUICKLY.
COME BACK IN A FEW WEEKS.
>> I HAVE TWO QUESTION.
ONE IS ABOUT DO YOU KNOW WHETHER
THEY SHARE THE SAME T CELL WITH
THE OTHER HETEROGENEITY OF T
CELL.
>> THEY ALL EXPRESS AND ALL
NAIVE THAT'S HOW YOU CAN TEST
AND YET IT SEEMS TO BE A BIT
DIFFERENT.
WE HAVE NOT DONE ENOUGH WORK TO
SEE IF IT CORRELATES WITH
DIFFERENT TCR BUT WE'RE
INTERESTED.
>> THEY'RE NAIVE T CELL?
>> YEAH.
>> BUT YOU PROLIFERATE --
>> SORRY, WHEN YOU SPEAK ABOUT
HUMAN ONES?
>> YEAH.
>> THE HUMAN ARE A DIFFERENT
STORY.
THE T CELL BLAST AND THE MAIN
POINT IS THE TELOMERES ARE DIN
DIFFERENT STATES AND THE WAY
THERE'S GET ACTIVATED 1% MAKING
IT WITHIN AN HOUR.
WE HAVE NOT DOWN ALL BUT MORE
SIMPLER AND A PHENOTYPE AND YOU
CAN END UP WITH A DIFFERENT
PLACE.
>> ANOTHER QUESTION IS YOU SAY
BASED ON YOUR MODEL YOU CAN
IDENTIFY THE SPARK CELL.
IN THIS MODEL YOU STILL HAVE
OTHER CELLS IN THERE.
IS THERE A WAY TO CALCULATE HOW
DOES THE OTHER CELL EFFECT THE
SPARK CELL RESPONSE?
>> GOOD QUESTION.
WE'RE WONDERING WHAT HAPPENS TO
THE BYSTANDERS ARE IMPORTANT.
THEY ARE JUMP IN AND GET
ACTIVATED.
WHEN YOU LOOK AT 48 HOURS THE
BULK OF THE T CELLS IN THE DISH
WILL BE ACTIVATED.
NOW, WHETHER THE BALANCE OR
RATIO OF SPARK TO NON-SPARK
CHANGES WE DON'T KNOW WE HAVE TO
CHECK.
>> THANK YOU.
>> THAT WAS GREAT.
SORRY, THE LIGHTS ARE EXTREMELY
BRIGHT.
GOING BACK TO MANUELS WORK ON
THE PROTO SPARK?
>> ONCE YOU GET ACTIVITY YOU'LL
BLAST THE CHROMATIN ANYWAY.
YOU'LL HAVE TO CHECK WHETHER YOU
GO BACK TO THE DATA -- I DOUBT
IT.
>> DOES IT REMAIN OPEN AND START
FROM AN OPEN CHROMATIN OR EVER
GO BACK?
>> WE CAN CHECK WITH MEMORY
CELLS.
WE HAVE NOT DONE IT, YEAH,
SORRY.
>> ANOTHER SPARK QUESTION.
IF YOU TAKE YOUR T CELL AND
CONTAINS A LOW PERCENTAGE OF
SPARK T CELLS AND TREAT THEM
GAMMA ACTIVATORSES AND TREX 1
CAN YOU GET A HIGHER PERCENTAGE?
>> WE'RE DOING THAT NOW.
BASED ON THE SIGNATURE AND THE
SEQ WAS DONE AND GIVE A LIST OF
FACTORS WHICH IMPLY RESPONSE
WE'RE THINKING RIGHT NOW.
>> WOULD BE NICE FOR HIGH
THROUGHPUT SCREENING.
>> SURE.
>> I WAS THINKING ABOUT THE
LATTER PART OF YOUR TALK ABOUT
THE ANTAGONISM OF CART.
WE KNOW CERTAIN CARS HAVE BASAL
SIGNALLING ON THEIR OWN.
DO YOU THINK THAT MAY ACTUALLY
BE INHIBITING THE ENGINE
SPECIFIC?
>> ABSOLUTELY.
WHEN YOU LOOK AT THE MODEL IT
CUTS BOTH WAYS.
YOU'RE IN DANGER BECAUSE YOU'RE
GOING TO ACTIVATE THE
PHOSPHATASES TO GO AFTER THE
TCR.
AND CONTROL WITH THE TCR SIGNAL
WITH THAT POSSIBILITY AND VICE
VERSA.
IF WE DON'T BE CAREFUL IT COULD
BE A BLOCK OF A TCR SIGNAL.
SO WE'VE NOT DONE ENOUGH AND
BASED ON THE MODEL WE CAN MAKE
PREDICTS AND WHOEVER WANTS TO
TEST IT, WELCOME TO IT.
>> THE INTERPLAY BETWEEN THE CAR
AND ENDOGENOUS TCR SIGNAL CAN
YOU OVERCOME THAT WITH
EXPRESSING THE CELLS.
>> THE SURPRISING PART IS THE
ANTAGONISM WORKS IN A LARGE
REGIME.
WE'RE IN OUR ORIGINAL MODEL IT
WAS WORKING ONLY FOR HIGH DOSE.
IT TURNS OUT THE WAY IT'S WIRED
EVEN LOW DOSE ANTAGONIST CAN BE
ENOUGH AND IF YOU OVERCOME THAT
THERE SEEMS TO BE A NEGATIVE
FEEDBACK AND SHOULD BE ABLE TO
BYPASS AND GET GOING.
WE'RE FOLLOWING UP ON THAT ONE
WILL THE T CELLS AND HOPEFULLY
THE CAR SOON.
THANK YOU SO MUCH.
THANK YOU ALL.