"Can New Risk Assessment Tools for Prostate Cancer Deliver Better Patient Outcomes?" and "Novel Insights in Obesity-driven Hepatocellular Carcinoma"

February 09, 2022

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Yale Cancer Center Grand Rounds | February 8, 2022

Presentations by: Dr. Michael Leapman and Dr. Carlos Fernandez-Hernando

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00:002 grand rounds.
00:03Virtually yet again.
00:06And we have two speakers today.
00:10Doctor Michael Lippman and
00:12Doctor Carlos Fernandez,
00:14Hernando and Doctor Leibman,
00:16is gonna be first and let me
00:19just briefly introduce him.
00:22So Michael Liebman is an assistant
00:25professor of Urology and takes care of
00:29the full range of patients with Gu cancers.
00:33He graduated from Cornell University
00:35and received his medical degree from
00:37the University of Maryland in Baltimore,
00:40completing his general surgery and
00:42urology at Mount Sinai before moving
00:44on to UCSF where he did it two
00:47year urologic Oncology fellowship.
00:49He was recruited to Yale in 2016
00:53specializing in urologic oncology with
00:57an appointment at Yale and at the VA.
01:01His research has largely focused on risk
01:05stratification and and clinical outcomes,
01:07and he is widely published and today
01:10is going to talk to us about Ken new
01:13risk assessment tools for prostate
01:15cancer deliver better patient outcomes.
01:18Michael welcome,
01:19thank you very much.
01:21Well, thanks so much for the warm
01:23introduction and good afternoon,
01:25so I'm happy to speak about this question.
01:27Can new risk assessment technologies for
01:30prostate cancer deliver better outcomes?
01:34I have no disclosures,
01:36so I'm a urologist whose interest,
01:39as mentioned,
01:40are really focused on urologic cancer,
01:42specifically prostate cancer,
01:43and this has fueled my interest
01:45in understanding how technology
01:46is aimed at decision making,
01:48are used in men with prostate cancer,
01:50a disease with a high burden
01:53of decisional conflict.
01:54Specifically,
01:54I'm interested in learning how
01:56they're being used whether or not
01:58they're meeting their intended goal,
01:59and how they can be optimized.
02:01So the overarching goal of this
02:03work is to improve how we screen
02:06for how we diagnose and how we
02:08manage early stage prostate cancer.
02:10So in my time I want to cover the
02:12rationale for active surveillance,
02:14the why and how of it,
02:16and then talk about a series of
02:18advances in the past decade that have
02:20been undertaken to help increase the
02:21precision of active surveillance,
02:23focusing on prostate MRI and tissue
02:26based gene expression signatures.
02:28And then talk about our work.
02:30Looking at real-world uptake and studies
02:33to estimate the effectiveness of testing.
02:35And lastly,
02:36take a close look at the question
02:38of the equity of the dissemination
02:41of new risk assessment tools.
02:43I want to start with a patient example.
02:45A common scenario that we see in the clinic.
02:47A gentleman referred for an elevated
02:50PSA to 8.1 on routine screening.
02:52He has diabetes,
02:54hypertension, hyperlipidemia.
02:55His father had localized prostate
02:58cancer but lived his mid 90s.
03:00He has a prostate biopsy showing
03:03three corps police in 3 + 3 or grade
03:06Group One prostate cancer and has
03:08come to see us for a second opinion.
03:10Based on standard clinical
03:12risk stratification,
03:13he falls in this green category.
03:14The low risk or very low risk criteria.
03:19So this patient is presented
03:21with a few different options.
03:22He can have surgery to remove his prostate,
03:25and that's what that's what I do.
03:26He can have radiation treatment
03:28to his prostate or monitoring
03:30known as active surveillance.
03:32His inclination is to be monitored and
03:34not be treated for his prostate cancer.
03:35He knows people who've had treatment
03:38and didn't like what he heard.
03:40So understandably has many
03:41questions about his options.
03:42How risky are the treatments?
03:43How might they affect his quality
03:45of life and particularly his
03:47urinary and sexual function?
03:48And what are the risks if he
03:50does active surveillance?
03:50Can the cancer spread?
03:54Our index patient is not alone.
03:55Prostate cancer is the most commonly
03:57diagnosed non skin cancer in men,
03:59accounting for nearly 270,000
04:02diagnosis estimated in 2022.
04:05And although the incidence,
04:06the ratio of incidents to
04:08mortality is heavily skewed,
04:09prostate cancer is still the second
04:11leading cause of cancer death in males,
04:14and this finding reflects both the
04:16wide heterogeneity of prostate
04:17cancer with some cancers bearing
04:19highly aggressive features,
04:21while others demonstrate an indolent
04:22course and may never be capable of
04:25metastasis or regional progression.
04:29For patients with low risk prostate cancer,
04:32such as our patient,
04:33we're fortunate that a vast
04:34amount of data has matured.
04:35Regarding the safety and long
04:38term outcomes of surveillance.
04:40And by active surveillance,
04:41I'm referring to the careful process
04:43of monitoring low risk prostate
04:45cancer with the intention of providing
04:47curative local treatment in the future.
04:49If progression is identified.
04:52It is the preferred management for
04:54very low risk and low risk prostate
04:56cancer by the NCCN and in longitudinal
04:59studies it is safe with less than 1%
05:01risk of mortality within 10 years,
05:03and it's effective at preserving
05:05long term quality of life.
05:08The monitoring that we refer to commonly
05:10involves periodic PSA monitoring,
05:12monitoring, prostate biopsy and imaging,
05:15including prostate MRI.
05:18How strong is the data for surveillance
05:20in this randomized trial published
05:22in 2016 from the UK of nearly 1500
05:26patients randomized to receive surgery,
05:28radiotherapy,
05:29or active monitoring for low
05:31risk prostate cancer,
05:32or the 10 year overall survival
05:34is nearly 100% in all groups
05:36without significant differences.
05:38These striking findings cement the
05:40long term safety of surveillance
05:42and its centrality in efforts
05:44to push back against decades of
05:46overtreatment of prostate cancer.
05:49As part of this work and as part
05:51of this mission, we've undertaken
05:52formative qualitative interviews to
05:54gain insights about the perspectives of
05:57patients diagnosed with prostate cancer.
05:59We spoke with patients recently
06:00diagnosed with low risk prostate cancer.
06:02To get a deeper sense about
06:05their experiences.
06:05And one patient poignantly told
06:07us it was very emotional for me.
06:09My first doctor told me that I
06:10needed to have surgery or radiation,
06:12just very matter of fact.
06:14After I heard the word cancer,
06:15I didn't know what to say.
06:16I just went blank.
06:19And another patient encapsulated it.
06:21Quite simply, I wanted to understand
06:23the reasons behind why my cancer
06:25was low risk or high risk,
06:26and why active surveillance
06:27could be reasonable for me.
06:30So when faced with a diagnosis
06:32that many of us consider indolent,
06:34patients frequently feel that their
06:36life has been upended and sometimes
06:38don't feel supported by their doctors.
06:40In any circumstance,
06:41the word cancer evokes very strong
06:43and intense emotions and clinicians,
06:45including us, are very
06:47frequently unaware or unprepared.
06:50And most notably,
06:50many of our patients want to be
06:52well informed about their cancer
06:53diagnosis and management to feel
06:55agency in their decision making
06:56and assess their choices from
06:58a variety of vantage points.
07:00And it's this last point that we
07:01really want to focus on today,
07:03particularly the emergence of
07:04precision diagnostic tools that
07:06seek to deliver on the goal of
07:09enhanced risk stratification and
07:10begin to unpack how their news is
07:12is delivering on this promise.
07:17So although we commonly distill prostate
07:19cancer into clinical risk groupings,
07:21the disease is in fact quite varied both in
07:24terms of its biology and clinical course,
07:26and I want to take a few minutes
07:27to also explain why A1 size fits
07:29all approach for prostate cancer.
07:31Even low risk prostate cancer may still
07:34be too inflexible and not optimally
07:36meet the needs of our patients
07:38enrolled in active surveillance.
07:40So I showed you earlier at the
07:42excellent data from the PROTECT study,
07:43which randomized patients to observation,
07:46radiation or monitoring.
07:47I'm sorry, observation, radiation or surgery.
07:50In this study, patients did not
07:52receive intensive monitoring,
07:54but rather we only followed at
07:56arms length with PSA monitoring
07:57and only had further work up if
08:00they had overt progression.
08:02This is pretty different from
08:03how we do things today.
08:04There is no MRI.
08:06There were no men mandated
08:08confirmatory biopsies,
08:09and although the overall survival
08:10at 10 years was quite good,
08:12there were beginning to see significantly
08:14higher risks of local progression and
08:16metastatic progression in this group,
08:18likely due to misclassification and
08:21therefore this data highlights the
08:23extent to which active monitoring
08:25must in fact be active.
08:27But just how good are are we at
08:29predicting boost disease is going to
08:31progress overtime and whose will not
08:33our best clinical models based on
08:35PSA Gleason score and stage actually
08:38performed quite model only modestly
08:40with C indices ranging from .52 to 0.7?
08:44So we're really not meeting the
08:45mark yet and has significant ground
08:47to cover in guiding our patients.
08:52The questions that we want to
08:53know are actually very practical.
08:55For example, how likely is a patient
08:57cancer to spread if not treated,
08:59how often will monitoring be needed
09:02and can treatment be given in time?
09:05Due to a very high prevalence of
09:07prostate cancer and it's desperate
09:08and it's decisional burden,
09:10there's perhaps equally important need
09:12to present this information coherently
09:14to our patients and enable them to
09:16make optimal decisions and also live
09:18for years with their diagnosis and
09:20manage the associated uncertainty.
09:25Several new tests have been developed
09:27and are now commercially integrated to
09:29improve prognostication for patients with
09:31localized prostate cancer considering
09:33or enrolled on active surveillance.
09:35These tests are all biopsy based.
09:37M RNA expression signatures that
09:39measure genes highly associated
09:41with prostate cancer outcomes.
09:43The decipher genomic classifier
09:45generates a score ranging from zero to
09:491 from microarray analysis of 22 genes.
09:52The uncle Type DX test measures
09:54the expression level of 12 genes
09:57reflecting androgen signaling
09:59cellular organization proliferation
10:00and stromal response pathways.
10:03And lastly, the Polaris signature is a
10:05cell cycle progression score calculated
10:07based on the expression levels of 31 genes.
10:10Each of these tests yields discrete
10:12predictions about cancer risk,
10:13including recommendations
10:14for clinical management.
10:19So all of these tests are independently
10:21provide prognostic value compared to the
10:23standard of care variables such as PSA,
10:26Gleason, score, and clinical stage.
10:28The disciple classifier is now the best
10:31studied and has been validated as both
10:33a prognostic and predictive marker.
10:35In one retrospective study where
10:37the decipher scores were calculated
10:39based on archival FFP specimens,
10:41patients in the highest group
10:43faced substantially greater risk of
10:45metastatic progression after treatment.
10:48However, a key point is that each of
10:50these tests have been studied only
10:51in retrospective cohorts of patients
10:53who have previously been treated,
10:55and comparatively little is known
10:56about their real-world use or the
10:58decisions that arise following testing.
11:03The other major advancement
11:04has been prostate MRI,
11:06something that Yale is truly a leader in.
11:09So high resolution prostate MRI affords
11:12reliable identification of prostate cancer
11:14and facilitates directed or fusion biopsies.
11:17It also substantially improves local staging.
11:21And it's now the standard
11:22of care in many countries,
11:23including the in the UK,
11:25where it's performed almost
11:27universally in patients with known
11:29or suspected prostate cancer.
11:31And actually, at Yale,
11:32in undertaking in the majority of
11:35patients in our diagnostic process.
11:38In one randomized trial of 500 patients,
11:40MRI led to increased detection of
11:42clinically significant prostate cancer,
11:44and in fact, and actually less
11:46detection of low grade cancer.
11:47So here's the breakdown that we can
11:49see in this chart over here that
11:51the majority of patients who have
11:53a high suspicion lesion on MRI are
11:56found to have clinically clinically
11:58significant or high grade cancer.
12:00Versus quite low on patients
12:02who have a lower suspicion.
12:07So based on improvements
12:09in diagnostic accuracy,
12:10it's been assumed that the routine use
12:13of prostate MRI will also enhance the
12:16use and safety of active surveillance.
12:18So in light of a major shift
12:20in the acceptance uptake,
12:21there is a pressing need to understand
12:23how these two new forms of testing,
12:25genomic testing and prostate
12:26MRI have impacted its practice.
12:31Use of active surveillance has increased
12:33significantly within the past decade.
12:35Between 2010 and 2015,
12:37data from SEER indicates that the rates
12:40have increased from 14.5 percent 2010 to
12:4542.1% in 2015 among low risk patients.
12:49But it's also worth noting how
12:51substantially practice patterns differ
12:53for prostate cancer by geography in
12:56this elegant study recently published,
12:58the authors contrasted changes
13:00in active surveillance use,
13:01which are these yellow bars on the right
13:04by sea region, and so Connecticut.
13:06We're doing quite well,
13:07but we really see how market the
13:09differences are between, for example,
13:11Connecticut and Greater Georgia.
13:14Showing that although changes appear to be.
13:16Continuing,
13:17there's also a really a substantial
13:19amount of heterogeneity.
13:23So it's within this context that we
13:25aim to evaluate the uptake of risk
13:27assessment tools with a particular
13:29emphasis on regional considerations,
13:31and in this analysis we focus
13:33on hospital referral regions,
13:34which are Regional Health care markets.
13:36Patricia Re medical care that
13:38have previously been defined and
13:40used to characterize variation
13:42in the intensity of health care.
13:44So we first sought to understand
13:46the use of prostate MRI and using
13:48Deidentified administrative claims
13:49from Blue Cross Blue Shield.
13:51We characterize the use of prostate
13:54MRI among beneficiaries who have
13:56recently diagnosed with prostate cancer.
13:58And we found that overall use of
14:01prostate cancer increased from
14:037.2% among patients diagnosed in
14:062012 to 16.7% in 2018 and 2019.
14:12However,
14:13it's clear that the vast variation
14:15by region continues to be a
14:17dominant theme in certain areas,
14:19such as the Northeast and HRR in
14:21Connecticut are high users of Mr.
14:23As our parts of the Mid Atlantic where,
14:27whereas others show minimal use.
14:32And genomic testing presents an
14:34interesting distinction because,
14:35in contrast to MRI,
14:36which has been available for years
14:38but only rose in popularity,
14:40slowly genomic testing has become approved
14:42and reimbursed by payers at roughly all
14:45at the same time beginning in 2013 and 2014.
14:49Another consideration is that testing is
14:51also performed at remote laboratories,
14:53so complex local infrastructure
14:55is generally not needed.
14:57And these tests are very much
14:59discretionary at the discretion
15:00of the position of the physician.
15:02So to answer the question about uptake,
15:04we evaluated trends and testing
15:07at the HRR level again.
15:09In addition to evaluating the
15:10presence of regional variation,
15:12we sought to also understand
15:14similarities among regions,
15:16and we use something called group
15:18based trajectory modeling perform of
15:20finite mixture modeling to identify
15:22shared phenotypes of adoption.
15:24So to just to say it's simply the big
15:26picture goal here is to understand how
15:29regional patterns cluster together and
15:31help understand what characteristics
15:33they might share in common.
15:35Using this approach,
15:36we uncovered 5 distinct regional
15:38clusters of adoption.
15:39We can think of these as the
15:42rapid adopters red.
15:43Be slow or minimal adopters in the bottom
15:46and those that sort of land in the middle.
15:49Clusters of regions with the
15:51largest expansion of genomic
15:53testing had hired median incomes
15:55and higher education levels,
15:57and we did not notably find any
15:59significant differences by race
16:01provider density or historical
16:03use of surgery or radiation.
16:06And these these findings are important
16:08because they provide the first indication
16:10of the extent to which discretionary
16:12testing varies geographically and
16:13also proposes shared conditions
16:15that may be associated with testing
16:17and from a practical perspective,
16:19this work also reveals potential
16:20gaps in how we are applying.
16:22Testing and get can give us a better
16:24sense of the need for consistency in
16:26our guidelines and care practices.
16:30So understanding that the clinical
16:32landscape is changing with
16:33the integration of new tools,
16:35we also wanted to understand the
16:37relation of taste testing to actual
16:39clinical management received by patients.
16:41But doing this experimentally is
16:43actually is difficult in observation.
16:45ULL data, given the absence of
16:47granular clinical information and
16:48the absence of randomization,
16:50a common theme in this work is seeking
16:52therefore to understand and account for.
16:54These unmeasured bias is associated with
16:56who gets a test and doesn't get a test.
16:59And this investigation may be
17:01increasingly valuable given the number
17:02of auxiliary services in cancer care,
17:04including many like MRI and genomics,
17:07whose clinical efficacy has not and may
17:10never be evaluated in a randomized trial.
17:14So we first sought to address
17:15this question of the association
17:17between prostate MRI use and initial
17:19management for prostate cancer.
17:21Answer Medicare.
17:21After identifying a cohort of
17:23patients with low risk prostate
17:26cancer by clinical criteria,
17:27we examine the association between
17:29receipt of a prostate MRI and initial
17:32observation for prostate cancer.
17:34And assess the association using
17:37conventional logistic regression
17:38and propensity score matching.
17:40In these analysis,
17:42we consistently found a strong association
17:44between MRI use and and observation
17:46with an odds ratio of nearly two.
17:52Taking advantage of the substantial
17:54of the substantial regional variation
17:56that we saw in earlier studies,
17:58we wanted to study whether a region's
18:00adoption of prostate MRI genomic testing
18:03was also associated with changes in
18:05clinical management for prostate cancer.
18:08To do this, we identified over 65,000
18:10patients with prostate cancer and
18:12Blue Cross Blue Shield and assess
18:14both individual and regional adoption
18:16of prostate MRI and genomic testing.
18:19And we sought to test the hypothesis
18:21that regions with high levels of
18:23uptake of MRI and genomic testing had
18:26greater changes favoring observation
18:29versus treatment for prostate cancer.
18:31And what we found was that those
18:34eight hours in the highest quartile
18:36of adoption of MRI,
18:37or associated with a four point 1%
18:39increase in observation versus treatment
18:42and those in the highest quartile.
18:44Genomic testing were associated
18:46with a 2.5% adjusted increase in
18:49observation versus definitive treatment.
18:52So the way I think to look at this
18:53is that these findings suggest
18:55alignment between a regions.
18:56Use of a new risk stratification
18:58technique occurring at the extremes
19:00and changes in observation,
19:02ULL management.
19:03However,
19:03owing to the limitations of
19:06this ecological study design,
19:07we're very careful not to directly
19:09extrapolate these to patient effects,
19:11but I think the consistency of these
19:14associations and the practical
19:15observation that there seems to be
19:17a certain type or inclination of
19:19institutions or providers who are much
19:21more invested in the idea of surveillance,
19:23suggests that these two may go hand in hand.
19:29Another major focus of our work has
19:31been to understand the experiences that
19:33patients with prostate cancer have.
19:35When using these patient facing tools.
19:38Through in-depth interviews, we've also
19:40specifically focused on this point.
19:43And would speak and when
19:44speaking with patients,
19:44the responses are really quite humbling
19:47and often clarifying in their insight.
19:49Patients say, often say things like
19:50the more data you can get the better,
19:52especially if it's noninvasive,
19:54like an MRI or genomic test.
19:56But they also expressed uncertainty.
19:58I wasn't really sure about the genetic thing,
20:00and we also hear very frank answers about
20:03the experiences of going through it.
20:04The MRI was loud and I couldn't breathe.
20:07No one told me about it and I
20:09wish I knew before.
20:11So many patients seem to express this sort
20:13of maximalist approach when it comes to
20:15information about their prostate cancer.
20:17However,
20:17we also have to realize that in the quest
20:19to deliver as much information as possible,
20:21we often fall short,
20:23especially when it comes to
20:25explaining complex predictions.
20:27Iterative testing is also
20:29not without downsides,
20:30as even small low risk procedures
20:31can be challenging for patients over
20:33the long course of their disease.
20:37And lastly, as we make strides in the science
20:39and clinical implementation of these tools,
20:41it's also vital to ask, are we ensuring
20:44that access to testing is equitable?
20:46Or are we perhaps widening gulfs?
20:49This is particularly
20:50relevant in prostate cancer,
20:51where there are entrenched racial
20:53disparities in diagnosis,
20:54treatment, and outcome.
20:55Black men with prostate cancer in
20:57the United States are more likely
20:59to be diagnosed with prostate cancer
21:00less likely to receive guideline,
21:02concordant care and experience.
21:04A nearly two fold greater risk
21:06of prostate cancer death.
21:08One mechanism through which differences
21:10in outcome might occur is less
21:12access and less use of diagnostic
21:15technologies involved in the timely
21:17detection of potentially lethal cancers.
21:19In our earliest work,
21:21we identified substantially lower
21:22use of prostate MRI,
21:24even adjusting for clinical characteristics
21:27among black versus white patients.
21:2938% lower odds of prostate MRI
21:32use in in in patients with low
21:35risk prostate cancer and although
21:37there are stark disparities,
21:38there are also very market
21:40differences by region.
21:41So, for example,
21:42in the Los Angeles City Registry,
21:4515% of patients of black patients
21:47with prostate cancer received an
21:50MRI versus 28% of white patients.
21:52We do see also disparities in Connecticut.
21:55But this is contrasted by some regions
21:57where things are relatively equal and
22:00Atlanta rates were at approximately 9%
22:02for black patients and white patients.
22:06So despite a growing recognition
22:08of the existence and pervasiveness
22:09of these disparities,
22:10little is known about the root causes.
22:13And recently we aim to under to
22:15identify factors that might underlie
22:17this disparity in the use of prostate
22:20MRI using a technique known as
22:22mediation analysis to breakdown the
22:24total effect of a patient race on
22:26their likelihood of receiving an MRI.
22:28And essentially what we're trying to
22:30do is explain where does this 38%
22:32difference come from, and to do this,
22:35we proposed a model.
22:37Through which the observed disparity
22:40may be explained by clinical
22:42mediators candidate mediators.
22:43In this sort of exist as
22:46intervening variables.
22:46Those might be explained by clinical factors,
22:49socioeconomic status, geography,
22:51and structural racism.
22:57Using multiple additive regression trees,
22:59a tool of for predictive data mining,
23:02we perform mediation analysis to
23:05decompose these known disparities
23:07into their potential components.
23:09Using this approach,
23:10we estimated that variation in
23:12region accounted for 24% of the
23:14of the observed affective race,
23:1619% to residential segregation,
23:18a manifestation of structural racism,
23:2119% to socioeconomic status.
23:23And 11% to dual eligibility.
23:25A marker for low income or disability.
23:29And to our knowledge,
23:30these with the first analysis to
23:31propose upstream contributors
23:33to inequalities in access to
23:35prostate cancer technologies,
23:36and we're hopeful that these results
23:38can help inform multi level efforts
23:40to improve equitable access and the
23:41quality of diagnostic cancer imaging
23:43beginning with efforts in our own backyard.
23:48So I want to start concluding here by
23:50saying that the way that we manage low
23:52risk prostate cancer is changing rapidly.
23:54One major change that we may see
23:55in the future is fewer diagnosis of
23:57low risk prostate cancer through
23:59the use of use of refined pre biopsy
24:01decision tools such as prostate MRI
24:04and other biomarkers with better
24:06specificity for high risk disease.
24:09But among patients with prostate cancer,
24:10we've also identified gaps in
24:12access comprehension and support for
24:15patients undergoing complex testing.
24:17To close this gap,
24:18I think that multifaceted efforts are
24:20needed to help improve the consistency
24:21and quality of care that we deliver,
24:23and this is going to be a clear
24:25focus of ours in the years to come.
24:27There are also clear opportunities to
24:29improve the quality of our predictions
24:31by leveraging institutional and
24:33national data sources such as baseline
24:35genomic and imaging characteristics
24:36to refine how we predict risk.
24:38So I think it's likely that we'll
24:41look back at these snapshots of gene
24:44expression as pretty antiquated
24:45relatively soon.
24:46And lastly,
24:47I think there's a great progress
24:48in the form of advanced imaging,
24:50including pet tracers with high
24:53sensitivity and specificity for
24:55prostate cancer that will soon likely
24:57be part of our diagnosis and tracking.
25:00So I want to stop there and conclude
25:02by saying that new technologies
25:03have been deployed to increased
25:05precision in the management of low
25:07risk prostate cancer patients.
25:08When you speak to them clearly value
25:11information about their cancer in one
25:13agency in the decision making process.
25:16Genomic testing and prostate MRI are
25:18associated with increased use of observation,
25:20but Kohl's relationship is
25:22still not clearly defined.
25:24And lastly,
25:25as we make strides in the science,
25:26we need to sharpen our attention to
25:28disparities in access that may in fact
25:31widen racial and geographic disparities.
25:35And I just want to say
25:36thank you for your time.
25:37I'm incredibly grateful to my wonderful
25:40mentors at the Yale Copper Center,
25:42particularly Kerry Gross.
25:43Shelmet Mott have been instrumental
25:45in developing this work.
25:47Extremely grateful to my colleagues
25:49in the Department of Neurology
25:50and the Yale Cancer Center has
25:52also been generous supporters
25:53of this work as well.
25:55Thank you.
25:57Thanks very much, Michael.
25:58If people have questions if they
26:01can put it in the chat and I'll
26:05I'll ask a question while we're
26:07waiting to see what people have.
26:09So is getting an MRI in it of itself
26:16something that leads to better care
26:17or is it a marker of doctors who
26:20provide a different kind of care?
26:23Yeah, it it's that's really.
26:24I think that the main question
26:25we're wrestling with it.
26:26It probably is a little bit of both.
26:28I mean, I think that the MRI
26:30you know if MRI is not even
26:31on the on the table for you,
26:33you're probably receiving one type of care.
26:35But I think but you know,
26:36with these very powerful tools you can,
26:39we can make.
26:39We can go in the wrong direction very
26:42easily because all of a sudden you have.
26:44A vast amount of data and one
26:46potential concern is that we may
26:48overestimate risk because we're
26:49finding you know things that we never
26:52found before and then therefore,
26:54patients veer off the path of
26:56surveillance because you've technically
26:58have found something that you had
27:00to work very hard to look for.
27:02Sure,
27:03thanks, and there's a question.
27:06Can you talk a little bit about
27:08what we're doing as an organization
27:11to minimize disparities?
27:13And I'll I'll focus this
27:15specifically on prostate cancer,
27:17although it wasn't written that way.
27:18Well, yeah, thank you. I mean,
27:19I think that you know the first step
27:21really is kind of understanding this,
27:23and I think that this when we
27:25you know we're so excited about
27:26the technology and we're only
27:28beginning to ask these questions.
27:29So it starts.
27:30I think with just very basic quality
27:33improvement efforts and we have an
27:35outstanding quality improvement team
27:37within the Department of Urology that's
27:39focused specifically on this question.
27:41And so I think that will be part of.
27:43Are you know?
27:44Interim reporting and quality
27:46improvement process to make sure that
27:48we are not disproportionately offering
27:50these services to certain groups?
27:53And and finally, so what's going
27:56on in California and Atlanta that
27:58that that we don't see the same
28:01kind of disparities? Any any clue?
28:05I, I think that I mean,
28:06that's really where I think that
28:08that you know major centers.
28:10You know it's this MRI and
28:11genomic testing are really
28:13an early adopter phenomenon.
28:14So I think we have a
28:16disproportionate influence.
28:16I think that in Los Angeles,
28:18certain medical centers probably also
28:20have a disproportionate influence,
28:21so all the more reason to be
28:24very circumspect and proactive
28:26in in when we roll the when
28:29we roll these things out.
28:31Great,
28:32well, I think we're going to move
28:33on to our next speaker, Michael.
28:35Thank you very much.
28:36It was really great. Thank you.
28:39So our next speaker is
28:43Carlos Fernandez Fernando,
28:46who is the Anthony and Brady Professor
28:49of Comparative medicine and pathology.
28:52He studied biochemistry and
28:54molecular biology at the University,
28:57Dodge Autonoma of Madrid,
28:59and received his PhD at Hospital,
29:02Vermont in Madrid as well.
29:05He did his postdoctoral work
29:08with Doctor William.
29:09Tessa here at Yale.
29:11His first position was
29:13faculty position was at NYU,
29:15and then he returned to Yale where
29:19his research seeks to identify novel
29:21mechanisms by which cholesterol and
29:25lipoprotein metabolism are regulated
29:28and without further comments,
29:31I'm going to turn this over to Carlos.
29:48You're still on mute.
30:00OK, now I see this working well
30:04so we can't see your slides
30:05at this month. Now we can
30:07OK. Thanks very much.
30:11I really appreciate the invitation for
30:13for giving the presentation today.
30:16Let me put this in full skin, uhm?
30:18As as you mentioned, I'm not a.
30:21I didn't never study much about
30:23two more biology or counselor.
30:26I did my PhD in biochemistry back
30:29in Madrid and at that time I was
30:32interested to study how cholesterol
30:34metabolism and other lipids regulated
30:38leukemia cell proliferation.
30:41Since then here I moved to the field of
30:44vascular biology for many years until,
30:46like about four years ago I
30:48get a very incredible,
30:50talented student come into my lap.
30:52To do that, PSD.
30:56As I do always with the people
30:57who has this passion for science,
30:59I ask them whether it is the product
31:01they want to do it and then he told me
31:03that he was very interested in deep.
31:04It's like he wanted to do something related
31:06to two more biology and immunology.
31:09And then we came up with this project
31:12because has something related to
31:14lipids and also it's a problem related
31:17to counter that is is based in the
31:20however city or lipid metabolism, Dr.
31:24Local phenomena using a mouse.
31:26Of the disease.
31:27Then I wouldn't guys like to
31:29get full credit to Jonathan,
31:31who actually wears the person.
31:33The driving force here.
31:35Who did this work? Why?
31:38Why this tumor?
31:39Why we were interested in a
31:42particular carcinoma as this?
31:44No more,
31:45he said he became more prevalent right now,
31:48particularly with the situation
31:49that we are with this crisis.
31:51So overeating and obesity
31:53and type 2 diabetes that is,
31:55having in all the Western societies.
31:57Then, as you probably are aware,
31:59about 30% of the population in the
32:02United States that accumulates large
32:04amount of neutral lipids in the
32:06liver and cause this pathology called
32:09non-alcoholic fatty liver disease
32:11that is quite prevalent from this situation.
32:14We saw that about 25% of the people
32:18that has Nathalie our transition to
32:21an estate of nasty is non alcoholic.
32:25Like that is,
32:26which is characterized for the
32:29cyclonic inflammation that occurs
32:30in the liver and the fibrosis.
32:35As well as by the damage and turnover
32:37that happened in this particular situation,
32:40then this is kind of a chronic disease,
32:42but in about 5% of the patients they
32:44are able to transition to develop.
32:49This is actually a pretty bad kind
32:52of concert, since the survival
32:54rate is pretty low in general.
32:57Then when we set up the
32:59idea for for his thesis,
33:01we were actually looking at that
33:04time for developing and noble mouse
33:06models to start exist and also to
33:08apply novel technologies that they
33:10start coming out at that time.
33:12We try to investigate the molecular
33:14level where what could be the driver.
33:16So the formation of the catalog in a
33:20model of obesity driving tumor formation.
33:22Uhm? Around that time,
33:25the group from Matthias Eichenwald,
33:28airing in the Cancer Research
33:30Center in Heidelberg,
33:32published this novel model of obesity,
33:35driving part of local phenomena that was
33:38based in feeding the mice without killing,
33:42defeating high fat diet.
33:45In the upper panel you see a
33:47number of papers that this group
33:50publishes recently using this.
33:52This model of the disease.
33:55It's a,
33:55it's a pretty good model in in our opinion,
33:59because, uh,
33:59the mice develop all the features that
34:01help the people that develop obesity,
34:03type 2 diabetes and ended up having this
34:06issue that is increasing body weight,
34:10type 2 diabetes, insulin resistance,
34:13and eventually none of them.
34:16But you know,
34:17substantial amount of mice develop.
34:21This is one of the benefits of the
34:22model that the capital quite well
34:24did not have any human disease.
34:26The downside of the model is
34:27not not all the mice developed,
34:29but of local phenomena only on a
34:31small fraction of mice, around 20%
34:33of the mice and develop the disease.
34:36In 12 months we were unable
34:39to reproduce this high.
34:42Incidence of.
34:42In my mind we have to extend our
34:45studies to 15 months to see that.
34:48And one of the first thing that
34:51we did was to fed the blocks
34:54is miles for about 20 months.
34:57With the this calling the fishing
34:59Haifa diet and we sacrifice this
35:02my son different time points
35:04three months and states that
35:05is considered to have NFLD.
35:07Six months the NASA state and then
35:10we were waiting on the 15 months to
35:13study the formation of tumors in mice.
35:16These are in police data from the
35:18the war from from Jonathan and and
35:21this is in the upper left corner.
35:23You see the loyal to experiments that
35:26we did here and all the analysis that
35:29we did in every day and in every time
35:32point I'm going to show you only a
35:34few data about the this the whole study.
35:36But then we actually collect
35:39issues at three six 12115 months
35:41to do lipidomic analysis.
35:43Public functionary sequence
35:44in single sequencing.
35:46Oregon municipal Chemistry not
35:47only for delivery but also for
35:50other tissues and also we track
35:52the glucosamine stasis and lipid
35:54metabolism every time point.
35:56As you can see here,
35:57when you start feeling this match
35:58with Pauline defeating high fat diet,
36:00this might gain significantly amount of
36:02body weight and disappears very early on.
36:04After putting the mice in this diet
36:06and the increasing body weight is
36:09accounting because the increasing
36:11pad mass and the lean mass in
36:14the masses similar during the
36:16feeding time but the fat mass is
36:19significantly increased up on high
36:20fat diet feeding and these mice in
36:23in addition to half obesity that
36:25develop significant dyslipidemia.
36:28Down in the lower panels.
36:30So in the high levels of cholesterol
36:33interpolation that are in significant
36:35increase in the later time points
36:37and this increase in cholesterol
36:40correspond to an increase.
36:42Circulating levels of LDL lipoproteins
36:44and stone in the middle panel.
36:47We also perform GTIT assets to
36:49demonstrate that this mass has
36:52insistent and glucose intolerance,
36:54and I'll show you here.
36:56Also,
36:57the fasting glucose in these mice
36:59aspects are also significantly elevated.
37:02Then we have a model that developed
37:04the three stages of the disease and
37:06also recapitulated quite well on the
37:08metabolic alteration that is found
37:10in in in in people with obesity and.
37:12Anti two diabetes.
37:14Then we also perform Mr.
37:16Local analysis in in these mice and
37:20Carter is well what's going on.
37:22And as you can see here,
37:23this might develop significant
37:25accumulation of lipids.
37:27You see the ballooning also there
37:28in after three months and six months
37:30in high point diet and also a
37:32significant fibrosis and damaging the liver
37:34as shown in the right panel by standing
37:38with serious right is most developed.
37:41Fibrosis early on as well,
37:43and the formation of fibrosis is
37:47also correlated with a significant
37:51increase in inflammation.
37:52So in the analysis and the flow cytometry
37:55analysis, or in the lower panel,
37:57I'm analyzing the 3:45 positive
37:58cells in the liver as well as
38:01neutrophils and also monocytes,
38:02and Cooper feels as well.
38:05Then one other result that we found here.
38:08We notice that after 12 months,
38:13efficiency of the of the the
38:14people of the development of
38:16commercial was quite restricted.
38:17We found seven of the 39 mice
38:20developed tumors and all of these
38:22correlate with the more or less with
38:25the simulating alpha fetal protein
38:27levels in circulation of the mice.
38:30As you can see in the in the 15 months group.
38:36The incidence of the two more simply
38:38significantly many of the money the mice
38:41around 50% of the mice develop tumors,
38:43and also they feel levels are
38:45very hot in the right panels you
38:46can see a representative image,
38:48so the kind of tumors that you observed
38:50in in this mouse model of the disease.
38:55Then we asked two questions
38:56and I'm going to be kind of.
38:58I'm going to sumarize all all all
39:00the other we have here, I mean.
39:03Happy to share more when next.
39:04I know you guys it can send us emails we can.
39:07We can meet with all of you
39:09and you know so with you.
39:10But the the two key aspects that
39:15you're not gonna want to address here,
39:16we're still first delineated,
39:18the metabolic changes that occurs in a party.
39:22They progress toward the Council
39:23felt I'm going to show you some.
39:25You know fewer slides about that.
39:28And the second part of the talk I'm
39:30going to focus a little bit more
39:32is about identification or novel.
39:33Potential targets that are
39:36associated in the development of
39:39the disease in this mouse model.
39:42Particularly in this protein fatty
39:44acid binding protein five that,
39:46as I will tell you in a minute,
39:48is a protein that is important not
39:50only in regulating lipid metabolism.
39:52Also preparation.
39:55Regulation of the suppression
39:56of this protein has been
39:59associated not only in liver.
40:02Humans, but also as a highly
40:05associated with prostate tumors,
40:07as engaging with the 1st.
40:10And part of the the first talk
40:12of the in the meeting today.
40:14Then if I will be 5 is highly elevated,
40:16doesn't prostate tumors,
40:16and there are a number of groups,
40:18particularly were collaborators
40:20that are looking at selling
40:22the efficacy of everything.
40:24In treating prostate cancer.
40:29Then this is a cartoon. That's true.
40:33Marissa little bit the.
40:35The son of the first experiment we did,
40:38we, we took a pentag here the 10 the
40:41the single cell RNA transcriptomics.
40:44Fight change in the pattern of gene
40:46expression not only in the patio sites but
40:49also in in the non parenchymal health,
40:52particularly in both illegal sales.
40:53Only minute sales.
40:56And then we will turn to look into it.
40:58Where where are the changes that occurs
41:00in the the tumor progression and how this
41:03has been associated with the disease?
41:06Then with the support of the
41:09liver center here ideal,
41:10we were able to isolate this quite
41:13well and and you're not on set up a
41:16very good protocol for keep this high
41:18school Bible on these cells and try
41:20to isolate these cells as soon as
41:22possible just to avoid any kind of a
41:25target effect giving their solution.
41:26Process.
41:27Then we did the analysis in
41:30different stages here and here.
41:32You have couple of humor plots.
41:35Then in the left plot the thing
41:39that you see here is all the single
41:42events thereby shown by dots that
41:45corresponding are grouped in different
41:47colors that correspond with the
41:48different cellular populations that
41:50you all share in the in the livers.
41:54Then here are input.
41:57Five different for different groups.
41:59One of them is the mice that
42:01are filled with the child diet.
42:04Then they might that they were filled with
42:07the five fat diet with low AFP expression.
42:10Then others would have high FPS
42:13present and then we also input
42:16directly the DDST carcinoma here.
42:18Then you can see here how all these
42:21sales group quite well and in the in the
42:24right panel that in that you see now is.
42:27How the diverse populations can be
42:29clustered and based in the differential
42:32gene expression and different stages
42:34from mice that are affecting child
42:37idea with versus mice that are
42:39fed with this calling deficient.
42:43Then you see there the the path
42:44aside and the concepts and cluster
42:46and that is in the in the red box.
42:49And as you can see here by the
42:50color you can see how the population
42:52shifted to the right.
42:54Since you had the 1st in in normal
42:57diet and how they transition to these
43:00cancer cells that are highlighted
43:02in the purple.
43:03These are the purple dots in the
43:06path aside group correspond to the
43:09the cancer cells also within the two
43:12more you see. Highly abundant also.
43:15The details suspected that is also
43:18in this plot.
43:20Then doing this kind of analysis,
43:22you can infer all the information
43:24coming from all the gene expression
43:26for every single event during
43:28the transition from the side.
43:30The healthy side do the content fell
43:32and you can do this kind of analysis
43:35called silver time that can tell you
43:37how these cells can transition from
43:39the healthy to the contact stage.
43:45Then this is the nicest.
43:46Save the time that you're not
43:47funded and you can see here.
43:49You can actually Coop at this quite
43:51well in the trajectories that instills
43:53come followed in the cellar time,
43:55and can group is also very well in
43:58the violent plug in the right panel
44:00you can see here probably better
44:02where the IPA or the pathways that
44:05appears to be deregulated in the
44:07process of the converting this healthy,
44:10but aside to two more two more
44:13two more cells.
44:15One of these boys are
44:18now under investigation.
44:19I'm going to talk about this.
44:22Like binding protein,
44:23but also we did metabolic analysis
44:25here in collaboration with
44:27Rachel Berry and follow up very
44:29well on these findings but.
44:31Things that you can see here is
44:33that there is a number of bad ways
44:35that appears to be regulated during
44:37the transition from the healthy to
44:39the malignant L and including the
44:42lipid oxidation understand toxic
44:44radical and then you have also
44:46significant and in regulation of
44:48further progress as people lipids,
44:50including the importance of transport.
44:53Then the the molecule I'm going
44:55to tell you about today,
44:56fatty acid binding protein actually
44:58play an important role in this pathway.
45:00So I want to show you why this
45:02could be very relevant.
45:04One of the question when when you do
45:07single task to mix now we are trying
45:09to do a special transcriptomics
45:10to see where I'm located.
45:12These cells within the tumor and how.
45:17How even is this pressing of the
45:20these genes across the tumors?
45:23This is decent ongoing war with
45:25the collection with sticking one.
45:26One of the things that we did in
45:29parallel in another different study
45:30that the Inmaculada Root Maldonado
45:32help help in this this world is
45:35trying to develop another mouse
45:36model here that is a rainbow.
45:38Mice that allow you to study more
45:41clonality this this mouse model that
45:44thing that does is randomly labeled
45:47the patio sites in three different colors.
45:50As you can see in the center pictures.
45:53These are the control mice and
45:55then you can see here where is the
45:57random distribution of all the paper
45:59cites in the three different colors
46:01and in green color are stain and
46:04the cells that are non epicycles.
46:07This correspond to the endothelial cells.
46:12And the mother was quite well to study.
46:14Regeneration is something I'm
46:15not going to touch today,
46:17but here in the right panel you see in
46:21a model of liver injury that treatment
46:24with carbon tetrachloride that induce death.
46:27And then you can start regeneration.
46:29You can see how you see a very nice
46:32clonal expansion of some of the
46:34existing apotheosized to develop
46:36these patches in different colors.
46:38Then we also employ in this same
46:40model to see where we actually
46:42happen in the context of aging and
46:43the context and situation where we
46:45have a chronic metabolic damage.
46:47What's happened with naphthalene,
46:50and this is actually very interesting,
46:52as you can see here,
46:53because even in US you see their
46:55selection on some specific clones that
46:57occurs in the liver in the left panel,
47:00and this is probably because you
47:01have this damage and regeneration.
47:03Delivery is a very interesting
47:05organ to study that.
47:06And you can see also accumulation of fat
47:09here with showing this like dark spot.
47:11But why I want to why I want to illustrate
47:14you this model and why it's very interesting.
47:17This model is because you can
47:19actually start to mortality.
47:20Then you can not only see.
47:22Diversity of two more sales by
47:24single seller and just get to me.
47:26But you can actually interrogate
47:28whether the two more unit for one
47:30cell that expanding a single clone of
47:32is coming from two different clones.
47:34And the thing that we are seeing now
47:36is preliminary is that many of these
47:38two more oligoclonal or monoclonal.
47:40This is the right you can see
47:42only two cells or maybe 2 patches
47:44and only in blue and yellow,
47:45suggesting that some of this too much
47:48pressure originated one or two cells.
47:51Doug knows the kissing status and
47:53started providing status started
47:56the United two more so in there.
47:58And we're using these kind of tools to map.
48:02The the molecular mechanism how?
48:05How diet induce?
48:08Obviously this is actually a very nice model.
48:10Also to study metastasis.
48:11The model can do it.
48:14You can actually track all this Cape
48:16and track those cells in different
48:18colors to see where the mass is being
48:21caused by the tumor's oriented in
48:23blue color or unity in jello color.
48:25And then this can be a stand and be
48:28using for other kind of tumors as well.
48:30Then one other thing that we're
48:32trying to look here is OK where
48:34you know this could be important.
48:36That may drive this,
48:37and this is when we identify 55
48:39then then in the in the left panel.
48:42This is uhm again.
48:43So in the suppression of alpha fetal
48:45protein and this is a totally restricted
48:47in most of all the content cells
48:50that is not actually expressed in
48:52this adult in the adult hepatocytes.
48:55Then if I will be 5,
48:57it's identified here as a very.
49:00You know remarkable and very specific
49:01for this tumour cells and and you can
49:04actually interpret this in the cellar time,
49:06if I will be 5 here.
49:07When you import the seller time and
49:09put here the events in different colors
49:11again in these dots corresponds to
49:14the patricide in different diets and
49:17coming from from different animals with low,
49:20high or directly from the Patella carcinoma.
49:23And you can see here that the
49:25expression of every five in healthy
49:27liver is pretty much nothing over.
49:30Very lowest price,
49:31but then they start to be highly suppressed
49:33when when the match start developing
49:35tumors which is actually there came out.
49:37Very interesting for us because if you want
49:39to target something or silence something,
49:42it's better to silence
49:43something in the liver that is
49:44not expressing a healthy tissue.
49:46Then you should expect a very low
49:48or non non off target effect or
49:51the therapeutical intervention.
49:53That is something is by expressing the
49:55liver and maybe you are messing around
49:57with another different function that
49:59could be important for other things.
50:00And This is why it was very interesting
50:03for us to follow at this target.
50:05Then then Jonathan went ahead
50:06here and try to identify also buy
50:09monistat demonstrate expression of
50:11F 55 in in in tomorrow's theses
50:13animals committee analysis so in very
50:15clearly here in the green color.
50:17The highest president of 85 and again
50:20highly restricted to 2 more when
50:22you compare with a healthy agent.
50:23Healthy liver in the in the lower in
50:26the lower panel and again this this.
50:30It's pretty much whistle,
50:31so corroborated by Western blot
50:33analysis in the right place.
50:35As I mentioned to you,
50:36and this was very exciting and now
50:38when you go to the human teeth that
50:40you can see that but you know not only
50:45my mentioned before so caustic content.
50:48Has high levels.
50:49I think that's enough and not
50:52only is very elevated,
50:54but also the overall survival of
50:57the persons with phenomenal at fast,
51:00high levels of advice significantly
51:02diminished with those that have
51:04low expression.
51:07Then then well, what is 55 doing and then
51:10when you start to see the territory?
51:13I'm not only in the context of the cancer,
51:17feel like another field,
51:19you see that you know we have as
51:21many scenes and it's not clear still
51:23is the mechanism of action that can
51:25have passed in the tumor pressing.
51:27Then I tell you be 5 and the renal name
51:30was given like SVP 5 because it's highly
51:32abundant in the epidermis is highly
51:35expressed. Their win was was found.
51:37Uhm, has been associated with multiple
51:40tumors, as I mentioned to you.
51:41Long trusted and then the mechanism faction
51:44that has been ascribed or if it be 5 is,
51:48there are several one of them that
51:50there probably is the most established
51:52is that the F B5 is a little chop
51:55around that binds to this party.
51:59Particularly by Mary got it appears
52:00to be a very potent Liam for that and
52:03activate the people better then and then
52:06regulate fatty acid synthesis and storage,
52:08but also regulates a lot preparation as well.
52:11There were also a number of other papers,
52:13particularly in San Fran,
52:14and it appears that they studied the role
52:17of T cells die actually discovered at
52:2055 is actually a mitochondrial protein.
52:23There that maybe the transfer of
52:25fatty acids and it's important for the
52:28Christian morphology in the mitochondrion,
52:30also controlling the fatty oxidation.
52:32Please dance as well.
52:33And also there were a number of
52:35other papers out there.
52:36Point out that 55 can control
52:39actually ER and maybe content and
52:42therefore control translation control.
52:45Also the activation of standard
52:47transcription factor that resides in
52:49the ER and also may regulate as well.
52:51The ER stress in in these cells.
52:55Then then we have generated the
52:59conditional local mass model that
53:00now are on the diet we we don't know.
53:03We we're looking forward for the
53:05genetic model,
53:06but in the meantime we call out my
53:09Martin Passando Gemma who got a
53:11multimillion ground with a group in
53:14Cold Spring Harbor to use the fighting
53:17hitters for treating prostate contact.
53:20Main character is so nice in the
53:22left the doctor organized the chair.
53:25Of that chemistry and they developed
53:28those specific incubators for 75.
53:31Then we call them Adam, basing the size.
53:33Large amount of this indicator for us
53:36for treating the HTC in this model.
53:38Then the thing that we did here is
53:41treating blacks eat mice with the
53:43high fat diet calling defeating diet
53:45for 12 months,
53:46then inject the inhibitor and then
53:48track the two more progression for the
53:50next demo and evaluate also potential tumor.
53:55And this is a data that was really incredible
53:58for us because the the data was stunning.
54:01I mean,
54:02we we we were not expecting such
54:05as a nice outcome in this model.
54:08The thing that you see here is that
54:10when you leave the mice to progress
54:12without treating or vehicle treated,
54:13you see that again we were able to
54:16reproduce the data we saw previously.
54:17That about 50% of the mice developed tumors.
54:21But when you treat these mice.
54:24For the last three months and keeping
54:26the mice and I do see that the two more
54:30incidents in significantly reviews
54:32which we only observe only 6 of 20.
54:35Did not work.
54:36Even more remarkable is when we actually
54:38measure that replating anything this nice.
54:40Yeah,
54:40you can see that.
54:42There you see levels increase in the
54:45mice that progress towards the disease,
54:47but those miles adapted with the.
54:51Incubator not only you stop the progression,
54:54but you see also a regulation of
54:56these bodies.
54:57Then this telling us that this
55:00track price not only preventing
55:03but it's only private private from
55:06something that we had to study.
55:08We had to do further studies with
55:10imaging and use my miles to to
55:13make this as a conclusion.
55:16Then you Nathan did a number of studies here,
55:19and this summarizing 3 or 4 slides.
55:22But this is the pathway analysis
55:24that he did in this tumor cells.
55:28This is coming also from the single
55:30cell RNA transcriptomics as well.
55:31Then this is actually restricted to.
55:35The the other side within the the phenomena.
55:39But things that you can see again is.
55:41You see also that the suppression of these
55:45five you see an increase in pressure.
55:47Someone living metabolism Bedok
55:49sedation things that you sit down in
55:52the model you start to see higher here.
55:54Uhm? He's also a number of.
55:59And all that analysis in in human cells.
56:02Then this is the the study that he did in
56:05in age who is 7 human local cinema line.
56:09And try to go even induce you know deeper
56:14study here where he treat with inhibitor.
56:18These tales for 48 hours and they are
56:21not sequencing analysis and again.
56:23He found many pathways that it was
56:25being out there and one of them that
56:28was remarkable clear is that they are
56:30stress and I'm going to show you some
56:33data regarding that thing appears
56:34to be significantly up regulated
56:36in my city with the the typing.
56:40Then for the Penguin analysis, the the data.
56:43These data suggest that this incubator
56:46influence or regulate the pool of
56:48industrial loan chain fatty acids.
56:50We are now doing a bit American
56:52alloces and those tumors and also
56:55that induced lipid peroxidation
56:57and free radical accumulation.
56:58Jonathan follow up is I'm
57:00going to show you another,
57:01but he has also data in these cells,
57:03proving that at the biochemical level
57:05and also so very clear data showing
57:07that the oxidative stress also in
57:09the US here stressing these tales.
57:10Unluckily,
57:11this induced cell death in this match.
57:14In there in these two more cells.
57:16This is the heat map from scientist pathways.
57:19You can see that the pair UTR padway being
57:23significantly unregulated in the mice.
57:25And just to finish,
57:26because I'm running out of time, yes,
57:29the last couple of slides this is.
57:32An example of some of the experiments
57:34he did here,
57:35and when we did with the cells and she
57:39very nicely over with very bad way.
57:41And not only that,
57:43but when he did also understanding here
57:45with PIE and also an accident to track.
57:47I felt better so that their treatment with
57:51the SP FY103 in DSL data higher dose.
57:55Obviously,
57:56one thing that we're really must looking
57:58for ways to the data with the genetic
58:00model that we're developing right now,
58:02we know that this inhibitor,
58:05despite you know they show a high
58:06efficacy and how specificity
58:07of inhibitor we know that they
58:09can have some after that fact,
58:11and we're trying to combine this with the
58:13genomic data just to demonstrate the role.
58:15Sucky role of fighting into more
58:18pressing then this is a little
58:20bit the summary of this of this
58:22work I'm doing so many things,
58:23but the thing that we. Trump
58:26carries that the suppression of.
58:30Mr accumulation of fatty acid.
58:33Resulting in increasing years, just new
58:35Paris Ponce leading to Apple classes.
58:38This is only like 50% on the part of
58:42the story because the thing that he
58:45Jonathan also observed here is that
58:47not only the inhibitor has a very
58:51important effect controlling the.
58:54And the cancer cell metabolism
58:56reducing this year stress and
58:59and dependent apoptosis in in.
59:01Directly related to other side,
59:03but also he found a very interesting
59:06wire in the two Micron violent.
59:08In these tumors.
59:09One thing that was very clear for the single.
59:14Analysis is that that would be 5 positive.
59:18Macrophage has more kind of and
59:20inflammatory terms and then when you
59:23actually suppress this you leave more.
59:25The formation of 19 presentation failed
59:27that they stimulate more T cells and this.
59:33Activation have working activity
59:34on these two more cells and then
59:37we we think that this inhibitor,
59:39which is actually very interesting,
59:40is working in different ways,
59:42not only reacting in the counterfoils.
59:45But those show acting at the in the two
59:48more microenvironment at the level of the
59:52immune response within the two months.
59:54Then with this I would like to to
59:57finish the presentation again.
59:58I put them in capital letter.
60:00Jonathan served all the credit for this work.
01:00:03He took the challenge to do it and.
01:00:06Did many models to study this
01:00:09and employing novel technology,
01:00:10then then he really did the the
01:00:13person who decided credit for this
01:00:15work and also my laboratory has
01:00:17been actively collaborating all the
01:00:18time with the laboratory Suarez.
01:00:22I would like to thank so Steven and
01:00:24you Meow who are helping us with
01:00:26the Murphy's technology to map out
01:00:28the special task atomic level with
01:00:30this happening in these tumors.
01:00:32Also Rachel Berry City than
01:00:35unbelievable work.
01:00:36Stop trying to show that I'm doing
01:00:39the metabolic analysis that happen
01:00:40within these tumors with this pinned
01:00:43analysis that is doing in her lab
01:00:45is fantastic collaboration and also
01:00:47marketing Stony Brook for providing
01:00:50not only inhibitor but also the FTP
01:00:53Firefox miles that they developed
01:00:55in the laboratory and with this
01:00:58habit technique question.
01:00:59So
01:01:00thank you very much, Carlos.
01:01:01We're a little short on time,
01:01:03but there are a couple of questions
01:01:06in and why don't we get to those?
01:01:09So the first is based on your mouse model.
01:01:11Do you have any explanation why
01:01:14Nash related liver cancer is less
01:01:16responsive to tyrosine kinase
01:01:18inhibitors or immunotherapy?
01:01:20Then viral related patterns
01:01:21cited against her.
01:01:24Well, I think it's a great.
01:01:25I think it's a great question
01:01:26and I think we will take note
01:01:28of that because I didn't know.
01:01:29But the thing that we see is a very
01:01:31strong component in the muni response
01:01:34in these tumors with high fat diet,
01:01:36we don't think that they discuss
01:01:38actually with with Jonathan is to
01:01:40look into the other data from Michael,
01:01:42Karen and others that use models
01:01:44of cathedral in induced coma
01:01:47and just to compare,
01:01:48where are the immune landscape in these
01:01:52tumors compared with the high fidelity user?
01:01:55Then, and this is a great question,
01:01:57then about the map kinase we got.
01:02:02We got a pilot grants here at the L
01:02:05and we partner with Anthony Bennett
01:02:07who actually work in mechanics and then
01:02:10we are looking in this pilot for the
01:02:13transition between novelty and mass,
01:02:15but the part of the things that
01:02:16they are going to study is with
01:02:18issues that we have in this nice.
01:02:20We're going to look how all these
01:02:22mechanics activity is being affected
01:02:23during the transition of friendliness
01:02:25and potentially in the cinema,
01:02:27but both both are great questions
01:02:28and we are looking into that.
01:02:30And in that question was from her to Chow,
01:02:34and this is from Claire Flannery great talk.
01:02:37Thank you. We're experiments for HTC
01:02:40development done in female mice.
01:02:42If so, were there any difference in HTC
01:02:45development time relative to male mice?
01:02:47Well, great question also and I
01:02:49think and I think it's a great
01:02:50question because you know today
01:02:51that the user not granted NIH.
01:02:53You have to have both. Then.
01:02:55Then I want to point out that
01:02:57we did this experiment with with money
01:02:58that it was not supported by grants.
01:03:00And obviously I mean you can.
01:03:02You can analyze the standard.
01:03:04The study take two years,
01:03:05but I'm going to be short in the answer.
01:03:08We need only in males,
01:03:09but will be extremely interesting
01:03:11to do in females then.
01:03:13Then we did some experiments in female there.
01:03:15Rainbow studies were done in females
01:03:17and the two more incidents actually in
01:03:19females are significantly lower than males.
01:03:22OK, then you have to wait even like two
01:03:24years in high fat diet and the tumor
01:03:27extent is not the thing that you see in then.
01:03:29It's difference in male and females.
01:03:31This has been shown this in modern cinema.
01:03:35In mice I I know, I know,
01:03:37much aware about the literature in human,
01:03:38I should be more aware now after I
01:03:41sent him again, I will read more,
01:03:43but but at least in mouse models OK?
01:03:47Because being shown that in in in the in
01:03:50other models the females asked happen
01:03:53here develop significantly as less tumors,
01:03:57and this has been associated in part
01:04:00to the adipose tissue production of
01:04:03adiponectin and another or modes.
01:04:05Then it's not clear whether this
01:04:07is translated to human,
01:04:08but looks at all the depots that
01:04:11are different fat depots that are
01:04:13different in male and female appears
01:04:15to be affecting the hormone secretion.
01:04:17It has an impact on the tumor formation,
01:04:20at least in mice.
01:04:21Then then it's a great question we should do.
01:04:23We have the rainbow Maes was done
01:04:25in females and This is why I tell
01:04:27you that in this model the two
01:04:29more incidences is less, but yes,
01:04:31I mean we should actually try
01:04:32to do them more,
01:04:33yeah?
01:04:34Well, there are other questions,
01:04:35but I think we're gonna have to
01:04:37end because it's five after one.
01:04:38You guys can send me by my email.
01:04:40Yeah, thank you. Yeah
01:04:42thanks thanks so much thanks.
01:04:44Thanks to thank you Carlos.
01:04:46And thanks to both of our speakers.
01:04:48See you all next week.