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

February 09, 2022

Yale Cancer Center Grand Rounds | February 8, 2022

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

ID
7433

Transcript

  • 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.