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

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.