Application of Team Science To Elucidate Sex-Specific Differences of Colorectal Cancer Metabolism

Transcript

• 00:00This this dynamic duo,
• 00:02that are gonna be giving
• 00:03us grand rounds today. It's
• 00:04my proud privilege to introduce
• 00:06doctor Khan. Doctor Saj Khan
• 00:08is our section chief of,
• 00:10HBB mixed tumors. He's a
• 00:12wonderful clinician,
• 00:14and an incredible researcher.
• 00:16We've known each other for
• 00:17over a decade. He finished
• 00:19his fellowship at the University
• 00:21of Chicago, and we tried
• 00:22recruiting him when I was
• 00:24at my first job, and
• 00:25now I have the proud
• 00:26privilege of working with him.
• 00:28His science has been focused
• 00:29a lot on the
• 00:31underpinnings of liver metastases,
• 00:32which you'll hear a little
• 00:33bit about, but also about
• 00:35other things, including, biomarkers or
• 00:37pancreas cancer.
• 00:39And then with him is
• 00:41doctor Johnson.
• 00:42Together, they have won numerous
• 00:44awards, numerous NIH peer funded
• 00:46grants,
• 00:48and I think, exemplify
• 00:50how team science works. Doctor
• 00:51Johnson did her PhD in
• 00:53analytical chemistry, which I didn't
• 00:55know you did,
• 00:56at the Imperial College of
• 00:58London, which is one of
• 00:59the premier schools,
• 01:01and she is now,
• 01:03held a position as an
• 01:04associate professor at the School
• 01:05of Public Health in epidemiology.
• 01:08Together, I think both of
• 01:09them have explored,
• 01:10tumor metabolites
• 01:12and genetic differences,
• 01:14around liver metastases,
• 01:16and they'll share some of
• 01:17their journey about the team
• 01:18science as well as,
• 01:21perhaps a little bit about
• 01:22liver metastases.
• 01:23So welcome, Saj and Caroline.
• 01:40Hey. Well, thank you very
• 01:41much, for the introduction. And
• 01:42we'd also like to thank
• 01:44the Yale Cancer Center for
• 01:45inviting us to give a
• 01:46talk today about
• 01:48our collaborative team science work,
• 01:50specifically looking at sex disparities
• 01:52in cancer.
• 01:53So as mentioned, I'm from
• 01:55the School of Public Health.
• 01:56I'm in the Department of
• 01:57Environmental Health Sciences, and doctor
• 01:59Khan
• 02:00is in, the Department of
• 02:02Surgery at Yale School of
• 02:03Medicine. And we've been working
• 02:04together for over nine years
• 02:06now.
• 02:07So over the next forty
• 02:08five minutes,
• 02:09Sajhi is going to start
• 02:11our presentation by introducing some
• 02:13of the background to sex
• 02:14disparities
• 02:15in
• 02:16both, basic research and also
• 02:18clinical research. And then we're
• 02:20gonna, take turns talking about
• 02:21some of our collaborative research,
• 02:23which is mostly in the
• 02:25area of looking at the
• 02:26role of asparagine
• 02:28in colorectal cancer for female
• 02:30patients. And then we'll end
• 02:31talking about some of our,
• 02:33future directions.
• 02:34Thank you.
• 02:41Okay. So
• 02:43so thanks again for, for
• 02:45the opportunity to speak. Thanks,
• 02:46Kieran, for that very nice
• 02:47introduction.
• 02:49You know, cancer is the
• 02:50second leading cause of deaths
• 02:52worldwide.
• 02:54About ten million people each
• 02:56year,
• 02:57die of cancer across the
• 02:58globe.
• 02:59Cancers,
• 03:00affects people of, every walk
• 03:02of life. It affects,
• 03:04females and males,
• 03:06and there's no race or
• 03:07ethnic background in the world
• 03:09that's immune to the development
• 03:10of cancer.
• 03:14Cancer is not just a
• 03:15global problem, but it is
• 03:16a local problem here in
• 03:18Connecticut for women and for
• 03:19men.
• 03:20Here are examples of three
• 03:22patients I've operated on over
• 03:23the last twelve and a
• 03:24half years at Yale throughout
• 03:25the health system. We have
• 03:26a large health system.
• 03:28The left picture is a
• 03:29patient of a seventy year
• 03:31old male with colorectal cancer
• 03:32liver metastases who I did
• 03:34a right hepatectomy on years
• 03:35ago.
• 03:36In the center is a
• 03:37seventy five year old female,
• 03:40who had a diagnosis of
• 03:40pancreas adenocarcinoma,
• 03:42who I did a WIPA
• 03:43on several years ago.
• 03:44And on the right is
• 03:45a seventy one year old
• 03:46female who had a gastric
• 03:48gastrointestinal
• 03:49stromal tumor,
• 03:50a giant one for which
• 03:51we did a a gastrectomy
• 03:53and a distal pancreatectomy.
• 03:58Here are some gross and
• 03:59microscopic
• 04:00pictures of patients who underwent
• 04:01who underwent of a patient
• 04:03who underwent a colectomy for
• 04:04colon adenocarcinoma.
• 04:06The pictures were nicely prepared
• 04:08for by doctor Zuchin Zhang
• 04:09from our department of pathology.
• 04:11On the top left, you
• 04:12can see a picture of,
• 04:14a gross picture of the
• 04:15ascending colon adenocarcinoma.
• 04:17And on the bottom and
• 04:18the right, you can see
• 04:19beautiful histology pictures of a
• 04:20lymph node metastasis
• 04:22and full thickness invasion.
• 04:30Here are the ten leading
• 04:31causes of new cancer diagnoses
• 04:33in the United States and
• 04:34the ten leading cause of
• 04:35cancer deaths in the United
• 04:36States in females and males.
• 04:39What I do want you
• 04:39to notice that even for
• 04:40the nonreproductive
• 04:41cancers,
• 04:42the incidence,
• 04:44is not equivalent.
• 04:46So it's something we'll get
• 04:47back to a little bit
• 04:48later in this talk.
• 04:50And the reason for that
• 04:51is because sex and gender
• 04:52have a major influence on
• 04:54human health.
• 04:55Generally speaking, sex refers to
• 04:57the biological characteristics of an
• 04:59individual,
• 05:00while gender refers to the
• 05:01sociocultural
• 05:03roles and sexual identity of
• 05:05an individual.
• 05:06So the perp for the
• 05:07purposes of this talk,
• 05:09Carol and I are gonna
• 05:10focus on the sex of
• 05:11an individual, and we'll interchangeably
• 05:13use the words female and
• 05:14women,
• 05:15males and men, when discussing
• 05:17the individual sexes.
• 05:20There are differences which exist
• 05:22between sexes in regards to
• 05:23disease development,
• 05:24diagnosis, treatment response, which are
• 05:27rooted in genetic differences between
• 05:29women and men.
• 05:30And understanding the effect of
• 05:31sex as a biological variable
• 05:33will improve the health of
• 05:35women and men
• 05:36across the globe.
• 05:39Unfortunately, there's been a global
• 05:41bias and historical neglect of
• 05:42studying sex as a biological
• 05:44variable in both basic and
• 05:45clinical research,
• 05:47over the years.
• 05:49In biomedical research, females are
• 05:51underrepresented
• 05:52in clinical trials and research,
• 05:54and, biological sex has long
• 05:56been ignored in sciences, and
• 05:58male animals have been predominantly
• 06:00used.
• 06:00So So the majority of
• 06:01research,
• 06:03in our world has been
• 06:04heavily biased towards men.
• 06:09The neglect of research where
• 06:11sex is considered as a
• 06:12biological variable includes cancer research,
• 06:14and we're here at a
• 06:15premier cancer center,
• 06:17and this is something that
• 06:18we wanna focus a bit
• 06:19more of this talk on.
• 06:21Sex disparities are evident in
• 06:23nonreproductive
• 06:23cancers as the incidence of
• 06:25mortality,
• 06:26are higher in males compared
• 06:27to females.
• 06:29And, unfortunately, sex disparities are
• 06:31poorly understood at a molecular
• 06:33level.
• 06:34This includes the impact which
• 06:35sex plays on genetic differences,
• 06:38epigenetic alterations,
• 06:40and sex hormones.
• 06:42Furthermore, there's emerging research, which
• 06:44reveals that x and y
• 06:46genes encode for regulators of
• 06:47metabolism,
• 06:48immunity,
• 06:49and tumor suppression.
• 06:55There are sex disparities that
• 06:56are evident in the major
• 06:57types of treatment we offer
• 06:58for our patients here. We
• 07:00have doctor Traga, who's a
• 07:01surgical oncologist. So when we
• 07:02look at cancer surgery performed,
• 07:04lung cancer surgery, believe it
• 07:06or not, for, female patients
• 07:08actually lead to a better
• 07:09are associated with a better
• 07:11survival
• 07:11and less likely experience of
• 07:13sepsis complications suggesting that there
• 07:15may be,
• 07:16differences in the biology
• 07:18of a female with a
• 07:19male even from surgery.
• 07:21Radiation treatment for esophagus squamous
• 07:23cell carcinoma has a better
• 07:24survival for females compared to
• 07:25males.
• 07:27And for systemic therapies, and
• 07:28we have a lot of,
• 07:29medical oncologists on the Zoom
• 07:31and on the audience, multiple
• 07:32chemotherapy regimens are associated with
• 07:34the longer survival for esophagus
• 07:36cancer,
• 07:37stomach cancer, non small cell
• 07:38lung cancer, and glioblastoma
• 07:40for females.
• 07:42EGFR inhibitors, the targeted drugs,
• 07:44lead to longer survival for
• 07:45females.
• 07:47And interestingly, immune therapies for
• 07:49non small cell lung cancer
• 07:51lead to longer survival for
• 07:52females, but actually for males
• 07:54that get into,
• 07:56IO,
• 07:57they have longer survivals for,
• 07:59again, implying that there's a
• 08:00difference in the biology,
• 08:02of, female and male cancers.
• 08:06There are multiple examples of
• 08:07sex not being considered in
• 08:09preclinical
• 08:10research settings as well.
• 08:12In cancer cell lines, there
• 08:14are, believe it or not,
• 08:14greater stocks of nonreproductive
• 08:16male cell lines compared compared
• 08:18to female cell lines.
• 08:20Single sex analyses fail to
• 08:22consider male and female responses.
• 08:25Many studies just show an
• 08:26averaging of responses across the
• 08:28models for both sexes.
• 08:30When one looks very carefully
• 08:32at cell culture media, the
• 08:33serum of the calf fetus
• 08:35is sometimes mixed with male
• 08:36and female derived,
• 08:38organism organs
• 08:40and more often is not
• 08:41scrutinized.
• 08:42There's a mismatch of cell
• 08:44culture media with cultured cells.
• 08:46And finally, with mouse models,
• 08:48you know, there are also
• 08:49problems. Cancer drugs are often
• 08:52tested in premenopausal
• 08:53mice where the majority of
• 08:54cancers diagnosed,
• 08:56in females are postmenopausal,
• 08:58of age.
• 08:59And there are, of course,
• 09:00financial considerations. We're in constrained
• 09:02times with our financial budgets
• 09:04right now for a lot
• 09:05of our research,
• 09:06and a lot of that
• 09:07leads investigators
• 09:08to,
• 09:10favoring single sex studies in
• 09:11younger mice for financial considerations.
• 09:15As you can imagine, if
• 09:16there's a problem with the
• 09:17basic science, it translates into
• 09:19clinical trials as well.
• 09:21Women have lower enrollment in
• 09:23colorectal cancer and lung cancer
• 09:25trials compared to males.
• 09:27Women are underrepresented
• 09:28in, IO, or immune therapy
• 09:31trials.
• 09:32In a study, that looked
• 09:34at clinical trials and FDA
• 09:35approved modern anticancer drugs,
• 09:38fifty percent of the studies
• 09:39did not report the drug
• 09:40and efficacy,
• 09:42of these drugs in females.
• 09:44And women are also known
• 09:46to be underrepresented in these
• 09:47trials, and this is most
• 09:48this is most pronounced in
• 09:50GI cancer
• 09:51clinical trials, lung cancer, and
• 09:52leukemia trials.
• 09:57Despite these challenges,
• 09:59which we've,
• 10:00mentioned, you know, there are
• 10:02some ongoing efforts which believes
• 10:04that which provide hope that
• 10:06things will get better in
• 10:07the future,
• 10:08and more sex is gonna
• 10:09be looked at as biological
• 10:10variable in scientific research.
• 10:13There have been efforts made
• 10:14to include females and males
• 10:15in cells, tissues, animals, and
• 10:18humans.
• 10:19And, notably, there have been
• 10:20some important policy changes made
• 10:22in the last twenty six
• 10:23years, which are helping helping
• 10:25helping move,
• 10:27biomedical research forward.
• 10:29In two thousand and one,
• 10:30the Institute of Medicine
• 10:31convened a think tank that
• 10:33concluded that sufficient evidence
• 10:35existed, which shows that there's
• 10:36biological differences of women and
• 10:38men that influence treatment and
• 10:40prevention strategies.
• 10:43In two thousand and seven,
• 10:44the World Health Organization
• 10:46passed resolution to urge researchers
• 10:48to split their data according
• 10:50to sex
• 10:51and include gender analysis when
• 10:53appropriate.
• 10:55In two thousand eleven, the
• 10:56Canadian Institutes of Health,
• 10:59expected research applicants to integrate
• 11:01sex and gender,
• 11:02into their research design. To
• 11:04show an example of progress
• 11:05from two thousand eleven to
• 11:07two thousand and twenty two,
• 11:08the research proposal for the
• 11:09Canadian Institute of Health Research
• 11:11increased from twenty two percent
• 11:13to eighty three percent.
• 11:15In nineteen ninety three, the
• 11:17NIH Revitalization Act required the
• 11:19inclusion of women in NIH,
• 11:22funded clinical research. Unfortunately, many
• 11:24researchers did not, follow this
• 11:26recommendation, and sometimes it takes
• 11:28a nudge and sometimes you
• 11:29have to bring back good
• 11:30ideas. And in two thousand
• 11:32sixteen, the NIH
• 11:33passed the Century Cures Act
• 11:35that called for the inclusion
• 11:37of male and female sexes
• 11:38in studies involving cells, tissues,
• 11:40and animals.
• 11:41And most recently,
• 11:43during the Biden administration, the
• 11:44White House initiative on women's
• 11:45health research called to improve
• 11:47women's health research by prioritizing
• 11:49investments
• 11:50in,
• 11:51women's health research.
• 12:01Let's see here.
• 12:04Okay.
• 12:07So, so, hopefully, so far,
• 12:09we've convinced you that,
• 12:11studying sex as a biological
• 12:12variable is an important thing
• 12:14to do, and it's gonna
• 12:15improve cancer outcomes.
• 12:16But how do you go
• 12:17about
• 12:18performing this kind of impactful
• 12:19research?
• 12:20Sometimes when one starts a
• 12:21research project,
• 12:23it can seem like a
• 12:24long windy road. You don't
• 12:25know where to start. You
• 12:26don't know where the the
• 12:27goal line is. You don't
• 12:28know where the end is.
• 12:29So so how do you
• 12:30get go about doing the
• 12:31research?
• 12:32So for one, no one
• 12:34can walk the road anymore,
• 12:35alone.
• 12:36The NIH,
• 12:38has realized that scientific problems,
• 12:40become more challenging given the
• 12:41vast amount of information, technologies,
• 12:43and resources that are ever
• 12:45present,
• 12:46and we're seeing this more
• 12:47and more exponentially in the
• 12:48last one or two years
• 12:49with AI.
• 12:50The increase in number of
• 12:51publications in science and engineering
• 12:54is
• 12:55with multiple authors is increasing
• 12:57exponentially.
• 12:59So in order to tackle,
• 13:01complex problems, the team science
• 13:03approach is the answer. And
• 13:04in order to understand this,
• 13:06we would first like to
• 13:06define what team science exactly
• 13:08is.
• 13:10Team science is a collaborative
• 13:11effort to address a scientific
• 13:13challenge,
• 13:14by leveraging experts in different
• 13:16fields,
• 13:17who need a compelling clinically
• 13:19driven research question to start.
• 13:22The tree the team,
• 13:23is comprised of, individuals that
• 13:25address a clinically focused
• 13:27research project.
• 13:29They are comprised of members
• 13:30with training and expertise,
• 13:33from multiple scientific disciplines.
• 13:35The team works together to
• 13:36combine and integrate,
• 13:38different knowledge skills perspectives,
• 13:40from different fields.
• 13:41And this approach,
• 13:44enables for medical and scientific
• 13:46communities to solve complex problems
• 13:48and help, people worldwide.
• 13:51To apply a team science
• 13:53approach, first, one needs to
• 13:54assemble a team. So the
• 13:56research question should drive who
• 13:57the members of the team
• 13:58should be. So to use
• 13:59a sports analogy, I I
• 14:01enjoy baseball and basketball.
• 14:03So, you know, we're the
• 14:05March Madness is underway right
• 14:07now. So,
• 14:08so in order for, you
• 14:10know, the Yukon women's team
• 14:11to win another championship, every
• 14:13member of the team doesn't
• 14:14need to be a three
• 14:15point shooter. You need a
• 14:16well balanced team with good
• 14:17defenders, good rebounders,
• 14:19a good point guard, so
• 14:20it's a well balanced team.
• 14:21To win a World Series
• 14:23in baseball so Caroline is
• 14:24a Red Sox fan. I'm
• 14:26a Yankees fan, but we
• 14:27still get along.
• 14:28But every player can't be
• 14:30a Derek Jeter. Every player
• 14:31can't be a Mookie Betts.
• 14:32You need a well rounded
• 14:33team of good pitchers, defenders,
• 14:36batters,
• 14:37in order to win a
• 14:38world series.
• 14:40And finally, you know, one
• 14:42needs to identify the right
• 14:43people for the team.
• 14:45Generally speaking, here's a solid
• 14:46component of what Caroline and
• 14:48I have used over the
• 14:49last, you know, nine years
• 14:50for our team.
• 14:52So,
• 14:53you know, you need good
• 14:54collaborators, biostatisticians,
• 14:56students and pre docs who
• 14:58are passionate about the research
• 14:59project that the PIs are
• 15:00interested in, good mentorship and
• 15:02advisorship. We both feel strongly
• 15:04about mentorship.
• 15:05Good lab staff that's supportive,
• 15:07and good postdocs in creating
• 15:09a good environment.
• 15:12Doctor Johnson and I both,
• 15:14have individual labs at the
• 15:15Yale School of Public Health
• 15:17and the Yale School of
• 15:18Medicine where we create synergy
• 15:20in our research projects,
• 15:21from the team members so
• 15:23the intellectual,
• 15:24sum is far greater than
• 15:25the individuals. So the intellectual
• 15:27sum is greater than the
• 15:28individuals. So so we strongly
• 15:30advocate for a team science
• 15:31approach,
• 15:32and together, our labs study
• 15:34how metabolism and biological sex
• 15:36influence colorectal cancer and patient
• 15:38outcomes.
• 15:40Our team science approach has
• 15:42resulted in science, which is
• 15:43moving the field forward by
• 15:44tackling GI cancers.
• 15:47Our approach has led to
• 15:48multiple high impact publications over
• 15:50the last nine,
• 15:51less than nine years in
• 15:53addition to millions of dollars
• 15:54of funding from the NIH,
• 15:55which has has enabled us
• 15:57to contribute to society's understanding
• 15:58of GI cancers
• 16:00both globally globally and locally
• 16:02here in New England.
• 16:04I'm gonna turn it over
• 16:05to doctor Johnson.
• 16:07Thank you, Saj. So,
• 16:09I'm now gonna be discussing
• 16:10some of our collaborative research
• 16:12that has used metabolomics
• 16:14to identify sex specific
• 16:16metabolism in colorectal cancer.
• 16:19So I'd first like to
• 16:20start by explaining why it's
• 16:22important to understand the diversity
• 16:23and functions of metabolites in
• 16:25cancer.
• 16:26Well, metabolites and actually metabolic
• 16:28rewiring can actually help the
• 16:30transition
• 16:31from a primary tumor to
• 16:32a metastasis. So you see
• 16:34here on this diagram
• 16:35that actually the production of
• 16:37acidic metabolites like lactate
• 16:39can help the the primary
• 16:40tumor cells undergo interversation
• 16:42into the,
• 16:44circulatory system.
• 16:46Once the tumor cells are
• 16:47in the circulatory system, they
• 16:48actually undergo a high amount
• 16:50of oxidative stress. So the
• 16:52cells ramp up their production
• 16:53of glutathione and NADPH
• 16:55to survive.
• 16:56Once they reach their new
• 16:58seeded sites, such as the
• 16:59liver or the lung, the
• 17:01cells are actually in a
• 17:02state of dormancy
• 17:03because this is a completely
• 17:04new,
• 17:05microenvironment,
• 17:06different nutrients present there.
• 17:08Then they start to ramp
• 17:09up their anabolic metabolism, producing
• 17:11more metabolites and also nucleic
• 17:13acids and proteins and lipids
• 17:14to grow in the new
• 17:16organ. So you can see
• 17:17there are many bottlenecks along
• 17:19this cascade that can actually
• 17:20be targeted,
• 17:22by using by looking at
• 17:23metabolites and targeting metabolites to
• 17:26improve prognosis.
• 17:29But, you know, we came
• 17:30to think about sex differences
• 17:32in metabolism because there is
• 17:33this fundamental fact that nearly
• 17:35all aspects of metabolism do
• 17:37show this sexual dimorphism.
• 17:39So even eighty years ago,
• 17:41the first paper that really
• 17:42showed this was, come from
• 17:44Marseille, and this professor noted
• 17:46differences in fact deposition.
• 17:48So this is a quote
• 17:49from his paper
• 17:53man's fat mass, though she
• 17:55is as often obese as
• 17:56a man or fatter, she's
• 17:57very nice,
• 17:59she dies less often from
• 18:00the metabolic complications of obesity.
• 18:03So women resist the loss
• 18:04of body energy stores while
• 18:06men mobilize energy stores promptly.
• 18:09And you can see this
• 18:10quite clearly if you look
• 18:11at carbohydrate and lipid metabolism
• 18:13at rest and exercise and
• 18:15the differences between men and
• 18:16women. So during rest, females
• 18:19tend to incorporate free fatty
• 18:20acids into triglycerides
• 18:22to store fat. And then
• 18:23in exercise, they preferentially use
• 18:25these, lipids as a fuse
• 18:27fuel source.
• 18:28Whereas for males, at rest,
• 18:29they tend to oxidize these
• 18:31free fatty acids. And during
• 18:33exercise, they preferentially use carbohydrates
• 18:35of a as a fuel
• 18:36fuel source. So this is
• 18:38just one example of how
• 18:39there are sex differences in
• 18:41metabolism, but it obviously goes
• 18:42much more broader than this.
• 18:46And, there was a review
• 18:47paper out about five years
• 18:48ago, which collated a lot
• 18:50of, studies that has seen
• 18:51the same effect in cancer
• 18:53cells between differences between males
• 18:55and females.
• 18:56So highlighted here in the
• 18:58green are is a metabolic
• 18:59pathway that's preferentially used
• 19:02by cancer cells in males,
• 19:03and in purple is a
• 19:05pathway that's used preferentially by
• 19:07cancer cells in females.
• 19:08So you can see glucose
• 19:10is converted into pyruvate and
• 19:12lactate in males preferentially.
• 19:14But for females, the intermediates
• 19:16in glycolysis
• 19:16actually get shunted through to
• 19:18the pentose phosphate pathway
• 19:20and to generate NADPH,
• 19:22and are used as metabolic
• 19:23building blocks as well in
• 19:25metabolites.
• 19:26So you can see even
• 19:27in these core metabolic pathways,
• 19:28there really are profound sex
• 19:30differences.
• 19:33And then if we look
• 19:34at colorectal cancer, as doctor
• 19:36Khan mentioned, there are sex
• 19:37differences in incidence of mortality,
• 19:39where males tend to have
• 19:40a higher incidence of mortality
• 19:42of this disease.
• 19:43But actually, new studies coming
• 19:44out from IARC just in
• 19:46the past year have seen
• 19:47that for early onset colorectal
• 19:48cancer
• 19:49in the UK and Australia,
• 19:51it's almost at parity now
• 19:52for men and women, which
• 19:53is very interesting as this
• 19:55may this must be some
• 19:56sort of environmental factor that's
• 19:58driving this.
• 19:59But if we look at
• 20:00the colorectal, we can see
• 20:01here that it's typically divided
• 20:03into these two parts, the
• 20:04right side and the left
• 20:05side. And you can see
• 20:06just how different they are
• 20:07in terms of the frequency
• 20:09of molecular features.
• 20:10So on the right side,
• 20:11highlighted here in green, you
• 20:13tend to have more,
• 20:15CpG islemethylated
• 20:16phenotype,
• 20:18more BRAF mutations, and more
• 20:20KRAS mutations in these tumors.
• 20:22Whereas those tumors on the
• 20:23left side tend to have
• 20:24more p fifty three and
• 20:25APC mutations.
• 20:27Importantly,
• 20:28women tend to develop more
• 20:30right sided cancers than left
• 20:31sided cancers, whereas it's the
• 20:33opposite for males.
• 20:35Another important fact is that
• 20:36a right sided patient tends
• 20:38to have overall worse prognosis
• 20:40than a left sided colorectal
• 20:42cancer patient. And this is
• 20:43even after studies have adjusted
• 20:45for things such as,
• 20:46therapy use when the stage
• 20:48of the cancer diagnosis
• 20:50and also the type of
• 20:51screening.
• 20:52So you can see just,
• 20:53the differences along
• 20:55the colorectum,
• 20:56for males and females.
• 20:58So then we had, the
• 20:59question if sex differences exist
• 21:02in where tumors occur within
• 21:04the colorectum, could metabolism
• 21:05also differ in these tumors?
• 21:09So the first collaborative study
• 21:10that we did, was with
• 21:12Yatzi San, who was a
• 21:13Miles per hour student and
• 21:14Doctor. Mironova, who was a
• 21:16resident,
• 21:17and they looked at publicly
• 21:18available data. So we went
• 21:19to NCBI's
• 21:21GEO
• 21:22and we found five gene
• 21:23expression datasets, which had information
• 21:25for colorectal cancer patients and
• 21:27also the sex of the
• 21:28patient
• 21:29and where the tumor occurred
• 21:31within the colorectum.
• 21:32And what you can see
• 21:33here is just a,
• 21:35diagram of principal components analyses.
• 21:38So basically each one of
• 21:39these dots represents a gene
• 21:40expression
• 21:42profile for one of these
• 21:43tumors.
• 21:44And when the dots are
• 21:45sort of more closely clustered
• 21:46together, it means they're more
• 21:47similar in their gene expression
• 21:49profile.
• 21:50So the figure on the
• 21:51left,
• 21:52shows
• 21:53differences in gene expression between
• 21:55the right side and the
• 21:56left sided tumors for women,
• 21:58and the figure in b
• 21:59shows the differences between men
• 22:01and women for right sided
• 22:02cancers.
• 22:03So you can see just
• 22:04how different even the gene
• 22:06expression at the gene expression
• 22:07level these,
• 22:09these
• 22:10are.
• 22:11We then model this gene
• 22:12expression data,
• 22:14using a tool called MetaCore
• 22:16and with help from Rolando,
• 22:18Garciemilian
• 22:19at the the Yale Medical
• 22:21Library.
• 22:22And what we found was
• 22:23five pathways that seem to
• 22:24be enriched in women that
• 22:26had right sided colorectal cancer.
• 22:28So this is just one
• 22:29of them here, and it's
• 22:30the carbohydrate responsive element binding
• 22:32protein pathway.
• 22:34And it basically shows that
• 22:35these genes work together to
• 22:36regulate
• 22:37protein kinase a signaling,
• 22:39also AMPK and GPCRs.
• 22:42And we've seen the same
• 22:43effect actually in more of
• 22:44our studies now, doing metabolomics.
• 22:46So we have replicated this.
• 22:51So now I'd like to
• 22:52move on to, our sort
• 22:53of metabolomics analysis.
• 22:55So we decided to,
• 22:57design this study to investigate
• 22:59sex differences in metabolism in
• 23:00tissues from the colorectal cancer
• 23:02patients.
• 23:03And doctor Khan and I
• 23:04collaborated with doctor Patty Patey,
• 23:06rather, at Memorial Sloan Kettering
• 23:08Cancer Center.
• 23:10But in the nineteen nineties,
• 23:11he collected tumor tissues and
• 23:13normal tissues
• 23:14and also liver metastases and
• 23:16normal liver from over seven
• 23:18hundred and sixty two cases.
• 23:21The tumors are actually taken
• 23:22in the OR and then
• 23:23snap frozen and put in
• 23:25the minus eighty freezer.
• 23:26So they're really perfect for
• 23:27mass spectrometry
• 23:28analysis.
• 23:30In twenty eighteen, we were
• 23:32lucky to receive a r
• 23:33twenty one grant, which enabled
• 23:35us to hire somebody to
• 23:36go to the medical vault
• 23:38at Sloan Kettering and transpose
• 23:40all of the medical records
• 23:41for the seven hundred and
• 23:43sixty two patients. So we
• 23:44then had information
• 23:46more information about these patients,
• 23:48including their out outcomes. So
• 23:50it's very valuable resource.
• 23:53So for our first study,
• 23:54we wanted to keep it
• 23:55very clean. So we just
• 23:56looked at stage one to
• 23:57three,
• 23:58cases.
• 23:59At the time, we didn't
• 24:00know if the stage four
• 24:01cases had been treated with
• 24:03chemotherapy
• 24:04beforehand, which could change the
• 24:05metabolism.
• 24:06And we just looked at
• 24:07the left and the right
• 24:08sided cases.
• 24:10So we had a hundred
• 24:11and two, tissues from males
• 24:13and ninety five from females,
• 24:15and then we also had
• 24:16about thirty nine normal mucosa.
• 24:21So doctors Kai and Retre,
• 24:23back in twenty eighteen, performed
• 24:25the untargeted metabolomic analysis.
• 24:27And basically what we did
• 24:29was extracted the tissues for
• 24:31metabolites,
• 24:32and then we ran them
• 24:33through our LC QTof,
• 24:35mass spectrometry
• 24:36system.
• 24:37And this system allows us
• 24:38to look in an unbiased
• 24:40manner at everything that's present
• 24:42within these tumor tissues that
• 24:44is below basically twelve hundred
• 24:45daltons, all these low molecular
• 24:47weight, metabolites.
• 24:49So in our analysis, we
• 24:51can look at about forty
• 24:52thousand different features.
• 24:54In our lab, we have
• 24:56a database now that's comprised
• 24:58of one thousand standards, so
• 24:59we're able to directly match
• 25:01these features to the standards.
• 25:03And then we performed a
• 25:05statistical analysis, and then we
• 25:06looked to see if any
• 25:07of these metabolites were prognostic
• 25:09by sex.
• 25:11So, actually, our initial analysis
• 25:13showed us,
• 25:14similar findings to what I
• 25:15showed you in that figure
• 25:16before where we saw that
• 25:17male tumors for males tended
• 25:19to have sort of a
• 25:20high production of lactate,
• 25:21whereas for females, it seemed
• 25:23that, glucose was shunted into
• 25:24the pentose phosphate pathway,
• 25:27and produce NADPH.
• 25:29But there was one pathway
• 25:30that really stood out to
• 25:31us, and that was the,
• 25:33asparagine metabolic pathway,
• 25:35where in female patients,
• 25:37we could see that asparagine
• 25:38was produced at a high
• 25:40amount in stage one, two,
• 25:41and three tissues.
• 25:43So this is just a
• 25:43diagram here of the abundance
• 25:45of asparagine in the normal
• 25:46tissues and then the stage
• 25:48one, two, and three
• 25:49for males and females. So
• 25:51you can see for male
• 25:52tissues that it was only
• 25:53at stage three that asparagine
• 25:54was significantly increased.
• 25:57Around the same time, we
• 25:59saw this paper from UCLA,
• 26:01and they noted that asparagine
• 26:03could actually be used when
• 26:04the tumor tissues are nutrient
• 26:06deplete and need to keep
• 26:07growing. And what asparagine
• 26:09does, it helps increase,
• 26:11mTOR and also protein synthesis
• 26:14and nucleotide synthesis.
• 26:15And it can actually,
• 26:17also increase the uptake of
• 26:19serine and threonine to help
• 26:20tumor growth.
• 26:21So we measured these metabolites,
• 26:23and again, we saw that
• 26:25they were increased,
• 26:26only really in the female
• 26:27tumors at stage one to
• 26:29three. So this sort of
• 26:30helped us develop our hypothesis
• 26:32that, you know, asparagine could
• 26:33be very important. And here
• 26:35you can see here that
• 26:36asparagine, anthranine, and serine are
• 26:38positively correlated as well.
• 26:40So this suggested to us
• 26:42that asparagine increases in tumors
• 26:44from female patients to drive
• 26:45growth under nutrient deplete conditions.
• 26:50So asparagine is a nonessential
• 26:52amino acid, so it can
• 26:53actually be derived from the
• 26:54diet. But the tumor cells
• 26:56can produce it themselves through
• 26:57this, for Asparte and through
• 26:59this asparagine synthetase,
• 27:02reaction,
• 27:03which require requires glutamine and
• 27:05ATP.
• 27:06So we went to TCGA
• 27:08and the CoEDGE,
• 27:10resource, and we looked at
• 27:11ASNS expression in females and
• 27:13males.
• 27:14And we saw that if
• 27:15a female patient had high
• 27:17ASNS expression, they had a
• 27:18poor outcome compared to or
• 27:20five year over so honestly,
• 27:22not five years. It's longer
• 27:23than that. Sorry.
• 27:24They had a poor outcome
• 27:25compared to those that had
• 27:27low medium ASNS expression.
• 27:29Whereas for males, there was
• 27:30no, difference whatsoever.
• 27:34So in summary for this
• 27:35section, we see that differences
• 27:37exist in tumor metabolites
• 27:39between females and males,
• 27:41That stage one to three
• 27:42tumors from females have elevated
• 27:44asparagine
• 27:45that could drive cancer growth
• 27:46during nutrient to complete conditions.
• 27:48And then overexpression
• 27:50of ASNS in TCGA is
• 27:52prognostic for females only. So
• 27:54I'm gonna turn the next
• 27:55section to such.
• 27:59So the next question we
• 28:00wanted to ask is, is
• 28:02there a link between colorectal
• 28:03cancer tumor metabolites
• 28:05and prognosis when stratified by
• 28:07sex?
• 28:10We used the same biobank
• 28:11that doctor Johnson had mentioned
• 28:13and evaluated individual metabolites for
• 28:15prognosis.
• 28:16In a multivariate
• 28:18in in the multivariable Cox
• 28:19proportional,
• 28:22hazard model where we controlled
• 28:23for clinical factors and established
• 28:26colorectal
• 28:27genes, such as MSI high
• 28:29status,
• 28:30BRAF,
• 28:31KRAS,
• 28:31we found that the eighteen
• 28:33listed metabolites
• 28:34on this slide were associated
• 28:36with prognosis in a self
• 28:37specific manner.
• 28:39Specifically, we found that asparagine,
• 28:42on the top in yellow,
• 28:44and serine,
• 28:45in the middle in blue,
• 28:47were associated with a poorer
• 28:48five year survival in females,
• 28:51whereas the same metabolites were
• 28:52associated with a better survival
• 28:54for males.
• 28:56Furthermore,
• 28:56succinate,
• 28:57was associated with a favorable
• 28:59survival in females and a
• 29:00worse survival for males.
• 29:05We next tested for sex
• 29:07specific associations after adjusting for
• 29:09the same clinical and pathologic
• 29:11variables,
• 29:12which I mentioned in the
• 29:13last slide, with a specific
• 29:14focus on the asparagine synthesis
• 29:16pathway.
• 29:18Asparagine abundance was very significantly
• 29:20associated with poor survival and
• 29:22poor recurrence free survival for
• 29:24females,
• 29:25whereas it was associated with
• 29:27the favorable survival for males.
• 29:29So females had a a
• 29:30hazard ratio of six point
• 29:32three nine for overall
• 29:33death with overexpression
• 29:35of asparagine.
• 29:38We then, performed a COX
• 29:40survival analysis looking at the
• 29:42asparagine levels.
• 29:44High expression of asparagine, shown
• 29:45in blue,
• 29:47on these Kaplan Meier curves
• 29:48had a worse overall survival
• 29:51and a worse recurrence free
• 29:52survival,
• 29:54compared to low asparagine levels,
• 29:55which are shown in red,
• 29:57and this finding was only
• 29:58found in female patients.
• 30:00In males in male patients,
• 30:02asparagine actually was not associated
• 30:03with survival at all. So
• 30:05just to summarize a bit,
• 30:06so Caroline had mentioned about
• 30:08ASNS. This is the asparagine
• 30:09metabolite, so,
• 30:11the story is starting to
• 30:12build. So in summary,
• 30:16sex specific differences
• 30:18exist between individual metabolites and
• 30:20prognosis.
• 30:23Asparagine metabolism is linked to
• 30:25poor survival in females.
• 30:28And that we talked about
• 30:29asparagine metabolite abundance, being,
• 30:32being the case.
• 30:34Serum metabolite abundance is linked
• 30:36to poor survival in males.
• 30:40Caroline's gonna take over each.
• 30:43So, yeah, so now we
• 30:44turned our research to look
• 30:45at the functional effect of
• 30:47ASNS and Asparagine
• 30:48on colorectal cancer metabolism.
• 30:51So this work has been
• 30:52primarily read, led by doctor
• 30:54Ola Aladelacun,
• 30:56And he's been looking at
• 30:58the role of the ASNS
• 30:59gene and also asparagine in
• 31:01driving
• 31:02cancer growth in in vitro
• 31:03and in vivo models.
• 31:05So the first first thing
• 31:06that we did was knockout
• 31:08ASNS in a colorectal cancer
• 31:10cell line. So HCT one
• 31:12one six is a male
• 31:13cell line. And you can
• 31:14see here from this diagram
• 31:16at the top,
• 31:17the two cell lines there,
• 31:20are cultured without sparigene in
• 31:21the media. And you can
• 31:23see how knockout of ASNS,
• 31:25really affects the way these
• 31:26spheroids grow. They have a
• 31:27really hard time growing.
• 31:29But if we add asparagine
• 31:30back into the media, you
• 31:31can see it actually does
• 31:33rescue cell growth. So this
• 31:35just shows for this cell
• 31:36line that ASNS and asparagine
• 31:38are important to maintain,
• 31:40the growth of the cancer.
• 31:43We then took these cell
• 31:44lines and did a subcutaneous
• 31:46xenograft,
• 31:47experiment
• 31:48using immunocompromised
• 31:49mice. This was done in
• 31:51collaboration
• 31:51with the Center for Precision
• 31:54Modeling Core.
• 31:55So we had ten mice
• 31:56per group, males and females
• 31:58inoculated with the ASNS,
• 32:00wild type or, knockout cell
• 32:03line, and then they were
• 32:04grown for about three weeks.
• 32:06So we looked at the
• 32:07growth kinetics and also the
• 32:09metabolomics of the tumors as
• 32:10well.
• 32:12So the diagram on the
• 32:13left shows you the asparagine
• 32:15abundance,
• 32:16within the tumors.
• 32:18You can see that in
• 32:19green, these are the knockout
• 32:21cell lines that were grown
• 32:22in the mice, and you
• 32:23can see they do produce
• 32:24less asparagine
• 32:25than the wild type cell
• 32:27line. But the difference is
• 32:28about the same for both
• 32:29males and females.
• 32:31When we look at tumor
• 32:32volume,
• 32:32we can see that the,
• 32:34the tumors that grow in
• 32:36the males
• 32:37grow the fastest, but the
• 32:38knockout does have an effect
• 32:39on how these tumors grow.
• 32:41They grow a lot slower.
• 32:43And and then for the
• 32:43female, the cell lines, they
• 32:45both grow slower in the
• 32:46females compared to the males,
• 32:47but the knockout has the
• 32:49slowest tumor growth.
• 32:51But our next question was,
• 32:53you know, what is the
• 32:54effect of asparagine supplementation?
• 32:56So thinking about this in
• 32:57terms of we find asparagine
• 32:59readily in our diet and
• 33:00can be produced by the
• 33:01microbiome as well.
• 33:03So we took the ASNS
• 33:05wild type,
• 33:06cell lines and did a
• 33:07xenograft again,
• 33:08in the males and females
• 33:10and gave the mice an
• 33:11IP injection of one or
• 33:13ten MPK asparagine
• 33:15about every three to seven
• 33:16days and, let these tumors
• 33:18grow again for about three
• 33:19weeks.
• 33:20So you can see in
• 33:21the in the males, the
• 33:23asparagine did slightly increase tumor
• 33:25volume at the earlier stages,
• 33:27but ultimately
• 33:28at the end of three
• 33:28weeks, the tumor volume was
• 33:30about the same.
• 33:31But surprising to us,
• 33:33we saw that Asparagine actually
• 33:35decreased tumor growth in the
• 33:37female mice, which was opposite
• 33:38to our hypothesis. But I
• 33:39like it when that happens
• 33:40because it leads to new
• 33:41questions
• 33:42and new findings.
• 33:44So one thing we thought
• 33:45is that perhaps the asparagine
• 33:46being present in the in
• 33:48the female mice is having
• 33:49a negative feedback on the
• 33:51production of asparagine in the
• 33:52tumor and maybe knocking you
• 33:54know, decreasing the expression of
• 33:55ASNS.
• 33:57So we looked at ASNS
• 33:58expression in the tumor tissues,
• 34:00and we saw that it
• 34:01was indeed decreased only in
• 34:03the female tumors, and it
• 34:04wasn't changed in the male
• 34:06tumors after asparagine supplementation.
• 34:09We then looked at asparagine
• 34:11and aspartate levels in the
• 34:12tumors, and we could see
• 34:13that for both the males
• 34:14and the females that were
• 34:16given,
• 34:16exogenous asparagine,
• 34:18asparagine levels did increase within
• 34:20the tumors.
• 34:21And then surprising to us,
• 34:22we saw that aspartate was
• 34:24decreased in the female tumors
• 34:26only
• 34:27that were given asparagine.
• 34:29So aspartate is a substrate
• 34:30for this reaction.
• 34:32So aspartate actually requires a
• 34:34transporter to be brought into
• 34:35the cell,
• 34:37the cancer cell, whereas asparagine
• 34:38can use a transporter or
• 34:40it can passively
• 34:41diffuse.
• 34:42So we looked at the
• 34:43transporter SLC one a three,
• 34:46and we saw again that
• 34:47this was significantly
• 34:48decreased in the female mice
• 34:50that received asparagine,
• 34:52but but it was actually
• 34:52increased in the male mice.
• 34:54You can just see how
• 34:55different these are between the
• 34:56mouse models just by sex
• 34:58of the mouse.
• 35:00So de novo asparagine production
• 35:02decreased in the female tumors
• 35:03only. So I'll just talk
• 35:05about sort of the hormonal
• 35:07changes that we saw as
• 35:08well in these mice that
• 35:09had asparagine
• 35:11supplemented.
• 35:12So we measured estradiol
• 35:14just using a serum estradiol
• 35:15using ELISA.
• 35:17And what we found was
• 35:18surprising to us again was
• 35:20that when the mice received
• 35:21asparagine,
• 35:22it's actually increased estradiol levels
• 35:24in the serum of the
• 35:25female mice, but it decreased
• 35:27estradiol levels in the serum
• 35:29of the male mice.
• 35:30So we didn't unfortunately have
• 35:31the ovaries from these,
• 35:34from these mice, but we
• 35:35did look at aromatase expression
• 35:37in the tumors.
• 35:38And we found as well
• 35:39that asparagine actually increased aromatase
• 35:42expression in the tumors themselves
• 35:43from female mice, but there
• 35:45was no effect in the
• 35:46males.
• 35:47And this is interesting because
• 35:48estradiol is a known anti
• 35:50proliferative for colorectal cancer and
• 35:52does provide protection for women
• 35:53against colorectal cancer. So this
• 35:55increase in estradiol and aromatase
• 35:57could be potentially
• 35:59having that sort of effect
• 36:01on decreasing the tumor growth.
• 36:04We then looked at the
• 36:04estrogen receptors, and we found
• 36:06no difference in estrogen receptor
• 36:08beta or alpha expression.
• 36:10So we looked at Gepa,
• 36:12which is
• 36:13a membrane bound and very
• 36:15nutrient sensitive estrogen receptor.
• 36:17And what we found was
• 36:18that this actually did change
• 36:19in expression in response to
• 36:21asparagine
• 36:22being supplemented.
• 36:23So it's significantly decreasing the
• 36:25females and again increasing the
• 36:27males.
• 36:28So we did some further
• 36:30investigations into the link between
• 36:31GPRA and ASNS,
• 36:33and we see they're actually
• 36:34linked to each other through
• 36:35this integrated stress response pathway,
• 36:38which is typically activated as
• 36:40well under nutrient stress.
• 36:41So you may recognize some
• 36:43of these,
• 36:44molecules here if you work
• 36:45in this area, but we
• 36:46could see that downstream of
• 36:48of GPO signaling,
• 36:49we have AMPK
• 36:50and we have p r
• 36:51three k and mTOR
• 36:53and also ASNS.
• 36:55So in our models, we
• 36:56see that in the females,
• 36:57when we give them asparagine,
• 36:59this has a knock on
• 36:59effect of decreasing this pathway
• 37:01and decreasing tumor growth. But
• 37:03in the males, it appears
• 37:04to be an opposite effect.
• 37:08In another study, led by
• 37:10Lingan Liu from, CDE at
• 37:12YSPH,
• 37:13he looked at G Pro
• 37:14expression
• 37:15in TCGA
• 37:16again. And what we saw
• 37:18was that at the advanced
• 37:19stages of tumor growth, so
• 37:21at stage three and four,
• 37:23female patients,
• 37:24with high GPRA expression have
• 37:26a poor outcome compared to
• 37:28low expression
• 37:29where we don't see that
• 37:30effect for males and we
• 37:31don't see that effect in
• 37:32the early stages as well.
• 37:36So I just wanna mention
• 37:37in two slides a resource
• 37:39that might also be useful
• 37:40for Yale Cancer Center members.
• 37:42So within this space, we
• 37:44were also wondering what the
• 37:45scale of sex disparities in
• 37:47cancers are.
• 37:48So I have this very
• 37:49talented PhD student, Shiny Shen,
• 37:51who should be graduating soon.
• 37:53She's commented by both me
• 37:55and Saaj, and she wanted
• 37:56to understand what really is
• 37:58the scale of these differences.
• 37:59So what are the molecular
• 38:01differences,
• 38:02potential therapeutic efficacy and adverse
• 38:05responses,
• 38:06and also some environmental factors
• 38:07that could affect risk, such
• 38:09as different exposures and the
• 38:11microbiome.
• 38:12But when she looked in
• 38:13the literature, she found this
• 38:14information
• 38:15very disparate,
• 38:16and there were no real
• 38:17databases that held this information.
• 38:20And if there was a
• 38:20database, it was quite old
• 38:22or didn't really have everything
• 38:23in there.
• 38:24So she decided to make
• 38:25her own database, which is
• 38:27called Onco Sexome.
• 38:29And what she's done is
• 38:30amass all of the sex
• 38:31differences that she could find
• 38:33on seventy one different cancers.
• 38:35So we have four domains
• 38:36here. I can't talk about
• 38:37it in detail because of
• 38:38time, but basically we have,
• 38:41sex differences on over two
• 38:43thousand drugs, the anticancer drugs,
• 38:45in terms of pharmacokinetics,
• 38:47adverse responses, side effects. You
• 38:49know, throughout we see with
• 38:51five Fluorouracil, it has poor
• 38:52efficacy and more side effects
• 38:54for females with colorectal cancer.
• 38:56That's just one finding from
• 38:57this.
• 38:59Over three hundred and sixty
• 39:00risk factors using data from
• 39:02IARC,
• 39:03and then nearly twelve thousand
• 39:04different molecular differences in terms
• 39:07of genes, immune cells,
• 39:10hormones, and proteins.
• 39:12And then nearly fifteen hundred
• 39:14different microbes that are changing
• 39:16their risk,
• 39:17for different types of cancers.
• 39:19So if you're working in
• 39:19this area,
• 39:21I'd encourage you to check
• 39:22it out.
• 39:24So just to summarize,
• 39:26we do see in our
• 39:27studies that ASNS knockout slows
• 39:29tumor growth and decreases tumor
• 39:31asparagine levels.
• 39:33Asparagine supplementation
• 39:34has this differential effect on
• 39:36CRC tumors that express ASNS,
• 39:40and GPO signaling could be
• 39:41driving these differences,
• 39:43where high GPO is actually
• 39:44linked to poor outcomes for
• 39:46advanced stage females only.
• 39:48So I'm gonna have, Serge
• 39:50wrap up the last.
• 39:55The team science approach for
• 39:57the Johnson and Khan Labs
• 39:58for nearly a decade has
• 40:00generated a lot of exciting
• 40:01findings,
• 40:02but we continue to build
• 40:03what we've done together.
• 40:05As part of our current
• 40:06r o one, our labs
• 40:07are looking at sex specific
• 40:09clinical factors which regulate asparagin
• 40:11metabolism in colorectal cancer patients.
• 40:14We are building a well
• 40:15annotated clinical database.
• 40:18Jean, who's in my lab,
• 40:20is working on this where
• 40:21he's building a robust database
• 40:24of patients with stage three
• 40:25colon cancer, stage four colon
• 40:27cancer, stage four colon cancer,
• 40:29liver metastases,
• 40:31and he is
• 40:32developing tissue microarrays,
• 40:34and he's about to start
• 40:36to doing spatial transcriptomics experiments
• 40:37and RNA sequencing experiments
• 40:40on the tumor blocks to
• 40:41better characterize the asparagine
• 40:43and the estrogen pathways.
• 40:45At the same time, we're
• 40:46also prospectively collecting tumors and
• 40:49stool from patients in collaborations
• 40:51with our wonderful colorectal colleagues
• 40:53at the Yale New Haven
• 40:54Hospital, Bridgeport Hospital. Again, we
• 40:56have a great health system
• 40:57here, so two different sites
• 40:59of surgeons,
• 41:00have are helping us, crew
• 41:02some of our patients here.
• 41:03And finally, we're closely collaborating
• 41:05with doctor Juthen Roper, who's
• 41:07a gastroenterologist
• 41:08at Duke University and has
• 41:09been a wonderful partner for
• 41:10Caroline and I.
• 41:13Through our collaboration with doctor
• 41:14Roper,
• 41:15we're using an orthotopic
• 41:17mouse model using
• 41:19CRISPR edited
• 41:20ASNS edited cells to better
• 41:22recapitulate
• 41:23the metabolic changes
• 41:25in the colorectal cancer during
• 41:26cancer development and the progression
• 41:28towards liver metastasis, which is
• 41:30a great model to study
• 41:31our, stage three and stage
• 41:33four colorectal cancer.
• 41:37In addition to our RO1,
• 41:39we are building on some
• 41:40of the additional high impact
• 41:41work, that we've done, some
• 41:43of which is already published,
• 41:44and some of which is
• 41:45based on one of our
• 41:46R21s
• 41:47on social disadvantages
• 41:49and patient outcomes, which are
• 41:51linked to increased deaths in
• 41:52African Americans with colorectal cancer,
• 41:55and the microbiome.
• 41:56We continue to use the
• 41:57National Cancer Database. I see
• 41:59Wafa here in the audience.
• 42:00She's another member of our
• 42:01lab who's working on that
• 42:02right now in the All
• 42:03of Us database, which, Sam
• 42:05Butinski, who's one of our
• 42:06former surgical who's a current
• 42:07surgical resident, was in the
• 42:09lab,
• 42:10has been using to examine
• 42:11risk factors and barriers to
• 42:13GI cancer care and the
• 42:14well-being.
• 42:15And finally, our labs have
• 42:17also shown,
• 42:18through the work of, Montana
• 42:20Morris, who's actually a research
• 42:22fellow at University of Pennsylvania,
• 42:23and Doctor Abhishek Jain, who
• 42:25is a member of the
• 42:26Johnson Lab, who is now
• 42:27a faculty at SUNY Albany,
• 42:28have shown that there and
• 42:30they've shown that there's clear
• 42:31metabolic
• 42:32heterogeneity
• 42:33on colon cancer liver metastases,
• 42:35based on the site of
• 42:36the primary tumor of the
• 42:37sidedness, the right sided versus
• 42:39the left side going to
• 42:40one of Caroline's earlier slides.
• 42:43So in conclusion,
• 42:44studying sex as a biological
• 42:46variable is important in the
• 42:47pursuit towards precision medicine.
• 42:51All the work presented by
• 42:52doctor Johnson and I, however,
• 42:54is clearly a team effort.
• 42:55So here's our acknowledgment slide,
• 42:57and it shows the current
• 42:58members of each of our
• 42:59labs, and it acknowledges the
• 43:00many previous members who we
• 43:02have been fortunate to mentor.
• 43:04We have had wonderful collaborations
• 43:06at Yale, both at the
• 43:07School of Public Health and
• 43:08the School of Medicine across
• 43:09multiple departments,
• 43:11and some of those individuals
• 43:12are listed here.
• 43:13Across the United States, we're
• 43:15fortunate to have great collaborations
• 43:16with doctor Jatin Roper at
• 43:18Duke University and, doctor Philip
• 43:20Pady at Memorial Sloan Kettering
• 43:21Cancer Center.
• 43:22So we thank you for
• 43:23your time, and the floor
• 43:25is open for questions.
• 43:27Thank you very much.
• 43:37I think there's some microphones
• 43:38here of, being with Mozart.
• 43:50That was a great talk.
• 43:52Thank you very much.
• 43:54In one of the slides,
• 43:56you mentioned that there
• 43:58were seven hundred and something
• 44:00specimens from the Sloan Kettering
• 44:02database over a thirty year
• 44:04period or something. It doesn't
• 44:06seem like very many. So
• 44:07did you have to choose
• 44:08different specimens, or how how
• 44:11come there were only seven
• 44:12hundred and sixty?
• 44:14Yeah. Do you want me
• 44:15to answer that? I don't
• 44:16know the answer to that.
• 44:17Okay.
• 44:18You know, that was developed
• 44:20over it's it it just
• 44:21shows how much hard work
• 44:22was to get to those
• 44:23seven hundred sixty two. So
• 44:25those were accrued,
• 44:26for, you know, about fifteen
• 44:28years or so,
• 44:29and part of it when
• 44:30I was a research fellow
• 44:31in, at Memorial Sloan Kettering.
• 44:33And
• 44:34but, you know,
• 44:36I think some of it
• 44:37came down to some being
• 44:38practical about certain things, about
• 44:40what time the tumor was
• 44:41removed in the operating room
• 44:43and who was around that
• 44:44day,
• 44:45because these had to be
• 44:47captured and snap frozen and,
• 44:49prepared freshly so they could
• 44:50be used for subsequent analyses.
• 44:52But,
• 44:53but, it's it's actually one
• 44:55of the largest biorepositories
• 44:57in the world.
• 44:58So, but it did you
• 45:00know, ideally, we would have
• 45:01had more, but I think,
• 45:02you know, seven hundred sixty
• 45:03two is, but those are
• 45:05all of the cases from
• 45:06the biorepository.
• 45:15Yeah.
• 45:19Thank you for the presentation.
• 45:22Did you,
• 45:23assess the the immunostatice
• 45:27of your patients?
• 45:28Particular, for instance,
• 45:30cytokine,
• 45:31chemokine,
• 45:33those parameters.
• 45:35That's one. Number two,
• 45:38you're you're talking about
• 45:40the difference of the, aging
• 45:42process,
• 45:43a sex
• 45:46and,
• 45:47among the patient with
• 45:49how about the print
• 45:51the picture of aging
• 45:54in other age
• 45:56into the picture,
• 45:58and then relate that to
• 46:00the
• 46:01sex hormone?
• 46:04Yeah. So that there are
• 46:05two great questions.
• 46:06For the immune status, we
• 46:08haven't looked at that, but
• 46:09I think that's something that
• 46:10we can look at with
• 46:11the micro rate, the
• 46:14TMAs rather that we're developing
• 46:16in the lab.
• 46:18In terms of age, so
• 46:19our studies primarily have focused
• 46:21on individuals
• 46:22over fifty five. So I
• 46:24think they're fifty five to
• 46:25eighteen. There probably is some,
• 46:27you know, range within
• 46:29that age group as well
• 46:30in terms of the metabolites.
• 46:33For the prognostic work, we
• 46:34do
• 46:35adjust for age to take
• 46:36that into account,
• 46:38but we have started some
• 46:39new studies,
• 46:40last year on early onset
• 46:41colorectal cancer. So for patients
• 46:44under the age of fifty,
• 46:45We had a paper out
• 46:46earlier this year with that
• 46:48data.
• 46:49We don't have a huge
• 46:50number of cases, so we
• 46:52didn't stratify by sex initially.
• 46:55I think there's about fifty
• 46:56cases for early onset, but
• 46:58we do see some, you
• 46:59know, striking differences compared to
• 47:01late onset.
• 47:03But we have a study
• 47:04right now where we are
• 47:05looking at early onset.
• 47:07We found
• 47:08actually, Asparagine doesn't seem to
• 47:09be coming up as an
• 47:11important pathway for the early
• 47:13onset, but we have,
• 47:15also found data in collaboration
• 47:17with other individuals to validate
• 47:19that work even though it's,
• 47:20like, smaller
• 47:21sample sizes. So, yeah, it's
• 47:23definitely
• 47:24an important thing to look
• 47:25at. And in our in
• 47:27our current collections,
• 47:29Yale, we are looking at
• 47:30things such as, oral contraceptive
• 47:33use,
• 47:34HRT use, and,
• 47:37anything
• 47:38else. Oophorectomy.
• 47:40Oophorectomy
• 47:41as well and things like
• 47:42that. So
• 47:49yep.
• 47:51Since you did
• 47:53metabolomics
• 47:55and also renate the asparagin
• 47:57sensitivities
• 47:58may play clear on there.
• 48:01Did you ever look into
• 48:02the possibility
• 48:04actually asparagin,
• 48:07over
• 48:07aspartate
• 48:09ratio
• 48:11as a perimeter.
• 48:13Maybe you'll find the more
• 48:14significance,
• 48:16more
• 48:16drastic difference there using the
• 48:19ratio and relating that to
• 48:21in the in the plasma.
• 48:23That's just one suggestion.
• 48:26Another is
• 48:28the we actually intend to
• 48:30assess the in the pancreatic
• 48:32cancer's
• 48:33situation.
• 48:34The cytokine, chemokine
• 48:37profile
• 48:38of the patient on the
• 48:40different
• 48:41treatment.
• 48:42So if you're interested, you
• 48:44can come to us. We
• 48:45use
• 48:46the beads to evaluate that.
• 48:48Okay. At least the more
• 48:50than ten
• 48:51cytokine, chemokine.
• 48:52Mhmm. Perhaps we can help
• 48:54you on some of those
• 48:56aspects. Thank you. I think
• 48:57it's very generous.
• 48:58Yeah. I think with the
• 48:59ratio, it's a little bit
• 49:00tricky because
• 49:01asparagine can come like, if
• 49:03we're looking at,
• 49:04clinical samples, asparagine can come
• 49:07from the diet as well
• 49:09and also from microbial production.
• 49:13So we have some studies
• 49:15planned in the mouse models,
• 49:17actually, where we're
• 49:19going to be or we
• 49:21currently have a study where
• 49:22we're knocking out ASNS in
• 49:23a bacterial cell line with
• 49:25Andy Goodman's lab. So we're
• 49:27going to see what effect
• 49:28that might have on the
• 49:29tumor and then also modulate
• 49:30the diet.
• 49:32So yeah. But that's a
• 49:33good idea. Thanks.
• 49:35I mean, I I think
• 49:36that's phenomenal. You know, obviously,
• 49:38it's a wonderful work over
• 49:40a decade for all the
• 49:41the samples that have been
• 49:42collected, the science that has
• 49:43come forth.
• 49:44I think one of the
• 49:45challenges for metabolomics is, of
• 49:47course, the,
• 49:49you know, there's so many
• 49:50variables to adjust for,
• 49:52including, say, genomics, including,
• 49:55you know, the dietary
• 49:57concerns, immune modulation.
• 50:01And and I think also
• 50:02this field of functional metabolomics
• 50:03of, like, how do you
• 50:04actually look at, you know,
• 50:05maybe,
• 50:06labeling some of these molecules
• 50:08and seeing if they're coming
• 50:09up in this.
• 50:10So, you know, I I
• 50:11I sense that you're thinking
• 50:12about animal models as your
• 50:14next step to to confirm
• 50:15your work. But how are
• 50:17you going to account for
• 50:17all this heterogeneity that's that's
• 50:19involved in thinking about colon
• 50:21cancers in general? You know?
• 50:23Because I think diet is
• 50:24different, and your CMS subclasses
• 50:26are different. So what are
• 50:27what are your guys' thoughts
• 50:28on how to address that?
• 50:30Try.
• 50:32Yeah. So that that's a
• 50:33great question, Kieran. So,
• 50:35so that's what we're, you
• 50:36know, working on right now
• 50:38to figure out,
• 50:39because there are different areas
• 50:41that could explain some of
• 50:42our findings in regards to
• 50:43asparagine. And the question is,
• 50:44is it inherent to the
• 50:45tumor in and of itself,
• 50:48which is where,
• 50:49the spheroid and the organoid
• 50:50model is becomes very useful.
• 50:52Is it coming from,
• 50:54the diet?
• 50:55So, hence, the animal model
• 50:57that we have, we can
• 50:58adjust the asparagine in the
• 50:59chow. But in addition to
• 51:01that, we're prospectively collecting,
• 51:03a questionnaire for our patients
• 51:05to see what their dietary
• 51:07intake may be representative of
• 51:08asparagine two to see if
• 51:09it's coming from the environment
• 51:10or the diet. And, the
• 51:12work that Caroline mentioned with
• 51:13Andy Goodman,
• 51:15there's a very good,
• 51:16mouse model that's being used,
• 51:18to look if the microbiome
• 51:19might be driving some of
• 51:20those findings as well too.
• 51:21So I think, you know,
• 51:23to answer the question, stay
• 51:25tuned. Hopefully, in a few
• 51:26years, we'll have some, more
• 51:27specific answers to that.
• 51:31Of course.
• 51:32I think the problem of
• 51:34current approach
• 51:36is too much reductionist
• 51:38approach.
• 51:39You have to consider
• 51:41system biology
• 51:42approach.
• 51:44So when you look at
• 51:45the those marker
• 51:47and then you're looking for
• 51:48the parameter in the plasma,
• 51:51so if you focus just
• 51:53on bacteria
• 51:55or just on the
• 51:57nutrition status may not be.
• 51:59You are looking the overall
• 52:01pictures.
• 52:02And,
• 52:03using the AI system, I
• 52:05think it's going to be
• 52:06discussed,
• 52:07you may end up with
• 52:09a surprising,
• 52:15more surprising results.
• 52:17That's what we did.
• 52:19We actually didn't do too
• 52:20many colon patients.
• 52:23In our clinic trial,
• 52:25we can predict that,
• 52:27we use metabolomics
• 52:29and cytokine chemokine profile,
• 52:32just this two parameter ink.
• 52:35For each patient, we're showing
• 52:36a deal with more than
• 52:37one hundred data points.
• 52:39Well, we have to use
• 52:41the statistics. This is with
• 52:47collaboration.
• 52:48We end up with the
• 52:49only three parameter can predict
• 52:51whether the patient that's a
• 52:54with limit patient,
• 52:56well, response,
• 52:57to the chemotherapy
• 52:59or not.
• 53:00And,
• 53:02if in the first two
• 53:03cycles.
• 53:04So then just make a
• 53:06point.
• 53:07Consider using system biology
• 53:09working together
• 53:11with your,
• 53:14with with with the if
• 53:15you'd,
• 53:16with with your
• 53:18statistician.
• 53:20They are really powerful
• 53:22tool to explore those complicated
• 53:25issues you brought in.
• 53:28Yeah. Thanks. Very good point.
• 53:30You know, the challenge sometimes,
• 53:32I think, with using the
• 53:32systems approach is, again,
• 53:34it can,
• 53:36you're not sure what the
• 53:37effect of where the variable
• 53:38is driving the the biological
• 53:41findings. So that's why we're
• 53:42trying to keep it simple
• 53:43and focus on the microbiome,
• 53:45the diet,
• 53:46and then
• 53:47the the cancer cells in
• 53:48and of itself. But,
• 53:50but, yeah, I think,
• 53:51very good points that you
• 53:52bring up.
• 53:53Well, I think it's about
• 53:55time right now. Doctor Khan
• 53:56and doctor Johnson are available
• 53:58by email. If you have
• 53:59questions or you wanna collaborate
• 54:01or wanna access the seven
• 54:02hundred and ninety two patient,
• 54:04databank.
• 54:06But thank you very much.
• 54:07It was a wonderful talk.
• 54:08Thank you both.
• 54:10Yes.