# Towards Predictive Biomarkers in Early-Stage Her2 Positive Breast Cancer

March 04, 2022## Information

March 3, 2022

Yale Pathology Grand Rounds

Christina L. Curtis, PhD

ID7503

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- 00:00OK, let's begin.
- 00:02Welcome everybody to
- 00:04pathology grand rounds today.
- 00:06I'm excited to introduce our speaker,
- 00:08Christina Curtis from Stanford.
- 00:10Christina comes has quite
- 00:12a long training history.
- 00:14Beginning in Heidel, Heidelberg,
- 00:16Germany for a Masters degree and
- 00:18then did her doctorate at USC and in
- 00:21computational and Molecular Biology program.
- 00:23Did a postdoc back over
- 00:25across the ocean in Cambridge,
- 00:26then came back to USC to begin
- 00:28as an assistant professor,
- 00:30but shortly thereafter.
- 00:31Joined Stanford as an assistant
- 00:33professor there and is now at the
- 00:36associate professor level at Stanford,
- 00:37and as you can see.
- 00:39We in pathology.
- 00:41Most of the people are in pathology,
- 00:43grand rounds or some way related to that.
- 00:45I've had a long appreciation for the
- 00:47importance of spatial information,
- 00:48but many of our genomics colleagues
- 00:51just grounded all up and I think
- 00:53that one of the reasons I was
- 00:55excited to invite Christina.
- 00:56She's one of those people that
- 00:58not only is an expert in genomics,
- 01:00as you can see,
- 01:01she's the director of the breast
- 01:02Cancer Translational unit and the Co
- 01:04director of the Molecular Tumor Board.
- 01:05But she's also very conscious,
- 01:07conscious of spatial information.
- 01:09And so the mixing of genomic information
- 01:12and spatial information is not an easy task,
- 01:15but I think Christina Curtis is
- 01:18one of the world leaders on this,
- 01:19even though she's still fairly
- 01:21junior and so rather than go through
- 01:23all the awards and fellowships
- 01:24and leadership positions she had,
- 01:26I'm going to let her speak for herself.
- 01:28And she's going to tell us about toward
- 01:29predictive markers and early stage.
- 01:31Her two positive breast cancer, Christina.
- 01:34Great, thanks so much for the
- 01:35kind introduction, David and I,
- 01:37I am delighted to share this with you.
- 01:39I hope next time. To be in person,
- 01:42as I'm sure we all do but but yeah,
- 01:45really delighted to share
- 01:46this sort of recent work,
- 01:48and I think often I have to say
- 01:50I've drawn a lot of inspiration
- 01:52from from you David in in this,
- 01:54as you were obviously a
- 01:55pioneer early in the field,
- 01:56and so we've been sort of waiting
- 01:58for these technologies to make
- 02:00it to a place where where they
- 02:02can be utilized by the masses,
- 02:04and so that that's what I'll speak to today.
- 02:05I'll just state these are my disclosures.
- 02:08The only point that is relevant to
- 02:10the discussion today is that I am a.
- 02:12Scientific advisor,
- 02:12banana string and I will discuss the
- 02:16mastering DSP technology and work
- 02:17that I had done Prior to joining.
- 02:20So as we all know,
- 02:22I'm a major objective of our current
- 02:26times is to affect precision oncology,
- 02:29and there's many pieces to this puzzle
- 02:33that range from really improving
- 02:35our understanding of prognostication
- 02:37through biomarker discovery,
- 02:38as well as predicting response to therapy,
- 02:41improving patient stratification,
- 02:42and ultimately,
- 02:43this goes on and actually can inform
- 02:46the drug development pipeline.
- 02:48And so there's a number of key goals.
- 02:50Here,
- 02:50mainly what I will focus on as a key
- 02:54area of interest from my own group is
- 02:56on patient stratification and really
- 02:59identifying aggressive subgroups of
- 03:01disease and tailoring our therapeutic
- 03:04approaches for these subgroups.
- 03:07Another key objective and and
- 03:08I will touch on this as well,
- 03:10is really on being able to
- 03:12predict response to therapy.
- 03:13And of course this is not only
- 03:15our new targeted and immunotherapy
- 03:17therapeutic agents,
- 03:18but also chemotherapeutic.
- 03:20Back bones that really remain the
- 03:23mainstay of many treatment regimes,
- 03:25but have been hard.
- 03:27A hard nut to crack with respect
- 03:29to prediction of response and
- 03:30ultimately a lot of the work in my
- 03:32own lab and I I won't dwell on this.
- 03:35I'll focus more on sort of the
- 03:36applications of these approaches
- 03:38is has been to use systems biology
- 03:40techniques and and I would say
- 03:41that one of the potential powers of
- 03:43this type of approach is that we're
- 03:45not only interested in developing
- 03:47predictive models or classifiers,
- 03:50but actually unraveling the biology
- 03:51so that we can.
- 03:53And develop mechanistic insights
- 03:56into into disease,
- 03:58and perhaps inform the next wave of
- 04:01therapeutic approaches and so really,
- 04:03what I'll talk about today is sort
- 04:06of a few pieces from my own labs work
- 04:09that have led from really omic technologies,
- 04:12but that are now moving
- 04:14towards clinical translation.
- 04:15Of course, across a very long road,
- 04:18and so the first story that
- 04:20I'll talk about really is about
- 04:22leveraging spatial approaches.
- 04:24In situ proteomic profiling to
- 04:25predict response in this case
- 04:27to her two targeted agents and
- 04:29her two positive breast cancer,
- 04:31and I'll describe really how
- 04:32we've gone about this.
- 04:34Actually,
- 04:34starting with dissociative and
- 04:36bulk technologies that that let
- 04:39us down some some harder paths and
- 04:41moving forward to use new technologies
- 04:44that are really quite emergent.
- 04:45So that's the first story that I'll
- 04:47share with you and really will be
- 04:49the bulk of my discussion today.
- 04:51Why I won't speak to some of the approaches
- 04:53that we've developed to for example,
- 04:56product, chemotherapy benefit and and
- 04:57these are really based on epigenomic
- 05:00biomarkers that have emerged from large
- 05:02transcriptional profiling efforts
- 05:03but also coupled with in vitro data.
- 05:06But this is of course another area of
- 05:08interest and I think that as we think
- 05:10about personalizing therapy again,
- 05:11we must be cognizant about how
- 05:14we do this for standard of care
- 05:17chemotherapeutic agents and really
- 05:19deescalating whenever possible.
- 05:21And of course. Escalating when necessary.
- 05:24I will try to close and and really
- 05:26speak to some of our other efforts
- 05:29that have been leveraging genomic
- 05:31biomarkers to guide therapy selection
- 05:33and high risk of relapse breast cancer
- 05:36and I'll just touch on some of the
- 05:39work that was foundational for this
- 05:41and our ongoing trials in this area.
- 05:44Right,
- 05:44so I think it goes without saying that
- 05:47really her two positive breast cancer
- 05:49is an archetype for precision medicine.
- 05:52This is of course one of our first
- 05:54exemplars where we had a targeted
- 05:56therapy for this copy number,
- 05:57amplified subgroup of disease,
- 05:59and we know that trustees map has
- 06:02been tremendously effective and
- 06:04has really changed.
- 06:05The landscape and outcomes
- 06:07for these patients.
- 06:08It's still the case,
- 06:09however,
- 06:10that despite the effectiveness of this
- 06:13agent that a subset of patients recur,
- 06:15and this has really led then.
- 06:16Down a path of developing a
- 06:19number of additional FDA approved
- 06:22agents including purchase Mab,
- 06:24TDM,
- 06:25one as well as small molecule
- 06:27inhibitors such as Neurontin and Pat
- 06:29nib to overcome this resistance,
- 06:32and so there's been numerous
- 06:33efforts in this area.
- 06:34We now have a wealth of FDA
- 06:36approved drugs and of course,
- 06:38in tandem to this.
- 06:39There have been considerable efforts to
- 06:41start to understand the mechanisms of
- 06:44resistance convergence on PR3 kinase.
- 06:46Pathway involvement of P-10 and
- 06:48so forth and and two dissect the
- 06:51contribution of these pathways to
- 06:53resistance and her two positive
- 06:55breast cancer.
- 06:55But really sort of coming back to this.
- 06:58It's still the case that while
- 06:59we need to escalate therapy for
- 07:01a subset of patients,
- 07:02there may be a subset of patients who
- 07:04actually do not require chemotherapy,
- 07:07and who could be spared these
- 07:09agents and so really,
- 07:10this sort of highlights the very
- 07:12critical need at this point in
- 07:14time to develop predictive mile
- 07:15markers to tailor therapy.
- 07:17And this is both for escalation but also
- 07:21deescalation and so just to highlight,
- 07:23you know how important this is.
- 07:25I I thought I would just demonstrate some
- 07:28of the pivotal trials in this space.
- 07:31Of course, NSA, BP,
- 07:33B31,
- 07:33amongst others that demonstrated
- 07:35the benefit of trustees Mab with
- 07:38respect to disease free survival.
- 07:39But since this time there's
- 07:41been numerous studies that have
- 07:43sought to further escalate therapy.
- 07:44These include the affinity trial of
- 07:47adjuvant trustees map in combination
- 07:49with pertuzumab as well as the
- 07:52Katherine trial which compared
- 07:53T DM one versus trustees map.
- 07:56And so really critical studies in
- 07:59the field have been highlighted
- 08:01very recently at ASCO and in tandem.
- 08:05There have been efforts to deescalate
- 08:07therapy and and one example of this is
- 08:10the a peachy trial which examined adjutant.
- 08:12Paclitaxel plus trustees map but
- 08:14with omission of chemotherapy,
- 08:16mainly anthracyclines.
- 08:16So this is a huge area and there's
- 08:19a lot happening in this space to
- 08:21really personalize therapy and
- 08:22in part enabled by by the many
- 08:25therapeutic options we do have.
- 08:26So I don't expect you to
- 08:27read this slide over here,
- 08:28but I want to say that this is really
- 08:31a place where there's been just
- 08:33tremendous efforts spanning multiple
- 08:35neoadjuvant trials in the early stage.
- 08:38Her two positive setting looking at both
- 08:41single and or dual agent approaches,
- 08:43and of course a key goal and the
- 08:45correlative science that has been
- 08:47done in tandem has really focused on.
- 08:49Can we develop predictive biomarkers?
- 08:53In the neoadjuvant setting,
- 08:54and so I'll just say that there's been a
- 08:57huge amount of sequencing of these cohorts.
- 08:59Calgb for 601 Pamela includes some of
- 09:01the most in depth data where there's
- 09:04been both EXO mandor targeted sequencing.
- 09:07There's been expression profiling some
- 09:08using arrays, some using RNA seek,
- 09:11really as discovery efforts to
- 09:13identify these biomarkers, and then,
- 09:15of course,
- 09:16a big component of embedded in that
- 09:18work has been the use and intrinsic
- 09:21subtyping or pan 50 based subtyping.
- 09:23To ask whether there is enrichment for
- 09:25the intrinsic subgroups and some of the
- 09:28associations that have been identified,
- 09:29there are an enrichment amongst
- 09:31responders in the her 2E or her two
- 09:34enriched group and then on treatment.
- 09:36As some of these trials have
- 09:39included a non treatment biopsy,
- 09:41there's been associations demonstrated
- 09:43between us which from her two enriched,
- 09:46for example,
- 09:47to normal like and so.
- 09:49This is clearly an important area where
- 09:51there's been numerous correlative studies.
- 09:53And then the other area that I
- 09:55that I also want to point out is
- 09:57that of course many have turned to
- 10:00assessment of tumor infiltrating
- 10:02lymphocytes or thiles in attempts
- 10:04to predict response to her two
- 10:06targeted therapy both at baseline
- 10:08and in a subset of trials on therapy.
- 10:11And so,
- 10:12just to speak a little bit more to that,
- 10:15this is one of the more recent studies
- 10:17that actually examined both the Pamela
- 10:20trial as well as the validation trial.
- 10:22And really, the goal was to ask,
- 10:24could we,
- 10:25for example,
- 10:26predict response to therapy and
- 10:28so just to highlight a few pieces
- 10:31of data from this study?
- 10:33And really one of the.
- 10:37If not further efforts to predict
- 10:40probably needs, but not at the moment.
- 10:43Sorry I hear some noise.
- 10:45Please please go on mute Susanna.
- 10:49Right so here really what they had
- 10:51done in this study was to assess
- 10:54tills at baseline, and at day 15.
- 10:57So this was our running biopsy
- 10:59prior to administration.
- 11:00Actually, of any chemotherapy it's
- 11:01not administered in this trial,
- 11:03and what you can look at is the change
- 11:05in tumor infiltrating lymphocytes,
- 11:07with orange showing an increase
- 11:09blew a decrease and stable,
- 11:12and in tandem in this study,
- 11:13they also actually examined
- 11:15tumor cellularity,
- 11:16and you can see some exemplars here.
- 11:18And so this really led to the development.
- 11:20Of an approach called cell till which
- 11:23attempts to combine the estimates
- 11:24based on tills with celularity you
- 11:26can see the area under the curve
- 11:29here about .7 in the panelist study.
- 11:31Actually it's higher for cellularity
- 11:33and so the goal was to sort of
- 11:36develop a combined classifier here,
- 11:38and this has really been.
- 11:39You know,
- 11:39one of the great successes in trying
- 11:41to predict response to her two targeted
- 11:43therapy all be it with variable
- 11:45responses across different cohorts.
- 11:47And so I'll just show that when
- 11:48this group went on to corroborate.
- 11:50These findings in the LPT trial.
- 11:52You can see that the performance was
- 11:56substantially inferior with an AUC
- 11:58under .7 using cell till here and
- 12:00so this shows you one of the recent
- 12:02attempts and of course this group
- 12:05probably needs no introduction to the
- 12:07opportunities and challenges around
- 12:09scoring tumor infiltrating lymphocytes
- 12:11and really standardizing these assays.
- 12:13But I wanted to present this to set
- 12:15the stage sort of for where the fields
- 12:16at and to say that you know really,
- 12:18despite many many attempts.
- 12:20To develop predictive biomarkers from
- 12:22the genomic from the transcriptomic,
- 12:25we still do not have a validated
- 12:27predictive biomarker and sell tools
- 12:29have emerged in the forefront.
- 12:30But there's more work to be done to
- 12:33really ask how consistent this is.
- 12:35Also across different agents and
- 12:37so this really sets the stage for
- 12:39where we began.
- 12:40Our journey in this field,
- 12:41which was a collaboration with Sarah
- 12:44Hurvitz and Dennis Slamon at UCLA
- 12:47on the trio USB 07 clinical trial.
- 12:49Now this trial looked at
- 12:51neoadjuvant trustees.
- 12:52Vanderlip at mid in early stage
- 12:53her two positive breast cancer.
- 12:55It was an investigator initiated
- 12:57trial and here you can see the sort
- 13:00of sample size is 130 patients.
- 13:02They were assigned either to our one
- 13:05trustees Mobileone ARM 2 lapatinib or
- 13:08the combination and what was you know,
- 13:10really intriguing to me about this
- 13:12trial was that core biopsies were
- 13:14collected not only at baseline but
- 13:16actually at run in after a single
- 13:18cycle of targeted therapy alone.
- 13:20Prior to the administration of chemotherapy.
- 13:22Now you can see the pathologic
- 13:24complete response rates,
- 13:25which was the primary endpoint here of 47%.
- 13:28The PATNAM was inferior here as it
- 13:30has been in other trials and the
- 13:33combination was modestly improved but
- 13:35not statistically significantly different,
- 13:37and so really this is this trial
- 13:40is distinct in the collection of
- 13:41a non treatment core biopsy prior
- 13:43to administration of chemo and at
- 13:45the time I felt that this would
- 13:47really afford us some unique
- 13:49insights into biomarkers of
- 13:51response after targeted therapy.
- 13:53Alone without having to deconvolve
- 13:55the effects of chemotherapy and so
- 13:57in initial work that was led by
- 13:59Sarah Hurvitz and Jennifer Castle,
- 14:01Gin a former fellow in my lab.
- 14:03Now, faculty at Stanford,
- 14:05we had embarked on an initiative,
- 14:07and this was actually embedded in the B
- 14:1007 trial design to leverage these pre
- 14:13on treatment and surgical samples to
- 14:16conduct bulk RNA expression profiling.
- 14:18And this was done using
- 14:21actually dual color microarrays.
- 14:23Which have now been largely
- 14:25supplanted by by RDC,
- 14:27but nonetheless you.
- 14:28You can see here the sort of try the
- 14:31design for this after a single cycle of
- 14:34of either of these agents we collected
- 14:37actually fresh frozen material and
- 14:38this was from a total of 89 patients.
- 14:40The fresh frozen material was then
- 14:42sent for RNA profiling and so of
- 14:44course the key question here is
- 14:45based on these expression profiles,
- 14:47could we predict pathologic
- 14:50complete response and?
- 14:52You can see that we took actually
- 14:54quite a deep dive into understanding
- 14:57the baseline clinical covariates and
- 14:59tumor features for these individuals.
- 15:02What you can see are the different
- 15:04measurements that were made.
- 15:05These include the her two fish ratio,
- 15:08which was.
- 15:10Obviously performed as part of
- 15:11the enrollment crunch cereal.
- 15:12We then went on to calculate
- 15:15the immune score.
- 15:16Her two IHC was also performed.
- 15:18Tills were scored and based on
- 15:20the RNA expression profiling,
- 15:22we were able to infer both
- 15:23intrinsic subtype M50 as well as
- 15:25the integrative subtypes which
- 15:27my group defined some years ago,
- 15:29and I'll speak to that more later
- 15:31and and then you have hormone
- 15:32receptor status and of course,
- 15:34Pathologic complete response.
- 15:35So really quite a rich data set with
- 15:38with numerous molecular correlate's.
- 15:39I'll cut to the chase and say that
- 15:42despite having all of these in hand,
- 15:44really none of them were
- 15:47robustly predictive of PCR.
- 15:48And that was true both at
- 15:50baseline as well as on therapy.
- 15:53But what we did learn from this
- 15:54study is that we were able to
- 15:56really start to deconvolve some of
- 15:58the contributions and something
- 15:59and illuminate some of the real
- 16:01challenges that come up with
- 16:04bulk admic sequencing data,
- 16:06and so many of the patterns that we
- 16:08saw are in line with what might.
- 16:10One might expect So what this plot
- 16:12shows here is actually the changes
- 16:15in gene expression during the
- 16:17short term on treatment biopsy.
- 16:19So after just a single cycle you
- 16:22can see the normalized enrichment
- 16:24scores for a variety of gene sets,
- 16:26and amongst those that are downregulated
- 16:29we have decreases in her two signaling
- 16:32proliferation and so forth.
- 16:34In in contrast we see increased enrichment
- 16:37for stromal and immune signatures.
- 16:40Just started after the short
- 16:42term therapy and there's a
- 16:43variety of signatures included.
- 16:44Many of them are of course correlated,
- 16:46and that's actually something that's
- 16:48really important to examine as we
- 16:50seek to parse these signatures,
- 16:51and so this is just showing really the
- 16:54Pearson correlation coefficient matrix
- 16:55based on the gene set enrichment,
- 16:58and hopefully what you can take away
- 17:00from this is that there's a huge
- 17:02degree of correlation for many of
- 17:04these pathways with one another,
- 17:05and this just sort of mirrors
- 17:07what we see over here. So indeed,
- 17:08in this short time course we are.
- 17:11Observing a number of patterns
- 17:12that we might expect to see.
- 17:16But there are, you know,
- 17:17we can also use these data to start to
- 17:19deconvolve what happens on therapy,
- 17:20and so, like many others,
- 17:22we also performed intrinsic subtyping.
- 17:25You can see that there's a preponderance
- 17:26of the her two enriched subgroup.
- 17:28This comprises roughly 53%
- 17:30of patients pretreatment.
- 17:32There's also a substantial normal,
- 17:33like composition here at treat.
- 17:36You know, prior to therapy, but on therapy,
- 17:39we do see this pretty dramatic switching,
- 17:41where a number of the her 2E
- 17:43cases actually become normalized.
- 17:44There is some other switching
- 17:46going on amongst.
- 17:47Luminous, but that's more modest,
- 17:48so we can assess these,
- 17:50and in these patterns are in
- 17:51line where others have reported
- 17:53and Pamela and similar trials.
- 17:55We can also use the bulk data to attempt
- 17:58to deconvolve the immune composition,
- 18:01but this is an exceedingly hard task,
- 18:04really, uh,
- 18:04you know there are many algorithms for this.
- 18:07I show one example,
- 18:08the cyber sort approach,
- 18:10which uses a reference matrix,
- 18:11and what I hope you take away from this
- 18:13is that you know apparent from these
- 18:15plots there's actually relatively.
- 18:17Modest changes from pre to on treatment
- 18:19or even at the time of definitive surgery.
- 18:22After completion of both
- 18:25targeted and chemotherapy.
- 18:26So this is a hard nut to crack.
- 18:29And actually the admixture
- 18:31in these populations.
- 18:33Really, we believe,
- 18:34hindered our ability to discover
- 18:36biomarkers in this context,
- 18:38and so I'll leave off there
- 18:40and say that this in tandem.
- 18:41You know,
- 18:42having sort of early reads into this
- 18:43and what others had been observing,
- 18:44which was similar.
- 18:46Let us too.
- 18:47Then take a new approach and so
- 18:50in collaboration with ministering
- 18:52while they were developing the
- 18:54digital spatial profiling platform,
- 18:56we really embarked on.
- 18:57This study was led by Katherine
- 18:59McNamara and MD,
- 19:00PhD student,
- 19:02who will match very soon and and
- 19:04was just a stellar lead and really
- 19:06taking on these new data types.
- 19:07And So what we did was to
- 19:09go back to this cohort.
- 19:10Now we turn to the formalin,
- 19:12fixed paraffin embedded tissue
- 19:14and we selected data from a.
- 19:16You know it was a total of 100 and.
- 19:18There were 122 samples that
- 19:20derived from 57 patients,
- 19:22and these were the ones that we
- 19:23felt we had adequate material
- 19:25left for for this assay,
- 19:26and so there were a total of
- 19:2820 in the discovery cohort,
- 19:2929 in the validation,
- 19:31again sampled at baseline,
- 19:33pretreatment at run in and at surgery.
- 19:36Right,
- 19:37so for those of you that are
- 19:39not familiar with this assay,
- 19:41we were focused on the Multiplex
- 19:44proteomic piece.
- 19:45There are indeed also technologies
- 19:48that allow one to profile the
- 19:50transcriptome that we're still very
- 19:52much in development at this time.
- 19:53The basic premise of this assay
- 19:55is that we have an indexing
- 19:57oligo nucleotide that is attached
- 19:59to this UV linker,
- 20:00and So what we can essentially
- 20:02do is to sustain our slide or FP
- 20:04slide with probes or antibodies of.
- 20:06Interest these oligos are UV,
- 20:09photo,
- 20:09cleavable and so that we can
- 20:11image the slides.
- 20:12Go in and select regions of interest or
- 20:14our allies and cleave off these oligos,
- 20:17aspirate them, and especially dispense them,
- 20:20and then they can be read off digitally,
- 20:22either using the Nanostring
- 20:23encounter or indeed via sequencing.
- 20:25And this process is repeated for a number
- 20:29of antibodies up to approximately 40 Plex,
- 20:31and so to give you a flavor for what we did.
- 20:34These were all four Micron
- 20:35sections that we took from this.
- 20:37You know almost this clinical trial for which
- 20:39we were using the sort of residual material,
- 20:42and we actually arrayed the pre on and
- 20:45surgical sample onto the same slide
- 20:47to mitigate batch effects and went
- 20:49in then and essentially use one of
- 20:51the the features of DSP which is to
- 20:54select based on phenotypic markers,
- 20:56in this case pants ID keratin to
- 20:59enrich for tumor cells as well as CD
- 21:0245 to illuminate immune cells.
- 21:05And of course this is coupled with the.
- 21:07Does DNA marker and so we can go
- 21:09in and really then take the tumor,
- 21:11enrich mask,
- 21:11or indeed take the inverted mask and enrich
- 21:15for the surrounding microenvironment?
- 21:16And we profiled a 43 Plex marker panel.
- 21:21This was really the panel that was in
- 21:23development at Nanostring at the time.
- 21:25We were fortunate that included a
- 21:28number of her two pathway members,
- 21:31AKT Phospho, AKT, and so forth.
- 21:33We actually had to add her to on.
- 21:34We could not convince them at
- 21:36the time to add.
- 21:38IAR, which was you know,
- 21:39disappointing in many respects,
- 21:41but I think many of the immune markers
- 21:44are really well represented here,
- 21:45and so this is the panel that we had now,
- 21:48just to give you a flavor,
- 21:49you know our eyes select selection.
- 21:51We could spend a lot of time talking about.
- 21:52There are huge study design
- 21:54considerations for how we do this.
- 21:56You know,
- 21:57in this trial cohort we essentially
- 21:59our goal was to select an average
- 22:02of four or ROI's part issue.
- 22:04This shows you an example where we
- 22:06selected 6 and in a different case where.
- 22:08You can see three of the four
- 22:10in this non PCR case,
- 22:12so you know we are essentially
- 22:14picking similar regions,
- 22:15but trying to be representative here and
- 22:17there are many ways to go about doing
- 22:19this now just to convince ourselves
- 22:21that the technology was working because
- 22:23we were amongst the first to view this,
- 22:26we went in and compared the
- 22:29normalized DSP KY 67 levels with the
- 22:31immunohistochemistry CHI 67 that we had
- 22:34for the same subset of samples and you
- 22:36can see that the correlation is .62.
- 22:38Reasonably good here.
- 22:40We did a similar analysis for her
- 22:43to where we had compared with,
- 22:45you know,
- 22:46I see based staining and again
- 22:48we're seeing trends that we would
- 22:50expect that convinced us of.
- 22:52You know that we were at least reading
- 22:54out some of this appropriately.
- 22:56So to give you a sense for the data.
- 22:59Really,
- 22:59what you can see here are just
- 23:01two examples
- 23:02of case 69.
- 23:03A pathologic complete response and case 58.
- 23:06I'm showing you individual regions.
- 23:09That we went in and profiled,
- 23:10and you can see the her two levels are
- 23:13indicated for these different regions above.
- 23:15And so this is just to show how
- 23:17we could visualize the data.
- 23:19But of course,
- 23:20what we're really interested in are the
- 23:22quantifications that we derive for this.
- 23:24So coming back to the sort of
- 23:27baseline characteristics that
- 23:28I had shared with you before,
- 23:30we were now very interested in looking at
- 23:32two key markers starting with her two.
- 23:35Of course as well as CD 45.
- 23:37So these are the protein markers of
- 23:39interest you can see on the bottom
- 23:41that we've stratified by intrinsic
- 23:42subtype as well as ER status obviously
- 23:44critical to account for here,
- 23:46and Patsy R,
- 23:46and so each of these dots represents
- 23:48a not at the log 2 normalized digital
- 23:51spatial profiling protein levels.
- 23:52You can see that there is reasonably.
- 23:54You know,
- 23:55good clustering for some of these in
- 23:57terms of the levels of expression
- 23:59within a sample, but some cases also exhibit,
- 24:01you know, pretty considerable variability,
- 24:04and so this is if we want to look at
- 24:07each of the regions individually.
- 24:08And that's of course something
- 24:10we can do with these data.
- 24:11So this gives us a sense for
- 24:15the heterogeneity present.
- 24:16In these markers,
- 24:17and neither were predictive at baseline,
- 24:19neither of these protein
- 24:21markers were predictive of PCR.
- 24:23We can also, of course,
- 24:25look at her two heterogeneity
- 24:27with far greater granularity,
- 24:29and so one of the ways that you
- 24:32could envision doing this is,
- 24:33as we've represented here,
- 24:34taking the PCR cases and non
- 24:36PCR cases on the bottom.
- 24:37This is again for each each individual
- 24:41region or summarizing the her two
- 24:44protein levels and you can see.
- 24:46How they look pretreatment
- 24:49versus on treatment.
- 24:50Whenever available,
- 24:51you can see that there's quite a bit
- 24:53more variability in the on treatment,
- 24:55her two levels,
- 24:55and actually we quantified this.
- 24:57You can look at the mean square error
- 25:01within patients versus between patients
- 25:03and see that they're far more comparable.
- 25:05Pretreatment versus on treatment.
- 25:07I mean,
- 25:08we really have a quite dramatic degree
- 25:10of change in her two heterogeneity,
- 25:14and so this this tells us
- 25:16something of course about.
- 25:17You know both that were
- 25:19likely hitting the target,
- 25:20but what that you know?
- 25:21How do we interpret the functional
- 25:24importance of this heterogeneity
- 25:25so you know the beauty of the
- 25:27multiplexing here is that it's not
- 25:29just her two that's of interest,
- 25:30but we can actually do these
- 25:32kinds of analysis for all of the
- 25:34markers that we've profiled.
- 25:35And so this is just stratifying
- 25:37by pretreatment on treatment,
- 25:39looking at all two all tumor markers.
- 25:42Not surprisingly,
- 25:43the greatest change in her two heterogeneity.
- 25:47Is in heterogeneous for her too,
- 25:49but this is followed by Phospho S6.
- 25:51If we instead look at the immune markers,
- 25:53we see that the change is
- 25:56overall relatively less compared
- 25:57to the tumor markers,
- 25:59but amongst the top markers we do
- 26:01see differences in our CD3 and CD8.
- 26:04And I should emphasize right now that all
- 26:06of the data that I'm showing you at this
- 26:08point is based on the pan cytokeratin
- 26:10enriched regions and focusing in on those.
- 26:12So similarly we can ask, well, you know,
- 26:14how do these markers change or differ?
- 26:16Or is there any association between patients
- 26:18that achieve a PCR versus those that don't?
- 26:21And this is now beginning to compare
- 26:23these on treatment values and and you
- 26:25can see that there are indeed pretty
- 26:27dramatic differences between responders
- 26:29and non responders.
- 26:30So this is all very well.
- 26:32I want to come back to say that
- 26:33of course you know we were.
- 26:34Being fairly pragmatic in
- 26:35our initial approach here,
- 26:36asking well can we.
- 26:37What can we learn from these
- 26:39samples at baseline?
- 26:40It would be ideal if we had strong
- 26:42predictors of response at baseline,
- 26:44so this is just showing you the CD
- 26:4745 pretreatment levels in a vial
- 26:49implant for the PCR cases versus
- 26:51non PCR as well As for CD 56 and
- 26:53what you can hopefully appreciate
- 26:55based on these violins are that
- 26:58there's really very no association
- 27:02in in the pretreatment markers,
- 27:04but once we start to look.
- 27:05On treatment,
- 27:06we're actually starting to see
- 27:07that there are significant
- 27:09differences between these markers
- 27:11in the PCR versus non PCR cases.
- 27:13So that gave us some,
- 27:14you know,
- 27:14sort of encouragement just in from
- 27:16a univariate analysis perspective.
- 27:18But what really got us excited?
- 27:22Was when we started to look at
- 27:24these markers in concert and So
- 27:26what I'm showing you here in this.
- 27:31It plot is that we're looking at the
- 27:33significance or the negative log 10
- 27:35FDR adjusted P value from the change
- 27:38from run into baseline again in the pan,
- 27:40CK enriched regions and so now you
- 27:43can look at the PCR cases and see that
- 27:45a whole host of markers are lower,
- 27:48quite dramatically lower at running.
- 27:50These include her two but also
- 27:52phospho 6 phospho Akt Chi 67,
- 27:54phospho, Erk and so forth.
- 27:57So really the whole ham pathway.
- 28:01Is showing a decrease?
- 28:03On treatment in patients that respond,
- 28:06there is a concomitant increase
- 28:07in a number of immune markers,
- 28:09including CD45, CD, eight others,
- 28:12really quite dramatic opposition here,
- 28:15and in contrast,
- 28:16we did not observe these patterns
- 28:18in the non PCR cases,
- 28:20so you can see that a handful of
- 28:22these markers are indeed achieved.
- 28:24Significance based on the FDR and there is a,
- 28:27you know, sort of a modest log 2 fold change,
- 28:30but it is much attenuated relative
- 28:33to the PCR cases,
- 28:34so this was the 1st.
- 28:35Sign that we had some signal and
- 28:37these data and was really quite encouraging.
- 28:41Now I wanted to come back to the
- 28:43RNA seek data RNA data that I
- 28:45showed you in the very beginning,
- 28:47and to say that we took the match samples.
- 28:49So just subsetting the larger
- 28:51RNA fresh frozen cohort where
- 28:53we failed to observe an association
- 28:55and asking what if we take these
- 28:57markers and just look at the
- 28:58RNA level and what you can see.
- 29:00Is that really we see no signal
- 29:03here now this of course could
- 29:06be attributed to many factors.
- 29:07It could be due to admixture in the bulk RNA.
- 29:11It could be due to the fact that
- 29:13we've actually enriched for a pan
- 29:15set of keratin tumor enriched,
- 29:16you know, population using DSP.
- 29:19And thirdly, it could be due to the fact
- 29:21that you know protein is more proximal.
- 29:23Readout of these signaling pathway
- 29:24changes that we want to observe.
- 29:25So multiple factors here, of course,
- 29:27the ideal comparator would be to do DSP RNA.
- 29:31Unfortunately,
- 29:32when we did this at the time,
- 29:34the RNA probes, it was a 96 Plex panel.
- 29:36We did do it the signal to noise was.
- 29:39Incredibly poor and we really
- 29:41couldn't use those data,
- 29:42so it is an experiment that that
- 29:43I'm sort of curious about to get
- 29:45back to which of these factors is
- 29:47driving our observations and I'll
- 29:49speak a little bit more to that more,
- 29:52but but you know,
- 29:53I will place some bets on the fact
- 29:56that we are enriching for tumor,
- 29:59and we are reading this out of the
- 30:01protein level so encouraged by our
- 30:03the previous slide and showing that
- 30:05there was an association between
- 30:07multiple markers in the PCR.
- 30:09Cases we then went on to take the
- 30:12logical next step which was to
- 30:14ask could we develop a classifier
- 30:16in our discovery cohort?
- 30:19Our very small discovery cohort and
- 30:22ask whether we could potentially
- 30:24predict response and so this is an L2
- 30:27regularized regression model because
- 30:28we were really trying to understand
- 30:30what these data could tell us.
- 30:32What we have done is to look at the
- 30:35DSP markers combined ingredients.
- 30:37So this is an and.
- 30:39Yep,
- 30:40pre and on treatment and we're
- 30:42comparing this with the sort of classic
- 30:44markers that we have in the field today.
- 30:46Which is estrogen receptor status,
- 30:49which we know is associated with PCR in the
- 30:51her two positive setting as well as Pam.
- 30:5350 You can see that this is this
- 30:54purple line with amine OC of .5
- 30:56so not telling us a whole bunch.
- 30:58We also try to combine these and
- 31:00ask whether this would improve our
- 31:02prediction and answer is no but but
- 31:04we were reasonably encouraged by
- 31:07this AUC of .733. Obviously this is.
- 31:10In, you know, in the discovery cohort alone,
- 31:14using cross validation and so you know,
- 31:17encouraged by this the next question was,
- 31:19well, OK,
- 31:20what markers are actually informative here,
- 31:22and so looking at the marker coefficients
- 31:24with this within this L2 model,
- 31:26what we noticed was that really CD
- 31:2945 and adjacent to this the next
- 31:31one up was Vista were amongst the
- 31:34the largest marker coefficient,
- 31:36so that gave us some clues and
- 31:38this was in the on treatment.
- 31:40Right,
- 31:40shown here in pink in the
- 31:43on treatment biopsy.
- 31:44So with this in hand we then you know,
- 31:46got a bit bolder and said, well, OK.
- 31:48What if we just look at CD 45 DSP alone?
- 31:52I mean, this would obviously be a
- 31:53simpler way to approach this problem,
- 31:55and the answer was that
- 31:57in our discovery cohort,
- 31:58so this is using cross validation.
- 32:00We got a very very high AUC,
- 32:03almost too good to be true of .9 and
- 32:05so then we actually took this into our,
- 32:09you know, withheld validation set where
- 32:11we assess this in the AC was .75.
- 32:14So not too bad.
- 32:15Still encouraging enough and and this
- 32:16LED us to then kind of come back to
- 32:18what I mentioned at the beginning,
- 32:19which was what we had on treatment pills.
- 32:22We had scored these for the entire cohort.
- 32:24We hadn't seen an association at
- 32:26large with that and we had reported
- 32:28that along with the trial.
- 32:30But you know what are these trends look like?
- 32:32So so here you can see the on
- 32:35treatment tell score broken down
- 32:37by non PCR versus PCR and you know
- 32:41we really see a far more striking
- 32:43separation of PCR versus non PCR cases.
- 32:45Saying CD 45 DSP they are correlated
- 32:47but not as well as one would like.
- 32:50And that begs a number of questions
- 32:52and and of course it would be really
- 32:55interesting to ask you know why is that
- 32:57the case in in this PO7 clinical trial?
- 32:59And why have others seen more of an
- 33:01association? For example in Pamela?
- 33:02But as I showed you before,
- 33:04not this doesn't always validate,
- 33:05and I think you know it raises the question.
- 33:07There's of course a lot of inter and intra
- 33:10observer variability in scoring chills,
- 33:12and so perhaps that's a contributing factor.
- 33:16But so with this information in hand
- 33:18and encouraged by the fact that CD
- 33:2145 DSP protein alone on treatment
- 33:23seem to be predictive of a PCR,
- 33:27we then went on to really try to reduce
- 33:30this approach to a more simplistic strategy.
- 33:33And So what we did was to then
- 33:35come back and perform CD.
- 33:3745 I mean a histochemistry on
- 33:40the cohort we had in hand,
- 33:42and we gathered as many additional
- 33:44cases from this clinical trial which
- 33:46was near expended at this point.
- 33:48To use that as a validation set,
- 33:50and so importantly,
- 33:52we built into this an effort to enrich
- 33:56with expert pathology guidance for
- 33:58tumor for tumor and rich regions,
- 34:00and this was really trying to mimic
- 34:02what we had done with the pants
- 34:05cytokeratin enrichment using DSP.
- 34:07We then use Q path to really
- 34:09automate this process and develop
- 34:10a digital pathology workflow.
- 34:12So how does this look?
- 34:13Well, so now this is taking, you know,
- 34:16a comparison of the match cases where we had.
- 34:19And then CD 45 immunohistochemistry
- 34:21shown in orange versus those
- 34:24cases where we had CD 45 DSP.
- 34:26You can see that they're
- 34:27largely in agreement.
- 34:28The IHC does slightly better,
- 34:31but really these are very,
- 34:34you know, reasonable AUC's to observe
- 34:35and just to put this in context,
- 34:37then what we're what we really want
- 34:39to be able to do is ask, well,
- 34:40what is the positive predictive value?
- 34:42The chance that a tumor will
- 34:45have a pathologic complete
- 34:47response to TCLTCH or TCHL?
- 34:49So the three different arms in the study
- 34:51and we were able to then, you know,
- 34:54use this to sort of set a cut point and
- 34:56the positive predictive value for a CD.
- 35:0045% positivity greater than 20% is .82 here,
- 35:03and this is in this combined cohort.
- 35:05If we do this only in the validation set.
- 35:08Where we have fewer cases,
- 35:10it drops to about .71.
- 35:12But Needless to say,
- 35:13this was really encouraging and
- 35:14begs the question, you know,
- 35:15sort of just to come back to this,
- 35:16but we could go from a Multiplex assay
- 35:18and reduce this down to a single
- 35:20marker that could be run in any lab.
- 35:22We know that CD 45 is incredibly
- 35:24robust and raises the possibility that
- 35:27perhaps this kind of approach could
- 35:30be used to guide therapy D escalation.
- 35:33Now I'll come back to this.
- 35:34This will require further validation
- 35:36in additional trial cohorts, namely.
- 35:39Ideally those that did not administer
- 35:42chemotherapy.
- 35:42Afterwards,
- 35:43and we are actively now validating
- 35:45us in a in a retrospectively in
- 35:48one of those trial cohorts,
- 35:50and if that pans out then of course then
- 35:53next steps would be a prospective effort.
- 35:55So moving you know back,
- 35:58I just want to spend a few more minutes
- 36:00saying that we of course had much more
- 36:02data in this cohort that we could mine.
- 36:04We are very keen to really leverage
- 36:07what started out as a discovery effort,
- 36:10but LED us to this new biomarker.
- 36:11I mean,
- 36:12this really was a piloting of the technology.
- 36:14That uncovered some pretty
- 36:15interesting biology here,
- 36:16but sort of coming back to the data
- 36:19I showed you before that the non
- 36:21PCR cases really didn't show very
- 36:23dramatic changes at the pretreatment
- 36:26versus run in time point.
- 36:28However,
- 36:28if we take those same patients and
- 36:30now look at them at at the surgical
- 36:33versus pretreatment time points,
- 36:34so after they've completed the full course,
- 36:36what we see is actually that by
- 36:39then they do indeed exhibit a
- 36:42a reduction in her two.
- 36:44We'll see that there is a reduction in
- 36:46CHI 67 these other downstream markers,
- 36:48however, are less downregulated,
- 36:50and this potentially suggests
- 36:52compensatory signaling in the non PCR
- 36:55cases that's active at the time of surgery.
- 36:57I will point out as well that we do
- 36:59see shifts by the time of surgery
- 37:01in immune markers, including CD 56.
- 37:03And of course you know this raises
- 37:07the question.
- 37:08Why that might be the most enriches
- 37:10this because we're seeing an
- 37:12effective natural killer cells
- 37:13in identifying and killing these
- 37:15chemotherapy stressed tumor cells.
- 37:17You know that's that's possible.
- 37:20And so really a lot to unpack here,
- 37:22but I think it's quite
- 37:23interesting that now we do
- 37:24start to see these changes now.
- 37:26On top of this, I haven't really
- 37:28talked at all beyond what we can
- 37:30do with the tumor in rich regions.
- 37:32But as I mentioned in the beginning,
- 37:33you know a benefit of the DSP
- 37:36approach and the phenotypic
- 37:37selection strategy that we deployed.
- 37:40There are many others that one could
- 37:42envision is that we can now select the
- 37:46surrounding tumor microenvironment area,
- 37:47and so we wanted to explore that.
- 37:50You know a bit here,
- 37:52and So what I'm showing you is
- 37:54now looking at the enrichment of
- 37:56just immune genes in either the
- 37:58surrounding microenvironment that's
- 37:59shown over here on the right.
- 38:02So that's an enrichment towards the TME.
- 38:05The TME area versus in the tumor over here,
- 38:08and so this is in the first instance
- 38:10looking at the pretreatment time point
- 38:12where you can see is that there's really
- 38:14a number of immune suppressive marks
- 38:16evident in the tumor in rich region.
- 38:18We see evidence for T cell exclusion.
- 38:20Based on you know, CD3 CD,
- 38:22four other markers.
- 38:23I do want to be cautious here
- 38:25and why we sort of, you know,
- 38:27interpret this a little bit
- 38:28carefully is that you know,
- 38:30I think there's a lot of
- 38:31open questions around,
- 38:32just the relative.
- 38:33So we say stickiness affinity of these
- 38:35antibodies in tumor versus immune
- 38:38populations that we don't understand well,
- 38:40and that will need to be parsed further.
- 38:43And I say that in part,
- 38:44just noting that we see very, you know,
- 38:46high enrichment of B7H4 over here.
- 38:48So we could take that with a grain of salt,
- 38:51but of course what we can do is
- 38:52assume that those are going to be
- 38:54equivalent overtime and now ask well
- 38:56what happens in terms of these marks
- 38:58from pretreatment to on treatment,
- 39:00and you can see that you know really
- 39:03in the tumor enriched region,
- 39:05things stay large,
- 39:05largely the same at this first time point,
- 39:07by the time we get to the post
- 39:09treatment time point,
- 39:10we actually do see that you know many
- 39:14of these markers have now shifted.
- 39:15We see far less evidence of, you know,
- 39:17sort of this T cell exclusion.
- 39:19Now they've.
- 39:19Appeared to infiltrate and and I'll
- 39:21point out that this Last Post treatment,
- 39:23timepoint were of course only
- 39:25looking at the non PCR cases.
- 39:27We're not looking at the responders who
- 39:29presumably had no tumor cells present.
- 39:32Right,
- 39:32so this this gives us some clues as
- 39:34to also potentially the timing of
- 39:37changes of these immune markers,
- 39:39and you know raises the question
- 39:41as to whether further efforts to
- 39:43profile at multiple time points
- 39:45on therapy might actually inform
- 39:47you know the timing of these,
- 39:49I mean infiltrates and maybe have
- 39:52relevance for contemplating the timing
- 39:54of immunotherapy in these populations.
- 39:55And of course that will
- 39:57require further study as well.
- 39:59So I guess you know just to
- 40:00really wrap up this part.
- 40:02I'll say that Multiplex proteomic profiling,
- 40:05coupled with pan Cytokeratin and
- 40:06Richemond can reveal dynamic changes
- 40:08in the tumor microenvironment
- 40:10during her two targeted therapy.
- 40:12These data are data really under score the
- 40:14value of having a non treatment biopsy.
- 40:16That's obviously can be difficult to achieve,
- 40:19but this was instrumental.
- 40:21And really where we saw the most predictive.
- 40:25A potential of any of these biomarkers.
- 40:29We see that CD 45,
- 40:31either expression or cell counts as measured
- 40:34by HC predicted PCR in an independent
- 40:37set and this really did outperform
- 40:40other candidate biomarkers such as ER
- 40:42or her to enrich status and so we do.
- 40:46We think that these findings have
- 40:48implications for tailoring therapy.
- 40:49Of course, far more work is needed,
- 40:51but really the dream would be to
- 40:53be able to scare to spare patients
- 40:56who safely can omit chemotherapy.
- 41:00And to make that determination
- 41:02early during their treatment course
- 41:03and so this will require further
- 41:06validation which is ongoing.
- 41:07But I'll just say that you know,
- 41:08I think there's many other open
- 41:10questions that this work informs.
- 41:12Of course,
- 41:12you know it will be interesting to see
- 41:15how predictive CD 45 is in other cohorts,
- 41:17as well As for other anti
- 41:19her two targeted agents.
- 41:20I'm still very interested to better
- 41:22understand the comparison with
- 41:24cell till that hasn't really been
- 41:26head-to-head in larger cohorts.
- 41:27I think this raises the question
- 41:28about what is the optimal timing.
- 41:30We had this essentially one cycle of
- 41:32targeted therapy two week window because
- 41:34that's when patients were biopsied,
- 41:36and it's convenient.
- 41:38There may be better windows,
- 41:40but of course understanding this
- 41:42timing is going to be critical for
- 41:45really optimizing our interventions.
- 41:46And then there's many other
- 41:49questions about whether CD 45,
- 41:51you know will correlate with long term
- 41:53outcomes and whether its prognostic
- 41:56and or predictive in other subgroups
- 41:58of breast cancer and other cancers.
- 42:01So I'll say that you know there are
- 42:03many other efforts to contemplate
- 42:05D escalation strategies.
- 42:07One of these is the ADAPT trial,
- 42:11which is looking at neoadjuvant
- 42:13pertuzumab plus trustees.
- 42:14Mab with or without paclitaxel.
- 42:16Another trial is for gain,
- 42:19which is looking at and FDG PET based
- 42:23biomarker of Pathologic complete
- 42:25response and of course these are
- 42:28you know ongoing and reporting out.
- 42:30And really, just highlight,
- 42:32I think the the real efforts of
- 42:35the Community to try to identify
- 42:37these biomarkers and two to optimize
- 42:40for our patients.
- 42:41Now I will say that of course
- 42:43I've I've mentioned this in the
- 42:45context of deescalation.
- 42:45Really the flip side of that coin is that
- 42:49ultimately we're very likely to need risk.
- 42:52Adapted novel trial designs to tailor
- 42:55therapy and deliver new drugs to
- 42:58high risk patients as needed and so.
- 43:00We've been contemplating that a little
- 43:02bit more in the ER positive her two
- 43:03negative setting and I'll just say that,
- 43:05you know,
- 43:05sort of building on from this work
- 43:07we've now embarked on a number of
- 43:09other efforts to really chart not only
- 43:12the tumor immune microenvironment,
- 43:13but to characterize tumor
- 43:15evolution through therapy.
- 43:16And that's been on a really
- 43:18long standing interest in
- 43:19my lab. We are using a
- 43:21variety of tools to do this,
- 43:22not only spatial proteomics,
- 43:24but also transcriptomics,
- 43:26which you know affords us maybe
- 43:29a less biased approach, and.
- 43:30Enables discovery efforts and and for many
- 43:32of these cohorts that we're working on,
- 43:35we've previously performed end up
- 43:37sequencing mainly at the bulk DNA level,
- 43:40but in some cases when possible.
- 43:42We're also doing this, you know,
- 43:44at the single cell level,
- 43:45to try to tease apart this biology,
- 43:47and so one of the areas that
- 43:50we're particularly interested in,
- 43:51and that I'll just summarize
- 43:53briefly is in understanding the
- 43:56determinants of breast cancer relapse
- 43:58and so to highlight this problem.
- 44:01You know,
- 44:01I think we all appreciate that
- 44:03prognosis has improved dramatically for
- 44:04early stage breast cancer patients,
- 44:06in part due to new therapeutic
- 44:08strategies and screening and,
- 44:10and we certainly know this is the
- 44:11case for her two positive disease,
- 44:13but but for many other subgroups.
- 44:15And yet at the same time more
- 44:17than 20% of patients will recur
- 44:19with Mets at distant sites,
- 44:22and this remains largely incurable.
- 44:24There was a very powerful meta
- 44:27analysis performed by panel
- 44:29published several years ago.
- 44:31Which demonstrated that there's a
- 44:32subset of women with early stage ER,
- 44:35positive breast cancer who have a
- 44:36persistent risk of recurrence and death
- 44:382 decades after their initial diagnosis,
- 44:41and these include women
- 44:43with node negative disease.
- 44:45As you can see over here,
- 44:48and so really illuminating what's
- 44:50been observed in clinical practice.
- 44:51Now,
- 44:52a key challenge of this has been
- 44:54that it's evident that our classic
- 44:57characteristics of nodal status,
- 44:59size, grade are insufficient.
- 45:01To predict recurrence,
- 45:02and really that the progress in the
- 45:04space has been impeded by the lack
- 45:07of cohorts with long term clinical
- 45:09follow-up and molecular data.
- 45:11And so this is really a segue to
- 45:15just a brief summary of of other
- 45:17work that we've been pursuing where
- 45:19several years ago now a decade ago.
- 45:21Actually,
- 45:22we sought to unpack the genomic
- 45:25landscape of breast cancer,
- 45:28really focusing on combining whole
- 45:30genome copy number based profiling.
- 45:33Quick transcriptomics and using
- 45:35unsupervised approaches we discovered
- 45:37that there are at least 10 molecularly
- 45:39distinct groups of disease.
- 45:41You can see these over here.
- 45:42This is the chromosome copy number
- 45:45in red shows amplifications,
- 45:46deletions in blue looking along
- 45:48the chromosome,
- 45:48and you'll recognize the Pam 50
- 45:50intrinsic subgroups and then our
- 45:52integrative subgroups on the outside.
- 45:54You can see that we really
- 45:56discover a number of groups.
- 45:58Not only do we recover, of course,
- 46:00the her two positive Group A great control,
- 46:02but we see other groups such
- 46:03as integrative cluster.
- 46:04One which has amplification of ARP,
- 46:056 KB,
- 46:06one on chromosome 17 Q integrative cluster,
- 46:096 amplification.
- 46:10Overexpression of FGFR,
- 46:11one on chromosome 8P12 and then this
- 46:15highly complex integrated cluster
- 46:17two with amplification of a cassette
- 46:19of chromatin regulators on 11 Q.
- 46:22So additional subgroups that are
- 46:24very much copy number defined
- 46:26and we were able to further show
- 46:28that these integrative
- 46:29subgroups really have
- 46:31distinct clinical outcomes.
- 46:32This is this cohort,
- 46:33obviously by virtue of the long
- 46:35follow-up predated the use of trustees,
- 46:37and Ave can see the integrative Cluster
- 46:385 or her two positive group here,
- 46:40but numerous other groups
- 46:42have very steep trajectories.
- 46:43So just to recap what what
- 46:45this really told us is that in
- 46:48addition to the her two positive
- 46:50subgroup or integrated Cluster 5.
- 46:52Many other subgroups are
- 46:53copying overdriven and might
- 46:54share similar characteristics,
- 46:56and so in recent years we've been
- 46:58able to go back and obtain the 20
- 47:01year clinical follow up for this
- 47:04metabolic cohort of over 2000 patients,
- 47:06and So what I'm showing you here is
- 47:08just a summary broken down by ear
- 47:10positive and ear negative patients.
- 47:12Each patient along the vertical.
- 47:14You can see the site of metastasis
- 47:16for these patients.
- 47:17You can see that they're vast,
- 47:18so there's a huge degree of organic tropism.
- 47:22But really,
- 47:23what's critical about these data
- 47:24with long term follow up is that
- 47:26now having this complete recurrence
- 47:28information allows us to study
- 47:29the rates and routes of distant
- 47:31relapse and their lethality,
- 47:32and so I won't dwell on these data.
- 47:35They're all publicly available,
- 47:37but what this led us to is to then really
- 47:40revisit these integrative subgroups
- 47:41and their association with relapse risk,
- 47:44and So what I'm showing you here is
- 47:47the probability of relapse ordered
- 47:49by increasing risk for individuals.
- 47:51And I'll walk through starting
- 47:53with her two positive.
- 47:53Again,
- 47:54before the use of trustees met Ian Black,
- 47:56you can see after surgery there risks
- 47:58over this 20 year interval in red
- 48:00after being disease free five years
- 48:02and in green disease free 10 years.
- 48:04And of course these trajectories
- 48:06are are very steep.
- 48:07We know her too is a bad actor
- 48:09prior to trustees moving.
- 48:10This has changed the game,
- 48:12but we were intrigued to see that just
- 48:14adjacent to the her two positive group
- 48:16where these four what we term high
- 48:18risk ER positive her two negative subgroups,
- 48:20integrative clusters 169 and two which
- 48:22happened to be defined by those.
- 48:24Hallmark copying appropriations.
- 48:25I just showed you,
- 48:27and so really.
- 48:28Hopefully you can appreciate that the
- 48:30risks of relapse are in excess of,
- 48:32you know,
- 48:32in some cases 55% and this persists.
- 48:35Five 1020 years after diagnosis,
- 48:37so we believe that this subset of
- 48:40patients may correspond to the late
- 48:42relapsing groups defined by PAN
- 48:44at all for which biomarkers have
- 48:46been lacking now adjacent to this.
- 48:48There are two subgroups of
- 48:49triple negative disease,
- 48:50so I see 10 is a classic baselight group.
- 48:53You can see the risk of relapse
- 48:55plateaus after five years.
- 48:56Integrative cluster for this,
- 48:57ER negative group actually has
- 48:59a increased risk of relapse,
- 49:00which better mirrors the ER positive
- 49:02groups and they have dramatically
- 49:04different immune landscapes and then
- 49:06over here we have our more typical risk.
- 49:08The majority of ER positive her
- 49:10two negative patients who really
- 49:12show show much more modest risk,
- 49:15so. Taking this information and comparing
- 49:17it to a sort of a typical risk group,
- 49:19we built the most powerful
- 49:21clinical models we could,
- 49:23incorporating all of the known covariates
- 49:25and what I'm comparing here is the
- 49:27clinical model plus immunohistochemistry
- 49:28versus integrative subtype information,
- 49:31and what I hope you can appreciate is that
- 49:33if we just look at immunohistochemistry
- 49:34data to separate these groups,
- 49:36we see that the risk is really
- 49:37homogenized the green line triangles
- 49:39are very similar across these groups,
- 49:40whereas when we incorporate
- 49:42intricate subtype,
- 49:42we see pretty dramatic separation and
- 49:44in this varies over time and it varies.
- 49:46In a subgroup,
- 49:47so we believe that this information
- 49:49informs the prediction of relapse risk.
- 49:51But critically, you know,
- 49:53really these groups have distinct drivers,
- 49:55and so I've already talked to you about
- 49:56what the integrative subtypes are.
- 49:58This is just showing you the
- 50:00landscape or amplification frequency
- 50:01for these different drivers.
- 50:02And really,
- 50:03there's many genes in these
- 50:05large copy number regions,
- 50:06so pinpointing the precise
- 50:07driver is a challenging task,
- 50:08but there's a number of candidates
- 50:10that emerge for each one and just to
- 50:13say that while individually these
- 50:15groups account for eight or five.
- 50:17Or you know, another 8% of the population.
- 50:19Together they account for 25% of all,
- 50:22ER positive, her two negative cancers,
- 50:24and the vast majority of distant relapses.
- 50:27And so we've been really intrigued
- 50:28to contemplate the fact that this
- 50:30may nominate new therapeutic targets
- 50:31in these high risk populations,
- 50:33thinking about honing in on their
- 50:35downstream targets, either the FGFR.
- 50:38Receptor itself, or indeed the ligands.
- 50:40And of course many downstream
- 50:42targets in the AKT mtor pathway.
- 50:44And so this actually motivated us to
- 50:47develop a window of opportunity trial
- 50:49to evaluate new therapeutic strategies
- 50:51in these high risk populations,
- 50:53and this is funded by the Department
- 50:56of Defense and and really this is
- 50:58a multicenter trial terpsichore,
- 51:00which we will biomarker stratify patients
- 51:03according to their integrative subtypes.
- 51:06Assign them into these individual groups.
- 51:08And conduct a window study where patients
- 51:11receive two weeks of targeted therapy.
- 51:13The readout of interest is a
- 51:16reduction in CHI 67 after therapy,
- 51:19and of course we're comparing the
- 51:21targeted agent alone or in combination.
- 51:22Sorry,
- 51:23the targeted agent in combination with
- 51:25ending therapy or endocrine therapy alone,
- 51:27and they are randomized,
- 51:28and so we're really excited about.
- 51:30This is a very ambitious trial,
- 51:31of course,
- 51:32to biomarker stratify in a very short window,
- 51:34and to do this in the early stage setting,
- 51:35but we believe that additionally,
- 51:37by collecting on treatment.
- 51:39And core biopsy surgical samples
- 51:41will actually be able to conduct
- 51:43similar studies to what I described
- 51:45before looking at the change in
- 51:47in these in these tissue samples
- 51:50in response to short term therapy.
- 51:52And so to enable this,
- 51:54we've really also set up a whole
- 51:56pipeline to do prospective biobanking.
- 51:58Both plasma tissue collection,
- 52:00but also the generation of organoids.
- 52:02And I'll say that that's been
- 52:04really ongoing work in
- 52:05my group to establish organized
- 52:07models that are representative of
- 52:09these high risk of relapse subgroups,
- 52:11because in fact they are vastly
- 52:13underrepresented by existing cell lines,
- 52:16and this is afforded us a real opportunity
- 52:18to have very high quality viable
- 52:20material for a number of assays and.
- 52:23And I hope to share with you
- 52:24some of that at another time,
- 52:26but also just to say that this is
- 52:28also really fueled a new center that
- 52:30we have for breast cancer metastasis.
- 52:32It's very much focused on delineating
- 52:34the evolutionary dynamics as well
- 52:35as micro environmental determinants
- 52:37of metastatic breast cancer,
- 52:38and it's really oriented around
- 52:40these integrative subgroups which
- 52:42we've defined seeking to define
- 52:44their definitive drivers as well
- 52:46as to leverage real-world data
- 52:48to evaluate these associations,
- 52:50but also to do a deep dive
- 52:52into the cellular topography.
- 52:54Of both the primary tumor and
- 52:56metastasis through therapy.
- 52:58And, of course,
- 52:59these are inexorably LinkedIn breast
- 53:01cancer and then a final piece of this
- 53:03and sort of give you the overwhelming
- 53:05schematic is actually now with these
- 53:07organoid models that we've established.
- 53:09We're actually conducting our very
- 53:11first crisper screens in 3D models
- 53:13to really pinpoint the definitive
- 53:15drivers of these subgroups,
- 53:16and and of course,
- 53:17we hope that this information
- 53:19will ultimately inform the next
- 53:21wave of clinical trials.
- 53:23It's all closed by thanking the many
- 53:26people in my lab who led this work.
- 53:28Fabulously.
- 53:28Talented scientists that it's really a
- 53:30privilege for me to work with every day.
- 53:33And you saw some of their
- 53:34pictures along the way.
- 53:35I just want to thank our collaborators at
- 53:37Sarah Hurvitz densely man and and Mike Press.
- 53:39And of course the initial work was also
- 53:41done in collaboration with Nanostring,
- 53:44and I'd be very happy to take any questions.
- 53:51Great thank you Christina.
- 53:53That was terrific.
- 53:55Vic lecture and we would all be
- 53:56clapping except that you can hear us.
- 53:58You can see is the logos these days,
- 54:01but I'm sure there must be questions.
- 54:04Anyone can either raise their
- 54:06hand or if they have questions
- 54:08just unmute and go ahead.
- 54:14Hi hi Christina, very nice work.
- 54:16This is iron crop.
- 54:19In the it's a very nice study when
- 54:22you have these the the neoadjuvant
- 54:24trial with the window of the her two
- 54:27therapy alone and you saw that you
- 54:29know this decrease in in her two
- 54:31signaling and increase in in immune
- 54:33markers in the responders or the people
- 54:35who eventually would be responders.
- 54:37Did you see any difference in that
- 54:40association when you looked at the
- 54:42type of her two therapy that they
- 54:44had given the potential for the
- 54:46putative differences met different
- 54:48mechanism action between.
- 54:49Antibodies and T keys.
- 54:51Yes we did and it was something near
- 54:54and dear to my heart to explore,
- 54:56but I have to say we were pretty
- 54:59underpowered because, you know,
- 55:00the whole trial whole trial was
- 55:02130 patients and then just for the
- 55:04subsets that we did DSP on that that
- 55:06that there was adequate material.
- 55:08The numbers in each of the arms were low.
- 55:10We kind of course grouped the
- 55:13trustees amebo only and the trustees
- 55:15met platinum are we see some hints
- 55:17of signal that would suggest that.
- 55:19You know, ABC mechanisms are are distinct,
- 55:22but we're really underpowered
- 55:24to fully tease that apart,
- 55:26and I think that it's ultimately just
- 55:28going to require a greater sample size,
- 55:30but it's a wonderful question,
- 55:31one that I ruminated on long and hard,
- 55:35and you know,
- 55:36I think I suspect that it's
- 55:38that that it will be different.
- 55:41We just didn't have the data.
- 55:42Yeah, thanks.
- 55:49Right, this is my year. Thank you.
- 55:51That was sharing the exciting
- 55:54part that was really nice.
- 55:57Difference in the pattern?
- 55:59You know signature or markers
- 56:01between the ACB 2 and three?
- 56:04If y'all look at those or with
- 56:06ACB 3 just you know worst thing
- 56:08you know the same type of markers
- 56:11persisting but much worse.
- 56:14Yeah, I mean we did.
- 56:16We did group them again
- 56:17because of power issues.
- 56:18You know just in terms of
- 56:20stratifying further on that.
- 56:21I agree it would be interesting
- 56:22we could go back.
- 56:23I think we're just we're really limited
- 56:25by the numbers here and you know.
- 56:27This was intended to be sort of a pilot
- 56:29effort to understand the technology,
- 56:31and we went back once we saw these
- 56:33results and gathered as many samples
- 56:35from this cohort as we could to go
- 56:37back and really bolster the numbers,
- 56:39but I think we're still just just fall short,
- 56:41and so you know,
- 56:42I think would be very
- 56:44interesting to to do this.
- 56:45Not not that there's so many
- 56:46of these additional cohorts
- 56:47that have on treatment,
- 56:48but there are others that are larger,
- 56:50so yeah,
- 56:51unfortunate.
- 56:54Thank you. Both very nice talk.
- 56:59So for the first part of Orlando strain
- 57:02studies when where you showed pretty
- 57:05good prediction power with on treatment
- 57:08parameter but not pre treatment.
- 57:10But I wonder whether you actually
- 57:12look like the the difference or ratio
- 57:15between some of the parameters and
- 57:16see whether there's a correlation.
- 57:18It might be better prediction power,
- 57:20that's right so we did so initially many
- 57:23of our models were actually combining
- 57:25sort of trying to get at this delta.
- 57:27The delta which I I thought was going to be,
- 57:29you know where it's at?
- 57:33Additional information that you're
- 57:34leveraging from having these
- 57:36pairs and it really turned out.
- 57:38That it seems that it's the
- 57:41that the on treatment value in
- 57:43and of itself held its own.
- 57:44It really held its own and we
- 57:46didn't get a huge benefit from that.
- 57:48Now there there may be many reasons
- 57:49why I mean, I'll say that you know,
- 57:52we were struck by the reduction in
- 57:54celularity in this cohort on treatment.
- 57:55Many of these patients,
- 57:56and of course we know that you know,
- 57:57there's cellularity estimates
- 57:59are not always concordant.
- 58:01We estimated this both molecularly,
- 58:03but obviously with expert pathology
- 58:05review and the sections were not
- 58:07identical for the different assays,
- 58:08of course.
- 58:09So there were a number of cases that
- 58:12you know had essentially 0 celularity
- 58:14upon path review on treatment more
- 58:17so than I would have expected we
- 58:18see in association with cellularity.
- 58:20But of course then what's interesting
- 58:22is you go in with DSP and you you start
- 58:24to illuminate this and we can see
- 58:26evident tumor markers there, right?
- 58:29So I mean just coming back to sort of
- 58:31some of the questions about the delta,
- 58:32I think.
- 58:35Yeah.
- 58:35You know some some questions emerge
- 58:37about like what's the heterogeneity
- 58:39in the pretreatment sample versus
- 58:41the on treatment?
- 58:43We're limited by the core biopsies.
- 58:44I think we see quite a bit of it and
- 58:46I I would say that we do detail some
- 58:48of this in the supplement and methods.
- 58:50There's a lot of comparisons one can
- 58:51do if you reduce the number of regions,
- 58:53which is one way to kind of come
- 58:55back to this question of, you know,
- 58:56are we measuring enough in terms
- 58:58of the delta?
- 58:59Because we had some cases for up
- 59:01to four ROI's and it looked to us,
- 59:03just, you know,
- 59:03sort of from a study design consideration
- 59:05that a minimum of two regions.
- 59:06Would be really helpful here,
- 59:09whether for improved that a ton is a you
- 59:11know another matter, not always feasible.
- 59:15But I think it's a.
- 59:16It's a great question that
- 59:17was our intuition as well,
- 59:19but for these various reasons
- 59:20we didn't really see a dramatic
- 59:22benefit of the delta.
- 59:27Maybe I'll take a chance to
- 59:28ask a question here as well.
- 59:29We're kind of running out of time,
- 59:31but maybe we have a couple more minutes.
- 59:33In particular one of those
- 59:34things we struggle with is how
- 59:36many fields of you are enough,
- 59:37and in your DSP study it's it
- 59:39looked like that there was.
- 59:41You were struggling with that same thing.
- 59:43Did you look at in in DSP?
- 59:45One of the blessings and curses of
- 59:47that technology is it's way more
- 59:49data than you can analyze years,
- 59:51and there's lots of other questions I
- 59:53have for you that I'll discuss later
- 59:55about other ways of analyzing that data.
- 59:57Did you look at averaging your
- 59:59fields of view versus looking at
- 01:00:01them individually and with that
- 01:00:03with the average or individual
- 01:00:05fields more informative?
- 01:00:07And did you look also at like how many
- 01:00:09you know comparing 2 versus 6 that
- 01:00:11looked like you had different numbers?
- 01:00:14Can you comment?
- 01:00:14That's right.
- 01:00:15Yeah, it's a hugely important question.
- 01:00:17I would say we were very.
- 01:00:19We spent a lot of time trying to parse
- 01:00:22this with this with the data that we had.
- 01:00:24I think that some of the analysis I've
- 01:00:27presented we did indeed take the average.
- 01:00:30There are ways to also,
- 01:00:32you know, wait that information,
- 01:00:34but in the sort of logistic regression
- 01:00:36models we were actually predicting,
- 01:00:38we did take an average across them.
- 01:00:41We subsequently, you know,
- 01:00:43we also investigated sort of.
- 01:00:44What if you had only?
- 01:00:45One versus 2 versus 4 and some had up
- 01:00:47to six and and as I sort of hinted at
- 01:00:50and actually analogous to what we've
- 01:00:52seen with genomic heterogeneity at
- 01:00:54minimum of two regions gets you a lot
- 01:00:56this ability to say anything about
- 01:00:58how heterogeneous these markers are,
- 01:01:00even between sort of two regions
- 01:01:03is hugely important,
- 01:01:05informative,
- 01:01:05and then there starts to be what appears
- 01:01:07to be a trail off in terms of the added
- 01:01:09benefit when you get up higher to six,
- 01:01:11you know,
- 01:01:11I think I wouldn't stake my life on
- 01:01:13whether choose the absolute Max we we
- 01:01:15try to collect more whenever possible
- 01:01:17because we're interested in the discovery.
- 01:01:19You know,
- 01:01:19and sort of questions around this.
- 01:01:20And I, I think also questions arise as to,
- 01:01:23you know how variable is this going to be
- 01:01:24for different subgroups of disease, right?
- 01:01:27So I don't think we've mastered that,
- 01:01:29but I felt at least encouraged that with two.
- 01:01:32We seem to be gaining information that that
- 01:01:35the heterogeneity itself was informative.
- 01:01:37Yeah,
- 01:01:38great thanks.
- 01:01:40Are there any other questions?
- 01:01:41Were already 5 minutes past,
- 01:01:42but if there's one more question,
- 01:01:44maybe we could take that if not.
- 01:01:48I don't see anyone raising their hands,
- 01:01:50so I'll assume that we're all
- 01:01:53happy with where we are and
- 01:01:55thank you very much for a very
- 01:01:57interesting lecture and well, thank.
- 01:01:59Thank you all for joining.
- 01:02:01Thank you.