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Supporting Hematology Innovation: Outcomes of an EHA Junior Research Grant (with Dr Ferran Nadeu Prat) · EHA Unplugged

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  One of my PhD students now has a talk here at DJ presenting some
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  of our next aims in this field. So we have basically to make the
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  the story simple, we have been able as I was saying, to
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  characterize the evolution of the CLL from time of diagnosis
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  to time of progression, relapse transformation, because we have
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  samples over a period of 20 years now.
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  We have tools that allows us to go even back in time so we can
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  reconstruct how the tumor has been evolving even before the
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  diagnosis of the disease. And we we do this and others in
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  the field. Welcome to EHA Unplugged, the
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  official podcast channel of the European Hematology Association.
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  EHA. I am Isabella Libre, your host,
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  and we have today the pleasure to have with us Doctor Ferran
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  Nadeo. Thank you very much for the
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  invitation. It's my pleasure to be here
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  today. Thank you for being here with
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  us. The doctor, Ferran Nadeo, is a
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  researcher from the Molecular Pathology in Lymphoid Neoplasm
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  group in Barcelona in Spain. He was awarded an EHA Junior
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  Research Grant in 2021, and we are here to learn about how this
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  grant has supported his career development and to learn what
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  your results have been. We're interested to see Doctor
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  Nadeo is a computational biologist and a molecular
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  pathologist. He's specializing in
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  hematological malignancies with particular expertise in chronic
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  lymphocytic leukemia, CLL. He has developed new
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  bioinformatics approaches to understand cancer evolution.
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  So we're going to talk about your project that was founded by
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  the EHA Grant Perfect. Can you please begin by
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  explaining what the aims of those that project were?
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  Yeah. So we decided to start chronic
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  leukemia from an evolutionary perspective because it's a very
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  intriguing disease. In some patients the disease
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  rapidly progresses while in others that the disease is a
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  very slow disease with a very slow turn around.
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  And and this at the resonated before therapy also goes after
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  therapy when we treat the patients, there are some that
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  response for a very long time, other that rapidly progress.
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  So we aim using different multiomics technologies,
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  genomics, transcriptomics to understand which is the biology
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  behind these different clinical patterns.
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  And this is more or less the general aim of this EAJ grant
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  that I conducted. So your project aims to decode
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  evolutionary pathways in CLL. What do you mean by an
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  evolutionary pathway? Is that what this what is the
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  biology behind this very different clinical course?
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  And in the first couple of years of the of my AJ Grand, we focus
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  on what we call Rich star transformation for instance,
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  with this Hoda CLL cells, which is a small cell leukemia mostly
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  indolene in many patients transformed into a very
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  aggressive disease. So understanding this kind of
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  evolutionary pattern is that what we call like the
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  evolutionary pathways of CLL, we could understand how this
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  dynamic process occurs within a time window of more than two
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  decades. Understanding which are the
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  early Internet alterations that are occur and how the cells
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  change and evolve during all this decades of evolution of a
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  tumor. Also giving us opportunity in
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  the filter for early detection of this aggressive clones and
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  this kind of things. So what is your, What is
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  especially in your approach to answer these questions, to
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  understand this evolution? So I think that would make our
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  project a special is that we have been in our hospital
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  collecting samples from the patients and storing them in the
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  freezer for more than 20 or 30 years.
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  So this gives us the opportunity to analyse the sample, for
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  instance, the time of Richter transformation which occur at
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  2020, let's say. But we had samples from 20 years
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  before and we could analyze who were who were these cells at
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  time of disease diagnosis and followed the changes during all
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  this period of the disease and the same patient.
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  We've had like 6 or 7 or 8 longitudinal samples collected
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  over a period of more than 20 years.
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  And this give us quite a unique scenario to really understand
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  how this evolution of the, the disease works at the, at the
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  genomic level and the symptomic level.
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  In, in our studies, we try to combine the different omics
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  layers. We usually start with the
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  genomics because it give us which is the, the, the genetic
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  drivers of the disease. Because we know that in CLL
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  there are some genetic drivers, so mutations, copy number
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  alterations that are associated with aggressive disease, others
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  to more indolent disease. So we have the genomic
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  information as a background on top of which we want to build on
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  the other omics layer. For instance, one we have the
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  genomics, we add the transcriptomics to see how this
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  genomic alterations might modulate the phenotype or the
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  transcriptomic landscape of the cells.
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  And in some of the studies we also add epigenetic layers, DNA
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  methylation and toxic, which basically gives you the idea of
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  how the chromatin is, is back or open in the in the nucleus, so
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  its enhancers may be active in the cells and so on.
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  So we aim to try to combine all these different layers to really
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  understand which is the the features that dictates these
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  different clinical path. Because we have seen that in
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  some cases genomics alone is not sufficient to understand this
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  process, but that there are some genomic basis.
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  And then at the later stages there are some transcriptomic
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  changes. That is what the end dictates,
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  these different evolutions. So you're also using computation
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  analysis in a special way. Can you explain what?
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  You are doing, Yeah, we try to make sense of how we can
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  integrate this data, the Soviet, because it's quite a
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  straightforward to the dynamic analysis or transcriptomic
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  analysis or the P genetic analysis.
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  The problem is how we can integrate all this.
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  This data have to say that we are still working on that
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  because it's a field that is evolving so fast and there is
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  every year new, new algorithms to do so.
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  So it's a constant process. But what we have tried to do is
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  to try to find in the different omics layers that we have some
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  features that can be combined. For instance, in the whole
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  genome we have the copy number alterations to say OK, these are
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  the genetic alterations that are present in a subset of the
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  cells. Then when we have this
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  transcriptomic using single cell RNA sequencing, we can also
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  identify copy number alterations there.
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  And then we can kind of map it. Cells carry this genomic
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  alterations with a transcriptom and link genomic and
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  transcriptomic information for instance.
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  And when we look at the epigenetic information, we can
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  see which are the transcription factors that bind in these
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  regions and which are this the the genes that are more close to
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  the the different enhancers that we find for instance, and is a
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  way to link transcriptomic or RNA sequencing data with a piece
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  of genetic information. So we tend to make this kind of
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  cross or mix integration of the different layers.
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  And now we are working on algorithms that can allow you to
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  put all of this together to have a much granularity in the
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  analysis. So you are developing your own
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  algorithms. In some of the analysis, we
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  develop custom algorithms to try to answer some of these
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  questions. We are also lucky that there is
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  this is a field work in which many people is working on this
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  kind of multi omic integration especially in the context of
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  single cell. So we are also reusing
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  algorithms that other colleagues from the community have
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  developed. So what have you observed?
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  But can you tell us what you what you have?
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  Seen. I think that one of the main
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  findings that we did during this year, J Grant, was what we
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  published about research transformation in which patients
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  nowadays have a very poor outcome that is a much unmet
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  medical need in this context. And this is a very aggressive
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  transformation. But when we did this
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  reconstruction of how this evolution takes place within the
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  patient, we found what we call the seeds of this transformation
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  up to 20 years before the clinical manifestation of the
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  disease. So we're now start to understand
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  how this very aggressive transformation at the clinical
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  level works or or develops at the biological level, which may
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  not be that aggressive because it takes up to 20 or 30 years.
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  So this has basically changed quite a bit the paradigm that we
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  had about to hold Rich the transformation occurs and and
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  this opens also a possibility to try to develop tools for early
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  detection of this aggressive research transformation.
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  And eventually maybe at some point to start thinking on early
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  intervention in patients in which we may identify this early
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  sets at the earlier time point. And in addition to this more
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  philosophical or evolutionary perspective of research
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  transformation, we have also identify a lot of metabolic
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  changes in these cells, transcriptomic changes that may
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  guide to develop potential new therapies for this group of
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  patients that now other groups are also following this research
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  line that we have started. Here So how can this work
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  translate into better diagnosis or prognosis with patients we
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  treated differently earlier? Our genomic and transcriptomic
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  characterization has allow us to identify which are the genetic
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  alterations that are associated to reach the transformation for
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  instance or to aggressive or to aggressive course of the CLL.
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  So this can help us to incorporate the analysis of
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  these alterations in the clinics to identify which patients might
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  be more prone to develop rich, the transformation or to have an
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  aggressive disease. And we are working now on
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  integrating this genomic information in clinical models
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  in which we want to combine clinical variables together with
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  genomic information to try to better predict who a patient at
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  time of diagnosis will evolve, if the disease will progress
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  faster, if it may have higher chances to develop rich or
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  transformation. So I think that from a
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  diagnostic point of view, it may help.
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  I also think that in defining which are the mechanisms that
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  lead to this aggressive transformation of CLL may also
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  identify potential new vulnerabilities of the cells.
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  And we have validated, for instance, in our study that by
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  targeting the metabolism of the rich star cells, we cannot stop
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  the proliferation. It was a proof of concept to
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  show that our findings may lead to the development of other
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  drugs. But this is something that our
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  group is not working on directly.
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  But we hope that the community can take some of our findings
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  and and take advantage of that. And as you said also from the
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  concept of early diagnosis of this aggressive clones, we have
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  shown that this can be indeed detected years before that
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  clinical manifestation. I have to say that we are still
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  don't know exactly if we can find this aggressive clones in
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  patients that do not develop. This such aggressive
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  transformation is something that we are still working on that we
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  wanted to finish during the AJ Grant.
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  It was not possible because every all these kind of things
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  always take a bit more time than what what we initially expect.
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  But we are working on trying to identify if this could be a
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  marker of which patients will develop.
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  We know that in those patients that will develop these seeds
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  are there so we could aim for the future and early diagnosis.
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  And for instance, we are working on developing single cell
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  technologies and algorithms that can identify robustly these
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  cells early in time and but we still have to make sure that
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  this is a real biomarker of this transformation.
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  So you were one of the ones who could fund the research with an
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  EHA grant. How does has this transformed
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  your career? Yeah.
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  I think that it was instrumental to be able to conduct the
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  postdoctoral fellow and together with my PhD mentor, because in
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  Spain it's quite hard to get or even impossible to get a grant
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  to do the post doctorate fellow in the same lab that you did the
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  PhD. But I think that this was the
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  the right idea to continue the research line that I started
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  because we have all the sample availability, we have lots of
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  international collaborations. It was like for me the perfect
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  environment to conduct this research and the AJ Grant
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  support not only my salary but also give me the opportunity to
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  have some funding to start creating my own research line
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  with independent from my mentor. And this I think that is
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  something that has been really crucial to help me apply for
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  additional funding in the last years.
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  And I have been lucky to get some additional grants in the
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  last period of my post doctoral failed to start kind of setting
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  my own track on on this field of genomics, cancer revolution.
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  And also I think that the an EJ grand, it's an important match.
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  It's on in ACV for its prestigious and, and as you
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  said, it's not a grand that many people can get.
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  So I'm very honored to have this, to have this opportunity
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  access a very, very grateful for that.
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  And also not only for the HA grammar, they will also take to
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  take this opportunity to mention the the computational biology
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  training in hematology that I also could also participate in
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  the 1st edition of the CBTH. And was also a very nice
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  experience to extend my collaborations and my network of
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  of colleagues. And also was a very nice
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  experience to keep growing and as a scientist, but also from a
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  personal side. Are you still participating in
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  CBTH? I participated in the 1st
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  edition and and it was very nice because I could present some of
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  the work that we were doing, some of the ideas that we want
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  to follow now in the next projects.
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  And I get extreme useful feedback from all the the
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  mentors, excellent mentors, all of them.
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  And it was very nice because the the mentors were super involved
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  in the projects if a lot of feedback and in with many of
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  them I still in contact and collaborating.
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  In addition to the AJ grind with with crucial as well for my
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  prospector or fellow. But also these mentoring
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  programs of the AJ are very, very important and very useful
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  for for searches that are starting their own research
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  lines in this field. So what comes next?
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  Are you going to become a mentor or what comes next for the
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  project in general? So for for the project did we
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  have one of my PhD students now has a talk here at DJ presenting
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  some of our next aims in this field.
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  So we have basically to make the, the story simple, we have
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  been able, as I was saying, to characterize the evolution of
• 14:44
  the CLL from time of diagnosis to time of progression, relapse,
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  transformation, because we have samples over a period of 20
• 14:51
  years now we have tools that allows us to go even back in
• 14:56
  time so we can reconstruct how the tumor has been evolving even
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  before the diagnosis of the disease.
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  And we, we do this and others in the field do this kind of things
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  using single cell whole genome sequencing, in which you can
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  sequence the whole genome of individual cells and then you
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  can reconstruct the phylogenetic tree of how the cells have been
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  evolving, just like the phylogeny of the species or this
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  famous tree of Darwin. So you can do the same for CLL
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  or for any cancer. And this gives you super
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  resolution to understand how this tumors evolve even before
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  the diagnosis of this, this the CLL in this case, but in in any
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  other tumor. And we are applying this
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  technology to better understand the process of restart
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  transformation, to really know when CLL starts, where the
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  earlier alterations of research transformation occur, to better
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  have an idea of which is the timing of this disease.
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  Also aiming for this better understanding of which is the
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  time window in which we could eventually develop early
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  intervention strategies. And in the context of CLF, we
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  are having now this this talk here, the TIJ in which we have
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  found that some patients don't have a single CLL, so a single
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  tumor, but have multiple independent CLLS, which is quite
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  fascinating from a biological point of view.
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  And we are studying this to see why this happens in some
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  patients. How this different tumors evolve
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  during the lifespan of the patient and grow it like in
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  parallel, which are the features that are in common in the
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  different clones, what make them different.
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  And all this from this more evolutionary perspective of how
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  this tumors evolve over the the course of the years of the the
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  patient even before the diagnosis of the disease.
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  Because within that this as has been shown in other neoplasms,
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  knowing how the tumor evolved in the past is also very important
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  to understand or to predict how this will evolve in the future.
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  So now, in the past, we can better predict the future, let's
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  say. So what would you tell other
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  young investigators that are thinking about how to fund the
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  research, are thinking about applying for EHA grants?
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  Yeah, I, I would really recommend to apply.
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  I know it's hard to get, I was privileged to get one of those,
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  but I really think that they are all the the, the support that
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  you get from the AJ and all the other activities that we have
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  mentioned CVTH, but there are many others for non
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  computational biologists, the TRTH and another it's a unique
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  opportunity to to extend the your network or some of them
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  become then colleagues and collaborators friends.
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  So it's really, I think that all this initiative on the EJR
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  cruise for young investigators to expand a bit there or to go
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  outside of their area of comfort, grow personally,
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  scientifically, get input from top mentors to have basically
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  changed a lot the way I understand how to do science or
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  to have to say a much broader perspective of how to tackle a
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  problem, how to collaborate and all these kind of things.
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  So if you want our audience to remember just one thing from
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  this podcast, what would it be? Well.
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  Maybe from scientific, scientific aspect, our work on
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  on understanding the evolution of CLL, especially on this Rich
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  the transformation and the paper is out published.
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  So I encourage them to read if they're interested in this
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  evolutionary perspective of Rich the transformation.
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  I think that this would be the highlight of my EHJ grant from a
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  scientific point of view, from more a personal experience.
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  I will strongly recommend to all the people that is my stage or
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  or be younger to apply for EJ grants to participate in the
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  mentoring programs that are available that are really,
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  really useful to to keep growing as scientists extend network of
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  collaborators and and I think that they are very, very useful.
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  So thank you very much, Doctor Nadeau, for being with us today.
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  Thank you, it was my privilege to be here.
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  And thank you to the audience for listening and please stay
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  tuned for more episodes.