Project

Next Generation Multi-Modal Analysis of Tumoral Heterogeneity in Peripheral Blood

Short Summary

Acute myeloid leukemia (AML) remains one of the most aggressive forms of cancer, solid or liquid, stalling at 25% survival at 5 years in adults. The scientific community now recognizes that multiple different tumoral cell populations are present simultaneously at diagnosis and throughout disease evolution. This has raised new challenges in how to tackle these diseases. Thus, new diagnostic tools are required in order to adequately characterize these multiple populations. In our lab, we are developing such a novel approach, namely an Interconnected Robotic Imaging and Single-cell RNA-sequencing (IRIS) platform that aims to provide different data types simultaneously for individual cells. By coupling imaging and gene sequencing data we aim to uncover new morphological biomarkers that may be specific to certain genetic profiles. This, in turn, may potentiate the discovery of de novo markers as well as reducing the times from diagnosis to treatment.

Goals

With the analysis of these different cell populations, whose distribution evolves from diagnosis to disease progression, resistance and relapse, we hypothesize that certain of these cell populations will be revealed to be major drivers of adverse outcomes. Hence, looking at how these cells react and change when we apply different clinical pressures (i.e. targeted vs non-targeted chemotherapies), could allow us to identify new biomarkers, aiding us to dissect the complicated clinical cases and to better adapt treatment protocols.

Significance

Further exploring tumoral heterogeneity will enable us to understand how different cell populations evolve throughout treatment, providing new insights into rarer cell populations and their characteristics driving treatment resistance and relapse. Characterizing such cell populations may reveal relevant molecular markers and targets. In addition, we may identify morphological features that could be characteristic of specific blood cancer subtypes across for example AML. If this is the case, then we could simply “look” at cells and already know some of the major mutations they carry, which could drastically improve diagnosis to treatment timeframes.

Background

Treatment protocols for AML require extensive characterization of cells and numerous analysis platforms have to be used. However, none of those currently utilized allow for the coupling of imaging and gene sequencing data. Using IRIS, this challenge can be resolved and yield molecular and imaging data on a per cell basis across the heterogenous population of cancer cells before, during and after treatment. This may help us better understand how blood cancers evolve, potentially opening the door to speedier and more specific characterization of AML.