DrSCS

Predicting subclonal drug response from single-cell sequencing for precision oncology

Started
September 29, 2022
Status
In Progress
Share this project

Abstract

Tumours evolve as heterogeneous populations of cells, which may be distinguished by different genomic aberrations and transcriptomic differences. The resulting intra-tumour heterogeneity plays an important role in cancer patient relapse and treatment failure, so that obtaining a clear understanding of each patient's tumour composition and evolutionary history is key for personalised therapies. Single-cell DNA sequencing now provides the possibility to resolve tumour heterogeneity at the highest resolution of individual tumour cells, while single-cell RNA sequencing offers expression profiling of tumour subpopulations as well as characterisation of the immune compartment of the tumourmicroenvironment. In parallel, ex-vivo drug screening gives a direct measurement of the aggregate response of tumour cells to various compounds.

With 136 samples profiled in a recent collaboration, we aim to develop and incorporate new prediction models for the drug response of the individual subclones in each patient's tumour. This approach will account for the tumour heterogeneity and for potentially different responses of constituent tumour parts to treatment. To integrate the different data modalities, we aim to first integrate the single-cell DNA and RNA information with joint modelling of the tumour evolution to more accurately understand its composition in terms of the existing subpopulations and their expression and copy number profiles. By additionally integrating drug response information, we can, for the first time, build and test drug prediction models at the level of tumour subclones. The potential of understanding the effect of different treatments on the heterogeneous parts of a tumour and thereby designing treatments for the full composite tumour could have great impact in aiding precision oncology.

This project is co-funded by PHRT.

People

Collaborators

SDSC Team:
Quentin Duchemin
Daniel Trejo Banos
Guillaume Obozinski

PI | Partners:

ETH Zurich, Department of Biosystems Science and Engineering:

  • Dr Jack Kuipers
  • Prof Niko Beerenwinkel

More info

ETH Zurich, Clinical Bioinformatics Unit:

  • Franziska Singer
  • Anne Bertolini

More info

description

Motivation

During tumour progression, cancers may evolve into a complex system of heterogeneous
subpopulations of cells harbouring different genomic aberration. Diversity and heterogeneity within a tumour is a considerable cause of treatment failure and relapse, since subclonal cell populations may be resistant to treatment and therefore progress. Effective treatment should target all subpopulations, or evolve with the tumour to adapt as new clones become dominant. Uncovering the genetic makeup and evolutionary history of each tumour and linking these molecular alterations to the chances of reatment success is thus key to developing targeted therapies.

Proposed Approach / Solution

The aim of this project is to combine knowledge about the tumour architecture uncovered through single-cell DNA and RNA sequencing with the measured drug response of the tumour cells (cf. Figure 1). In particular we aim to integrate copy number and expression data from the same tumour to best describe the different tumour subpopulations and their aberrations, along with characterising the immune component. From this, and comparing to the aggregate drug responses of the tumour, we aim to build multitask learning predictors of drug sensitivity at the subclonal level. The mission of the SDSC in the project is to successfully integrate single-cell data modalities and implement the subclonal prediction model of drug response from single-cell DNA and RNA data.

Impact

This project offers the potential to determine which treatments could control the full tumour for precision oncology, allowing to set up protocols suitable for translating drug predictions to clinical usage.

Figure 1: From single-cell RNA and DNA sequencing we obtain immune cell informationand expression clusters along with copy number clones.  From the measured aggregatedrug response, we learn a model predicting the drug sensitivity at thesubclonal level to target treatment of the entire tumour.

Gallery

Annexe

Additional resources

Bibliography

  1. Kuipers, J., Tuncel, M. A., Ferreira, P., Jahn, K., & Beerenwinkel, N. (2020). Single-cell copy number calling and event history reconstruction. doi:10.1101/2020.04.28.065755
  2. Ferreira, P. F., Kuipers, J., & Beerenwinkel, N. (2021). Mapping single-cell transcriptomes to copy number evolutionary trees. doi:10.1101/2021.11.04.467244
  3. Bertolini, A., Prummer, M., Tuncel, M. A., Menzel, U., Rosano-González, M. L., Kuipers, J., … Singer, F. (2022). scAmpi-A versatile pipeline for single-cell RNA-seq analysis from basics to clinics. PLoS Computational Biology, 18(6), e1010097. doi:10.1371/journal.pcbi.1010097
  4. Irmisch, A., Bonilla, X., Chevrier, S., Lehmann, K.-V., Singer, F., Toussaint, N. C., … Levesque, M. P. (2021). The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support. Cancer Cell, 39(3), 288–293. doi:10.1016/j.ccell.2021.01.004

Publications

Related Pages

More projects

ML-L3DNDT

Completed
Robust and scalable Machine Learning algorithms for Laue 3-Dimensional Neutron Diffraction Tomography
Big Science Data

BioDetect

Completed
Deep Learning for Biodiversity Detection and Classification
Energy, Climate & Environment

IRMA

In Progress
Interpretable and Robust Machine Learning for Mobility Analysis
No items found.

FLBI

In Progress
Feature Learning for Bayesian Inference
No items found.

News

Latest news

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data
May 1, 2024

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

Smartair | An active learning algorithm for real-time acquisition and regression of flow field data

We’ve developed a smart solution for wind tunnel testing that learns as it works, providing accurate results faster. It provides an accurate mean flow field and turbulence field reconstruction while shortening the sampling time.
The Promise of AI in Pharmaceutical Manufacturing
April 22, 2024

The Promise of AI in Pharmaceutical Manufacturing

The Promise of AI in Pharmaceutical Manufacturing

Innovation in pharmaceutical manufacturing raises key questions: How will AI change our operations? What does this mean for the skills of our workforce? How will it reshape our collaborative efforts? And crucially, how can we fully leverage these changes?
Efficient and scalable graph generation through iterative local expansion
March 20, 2024

Efficient and scalable graph generation through iterative local expansion

Efficient and scalable graph generation through iterative local expansion

Have you ever considered the complexity of generating large-scale, intricate graphs akin to those that represent the vast relational structures of our world? Our research introduces a pioneering approach to graph generation that tackles the scalability and complexity of creating such expansive, real-world graphs.

Contact us

Let’s talk Data Science

Do you need our services or expertise?
Contact us for your next Data Science project!