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

September 29, 2022
In Progress
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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.



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



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.


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.



Additional resources


  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


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