ROBUST SCORING OF SELECTIVE DRUG RESPONSES FOR PATIENT-TAILORED THERAPY SELECTION
The widely-used drug sensitivity score (DSS) developed at FIMM has been extensively upgraded for more robust quantification of selective drug responses in patient-derived cells. The novel features in the open-source software support systematic identification of candidate compounds for effective personalised therapies.
Article originally published at the FIMM website: https://www.helsinki.fi/en/hilife-helsinki-institute-life-science/news/robust-scoring-selective-drug-responses-patient-tailored-therapy-selection
Functional testing of molecularly targeted drugs in patient cells (so-called ex vivo drug testing) has shown clinical benefits in terms of improved patient outcomes and personalized treatment options both in hematological malignancies and in solid tumors.
In 2013, a research team led by Tero Aittokallio at the Institute for Molecular Medicine Finland (FIMM), developed a new computational approach to score drug sensitivity, called DSS metric. Their approach integrates multiple dose-response parameters into a single response metric, thus identifying differential drug response patterns between cancer and healthy control cells. The selective scoring of drug responses supports the identification of targeted and non-toxic compounds as effective and safe individualized treatment options for guiding clinical decision-making.
During the past years, the team has continued the DSS development work by extending the features of the selective scoring approach, and together with researchers in other cancer centers demonstrated its applicability in various precision medicine study settings. The results of this joint work have just been published in Nature Protocols.
In this article, the research team presents several case studies and real-life examples that showcase the best use of selective DSS calculation when optimizing treatments for leukemia patients in three ongoing precision medicine studies in Europe and in the US. The generic methods are widely applicable also to other malignancies that are amenable to ex vivo drug testing.
“We implemented several new features for the DSS calculation, such as robust statistics and batch effect correction, which enable more accurate quantification of the cancer-selective effects of drugs”, says FIMM Doctoral Researcher Yingjia Chen, the first author of the study.
The protocol paper also makes available a standardized set of drug response profiles to 527 anticancer compounds tested in ten healthy bone marrow samples as reference control data for selective scoring and avoiding potential toxic effects in future studies.
“Importantly, these methods can also be used to score selective responses of drugs by comparing their responses between cancer and non-cancer cells using single-cell drug testing assays such as flow cytometry or imaging methods”, says Postdoctoral Researcher Liye He from FIMM.
The study was done in collaboration with researchers from Biotech Research & Innovation Centre (BRIC), Helsinki University Hospital, Orton Orthopaedic Hospital, Tampere University and Oslo University Hospital.
The open-source R-codes provide a robust means to tailor personalized treatment strategies based on increasingly available ex vivo drug testing data from patients in real-world and clinical trial settings. The source codes and the drug response data consisting of the ten healthy control responses and example leukaemia patient response data are openly available in GitHub.
The new DSS metrics are additionally implemented in the Breeze data analysis platform maintained by the FIMM High Throughput Biomedicine (HTB) unit. The open-source codes enable others to easily implement the selective methodology in the in-house drug screening pipelines.
Original publication:
Robust scoring of selective drug responses for patient-tailored therapy selection. Yingjia Chen, Liye He, Aleksandr Ianevski, Pilar Ayuda-Durán, Swapnil Potdar, Jani Saarela, Juho J. Miettinen, Sari Kytölä, Susanna Miettinen, Mikko Manninen, Caroline A. Heckman, Jorrit M. Enserink, Krister Wennerberg & Tero Aittokallio. Nature Protocols (2023). DOI: https://doi.org/10.1038/s41596-023-00903-x
Further information:
Yingjia Chen, FIMM Doctoral student
Institute for Molecular Medicine Finland FIMM, HiLIFE, University of Helsinki
Tero Aittokallio, PhD, Professor
Institute for Molecular Medicine Finland FIMM, HiLIFE, University of Helsinki
Email: tero.aittokallio@helsinki.fi