Artificial intelligence helps researchers find suitable drugs based on patient’s genetic data and cancer cell samples

The iCAN-PEDI study, investigating drug treatments and drug responses in children with cancer, is a part of the large-scale iCAN Flagship project of Academy of Finland. The study combines genetic and epigenetic information on the patients’ cancer with data on the drug testing of patient derived cancer cells. Together with collaborators, the team also develops artificial intelligence (AI) -guided analysis for the drug testing data. The project aims to deliver findings that may affect treatment approaches back to the doctors. This helps the doctors to construct more individualised treatment approaches.

Vilja Pietiäinen, senior scientist and adjunct professor (docent) in cell and molecular biology at the Finnish Institute for Molecular Medicine (FIMM), leads the iCAN-PEDI project with Minna Koskenvuo, a clinician (in Pediatric Hematology and Oncology) at the HUS New Children’s hospital and at Turku University hospital. In addition, many clinicians from HUS’s New Children’s Hospital and researchers from the University of Helsinki are involved in the project. Pietiäinen says she wants to make cancer treatments more individualised.

“From a medical perspective, the way we treat childhood cancers is already individualised. However, studying children’s tumours on a molecular level can help us find more effective drugs for specific types of cancers. The solid tumours in children are often heterogeneous and difficult to diagnose only based on pathology. In addition, some of these tumours are very rare. The types of cancer seen in children generally include fewer genetic changes, which means more molecular-level data is required for a diagnosis. The diagnosis, in turn, will affect the treatment approach.”

According to Pietiäinen, collecting large amounts of data on individual patients can significantly improve the diagnostic process and help find new ways to classify different cancers. It will also allow researchers to understand how much variation can be found even within well-known types of cancer.

“We want to better understand why a certain patient responds to drugs the way that they do. This will allow us to develop better and more individualised ways for choosing a treatment approach for a specific patient.”

She and her team combines patient’s molecular-level cancer data with cell models that represent each patient’s individual cancer cells. Exome sequencing allows researchers to examine the information of roughly 20,000 genes in a single run. Transcriptomics, in turn, makes it possible to analyse thousands of RNA molecules simultaneously. This process provides information on how different genes are expressed. Tissue imaging serves to illustrate the biomarkers expressed by different types of cancer tissue. The resulting data is stored in HUS Acamedic, the secure environment used by the iCAN project.

Pietiäinen says that often, genetic data alone is not enough to determine how an individual patient’s cancer will respond to a specific drug.

“We need to study the drug responses of individual patient’s cancer cells with the help of microscopic imaging in the laboratory. Cancer is a very heterogeneous disease: not all cells will necessarily respond to the same drug(s). However, we are also interested in those cells that do not show a response, and have developed the resistance to the used treatments. A combination of different drugs may be required to eradicate all the cancer cells.”

Imaging data used to construct a machine learning model

Once the study-consented patient has been operated on, a cancer tissue sample is sent directly to a pathologist, who do the diagnosis and will forward the additional sample directly to the researchers involved in the study. Drug susceptibility is tested with a multi-well cell culturing plate in a process that utilises robotics. The wells are very small, which means that only a small amount of the valuable cell samples is required. A single plate can be used to test dozens of drugs at a time.

Researchers fill the wells with different concentrations of specific drugs and cancer cell samples. With the help of microscopic imaging, they can inspect how the drugs influence the cancer cells in the wells of the plate. Machine learning models allow researchers to more effectively analyse the images of cancer cells. The artificial intelligence used by the research team has been trained with the Finnish ELIXIR node’s, CSC’s high-performance computing clusters.

“We call this phenotypic imaging. Microscopic imaging allow us to identify hundreds of different cell characteristics from images of drug-treated cancer cells. This information is important for further training the machine learning model. If we are able to clearly identify certain phenotypes, we can also teach the machine to do the same by showing it how certain cells have responded to a certain drug. After this, we can provide the machine with a new dataset, in which case it will be able to classify the cells by how they show up in the images. On the other hand, artificial intelligence, especially deep-learning solutions, can also help us to discover traits or phenotypes that we as humans are not able to either detect or classify.”

Once the hundreds of analysed traits are fed into the artificial intelligence, it will be able to differentiate between different drug responses at single-cell level. The data can also be used to sort patients into groups based on the drug responses they exhibit.

Identifying the optimal drug response requires several different data sources. As an example, Pietiäinen mentions a large European project (ERA PerMed) that the project researchers were previously involved in.

“We know that there is currently no targeted drug treatment for up to 90 per cent of cancerous gene mutations. Therefore, we were only able to partially determine the efficacy of different drugs and drug targets for different drugs based on genetic information. However, drug testing did show that patients’ cells responded to certain drugs.”

Pietiäinen considers it crucial to be able to compare drug testing data from cancer studies to the response shown by healthy cells, for example.

“This way, we will be able to see such things as whether a particular patient’s cells respond particularly well to certain drugs. This information can then be compared to patients’ genetic and gene expression data. For instance, we could find out that a specific patient has a mutation that makes the cancer more susceptible for a certain drug, causing them to respond better to that drug. On the other hand, non-mutational information, such as how genes are expressed, how signal pathways are activated, or how epigenetic changes arise, may help us better understand how cells respond to different drugs. These different types of data can then be used at the individual level but also to divide patients into different subgroups to find more suitable treatment approaches.”

The patient’s blood samples or cerebrospinal fluid samples can be used for fluid biopsies and used to inspect how the tumour’s DNA is expressed. This will show how well the patient is responding to the drug, or if the cancer has recurred.

Technology for combining data

The iCAN research project, which is funded by Academy of Finland, covers most currently known types of cancer. Several research groups who concentrate on different types of cancers and research groups working on improving the relevant research methods are involved in the project. Information on the cancer is compared with the patient’s other health data in the secure HUS Acamedics environment.

“All the data we upload on Acamedics is available to all researchers participating in the iCAN project. We have a wealth of material that we can compare our findings against. This allows us to identify, for example, patient group and patient specific genetic markers in the genetic and other omics data.

All data, which includes data types such as drug testing data, genetic data and transcriptomics data, are combined using a powerful tool called an Integrated Molecular Tumour Board system (iMTB). In their research project on children with cancer, Pietiäinen and her colleagues are also evaluating how doctors can quickly make use of the results of recent or ongoing research.

“We aim to report clinically relevant findings to the doctors, thereby hopefully helping to choose a better treatment approach if they have ran out of recommended approaches.”

Cancer tissue profiling data transmitted with the help of the Finnish ELIXIR node CSC

The iCAN utilises the SD Connect service provided by the Finnish ELIXIR node CSC for transferring sequencing data to the Academics environment.

The data is encrypted using Crypt4GH, a protected standard encryption method developed by the Global Alliance for Genomics & Health for sharing human genetic information.

“This ensures that the information can be used in all of the services included in CSC’s SD service suite, and may even be potentially shared with other service providers who possess similar information.”

The sheer magnitude of the iCAN project is illustrated by the fact that the accumulated data is expected to reach three petabytes by 2026.

“All of this data makes it possible for us to understand the molecular makeup of different types of cancer and patients’ drug responses.”

Ari Turunen

29.9.2023

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