Despite the great progress in cancer research in recent years, the clinical treatment of cancer patients is still challenged by the great molecular variability between patients with seemingly similar cancers.
Jing Tang, from the Institute for Molecular Medicine Finland (FIMM), is tackling this problem with data integration approaches. He is developing mathematical and computational tools, and is applying them to the rational selection of cancer drug combinations that can be used to provide personalised and more effective therapeutic strategies.
“Recent biotechnical advances combined with computing and network modelling of pathological processes can fundamentally impact the way we approach drug discovery. We want to be able to provide personalised treatment options for cancer patients based on both genomic and drug sensitivity profiling data,” Jing Tang explains.
Many of the questions Tang wants to answer are the same that puzzle clinicians every day: Why did these two patients respond so differently to the same treatment? Why did this patient develop resistance to a drug that was so effective earlier? How can we determine the best treatment for this particular patient?
Building on solid groundwork, then beyond
Jing Tang is the first researcher at FIMM to receive an ERC grant. The 5-year grant totals 1.5 million euros.
“I believe that my strengths in this very tough competition were my multidisciplinary background and the strong translational aspect of my proposal, but also, and importantly, the fact that my proposal builds on the well-established personalised medicine research programme and state-of-the-art infrastructure available at FIMM.”
Instead of the more common PI-centric approach, FIMM has decided to focus on a few of the ‘grand challenges’ in today’s world, and almost the entire institute is working towards meeting these challenges. Notably, since the establishment of the institute, researchers and clinical collaborators have worked together on a programme called ‘Individualised Systems Medicine in cancer’, aiming to develop strategies to accelerate translational cancer research.
“It has become very clear that we are able to produce diverse, high-quality molecular profiling data. The bottleneck is now the interpretation of the data, not generating it. Without introducing a data integration and mathematical modelling framework, however, we can easily get lost in translating the ‘big data’ into valid treatment options.”
Support from funding specialists and from colleagues
This year, ERC Starting Grants were awarded to 325 early-career researchers throughout Europe. Only 11% of the proposals were funded.
“One of the eligibility criteria is that the applicants need to have two to seven years of experience after completion of the PhD. This was the first time I applied and also my last chance to get the starting grant.”
If fortunate enough to be among the 30% of ERC applicants who are invited to an interview, the researcher has only ten minutes to convince the expert panel, so the time must be spent wisely. Both the University of Helsinki and Academy of Finland support ERC applicants by organising training sessions. Jing Tang found the practice sessions invaluable, and believes that his interview would not have been as successful without the training and support provided.
“There were some very difficult questions, but thanks to the practice sessions, I was prepared for all of them. After the interview I felt satisfied with my performance.”
Research for the benefit of patients
With the ERC funding, Tang will recruit both post-doctoral researchers and doctoral students to his group. He wants to build a multidisciplinary team having open-minded people with different academic backgrounds.
“I would like to overcome the barriers between bioinformaticians and clinical researchers.”
“We have started the drug combination prediction work with haematological cancers but our methods are widely applicable, and next we’ll test them with ovarian cancers. Any cancer types and later also other complex diseases can be considered. I am always looking for new clinical collaborators and am open to all data-intensive biomedical questions,” Tang points out.
Tang is committed to making all the informatics tools and software applications developed by his group autonomously usable by translational scientists and clinical practitioners.
“I envision that in five years we’ll be able to provide relevant ‘personalised drug-disease network’ information to all patients. Newly identified drug and disease associations should provide significant added value to clinicians when monitoring the progress of the disease or making treatment decisions for their patients.”