Predicting treatment response to improve outcomes and reduce side effects

No two patients respond to cancer treatment in the same way. Work Stream 2 aims to understand why some patients benefit from a particular treatment while others experience limited response, serious side effects, or long-term health consequences. By identifying these differences, we seek to make cancer treatment safer, more effective, and better tailored to each patient.

We integrate tumor genomics, medical imaging, immune profiling, clinical data, patient-reported outcomes, and Swedish healthcare and population registers to develop predictive models of treatment response, toxicity, and long-term outcomes. By combining biological, clinical, psychological, and social factors, we aim to better understand the complex determinants of treatment effectiveness and the unintended consequences of cancer therapy.

A key focus is identifying patients at increased risk of treatment-related complications—including cardiotoxicity, infertility, neuropathy, cognitive and mental health effects, lymphedema, and other long-term sequelae. We also investigate how health across the life course, pregnancy history, environmental exposures, and social conditions influence treatment outcomes and recovery.

The knowledge generated will be translated into clinician-facing decision-support tools and patient-friendly resources that support shared decision-making and personalized treatment planning. Ultimately, Work Stream 2 aims to optimize treatment selection, minimize harm, improve quality of life, and ensure that every patient receives the right treatment at the right time.

Participating investigators

Carina Bernardo, Ingrid Hedenfalk, Lao Saal, Lisa Rydén, Niklas Loman, Päivi Kannisto, Pamela Buschwald,  Sean Perrin, Sophia Zackrisson

Research focus

• Predict treatment response by integrating genomic, imaging, immune, and clinical data.
• Identify risk factors for treatment toxicity and long-term complications.
• Understand how biological, psychological, and social factors influence treatment outcomes.
• Investigate the long-term and intergenerational consequences of cancer treatment.
• Develop decision-support tools that enable personalized treatment and shared decision-making.
• Improve quality of life by reducing treatment-related harm and optimizing cancer care.