Researchers at the University of East Anglia have identified at least three distinct subtypes of osteosarcoma, a rare bone cancer primarily affecting children and teenagers. This breakthrough, achieved through advanced mathematical modeling and machine learning, promises to transform clinical trials and patient care by enabling more targeted treatment strategies.
Overcoming Treatment Challenges in Osteosarcoma
For decades, osteosarcoma treatment has relied on untargeted chemotherapy and surgery, often resulting in limb amputation and severe side effects. The survival rate has stagnated at around 50% for the past 45 years. Multiple international clinical trials investigating new drugs have been deemed failures, but this new research suggests that some patients did respond to these drugs, indicating the existence of subtypes that could benefit from targeted therapies.
Dr. Darrell Green, of UEA's Norwich Medical School, explained, "Since the 1970s osteosarcoma has been treated using untargeted chemotherapy and surgery, which sometimes results in limb amputation as well as the severe and lifelong side effects of the chemotherapy... This new research found that in each of these 'failed' trials, there was a small response rate (around five to 10 per cent) to the new drug, suggesting the existence of osteosarcoma subtypes that did respond to the new treatment."
Latent Process Decomposition (LPD) for Subtype Discovery
Traditional methods of predicting osteosarcoma subtypes have been limited by their inability to account for the heterogeneity within individual tumors. To address this, the researchers employed a novel approach called Latent Process Decomposition (LPD). LPD considers the tumor as a mix of hidden patterns in gene activity, representing different functional states, each with its specific gene expression profile. This method allows for a more nuanced understanding of tumor biology and facilitates the identification of distinct subtypes.
The research uncovered three osteosarcoma disease subtypes, one of which was found to respond poorly when treated with the standard chemotherapy drug combination called MAP. This finding suggests that by grouping patients based on these patterns, doctors could make more informed decisions about treatment, potentially avoiding ineffective therapies and improving patient outcomes.
Implications for Clinical Trials and Patient Care
The identification of osteosarcoma subtypes has significant implications for future clinical trials. By stratifying patients based on their subtype, researchers can better assess the efficacy of new drugs and identify those that are most likely to benefit from specific treatments. This approach could lead to more successful clinical trial outcomes and the development of targeted therapies that are tailored to the individual patient's cancer.
Dr. Green added, "We hope that in the future, grouping patients using this new algorithm will mean successful outcomes at clinical trial, for the first time in over half a century... When patients can be treated using targeted drugs specific to their cancer subtype, this will facilitate a move away from standard chemotherapy."
Study Limitations and Future Directions
The researchers acknowledged that the study had some limitations, including a small dataset for the LPD model development and incomplete clinical data in the validation cohort. Access to tissue and linked clinical data is particularly challenging for osteosarcoma due to the rarity of cases, limited biopsy material, and the extensive chemotherapy-related damage present in post-treatment samples. Despite these challenges, the LPD method proved to be reliable, as it identified consistent subgroups of osteosarcoma across four different sets of independent data.
With improved guidelines for bone cancer sample and clinical data collection, researchers anticipate refining the LPD model further, potentially uncovering even more specific osteosarcoma subtypes and paving the way for more effective, personalized treatments.
