The Current Approach to Cancer Treatment
The current approach to cancer treatment can be best described as one-size-fits-all. But the fact is, one size does not fit all and cancer drugs don’t work the same way for everyone. A report published by Personalized Medicine Coalition in 2017, pointed out that any particular class of cancer drugs is ineffective in a startling 75% of patients!
Due to the one-size-fits-all approach, a large number of cancer patients fail to respond to their prescribed treatments and experience serious negative side effects, causing a big impact on society and the economy. In the United States alone, the economic burden due to adverse drug reactions is more than 30 billion dollars annually.
In today’s era of targeted therapy, it is essential to identify patient subsets that are either likely or unlikely to respond to a particular drug. Doing so would maximize the efficacy and minimize unnecessary toxicity associated with the use of the drug in question.
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Precision Medicine – The Way Forward, But Cancer Heterogeneity a Major Bottleneck
This is where Precision Medicine comes in. Precision Medicine is an emerging approach to medical treatment that allows doctors to select treatment regimes that are most likely to help patients based on the genetic understanding of their disease.
For cancer therapy, however, there are significant challenges in applying personalised and precision medicine approaches. Cancer is a genetically heterogeneous disease, with a large number of genetic alterations present within a patient’s cancerous growth (typically, a single tumour contains anywhere from tens to millions of genomic aberrations). This represents a major impediment to designing effective personalised treatment strategies against cancer.
Stratifying patients by mutation status, for e.g., PIK3Ca mutation, has resulted in improvement in response rates in the clinical trials testing inhibitors to PI3K/AKT/mTOR pathway. Non-responders to treatment, though, are still common. Given the highly heterogeneous and chaotic landscape in cancer biology, the isolated molecular entities have little/no value on their own for precision medicine purposes.
To address the problem of heterogeneity in precision cancer diagnosis and treatment, one promising solution is to optimise the process through molecular sub-typing based on the intrinsic biology of cancer. The current application of molecular classifiers is still restricted to the prediction of cancer recurrence risk. What is needed are classification systems that can be correlated with the outcomes of various specific treatment regimes.
Image credit: Prostate Cancer Foundation
References
1. Personalized Medicine Coalition. The personalized medicine Report. Opportunity, challenges, and the future (2017)
2. Sultana J et al. Clinical and economic burden of adverse drug reactions. J Pharmacol Pharmacother. 2013, 4:S73-S77.
3. Gary Kurtzman. A Business Model for Diagnostic Startups-A Business Model for a New Generation of Diagnostics Companies. Biotechnol Healthc. 2005, 2:50-55.
4. BIS Research. Global Precision Medicine Market: Focus on Precision Medicine Ecosystem, Applications, 13 Countries Mapping, and Competitive Landscape - Analysis and Forecast, 2018-2028.
5. Kenneth Research. Cancer Biomarkers Market Size, Growth, Opportunity and Forecast to 2025.
6. Drier Y and Domany E. Do Two Machine-Learning Based Prognostic Signatures for Breast Cancer Capture the Same Biological Processes? 2011, 6: e17795.