Recently, the American Board of Internal Medicine, in conjunction with the American Society of Clinical Oncology, released a list of “Five Things Physicians and Patients Should Question”, which highlighted procedures and treatment modalities that lack evidence supporting their efficacy and clinical value.1 Although it would be reasonable to assume that healthcare professionals regularly treat their patients with evidence-based therapies and medicines, healthcare costs may demonstrate otherwise.
From 2008 to 2009, national healthcare costs increased by approximately $94.6 billion dollars.3 A study by Kale and colleagues in 2011 revealed some potential reasons for the increased costs. Kale’s study analyzed the cost of the top five most overused clinical procedures as defined by the Good Stewardship Working Group and found unnecessary practices, such as ordering a CBC for a general medical check-up, and other nonessential procedures, contributed to $6.76 billion dollars in unnecessary healthcare expenditures in 2009.
With the country’s economic stability in question, and Medicare transitioning from a fee-for-service to a performance-based model, it is becoming increasingly clear that fiscal constraints will lead to little room for second guessing for both patients and physicians when it comes to healthcare. However, a new—albeit costly—but promising approach to cancer treatment, in particular, is emerging in personalized medicine through genetic profiling. As new economic policies begin to change the way healthcare is practiced, innovation must abound so that healthcare professionals are still able to provide treatments that result in the best outcomes; when it comes to treating cancer in the era of personalized medicine, less may mean more.
A study in 2012 from the Cancer Genome Atlas Research Network profiled 178 squamous cell lung carcinomas in an attempt to analyze genetic and epigenetic abnormalities. Researchers found high rates of genomic complexity and mutations (8.1mutations/megabase), altered cellular pathways, and similar alterations between squamous cell lung carcinomas and head and neck squamous carcinomas.4
Researchers also found 114 cases of somatic alterations in which there was a potentially targetable gene based on the following: mutation assessor score; confirmations of altered alleles; and the availability of agents undergoing clinical trials and those agents currently approved by the US Food and Drug Administration.4 This study is evidence that understanding genetic mutations in cancer is key. While they may present in similar fashion histologically, the variations in the genetic make-up of each individual patient requires a personalized approach to treatment.
An example of this approach starts with the work of Timothy Ley and his colleagues who have published papers on topics such as the DNA sequence of cytogenetically normal acute myeloid leukemia (AML) patient and the somatic mutations and germline sequence variants in patients with de novo AML. A few years after publishing this work, one of Dr. Ley’s colleagues, Lukas Wartman, would present with a second relapse of acute lymphoblastic leukemia (ALL). In an effort to help their colleague, the original research team built upon their previous works and sequenced Dr. Wartman’s genome.
Results showed multiple mutations in his ALL genome, and with further analysis they were able to determine the key culprit of Wartman’s relapse: an over productive FLT3 gene. Dr. Wartman was subsequently treated with the FLT3 inhibitor sunitinib,5 an agent currently indicated for gastrointestinal stromal tumors, advanced renal cell carcinoma, and advanced metastatic pancreatic neuroendocrine tumors, but not for ALL. To this day, Dr. Wartman remains in remission as a result of this personalized treatment approach.6
Current cancer therapies can be extremely expensive and financially burdensome to patients; adding genetic profiling to treatment costs would not be a viable option for many people. While more research needs to be done to assess this approach, key questions arise. Is it worth exploring whether genetic profiling and targeted therapies would be more cost effective in the long term than continuing with current conventional therapies? Take Dr. Wartman’s experience. Would he have had to pay for two extra rounds of chemotherapy and care had his ALL been treated through a personalized approach the first time? Should current treatment models for cancer be reassessed as further genomic evidence comes to light? Doing so may eventually lead to a decrease in disease recurrence rates, or perhaps even a “cure” for certain cancers.
While these questions remain unanswered, new data validates the need for a personalized approach as it becomes increasingly evident that carcinogenesis varies from patient to patient. While expensive, the possibilities of such an approach have the potential to ultimately outweigh such costs.
1) ABIM. Five Things Physicians and Patients Should Question. Accessed at: http://www.choosingwisely.org/doctor-patient-lists/american-society-of-clinical-oncology/
2) Kale MS, Bishop TF, Federman AD, Keyhani S. “Top 5” lists top $5 billion. Arch Intern Med. 2011 Nov 14;171(20):1856-8.
3) Centers for Medicaid and Medicare Services. National Health Expenditures; Aggregate and Per Capita Amounts, Annual Percent Change and Percent Distribution: Selected Calendar Years 1960-2011. Accessed at: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/downloads/tables.pdf
4) The Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature. 2012 Sep 27;489(7417):519-25.
5) The Genome Institute at Washington State University. Doctor Survives Cancer he Studies. Accessed at: http://genome.wustl.edu/articles/detail/doctor-survives-cancer-he-studies/
6) Kolata G. In Treatment for Leukemia, Glimpses of the Future. New York Times. 2012 Jul 8. Accessed at: http://www.nytimes.com/2012/07/08/health/in-gene-sequencing-treatment-for-leukemia-glimpses-of-the-future.html?pagewanted=1&_r=1