Personalized Medicine: The Force of the Future in the Fight Against Cancer
Personalized Medicine: The Force of the Future in the Fight Against Cancer
Akash Jagdeesh
Thomas Jefferson High School for Science and Technology
This article was the 2nd place winner in Teknos 2021 Summer Writing Competition
A patient walks into the doctor’s office, filled with anxiety. They see the pensive face of the doctor peering at their laptop screen. After some discussion, the doctor finally breaks the news: “We have detected the presence of a growing tumor in your body. You have cancer.” These dreaded words are enough to turn someone’s life upside down. What comes next? Months of radiation therapy? Chemotherapy? What happens if little to no progress is made? How many years will I have to suffer before I recover? Will I even be able to beat this terrible disease?
These are the thoughts that race through the minds of the approximately 18 million people around the world who are diagnosed with cancer each year. Causing approximately 10 million deaths per year, cancer is one of the deadliest diseases in the world. The International Agency for Research on Cancer predicts that these numbers will rise to about 30 million new cancer patients and 16 million deaths annually by 2040 [3]. As the threat of the disease grows each year, the need for improvements in cancer health care becomes more and more urgent.
But first, what is cancer, and what makes it so hard to cure? Cancer is a disease that involves uncontrolled growth of mutant cells, forming masses known as tumors. These rogue cells can travel to other parts of the body and establish new sites for tumor growth. Tumors can damage nearby tissue and overwhelm the immune system, which can ultimately result in death. While many factors contribute to the formation of cancer, genetics is the primary cause of tumor development [2]. When DNA in cells accumulates enough deleterious mutations to bypass regulation mechanisms for cell division, cells grow uncontrollably and form tumors. Mutations in approximately 3.3% to 4% of our genes can result in a cancerous tumor, although multiple key mutations are usually needed to cause cancer [1]. In traditional cancer medicine, age, lifestyle, familial history, and exposure to carcinogens are some of the risk factors that oncologists use to determine how many mutations one’s cells can develop. Based on this information, the doctor may decide to conduct a cancer screening. New growths would be recognized through visual inspection (spotting lumps on a patient), palpation or physical inspection, or imaging tests (CT scan or MRI), methods that have all been around for decades [7].
Treatment involves surgical removal of the tumor before it spreads, toxic chemotherapy, or radiation therapy. Therapeutic drugs usually target normal cell activity in tumors, resulting in unnecessary harm to nearby tissues. Cancer treatment becomes a guessing game; oncologists continually try treatments that have historically been successful until they arrive at one that makes meaningful progress [1]. If a treatment does not succeed, then doctors move on to the next one, resulting in long stretches of time with little to no progress. The lack of effectiveness of this strategy usually means that patients suffer mentally and physically for months, experience significant side effects, and bear large expenses [1, 5]. Moreover, cancer treatment and drugs have not significantly improved in decades [1]. If we want to make strides in the battle against cancer, there must be a better way.
This is where personalized medicine comes in. Personalized medicine recognizes that all humans are unique in their physical characteristics, biological characteristics, and lifestyles. It attempts to customize health care to suit each patient, since all patients have different needs, are predisposed to different diseases, and respond differently to treatments. Physicians that practice personalized medicine use advanced testing to gather a wealth of information about a patient in order to guide health care decisions for that individual patient [4]. This information includes genomic, molecular, clinical, pathologic, pharmacological, and epidemiological data [1]. By analyzing these data, physicians and oncologists are armed with the information required to provide the best care possible.
One of the main benefits of personalized medicine is the ability to predict the onset of disease in patients before it happens. Genomic data is the primary tool used to predict the risk of cancer. This is only possible because of the Human Genome Project, a global effort to sequence the entire human genome, identify its genes, and discover the protein products of these genes. The International HapMap Project began a few years after the Human Genome Project and attempted to identify and categorize genetic variations called single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) in large databases. SNPs occur in one allele of a gene, while CNVs relate to the number of times a certain section of the genome is repeated. A similar project called the 1000 Genomes Project commenced in 2008, aiming to create a comprehensive database of common SNPs and rare SNPs. Since humans share about 99.9% of genetic information, genetic differences such as SNPs and CNVs provide vital information in healthcare [2, 5]. Using association studies, scientists can pinpoint certain allele loci of interest in disease prediction [2].
SNPs and/or CNVs in these alleles allow scientists to calculate a comprehensive, quantitative measure of disease risk for a patient, known as a genetic risk score. This risk assessment is key in the field of personalized cancer medicine since it allows scientists and doctors to predict and prevent disease through personalized treatment plans and lifestyle modifications based on an individual’s unique risk [2]. For example, mutations in certain genes, recognized by analyzing SNPs and CNVs, may increase a patient’s risk for ovarian cancer from the baseline chance of 1.7% to a chance of 25%-60%. A preventative care plan and cancer screening schedule for a patient with a 60% chance of developing ovarian cancer would greatly contrast a care strategy for a patient that only has a 1.7% chance of developing ovarian cancer. Knowledge of these risks can guide physicians to better care for at-risk patients, reducing the threat of cancer [5, 6]. In most cases, preventative medicine results in fewer costs, higher success rates, and less suffering for the patient since the eventual cost and chance of death greatly increases once the cancer forms and evolves [1, 5]. Personalized medicine strategies and advancements in genomic testing make these improvements possible.
Once the cancer establishes itself in the body, the focus shifts from preventative care to diagnosis and treatment. The lack of efficient and inexpensive methods of analyzing tumors and collecting patient data hindered the implementation of personalized medicine in oncology for years. However, with recent advancements, examining the various characteristics of tumors is easier and cheaper than ever before, allowing scientists to characterize cancer into specific subtypes that can be mitigated with certain drugs. The Human Genome Project greatly contributed to this advancement, allowing scientists to compare the patient’s normal genome with the cancerous genome to identify genetic variations down to a single nucleotide. Physicians can use biomarker data and advanced testing to diagnose cancer patients earlier and more accurately, unlike the CT scans and MRIs of the past that could not identify cancerous growths until they reached a significant size. Smaller tumors are much easier to treat since the cancerous cells will have less genetic variation [1, 8]. Genetic homogeneity in a tumor will increase the chance that a specific drug will succeed, as the cancer cells are less diverse [1]. These reasons underscore the importance of early detection and diagnosis.
Cancer is dynamic, driven by mutations that change over time within a tumor. For this reason, one patient with a specific type of cancer will have a tumor with different mutations and characteristics from a tumor in another patient with the same type of cancer. This difference in tumor characteristics results in varying levels of success of certain treatments. As a result, traditional treatment strategies have not been as effective as desired [1]. Advanced genomic testing and patient information also allows oncologists to predict the chance that a specific drug will be successful, so that the optimal treatment and dosage can be chosen. In one case, a patient may have certain SNPs that indicate a decreased ability to metabolise a target drug, requiring a higher dosage to achieve the drug’s intended effect [2]. Physicians will be able to predict the chance of success of various treatments, eliminating the trial and error aspect of traditional care. For example, the mutations in chronic myelogenous leukemia make it genetically closer to gastrointestinal stromal tumor sarcoma instead of other types of leukemia, so it would respond better to drugs that treat gastrointestinal stromal tumor sarcoma than drugs used to treat other types of leukemia [1]. This important information would not even be known in traditional medicine, resulting in ineffective treatment. This approach of using patient data, chiefly genomic data, to improve the efficiency of treatment minimizes cost, time of treatment, potential toxicity, and adverse side effects, resulting in higher quality care.
However, the personalized medicine model also comes with some limitations. With a high focus on targeting tumor growth, the effects of cancer on the body are often overlooked. Research on how cancer causes death is not profound, so although tumors can be identified and characterized, information about their specific effects on human health is limited. One of the reasons that personalized medicine greatly reduces tumor growth but sometimes does not significantly affect patient prognosis is that the tumor is never truly eliminated, and can continue to grow while it evades the body’s immune system. Without targeting the tumor’s evasion of the immune system, that tumor or other tumors will be able to use that mechanism to proliferate. Rather than simply attacking the growth and malignancy of the tumor, preventing the tumor from influencing the immune system would allow the body to identify and eliminate any growths that use the same mechanism to avoid the immune system. This approach has achieved effective, long-lasting results in cases where this treatment is feasible [1]. However, drugs that target the mechanisms that tumors use to avoid detection by the immune system can have unintended consequences such as autoimmune toxicity, where the body’s own immune system harms healthy cells. When such consequences are eliminated, these drugs can partner treatments that reduce the proliferation of tumors, reducing lethality and resulting in high rates of success.
Even with its limitations, personalized medicine introduces an innovative approach to cancer health care and provides us with new opportunities to learn and make progress in the fight against cancer. New technology will allow us to gather more information about patients and their tumors in order to develop more sophisticated treatments. Advancements in data science and software engineering will allow us to comprehensively analyze patient data, tumor data, historical clinical data of similar patients, and their responses to various treatments in order to assign the most effective treatment to that individual patient [1, 6]. The International Consortium for Personalised Medicine believes that personalized medicine will drastically revolutionize health care by 2030, shifting the emphasis from diagnosis and treatment to risk determination, personalized strategies for disease prevention, and personalized health and lifestyle plans [8]. In a field that has seen limited progress in the past couple decades, personalized medicine gives us the tools to transform oncology and establish a turning point in the fight against cancer. Soon, the diagnosis of cancer may carry the same weight as a broken arm. Who knows how far personalized medicine will take us?
References
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[2] Brunicardi, F. C., Gibbs, R. A., Wheeler, D. A., Nemunaitis, J., Fisher, W., Goss, J., & Chen, C. (2011). Overview of the development of personalized genomic medicine and surgery. World Journal of Surgery, 35(8), 1693-1699. doi.org/10.1007/s00268-011-1056-0
[3] Cancer statistics. (2020, September 25). National Cancer Institute: Comprehensive Cancer Information. Retrieved August 7, 2021, from https://www.cancer.gov/about-cancer/understanding/statistics
[4] Golden, J. A. (2015, August 26). Transforming health care: The impact of precision medicine [Interview transcript]. Brigham Health Hub. Retrieved August 7, 2021, from https://brighamhealthhub.org/precision-cancer-medicine-five-things-you-should-know/
[5] Hood, L. (2012). Informatics and personalized medicine. Informatics Needs and Challenges in Cancer Research: Workshop Summary, 31-42. National Academies Press. https://www.nap.edu/read/13425/chapter/4
[6] Joint Center for Cancer Precision Medicine established. (2013, November 12). Dana-Farber Cancer Institute. Retrieved August 7, 2021, from https://www.dana-farber.org/newsroom/news-releases/2013/joint-center-for-cancer-precision-medicine-established/
[7] Screening tests. (2021, March 12). National Cancer Institute: Comprehensive Cancer Information. Retrieved August 7, 2021, from https://www.cancer.gov/about-cancer/screening/screening-tests
[8] Vicente, A. M., Ballensiefen, W., & Jönsson, J.-I. (2020). How personalised medicine will transform healthcare by 2030: The ICPerMed vision. Journal of Translational Medicine, 18(1). https://doi.org/10.1186/s12967-020-02316-w
