According to the CDC, cancer is the second leading cause of death in America, even though the overall cancer death rate has declined since 2010. More people live with cancer now than ever before, largely thanks to significant breakthroughs in cancer treatment.
In the last decade, 27% of all new drug approvals belonged to cancer drugs, which is an unprecedented amount. Between 2010 and 2020 marked the meteoric rise of immunotherapy, precision medicine, the development and use of liquid biopsies, and the inclusion of machine learning in cancer treatment.
Here is a more detailed overview of some of the most meaningful cancer drugs and treatment and diagnostic methods approved in the last ten years.
While the concept of immunotherapy is not new (dating back to the 1800s), it is only in the past decade that it has been transformed into a promising method of treating a range of life-threatening illnesses. Immunotherapy is effective against many cancers, including leukemia, lymphoma, lung cancer, melanoma, etc.
In essence, immunotherapy is based on the idea of boosting one’s immune system to fight cancer. It does this in various ways, as evident from the revolutionary discoveries in the past decade.
In 2010, the FDA approved the first immunotherapy therapeutic cancer vaccine under the name of Provenge (sipuleucel-T). This vaccine is effective against advanced prostate cancer and is performed by injecting a patient with their own cells that have been engineered to produce immune-stimulating proteins.
A year later, Dr. James Allison’s research related to the use of blocking CTLA-4 in eliminating cancer led to the FDA approving the drug Yervoy (ipilimumab). This monoclonal antibody can be injected into cancer patients to help block an immune system inhibitory receptor, CTLA-4, which leads to the destruction of cancer cells. It is now effectively used for the treatment of metastatic melanoma, and Dr. James Allison was one of the scientists who received a Nobel Prize for this breakthrough in 2018.
Another revolutionary immunotherapy method is CAR-T therapy. CAR-T therapy involves extracting immune cells known as T-cells from the patient’s blood or bone marrow before being reprogrammed in the lab by adding new genes. These CAR-T cells are then injected back into the patient, where they target and attack cancerous cells.
In 2017, the FDA approved the first two CAR-T therapies for treatment against leukemia and lymphoma in children and adults – Kymriah (tisagenlecleucel) and Yescarta (axicabtagene ciloleucel).
Since the previous decade began with no mention of immunotherapy and ended with several great leaps forward in this field of science, it is safe to say that we expect exciting things to come in the following years.
Precision medicine is the process of tailoring the treatment to individual genetic, environmental, and lifestyle factors. It was introduced in cancer treatment when researchers started exploring ways to personalize treatments by finding out which patients would benefit from them and how they should be used.
The advent of precision medicine has made it possible for physicians to prescribe drugs that are less prone to cause side effects. The ability to monitor patients’ responses and adjust treatments accordingly has been instrumental in improving treatment outcomes.
With fast and less expensive gene sequencing to identify a patient’s cancer cells, this type of medicine has been used to treat specific types of lung cancer, pancreatic cancer, brain cancers, and triple-negative breast cancer.
In addition, new precision medicine methods aim at getting rid of cancer cells before they turn into tumor masses by targeting specific mutations or genetic abnormalities as soon as tumors are detected.
One of the most important diagnostic methods that will assist in precision medicine is liquid biopsy.
A biopsy is a widely accepted test of checking for cancer. It requires removing a tissue sample from the patient and then checking it for signs of cancer. This process can be uncomfortable and even painful for the patient, making it less ideal for cancer screening.
For this reason, liquid biopsies are being researched and developed to detect various cancers without removing any tissue cells from the patient’s body.
Liquid biopsy is a technique that can be done by sampling blood or urine from the patient.
One type of liquid biopsy includes looking for circulating tumor DNA (ctDNA) in body fluid samples. It works by identifying mutations in ctDNA that occur when cancer cells divide.
The DNA is then sequenced to identify the type of cancer and what treatment will be most effective for that individual patient.
Another type of liquid biopsy includes microRNA, which are small molecules in the blood that can indicate whether cell transformation has occurred due to a high-risk mutation or other factors. These mutations can be tracked down by sequencing the microRNA.
The ultimate goal of liquid biopsy is to find cancer as early in its progression as possible because this leads to better treatment. The process of taking a blood or a urine sample from a patient is also a lot more comfortable than taking a tissue sample.
All of the data gathered from liquid biopsies and similar tests are slowly but surely being analyzed with the help of machine learning.
Machine learning or artificial intelligence is not just a concept for sci-fi novels.
It represents a branch of computer science that focuses on constructing and studying algorithms that can learn from data without being explicitly programmed. These algorithms take in information as input and then make predictions or decisions based on this input. They are often called ‘machines’ because they operate like machines – without any conscious comprehension about what they are doing.
AI is more prevalent than you might think – in 2021, it has found its rightful place in a range of industries, from retail to finance.
The technology is now being used in the healthcare sector for research, diagnosis, and treatment. AI can analyze large quantities of data with speed and precision that no human would be capable of – helping scientists find links between genetic mutations and cancer diagnoses at an unprecedented pace. It also makes it possible to predict how a patient will respond to a specific treatment.
Soon, AI will be able to perform more complex tasks – even augmenting doctors’ abilities with machine learning technology that can help them make better decisions for their patients.
Here are the six most common applications of machine learning in medicine today:
- Robotic surgery
- Medical imaging diagnosis
- Personalized medicine
- Electronic health records (EHRs)
- Robotic patient support tasks
- Disease identification and diagnosis
We can only expect more complex applications in the future.
In the last decade, there have been many advances in cancer treatment. The most recent advancements are precision medicine and AI, which will continue to improve over time as the technology improves.
All of the new drugs and therapy methods are focusing on individual treatment approaches. Personalization of medicine is the latest trend and will continue to grow as the population continues to age. This is a massive step in the right direction, as cancer can be treated more precisely and effectively.
Thanks to AI in cancer research and care, the future of medicine is looking brighter, but it’s still important for patients to keep their regular checkups with their physicians. Even though healthcare technology is developing rapidly, we must not forget that providers also play a significant role.
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