The Voice
Lung Cancer Care Today and Tomorrow
Although we’re far from having a cure for lung cancer for all, recent research on many different aspects of the disease and its treatment offers hope today and for the future.
Researchers are still a few years away from being able to have blood tests, as a standard of care, to diagnose lung cancer before symptoms appear. We do have techniques to test blood samples for antibodies, proteins or abnormal DNA that could signal the presence of cancer. Because blood tests are often routine in office visits, and they are much less invasive than some other testing procedures like biopsies, they present an attractive option for potentially detecting lung cancer early. Detecting lung cancer in the early stages is the opportunity for surgical removal and for radiation of the tumor that can lead to a cure. Finding the disease early gives physicians and patients more time to strategize and consider options to treat the disease for better outcomes.
Another important advance in lung cancer is using machine learning to analyze huge datasets to help physicians and researchers understand complex issues around early detection, diagnosis, prognosis, and appropriate uses for chemotherapy, targeted therapy, and immunotherapy. Collaborative research efforts have generated huge lung cancer databases which can be used to facilitate machine learning. Although physicians do not currently use machine learning for an initial diagnosis, it can be used to enable systematic advances in clinical studies of lung cancer. The benefit could be in improving accuracy in interpreting CT scans. Machine learning is a form of artificial intelligence that uses mathematical algorithms in order to make predictions by identifying patterns in the data. It’s been used for years advanced approaches for early detection, cancer type classification, prognosis prediction, and for evaluating drug response. As such, it can also serve as a tool in diagnostic decision making or a second opinion. Machine learning can also help to choose the right targeted treatments for some of the more common types of lung cancer. Machine learning models can better understand the prognosis for a certain type of lung cancer. They can aid in proposing certain types of targeted therapy, chemotherapy and combinations that improve chances of a successful recovery. In addition, immunotherapy, or getting the body’s immune system to attack cancerous cells, has proven a promising new treatment for lung cancer. Since not everyone responds to immunotherapy in the same way, based on how their tumors develop, machine learning experts have developed models to predict how well certain patients will respond to immunotherapy.
There have also been surgical and non-surgical advances in how lung cancer is treated after it is diagnosed. One of the surgical advances involves decreasing how much of the lung surgeons remove. Did you know that your lungs are each made up of different numbers of lobes? Your right lung has three lobes, and your left lung has two. In the past, when people had non-small cell lung cancer, surgeons would often remove an entire lobe of the lung. This was because a 1995 study found that removing only part of a lobe meant that lung cancer was more likely to come back. But with advances in technology in the decades since, doctors and researchers wanted to revisit that finding with a large-scale, international clinical trial. The study found that removing part of a lung lobe was just as effective as removing the whole lobe. While operating on a smaller part of a lung does not necessarily mean the lung will function better afterwards, the less invasive surgery lessens the risk of other complications.
Researchers have also continued studying non-surgical treatments for lung cancer. A study published this year in JAMA Open Network found that early-stage cancer patients that were treated with immunotherapy and chemotherapy at the same time had better survival rates than those who were treated with chemotherapy alone. The patients who were given both types of treatment also were more likely to see all signs of their cancer disappear. Although immunotherapy was developed in 1981 and is not new, it has advanced rapidly in recent years. The results of the study are promising, but more research is needed on whether the findings hold up wide-scale, how effective it is, and which types of lung cancer it can be used for.
Targeted therapy is another promising area of treatment research. It involves finding changes in the DNA that is unique to each patient’s specific tumor and developing treatments to target those changes or cancer cell weaknesses instead of harming healthy and benign cells. One of the targeted treatments involves epidermal growth factor receptor (often called EGFR), a protein which causes cells to grow. However, if there is a mutation that produces too much of this protein, it may cause cancer. Another commonly targeted treatment involves the ALK gene, which helps your body develop its gut and nervous system, but then should get turned off. If the ALK gene gets turned back on again, it can fuse with another gene in a way that causes cancer. The ROS-1 and NTRK genes are other genes that, if they fuse with other genetic material, cause uncontrolled growth, which leads to cancer. Because of this, they are also the subject of new targeted therapies.
Sources
Blood Tests For Detecting Lung Cancer: How They Might Work | healthline.com
Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis | ScienceDirect
Lung-Sparing Surgery Effective for Early-Stage Lung Cancer | NCI
Lung cancer treatment takes step forward with immunotherapy and chemotherapy research | Healthing.ca
Targeted Therapies for Lung Cancer | American Lung Association