Cancer is nothing new in the medical field, being one of the leading causes of death worldwide . If it is so common, then why is it so hard to treat? Though the disease has several subtypes, the defining features of cancer include the rapid proliferation of abnormal cells (tumor) and spread to surrounding organs of the body (metastasis). As each variation of cancer responds to therapy differently, personalized cancer treatment is one of the main challenges to overcome in the oncology field.
Treating cancer effectively involves: 1) an early diagnosis, 2) the development of an appropriate treatment regimen, and 3) a prognosis, predicting possible post-treatment responses in order to maximize cancer cell death and minimize negative side effects (e.g. the formation of blood clots). Currently, researchers are looking towards biomarkers as an improved method of predicting tumor response to therapy. Biomarkers are biological molecules that are secreted by tumors as a specific response produced by the body in the presence of cancer. For example, DNA, RNA, protein or metabolomic profiles that are specific to the tumor can help characterize alterations in a tumor . Identifying biomarkers at different stages of tumor growth can be used to develop therapies that specifically target tumor abnormalities and improve patient outcomes. Although immunotherapy has transformed how cancer treatment is delivered, not all patients respond in the same manner. Thus, it becomes relevant to classify biomarkers that can predict immune responses to better match patients with immunotherapies that would best suit their biological profiles .
Since 2019, a group of researchers across multiple institutions decided to tackle this challenge by studying the biomarkers of non-small lung cancer (NSLC), the most common form of lung cancer, to help in clinical decisions surrounding cancer therapeutics . NSLC is characterized by the rapid growth of abnormal cells lining the surface of the lung airways . A key biomarker pathway that was investigated in their study was PD-L1/PD-1, also known as programmed death ligand-1 or programmed death protein-1. The action of PD-L1 binding to PD-1 results in T lymphocyte-activation inhibition, reduces cytokine levels, and exhausts CD8+ T lymphocytes . This is carried out by tumor cells in response to endogenous immune anti-tumor activity, allowing cancer cells to “hide” from the immune system. Therefore, blocking the binding of PD-L1 to PD-1 with an immune checkpoint inhibitor (anti-PD-L1/anti-PD-1) allows the T cells to kill tumor cells and mediate an immune response against the tumor. Unfortunately, immune checkpoint inhibitor therapy doesn’t work on all patients affected by NSLC.
To better understand what characterizes the differences in patient response to immune checkpoint inhibitors, the researchers decided to use a certain type of DNA mutation, called a frameshift mutation, as a biomarker in these patients. Frameshift mutations are pieces of the gene sequence that are deleted/added, resulting in a change in every amino acid being translated from their RNA transcript. This leads to the cell producing abnormal proteins called neoantigens that can be detected by the patient’s immune system and get marked as an enemy cell that can later be visualized when conducting a tumor biopsy searching for biomarkers . The researchers sought to answer whether there was a positive correlation between the tumor’s expression of frameshift mutation proteins and an increased response to checkpoint inhibitor drugs.
First, they explored the Cancer Genome Atlas, a database of clinical cancer samples of copied DNA sequences for an RNA molecule, looking for two types of NSLCs: adenocarcinoma and squamous cell carcinoma . They then divided the RNA expression data into four groups: low, medium-low, medium-high, and high levels of frameshift mutations. If there was a positive relationship between the presence of neoantigens and tumor susceptibility, this would support the expectation of a heightened immune response to tumors. Based on their findings in both lung adenocarcinoma and squamous cell lung carcinoma, the group with the highest number of frameshift mutations in the tumor had more immune cells present in the tumor, thus supporting their hypothesis. In other words, the immune response against tumors with greater neoantigens was intensified, allowing one to track and measure attacking tumors more easily .
The researchers also wanted to compare the immune response of NSLC patients with and without frameshift mutations. They analyzed the two groups of patients based on the length of their treatments before the tumor growth returned: patients with the frameshift mutation had a longer time period before tumor progression and prolonged response to treatment compared to the patients without the frameshift mutation . Although the results seemed to conclude that fewer frameshift mutations affected the time until the tumor grew again, the difference in their total survival rates was not statistically significant.
While the researchers knocked down a couple of roadblocks in the improvement of tumor response to immunotherapy, there are still several more obstacles to overcome before we can eliminate cancer once and for all. For example, one step could be identifying more biomarker pathways besides PD-L1/PD-1 to increase the range of treatable cancer types. Overall, the study showed that cancer patients with more frameshift mutations produced a more robust immune response and had slower tumor growth. This seemed counterintuitive initially: how can the presence of more mutations lead to better treatment responses? It turned out that a common ground for developing personalized cancer therapy is having the unique signature of these frameshift mutations - maybe two wrongs can make a right?
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