By: Yazmin I. Rovira Gonzalez, Ph.D.
The fight against cancer has been boosted tremendously since the discovery of checkpoint inhibitor therapy – a form of cancer immunotherapy where key regulators of the immune system are blocked so that immune cells can attack the tumor. However, the level of patient response to currently approved therapies is quite variable, restricting the chances of success of a checkpoint inhibitor therapy. Not knowing whether a patient will respond to immunotherapy and if there are specific molecular features that can help predict a response is a substantial limitation to immune checkpoint inhibitor drugs.
What if there was a way you could precisely measure the number of mutations carried by tumor cells and predict whether a patient is likely to respond to currently approved therapies? In a recently published article in Nature Cancer, scientists at the Johns Hopkins Kimmel Cancer Center, the Bloomberg-Kimmel Institute for Cancer Immunotherapy, and the Johns Hopkins University School of Medicine developed an integrated genomic method that could potentially assist physicians in predicting which patients with non-small cell lung cancer (NSCLC) will respond to therapy with immune checkpoint inhibitors.
The study describes how investigators developed a novel computational approach that more accurately computes the number of acquired mutations in the tumor, also known as the tumor mutational burden (TMB). Although TMB values are considered an emerging genomic biomarker of patient response to therapy, they can be confounded by the ratio of tumor versus normal cells of the samples analyzed. For instance, some tumors with a high TMB do not respond to immunotherapy while some tumors with low TMB benefit from immunotherapy, stressing the urgent need to develop integrated biomarkers that can better inform physicians of the clinical course of the patient. The new method described could be used to accurately estimate TMB and optimize prediction of response to immunotherapy among patients with lung cancer, colon cancer, melanoma, and other solid tumors.
To produce this method, researchers evaluated 3,788 tumor samples (originating from bladder, breast, colon, head and neck, kidney, and non-small cell lung cancers and melanomas) from the National Cancer Institute’s Cancer Genome Atlas database, and 1,661 tumor samples from previously published cohorts of immunotherapy-treated patients. Specifically, they investigated TMB estimates from whole-genome sequencing (entire protein-coding region of genes in a genome is sequenced) and targeted next-generation sequencing (regions of interest in a genome are sequenced) from all 5,449 tumors. They found a significant correlation between TMB and the percentage of cancer cells in a solid tumor sample, also known as “tumor purity.” The higher the tumor purity, the closer it is to the true TMB of the tumor, while the lower the tumor purity, the more likely the TMB estimate will be inaccurate (Figure 1).
In other words, the observed TMB value was strongly affected by low tumor purity. To overcome this limitation, the researchers created a computational approach to estimate corrected TMB values for each tumor based on tumor purity, and called it corrected TMB value (cTMB). Using comprehensive analysis of whole-genome sequencing for 104 lung tumors treated with immune checkpoint inhibitors, the researchers also discovered more activating mutations in genes of receptors that regulate key cellular processes, (including cell proliferation, survival and metabolism) among tumors that did not respond to immunotherapy in some cohorts of patients. Moreover, they identified a large number of smoking-related mutations in patients that respond to therapy. Altogether – corrected TMB, receptor mutations, smoking-related mutations, and the number of cell-surface proteins responsible for immune response – provided the team with an extra-accurate prediction of patient response to immunotherapy when compared to TMB alone, and even to corrected TMB value.
If incorporated into clinical practice, this method would offer a way for clinicians to provide immunotherapy alone to patients with a high tumor mutation burden, while give chemotherapy in combination with immunotherapy to patients with low tumor mutation burden. This exciting approach offers a potential change in the way clinicians make decisions about the course of therapy for their patients.