Extrachromosomal DNA (ecDNA) is large, circular, highly amplified pieces of DNA that have untethered themselves from chromosomes. ecDNAs are not found in normal tissues, and they are commonly detected in many of the most aggressive forms of cancer among children and adults (669), including during the transition from precancerous condition to cancer (670). Extrachromosomal DNAs are enriched for cancer-promoting oncogenes that drive tumor formation and growth, as well as gene regulatory elements that control their expression (671). Extrachromosomal DNAs can also contain genes, the products of which may help tumors escape immunotherapies (670). Because ecDNAs do not follow the normal rules of chromosomal inheritance and are randomly inherited by daughter cells during cell division, much like bacteria, ecDNA enables tumor cells to change quickly, potently contributing to tumor heterogeneity, high oncogene copy number, and rapid treatment resistance (672), and resulting in shorter survival for patients (669). Extrachromosomal DNA was first discovered in the 1960s, but was thought to be rare and of unclear importance. The application of powerful basic science technologies, DNA sequencing, and computational tools has revealed that ecDNA is very common among many of the most aggressive forms of cancer, contributing to poor outcomes for patients (669,673-677). The first ecDNAdirected anticancer therapy is now in clinical trials (678). The 2022 Nobel Prize in Chemistry was awarded to K. Barry Sharpless, PhD, Morten Meldal, PhD, and Carolyn Bertozzi, PhD, for their pioneering roles in the discovery of click chemistry. Click chemistry refers to a class of simple chemical reactions that permit the joining of two molecules together. In medical research, click chemistry allows researchers to attach a chemical, such as a fluorescent probe that can be visualized by imaging, to a molecule or a protein present on or inside cells (679). Click chemistry is poised to revolutionize drug discovery and treatment of diseases, including cancer (680). As one example, researchers used click chemistry to specifically remove a type of sugar molecule commonly present on the surface of cancer cells, which resulted in enhanced antitumor immune response and improved effectiveness of immunotherapy (681). The approaches highlighted here are fueling our ever-expanding knowledge of cancer initiation and progression and are providing new and better ways to understand and treat cancer. The success of mRNA-based vaccines in controlling the COVID-19 pandemic has resulted in renewed interest in developing therapeutic (see A New Era of mRNA-based Cancer Vaccines, p. 128) and preventive vaccines for use in clinical cancer care. For example, researchers are investigating the potential of vaccines in preventing cancers in individuals who are diagnosed with inherited cancer syndromes, such as Lynch syndrome (see Figure 5, p. 31). As genetic testing becomes more common and more people get tested for hereditary cancer syndromes, researchers estimate that one in every 288 people may be diagnosed with Lynch syndrome. These findings underscore the need to develop vaccines that can prevent cancer initiation and progression in individuals with hereditary cancer syndromes. Thanks to research, a new clinical trial is investigating a vaccine that will offer an effective, safe, and easy method of preventing Lynch syndrome-related cancers (682). Success of this study will lead to a new frontier in cancer prevention where preventive vaccines will play a pivotal role in reducing cancer burden. Artificial Intelligence Artificial intelligence (AI) is the ability of a computer to perform tasks commonly associated with human intelligence, such as how to act, reason, and learn. The use of AI in aiding health care professionals for early detection of cancer has shown tremendous potential, and several AI-assisted software and medical devices have already been approved by FDA for use in the clinic (see Realizing the Potential of Artificial Intelligence for Early Detection of Cancers, p. 65). Ongoing research, some of which is highlighted below, is exploring the potential of AI in other aspects of cancer research and patient care (683). One exciting use of AI-assisted software is to extract existing knowledge from different sources of information—genomic data, test results, health care professionals’ notes during clinic visits, patient reported outcomes, data from wearables, and scientific publications related to a patient’s cancer—and present a complete view of a patient’s health to clinicians. As one example, researchers used available clinical information from 1,348 patients with early-stage lung cancer to develop an AIassisted model which accurately identified patients who were at low or high risk of cancer recurrence (684). Another machine learning model analyzed images and genomic datasets from 14 different types of cancer and discovered features that could accurately predict poor or favorable health outcomes (685). Another way AI is helping to accelerate the pace of progress against cancer is by uncovering previously unknown aspects of cancer cells. For example, a deep learning model revealed that mitochondria—the powerhouses of cells—are organized differently in lung tumors with high metabolism (more aggressive), compared to those with low metabolism (less aggressive) (686). Researchers can use this new information in a number of ways. HOW CLICK CHEMISTRY WORKS CLICK Reaction Product Reagent 1 Reagent 2 AACR Cancer Progress Report 2023 Envisioning the Future of Cancer Science and Medicine 146
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