NEW FINDINGS — CANCER
Cancer is a complex genetic disease commonly caused by the accumulation of many different types of DNA mutations. Because of the wide genetic diversity of cancer, one patient’s cancer could react differently to the same treatment when compared to another patient with a similar cancer. A small percentage of patients with advanced metastatic cancer survive significantly longer than patients with a similar severity of disease. Physicians classify these patients as exceptional responders. Acknowledging that they could learn a lot from these patients, the National Cancer Institute (NCI) initiated the Exceptional Responders Initiative in 2014. The goal of the program was to understand the molecular basis of exceptional responses to treatment. The criteria for enrollment as an exceptional responder was having a response to a treatment that would be effective in less than 10 percent of similar patients that lasted for three times the typical response time. Over the course of the initiative, scientists collected and analyzed tumor biopsies frommore than 110 exceptional responders. In 23.4 percent of responders, the scientists were able to identify molecular features that could potentially explain their exceptional response to treatment. The mechanisms underlying the exceptional responses fit into two main categories: the body’s ability to repair DNA damage and the immune system’s response to tumors. The results demonstrate that genomic characterizations of cancer can uncover changes that may contribute to unexpected and long-lasting responses to treatment. Exceptional Responders cancer therapy
AI improves cancer detection and diagnosis Once an idea that existed only in science fiction, computers and artificial intelligence (AI) technology are now a part of our everyday lives. Scientists and clinicians have even begun using AI programs to help make earlier and more accurate diagno- ses for cancer. Cancer screening is one of the most effective ways to increase cancer survival rates because it detects disease earlier when doctors have the best chance of treating it. The CDC currently supports screening for breast, cervical, colorectal, and lung cancers as recommended by the US Preventive Services Task Force. However, there are several concerns with the accuracy and sensitivity of current cancer screening tests. AI can identify characteristics in data that cannot be perceived by the human brain. Taking advantage of this feature, doctors and scientists have begun training AI applications to help them look for cancer in common screening tests to reduce inaccu- rate results and increase screening sensitivity. For example, a group of researchers trained an AI system to find the early stages of lung cancer in CT scans because they did not have enough radiologists to increase early detection programs for lung cancer at their hospital. The researchers used a database of more than 40,000 CT scans to train the system. In the beginning, the researchers told the computer which early-stage scans turned out to contain cancerous spots and which did not. Eventually, the computer learned what the cancerous tissue looked like and could flag early signs of cancer. The system is so sensitive that it can detect small tumors around one to three millimeters in size about 95 percent of the time (the accuracy rate of radiologists is about 65 percent). For comparison, that is about the size of the tip of a pen. The system can even detect subtle changes that doctors could not see, detecting cancer in scans taken two years before a doctor even detected the cancer. This is just one example of the ways in which artificial intelligence algorithms are helping doctors diagnose and predict disease. REFERENCES: Svoboda, E. Deep learning delivers early detection, Nature (2020) 587:S20-S22. Ardila, D et al., End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography, Nature Medicine (2019) 25: 954-961. DOI: 10.1038/s41591-019-0447-x.
REFERENCE: Wheeler D.A. et al. Molecular features of cancers exhibiting exceptional responses to treatment, Cancer Cell (2021) 39:38-53. DOI: 10.1016/j.ccell.2020.10.015.
Fecal transplants boost cancer immunotherapy response for melanoma
melanoma
Cancer immunotherapy is a type of treatment that uses a person's own immune system to fight cancer. While it has been a lifesaver for some patients, it does not work for all patients or cancers. Two recent studies reported an unlikely treatment that could help increase the efficacy of immunotherapy for some patients. The treatment, called fecal transplantation, involves transplanting a stool sample from an individual who responded positively to immuno- therapy into the large intestine of a patient who has not responded to immunotherapy. Fecal transplant is an accepted treatment for Clostridium difficile infection of the intestines and has been tested for other conditions linked to changes in the gut microbiome. Previous studies show an association between certain types of bacteria in patients’ guts and the effectiveness of their immunotherapy. In the current studies, patients who initially did not benefit from immu- notherapy drugs saw their tumors stop growing or even shrink after receiving a stool sample from patients for whom the drugs worked. Although both studies were small Phase I trials, the results are promising and could help scientists determine which type of gut bacteria improve patients’ responses to immunotherapy to develop more targeted treatments in the future. REFERENCES: Baruch E.N. et al. Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients, Science (2021) 371, 602–609. DOI: 10.1126/ science.abb5920. Davar D. et al. Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients, Science (2021) 371, 595–602. DOI: 10.1126/science.abf3363.
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SCIENCE FOR LIFE
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