CANCER NEWS
City of Hope developed cancer-killing virus: activates immune system against colon cancer
The preclinical research demonstrates that combining the oncolytic virus CF33 with an immune checkpoint inhibitor results in lasting resistance to certain tumors
DUARTE, Calif. -- A cancer-killing virus that City of Hope scientists developed could one day improve the immune system's ability to eradicate tumors in colon cancer patients, reports a new study in Molecular Cancer Therapeutics, a journal of the American Association for Cancer Research.
The preclinical research is a first step to showing that City of Hope's oncolytic virus CF33 can target hard-to-treat tumors that "handcuff" the immune system and keep T cells from activating the immune system to kill cancer cells. More specifically, the researchers demonstrated in mouse models that CF33 appears to increase PD-L1 expression in tumor cells and causes them to die in a way that stimulates an influx of activated immune cells.
"CF33 is a safe, innovative virus City of Hope developed that can become a gamechanger because of how potent it is and because of its ability to recruit and activate immune cells," said Susanne Warner, M.D., a surgical oncologist at City of Hope and senior author of the study. "Our oncolytic virus trains the immune system to target a specific cancer cell. Preclinical models show that a combination treatment of oncolytic virus CF33 with anti-PD-L1 checkpoint inhibition leads to lasting anti-tumor immunity, meaning if a similar cancer cell ever tries to regrow, the immune system will be ready and waiting to shut it down."
Colorectal cancer is the third leading cause of cancer-related deaths in the United States and is expected to cause 53,200 deaths in 2020, according to the American Cancer Society. City of Hope researchers are excited about the potential of CF33 to enhance colon cancer treatment and point out that CF33 has been effective preclinically against a wide variety of cancers.
Yuman Fong, M.D., the Sangiacomo Family Chair in Surgical Oncology at City of Hope, and his team created oncolytic virus CF33 and expect to open a clinical trial to test the safety of this treatment in human patients in 2021. This treatment addresses a problem in cancer: Most solid tumors do not respond to checkpoint inhibitors because the "uncloaked tumor cell" still isn't recognized by the immune system, Fong said.
"CF33 selectively infects, replicates in and kills cancer cells. This study demonstrates that a designer virus we created to infect a wide variety of cancers can make tumor cells very recognizable to the immune system," Fong said. He, Warner and other City of Hope physician-scientists are working on turning "cold tumors" resistant to treatment into "hot tumors" that can be killed by a well-trained immune system.
The U.S. Food and Drug Administration has approved only one oncolytic virus thus far: T-VEC, which is a local immunotherapy treatment that kills melanoma cells.
To confirm their hypothesis, City of Hope scientists tested four groups: control with no treatment, anti-PD-L1 alone, CF33 alone, and a combination of CF33 and anti-PD-L1. Results indicated that a combined treatment of City of Hope's oncolytic virus and anti-PD-L1 appeared to be most effective. It also increased CD8+ T cells, which are immune cells that remember previous diseases and are trained to kill them if they are reintroduced later. In other words, the models developed anti-tumor immunity. This means that animals cured of their cancer were effectively immune to future tumor growth.
Fong and colleagues have demonstrated CF33's anti-tumor immune efficacy against triple-negative breast cancer cell lines, in brain tumor cells, in liver cancer models, and in pancreatic, prostate, ovarian, lung and head and neck cancer. Moreover, a recent City of Hope-led study found that CF33 could be combined with chimeric antigen receptor (CAR) T cell therapy to target and eliminate solid tumors that are otherwise difficult to treat with CAR T therapy alone. City of Hope has licensed CF33 to Imugene Limited, a company developing novel therapies that activate the immune system against cancer.
Notably, the CF33 virus may be tracked by non-invasive PET scanning. "If we can perfect the technique, we can give someone a viral injection and watch it work - see where it goes and identify cancer cells that we didn't even know existed," Warner said. "Doctors would have real-time data and know if we should give a patient a higher dose or where to direct the treatment based on tumors that have not yet been killed."
What Warner describes is a developing field called theranostic precision medicine, meaning doctors are able to give patients therapies and concurrently diagnose them to provide the most appropriate treatment for that patient. It is one of many precision medicine approaches City of Hope is developing and offering to patients.
The next step for the current study is to test the innovative CF33 virus platform in different solid tumor models.
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This research was supported by the American Cancer Society Mentored Research Scholar Grant (MRSG-16-047-01-MPC) and through the generosity of the Natalie and David Roberts Family.
About City of Hope
City of Hope is an independent biomedical research and treatment center for cancer, diabetes and other life-threatening diseases. Founded in 1913, City of Hope is a leader in bone marrow transplantation and immunotherapy such as CAR T cell therapy. City of Hope's translational research and personalized treatment protocols advance care throughout the world. Human synthetic insulin and numerous breakthrough cancer drugs are based on technology developed at the institution. A National Cancer Institute-designated comprehensive cancer center and a founding member of the National Comprehensive Cancer Network, City of Hope has been ranked among the nation's "Best Hospitals" in cancer by U.S. News & World Report for 14 consecutive years. Its main campus is located near Los Angeles, with additional locations throughout Southern California. For more information about City of Hope, follow us on Facebook, Twitter, YouTube or Instagram.
AI predicts which drug combinations kill cancer cells
A machine learning model developed in Finland can help us treat cancer more effectively
IMAGE: AI METHODS CAN HELP US PERFECT DRUG COMBINATIONS. view more
CREDIT: MATTI AHLGREN, AALTO UNIVERSITY
When healthcare professionals treat patients suffering from advanced cancers, they usually need to use a combination of different therapies. In addition to cancer surgery, the patients are often treated with radiation therapy, medication, or both.
Medication can be combined, with different drugs acting on different cancer cells. Combinatorial drug therapies often improve the effectiveness of the treatment and can reduce the harmful side-effects if the dosage of individual drugs can be reduced. However, experimental screening of drug combinations is very slow and expensive, and therefore, often fails to discover the full benefits of combination therapy. With the help of a new machine learning method, one could identify best combinations to selectively kill cancer cells with specific genetic or functional makeup.
Researchers at Aalto University, University of Helsinki and the University of Turku in Finland developed a machine learning model that accurately predicts how combinations of different cancer drugs kill various types of cancer cells. The new AI model was trained with a large set of data obtained from previous studies, which had investigated the association between drugs and cancer cells. 'The model learned by the machine is actually a polynomial function familiar from school mathematics, but a very complex one,' says Professor Juho Rousu from Aalto University.
The research results were published in the prestigious journal Nature Communications, demonstrating that the model found associations between drugs and cancer cells that were not observed previously. 'The model gives very accurate results. For example, the values ??of the so-called correlation coefficient were more than 0.9 in our experiments, which points to excellent reliability,' says Professor Rousu. In experimental measurements, a correlation coefficient of 0.8-0.9 is considered reliable.
The model accurately predicts how a drug combination selectively inhibits particular cancer cells when the effect of the drug combination on that type of cancer has not been previously tested. 'This will help cancer researchers to prioritize which drug combinations to choose from thousands of options for further research,' says researcher Tero Aittokallio from the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki.
The same machine learning approach could be used for non-cancerous diseases. In this case, the model would have to be re-taught with data related to that disease. For example, the model could be used to study how different combinations of antibiotics affect bacterial infections or how effectively different combinations of drugs kill cells that have been infected by the SARS-Cov-2 coronavirus.
Cancer cases are rising in adolescents and young adults
HERSHEY, Pa. -- Cancer cases in adolescents and young adults have risen by 30% during the last four decades, with kidney cancer rising at the greatest rate, according to researchers at Penn State College of Medicine. The team said further research into screening, diagnosis and treatment are needed to address the growing trend in this age group.
Dr. Nicholas Zaorsky, assistant professor of radiation oncology and public health sciences, said that cancer is the leading cause of disease-related death in this age group and that the increasing number of cases is concerning.
"Adolescents and young adults are a distinct cancer population," Zaorsky said. "But they are often grouped together with pediatric or adult patients in research studies. It is important to study how this group is distinct so that care guidelines can be developed to address the increase in cases."
The researchers analyzed data -- including sex, age at diagnosis and type of cancer -- from nearly half a million cancer patients in the United States between 15 and 39 years old across more than four decades. The data were collected by the National Cancer Institute's Surveillance, Epidemiology and End Results Program. The team's goal was to determine the number of cancer cases in adolescents and young adults between 1973 and 2015. The results published today (Dec. 1) in JAMA Network Open.
During the time period studied, the researchers found cancer diagnoses increased from 57 to 74 per 100,000 adolescents and young adults. The most common types in males were testicular, melanoma and non-Hodgkin lymphoma. The most common types in females were breast, thyroid, cervical and uterine cancers. Zaorsky, a Penn State Cancer Institute researcher, said that the rates of kidney, thyroid and gastrointestinal cancers are increasing in this age group.
"Other studies have shown these types are increasing among this age group," said Zaorsky. "Our data reinforces the fact that clinicians should be on the lookout for these cancers in their adolescent and young adult patients."
According to Zaorsky, further research is needed to determine why kidney, thyroid, gastrointestinal and other types of cancer are on the rise in adolescents and young adults. He said that environmental, dietary and screening changes during the time period studied may have contributed to the increased incidences.
"These cancers all have unique risk factors," Zaorsky said. "Now that there is a better understanding of the types of cancer that are prevalent and rising in this age group, prevention, screening, diagnosis and treatment protocols specifically targeted to this population should be developed."
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Alyssa Scott, Kelsey Stoltzfus, Leila Tchelebi and Pooja Rao of Penn State College of Medicine; Daniel Trifiletti of Mayo Clinic; Eric Lehrer of Icahn School of Medicine at Mount Sinai; and Archie Bleyer of Oregon Health and Science University and McGovern Medical School also contributed to this research.
This research was supported by the National Center for Advancing Translation Sciences (Grants TL1 TR002016 and UL1 TR002014) through Penn State Clinical and Translational Science Institute's Translational Science Fellowship. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The authors disclose no conflict of interest.
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