Viron Therapeutics, Inc.

Using artificial intelligence technology to identify patterns of DNA fragments associated with lung cancer, researchers have developed and validated a liquid biopsy that may help identify lung cancer earlier. Using artificial intelligence technology to identify patterns of DNA fragments associated with lung cancer, researchers from the Johns Hopkins Kimmel Cancer Center and other institutions have developed and validated a liquid biopsy that may help identify lung cancer earlier. In a prospective study published June 3 in Cancer Discovery, the team demonstrated that artificial intelligence technology could identify people more likely to have lung cancer based on DNA fragment patterns in the blood. The study enrolled about 1,000 participants with and without cancer who met the criteria for traditional lung cancer screening with low-dose computed tomography (CT). Individuals were recruited to participate at 47 centers in 23 U.S. states. By helping to identify patients most at risk and who would benefit from follow-up CT screening, this new blood test could potentially boost lung cancer screening and reduce death rates, according to computer modeling by the team. "We have a simple blood test that could be done in a doctor's office that would tell patients whether they have potential signs of lung cancer and should get a follow-up CT scan," says the study's corresponding author, Victor E. Velculescu, M.D., Ph.D., professor of oncology and co-director of the Cancer Genetics and Epigenetics program at the Johns Hopkins Kimmel Cancer Center.Lung cancer is the deadliest cancer in the United States, according to the National Cancer Institute, and worldwide, according to the World Health Organization. Yearly screening with CT scans in high-risk patients can help detect lung cancers early, when they are most treatable, and help avert lung cancer deaths. Screening is recommended by the U.S. Preventive Services Task Force for 15 million people nationally who are between ages 50 and 80 and have a smoking history, yet only about 6%-10% of eligible individuals are screened each year. People may be reticent to follow through on screening, Velculescu explains, due to the time it takes to arrange and go to an appointment, and the low doses of radiation they are exposed to from the scan. To help overcome some of these hurdles, Velculescu and his colleagues developed a test over the past five years that uses artificial intelligence to detect patterns of DNA fragments found in patients with lung cancer. It takes advantage of differences in how DNA is packaged in normal and cancer cells. DNA is neatly and consistently folded up in healthy cells, almost like a rolled-up ball of yarn, but DNA in cancer cells is more disorganized. When both types of cells die, fragments of DNA end up in the blood. The DNA fragments in patients with cancer tend to be more chaotic and irregular than the DNA fragments found in individuals who do not have cancer. The team trained artificial intelligence software to identify the specific patterns of DNA fragments seen in the blood of 576 people with or without lung cancer. Then, they verified that the method worked in a second group of 382 people with and without cancer. Based on their analyses, the test has a negative predictive value of 99.8%, meaning that only 2 in 1,000 individuals tested may be missed and have lung cancer. The group's computer simulations showed that if the test boosted the rate of lung cancer screening to 50% within five years, it could quadruple the number of lung cancers detected and increase the proportion of cancers detected early -- when they are most treatable -- by about 10%. That could prevent about 14,000 cancer deaths over five years. "The test is inexpensive and could be done at a very large scale," Velculescu says. "We believe it will make lung cancer screening more accessible and help many more people get screened. This will lead to more cancers being detected and treated early." The test is currently available through DELFI Diagnostics for use as a laboratory-based test under the Clinical Laboratory Improvement Amendments. However, the team plans to seek approval from the U.S. Food and Drug Administration for lung cancer screening. Velculescu colleagues also plan to study whether a similar approach could be used to detect other types of cancer. Robert B. Scharpf of Johns Hopkins co-authored the study. Additional co-authors were from the Cleveland Clinic, DELFI Diagnostics, Medicus Economics LLC, Miami Cancer Institute, the Pan American Center for Oncology, Washington University, Centura Health, Vanderbilt Health, Stratevi, Massachusetts General Hospital, the Medical University of South Carolina, the Department of Veterans Affairs, the Perelman School of Medicine at the University of Pennsylvania, New York University Langone Health, Allegheny Health Network and Memorial Sloan Kettering Cancer Center. The work was supported in part by DELFI Diagnostics, the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, Stand Up To Cancer-LUNGevity-American Lung Association Lung Cancer Interception Dream Team Translational Research Grant, Stand Up To Cancer-DCS International Translational Cancer Research Dream Team Grant, the Gray Foundation, The Honorable Tina Brozman Foundation, the Commonwealth Foundation, the Cole Foundation and the National Institutes of Health. Velculescu and Scharpf are inventors on patent applications submitted by The Johns Hopkins University related to cell-free DNA for cancer detection that have been licensed to DELFI Diagnostics, LabCorp, Qiagen, Sysmex, Agios, Genzyme, Esoterix, Ventana and ManaT Bio. Velculescu divested his equity in Personal Genome Diagnostics (PGDx) to LabCorp in February 2022. Velculescu is a founder of DELFI Diagnostics, serves on the board of directors, and owns DELFI Diagnostics stock. Scharpf is a founder and consultant of DELFI Diagnostics and owns DELFI Diagnostics stock. Velculescu, Scharpf and Johns Hopkins receive royalties and fees from the company. The Johns Hopkins University also owns equity in DELFI Diagnostics. Velculescu is an adviser to Viron Therapeutics and Epitope. These relationships are managed by Johns Hopkins in accordance with its conflict-of-interest policies.

Repeats of DNA sequences, often referred to as 'junk DNA' or 'dark matter,' that are found in chromosomes and could contribute to cancer or other diseases have been challenging to identify and characterize. Now, researchers have developed a novel approach that uses machine learning to identify these elements in cancerous tissue, as well as in cell-free DNA (cfDNA) -- fragments that are shed from tumors and float in the bloodstream. This new method could provide a noninvasive means of detecting cancers or monitoring response to therapy. Repeats of DNA sequences, often referred to as "junk DNA" or "dark matter," that are found in chromosomes and could contribute to cancer or other diseases have been challenging to identify and characterize. Now, investigators at the Johns Hopkins Kimmel Cancer Center have developed a novel approach that uses machine learning to identify these elements in cancerous tissue, as well as in cell-free DNA (cfDNA) -- fragments that are shed from tumors and float in the bloodstream. This new method could provide a noninvasive means of detecting cancers or monitoring response to therapy. Machine learning is a type of artificial intelligence that uses data and computer algorithms to perform complex tasks and accelerate research. In laboratory tests, the method, called ARTEMIS (Analysis of RepeaT EleMents in dISease) examined over 1,200 types of repeat elements comprising nearly half of the human genome, and identified that a large number of repeats not previously known to be associated with cancer were altered in tumor formation. The investigators also were able to identify changes in these elements in cfDNA, providing a way to detect cancer and determine where in the body it originated. A description of the work is to be published March 13 in Science Translational Medicine. "When you think about existing cancer genes and the DNA sequences around them, they're just chock full of these repeats," says Victor E. Velculescu, M.D., Ph.D., a professor of oncology and co-director of the Cancer Genetics and Epigenetics Program at the Johns Hopkins Kimmel Cancer Center, who led the study with Akshaya Annapragada, an M.D./Ph.D. student at the Johns Hopkins University School of Medicine, and Robert Scharpf, Ph.D., an associate professor of oncology at Johns Hopkins. "Until ARTEMIS, this dark matter of the genome was essentially ignored, but now we're seeing that these repeats are not occurring randomly," Velculescu says. "They end up being clustered around genes that are altered in cancer in a variety of different ways, providing the first glimpse that these sequences may be key to tumor development." In a series of laboratory tests, the researchers first examined the distribution of 1.2 billion kmers (short sequences of DNA) defining unique repeats, finding them enriched in genes commonly altered in human cancers. For example, of 736 genes known to drive cancers, 487 contained an average fifteenfold higher than expected number of repeat sequences. These repeat sequences also were significantly increased in genes involved in cell signaling pathways that are commonly dysregulated in cancers.Using next-generation sequencing, technology that allows researchers to rapidly examine the sequences of entire genomes, the researchers also looked to see if repeat sequences were directly altered in cancers. They used ARTEMIS to analyze over 1,200 distinct types of repeat elements in tumor and normal tissues from 525 patients with different cancers participating in the Pan-Cancer Analysis of Whole Genomes (PCAWG), and found a median of 807 altered elements in each tumor. Nearly two-thirds of these elements (820 of 1,280) had not previously been observed as being altered in human cancers. Then, they used a machine-learning model to generate an ARTEMIS score for each sample to provide a summary of genome-wide repeat element changes that were predictive of cancer. ARTEMIS scores distinguished the 525 PCAWG participants' tumors from normal tissues with a high performance (AUC=0.96) across all cancer types analyzed, where 1 is a perfect score. Increased ARTEMIS scores were associated with shorter overall and progression-free survival regardless of tumor type. The investigators next evaluated ARTEMIS' potential for noninvasive detection of cancer. They applied the tool to blood samples from 287 individuals with and without lung cancer participating in the Danish Lung Cancer Screening Study (LUCAS). ARTEMIS classified patients with lung cancer with an area under the curve (AUC) of 0.82. But when used with another method called DELFI (DNA evaluation of fragments for early interception) -- an assay previously developed by Velculescu, Scharpf and other members of their group that detects changes in the size and distribution of cfDNA fragments across the genome -- the combination model classified patients with lung cancer with an AUC of 0.91. Similar performance was observed in a group of 208 individuals at risk for liver cancer, in which ARTEMIS detected individuals with liver cancer among others with cirrhosis or viral hepatitis with an AUC of 0.87. When combined with DELFI, the AUC increased to 0.90. Finally, they evaluated whether the ARTEMIS blood test could identify where in the body a tumor originated in patients with cancer. When trained with information from the PCAWG participants, the tool could classify the source of tumor tissues with an average 78% accuracy among 12 tumor types. The investigators then combined ARTEMIS and DELFI to assess blood samples from a group of 226 individuals with breast, ovarian, lung, colorectal, bile duct, gastric or pancreatic tumors. Here, the model correctly classified patients among the different cancer types with an average accuracy of 68%, which improved to 83% when the model was allowed to suggest two possible tumor types instead of a single cancer type. "Our study shows that ARTEMIS can reveal genome-wide repeat landscapes that reflect dramatic underlying changes in human cancers," Annapragada says. "By illuminating the so-called 'dark genome,' the work offers unique insights into the cancer genome and provides a proof-of-concept for the utility of genome-wide repeat landscapes as tissue and blood-based biomarkers for cancer detection, characterization and monitoring." Next steps are to evaluate the approach in larger clinical trials, says Velculescu: "You can imagine this could be used for early detection for a variety of cancer types, but also could have uses in other applications such as monitoring response to treatment or detecting recurrence. This is a totally new frontier." Additional study co-authors were Noushin Niknafs, James R. White, Daniel C. Bruhm, Christopher Cherry, Jamie E. Medina, Vilmos Adleff, Carolyn Hruban, Dimitrios Mathios, Zachariah H. Foda and Jillian Phallen. The work was supported in part by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, Stand Up to Cancer (SU2C) in-Time Lung Cancer Interception Dream Team Grant, SU2C-Dutch Cancer Society International Translational Cancer Research Dream Team Grant (SU2C-AACR-DT1415), the Gray Foundation, The Honorable Tina Brozman Foundation, the Commonwealth Foundation, the Mark Foundation for Cancer Research, the Cole Foundation, a research grant from Delfi Diagnostics and U.S. National Institutes of Health grants CA121113, CA006973, CA233259, CA062924, CA271896 and 1T32GM136577. Annapragada, Scharpf and Velculescu are inventors on patent applications submitted by The Johns Hopkins University related to genome-wide repeat landscapes in cancer and cfDNA. Annapragada, Bruhm, Adleff, Mathios, Foda, Phallen and Scharpf are inventors on patent applications submitted by The Johns Hopkins University related to cell-free DNA for cancer detection that have been licensed to Delfi Diagnostics. White is the founder and owner of Resphera Biosciences LLC and serves as a consultant to Personal Genome Diagnostics Inc. and Delfi Diagnostics Inc. Cherry is the founder and owner of CMCC Consulting. Phallen, Adleff and Scharpf are founders of Delfi Diagnostics, and Adleff and Scharpf are consultants for this organization. Velculescu is a founder of Delfi Diagnostics, serves on the board of directors and owns Delfi Diagnostics stock, which is subject to certain restrictions under university policy. Additionally, The Johns Hopkins University owns equity in Delfi Diagnostics. Velculescu divested his equity in Personal Genome Diagnostics (PGDx) to LabCorp in February 2022. He is an inventor on patent applications submitted by The Johns Hopkins University related to cancer genomic analyses and cell-free DNA for cancer detection that have been licensed to one or more entities, including Delfi Diagnostics, LabCorp, Qiagen, Sysmex, Agios, Genzyme, Esoterix, Ventana and ManaT Bio. Under the terms of these license agreements, the university and inventors are entitled to fees and royalty distributions. Velculescu is also an adviser to Viron Therapeutics and Epitope. These arrangements have been reviewed and approved by The Johns Hopkins University in accordance with its conflict-of-interest policies.

Novel blood testing technology that combines genome-wide sequencing of single molecules of DNA shed from tumors and machine learning may allow earlier detection of lung and other cancers. Novel blood testing technology being developed by researchers at the Johns Hopkins Kimmel Cancer Center that combines genome-wide sequencing of single molecules of DNA shed from tumors and machine learning may allow earlier detection of lung and other cancers. The test, called GEMINI (Genome-wide Mutational Incidence for Non-Invasive detection of cancer), looks for changes to DNA throughout the genome. First, a blood sample is collected from a person at risk for developing cancer. Then, cell-free DNA (cfDNA) shed by tumors is extracted from the plasma and sequenced using cost-efficient whole genome sequencing. Single molecules of DNA are analyzed for sequence alterations and are used to obtain mutation profiles across the genome. Finally, a machine learning model trained to identify changes in cancer and non-cancer mutation frequencies in different regions of the genome is used to distinguish people who have cancer from those who do not have cancer. The classifier generates a score ranging from 0 to 1, with a higher score reflecting a higher probability of having cancer. In a series of laboratory tests of GEMINI, investigators found that the approach, when followed by computerized tomography imaging, detected over 90% of lung cancers, including among patients with stage I and II disease. A description of the work, a proof-of-concept study, was published online July 27 in the journal Nature Genetics. "This study shows for the first time that a test like GEMINI, incorporating genome-wide mutation profiles from single molecules of cfDNA, in combination with other cancer detection approaches, may be used for early detection of cancers, as well as for monitoring patients during therapy," says senior study author Victor Velculescu, M.D., Ph.D., professor of oncology and co-director of the cancer genetics and epigenetics program at the Kimmel Cancer Center. The study mostly focused on detection of lung cancer in high-risk populations, says Daniel Bruhm, lead study author and graduate student in the human genetics program at the Johns Hopkins University School of Medicine. "However, we detected altered mutational profiles in cfDNA from patients with other cancers, including liver cancer, melanoma or lymphoma, suggesting it may be used more broadly," Bruhm says. To develop GEMINI, investigators examined whole-genome sequences of cancers from 2,511 people across 25 different cancers from the Pan-Cancer Analysis of Whole Genomes study, identifying distinct mutation frequencies across the genome in different tumor types. For example, lung cancers were found to have an average of 52,209 somatic mutations per genome. The investigators also identified genomic regions with the highest number of mutations, finding that genome regions with a high frequency of mutations were similar between tumor tissue and blood-derived cfDNA from patients with lung cancer, melanoma or B cell non-Hodgkin lymphoma. Researchers evaluated GEMINI's ability to detect sequence alterations in cfDNA from 365 people in the Longitudinal Urban Cohort Ageing Study (LUCAS), a cohort of people at high risk of having lung cancer. GEMINI scores were higher in people with cancer than in those without cancer. Researchers also assessed whether GEMINI could be combined with DELFI (DNA evaluation of fragments for early interception) -- a previously developed test that detects changes in the size and distribution of cfDNA fragments across the genome -- to improve detection of early-stage lung cancer. Several cancer samples that GEMINI missed were detected using the combined technique. In 89 samples from patients from the LUCAS cohort who had lung cancer, GEMINI combined with DELFI correctly detected cancers 91% of the time. Similar results were obtained in a separate validation cohort of 57 people who mostly had early-stage lung cancer. The investigators also studied the use of GEMINI in other study samples, including seven patients who did not have any detectable tumors at the time of blood collection. They had a median GEMINI score of 0.78, which is higher than people without cancer. Six tested positive using GEMINI and were later diagnosed with lung cancer from 231 to 1,868 days after samples were obtained, suggesting that abnormalities in cfDNA mutation profiles can be detected years before standard diagnoses. Additional experiments determined that GEMINI could distinguish between subtypes of lung cancers and could detect early liver cancers. In a group of patients receiving lung cancer drug treatment, GEMINI scores decreased during initial response to therapy, suggesting the testing could be used to monitor patients during therapy. Together, the results indicate that the combination of genome-wide GEMINI mutation analyses and DELFI fragmentation analyses of cfDNA "may provide an opportunity for cost-efficient, scalable detection of cancers," says Rob Scharpf, Ph.D., associate professor of oncology at the Kimmel Cancer Center. Larger clinical trials are needed to validate the tool before it could become available for clinical use, he says. Additional study co-authors include Dimitrios Mathios, Zachariah Foda, Akshaya Annapragada, Jamie Medina, Vilmos Adleff, Elaine Jiayuee Chiao, Leonardo Ferreira, Stephen Cristiano, James White, Amy Kim, Valsamo Anagnostou and Jillian Phallen of Johns Hopkins. Other authors are from Boston University and the Allegheny Health Network Cancer Institute in Pittsburgh. The work was supported in part by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, the Stand Up to Cancer (SU2C) InTime Lung Cancer Interception Dream Team Grant, the SU2C-Dutch Cancer Society International Translational Cancer Research Dream Team Grant (SU2C-AACR-DT1415), the Gray Foundation, the Commonwealth Foundation, the Mark Foundation for Cancer Research, the Cole Foundation, a research grant from Delfi Diagnostics and U.S. National Institutes of Health grants CA121113, CA006973, CA233259, CA062924 and 1T32GM136577. Bruhm, Mathios, Cristiano, Phallen, Scharpf and Velculescu are inventors on patent applications submitted by The Johns Hopkins University that are related to cfDNA use for cancer detection. Cristiano, Phallen, Adleff, Scharpf and Velculescu are founders of Delfi Diagnostics, and Adleff and Scharpf are consultants for this organization. Velculescu also serves on the board of directors and is an officer for Delfi Diagnostics, and he owns stock in the business. White is the founder and owner of Resphera Biosciences. Additionally, The Johns Hopkins University owns equity in Delfi Diagnostics. Velculescu is an inventor on patent applications submitted by Johns Hopkins related to cancer genomic analyses and cfDNA for cancer detection that have been licensed to various entities. Under these license agreements, the university and inventors are entitled to fees and royalty distributions. Velculescu is an adviser to Viron Therapeutics and Epitope. These arrangements have been reviewed and approved by The Johns Hopkins University in accordance with its conflict-of-interest policies.