Viron Therapeutics, Inc.

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.

A novel artificial intelligence blood testing technology has been used to successfully detect lung cancer in a 2021 study has now detected more than 80% of liver cancers in a new study of 724 people. A novel artificial intelligence blood testing technology developed and used by Johns Hopkins Kimmel Cancer Center researchers to successfully detect lung cancer in a 2021 study has now detected more than 80% of liver cancers in a new study of 724 people. The blood test, called DELFI (DNA evaluation of fragments for early interception) detects fragmentation changes among DNA from cancer cells shed into the bloodstream, known as cell-free DNA (cfDNA). In the most recent study, investigators used the DELFI technology on blood plasma samples obtained from 724 individuals in the U.S., the European Union (E.U.) and Hong Kong to detect hepatocellular cancer (HCC), a type of liver cancer. The researchers believe this is the first genome-wide fragmentation analysis independently validated in two high-risk populations and across different racial and ethnic groups with different causes associated with their liver cancers. Their findings were reported Nov. 18 in Cancer Discovery and at the American Association for Cancer Research Special Conference: Precision Prevention, Early Detection, and Interception of Cancer. It is estimated that 400 million people worldwide are at higher risk of developing HCC because of cirrhosis from chronic liver diseases including chronic viral hepatitis or non-alcoholic fatty liver disease, according to a worldwide analysis of the burden of liver disease (J. Hepatology, 2019). "Increased early detection of liver cancer could save lives, but currently available screening tests are underutilized and miss many cancers," says Victor 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, who co-led the study with Zachariah Foda, M.D., Ph.D., gastroenterology fellow, Akshaya Annapragada, M.D./Ph.D. student, and Amy Kim, M.D., assistant professor of medicine at the Johns Hopkins University School of Medicine. Of the 724 plasma samples studied, 501 were collected in the U.S. and E.U. and included samples from 75 people with HCC to train and validate the machine learning model, a type of artificial intelligence that uses data and algorithms to improve accuracy, explains Foda. For validation, an additional 223 plasma samples were analyzed from individuals in Hong Kong and included samples from 90 people with HCC, 66 with hepatitis B virus (HBV), 35 with HBV-related liver cirrhosis and 32 people with no underlying risk factors. The DELFI technology uses a blood test to measure the way DNA is packaged inside the nucleus of a cell by studying the size and amount of cell-free DNA present in the circulation from different regions across the genome. Healthy cells package DNA like a well-organized suitcase, in which different regions of the genome are placed carefully in various compartments. The nuclei of cancer cells, by contrast, are like more disorganized suitcases, with items from across the genome thrown in haphazardly. When cancer cells die, they release DNA fragments in a chaotic manner into the bloodstream. DELFI identifies the presence of cancer by examining millions of cfDNA fragments for abnormal patterns, including the size and amount of DNA in different genomic regions. The DELFI approach only requires low-coverage sequencing, enabling this technology to be cost-effective in a screening setting, the researchers say. In the latest study, researchers performed the test -- which was previously shown to accurately classify lung cancer -- on cfDNA fragments isolated from the plasma samples. They analyzed the patterns of fragmentation across each sample to develop a DELFI score. Scores were low for cancer-free individuals with viral hepatitis or cirrhosis (median DELFI score was 0.078 and 0.080, respectively), but, on average, 5 to 10 times higher for the 75 HCC patients in the U.S./E.U. samples, with high scores observed across all cancer stages, including early-stage disease (DELFI scores for Stage 0 = 0.46, Stage A = 0.61, Stage B = 0.83, and Stage C = 0.92). In addition, the test detected fragmentation changes in the content and packaging of liver cancer genomes, including from genome regions associated with liver-specific activity. The DELFI technology detected liver cancers at their earliest stages, with an overall sensitivity -- or ability to accurately detect a cancer -- of 88% and a specificity of 98%, which means it almost never incorrectly provided a false positive result, among people at average risk. In samples collected from those at high risk of HCC, the test had 85% sensitivity and 80% specificity. "Currently, less than 20% of the high-risk population get screened for liver cancer due to accessibility and suboptimal test performance. This new blood test can double the number of liver cancer cases detected, compared to the standard blood test available, and increase early cancer detection," says Kim, co-senior author on the study. The researchers say next steps include validating this approach in larger studies for clinical use. More than 800,000 people are diagnosed with liver cancer worldwide each year, and it is a leading cause of cancer deaths worldwide, according to the American Cancer Society. In addition to Velculescu, Foda, Annapragada and Kim, other researchers were Kavya Boyapati, Daniel Bruhm, Nicholas Vulpescu, Jamie Medina, Dimitrios Mathios, Stephen Cristiano, Noushin Niknafs, Harry Luu, Michael Goggins, Robert Anders, Jing Sun, Shruti Meta, David Thomas, Gregory Kirk, Vilmos Adleff, Jillian Phallen and Robert Scharpf. The research was supported by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, Stand Up to Cancer-Dutch Cancer Society International Translational Cancer Research Dream Team Grant (SU2C-AACR-DT1415), the Gray Foundation, the Commonwealth Foundation, SU2C INTIME Lung Cancer Interception Dream Team Grant, the Mark Foundation for Cancer Research, a research grant from Delfi Diagnostics, the National Institutes of Health grants CA121113, CA006973, CA233259, GM136577, CA237624 and CA062924, and Department of Defense CDMRP Award W81XWH-20-1-0605. Stand Up to Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research. The researchers disclose the following competing interests: Velculescu is a founder of Delfi Diagnostics, serves on the board of directors and as a consultant for this organization, and owns Delfi Diagnostics stock, which is subject to certain restrictions under Johns Hopkins 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 an adviser to Danaher, Takeda Pharmaceuticals and Viron Therapeutics. Scharpf is a founder and consultant of Delfi Diagnostics and owns Delfi Diagnostics stock subject to certain restrictions under university policy. These arrangements have been reviewed and approved by The Johns Hopkins University in accordance with its conflict-of-interest policies.