Promising Blood Test Technology Detects Lung and Other Cancers Early
Researchers at the Johns Hopkins Kimmel Cancer Center have developed a groundbreaking blood test technology that shows great promise in detecting lung and other cancers at an early stage. The test, known as GEMINI (Genome-wide Mutational Incidence for Non-Invasive detection of cancer), combines genome-wide sequencing of single molecules of DNA with machine learning to identify mutations associated with cancer. This innovative approach could revolutionize cancer detection by enabling earlier diagnoses and monitoring of patients during therapy.
To conduct the test, a blood sample is collected from individuals at risk of developing cancer. Tumor-derived cell-free DNA (cfDNA) is then extracted from the plasma and subjected to whole genome sequencing, a cost-efficient method for analyzing DNA sequences. By analyzing single molecules of DNA, researchers can identify sequence alterations and assess mutation profiles across the entire genome. To distinguish between individuals with cancer and those without, a machine learning model trained on cancer and non-cancer mutation frequencies in different genomic regions assigns a score ranging from 0 to 1, with higher scores indicating a higher probability of cancer.
In laboratory tests, GEMINI combined with computerized tomography imaging detected over 90% of lung cancers, including those at stage I and II. This proof-of-concept study, published in the journal Nature Genetics, highlights the potential of GEMINI as an early detection tool for various cancers. While the focus of the research was primarily on lung cancer in high-risk populations, altered mutational profiles were detected in cfDNA from patients with liver cancer, melanoma, and lymphoma, suggesting broader applications for this test.
The development of GEMINI involved an analysis of whole-genome sequences from 2,511 individuals across 25 different cancer types, revealing distinct mutation frequencies specific to each tumor type. By identifying genomic regions with high mutation rates, researchers found similarities between tumor tissue and cfDNA from patients with conditions such as lung cancer, melanoma, and B cell non-Hodgkin lymphoma.
The ability of GEMINI to detect sequence alterations was evaluated in a prospective observational cohort study (LUCAS), which included individuals at high risk of lung cancer. Notably, GEMINI scores were higher in people with cancer compared to those without. To improve the detection of early-stage lung cancer, GEMINI was combined with DELFI (DNA evaluation of fragments for early interception), a previously developed test that assesses changes in the size and distribution of cfDNA fragments across the genome. This combined approach successfully detected cancers that GEMINI alone had missed.
Furthermore, the study demonstrated GEMINI’s potential to identify subtypes of lung cancer and detect early liver cancers. In a group of patients receiving lung cancer treatment, GEMINI scores decreased during the initial response to therapy, suggesting the test could be employed for patient monitoring.
While the results are promising, larger-scale clinical trials are required to validate GEMINI before it can be utilized in a clinical setting. Nevertheless, the combination of genome-wide GEMINI mutation analysis and DELFI fragmentation analysis of cfDNA presents an opportunity for cost-efficient and scalable cancer detection.
The development of GEMINI and its potential applications in cancer detection represent a significant step forward in the fight against this devastating disease. With further validation and refinement, this blood test technology could save lives by facilitating earlier diagnoses and more effective monitoring of cancer patients.
