Massachusetts General Hospital Researchers Develop Tool for Detecting Lung Cancer from the Metabolites in a Drop of Blood

Potential is for a clinical laboratory test that can help pathologists identify early-stage lung cancer in people long before symptoms appear

In a proof-of-concept study, researchers from Harvard-affiliated Massachusetts General Hospital (MGH) have created a metabolomic screening model that can uncover early-stage lung cancer in asymptomatic patients from a single drop of the patient’s blood.

The NIH’s National Center for Biotechnology Information defines metabolomics as the “comprehensive analysis of metabolites in a biological specimen” and states that the emerging technology “holds promise in the practice of precision medicine.”

The technology is similar to the concept of a liquid biopsy, which uses blood specimens to identify cancer by capturing tumor cells circulating in the blood.

According to the American Cancer Society, lung cancer is responsible for approximately 25% of cancer deaths in the US and is the leading cause of cancer deaths in both men and women. The ACS estimates there will be about 236,740 new cases of lung cancer diagnosed in the US this year, and about 130,180 deaths due to the disease.

Early-stage lung cancer is typically asymptomatic which leads to later stage diagnoses and lowers survival rates, largely due to a lack of early disease detection tools. The current method used to detect early lung cancer lesions is low-dose spiral CT imaging, which is costly and can be risky due to the radiation hazards of repeated screenings, the news release noted.

MGH’s newly developed diagnostic tool detects lung cancer from alterations in blood metabolites and may lead to clinical laboratory tests that could dramatically improve survival rates of the deadly disease, the MGH scientist noted in a news release.

Leo Cheng, PhD
“Our study demonstrates the potential for developing a sensitive screening tool for the early detection of lung cancer,” said Leo Cheng, PhD (above), in the news release. Cheng is Associate Professor of Radiology at Harvard Medical School and Associate Biophysicist in Radiology at Massachusetts General Hospital. “The predictive model we constructed can identify which people may be harboring lung cancer. Individuals with suspicious findings would then be referred for further evaluation by imaging tests, such as low-dose CT, for a definitive diagnosis,” he added. Oncologists may soon have a clinical laboratory test for screening patients with early-stage lung cancer. (Photo copyright: OCSMRM.)

The researchers published their findings in Proceedings of the National Academy of Sciencestitled, “Screening Human Lung Cancer with Predictive Models of Serum Magnetic Resonance Spectroscopy Metabolomics.”

Detecting Lung Cancer in Blood Metabolomic Profiles

The MGH scientists created their lung-cancer predictive model based on magnetic resonance spectroscopy which can detect the presence of lung cancer from alterations in blood metabolites.

The researchers screened tens of thousands of stored blood specimens and found 25 patients who had been diagnosed with non-small-cell lung carcinoma (NSCLC), and who had blood specimens collected both at the time of their diagnosis and at least six months prior to the diagnosis. They then matched these individuals with 25 healthy controls.

The scientists first trained their statistical model to recognize lung cancer by measuring metabolomic profiles in the blood samples obtained from the patients when they were first diagnosed with lung cancer. They then compared those samples to those of the healthy controls and validated their model by comparing the samples that had been obtained from the same patients prior to the lung cancer diagnosis.

The predictive model yielded values ​​between the healthy controls and the patients at the time of their diagnoses.

“This was very encouraging, because screening for early disease should detect changes in blood metabolomic profiles that are intermediate between healthy and disease states,” Cheng noted.

The MGH scientists then tested their model with a different group of 54 patients who had been diagnosed with NSCLC using blood samples collected before their diagnosis. The second test confirmed the accuracy of their model.

Predicting Five-Year Survival Rates for Lung Cancer Patients

Values ​​derived from the MGH predictive model measured from blood samples obtained prior to a lung cancer diagnosis also could enable oncologists to predict five-year survival rates for patients. This discovery could prove to be useful in determining clinical strategies and personalized treatment decisions.

The MGH study was co-led by Cheng and David Christiani, MD, Professor of Environmental Health at Harvard TH Chan School of Public Health, and was funded by the National Cancer Institute.

The researchers plan to analyze the metabolomic profiles of the clinical characteristics of lung cancer to understand the entire metabolic spectrum of the disease. They hope to create similar models for other illnesses and have already created a model that can distinguish aggressive prostate cancer by measuring the metabolomics profiles of more than 400 patients with that disease.

In addition, they are working on a similar model to screen for Alzheimer’s disease using blood samples and cerebrospinal fluid.

More research and clinical studies are needed to validate the utilization of blood metabolomics models as early screening tools in clinical practice. However, this technology might provide pathologists and clinical laboratories with diagnostic tests for the screening of early-stage lung cancer that could save thousands of lives each year.

J. P. Schlingman

Related Information:

Early-stage Lung Cancer May be Detected from a Drop of Blood

Cancer Clues in a Drop of Blood

Screening Human Lung Cancer with Predictive Models of Serum Magnetic Resonance Spectroscopy Metabolomics

Metabolomics: An Emerging but Powerful Tool for Precision Medicine

Metabolomics Promises to Provide New Diagnostic Biomarkers, Assays for Personalized Medicine and Medical Laboratories

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