Identification of specific lung cancer pathway increases the ability to predict which patients are likely or unlikely to benefit from a particular treatment, reveals a new study. The findings of the study are published in the journal Cancer Cell.
Known for its poor prognosis, lung adenocarcinoma is the most common type of lung cancer, responsible for about 4 of 10 diagnoses, according to the National Cancer Institute. Researchers on Mayo Clinic's Florida campus can now distinguish between two pathways where this deadly cancer can develop. They say their discovery could help future patients.
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‘Discovery of distinct lung cancer pathways may help lead to more targeted treatments.’
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"The ability to identify the specific pathway by which a patient's lung adenocarcinoma came about increases our ability to predict which patients are likely or unlikely to benefit from a particular treatment, and hopefully offer alternative options to patients whose cancer subtype is unlikely to respond," says Alan Fields, Ph.D., a cancer biologist and the study's senior author. Dr. Fields is the Monica Flynn Jacoby Professor of Cancer Research at Mayo Clinic.
He and his team studied the molecular features of lung adenocarcinoma in mice and found two ways that this cancer can develop. The first way depends on the cancer-causing gene known as "protein kinase C iota (PKCiota)." The second pathway, identified as the "Wnt/Beta-catenin signaling pathway," was found to operate independently of PKCiota. Lung adenocarcinomas that stem from the two different pathways were also found to form in different regions of the lung and through different cells of origin.
![Artificial Intelligence Detects Lung Cancer Before Radiologists]( "Artificial Intelligence Detects Lung Cancer Before Radiologists")
Artificial intelligence (AI)-based deep learning technology can detect lung cancer faster and more accurately than radiologists. This will make lung cancer diagnosis easier and help save many lives.
Once these two pathways were revealed in mice, Dr. Fields and colleagues considered how their discovery may apply to people. To begin, they compared the pathways in the mouse model to the six known molecular subtypes of this cancer in humans. The scientists found a match: a molecular marker that allowed them to predict which human lung adenocarcinoma cells originated from the PKCiota-independent pathway that they'd discovered in mice.
To test whether the tumors arising from the PKCiota-dependent and independent pathways might be sensitive to specific cancer therapies, Dr. Fields' team then conducted an experiment on human cells and the mouse model. As hypothesized, they found that two drugs affected the adenocarcinoma subtypes differently, depending on their underlying pathway. These results suggested to the investigators that they can predict how these cancer subtypes will respond to targeted therapies.
As a next step, the team will work to determine whether they can effectively and specifically identify PKCiota-dependent versus independent lung adenocarcinoma in human patients and confirm whether experimental drugs can predictably inhibit the growth of lung cancer in the PKCiota-dependent and independent patients.
Because the PKCiota pathway is also important to other cancers, the researchers think that their findings may apply beyond lung cancer.
This work builds on previous efforts by Dr. Fields and colleagues, who were the first to discover the connection between PKCiota, and the initiation, promotion and spread of lung cancers.
Source: Eurekalert
Artificial Intelligence Detects Lung Cancer Before Radiologists
Artificial intelligence (AI)-based deep learning technology can detect lung cancer faster and more accurately than radiologists. This will make lung cancer diagnosis easier and help save many lives.
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Once these two pathways were revealed in mice, Dr. Fields and colleagues considered how their discovery may apply to people. To begin, they compared the pathways in the mouse model to the six known molecular subtypes of this cancer in humans. The scientists found a match: a molecular marker that allowed them to predict which human lung adenocarcinoma cells originated from the PKCiota-independent pathway that they'd discovered in mice.
To test whether the tumors arising from the PKCiota-dependent and independent pathways might be sensitive to specific cancer therapies, Dr. Fields' team then conducted an experiment on human cells and the mouse model. As hypothesized, they found that two drugs affected the adenocarcinoma subtypes differently, depending on their underlying pathway. These results suggested to the investigators that they can predict how these cancer subtypes will respond to targeted therapies.
As a next step, the team will work to determine whether they can effectively and specifically identify PKCiota-dependent versus independent lung adenocarcinoma in human patients and confirm whether experimental drugs can predictably inhibit the growth of lung cancer in the PKCiota-dependent and independent patients.
Because the PKCiota pathway is also important to other cancers, the researchers think that their findings may apply beyond lung cancer.
This work builds on previous efforts by Dr. Fields and colleagues, who were the first to discover the connection between PKCiota, and the initiation, promotion and spread of lung cancers.
Source: Eurekalert
Lung Cancer Awareness Month - Improving Lung Cancer Survival
Lung cancer awareness month is observed in November and aims to educate the general public about lung cancer and improve survival. Lung cancer is the most common cancer in men and the third most common cancer in women.
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 Common Blood Pressure Tablets May Increase Risk of Lung CancerA common blood pressure drug belonging to the class angiotensin-converting enzyme inhibitors (ACEI) can increase the risk of lung cancer. The risk increases after 5 years of use of ACEIs, which peaks after 10 years. | Advertisement
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