Do you want chemotherapy? Genetic testing is quietly changing the cancer treatment process

Do you want chemotherapy? Genetic testing is quietly changing the cancer treatment process

September 14, 2016 Source: Singularity Network

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When you hear "hualiao" (whether it is "chemotherapy" or "therapy"), ordinary people will have a stereotype of chemotherapy, such as hair loss, vomiting, fatigue, and even death. Of course, these negative effects of chemotherapy are not only reflected in the patient's clinical manifestations, but clinicians are also increasingly concerned about the harm of chemotherapy to patients. More and more studies have shown that systemic chemotherapy poses a greater risk and may even threaten the lives of patients. It is time to find a solution to this historical problem.

At the end of last month, a group of British scientists used the national breast cancer and lung cancer chemotherapy data from the public health department in England to comprehensively analyze the 30-day mortality rate of patients after chemotherapy. This is the first time ever to use national data to analyze the survival of cancer after chemotherapy. Using these data, the researchers analyzed the effects of age, health, and other factors on the outcome of chemotherapy. Provides a reliable baseline "chemotherapy" for chemotherapy in UK patients. Their research results were finally published in The Lancet Tumor (1).

In today's advocacy of "precise medical care", it is often difficult for doctors to make clinical treatment decisions simply relying on the "baseline" of thick lines. Doctors obviously need more data to make accurate judgments and decisions. In recent years, the cost of gene sequencing has been decreasing, and more and more cancer patients are beginning to try genetic testing as an aid to cancer diagnosis. With the continuous accumulation of genetic testing data, the role of genetic testing results in cancer treatment decision-making has gradually emerged.

Chemotherapy, or no chemotherapy?

There are some patients with early breast cancer who have completed surgery, but traditional clinical tests still indicate that the patient is at a high risk of recurrence. For these patients, doctors usually arrange for further chemotherapy. Although doctors know that this is superfluous for some patients, for the sake of caution, doctors will still arrange chemotherapy for patients because doctors can't distinguish between these "high-risk" patients, which are really high and which are false. A research paper published in the New England Journal (2) on August 25 provides doctors with a reliable way to judge whether to do or not. This method may solve the problem of over-treatment of patients to a certain extent.

Dr. Fatima Cardoso and others tested 70 breast cancer-related genes in nearly 7,000 early breast cancer patients. They found that 1,550 patients who showed a high risk of clinical deterioration showed a low risk of worsening the genetic test results. They randomly divided these patients into two groups, one receiving adjuvant chemotherapy and the other not receiving chemotherapy.

After 5 years of follow-up, Dr. Cardoso found that 94.7% of the patients who did not receive chemotherapy did not deteriorate. In the group receiving chemotherapy, only 96.2% of the patients did not deteriorate. The group was 1.5% higher; obviously, there was no significant difference between the two. In all patients with no worsening of the disease, there was no difference in 5-year survival rates between patients receiving chemotherapy and those who did not receive chemotherapy. Therefore, the researchers believe that genetic testing can identify patients with high clinical risk of early breast cancer who do not need chemotherapy, and can help patients avoid unnecessary chemotherapy in many cases.

In this study, the MammaPrint gene test used by Dr. Cardoso was produced by Agendia. This early breast cancer genetic test kit was approved by the FDA in 2007. Although the test price is as high as $4,200, it can effectively help some patients avoid unnecessary chemotherapy damage, and this part of the cost has been covered by medical insurance.

Dr. Cardoso demonstrated that MammaPrint gene testing can be used to determine whether early breast cancer patients at high risk of clinical exacerbation require chemotherapy. So if the patient needs chemotherapy, how to treat it, which treatment plan will it respond to?

Early genetic testing can guide the choice of radiotherapy and chemotherapy

As new drugs and technologies for treating cancer continue to emerge, doctors are choosing more and more. Which treatment is the most suitable for the current patient? Becoming one of the biggest problems facing doctors. The Gary H. Karpen team from the University of California at Berkeley is trying to give doctors a way to solve problems. The Karpen team developed a CES scoring system that uses genetic testing results to determine the severity of cancer and which drugs are more sensitive to patients. This important research result of the Karpen team was published on August 31 in Nature Newsletter (3).

In different types of cancer tissues, the expression of genes is also different. The Karpen team analyzed 14 genes that are highly expressed in many types of cancer. Then, the score of each patient is calculated by the high expression of the gene, and the higher the score, the worse the prognosis of the patient. At the same time, they found in drug research that cancer cell CES scores are related to drug sensitivity. For example, the higher the CES scores of cancer cells, the more sensitive they are to the two topoisomerase I inhibitors, irinotecan and topotecan. This is a very important message for doctors. Therefore, the Karpen team believes that this finding can be used not only to predict the progress of a patient's cancer; more importantly, it is possible to accurately predict a patient's sensitivity to specific drugs. Of course, this is just the beginning for the Karpen team, who are still looking for more correlations between genes and disease and drug reactions.

Apgar newborn score sheet

Judging the development trend of the patient's condition through the combination of genetic markers and the sensitivity of the patient to the drug reminds me of the "Apugamian Neonatal Score Sheet" introduced by Artu Gwend in "The Doctor's Advance".

"In the United States in the 1930s, one in every 30 newborns died at birth. This number has barely changed in the past 100 years. People have been unaware of how to improve this situation. Later, a name is Virgin. Virginia Apgar's New York doctors came up with a solution. Her approach was incredibly simple, but it revolutionized the way hospitals deliver and receive neonatal care."

Apgar "takes a less direct but ultimately more effective approach. She designed a score sheet. Nursing staff assess the condition of the newborn according to the 0 to 10 scale. For example, the baby is pink. 2 points, 2 points for crying, 2 points for good eating, 2 points for breathing, 2 points for all limbs, 2 points for heart rate over 100. 10 points means that the newborn is in the best condition at birth. 4 points or less means that the baby is not healthy and the body is weak."

"This score sheet was published in 1953 and brought revolutionary impact. Originally in the clinic, the condition of the newborn was an abstract concept that was difficult to determine and judged by impression. This score sheet turned it into a collection that people can collect. And comparative figures. Later, around the world, almost all babies born in hospitals were assessed using the Apgar score sheet, recorded once every 1 minute after birth and 5 minutes after birth. Soon, people found that even The baby's score of 1 minute after birth is very bad. They are often saved by oxygen and warmth. The scores are good after 5 minutes. In today's United States, only 1 of the 500 full-term babies are born. One death, according to the mortality rate of the 1930s, there will be 160,000 newborn deaths last year (the actual number is only 1/8)."

I believe that the application of genetic testing in cancer diagnosis and treatment is similar to the role of the "Apgar Newborn Scale" in neonatal care. Through continuous exploration and analysis, a correlation is established between the combination of genetic biomarkers and diseases, and then this correlation analysis is used to predict the trend of the disease, or the effect of the drug. It seems that the feasibility of this correlation analysis is gradually being established.

With the accumulation of genetic data, we can establish more and more correlations between genes and diseases, and this correlation will eventually revolutionize the treatment process and methods of cancer. Now is just the beginning, more and better applications will inevitably happen in the near future.

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