Weigh cancer cells?

A new study shows that after treatment for glioblastoma, there is a link between patient survival and changes in the number of tumor cells
.

Researchers at the Massachusetts Institute of Technology (MIT) and the Dana-Farber Cancer Institute have developed a new method to determine whether a single patient will respond to a specific cancer drug
.

This new technology involves taking tumor cells from a patient, treating the cells with drugs, and then measuring changes in cell quality.
It can be applied to various cancers and drug treatments, said Scott Manalis, a professor in the Department of Bioengineering and Mechanical Engineering
.

Manalis said: "Basically all anti-cancer drugs used clinically directly or indirectly prevent the growth of cancer cells
.

This new research focuses on malignant glioma (a malignant brain cancer) and is part of a collaboration between the Koch Institute and the Dana-Farber Precision Medicine project to find New biomarkers and cancer diagnostic tests
.

Manalis and Dana - Farber, director of patient-derived model Center, Harvard Medical School associate professor Keith Ligon is the senior author of the study, the study on October 5, 2021, published in " the Cell Reports on"
.

Measure tumor cells

Approximately 13,000 Americans are diagnosed with glioblastoma each year.
This disease is incurable, but radiotherapy and drug therapy can help extend the life expectancy of patients
.

"For this disease, you don't have much time to make adjustments
.

Patients diagnosed with glioblastoma usually take a chemotherapy drug called temozolomide (TMZ)
.

Currently, doctors can use a genetic marker-methylation of the MGMT gene-to predict whether patients will respond to TMZ treatment
.

In recent years, Manalis and Ligon have been working on a new method of predicting patient response based on measuring the response of tumor cells to treatment rather than genomic characteristics
.

Ligon said: "The idea behind functional precision medicine is that for cancer, you can take out the patient's tumor cells, give them the drugs that the patient may get, and predict what will happen before giving them to the patient
.
"

Scientists are studying many different methods to achieve functional precision medicine.
One technique that Manalis and Ligon have been studying is to measure changes in cell quality after drug treatment
.
This method is based on a technology developed by the Manalis laboratory, which allows single cells to flow through vibrating microchannels and weighs with extremely high accuracy
.

A few years ago, Manalis, Ligon and their colleagues demonstrated that they can use this technology to analyze the response of two types of cancer, glioblastoma and acute lymphoblastic leukemia
.
This result is based on multiple measurements of individual cells after drug treatment, allowing researchers to calculate their growth rate over time after treatment
.
They pointed out that this statistic, which they called the Mass Accumulation Rate (MAR), is a good predictor of whether a cell is sensitive to a given drug
.

Using the high-throughput version of the system they developed in 2016, they can calculate the precise MAR using only 100 cells per patient
.
However, the disadvantage of MAR technology is that the cells must stay in the system for several hours, so they can be weighed repeatedly to calculate the growth rate over time
.

In their new study, the researchers decided to see if a simpler and significantly faster method-measuring subtle changes in the mass distribution of single cells between drug-treated and untreated cancer cells-could predict patient survival
.
They retrospectively studied a group of live glioblastoma cells from 69 patients.
These cells were donated to the Ligon Laboratory and Dana-Farber Patient Derived Model Center and used them for 3D spherical tissue culture
.
After separating the cells, the researchers treated them with TMZ and measured their quality a few days later
.

They found that by simply measuring the quality difference between the cells before and after treatment, using only 2,000 cells per patient sample, they can accurately predict whether the patient will respond to TMZ
.

Better predict drug response

The researchers showed that their measurement results are as accurate as MGMT methylation markers, but the measurement has an additional advantage that it can work in patients whose genetic markers cannot reveal TMZ susceptibility
.
For many other types of cancer, there are no biomarkers that can be used to predict drug response
.

"Most cancers simply don't have genomic markers
that can be used .
We believe that this functional approach can work in the absence of any genomic marker selection," Manalis said
.

Because this test is performed by measuring changes in quality, it can be used to observe the effects of many different types of anticancer drugs, regardless of their mechanism of action
.
TMZ works by stopping the cell cycle, which causes cells to grow larger because they can no longer divide, but their mass still increases
.
Other anti-cancer drugs work by interfering with cell metabolism or destroying its structure, which also affects cell quality
.

The long-term hope of the researchers is that this method can be used to test several different drugs on the cells of a single patient to predict which treatment is most effective for that patient
.

"Ideally, we would test the drugs that patients are most likely to get, but we would also test alternatives
.
"

Manalis and Ligon co-founded a company called Travera.
The company has obtained a license for this technology and is currently collecting data from samples of several different types of cancer patients, hoping to develop clinically validated laboratory tests.
Used to help patients
.

Original search: "Functional drug susceptibility testing using single-cell mass predicts treatment outcome in patient-derived cancer neurosphere models" by Max A.
Stockslager, Seth Malinowski, Mehdi Touat, Jennifer C.
Yoon, Jack Geduldig, Mahnoor Mirza, Annette S.
Kim, Patrick Y.
Wen, Kin-Hoe Chow, Keith L.
Ligon and Scott R.
Manalis, 5 October 2021, Cell Reports .

DOI: 10.
1016/j.
celrep.
2021.
109788