Researchers Predict Age of T Cells to Improve Cancer Treatment

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BlankResearchers Predict Age of T Cells to Improve Cancer Treatment

Released: 3/2/2011 7:00 AM EST

Source: Georgia Institute of Technology, Research Communications

Newswise — Manipulation of cells by a new microfluidic device may help

clinicians improve a promising cancer therapy that harnesses the body’s own

immune cells to fight such diseases as metastatic melanoma, non-Hodgkin’s

lymphoma, chronic lymphocytic leukemia and neuroblastoma.

The therapy, known as adoptive T cell transfer, has shown encouraging results in

clinical trials. This treatment involves removing disease-fighting immune cells

called T cells from a cancer patient, multiplying them in the laboratory and

then infusing them back into the patient’s body to attack the cancer. The

effectiveness of this therapy, however, is limited by the finite lifespan of T

cells -- after many divisions, these cells become unresponsive and inactive.

Researchers at Georgia Tech and Emory University have addressed this limitation

by developing a microfluidic device for sample handling that allows a

statistical model to be generated to evaluate cell responsiveness and accurately

predict cell “age” and quality. Being able to assess the age and responsiveness

of T cells -- and therefore transfer only young functional cells back into a

cancer patient’s body -- offers the potential to improve the therapeutic outcome

of several cancers.

“The statistical model, enabled by the data generated with the microfluidic

device, revealed an optimal combination of extracellular and intracellular

proteins that accurately predict T cell age,” said Kemp, an assistant

professor in the Wallace H. Coulter Department of Biomedical Engineering at

Georgia Tech and Emory University. “Knowing this information will help

facilitate the clinical development of appropriate T cell expansion and

selection protocols.”

Details on the microfluidic device and statistical model were published in the

March issue of the journal Molecular & Cellular Proteomics. This work was

supported by the National Institutes of Health, Georgia Cancer Coalition, and

Georgia Tech Integrative Biosystems Institute.

Currently, clinicians measure T cell age by using multiple assays that rely on

measurements from large cell populations. The measurements determine if cells

are exhibiting functions known to appear at different stages in the life cycle

of a T cell.

“Since no one measurement is a perfect predictor, it is advantageous to

concurrently sample multiple proteins at different time points, which we can do

with our microfluidic device,” explained Kemp, who is also a Georgia Cancer

Coalition Distinguished Professor. “The wealth of information we get from our

device for a small number of cells far exceeds a single measurement from a

population the same size by another assay type.”

For their study, Kemp, electrical engineering graduate student Rivet

and biomedical engineering undergraduate student Abby Hill analyzed CD8+ T cells

from healthy blood donors. They acquired information from 25 static biomarkers

and 48 dynamic signaling measurements and found a combination of phenotypic

markers and protein signaling dynamics -- including Lck, ERK, CD28 and CD27 --

to be the most useful in predicting cellular age.

To obtain biomarker and dynamic signaling event measurements, the researchers

ran the donor T cells through a microfluidic device designed in collaboration

with Hang Lu, an associate professor in the Georgia Tech School of Chemical &

Biomolecular Engineering. After stimulating the cells, the device divided them

into different channels corresponding to eight different time points, ranging

from 30 seconds to seven minutes. Then they were divided again into populations

that were chemically treated to halt the biochemical reactions at snapshots in

time to build up a picture of the signaling events that occur as T cells respond

to antigen.

“While donor-to-donor variability is a confounding factor in these types of

experiments, the technological platform minimized the experimental data variance

and allowed stimulation time to be precisely controlled,” said Lu.

With the donor T cell data, the researchers developed a model to assess which

biomarkers or dynamical signaling events best predicted the quality of T cell

function. The model found the most informative data in predicting cellular age

to be the initial changes in signaling dynamics.

“Although a combination of biomarker and dynamic signaling data provided the

optimal model, our results suggest that signaling information alone can predict

cellular age almost as well as the entire dataset,” noted Kemp.

In the future, Kemp plans to use this approach of combining multiple cell-based

experiments on a microfluidic chip to integrate single-cell information with

population-averaged techniques, such as multiplexed immunoassays or mass

spectrometry.

This project is supported in part by the National Institutes of Health

(NIH)(Grant No. R21CA134299). The content is solely the responsibility of the

principal investigator and does not necessarily represent the official views of

the NIH.