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Patient-Specific Cancer Cell Lines Designed To Predict Chemotherapy Sensitivity

gdpawel's picture
Posts: 545
Joined: May 2001






In one experiment, they successfully pinpointed the two anticancer drugs from among more than 3,000 that were the most effective in killing cells in one of the pancreatic cancer cell lines. A report on the success was published online recently in the journal Clinical Cancer Research. 











Citation: Johns Hopkins Medicine. "Patient-Specific Cancer Cell Lines Designed To Predict Chemotherapy Sensitivity." Medical News Today. MediLexicon, Intl., 19 Feb. 2013

gdpawel's picture
Posts: 545
Joined: May 2001

Patient-Specific Biomarker Information to Generate a Treatment Approach

Cell-lines (as described in the article above) are useful for experimentation in labs as they are always available to researchers as a product and do not require harvesting (acquiring of tissue from a host) every time cells are needed in the lab.

Problem is, cell-lines don't recapitulate drug response patterns which exist in the body. For drug selection, it is better to directly remove tumor "microclusters" straight from the body and immediately test them, before they change.

The problem with using cell-lines is that they do not predict for disease or patient specific drug effects. If you can kill ovarian cancer cell-lines with a given drug, it doesn't tell you anything about how the drug will work in real world, clinical ovarian cancer (real-world conditions).




In regards to tissue samples injured by shipping to a lab, compromising test results, what what cell culture assaysists do is they start with a piece of tumor, chop it into little pieces, digest it with collagenase, which digests the connective tissue strands which may be present.

This releases little microclusters of tumor cells, containing between 5 cells and 500 cells. These little microclusters also contain tiny capillary (endothelial) cells, running throughout the microcluster. Thus, this is "native" tumor and "native" tumor architecture. There is no growth. It is precisely as it existed within the body.

These microclusters are plated into medium on polypropylene, which does not allow them to stick to the bottom and spread out and grow. They remain just as they were (although there is a strong tendency for these microclusters to aggregate, basically grab hold of one another, so the microclusters get bigger, but this is usually not owing to cell growth but rather to cell aggregation).

Anyway, drugs are added immediately, before the cells and cultures have had the chance to change in any way. When the drugs work, they trigger apoptosis and the cells die and they detect cell death 96 hours later.

In regards to some companies offering commercial tests on surgically removed tumors using a small number of anticancer drugs, there are private laboratories that have been offering "cell-death" functional profiling assays as a non-investigational, paid service to cancer patients, in a situation where up to 30 different drugs and combinations are tested, at two drug concentrations in three different assay systems.

Another problem with cell-line technology is that cancer doesn't grow too much, it dies too little. This is why old cell-growth-based assays didn't work and why cell-death-based assays do.

The concern should be in killing the cancer cell. When the cancer cell is so damaged that it can not longer perform its duty, it will die. Scientists know that cancer is not a disease in which cells grow too abundantly, but the failure of cells to expire at their appointed time. Most basic cell biologists acknowledge that cancer is characterized by a failure of programmed cell death (apoptosis).

One way to look at programmed cell death, is with cell-death assays utilizing functional profiling, which use apoptotic endpoints (point of termination) and also a number of other indicators of programmed cell death. The advantage of this is that they are more reflective of chemotherapy’s actual effects in the human body. Genetic testing can’t and doesn’t do this.

Source: Cell Function Analysis

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