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Personalized Medicine And Cancer Care
By gdpawel at 2012-06-24 00:42
Personalized Medicine And Cancer Care

As we enter the era of "personalized" medicine, it is time to take a fresh look at how we evaluate treatments for cancer patients. More emphasis is needed matching treatment to the patient. Patients would certainly have a better chance of success had their cancer been chemo-sensitive rather than chemo-resistant, where it is more apparent that chemotherapy improves the survival of patients, and where identifying the most effective chemotherapy would be more likely to improve survival.

Findings presented at the 41st Annual Meeting of the European Society for Clinical Investigation in Uppsala, Sweden and the Annual Meeting of the American Assoication for Cancer Research (AACR) in San Diego, CA concluded that the functional cytometric profiling platform is relevant for the study of both "conventional" and "targeted" anti-neoplastic drug agents (anti-tumor and anti-angiogenic activity) in primary cultures of "fresh" human tumors.

Cell-based assays with "cell-death" endpoints can show disease-specific drug activity, are useful clinical and research tools for "conventional" and "targeted" drugs, and provide unique information complementary to that provided by "molecular" tests. There have been more than 25 peer-reviewed publications showing significant correlations between cell-death assay results and patient response and survival.

Many patients are treated not only with a "targeted" therapy drug like Tarceva, Avastin, or Tykerb, but with a combination of chemotherapy drugs. Therefore, existing DNA or RNA sequences or expression of individual proteins often examine only one compenent of a much larger, interactive process. The oncologist might need to administer several chemotherapy drugs at varying doses because tumor cells express survival factors with a wide degree of individual cell variability.

There is a tactic of using biopsied cells to predict which cancer treatments will work best for the patient, by taking pieces of live "fresh" tumor tissue, applying different chemotherapy treatments to it, and examining the results to see which drug or combination of drugs does the best job killing the tumor cells. An assay test with the functional cytometric profiling platform, using a cell-death endpoint, can help see what treatments will not have the best opportunity of being successful (resistant) and identify drugs that have the best opportunity of being successful (sensitive).

Funtional cytometric profiling measures the response of the tumor cells to drug exposure. Following this exposure, they measure both cell metabolism and cell morphology. The integrated effect of the drugs on the whole cell, resulting in a cellular response to the drug, measuring the interaction of the entire genome. No matter which genes are being affected, functional profiling is measuring them through the surrogate of measuring if the cell is alive or dead.

For example, the epidermal growth factor receptor (EGFR) is a protein on the surface of a cell. EGFR-inhibiting drugs certainly do target specific genes, but even knowing what genes the drugs target doesn't tell you the whole story. Both Iressa and Tarceva target EGFR protein-tyrosine kinases. But all the EGFR mutation or amplificaton studies can tell us is whether or not the cells are potentially susceptible to this mechanism of attack. They don't tell you if Iressa is better or worse than Tarceva or other drugs which may target this. There are differences. The drugs have to get inside the cells in order to target anything. So, in different tumors, either Iressa or Tarceva might get in better or worse than the other. And the drugs may also be inactivated at different rates, also contributing to sensitivity versus resistance.

As an example of this testing, researchers have tested how well a pancreatic cancer patient can be treated successfully with a combination of drugs commonly used to fight lung, pancreatic, breast, and colorectal cancers. The pre-test can report prospectively to a physician specifically which chemotherapy agent would benefit a cancer patient. Drug sensitivity profiles differ significantly among cancer patients even when diagnosed with the same cancer.

The funtional profiling technique makes the statistically significant association between prospectively reported test results and patient survival. It can correlate test results that are obtained in the lab and reported to physicians prior to patient treatment, with significantly longer or shorter overall patient survival depending upon whether the drug was found to be effective or ineffective at killing the patient's tumor cells in the laboratory.

This could help solve the problem of knowing which patients can tolerate costly new treatments and their harmful side effects. These "smart" drugs are a really exciting element of cancer medicine, but do not work for everyone, and a pre-test to determine the efficacy of these drugs in a patient could be the first crucial step in personalizing treatment to the individual.

Literature Citation:Functional profiling with cell culture-based assays for kinase and anti-angiogenic agents Eur J Clin Invest 37 (suppl. 1):60, 2007Functional Profiling of Human Tumors in Primary Culture: A Platform for Drug Discovery and Therapy Selection (AACR: Apr 2008-AB-1546)



8 comments | 4945 reads

by gdpawel on Sun, 2012-06-24 00:58
Pharmacogenomics can be defined as the study of how a person's genetic makeup determines response to a drug. Whether a medicine works well for you or whether it causes serious side effects, depends, to a certain extent, on your genes.

A challenge facing pharmacogenomic profiling in cancer cell lines is the number and complexity of interactions a drug has with biological molecules in the body. Variations in many different molecules may influence how someone responds to a medicine. Teasing out the genetic patterns associated with particular drug responses involves some intricate and time-consuming scientific detective work.

DNA is not the whole story. Genomics has provided sophisticated target therapies, but cellular pathways contain redundancies that can be activated in response to inhibition of one or another pathway, thus promoting emergence of resistant cells and clinical relapse.

Cancer cells utilize cross-talk and redundancy to circumvent targeted therapies. They back up, zig-zag and move in reverse, regardless of what the sign posts say. Using genomic signatures to predict response is like saying the Dr. Seuss and Shakespeare are truly the same because they use the same words.

The building blocks of human biology are carefully construed into the complexities that we recognize as human beings. However, appealing genotyping analysis may appear to those engaged in this field, it will be years before these profiles can approximate the vagaries of human cancer.

The endpoints genotyping analysis are gene expression, examining a single process (pathway) within the cell or a relatively small number of processes (pathways) to test for "theoretical" candidates for targeted therapy.

The endpoints of phenotyping analysis are expression of cell-death, both tumor cell-death and tumor associated endothelial (capillary) cell-death (tumor and vascular death), and examines not only for the presence of the molecular profile, but also for its functionality, the interaction with other genes, proteins and other processes occurring within the cell, and for its "actual" response to anti-cancer drugs (not theoretical susceptibility).

Phenotyping analysis measures biological signals rather than DNA indicators, provides clinically validated information and plays an important role in cancer drug selection. The data that support phenotyping analysis is demonstrably greater and more compelling than any data currently generated from genotyping analysis.

Phenotyping measures the response of the tumor cells to drug exposure. Following this exposure, it measures both cell metabolism and cell morphology. The integrated effect of the drugs on the whole cell, resulting in a cellular response to the drug, measuring the interaction of the entire genome. No matter which genes are being affected, it is measuring them through the surrogate of measuring if the cell is alive or dead.

We don't know how to handle one gene, never mind 20,000 genes. To put this in context, two percent of the human genome that codes for known proteins (the part that everyone currently studies) represents only 1/20 of the whole story.

It's not just PCR and the microarrays, the whole concept of using molecular "signatures" of any kind to do anything beyond the most straightforward of cases (i.e. single gene mutations, etc.) is so flawed that everyone should have seen the problems at the beginning.

The reason why no one seemingly sees it now can be explained by the facts that the technology itself is so elegant and beautiful. But a beautiful biological technology is do different than a beautiful computer technology - it's not worth much without some very good applications ("apps") and we will confront one targeted drug after another.

A more highly productive direction would be to investigate the targeting agents in each individual patient's tissue culture, alone and in combination with other drugs, to guage the likelihood that the targeting will favorably influence each patient's outcome.

The need for phenotyping analysis has never been greater. As systems biologists point out, complexity is the hallmark of biological existence. Any attempts to oversimplify phenomena that cannot be simplified, have, and will continue to lead us in the wrong direction.

Systems Biology Is The Future Of Medical Research

[url]http://cancerfocus.org/forum/showthread.php?t=3473

by gdpawel on Mon, 2012-08-13 11:20
Robert Nagourney, M.D., PhD., one of the pioneers of cell culture assays, has often described his personal misgivings surrounding the application of gene profiles for the prediction of response to therapeutics. His initial concerns regarded the oversimplification of biological processes and the attempt of analyte-driven investigators to ascribe linear pathways to non-linear events.

The complexities of human tumor biology took a turn toward the incomprehensible with the publication of a lead article in Nature by the group from Harvard under Dr. Pier Paulo Pandolfi. Dr. Nagourney sat in as Dr. Pandolfi reviewed his work during the Pezcoler Award lecture, held Monday, April 4, 2011, in Orlando at the AACR meeting.

What Dr. Pandolfi’s group found was that gene regulation is under the control of messenger RNA (mRNA) that are made both by coding regions and non-coding regions of the DNA. By competing for small interfering RNAs (siRNA) the gene and pseudogene mRNAs regulate one another. That is to say that RNA speaks to RNA and determines what genes will be expressed.

To put this in context, Dr. Pandolfi’s findings suggest that the 2 percent of the human genome that codes for known proteins (the part that everyone currently studies) represents only 1/20 of the whole story. One of the most important cancer related genes (PTEN), is under the regulation of 250 separate, unrelated genes. Thus, PTEN, KRAS and all genes, are under the direct regulation and control of genetic elements that no one has ever studied.

This observation represents one more nail in the coffin of unidimensional thinkers who have attempted to draw straight lines from genes to functions. This further suggests that attempts on the part of gene profilers to characterize patients likelihoods of response based on gene mutations are not only misguided but, may actually be dishonest.

The need for phenotype analyses like the functional cytometric profiling performed at laboratories like Rational Therapeutics, Inc. and Weisenthal Cancer Group, has never been greater. As the systems biologists point out, complexity is the hallmark of biological existence. Attempts to oversimplify phenomena that cannot be simplified, have, and will continue to, lead us in the wrong direction.

Dr. Larry Weisenthal, one of the pioneers of functional cytometric profiling analysis, has described the use of RT-PCR and DNA microarrays in personalized oncology as analogous to the introduction of the personal computer. Dazzling hardware in search of a killer application. This was wonderful technology and the geekiest of people bought them and played with them, but they really didn’t start to do anything for a mass market until the introduction of the first killer application, which was a spreadsheet program called Visicalc.

So what research scientists in universities and cancer centers have been doing for the past ten years is to try and figure out a way to use this dazzling technology to look for patterns of gene expression which correlate with and predict for the activity of anticancer drugs. Hundreds of millions of dollars have been spent on this effort. Objectively speaking, it’s like the emperor’s new clothes. So far, a qualified failure.

Academics are besides themselves over the promise of the new technology. It seems so cool that it simply must be good for something. How about in the area of identifying drugs which will work in individual patients? It has been a major bust by whatever standard you choose to apply. Objectively, if you compare and contrast the peer-reviewed medical literature supporting the use of functional cytometric profiling for personalizing drug selection versus the correspond literature supporting molecular profiling, the literature supporting functional profiling wins.

Literature Citation: Poliseno, L., et al. 2010. A coding-independent function of gene and pseudogene mRNAs regulates tumor biology. Nature. 2010 Jun 24; 465(7301):1016-7.)

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