Lung Metagene Predictor

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Lung Metagene Predictor

by Gdpawel on Tue Apr 03, 2007 12:00 AM

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The new genomic test - Lung Metagene Predictor - is supposed to tell physicians which lung cancer patients will benefit from chemotherapy and which ones do not need to be unnecessarily exposed to toxic chemotherapy cocktails.

The test doesn't predict which patients will benefit from chemotherapy (i.e. which patients are chemosensitive). Rather, it's like the Oncotype Dx test, which identifies patients who are unlikely to have a recurrence if treated with surgery alone. If you aren't going to have a recurrence, you don't need chemotherapy.

The test doesn't do anything to indicate if chemotherapy would or would not be helpful for those patients at higher risk for recurrence, much less which chemotherapy would be most likely to be helpful. Also, the test has a 10% false reassurance rate (10% of the good prognosis patients none the less recurred).

A genomic test can help to find out if a cancer patient will benefit from chemotherapy or not, and if they do, Whole Cell Profiling can help see what treatments have the best opportunity of being successful. Other tests, such as those which identify DNA, or RNA sequences or expression of individual proteins often examine only one component of a much larger, interactive process.

Whole Cell Profiling (via Cell Function Analysis) 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, Whole Cell 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.

It doesn'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.

One of the most promising new approaches that may deal with early detection of cancer is called Proteomics (Protein Expression Analysis), the study of proteins in the cells, tissues and body fluids. Even before a tumor can be felt, some researchers have found, the tumor begins secreting a distinctive pattern, or fingerprint of proteins. Here, you go beyond genes (DNA, the Genomic Analysis or structure of the human genome) and beyond Gene Expression (the measure of RNA content, like Her2/neu in breast cancer) to measure the actual proteins themselves.

Genomic Analysis is only important insofar as it influences Gene Expression Analysis, which is only important insofar as it influences Protein Expression Analysis (Proteonomics), which is only important insofar as it influences Protein Function Analysis (are proteins active or inactive), which is only important insofar as it influences Cell Function Analysis (cell culture testing), which is only important insofar as it influences Disease Analysis (doing something to treat the patient and then making a measurement on the patient with CT/PET scanning), in that order. There is an inverse hierachy between relevance and ease of measurement.

There are many pathways to altered cellular (forest) function (hence all the different "trees" which correlate in different situations). It serves to validate Whole Cell Profiling. The forest is looked at, and not the trees. Whole Cell Profiling measures what happens at the end (the effects on the forest), rather than the status of the individual trees. Cancer is a complex disease and needs to be attacked on many fronts. The best thing to do is to combine these different tests in ways which make the most sense. The future of cancer therapy will be personalized treatments for individual patients, and will require a combination of novel diagnostics and therapeutics.

Improving cancer patient diagnosis and treatment through a combination of cellular and gene-based testing will offer predictive insight into the nature of an individual's particular cancer and enable oncologists to prescribe treatment more in keeping with the heterogeneity of the disease. The biologies are very different and the response to given drugs is very different.

Gene-Expression Signatures in Lung Cancer: Not Ready Yet

by Gdpawel on Thu May 12, 2011 08:37 PM

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It was the hope is that any patient with cancer would have their tumor biopsied and profiled. The profile would then be displayed as a unique genetic signature, which would in turn predict which therapy is most likely to work. However.....

Gene-Expression Signatures in Lung Cancer: Not Ready Yet

Roxanne Nelson - Medscape Medical News

March 17, 2010 — The identification of prognostic markers could assist in the clinical management of nonsmall-cell lung cancers (NSCLC). Although molecular profiling of tumors has led to the identification of gene-expression patterns, a new review has found "little evidence" that any of the signatures are ready for use in the clinical setting.

In addition, the researchers reported that they found "serious problems in the design and analysis of many of the studies" that were included in their review, published online March 16 in the Journal of the National Cancer Institute.

Even in its earliest stages, lung cancer has a very high recurrence rate and mortality, the authors note. Current clinical staging techniques have limitations in terms of predicting recurrence and guiding treatment, but the ability to identify new molecular targets using techniques such as microarray-based gene-expression profiling has the potential to improve patient care.

Inconclusive Results Thus Far

Studies have reported mixed results. As previously reported by Medscape Oncology, one recent review article found that gene-expression profiling failed to outperform standard histologic examinations. However, another study reported that a "5-gene signature" was closely associated with relapse-free and overall survival among patients with NSCLC.

More recently, at the 2010 Joint Conference on Molecular Origins of Lung Cancer, researchers reported that a mutated epidermal growth-factor receptor (EGFR) gene signature was a validated therapeutic target in NSCLC, and suggested that this gene signature might provide "predictive value and biological insights" into EGFR inhibitor responses in lung adenocarcinomas.

For the current review, Jyothi Subramanian, PhD, and Richard Simon, DSc, from the Biometric Research Branch at the National Cancer Institute in Bethesda, Maryland, conducted a literature search of studies published from 2002 to 2009 to critically evaluate studies that reported prognostic gene-expression signatures in NSCLC.

Little Evidence of Gene Signatures

The authors selected 16 studies as being most relevant, and closely assessed them for a number of criteria, including the appropriateness of the study design, the statistical validation of the prognostic signature on independent datasets, the presentation of results in an unbiased manner, and the demonstration of medical utility for the new signature beyond that obtained using existing treatment guidelines.

They noted that one of the "striking findings" is that none of the studies succeeded in showing that gene-expression signatures had better predictive power "over and above known risk factors." In fact, they note, the majority of the risk factors outlined by the National Comprehensive Cancer Network (NCCN) guideline were not even considered by most of the studies they reviewed.

For example, the extent of residual tumor after resection is the most important variable, after stage, when making decisions about adjuvant chemotherapy, according to the NCCN guideline. But only 7 of the studies stated that completeness of resection was a criterion for patient selection.

Drs. Subramanian and Simon point out that "the most important medical question that needs to be answered by a new prognostic signature in NSCLC is whether it can identify the subset of stage IA patients who might benefit from adjuvant chemotherapy." But only 2 studies in their survey included validation results for this subpopulation.

The majority of papers presented overall validation results for stage I patients, and some of the signatures were successful in identifying high-risk stage I patients. However, whether or not the signature was better at predicting overall survival than tumor size or other standard risk factors was not adequately addressed and was unclear from most of these studies, the authors report. Only 1 study, they note, reported a marginal improvement in the predictive accuracy for their gene-expression signature, compared with tumor size, for stage I patients

Another important medical need is the ability to identify the subset of stage IB and stage II patients who are at a low risk for disease recurrence without chemotherapy, the authors explain. But only one of the studies presented separated validation results for this subgroup of patients; a second study was the only one that reported the statistical significance of the prognostic signature for validation in stage II samples. The lack of predictiveness for stage II patients could be the result of the small number of such patients in the study samples, they note.

Most of the studies presented validation results on data that were not used for developing the predictive signatures.

"Most of the studies presented validation results on data that were not used for developing the predictive signatures," they write; in addition, "none of the 16 studies reviewed adequately addressed the question of the predictive power that could be attained by using easily measurable clinicopathological factors for stage I samples."

On the basis of their observations and analyses, the authors suggest a set of guidelines to aid the design, analysis, and evaluation of prognostic gene-expression studies, with a focus on NSCLC.

"Clinical validity of a prognostic signature implies demonstrating that the test result correlates with clinical outcome," they write, whereas "medical utility of a prognostic signature means that the test result is actionable, leading to patient benefit."

Therefore, the ultimate test of clinical validity for a prognostic signature is how well it performs in a prospective clinical trial. Several such trials are currently underway, including the CALGB 30506 trial that was recently initiated to clinically test the lung metagene prognostic signature in lung cancer, the authors point out.

"Regardless of clinical validation, unless a new prognostic signature provides additional risk stratification within the stage and risk-factor groupings on which current treatment guidelines are based, its broad acceptance in medical practice is unlikely," the authors conclude.

J Natl Cancer Inst. Published online March 16, 2010

Gene-Guided Chemotherapy Research Questioned

by Gdpawel on Thu May 12, 2011 08:41 PM

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Gene-Guided Chemotherapy Research Questioned as Three NCI Trials Are Halted

July 27, 2010

Three ongoing cancer trials funded by the National Cancer Institute have been suspended after the validity of the technology being used was called into question by a large group of US scientists.

Developed at Duke University, the technology now under question uses gene signatures to predict responses to chemotherapy. Two of the trials involve patients with non-small cell lung cancer (NCT00545948 and NCT00509366), and the third is in patients with breast cancer (NCT00636441).

The trials were suspended on July 22 and 23.

The move was made after a group of 31 scientists called on the National Cancer Institute to suspend the trials because of concerns over the prediction models that were being used. The models were developed on the basis of research reported by Anil Potti, MD, and Joseph Nevins, PhD, from Duke University, Durham, North Carolina, but the validity of those models has been questioned by peer-reviewed reanalyses of their work, the scientists note.

In a letter dated July 19 and addressed to the new National Cancer Institute director, Harold Varmus, MD, the group of researchers called for the trials to be suspended until a "fully independent review is conducted of both the clinical trials and of the evidence and predictive models being used to make cancer treatment decisions."

At the same time, one of the Duke scientists involved in developing the technology has been suspended from his place of work. Dr. Potti was placed on administration leave while the university investigates allegations that he had falsely claimed to be Rhodes scholar, according to a report in the New York Times.

In addition, one of the published papers that reported this technology has now come under scrutiny. The Lancet Oncology has issued an "expression of concern" over a paper published in the journal in 2007, which described the validation of gene signatures to predict the response of breast cancer to neoadjuvant chemotherapy (Lancet Oncol. 2007;8:1071-1078).

That research was praised by an independent expert contacted by Medscape Medical News at the time, as it showed for the first time that gene signatures could predict responses to individual chemotherapy regimens.

However, since its publication in 2007, the methodology used to generate the response predictions has been questioned by statisticians from the M.D. Anderson Cancer Center in Houston, Texas, the journal notes.

The Lancet Oncology was contacted by senior author Richard Iggo, PhD, from the Swiss Institute for Experimental Cancer Research in Epalinges, Switzerland, and first author Herv Bonnefoi, MD, from the Institut Bergoni, University of Bordeaux, France. They "expressed grave concerns about the validity of their report in light of evolving events," and said they had repeatedly tried to contact their coauthors at Duke University (including Dr. Potti) without success.

The journal notes that the 15 European coauthors of the paper concur with the "expression of concern" notice that the journal has posted online and said that the 4 coauthors from Duke University have been contacted separately.

Controversy Surrounding Dr. Anil Potti and Duke University

The controversy surrounding Dr. Potti and his team's research at Duke University is outlined in exhaustive detail in a report published in the July 16 issue of The Cancer Letter. This publication found Dr. Potti's false claim of being Rhodes scholar in multiple grant applications submitted by him, and notes that the claim was also featured in a Duke newsletter in January 2007. However, this credential "disappeared" from Dr. Potti's biography later in 2007. The publication also found mentions of 2 other awards that it was unable to verify.

In addition to questions about Dr. Potti's credentials, The Cancer Letter notes that research coming out of his group has been "marred by corrections and even corrections of corrections," and points out that "errors in genomics research could have direct implications for patients."

Dr. Potti is considered to be a pioneer of personalized medicine because of his team's work on using gene signatures to predict responses to chemotherapy, and he has been featured in Duke University commercials aimed at the general public, the publication notes.

However, this work has been questioned by other scientists, it points out.

Two biostatisticians at the M.D. Anderson Cancer Center, Keith Baggerley, PhD, and Kevin Coombes, PhD, attempted to verify this work but found a series of errors, including mislabeling and mismatching of gene probe identifiers. They published their findings in November 2009 in the Annals of Applied Statistics (2009;3:1309-1334) and concluded: "Unfortunately, poor documentation can shift from an inconvenience to an active danger when it obscures not just methods but errors."

The biostaticians also suggested that the errors they found in the technology which was being used in ongoing clinical trials to allocate patients to treatment group may be putting patients at risk.

The Cancer Letter reports that as a result of that publication, Duke University temporarily suspended 3 clinical trials that were using gene signatures to assign patients to treatment these are the same 3 trials that were suspended again a just few days ago.

However, even though Duke suspended those trials in October 2009, they were restarted again in January 2010 after an internal investigation by Duke's Institutional Review Board confirmed the research and concluded that this approach was "viable and likely to succeed."

When contacted by The Cancer Letter and shown documents obtained under the Freedom of Information Act, the 2 statisticians from M.D. Anderson who had questioned the technology said they were not satisfied by the internal review. "Duke's statement implies that other members of the scientific community should be able to replicate the reported results with the data available," they told the publication. "Having tried, we can confidently state that this is not yet true."

The letter to the National Cancer Institute from the group of 31 scientists, which comprises many professors of statistics and biostatistics from prestigious US universities, including Johns Hopkins, Harvard, and Princeton, refers both to the Annals of Applied Statistics paper and The Cancer Letter reports.

"It is absolutely premature to use these prediction models to influence the therapeutic options open to cancer patients," the letter says, as independent experts have been unable to substantiate the researchers' claims using the researchers' own data. If the data and analysis can be validated, then it would be appropriate to reinitiate the trials, but until then, suspension of the ongoing trials is necessary, "given the potential of patients being assigned to improper treatment arms...[and] the associated potential risk posed to these patients."

Has Molecular Profiling Come of Age?

by Gdpawel on Sun May 15, 2011 04:35 AM

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Since the new millenium there has been the increasing acceptance of the concept that cancer is a very heterogenous disease and that it would be a good thing to try and individualize treatment. Oncologists are increasingly open to the concept of personalized therapy. Driving this change has been the success of a few drugs which target specific molecular targets within cancer cells. For instance, Gleevec in a relatively rare disease called chronic myelogenous leukemia (CML). Herceptin, which targets a mutation present in some patients with breast cancer. Iressa and Tarceva, which help some patients with a mutation in lung cancer. It has become routine to test breast cancer patients for the mutation conferring sensitivity to Herceptin. It is becoming routine to test lung cancer patients for the mutation conferring sensitivity to Iressa and Tarceva. When a tumor has certain KRAS mutations, the partially effective colon cancer drug Erbitux, is very unlikely to work. So we've have Her2 testing for predicting Herceptin activity in breast cancer. EGFR mutation testing to predict for Iressa and Tarceva (two different flavors of the same, similar type of drug) in lung cancer. KRAS mutation to predict for Erbitux in colon cancer. Of course, this leaves out the three dozen other drugs and a myriad of drug combinations, which may often be even more effective in each of these diseases, and leaves out virtually all of the other forms of cancer. Beyond this, there have been attempts to develop molecular-based tests to examine a broader range of chemotherapeutic drugs. New technologies for measuring the expression (biological activity) of literally hundreds to thousands of genes as part of a single test. There are two main technologies involved: RT-PCR (reverse transcription polymerase chain reaction) and DNA microarray. Dr. Larry Weisenthal, one of the pioneers of functional 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 profiling for personalizing drug selection versus the correspond literature supporting molecular profiling, the literature supporting functional profiling wins (big time!). The scientist who reported the best results with molecular profiling (Dr. Anil Potti of Duke University) has recently been accused of fraud and his clinical trials have been suspended.
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