Functional Profiling to Select Chemotherapy in Advanced or Metastatic NSCLC
Functional profiling using ex-vivo analysis of programmed cell death (EVA/PCD) doubles the response rate and improves time-to-progression and survival in patients with advanced lung cancer, according to a Phase II clinical trial conducted by investigators at Rational Therapeutics and the MemorialCare Todd Cancer Institute (Long Beach, CA) and published in the October issue of Anticancer Research.
"Medical oncologists have long pursued methods that can match patients to available therapies," said Dr. Robert Nagourney, lead investigator. "This study confirms the ability of a laboratory test to accurately predict drug activity for individual patients."
Functional profiling provides a window into the dynamic process by which human tumor cells respond to therapy. By capturing cells within their natural microenvironment, human biology is recreated in the laboratory.
The article, titled "Functional Profiling to Select Chemotherapy in Untreated, Advanced or Metastatic Non-Small Cell Lung Cancer," describes results achieved in patients who received first-line chemotherapy based on their individual ex-vivo analysis.
Using only FDA-approved, standard lung cancer drugs available to all oncologists, this process of laboratory selection provided a 64.5 percent response rate - more than double the national average of 30 percent (p = 0.00015), well established in the literature. More importantly, the median overall survival of 21.3 months was nearly two-fold longer than the best results of 13.5 months reported for non-assay based standard treatments. Strikingly, among the Stage IV (metastatic) patients, there are several who remain alive approaching eight years since diagnosis.
"These results suggest that laboratory selection of chemotherapy can change the natural history of this lethal disease," said Dr. Nagourney. "What makes the EVA/PCD approach unique is its capacity to capture human tissues in their native state, recreating conditions found in the human body."
Attempts to use gene profiling in this disease resulted in failure and controversy ("How Bright Promise In Cancer Testing Fell Apart." - Gina Kolata, New York Times, July 7, 2011). Contrary to gene-based methods, functional platforms capture the systems biology of human tumors in real-time providing therapeutic insights that translate directly into improved clinical outcomes.
Source: Rational Therapeutics
Comments
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Thanks for sharing.
Always looking for hope. Thank you. Let's keep this thread near the top for awhile. Rick.0 -
Type I ErrorToBeGolden said:Thanks for sharing.
Always looking for hope. Thank you. Let's keep this thread near the top for awhile. Rick.
Something else Dr. Nagourney pointed out about this study. Scientific proof is rarely proof, but instead our best approximation. Beyond death and taxes, there are few certainties in life. That is why investigators rely so heavily on statistics.
Statistical analyses enable researchers to establish “levels” of certainty. Reported as “p-values,” these metrics offer the reader levels of statistical significance indicating that a given finding is not simply the result of chance. To wit, a p-value equal to 0.1 (1 in 10) means that the findings are 90 percent likely to be true with a 10 percent error. A p-value of 0.05 (1 in 20) tells the reader that the findings are 95 percent likely to be true. While a p-value equal to 0.01 (1 in 100) tells the reader that the results are 99 percent likely to be true. For an example in real time, we are just reporting a paper in the lung cancer literature that doubled the response rate for metastatic disease compared with the national standard. The results achieved statistical significance where p = 0.00015. That is to say, that there is only 15 chances out of 100,000 that this finding is the result of chance.
Today, many laboratories offer tests that claim to select candidates for treatment. Almost all of these laboratories are conducting gene-based analysis. While there are no good prospective studies that prove that these genomic analyses accurately predict response, this has not prevented these companies from marketing their tests aggressively. Indeed, many insurers are covering these services despite the lack of proof.
So let’s examine why these tests may encounter difficulties now and in the future. The answer to put it succinctly is Type I errors. In the statistical literature, a Type I error occurs when a premise cannot be rejected. The statistical term for this is to reject the “null” hypothesis. Type II errors occur when the null hypothesis is falsely rejected.
Example: The scientific community is asked to test the hypothesis that Up is Down. Dedicated investigators conduct exhaustive analyses to test this provocative hypothesis but cannot refute the premise that Up is Down. They are left with no alternative but to report according to their carefully conducted studies that Up is Down.
The unsuspecting recipient of this report takes it to their physician and demands to be treated based on the finding. The physician explains that, to his best recollection, Up is not Down. Unfazed the patient, armed with this august laboratory’s result, demands to be treated accordingly. What is wrong with this scenario? Type I error.
The human genome is comprised of more than 23,000 genes: Splice variants, duplications, mutations, SNPs, non-coding DNA, small interfering RNAs and a wealth of downstream events, which make the interpretation of genomic data highly problematic. The fact that a laboratory can identify a gene does not confer a certainty that the gene or mutation or splice variant will confer an outcome. To put it simply, the input of possibilities overwhelms the capacity of the test to rule in or out, the answer.
Yes, we can measure the gene finding, and yes we have found some interesting mutations. But no we can’t reject the null hypothesis. Thus, other than a small number of discreet events for which the performance characteristics of these genomic analyses have been established and rigorously tested, Type I errors undermine and corrupt the predictions of even the best laboratories. You would think with all of the brainpower dedicated to contemporary genomic analyses that these smart guys would remember some basic statistics.0 -
The Biomarker-based Paradigmgdpawel said:Type I Error
Something else Dr. Nagourney pointed out about this study. Scientific proof is rarely proof, but instead our best approximation. Beyond death and taxes, there are few certainties in life. That is why investigators rely so heavily on statistics.
Statistical analyses enable researchers to establish “levels” of certainty. Reported as “p-values,” these metrics offer the reader levels of statistical significance indicating that a given finding is not simply the result of chance. To wit, a p-value equal to 0.1 (1 in 10) means that the findings are 90 percent likely to be true with a 10 percent error. A p-value of 0.05 (1 in 20) tells the reader that the findings are 95 percent likely to be true. While a p-value equal to 0.01 (1 in 100) tells the reader that the results are 99 percent likely to be true. For an example in real time, we are just reporting a paper in the lung cancer literature that doubled the response rate for metastatic disease compared with the national standard. The results achieved statistical significance where p = 0.00015. That is to say, that there is only 15 chances out of 100,000 that this finding is the result of chance.
Today, many laboratories offer tests that claim to select candidates for treatment. Almost all of these laboratories are conducting gene-based analysis. While there are no good prospective studies that prove that these genomic analyses accurately predict response, this has not prevented these companies from marketing their tests aggressively. Indeed, many insurers are covering these services despite the lack of proof.
So let’s examine why these tests may encounter difficulties now and in the future. The answer to put it succinctly is Type I errors. In the statistical literature, a Type I error occurs when a premise cannot be rejected. The statistical term for this is to reject the “null” hypothesis. Type II errors occur when the null hypothesis is falsely rejected.
Example: The scientific community is asked to test the hypothesis that Up is Down. Dedicated investigators conduct exhaustive analyses to test this provocative hypothesis but cannot refute the premise that Up is Down. They are left with no alternative but to report according to their carefully conducted studies that Up is Down.
The unsuspecting recipient of this report takes it to their physician and demands to be treated based on the finding. The physician explains that, to his best recollection, Up is not Down. Unfazed the patient, armed with this august laboratory’s result, demands to be treated accordingly. What is wrong with this scenario? Type I error.
The human genome is comprised of more than 23,000 genes: Splice variants, duplications, mutations, SNPs, non-coding DNA, small interfering RNAs and a wealth of downstream events, which make the interpretation of genomic data highly problematic. The fact that a laboratory can identify a gene does not confer a certainty that the gene or mutation or splice variant will confer an outcome. To put it simply, the input of possibilities overwhelms the capacity of the test to rule in or out, the answer.
Yes, we can measure the gene finding, and yes we have found some interesting mutations. But no we can’t reject the null hypothesis. Thus, other than a small number of discreet events for which the performance characteristics of these genomic analyses have been established and rigorously tested, Type I errors undermine and corrupt the predictions of even the best laboratories. You would think with all of the brainpower dedicated to contemporary genomic analyses that these smart guys would remember some basic statistics.
The biomarker-based paradigm will require us to consider the level of evidence necessary to declare true activity. Daniel J. Sargent, PhD, Professor of Cancer Research at the Mayo Clinic, tells us that it may become impossible to perform traditional trials with requirements to achieve a P-value less than 0.05, high statistical power, and an OS advantage.
When the patient population becomes small, we’re going to have to consider either other endpoints or other statistical philosophies. Should we use a Bayesian strategy, in which we borrow information from other clinical trials to help make decisions? Or do we loosen the P-value requirements, that a P-value of less than 0.1 or 0.2, for example, be considered a sufficient level of evidence for activity?
These are active areas of research that need to be fully considered as we enter this era of truly personalized therapy with patient populations that are becoming smaller and smaller. I do know that the Bayesian method is no stranger to the functional profiling platform. It’s what gives credit to the accuracy of the assay tests.
The absolute predictive accuracy of cell culture assay tests varies according to the overall response rate in the patient population, in accordance with Bayesian principles. The actual performance of assays in each type of tumor precisely match predictions made from Bayes’ Theorem.
Bayes’ Theorem is a tool for assessing how probable evidence makes some hypothesis. It is a powerful theorem of probability calculus which is used as a tool for measuring propensities in nature rather than the strength of evidence (Solving a Problem in the Doctrine of Changes).
Daniel J. Sargent, PhD, is the Ralph S. and Beverly E. Caulkins Professor of Cancer Research at the Mayo Clinic Cancer Center in Rochester, Minnesota, and Group Statistician for the Alliance for Clinical Trials in Oncology. "Commentary on clinical endpoints, validation of surrogate endpoints and biomarkers in oncology clinical trials."0 -
Systems Biology Comes of Age: Lung Cancer in the Crosshairsgdpawel said:The Biomarker-based Paradigm
The biomarker-based paradigm will require us to consider the level of evidence necessary to declare true activity. Daniel J. Sargent, PhD, Professor of Cancer Research at the Mayo Clinic, tells us that it may become impossible to perform traditional trials with requirements to achieve a P-value less than 0.05, high statistical power, and an OS advantage.
When the patient population becomes small, we’re going to have to consider either other endpoints or other statistical philosophies. Should we use a Bayesian strategy, in which we borrow information from other clinical trials to help make decisions? Or do we loosen the P-value requirements, that a P-value of less than 0.1 or 0.2, for example, be considered a sufficient level of evidence for activity?
These are active areas of research that need to be fully considered as we enter this era of truly personalized therapy with patient populations that are becoming smaller and smaller. I do know that the Bayesian method is no stranger to the functional profiling platform. It’s what gives credit to the accuracy of the assay tests.
The absolute predictive accuracy of cell culture assay tests varies according to the overall response rate in the patient population, in accordance with Bayesian principles. The actual performance of assays in each type of tumor precisely match predictions made from Bayes’ Theorem.
Bayes’ Theorem is a tool for assessing how probable evidence makes some hypothesis. It is a powerful theorem of probability calculus which is used as a tool for measuring propensities in nature rather than the strength of evidence (Solving a Problem in the Doctrine of Changes).
Daniel J. Sargent, PhD, is the Ralph S. and Beverly E. Caulkins Professor of Cancer Research at the Mayo Clinic Cancer Center in Rochester, Minnesota, and Group Statistician for the Alliance for Clinical Trials in Oncology. "Commentary on clinical endpoints, validation of surrogate endpoints and biomarkers in oncology clinical trials."
Systems Biology Comes of Age: Metastatic Lung Cancer in the Crosshairs
Robert A. Nagourney, M.D.
Cancer therapists have long sought mechanisms to match patients to available therapies. Current fashion revolves around DNA mutations, gene copy and rearrangements to select drugs. While every cancer patient may be as unique as their fingerprints, all of the fingerprints on file with the federal AFIS (automated fingerprint identification system) database don’t add up to a hill of genes (pun intended), if you can’t connect them to the criminal.
To continue the analogy, it doesn’t matter why the individual chose a life of crime, his upbringing, childhood traumas or personal tragedies. What matters is that you capture him in the flesh and incarcerate him (or her, to be politically correct).
The term we apply to the study of cancer, as a biological phenomenon is “systems biology.” This discipline strikes fear into the heart of molecular biologists, for it complicates their tidy algorithms and undermines the artificial linearity of their cancer pathways. We frequently allude to the catchphrase, genotype ≠ phenotype, yet it is the cancer phenotype that we must confront if we are to cure this disease.
Using a systems biology approach, we applied the ex-vivo analysis of programmed cell death (functional profiling) to the study of previously untreated patients with non-small cell lung cancer. Tissue aggregates isolated from their surgical specimens were studied in their native state against drugs and signal transduction inhibitors. This methodology captures all of the interacting “systems,” as they respond to cytotoxic agents and growth factor withdrawal. The trial was powered to achieve a two-fold improvement in response.
At interim analysis, we had more than accomplished our goal. The results speak for themselves.
First: a two-fold improvement in clinical response – from the national average of 30 percent we achieved 64.5 percent (p – 0.00015).
Second: The median time to progression was improved from 6.4 to 8.5 months.
Third: And most importantly the median overall survival was improved from an average of 10 – 12 months to 21.3 months, a near doubling.
These results, from a prospective clinical trial in which previously untreated lung cancer patients were provided assay directed therapy, reflects the first real time application of systems biology to chemotherapeutics. The closest comparison for improved clinical outcome with chemotherapeutic drugs chosen from among all active agents by a molecular platform in a prospective clinical trial is . . . Oh, that’s right there isn’t any.
http://www.rationaltherapeutics.com/the-science/published-reports.aspx
Systems Biology Is The Future Of Medical Research
http://cancerfocus.org/forum/showthread.php?t=34730 -
Incorporating Avastin and Tarceva in Combined-Modality Treatmentgdpawel said:Systems Biology Comes of Age: Lung Cancer in the Crosshairs
Systems Biology Comes of Age: Metastatic Lung Cancer in the Crosshairs
Robert A. Nagourney, M.D.
Cancer therapists have long sought mechanisms to match patients to available therapies. Current fashion revolves around DNA mutations, gene copy and rearrangements to select drugs. While every cancer patient may be as unique as their fingerprints, all of the fingerprints on file with the federal AFIS (automated fingerprint identification system) database don’t add up to a hill of genes (pun intended), if you can’t connect them to the criminal.
To continue the analogy, it doesn’t matter why the individual chose a life of crime, his upbringing, childhood traumas or personal tragedies. What matters is that you capture him in the flesh and incarcerate him (or her, to be politically correct).
The term we apply to the study of cancer, as a biological phenomenon is “systems biology.” This discipline strikes fear into the heart of molecular biologists, for it complicates their tidy algorithms and undermines the artificial linearity of their cancer pathways. We frequently allude to the catchphrase, genotype ≠ phenotype, yet it is the cancer phenotype that we must confront if we are to cure this disease.
Using a systems biology approach, we applied the ex-vivo analysis of programmed cell death (functional profiling) to the study of previously untreated patients with non-small cell lung cancer. Tissue aggregates isolated from their surgical specimens were studied in their native state against drugs and signal transduction inhibitors. This methodology captures all of the interacting “systems,” as they respond to cytotoxic agents and growth factor withdrawal. The trial was powered to achieve a two-fold improvement in response.
At interim analysis, we had more than accomplished our goal. The results speak for themselves.
First: a two-fold improvement in clinical response – from the national average of 30 percent we achieved 64.5 percent (p – 0.00015).
Second: The median time to progression was improved from 6.4 to 8.5 months.
Third: And most importantly the median overall survival was improved from an average of 10 – 12 months to 21.3 months, a near doubling.
These results, from a prospective clinical trial in which previously untreated lung cancer patients were provided assay directed therapy, reflects the first real time application of systems biology to chemotherapeutics. The closest comparison for improved clinical outcome with chemotherapeutic drugs chosen from among all active agents by a molecular platform in a prospective clinical trial is . . . Oh, that’s right there isn’t any.
http://www.rationaltherapeutics.com/the-science/published-reports.aspx
Systems Biology Is The Future Of Medical Research
http://cancerfocus.org/forum/showthread.php?t=3473
The November 10, 2012 issue of the Journal of Clinical Oncology published a highly instructive report on the incorporation of Avastin (bevacizumab) and Tarceva (erlotinib) into the treatment of Stage III NSCLC in combination with radiation for the treatment.
The article regarded a pilot study that incorporated an anti-VEGF antibody Avastin (bevacizumab) with EGFR TKI Tarceva (erlotinib) along with chemotherapy and radiation. In this trial the objective response rate of 39 percent, median progression-free survival of 10.2 months and median overall survival of 10.4 months, were not demonstrably superior to contemporary results, yet toxicity was significantly enhanced.
The investigators recommended against further exploration of this combination. Here the aggressive integration of targeted and conventional therapies proved a misadventure.
According to Dr. Robert A. Nagourney of Rational Therapeutics, the trial represented clinicians’ desire to engage in theoretically attractive clinical trials only to find that they reflect ineffective and/or more toxic treatment regimens. This lung cancer experience reflects the failure of the research community to dedicate adequate resources to predictive clinical models.
Combinations of chemotherapy with target therapies have been the subject of investigation at Rational Therapeutics in Long Beach, California for more than a decade. For example, they observed antagonism between platins and the EGFR antagonists Iressa (gefitinib) and Tarceva (erlotinib) two years before publication of the unsuccessful INTACT I and II Trials and three years before the unsuccessful TALENT and TRIBUTE trials.
All four of these trials combined platin based doublets with EGF-TKI’s. More recently Rational Therapeutics successfully identified favorable interactions between Tarceva (erlotinib) and VEGF inhibitors in individual patients that have provided durable responses in their NSCLC patients as first line therapy, now out to four and five years since diagnosis.
These experiences represent opportunities to explore novel therapies and avoid inadvertent antagonisms and misadventures. In the recent JCO, a good treatment was missed while a bad treatment was advanced.
Functional profiling through use of the EVA-PCD assay may represent the critical path from bench to bedside that the deputy director of the Center for Drug Evaluation and Research at the Food and Drug Administration, Janet Woodcock has described as a crying need.
Incorporating Bevacizumab and Erlotinib in the Combined-Modality Treatment of Stage III Non–Small-Cell Lung Cancer: Results of a Phase I/II Trial
http://jco.ascopubs.org/content/30/32/3953.abstract0 -
The one-gene/protein, one-target, one-drug paradigmgdpawel said:Incorporating Avastin and Tarceva in Combined-Modality Treatment
The November 10, 2012 issue of the Journal of Clinical Oncology published a highly instructive report on the incorporation of Avastin (bevacizumab) and Tarceva (erlotinib) into the treatment of Stage III NSCLC in combination with radiation for the treatment.
The article regarded a pilot study that incorporated an anti-VEGF antibody Avastin (bevacizumab) with EGFR TKI Tarceva (erlotinib) along with chemotherapy and radiation. In this trial the objective response rate of 39 percent, median progression-free survival of 10.2 months and median overall survival of 10.4 months, were not demonstrably superior to contemporary results, yet toxicity was significantly enhanced.
The investigators recommended against further exploration of this combination. Here the aggressive integration of targeted and conventional therapies proved a misadventure.
According to Dr. Robert A. Nagourney of Rational Therapeutics, the trial represented clinicians’ desire to engage in theoretically attractive clinical trials only to find that they reflect ineffective and/or more toxic treatment regimens. This lung cancer experience reflects the failure of the research community to dedicate adequate resources to predictive clinical models.
Combinations of chemotherapy with target therapies have been the subject of investigation at Rational Therapeutics in Long Beach, California for more than a decade. For example, they observed antagonism between platins and the EGFR antagonists Iressa (gefitinib) and Tarceva (erlotinib) two years before publication of the unsuccessful INTACT I and II Trials and three years before the unsuccessful TALENT and TRIBUTE trials.
All four of these trials combined platin based doublets with EGF-TKI’s. More recently Rational Therapeutics successfully identified favorable interactions between Tarceva (erlotinib) and VEGF inhibitors in individual patients that have provided durable responses in their NSCLC patients as first line therapy, now out to four and five years since diagnosis.
These experiences represent opportunities to explore novel therapies and avoid inadvertent antagonisms and misadventures. In the recent JCO, a good treatment was missed while a bad treatment was advanced.
Functional profiling through use of the EVA-PCD assay may represent the critical path from bench to bedside that the deputy director of the Center for Drug Evaluation and Research at the Food and Drug Administration, Janet Woodcock has described as a crying need.
Incorporating Bevacizumab and Erlotinib in the Combined-Modality Treatment of Stage III Non–Small-Cell Lung Cancer: Results of a Phase I/II Trial
http://jco.ascopubs.org/content/30/32/3953.abstract
Companion diagnostics and their companion therapies are what's being pushed as "personalized medicine" as they enable the identification of likely responders to therapies that work in patients with a specific molecular profile.
However, companion diagnostics tend to only answer a targeted drug-specific question and may not address other important clinical decision needs.
These companion diagnostics are being used to predict responsiveness and determine candidacy for a particular therapy often included in drug labels as either required or recommended testing prior to therapy initiation.
Landscape trends suggest companion diagnostic tests in their current "one-test/one-drug" embodiment will not adequately cover decision support needs as physicians become inundated with more biomarker data likely to be interrelated, nuanced and at time even contradictory.
Two years ago, Ryan Kuper was diagnosed with lung cancer. A very compelling aspect of Ryan's journey was Rational Therapeutic's ability to recognize Ryan's sensitivity to the drug Xalkori (crizotinib), after his genomic analysis failed to identify the gene target.
In the end, it was the functional platform that provided Ryan's doctors with the treatment plan that has proven to be effective against his widely metastatic non-small cell lung cancer.
Hear more about Ryan and the functional cytometric profiling assay.
http://www.youtube.com/watch?v=BKh-rMCc4dQ&feature=youtu.be 0 -
Functional Profiling to Select Chemotherapy
Functional Profiling to Select Chemotherapy in Untreated, Advanced or Metastatic Non-Small Cell Lung Cancer
Robert A. Nagourney 1,2, Jonathan B. Blitzer 1, Robert L. Shuman 1, Thomas J. Asciuto 1, Eknath A. Deo 1, Marylyn Paulsen 1, Robert L. Newcomb 3, Steve S. Evans 2
1. Memorial Medical Center of Long Beach, Todd Cancer Institute, Long Beach, CA
2. Rational Therapeutics, Long Beach, CA
3. Institute for Clinical & Translational Science, University of California, Irvine, CA
Abtract
Background Aim:
To assess the impact of drug selection upon the treatment of advanced and metastatic non-small cell lung cancer (NSCLC), we applied a functional platform that measures drug induced cell death in human tumor primary-culture micro-spheroids isolated from surgical specimens.
Patients and Methods:
At diagnosis, microspheroids isolated by mechanical and enzymatic disaggregation were examined for drug-induced cell-death by morphology and staining characteristics. Drugs were administered using standard protocols. Thirty-one patients, who received at least one cycle of therapy, were evaluable. All patients signed informed consent.
Results:
Twenty of 31 patients responded (64.5%). 1 completely and 19 partially, providing a two-fold improvement over historical control of 30% (p=0.00015), a median time to progression of 8.5 months and a median overall survival of 21.3 months.
Conclusion:
This functional platform is feasible and provides a favorable objective response rate, time to progression and survival in advanced, metastatic, untreated NSCLC, and warrants further evaluation.
Source: Anticaner Research October 2012 vol. 32 no. 10; 4454-4460
http://ar.iiarjournals.org/content/32/10/4453.abstract?sid=eb8c3504-bee5-4756-a1a8-1ae7c0d554dc
http://www.rationaltherapeutics.com/downloads/pdfs/EVAPCD.pdf
Using only FDA-approved, standard lung cancer drugs available to all oncologists, this process of laboratory selection provided a 64.5 percent response rate – more than double the national average of 30 percent (p = 0.00015), well established in the literature. More importantly, the median overall survival of 21.3 months was nearly two-fold longer than the best results of 13.5 months reported for non-assay based standard treatments. Strikingly, among the Stage IV (metastatic) patients, there are several who remain alive approaching eight years since diagnosis.
Standard treatment protocols, administered in accordance with published results in thoracic oncology literature, included: Carboplatin & Paclitaxel (Taxol); Cisplatin & Navelbine (vinorelbine); Cisplatin & Gemzar (gemcitabine); Carboplatin & Gemzar; Carboplatin & Alimta (pemetrexed); Tarceva (erlotinib); Tarceva & Avastin (bevacizumab); Carboplatin & Taxol & Avastin; Cisplatin & Navelbine & Avastin; Taxol; Docetaxel (Taxotere); Navelbine; Taxotere & Gemzar; Campto (Irinotecan); Campto & Cisplatin.
To date, they are tracking a 100% response rate to Tarceva in the select populations; even patients who have not been found to carry recognized mutations.
Assay Results and Bayes' Theorem
http://cancerfocus.org/forum/showthread.php?t=37540 -
Cost?
How much does this test cost? I don't think it is covered by insurance...
0
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