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Genomic testing for predicting response to targeted therapy


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The role of genomic testing for predicting response to targeted therapy

[Dr. Nagourney is medical and laboratory director at Rational Therapeutics, Inc., in Long Beach, California, and an instructor of Pharmacology at the University of California, Irvine School of Medicine. He is board-certified in Internal Medicine, Medical Oncology and Hematology.]

I read — with great interest — the recent study by Von Hoff, et al. in the Nov. 20, 2010 issue of the Journal of Clinical Oncology, as well as the associated editorial in the same issue. The manuscript, titled Pilot Study Using Molecular Profiling of Patient Tumors to Find Potential Targets and Select Treatments for the Refractory Cancers, reported the results obtained in 106 patients who consented to study using immunohistochemistry and microarray analysis for the identification of treatment process.

Of the original 106 patients:

There were 86 who underwent profiling and were considered for therapy

Of those, 68 were treated

Of whom, 66 received the recommended treatment.

The objective of the trial was to improve progression-free survival over that associated with the most recent prior therapy; to determine the percentage of time a target was identified; and finally, to gauge objective response rates by RECIST criteria.

The patients in the study:

Were a mixture of solid tumors, including breast, colon, ovary and others.

Had failed prior therapy.

The median age was 60 years and the majority of patients were female

Only the breast cancer patient population was defined in terms of the number of prior therapies — five.

At first blush, this paper would suggest that the era of molecular profiling has arrived. We need only obtain a small biopsy of tissue to identify the “targets” most likely to respond to available or investigational agents. At a closer look, however, we find that the INVESTIGATORS ON THE TRIAL INVENTED A CRITERION OF RESPONSE, namely a 1.3 fold improvement in time to progression. What that means is that patients who received an ineffective therapy and showed disease progression, need only improve upon that short response by a mere 30 percent to be counted among the “responders.”

Thus, a patient who failed a therapy after 10 days could theoretically be counted among the successes if their subsequent response to directed therapy was a meager 13 days in duration.

Even using this soft-boiled endpoint, only 18 of the 66 patients (27 percent) met criteria for response. However, these 18 responders should really be calculated against the total 88 patients approved for study, providing an even lower 20 percent result. Indeed, the most rigorous investigators would demand that these 18 be measured against the total 106 patient cohort, which would provide a response rate of a mere 16.9 percent.

Since most investigators don’t have the luxury of inventing their own criteria for response, we might examine this manuscript in the context of more widely used criteria like RECIST. In this context, the objective response rate of six out of 66 was 10 percent, with an additional 14 patients (21 percent) revealing stable disease for four months. However, again using the intention-to-treat analysis (the criteria other investigators must live by) the objective response rate falls to more like 6.8 percent (6/88) or most rigorously 5.7 percent (6/106).

Furthermore, four of the six (66 percent) objective responders, by RECIST criteria, and nine of the 18 (50 percent) were found in breast and ovarian cancers (mostly breast) known to be among the most chemo-responsive of all epithelial neoplasms. By these standards, the capacity of molecular profiling to identify responders begins to seem a bit underwhelming.

The design of the trail also raises some questions:

First, the principle end point is IHC (immunohistochemistry), followed by microarray

Yet, the specific predictive validity of the micro-array analysis is not addressed

While the authors note that IHC is a well-established and widely used methodology, they largely skirt the second issue noting only that “For MA (microarray), excellent reviews and commentary have been written on the subject of gene arrays and their potential and actual use for predicting clinical response for chemotherapy.”

In essence, we are left with a report that provides a very low objective response rate and succeeds only by meeting its own invented criteria to support the predictive validity for what appears to be mostly an established use of IHC. Should we consider this the birth of molecular profiling? By comparison, our functional platform in similarly heavily pretreated patients has consistently provided significantly higher response rates than those reported in the current analysis. Is it not time for the molecular profiles to match our results?

The incidence of ALK gene rearrangement in patients with NSCLC is in the range of 2-4 percent, while EGFR mutations are found in approximately 15 percent. These are largely mutually exclusive events. Yet with an objective response rate of 10 percent (Von Hoff, et al JCO, Nov 2011) reported for a gene array/IHC platform that attempted to select drugs for individual patients, it doesn't seem to be a very accurate or validated methodology to use in patients with advanced NSCLC. And those patients who do test negative for ALK and EGFR are left to the same guesswork that has provided responses in the range of 30 percent and survivals in the range of 12 months. It's interesting to note how quickly organizations like ASCO have embraced the expensive and comparatively inefficient molecular testing.

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Study: No Correlations Between Gene Mutations and Patient Survival

Tarceva (erlotinib) in Lung Cancer — Molecular and Clinical Predictors of Outcome

Ming-Sound Tsao, M.D., Akira Sakurada, M.D., Ph.D., Jean-Claude Cutz, M.D., Chang-Qi Zhu, M.D., Ph.D., Suzanne Kamel-Reid, Ph.D., Jeremy Squire, Ph.D., Ian Lorimer, Ph.D., Tong Zhang, M.D., Ni Liu, M.Sc., Manijeh Daneshmand, M.D., Paula Marrano, M.Sc., Gilda da Cunha Santos, M.D., Ph.D., Alain Lagarde, Ph.D., Frank Richardson, D.V.M., Ph.D., Lesley Seymour, M.D., Ph.D., Marlo Whitehead, M.Sc., Keyue Ding, Ph.D., Joseph Pater, M.D., and Frances A. Shepherd, M.D.

N Engl J Med 2005; 353:133-144July 14, 2005



A clinical trial that compared erlotinib with a placebo for non–small-cell lung cancer demonstrated a survival benefit for erlotinib. We used tumor-biopsy samples from participants in this trial to investigate whether responsiveness to erlotinib and its impact on survival were associated with expression by the tumor of epidermal growth factor receptor (EGFR) and EGFR gene amplification and mutations.


EGFR expression was evaluated immunohistochemically in non–small-cell lung cancer specimens from 325 of 731 patients in the trial; 197 samples were analyzed for EGFR mutations; and 221 samples were analyzed for the number of EGFR genes.


In univariate analyses, survival was longer in the erlotinib group than in the placebo group when EGFR was expressed (hazard ratio for death, 0.68; P=0.02) or there was a high number of copies of EGFR (hazard ratio, 0.44; P=0.008). In multivariate analyses, adenocarcinoma (P=0.01), never having smoked (P<0.001), and expression of EGFR (P=0.03) were associated with an objective response. In multivariate analysis, survival after treatment with erlotinib was not influenced by the status of EGFR expression, the number of EGFR copies, or EGFR mutation.


Among patients with non–small-cell lung cancer who receive erlotinib, the presence of an EGFR mutation may increase responsiveness to the agent, but it is not indicative of a survival benefit.

Source Information

From the University Health Network, Princess Margaret Hospital Site, and the Ontario Cancer Institute, University of Toronto, Toronto (M.-S.T., A.S., J.-C.C., C.-Q.Z., S.K.-R., J.S., T.Z., N.L., P.M., G.C.S., F.A.S.); the Ottawa Health Research Institute, University of Ottawa, Ottawa (I.L., M.D., A.L.); OSI Pharmaceuticals, Boulder, Colo. (F.R.); and the National Cancer Institute of Canada Clinical Trials Group and Queen's University, Kingston, Ont., Canada (L.S., M.W., K.D., J.P.).

http://www.nejm.org/doi/full/10.1056/NE ... t=abstract

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Study: Correlations Between Cell Culture Assay Results and Patient Survival

An abstract was presented to the 2006 ASCO meeting describing correlations between cell culture assay results (cell death in response to Iressa (gefitinib) exposure) and survival of 31 patients with non-small cell lung cancer who had received extensive prior chemotherapy.

These correlations were based on the actual assay results which had been reported, in real time, prospectively to the doctors who had ordered the laboratory tests. There were striking correlations between test results and patient survival. The abstract was published in the ASCO Proceedings.

What do the EGFR gene mutations code for? Anti-apoptotic pathways. So anti-apoptosis is inhibited with gefitinib or erlotinib, and the cells undergo apoptosis and die. And it is detected at the whole cell level with cell culture assays and reported out -- prospectively -- that this correlates strikingly with patient survival.

Not only is this a very important predictive test, but a unique tool for identifying newer, better drugs, testing drug combinations, serving as a "gold standard" to develop new DNA, RNA, and protein-based tests of drug activity.

Gefitinib-induced cell death in short term fresh tumor cultures predicts for long term patient survival in previously-treated non-small cell lung cancer.

Sub-category: Non-Small Cell Lung Cancer

Category: Lung Cancer

Meeting: 2006 ASCO Annual Meeting

Abstract No:17117

Citation:Journal of Clinical Oncology, 2006 ASCO Annual Meeting Proceedings Part I. Vol 24, No. 18S (June 20 Supplement), 2006: 17117

Author(s):L. Weisenthal



Gefitinib (GEF) may act by inhibiting anti-apoptotic signals transduced by mutant EGFR kinase (Science 305:1163,04). Cell culture assays with cell death endpoints could be informative for GEF activity.


We tested 568 biopsies of fresh human tumors (TUM) with 2 concentrations of GEF (22 and 11 µg/ml) for 96 hrs, each with 2 separate cell death endpoints (DISC and MTT), detailed methods http://weisenthal.org/w_ovarian_cp.pdf . Results classified as resistant (RES), intermediate (INT), or sensitive (SEN) based on means and standard deviations of training set data (ref ibid), reported prospectively to 3 different physicians: surgeon, pathologist, and oncologist. Assay evaluability rate > 90%.


Based on overall % control cell death, the following TUM showed (on average) no greater RES or SEN than the universe of 568 assays: NSCLC (n = 72), colon (33), breast (106), ovarian (109), melanoma (23), pancreatic (20), endometrial (12). The following showed (on avg) significantly greater RES: soft tissue sarcomas (n = 24), carcinoid/islet (16), renal (15), and mesothelioma (8). For NSCLC, there was no avg difference between female (32) vs male (35) or untreated (34) vs previously treated (38). For 32 unRxd pts with survival data, there was no significant difference in overall surv for 20 pts with prospectively reported GEF RES (GR) assays vs 12 pts with SEN or INT (GSI) assays. For 31 pts with prior chemoRx (med surv = 155 days), there was significant survival disadvantage for 14 pts with prospectively reported GR vs 17 pts with GSI (median 85 vs 380 days, P2 < 0.0001, HR 3.7; 95% C.I. 2.6-19) (Click here to see Kaplan-Meier Curves). For pts with known post-assay Rx, there were 7 pts with GSI subsequently receiving GEF or erlotinib (ERLOT), with med surv = 485 days; 9 pts with GSI not receiving GEF or ERLOT, med surv = 135 days; 10 pts with GR not receiving GEF or ERLOT, med surv = 76 days, and 3 pts with GR receiving GEF or ERLOT, med surv = 75 days. Survival of group of 7 pts was significantly greater than those of groups of 9, 10, and 3 pts (P2 = 0.037, P2 < 0.0001, and P2 = 0.0008, respectively, click here to see Kaplan-Meier Curves).


GEF-induced cell death in cultures of fresh TUM from prev-treated NSCLC pts may identify pts with favorable prognosis, particularly when treated with GEF or ERLOT.

http://weisenthal.org/asco_06_egfr_gefi ... enthal.htm

A subsequent news story in European Hospital Journal, June 5, 2006


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Cancer Cytometric Testing More Accurate than Molecular Gene Testing

Cancer Cytometrics (also known as functional tumor cell profiling or chemosensitivity testing) was found to be substantially more accurate than molecular profiling (gene testing).

In a clinical trial involving ovarian cancer patients, patterns of gene expression identified through molecular profiling were compared with results of cancer cytometric testing, in which whole, living cancer cells are exposed to candidate chemotherapy drugs.

In this, the first ever, head-to-head study of test accuracy, cancer cytometrics was found to be highly accurate when it came to identifying effective drugs (drug selection). It had 90% concordance with treatment outcome, while gene tessting had considerably less relevant, 0%, 25% or 75%, depending upon which genes were studied.

The accuracy levels found in this trial for cancer cytometric testing are strikingly consistent with those documented in dozens of previous studies, published by respected cancer researchers around the world.

In those studies, as in this one, extremely high levels of correlation (in other words, high levels of test accuracy) were found for cancer cytometrics, in which cell death-based assays, accurately and reproducibly measured drug-induced cell death and correlated with individual patient chemotherapy response and survival.

Overall, the studies found that drugs which successfully killed patients’ tumor cells in pre-treatment cytometric profiling were 7.5 times more likely to improve clinical response rates and prolong the lives of cancer patients than drugs identified in cytometric profiling as ineffective for those patients.


Arienti et al. Peritoneal carcinomatosis from ovarian cancer: chemosensitivity test and tissue markers as predictors of response to chemotherapy. Journal of Translational Medicine 2011, 9:94.

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There were interesting comments made by Dr. Len Lichtenfeld, on his American Cancer Society blog, about frankly pessimistic discussions at a meeting convened by the Institute of Medicine (IOM) about the current status of genomics and drug discovery in cancer.

From what he gathered, the "trick" with targeted therapy is figuring out which mutations really make a difference in the survival of cancer cells and then find a treatment that takes advantage of that vulnerability.

Tumors are complex and the targets are numerous. Our ability to target all of these challenges are very daunting. What may look promising in a clinical setting is not turning out be be successful in the real world setting.

Dr. Len said that one speaker at the IOM meeting offered, it's like "Eroom's Law" (Moore's Law spelled backwards), we are experiencing in the targeted therapy world, "increasing costs with decreasing success."

Then there is the issue the more we "slice and dice" the genome, the smaller the number of people who will benefit from medicines focused on the particular "targets" we uncover.

As we find out what and where the targets are on specific cancers, we are now beginning to develop very small, very carefully analyzed group of patients who are well documented to serve as in an adaptive clinical trial to warrant approval of the new targeted drug.

Perhaps 20 patients, well-studied, could allow the FDA to approve a drug for a particular targeted indication?

I replied to Dr. Len's comments that I told his old colleague, Dr. Herman Kattlove, who posted about this on his blog, I thought his "genetic heterogeneity" terminology was more befitting than what was used in the title of the recent British study (Intratumor Heterogeneity).

"Taking one biopsy sample of a tumor may not be enough to reveal its full genetic identity," was described by Medical News Today's Catharine Paddock, PhD. The study is significant because it suggests relying on one sample could overlook (other) important biomarkers that help make tailored treatments effective, explaining perhaps why personalized cancer therapy has been less successful than expected.

Dr. Robert Nagourney, Medical and Laboratory Director at Rational Therapeutics, Inc., Long Beach, California, pointed out on his blog, the disturbing news regarding the predictive validity and clinical applicability of human tumor genomic analysis for the selection of (targeted) chemotherapeutic agents. He also pointed out the accompanying editorial by Dr. Dan Longo, which made several points worth noting.

First, he states that "DNA is not the whole story." This should be familiar to those who follow cell function analysis. Dr. Longo references Albert Einstein, who said, "Things should be made as simple as possible, but not simpler."

Dr. Nagourney appreciates and applauds Dr. Long's comments for they echo his sentiments completely. The article of the study is only the most recent example of a growing litany of observations that call into question molecular biolgist's preternatural fixation on genomic analyses.

Human biology is not simple and malignantly transformed cells more are more complex still. Investigators who insist upon using genomic platforms to force disorderly cells into artificially ordered sub-categories, have once again been forced to admit that these oversimplifications fail to provide the needed insights for the advancement of cancer therapeutics.

Those laboratories and corporations that offer "high price" genomic analyses for the selection of "high price" chemotherapy drugs take notice of this and related articles carefully as these reports portend a troubling future for their current business model ("personalized" cancer treatment).

http://www.cancer.org/AboutUs/DrLensBlo ... eries.aspx

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  • 3 weeks later...

Horizontal & Vertical Signal Pathway Inhibitiion Tumor Primary Culture Microspheroids

Signal transduction pathways are important targets in cancer therapy. Small molecule inhibitors for the tyrosine kinase and serine threonine kinase pathways are already available for clinical therapy.

Additionally, compounds targeting the PI3K, AKT and MEK pathways will be available in the coming years. To explore the interaction of these parallel survival pathways, laboratories utilizing the functional cytometric profiling platform have compared activity and combined these inhibitors in human tissues.

Their findings include favorable interactions between EGFR tyrosine kinase inhibitors and compounds that block the PI3K pathways. The most active combinations were those that inhibited the cross-talk between pathways (horizontal inhibition) over drug combinations that targeted the same pathway at different downstream points (vertical inhibition).

Similar observations have been made combining PI3k inhibitors and MEK inhibitors. One such report in PNAS (May 10, 2010) closely paralleled the work conducted by Rational Therapeutics, that was reported at the AACR, showing the importance of signal pathway inhibition in human tumor primary culture microspheroids.

A series of studies utilizing these combinations, indicate that certain tumors respond to their drug exposure by undergoing autophagic death, not necrotic or apoptotic. This distinction is possible, as the functional cytometric profiling platform has the capacity to measure all forms of cell death – apoptotic and non-apoptotic.

In some tumors, one targeted drug may be more active. In other tumors, the combination proves best. This would suggest that some tumors are driven by one pathway while others have dual triggers that both must be shut off to achieve total cell kill.

Horizontal and vertical signal pathway inhibition in human tumor primary culture micro-spheroids

Robert Alan Nagourney, Paula J. Bernard, Federico R. Francisco, and Steven S. Evans

Rational Therapeutics, Long Beach, CA.


Signal transduction pathways are targets for small molecule tyrosine and serine-threonine kinase inhibitors. Redundancy and cross talk between pathways can complicate the application of genomic signatures but proteomic and functional platforms may more closely approximate tumor phenotypes. We used EVA/PCD (ex vivo analysis of programmed cell death), a functional platform measuring both apoptotic & non-apoptotic events to examine the PI3K and EGFr pathways in human 1° culture micro-spheroids.


337 tumor specimens provided from the OR were mechanically and enzymatically disaggregated and exposed to EGFr (Gefitinib), AKT (B-15), PI3K (LY294002) & mTOR (Rapamycin) inhibitors with LC50 values interpolated from 5-point dose response curves. Synergy was assessed by median effect and correlation coefficients by Pearson Moment.


Parallel Pearson Moment analyses in 51 specimens by 2-tailed T, revealed LY vs. B15 (NS); LY vs. Rapa (P<0.02); B15 vs. Rapa (P<0.0025); Rapa vs. Gefitinib (P <0.001). Synergy analyses for Rapa & Gefitinib revealed synergy in 29%, additivity in 41%, sub-additivity in 13% and antagonism in 17%. Exploratory synergy analyses that combined Gefitinib & LY and Gefitinib & B-15, favored the Gefitinib& B-15 combination with 100% synergy in this small series.


EVA/PCD analyses provide insights into cellular response to targeted therapies, alone and in combination. Correlative analyses identify points of commonality and/or disparity in downstream signaling that can be exploited in drug development and therapy. Synergy analyses reveal potentially important drug combinations. The EVA/PCD human tumor micro-spheroid platform provides a functional tool capable of streamlining drug development and cancer therapy.

Supported by The Vanguard Cancer Foundation and the Nagourney Institute.

Citation: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1764.

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In a presentation at the 2010 ASCO Annual Meeting, Dr. Robert Nagourney showed clinical response rates were doubled by using the Phenotype Analysis of Programmed Cell Death (functional profiling) platform with standard FDA approved chemotherapeutic agents in NSCLC patients.

If he can achieve these types of results by simply reconfiguring existing drugs, it suggests that the functional profiling platform could provide even better results as they introduce larger numbers of active, targeted agents.

One such agent, PF-1066 provided an overall response rate of 64 percent when patients were selected for the EML4-ALK fusion oncogene. With this type of approach, the selection of candidates for therapy predicated upon the biology of the patient, is precisely the premise underlying all of phenotype analysis.

While the PF-1066 data was strongly positive, it represented a very select population of lung cancer patients who carry a specific gene profile. Of all NSCLC patients, only 3-4 percent carry this gene.

While recognizing targets like EGFR and ALK continue to improve responses, the functional profiling platform is capable of identifying patients for response even when the specific underlying genetic mechanism may be less well characterized.

The capacity of the functional profiling platform to measure global cellular response enables his lab to select candidates for whom no known genetic predisposition exists.

At an experimental and molecular therapeutics session at the AACR 102nd annual meeting in 2011, Dr. Robert Nagourney may a presentation on signal transduction inhibitors. Using MEK/ERK and PI3K-MTOR inhibitors he explored the activities, synergies and possible clinical utilities of these novel compounds.

The findings were instructive. First, he saw a good signal for both compounds utilizing the Phenotype Analysis of Programmed Cell Death (functional profiling) platform. Second, he saw disease-specific activity for both compounds. For the MEK/ERK inhibitor, melanoma appeared to be a favored clinical target. This is highly consistent with their expectation.

After all, many melanomas carry mutations in the BRAF gene, and BRAF signals downstream to MEK/ERK. By blocking MEK/ERK, it appeared that he blocked a pathway fundamental to melanoma progression. Indeed, MEK/ERK inhibitors are currently under investigation for melanoma.

For PI3K inhibitors, the highest activity was observed in uterine cancers. This is interesting, because uterine carcinomas are often associated with a mutation in the PTEN gene. PTEN is a phosphatase tumor suppressor that functions to block activation of the PI3K pathway.

Thus, mutations in the tumor suppressor unleash PI3K signaling, driving tumors to grow and metastasize. Blocking PI3K provided a strong signal, indicating that this approach may be very active in tumors associated with these oncogenic events.

The third point of interest in his report was, perhaps, its most important. Specifically, that his lab can explore those diseases where MEK-ERK, PI3K and mTOR signaling are less established targets. Cancers of the lung, ovary, colon or breast all manifested profiles of interest.

When they combined both pathway inhibitors in a process called horizontal inhibition, renal cell carcinoma popped up as the best target. These results, though exploratory, suggest a superior approach for drug development, allowing the functional profiling platform to identify important leads much faster than the clinical trial process.

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The exon 18 mutation known as a G719X can respond to Tarceva, even though a patient made be EGFR negative. Fortunately, some physicians don't follow the so-called rules of chasing gene mutations. If you have the EGFR mutation, give it out. If you don't have the EGFR mutation, don't give it out.

When it comes to "drug selection" though, the molecular investigator can only measure those analytes in paraffin wax that they know to measure. If you are not aware of and capable of measuring a biologically relevant event, you cannot seek to detect it. If you don't know about the G719X, you don't know what to look for, and you aren't going to find it. The same goes for all the other loads of mutations clinicians don't know about, ABC, XYZ, you name it.

Originally, Xalkori was developed for patients who carried the CMET mutation. However, they later found a responding subpopulation that was actually carrying an unrecognized ALK gene rearrangement. Nexavar was originally evaluated for the treatment of BRAF mutation positive patients. Yet it was the drug's cross reactivity with the VEFG tyrosine kinases that lead to broader clinical applications.

The point is, each of these phenomena represents accidental successes. The lessons learned from this is that cancer biology is complex. Actually, it doesn't matter why Tarceva worked, so long as it did. I understand that there is reason to believe that the more potent irreversible EGFR/HER2 dual inhibitor HKI-271 may be even more selective for this point mutation.

The premise of the functional (cytometric) profiling platform is that the observation of a biological signal identifies a candidate for therapy whether we understand or recognize the target. Were it not for the clinical observation of response in patients, investigators would been unlikely to make the discoveries that provide such good clinical responses.

When cell function analysis first identified lung cancer as a target for Iressa, and began to administer the closely related Tarceva to lung cancer patients, neither Lynch nor Paez had identified the sensitizing EGFR mutations. That had absolutely no impact upon the excellent responses that were observed with cell function analysis.

It didn't matter why it worked, but that it worked. Might we not use functional analytical platforms (functional cytometric profiling) to gain insights into the next, and the next, and the next generation of drugs and therapies that target pathways like MEK, ERK, SHH, FGFR, PI3K, etc.?

Source: Cell Function Analysis

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  • 3 months later...

Why doesn't Rational Therapeutics/Weisenthal Cancer Group do gene mutation analyses?

Many patients inquire why these two laboratories have not focused upon genomic analyses as part of the services they offer. There are several reasons why they haven't focused on genomics.

The first reason is that there are many laboratories that already commercially offer these analyses. These gene array methods can be automated and conducted comparatively cheaply. As they have worked hand-in-hand with many of the commercial purveyors of these techniques, they have seen little advantage in reproducing these methodologies in their facilities.

The second and more important reason that they have not pursued genomics reflects their belief that cancer is more complex than its gene signature. This point is critical to an understanding of what functional analyses are. Both know that contained within the genes of each human is the information to create every protein, every enzyme, every lipid, every carbohydrate and all the organs and systems dependant upon their function. What is not known is how all of those 25,000+ genes are regulated to produce the unique features that constitute us as human entities.

From the moment of conception, when the male and female genetic materials are fused into what is known as a zygote, our informatics are established. What enables that single cell to become the multi-trillion-cell organism that we recognize as human is not the gene, but the gene regulation.

The informatics are static — the regulation is highly fluid.

Simply exploring the information contained within the human cell provides you with a blueprint of what may be, but no clear evidence that the outline structure will ever come to be in all of its functional complexity. In this regard, genomic analyses cannot approximate the vagaries and manifold variations that define us as individuals.

To look at this a different way, we can describe genetic information as “permissive, ” that is it tells you what you may or may not become. Functional information is “predictive,” it tells you exactly what you are. They have moved away from genomic analyses for the very reason that they provide only a veneer of information. The substance of cancer, its responsiveness to therapeutics and its ultimate cure, require a more definitive analysis. By studying human cellular behavior within the context of vascular, stromal and inflammatory elements, the functional profiling platform provides the closest approximation of human biology possible short of a clinical trial.

Human beings are demonstrably more than the sum of their genes. Cancer biology and the study of cancer therapy are many things, but simple is not one of them. Complex problems require solutions that incorporate all of their complexities, however uncomfortable this may be for genomic investigators.

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Blood Test Instead of Biopsy Could Predict Tarceva (erlotinib) Benefit in NSCLC?

(Medscape Oncology) A blood test instead of tumor tissue sampling could identify patients with non-small-cell lung cancer (NSCLC) who are likely to have a good response to treatment with erlotinib (Tarceva), new research suggests.

Clinically, the best responses to erlotinib (Tarceva) and other drugs that inhibit epidermal growth-factor receptors (EGFR) are seen in patients with NSCLC tumors that have a high number of these receptors. This is usually determined by fluorescent in situ hybridization (FISH) techniques carried out on tumor tissue samples obtained on biopsy.

In this study, researchers analyzed blood samples for specific proteomic profiles that have been shown to predict outcomes in patients treated with EGRF inhibitors.

The results for the blood tests were presented by David Carbone, MD, PhD, director of the Specialized Program of Research Excellence (SPORE) in Lung Cancer at the Vanderbilt-Ingram Cancer Center in Nashville, Tennessee, at the 2nd European Lung Cancer Conference in Geneva, Switzerland.

"FISH overall was a better predictor of benefit, but can only be done with adequate biopsy tissue, which was available in this study only in 22% of patients," Dr. Carbone said.

"With the serum test, 99% of patients had a successful determination of proteomic status," he added.

"I think that this test may be of potential value in identifying a subgroup of patients with a good prognosis who are likely to have a response to erlotinib (Tarceva)," Dr. Carbone said in a statement. "It may be of particular value for those in whom tumor tissue is inadequate or unavailable," he added.

When approached for comment, Howard (Jack) West, MD, from the Swedish Cancer Institute in Seattle, Washington, said that the work is "interesting" but that it does not clarify how the response to EGRF inhibitors compares with that to chemotherapy.

"Overall, I consider this work to be promising, but not yet at the point of being able to answer the clinical questions we need to address in practice with enough certainty to use it routinely," he told Medscape Oncology.

Serum Samples From Previous Studies

The study analyzed baseline blood samples taken from patients enrolled in the pivotal BR.21 study.

The BR.21 study, conducted by the National Cancer Institute of Canada Clinical Trials Group, showed that erlotinib (Tarceva) improved survival (by 2 months) when the drug was added to regular supportive care in patients with advanced NSCLC who had failed on at least 1 chemotherapy (J Clin Oncol. 2004;22[suppl 14]:7022). This study formed the basis of approval for erlotinib (Tarceva) use in the treatment of NSCLC, and established it as a standard treatment for patients with advanced NSCLC.

The BR.21 trial enrolled 731 patients. Dr. Carbone and colleagues obtained blinded baseline plasma samples for 441 patients.

The blood samples were analyzed by Biodesix, in Broomfield, Colorado, to identify specific proteomic profiles that are already known to predict outcomes in patients treated with EGRF inhibitors.

The analysis found 266 patients (60.3%) with a good proteomic profile, 170 patients (38.5%) with a poor profile, and 5 patients (1.1%) with an intermediate profile.

"The bottom line is that the proteomic test comparing good and poor profiles was strongly prognostic in both the erlotinib (Tarceva) and placebo arms," Dr. Carbone said.

In addition, patients with a good proteomic profile had a significantly higher response rate to erlotinib (Tarceva) than patients with a poor proteomic profile (9.8% vs 0.9%; P = .002), he reported.

However, Dr. West explained that this 9.8% response rate in the patients with the good proteomic profile is very comparable to that seen in the overall patient population. Plus, he added, the 0.9% response rate in those with a poor proteomic profile does not rule out benefit from erlotinib (Tarceva).

In addition, Dr. West said that this work does not clarify whether an EGRF inhibitor is likely to be superior, inferior, or equal to standard chemotherapy in either the good or poor profile groups, which is a pressing clinical question.

Dr. Carbone reports serving on the advisory board for Genentech and Biodesix, Inc. Coauthor H. Roder is an employee of Biodesix, and another coauthor, F.A. Shepherd, reports consulting for Genentech.

Source: 2nd European Lung Cancer Conference: Abstract 203O. Presented April 30, 2010.


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Getting tumor cells from blood maybe feasible for solid tumors, though ususally only when the tumor is very advanced, and then only in small numbers.

It seems plausible to get enough specimen from circulating tumor cells for solid tumors. It may be possible using PCR (Polymerase Chain Reaction) or similar technology for specific agents such as Herceptin.

Only minute quantities of DNA are necessary for PCR. DNA can be amplified from a single cell. PCR amplification techniques raise considerable concerns regarding contamination from one specimen to another, creating the potential for false positive results. Clinical interpretation of PCR results may also be challenging.

But, PCR may be useful when culture is difficult due to the low numbers of the organisms, for lengthly culture requirements, or when there is difficulty in collecting an appropriate sample. However, the results would not be indicative of what would happen inside the human body.

They ususally proliferate (grow) cancer cells from a small sample and subject those cells to chemo. Cells 'grown' in the lab will not behave the same way as the actual cancer cells do in your body's own environment. Because they test on subcultured cells (as opposed to fresh tumor cultures) and test the cells in monolayers (as opposed to three dimensional cell clusters), the cell grown in the lab will not behave the same way as the actual cancer cells do in your body's own environment.

Older technology cell-based assay tests failed because scientists looked to see which drugs inhibited the cancer cells' growth (cell-growth endpoint), not which chemotherapies actively killed the tumor cells (cell-death endpoint). Cancer wasn't growing faster than other cells, it's just dying slower. The newer assay testing technology connects drugs to patients by what 'kills' their cells, not by what 'slows' them down.

All of the work in the past almost twenty years in the cell culture field has been carried out largely on three dimensional clusters of cells (not monolayers). Work is done exclusively with three dimensional, floating, tumor spheroids. When you test the cells as three dimensional spheroids, they are many-fold resistant in vitro, just as they are in vivo (multicellular resistance).

Even the researchers at Johns Hopkins and Washington University in St. Louis have found out, our body is 3D, not 2D in form, undoubtedly, this novel step better replicates that of the human body. Traditionally, in-vitro (in lab) cell-lines have been studied in 2 dimensions (2D) which has inherent limitations in applicability to real life 3D in-vivo (in body) states. Recently, other researchers have pointed to the limitations of 2D cell line study and chemotherapy to more correctly reflect the human body.

Both genomics and proteomics can identify potential new therapeutic targets, but these targets require the determination of cellular endpoints, composed of hundreds of complex molecules that regulate the pathways necessary for vital cellular functions. If a targeted drug could perturb any one of these pathways, it is important to examine the effects of the drug within the context of the cell.

The cell-based profiling platform has the capacity to measure genetic and proteomic events as a functional, real-time adjunct to static genomic and proteomic platforms. By examining small clusters of cancer cells (microspheroids) in their native state, a snapshot can be presented of the response of tumor cells to chemotherapy, combinations and targeted therapies.

I would agree with Dr. West that the proteomic platform does not clarify how the response to EGFR inhibitors compares with that to chemotherapy, combinations, or other targeted therapies. There is a challenge to identify which patients the targeted treatment will be effective.

The introduction of targeted drugs has not been accompanied by specific predictive tests allowing for a rational and economical use of these drugs. However, given the technical and conceptual advantages of cell-based functional analysis, together with its performance and the modest efficacy of therapy prediction on analysis of genome and proteome expression, there is reason for a renewal in the interest of functional profiling assays for optimized use of medical treatment of malignant disease.

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