Gene-expression signatures not ready for prime-time

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Why hasn't there been any progress at all in drug selection through the use of molecular diagnostics and biomarkers? Simply put, they do not work! Little progress has been made in identifying which therapeutic strategies are likely to be effective for individual patients by molecular prognostic and predictive markers.

It was hoped 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 would most likely work. However, gene-expression signatures are not ready for prime time.

Identifying DNA expression of individual proteins (the measure of RNA content, like Her2, EGFR, KRAS or ALK) often examine only one component of a much larger, interactive process. Gene (molecular) profiling measures the expression only in the "resting" state, prior to drug exposure. There is no single gene whose expression accurately predicts clinical outcome. Efforts to administer targeted therapies in randomly selected patients often will result in low response rates at significant toxicity and cost.

Finding what targeted therapies would work for what cancers is very difficult. A lot of "trial-and-error" goes along trying to find out. However, finding the right targeted therapies for the right "individual" cancer cells can be improved by cell-based assays, using functional profiling.

Functional profiling measures proteins before and after drug exposure. It measures what happens at the end (the effects on the forest), rather than the status of the individual trees. Molecular profiling is far too limited in scope to encompass the vagaries and complexities of human cancer biology when it comes to drug selection. The endpoints of molecular profiling are gene expression. The endpoints of functional profiling are expression of cell death (both tumor cell death and tumor associated endothelial [capillary] cell death).

In testing for all "known" mutations, if you miss just one, it may be the one that gets through. And it's not just only targeted drugs that may be effective as first-line treatment on your "individual" cancer cells. Cancers share pathways across tumor types. There really is no lung cancer chemos, or breast cancer chemos, or ovarian cancer chemos. There are chemos that are sensitive (effective) or resistant (ineffective) to each and every "individual" cancer patient, not populations. There are chemos that share across tumor types.

Until such time as cancer patients are selected for therapies predicated upon their own unique biology (and not population studies), we will confront one targeted drug after another. The solution to this problem has been to investigate the targeting agents in each individual patient's tissue culture, alone and in combination with other drugs, to gauge the likelihood that the targeting will favorably influence each patient's outcome. Functional profiling results to date in patients with a multitude type of cancers suggest this to be a highly productive direction.

Sources:

Nagourney Proc ASCO, 2007
J Natl Cancer Inst. March 16, 2010

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An important drawback of tyrosine kinase inhibitor (TKI) therapy is the development of resistance, frequently through the acquisition of mutations. Mutations at the gatekeeper residues and other oncogenic kinases have proven highly resistant to currently available TKIs.

Until such time as cancer patients are selected for therapies predicated upon their own unique 'systems' biology, we will confront one resistant targeted drug after another (keep on experimenting until they get it right).

The solution to this problem is to investigate the targeting agents in each individual patient's tissue culture, alone and in combination with other drugs, to gauge the likelihood that 'targeting' will favorably influence each patient's outcome.

A systems biology approach towards the understanding and treatment of cancer examines the many components of the disease simultaneously.

I just hate to see cancer patients chase one mutation after another and then finding out it doesn't work. 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. There is the KRAS test in colon cancer.

This, of course, leaves out the three dozen other drugs (and myriad drug combinations) which may often be even more effective in each of these diseases, and it leaves out virtually all of the other forms of cancer.

There are lots of things which determine if drugs work, beyond the existence of a given "target." Does the drug even get into the cancer cell? Does it get pumped out of the cell? Does the cell have ways of escaping drug effects? Can cells repair damage caused by the drug? Do combinations of drugs work in ways which can't be predicted on the basis of static gene expression patterns?

Tumor biology is a lot more complex than we'd like it to be. Cancer is more complex than its gene signature. Many common forms of cancer present as a host of mutated cells, each with a host of mutations. And they're genetically unstable, constantly changing. That's why so many cancers relapse after initially successful treatment. You kill off the tumor cells that can be killed off, but that may just give the ones that are left a free reign.

The idea of searching for clinical responders by testing for a single gene mutation seems nice, but you may have to test for dozens of protein expressions that may be involved in determining sensitivity/resistance to a given drug. Because if you miss just one, that might be the one which continues cancer growth

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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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

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.).

[url]http://www.nejm.org/doi/full/10.1056/NEJMoa050736#t=abstract

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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

Abstract:

Background:

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.

Methods:

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 [url]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%.

Results:

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).

Conclusions:

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.

[url]http://weisenthal.org/asco_06_egfr_gefitinib_nsclc_weisenthal.htm

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

[url]http://www.european-hospital.com/en/article/239.html

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Medscape Oncology

April 3, 2012 (Chicago, Illinois) — When it comes to predicting the risk for common diseases, including cancer, genome sequencing is not a magic bullet. It might be a valuable tool for people with a strong family history of a disease, but not for the vast majority of people, researchers report.

Genomic sequencing will never be a crystal ball that can reliably predict future health issues, explained researcher Bert Vogelstein, MD, Clayton Professor of Oncology and Pathology at the Johns Hopkins Kimmel Cancer Center in Baltimore, Maryland.

"It cannot substitute for conventional risk-management strategies, including routine check-ups and lifestyle optimization," he said at a press briefing here at the American Association for Cancer Research 103rd Annual Meeting.

Dr. Vogelstein was summarizing the results of a study presented at the meeting and simultaneously published online April 2 in Science Translational Medicine.

The researchers analyzed data collected from thousands of twin-pair groups on the incidence of 24 diseases, including cancer and autoimmune, cardiovascular, genitourinary, neurologic, and obesity-associated conditions. They used mathematical models to predict disease risk.

For the majority of tested individuals, the results would be negative for most diseases. In addition, the predictive value of these negative tests would generally be quite modest, because "the total risk for acquiring the disease in an individual testing negative would be similar to that of the general population," according to the researchers.

Conversely, in the best-case scenario, the results show that the majority of people tested might be alerted to a clinically meaningful risk for at least 1 disease with whole-genome sequencing.

"We stand on the verge of a revolution, and advances in technology and sequencing that have immense implications for many fields of science," said Dr. Vogelstein. "But, as we all know from the recent revolutions in the Middle East, we can't always predict the final outcomes of revolutions."

He added that in genetics, and specifically in personalized medicine, many of the predictions have been based on qualitative arguments and anecdotal reports.

Positive and Negative Tests

A positive test result should indicate that a person has at least a 10% risk for disease. "That means 1 in 10 would develop the disease from all factors combined," he explained.

The usefulness of a negative test result "is in the eye of the beholder," Dr. Vogelstein noted. To be medically useful, the risk would have to be much lower than in the general population.

As an example, Dr. Vogelstein explained that 2% of those taking the test would get positive results for ovarian cancer. "That is 1 in 50 women, and that is the maximum — the best-case scenario," he said. "That can be useful for those women so they can have closer surveillance."

On the flipside, the other 98% of women would get a negative test. "Unfortunately, the negative test is not that informative because it only shows that they have a risk that is slightly lower than the general population," Dr. Vogelstein said.

These results were similar for the other diseases that the researchers looked at, although there were a few "outliers," Dr. Vogelstein explained. "In theory, with coronary heart disease — at least in males — it might be possible that many individuals in the population would have a positive test; this might put them on the alert for heart disease."

Cancer risk is influenced by both environmental and stochastic factors, which further dilutes the ability of whole-genome sequencing to predict disease risk.

To illustrate the limits of genetic testing, Dr. Vogelstein noted that currently, men have a 45% lifetime risk for cancer and women have a 38% lifetime risk. Having a negative test result would lower the risk to 32% to 42% in men and 27% to 36% in women, which is only a slight difference from that of the general population.

Dr. Vogelstein emphasized that information about the genome will not change these estimates, which "are made under the assumption that we are omniscient and understand the effects of every variant and their interactions with one another."

Benefit Seen for Some Conditions

Dr. Vogelstein and his team derived their estimates from 53,666 monozygotic twin pairs and clinical data from registries all over the world. Their analyses suggest that for 23 of the 24 diseases studied, the majority of individuals will receive negative test results, which will probably not be very informative.

With a negative test result, they estimate that the risk of developing 19 of the 24 diseases would be 50% to 80% of that in the general population, at a minimum.

For 13 of 27 disease categories, the researchers note that the majority of patients who would ultimately develop these diseases would not test positive, even in the best-case scenario. For 4 of the disease categories — thyroid autoimmunity, type 1 diabetes, Alzheimer's disease, and coronary heart disease deaths in men — genetic testing might be able to identify more than three quarters of people who subsequently will develop the disease.

Not Ready for Prime Time

A panel of discussants agreed with Dr. Vogelstein's conclusions and pointed out the implications of the study.

Timothy Rebbeck, PhD, professor of epidemiology at the University of Pennsylvania Perelman School of Medicine in Philadelphia, and editor-in-chief of Cancer Epidemiology, Biomarkers & Prevention, noted that "we are going to have to reconsider the value of genetic information and rethink new models and when this information is valuable and when it may not be."

He added that "what we are learning" from this study and previous research is that genetics might not be "the magic cure-all" for all things.

Thomas Sellers, PhD, MPH, executive vice president and director at the H. Lee Moffitt Cancer Center & Research Institute in Tampa, Florida, agreed "with the primary conclusion of this report," adding that this is a very "provocative" study that puts very important issues into perspective.

"Genome sequencing is not going away; there are questions that we have to look at," he said.

The third discussant, Olufunmilayo I. Olopade, MD, professor of medicine and human genetics and director of the cancer risk clinic at the University of Chicago School of Medicine in Illinois, pointed out how many researchers said the same thing about BRCA testing.

"I remember the argument we had almost 20 years ago about BRCA testing," she said. "Some thought nothing good could come out of that research..., now it has been adopted," Dr. Olopade said. "Many women died from ovarian cancer, and we could have prevented it if we had known."

She emphasized that "we are now just beginning our understanding," and that to have an impact on prevention, "we need to have a more elaborate approach."

"I think that genome sequencing can improve public health, but we need to know how we are we going to do it," she said. "We are not there yet, it's not ready for prime time.

American Association for Cancer Research (AACR) 103rd Annual Meeting. Presented April 2, 2012.

Sci Transl Med. Published online April 2, 2012.

[url]http://stm.sciencemag.org/content/early/2012/04/02/scitranslmed.3003380

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The ultimate 'driver' is functional profiling

The assumption behind all these recent efforts has been the gene mutation theory of cancer. Mutated genes somehow either cause cancer directly or inactivategenes though to guard against cancer, the so-called oncogenes and tumor suppressor genes.

However, there is no functional proof that the gene mutation theory is correct. Only 1 to 2 percent of the genome consists of genes. DNA is not the whole story.

Cells speak to each other and the messages they send are interpreted via intracellular pathways. You wouldn't know this using genotype analysis. Phenotype analysis provides the window. It can test various cell-death signaling pathways downstream.

While most scientists use genotype platforms to detect mutations in these pathways that might result in response to chemicals, phenotype platforms have taken a different tack. By applying cell functional analysis, to measure the end result of pathway activation or deactivation, it can predict whether patients will "actually" respond, not theoretical susceptibility.

Even if cancers are from the same tissue, and are generated with the same carcinogen, they are never the same. There is always a cytogenetic and a biochemical individuality in every cancer.

The phenotype platform has the capacity to measure genetic and epigenetic events as a functional, real-time adjunct to static genotype platforms. The "key" to understanding the genome is understanding how cells work. The ultimate "driver" is functional profiling.

Sometimes the genetic signal may not be the driver mutation. Other signaling pathways, like passenger mutations, could be operative. Driver mutations are the ones that cause cancer cells to grow, whereas passengers are co-travellers that make no contribution to cancer development. It turns out that most mutations in cancers are passengers. However, buried among them are much larger numbers of driver mutations than was previously anticipated. This suggests that many more genes contribute to cancer development than was thought.

Cells speak to each other and the messaages they send are interpreted via these intracellular pathways. You wouldn't know this using analyte-based genomic and proteomic methodologies. However, functional profiling provides the window. It can test various cell-death signaling pathways downstream.

While most scientists use genomic or proteomic platforms to detect mutations in these pathways that might result in response to chemicals, cell-based functional profiling platforms have taken a different tack. By applying functional analysis, to measure the end result of pathway activation or deactivation, they can predict whether patients will actually respond.

The cell-based functional profiling platform has the capacity to measure genetic and epigenetic events as a functional, real-time adjunct to static genomic and proteomic platforms.

Multiple mutations and cancer

[url]http://www.ncbi.nlm.nih.gov/pmc/articles/PMC298677/

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In molecular testing of lung cancer, there are patients that have driver mutations such as EGFR and ALK. Some of these patients treated with appropriate targeted drugs like Tarceva and Xalkori, respectively, often have responses and somewhat improved survival.

The problem, according to Dr. Joan Schiller, Professor of Hematology and Oncology, and Deputy Director of the Simmons Cancer Center at the University of Texas Southwestern Medical School, is that those two mutations only represent a small minority of lung cancers.

The Lung Cancer Mutation Consortium (LCMC), a group of 14 institutions that banded together to find more driver mutations that are actionable in lung cancer, collected and analyzed about 1,200 patients with stage IV adenocarcinoma of the lung, of which 1,000 usable tumors and data were obtained.

Of those, KRAS represented about 23% of all the mutations, EGFR about 20% and EML4-ALK about 10%. The remainder 1%-2% were PI-3 kinase, RAS-1, RET, MEC, MET, ALT, BRAF, and HER2. Therefore, in 45% of patients, they were not able to identify a targetable mutation. This does not include squamous cell carcinoma, although they hope to extend the data collection to include this segment.

In the future, they hope to be able to have a physician order genomic testing on a patient's lung cancer and have it come back with appropriate mutations identified so that they can target the right drug to the right patient.

Medical research has focused a great deal on developing DNA (genomic) tests to identify gene expressions, amplifications and mutations relevant to cancer. The hope is that genetic information will enable researchers to better predict how you will respond to various treatment options.

However, when it comes to predicting the best treatment, unlocking the complexities of your DNA is simply not the answer. In fact, a March 2010 study in the Journal of the National Cancer Institute looked at the value of a number of gene tests and concluded none of the studies showed “clear usefulness.”

While genomic analysis can provide a veneer of information, unraveling the complexity of human tumor biology is beyond the scope of these analyses. Gene tests cannot capture the myriad of factors that ultimately determine how tumor cells will behave inside the body. Simply put, the human body is much more complex than the sum of its genes.

For example: a flower seed may have the genetic instructions to become a rose. But, its genes will not necessarily determine its size, number of blooms, etc. These features are heavily influenced by non-genetic and environmental factors, such as the soil, nutrients, water, sun exposure, pathogens and the climate in which the seed is nurtured.

No good gardener would attempt to tell you how your future bouquet will look by simply examining a packet of flower seeds. Similarly, no good doctor should attempt to choose drugs based solely on genomic analyses. Most physicians realize that genotype does not equal phenotype.

By testing your tumor in its native state, “functional profiling” takes not just your genomic make-up into consideration, but your cells’ entire biology. Treatment based on genetic testing is still a guessing game. Selecting drugs and combinations through the functional profiling of a tumor sample can predict response to treatment.

References:

Kris MG, Johnson BE, Kwiatkowski DJ, et al. Identification of driver mutations in tumor specimens from 1,000 patients with lung adenocarcinoma: The NCI's Lung Cancer Mutation Consortium (LCMC). Program and abstracts of the American Society of Clinical Oncology Annual Meeting; June 3-7, 2011; Chicago, Illinois. Abstract CRA7506.

Varella-Garcia M, Berry LD, Su PF, et al. ALK and MET genes in advanced lung adenocarcinomas: The Lung Cancer Mutation Consortium experience. Program and abstracts of the American Society of Clinical Oncology Annual Meeting and Exposition; June 1-5, 2012; Chicago, Illinois. Abstract 7589.

[url]http://cancerfocus.org/forum/showthread.php?t=3701

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According to Dr. Eric Topol, Director, Scripps Translational Science Institute, recent studies have highlighted the potential value of whole genome or exome sequencing to precisely guide therapy for patients with cancer. However, almost all samples today go into formalin-fixed, paraffin embedded (FFPE) blocks, which alters the DNA and makes sequencing quite compromised and difficult.

He told Medscape Connect that the company, Foundation Medicine, which works with formalin-fixed paraffin-embedded (FFPE) blocks, and gets about 250-300 genes, the exons or coding elements in those genes, and reads out any potential links to drugs. But the rate-liimiting step appears to be getting something beyond these paraffin blocks. This is, we could do better if we could use either fresh formalin-fixed or frozen tissue samples from a biopsy or surgical specimen.

Topol says the problem is that pathologists are seemingly quite ritualistic. They don't want to go to frozen samples, which would be the best for whole genome sequencing. We're just at the cusp of getting started with this type of limited, not even full exome sequencing, just a few hundred genes, but that isn't enough.

Rencent papers in multiple journals in Nature, Science, Nature Genetics and Cell have shown that with hundreds of tumor samples fully sequenced, no two cancers are the same and a lot of the action is not in the coding elements of the genes per se. Whole genome sequencing certainly appears to be an ideal path to pursue, but we can't do it with the fixed problems that we have with the way samples are handled today.

Topol thinks that maybe we could get fresh formalin-fixed samples, as those appear to be well-suited to whole genome sequencing, although this is still a somewhat bootstrapped situation, like the paraffin-embedded samples. It appears that the long those samples are embedded, the harder it is to get a reasonable sequence beyond very targeted regions.

There are no two cancer tissues that are the same on a molecular basis. There's quite a bit of heterogeneity within the samples and multiple sequencing could account for that. And we also want to anticipate recurrence, match up the right driver mutations and the backseat passenger mutations, whether or not there's needed immunotherapy; all those things that could be done if we could get the right information from the get go.

So Dr. Topol asks this: How are we going to move to a world with a clinic of the future where patients with cancer can get whole genome sequencing rapidly? That is, to have annotation and interpretation of the genome with a day, and have your therapy precisely guided genomically?

Gene Sequencing for Drug Selection?

Researchers have realized that cancer biology is driven by signaling pathways. Cells speak to each other and the messages they send are interpreted via intracellular pathways known as signal transduction. Many of these pathways are activated or deactivated by phosphorylations on select cellular proteins.

Sequencing the genome of cancer cells is explicitly based upon the assumption that the pathways - network of genes - of tumor cells can be known in sufficient detail to control cancer. Each cancer cell can be different and the cancer cells that are present change and evolve with time.

Although the theory behind inhibitor targeted therapy is appealing, the reality is more complex. Cancer cells often have many mutations in many different pathways, so even if one route is shut down by a targted treatment, the cancer cell may be able to use other routes.

In other words, cancer cells have "backup systems" that allow them to survive. The result is that the drug does not affect the tumor as expected. The cancer state is typically characterized by a signaling process that is unregulated and in a continuous state of activation.

In chemotherapy selection, molecular profiling examines a single process within the cell or a relatively small number of processes. All a gene mutation study can tell is whether or not the cells are potentially susceptible to a mechanism of attack. The aim is to tell if there is a theoretical predisposition to drug response.

It doesn't tell you the effectiveness of one drug (or combination) or any other drug which may target this in the individual. There are many pathways to altered cellular function. Functional Profiling measures the end result of pathway activation or deactivation to predict whether patients will actually respond (clinical responders).

It measures what happens at the end, rather than the status of the individual pathway, by assessing the activity of a drug (or combinations) upon combined effect of all cellular processes, using combined metabolic and morphologic endpoints, at the cell population level, measuring the interaction of the entire genome.

Translational science: past, present, and future

[url]http://www.biotechniques.com/multimedia/archive/00003/BTN_A_000112749_O_3671a.pdf

Note: Foundation Medicine is not any different than Caris Diagnostics in Phoenix (now Miraca Life Sciences), beyond testing for standard pathology "targets" such as ER, PR, Her2, EGFR mutations, KRAS, BRAF. They aren't worth much for the sorts of chemotherapy which is used in 95% of all cancers and useless with respect to drug combinations. While fresh tissue is very dear and hard to come by, function trumps structure, in terms of potency and robustness of information provided than using archival paraffin blocks.