gpawelski

The particular sequence of DNA that an organism possesses (genotype), or mutational assay, does not determine what bodily or behavioral form (phenotype), or cellular assay, the organism will finally display. Among other things, environmental influences can cause the suppression of some gene functions and the activation of others. The knowledge of genomic complexity tells us that genes and parts of genes interact with other genes, as do their protein products, and the whole system is constantly being affected by internal and external environmental factors. The gene may not be central to the phenotype at all, or at least it shares the spotlight with other influences. Environmental tissue and cytoplasmic factors clearly dominate the phenotypic expression processes, which may in turn, be affected by a variety of unpredictable protein-interaction events. Until such time as cancer patients are selected for therapies predicted upon their own unique biology, we will confront one targeted drug after another. A better solution to this problem would be 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. Functionally (cellular assay) profiling these results in patients with a multitude type of cancers suggest this to be a highly productive direction. There is a ray of hope with immunotherapy, after the elements of the cancer industry had put it under a breadbox for over twenty years. Immunotherapy actually does work. However, researchers have no idea why it benefits some people but not others, because releasing the brake facilitates an all-out attack by the immune system, it can cause serious side effects - colitis, skin rashes, impaired pituitary function - that must be managed. The key is identifying the individual patients who stand to benefit (not average populations). Certainly new approaches to immunotherapy are both needed and welcome. It's not the answer to all of cancer, certainly, but when it works, it's helpful.

When they did comprehensive screening for gene mutations at MD Anderson, in a huge number of patients, they found an actionable target in 31%, but, of these 31%, only 10% responded to the targeted therapy. Overall, only 2.4% of all the patients receiving genomic profiling had a response. This is absolutely horrible. I think that genomic profiling is in general a scam, yet virtually everyone is doing it. I think that certain types of targeted genomic profiling is probably worthwhile -- e.g. EGFR mutations in lung cancer, but phenotype analysis can test for the same drugs using cell culture as a platform. San Francisco, CA—Neal J. Meropol, MD, of the University Hospitals Seidman Cancer Center, and Case Western Reserve University, Cleveland, OH, has long advocated against unnecessary treatment and testing of patients with cancer. At the 2015 Gastrointestinal Cancers Symposium, Dr Meropol outlined his reasons why clinicians should not bend to pressure to routinely test all tumors. “Here are my top 10 reasons why I believe we are not ready for routine molecular profiling of tumors,” he told the audience, listing the following reasons. 1. Assay Platform Limitations Variability in the sensitivity and specificity of the many platforms being promoted raises questions regarding analytic validation, said Dr Meropol, asking questions that raise concern. How are the genes selected for these panels? Does the platform look at the transcriptome or just the genome? Are we looking at epigenomic changes that may be relevant in selecting treatments? What is the turnaround time for the results? And what is the cost of these assays? 2. Tumors Are Heterogenous and Complex Mutations in a tumor may be different between sites. “Although they may look the same under a microscope, colon cancers and gastric cancers are extremely complex, and extremely heterogenous in terms of their molecular profiles,” Dr Meropol said. Nor will finding a single driver mutation guarantee that a single drug intervention will be effective, because of “cross talk” between pathways occurring downstream of a key mutation. “If we’re going to use a tumor biopsy for selecting treatment for an individual patient,” he said. “This biopsy should be done proximate to the time that we’re going to intervene with a new therapy.” 3. We Don’t Know the Drivers According to Dr Meropol, definitions used in the MATCH trial for identifying drivers to explore in a clinical trial are not yet good enough for routine care. “The reasons not to try it are that these are costly interventions, they may not work, they have side effects, and they provide false hope. This should not be our routine approach with patients,” he advised. 4. We Don’t Have the Evidence that Links Drugs to These Drivers The best level evidence is an FDA-approved dyad, but Dr Meropol stressed that this level of evidence is missing in nearly all drug cases. “Even if an agent meets a clinical end point, and there’s evidence of target inhibition, and there’s plausible evidence of a predictive or selection assay or analyte,” he said, “until it’s been proved in a prospective clinical trial, the evidence may be viewed as weak in terms of routine clinical practice.” Preclinical evidence is an even poorer prognosticator for patient outcomes. Dr Meropol iterated the need for incentivizing the development of biomarkers worldwide. 5. Investigational Drugs Are Not Widely Available There are limited clinical trial sites; patients have to seek studies and travel for them. “Not everybody lives in close proximity to a research center that has access to multiple clinical trials and new agents,” Dr Meropol said, “and getting compassionate access to a new drug in development is a logistically complicated process. It’s time-consuming and rather opaque.” 6. It Isn’t Practical to Screen Many to (Maybe) Help a Few The evidence is simply not there at this time, he said, highlighting a phase 1 study conducted at M.D. Anderson Cancer Center that intended to show the benefit of identifying clinical trials that might be appropriate for patients’ tumors. Of the 1283 patients assessed, 31% had at least 1 mutation, and among those who could be matched for treatment, approximately 10% had an antitumor response. But the overall response rate based on matching was only 2.4%. 7. There Is No Mechanism to Pay for Drugs for Off-Label Use Off-label use of expensive targeted agents is increasingly scrutinized by payers, and costs are falling on patients. “Recommending cancer drugs with high copays may not be ethical without strong evidence that it’s going to help that individual patient,” he said. 8. Drug Approval Based on Tumor Type, Not on Genotype The current (and old) paradigm is histology-based and requires large prospective phase 3 trials to provide the level of evidence that leads to a FDA and worldwide drug approval. But an emerging paradigm for drug approval is genome-based, using small studies looking for big effect to make decisions, “but we’re simply not there yet,” he said. 9. No Statistical Approach to Interpreting a Series of Anecdotes Dr Meropol asked: How much evidence is needed to conclude that a particular mutation should receive a particular therapy? How many patients are needed with outcome data across how many tissues of origin? And when looking at a major response, how can we distinguish between a fluke and an outcome that is real? The answers to these questions remain unknown. 10. Unintended Consequences for Patients Given the potential for confusion over interpretation, it is important to know who is interpreting the data and making the recommendations. “We don’t want to give our patients false hope,” said Dr Meropol. “We don’t want to subject them to the risks of needless biopsies, and we don’t want to subject them to the financial burden of therapies and procedures that are not destined to help them.” Source: Association for Value-Based Cancer Care (February 2015, Vol 6, No 1 - Personalized Medicine)

Therapies targeted at the specific genetics of a patient's lung cancer have proved harder to realize than expected. Ramaswamy Govindan vividly remembers the first time he treated his patients with the cancer drug gefitinib. It was the start of the millennium, and the outlook for patients with metastatic non-small-cell lung cancer (NSCLC) was dire: less than 40% survived a year after diagnosis. “The second patient I treated was about to go into hospice care,” recalls Govindan, a medical oncologist at Washington University School of Medicine in St Louis, Missouri. “But she went on to live three years before dying of a heart attack.” Gefitinib was approved by the US Food and Drug Administration (FDA) in 2003. Marketed as Iressa by AstraZeneca, its arrival was a watershed moment in the treatment of NSCLC, the most common type of lung cancer. The drug blocks a protein called epidermal growth factor receptor (EGFR), which transmits signals that help to control the division and migration of cancer cells. However, although some patients responded well to the treatment, many others did not. The same was true for another drug that targets EGFR: erlotinib (Tarceva), developed by Genentech and OSI Pharmaceuticals and approved by the FDA in 2004. The only apparent trend was that non-smokers were more likely than smokers to respond to erlotinib. “Back in the day, you would give Tarceva to somebody because they didn't smoke, but in the vast majority of those people it didn't help,” says Mark Kris, a thoracic oncologist at Memorial Sloan Kettering Cancer Center in New York City. In 2004, two research teams — one of which included Kris — discovered the secret1, 2. Both gefitinib and erlotinib were selectively active against lung cancers with hyperactive, mutated versions of the EGFR gene, but ineffective against tumours in which the gene was not mutated. Mutated EGFR is predom-inantly found in a type of NSCLC called adenocarcinoma, which accounts for 40% of lung cancers and is the most common form of the disease in people who have never smoked. The realization that specific genetic variants might help researchers to develop personalized lung-cancer treatments has launched a generation of targeted drugs that can deliver years of additional life to certain subgroups of patients. But some patients are still waiting to reap the medical benefits of the post-genomic era, and many doctors and clinical researchers fear that the low-hanging fruits of lung-cancer genetics may already have been picked. The cancer genome is a battered and scarred landscape of DNA-sequence changes as well as swapped, duplicated and deleted regions. The therapeutic focus is on the subset of these mutated genes — 'drivers' — that are essential for aggressive cell growth. The most useful drivers from a therapeutic perspective are oncogenes, which encode proteins that promote uncontrolled cell division and have the potential to convert a normally functioning cell into a cancer cell. Drugs that target mutant oncogenes might halt or reverse tumour growth. One major lung-cancer oncogene is EGFR. Mutations to the EGFR oncogene are detected in more than 40% of adenocarcinomas. Three drugs are commercially available for EGFR-mutant cancers, and more are in trials. In 2007, researchers uncovered a second driver oncogene that is present in 5–7% of adenocarcinomas. Called ALK, this gene encodes a poorly understood signalling protein and occasionally undergoes a genomic rearrangement that leaves the resulting protein permanently turned on. In 2011, the FDA approved crizotinib (marketed by Pfizer as Xalkori) for NSCLC patients whose tumours exhibit such rearrangements. Phase III trial data presented by Pfizer at the 2014 annual meeting of the American Society for Clinical Oncology (ASCO) indicate that crizotinib can extend the life of patients whose tumours have mutations in ALK. However, the benefits of these targeted drugs are only temporary — after about a year of remission, most tumours acquire resistance. For example, more than half of the tumours treated with EGFR inhibitors acquire a mutation called T790M in the EGFR gene3. This blocks the drug without interfering with the mutant protein's signalling. Tumours often contain genetically distinct cell populations, and many researchers believe that cancer recurrence may represent the evolutionary victory of an already-resistant minority. “Once we start to kill off cells that have the sensitizing mutation, the intrinsically resistant cells start to grow,” says Tony Mok, a clinical oncologist at the Chinese University of Hong Kong. Presentations at this year's ASCO meeting revealed promising clinical-trial data on drugs being developed by Clovis Oncology and AstraZeneca that inhibit the T790M mutant receptor. One molecule induced tumour shrinkage in almost two-thirds of patients. Patients with crizotinib-resistant tumours also received hopeful news this year. Such resistance often arises in the absence of a detectable mutation, which suggests that other mechanisms increase ALK activity to overwhelm crizotinib's modest capacity for inhibition. In April 2014, the FDA moved with unprecedented speed to approve the drug ceritinib (marketed by Novartis as Zykadia) based purely on a phase I trial4 showing a strong clinical response in resistant patients. Subsequent data suggest that ceritinib works equally well in both previously untreated and crizotinib-resistant patients. Ceritinib is 5 to 20 times more potent than crizotinib as an ALK inhibitor, and it is also more selective, says Alice Shaw, an oncologist at Massachusetts General Hospital in Boston, whose team led the phase I trial. At least nine other ALK drugs are in development. Targeted treatments benefit only a minority of lung-cancer patients. For the rest, the hunt continues for drivers that might prove vulnerable to therapy. Most progress has been seen in people diagnosed with adenocarcinoma and who do not smoke, many of whom have cancers that have arisen through one primary driver mutation (see page S12). By contrast, the mutational load in a smoker's tumour can be overwhelming, making it a challenge to separate the signals of likely driver mutations from the noise generated by large numbers of 'passenger' mutations that make a minimal contribution to tumour growth. But even targeting the genetic culprit in a single driver mutation can be tricky. Take the example of the oncogene KRAS, which encodes a signalling protein involved in cell proliferation. KRAS mutations appear in as many as one-quarter of adenocarcinomas, but attempts at targeted therapy have so far failed. A study reported at the 2014 ASCO meeting suggests that a subset of patients with KRAS-mutant NSCLC may benefit from a combination of drugs that target several proteins in the same biological pathway as KRAS. So far, only 10–15% of KRAS-mutant tumours respond to combination treatment, says Vassiliki Papadimitrakopoulou, a medical oncologist at the MD Anderson Cancer Center in Houston, Texas, who helped to coordinate the study. “We would like to see more than that.” For patients with non-adenocarcinoma lung cancers, targeted options are limited. Very few patients with squamous cell carcinoma (SCC) — the second most common form of lung cancer — have EGFR or ALK driver mutations. Most SCC tumours occur in smokers, and are plagued by the same extensive genomic mutation that is confounding efforts to apply targeted treatment to smokers' adenocarcinomas. This may be about to change, thanks to the work of Govindan and his colleagues at the Cancer Genome Atlas (TCGA), which in 2012 published a detailed assessment of the SCC genomic landscape derived from tissue samples from 178 SCC tumours5. The results suggested a number of avenues for potential intervention. A mutation in the gene CDKN2A, for example, is found in 70% of SCC tumours and could be a target. The urgent need for progress in lung-cancer treatment has inspired Papadimitrakopoulou, who is collaborating with other US investigators on the Lung Cancer Master Protocol. Launched in June, this multi-arm, multi-institutional clinical trial will use sequencing to match SCC patients with targeted drug candidates. It will also accumulate a lot of cancer genomic data. “We will be characterizing the largest set of SCCs across the United States,” says Papadimitrakopoulou. Govindan and his colleagues are also working on large-scale genomic analysis. After a genomic survey of mutations in 230 adenocarcinoma tumours6, published in July 2014, he and fellow TCGA coordinators Louis Staudt and Matthew Meyerson are working on plans to study a larger number of tumour samples in the hope of detecting additional targetable drivers. The robust performance of drugs that target ALK and EGFR has made testing for mutations in these genes routine. But as the cost of sequencing plummets, some clinicians believe that it makes more sense to survey hundreds of cancer-related genes rather than just those two to provide a larger set of potential targets. Kris is among the evangelists for extensive clinical sequencing. “If you have lung cancer in 2014, the first thing we do is a biopsy that includes a comprehensive genetic test for all potential drivers,” he says. Companies are also providing the tools to do this. Foundation Medicine, a company in Cambridge, Massachusetts, co-founded by TCGA scientists, generates oncology diagnostic reports for clinicians based on sequencing data from 236 cancer-associated genes. The company expects to do 25,000 tests in 2014, up from 9,000 in 2013. In June, the Memorial Sloan Kettering Cancer Center forged a partnership with Quest Diagnostics of Madison, New Jersey, to broaden clinician access to the centre's in-house genetic test, which also surveys numerous oncogenes in parallel. Genetic analyses could help to identify patients with mutations that are rare in lung cancer but are common in other tumour types. For example, a subset of adenocarcinoma patients with mutations affecting the RET gene might benefit from cabozantinib, a drug that targets this alteration in thyroid cancer7. And with much of the pharmaceutical industry's oncology efforts focused on developing targeted drugs, data from sequencing the genes of lung-cancer patients can also help to direct those patients to clinical trials. To assess the impact of sequencing on lung-cancer care, Kris and other scientists — who formed a group called the Lung Cancer Mutation Consortium — sequenced as many as 10 known oncogenes in more than 1,000 patients. Kris reports that 28% of the people tested were matched to clinical trials they might not otherwise have known about8. As with KRAS, many oncogenes are informative scientifically but are not medically useful, leading some researchers to question the short-term benefits of routine, large-scale tumour sequencing in patients — a practice Mok says is unlikely to improve lung-cancer care significantly until the next EGFR comes along. Still, he believes that genetic analysis must be embedded into the diagnostic process so that drugs can be matched to a patient as quickly as possible — he holds out hope that new drivers will soon join ALK and EGFR. As would everyone struggling to find new weapons against this lethal disease. With such resources at hand, more doctors might look forward to experiencing the sweet satisfaction Govindan encountered on providing his patient with just the treatment she needed to buy years of additional life. http://www.nature.com/nature/journal/v5 ... 13S8a.html

Employing multi-dimensional analyses of both genomic and phenotypic platforms Robert A. Nagourney, M.D. Oncologists confront numerous hurdles as they attempt to apply the new cancer prognostic and predictive tests. Among them are the complexities of gene arrays that introduce practicing physicians to an entirely new lexicon of terms like “splice variant, gene-rearrangement, amplification and SNP.” Although these phrases may roll of the tongue of the average molecular biologists (mostly PhDs), they are foreign and opaque to the average oncologist (mostly MDs). To address this communication shortfall laboratory service providers provide written addenda (some quite verbose) to clarify and illuminate the material. Some institutions have taken to convening “molecular tumor boards” where physicians most adept at genomics serve as “translators.” Increasingly, organizations like ASCO offer symposia on modern gene science to the rank and file, a sort of Cancer Genomics for Dummies. If we continue down this path, oncologists may soon know more but understand less than any other medical sub-specialists. However well intended these educational efforts may be, none of them are prepared to address the more fundamental question: How well do genomic profiles actually predict response? This broader issue lays bare our tendency to confuse data with results and big data with big results. To wit, we must remember that our DNA, originally provided to each of us in the form of a single cell (the fertilized ovum) carries all of the genetic information that makes us, us. From the hair follicles on our heads to the acid secreting cells in our stomach, every cell in our body carries exactly the same genetic data neatly scripted onto our nuclear hard-drives. What makes this all work, however, isn’t the DNA on the hard drive, but instead the software that judiciously extracts exactly what it needs, exactly when it needs it. It’s this next level of complexity that makes us who we are. While it is true that you can’t grow hair or secrete stomach acid without the requisite DNA, simply having that DNA does not mean you will grow hair or make acid. Our growing reliance upon informatics has created a “forest for the trees” scenario, focusing our gaze upon nearby details at the expense of larger trends and insights. What is desperately needed is a better approximation of the next level of complexity. In biology that moves us from the genotype (informatics) to the phenotype (function). To achieve this, our group now regularly combines genomic, transcriptomic or proteomic information with functional analyses. This enables us to interrogate whether the presence or absence of a gene, transcript or protein will actually confer that behavior or response at the system level. I firmly believe that the future of cancer therapeutics will combine genomic, transcriptomic and/or proteomic analyses with functional (phenotypic) analyses. Recent experiences come to mind. A charming patient in her 50s underwent a genomic analysis that identified a PI3K mutation. She sought an opinion. We conducted an EVA-PCD assay on biopsied tissue that confirmed sensitivity to the drugs that target PI3K. Armed with this information, we administered Everolimus at a fraction of the normal dose. The response was prompt and dramatic with resolution of liver function abnormalities, normalization of her performance status and a quick return to normal activities. A related case occurred in a young man with metastatic colorectal cancer. He had received conventional chemotherapies but at approximately two years out, his disease again began to progress. A biopsy revealed that despite prior exposure to Cetuximab (the antibody against EGFR) there was persistent activity for the small molecule inhibitor, Erlotinib. Consistent with prior work that we had reported years earlier, we combined Cetuximab with Erlotinib, and the patient responded immediately. Each of these patients reflects the intelligent application of available technologies. Rather than treat individuals based on the presence of a target, we can now treat based on the presence of a response. The identification of targets and confirmation of response has the potential to achieve ever higher levels of clinical benefit. It may ultimately be possible to find effective treatments for every patient if we employ multi-dimensional analyses that incorporate the results of both genomic and phenotypic platforms.

Mark Pegram, M.D. The use of cutting-edge technology and bioinformatics to inform clinical decision-making in oncology is still a ways off, according to Mark Pegram, MD, the Susy Yuan-Huey Hung Professor of Oncology and Director of the Stanford Breast Oncology Program, Stanford University, Palo Alto, California. At the 9th Annual New Orleans Summer Cancer Meeting, Dr. Pegram said the “lofty goal” of targeted therapeutic “cocktails”—which will be needed to address the molecular diversity of tumors—is proving hard to achieve. Circulating Tumor Cells Circulating tumor cells as an alternative to serial biopsies of metastatic lesions has great appeal, but the uptake of this technology has been somewhat anemic. One problem is obtaining a consistent definition of a circulating tumor cell. The cell must be positive for cytokeratin, must have a nucleus, must have a negative control, must be negative for leukocyte cytoplasm (white cell markers), and the nucleus must fit inside the cytoplasm. “These are the things measured using a huge variety of different approaches for defining and capturing [circulating tumor cells],” he said. The most clinically advanced is the CellSearch System, which uses an antibody/ferrofluid combination to attach specifically to circulating tumor cells, and magnets to draw those cells out of the blood sample to be stained and identified. The test enumerates the number of circulating tumor cells in a patient with metastatic disease, and this is correlated with overall survival. “The problem with this assay is that it is not sensitive enough to capture [circulating tumor cells] in early stages of disease. While enumeration of [circulating tumor cells] is prognostic, let’s be honest: that’s not what we are interested in,” Dr. Pegram said. “We are interested in predicting response to treatment.” He has observed that while some clinicians are “enamored” of this technology and do use it, others realize that it holds little value over routine restaging with radiographic studies. The assay also reveals little as to what is happening in these cells, and the small number of circulating tumor cells captured—five or so—is insufficient for fully deciphering the tumor, he said. Capturing more cells could help, and that is what microfluidics-based cell separation does. This new, simpler technology passes blood through a membrane, separating larger tumor cells from other blood elements and yielding thousands of cells upon which clinically relevant tests can be performed. “The approaches that have much higher yields will be more useful because they will be informative as to what cells are doing at a molecular level,” he predicted. Even more sophisticated blood-based technology will someday be better able to capture the genetic heterogeneity of advanced solid tumors at a gene-expression level so they can be compared with the primary tumor. This, however, will present other challenges. “In one blood sample there are multiple populations of [circulating tumor cells] that are different from another. This will pose a diagnostic challenge and a treatment challenge, as well, if we find unique targets within the same patient at the same time,” he said. “Until we can come to terms with the complexity of solid tumor malignancies, we can’t make informed decisions.” At this point, guideline committees “have not latched on to [circulating tumor cells] as a ‘must’ in clinical practice,” he indicated, calling circulating tumor cell determination a “consideration,” but one lacking in great value until emerging technologies can interrogate circulating tumor cells at a molecular level. Genomics and Drug Development The promise of genomics was to identify mutations within a tumor and thus allow the clinician to concoct a tailored therapeutic cocktail. In reality, however, the scenario is infinitely complex. Within a single MCF-7 human breast cancer cell, for instance, 157 chromosomal break points have been found. “We have rich genomic information in a tumor cell, but this does not tell the doctor how to treat the patient,” he said. The Cancer Genome Atlas (TCGA) Network, in its examination of its first 507 breast cancer samples, revealed only four frequently mutated genes out of 50 that were identified: PIK3CA, TP53, MAP3K1, and GATA3. “This was a stunning observation,” commented Dr. Pegram. “We thought we would discover multiple new therapeutic targets in breast cancer and therefore have home runs in drug development, but we found only four, and all four were already known to be common mutations.” Drugs are already targeting PI3K, the other three frequent mutations are not druggable, and the rest of the 50 genes are low-frequency mutations (affecting about 2% of breast cancers) for which pharmaceutical companies are unlikely to invest. “This will pose a challenge because our current models of drug development will not survive this reality,” he predicted. Furthermore, according to Dr. Pegram, deep sequencing identifies even more heterogeneity, revealing individual clones with different mutational profiles within the same tumor. The current next-generation diagnostics are not performing deep sequencing and therefore are not demonstrating the molecular heterogeneity that is critical for selecting the best targeted agent, he said. Even “more sobering,” he continued, is that this complexity is present at the time of diagnosis, with further alterations piled on due to drug resistance. Cancer and genomes are not static; they are a moving target, he reiterated. While the situation is clinically frustrating now, there is the potential to tease apart the molecular evolution of cancers with future sequencing technology, and this “extraordinary” achievement could give insights into prevention strategies. Adding Proteomic Data Even more complex than genomics is proteomics, the large-scale analysis of protein-expression profiles through mass spectrometry. Proteomic information on post-translational modifications in the tumor (ie, phosphorylation, glycolisation, etc) could be a useful adjunct to genomic information, producing a more “holistic view” of pathway regulation. “The hope is that mixing proteomic work along with genomic work will facilitate our understanding of what is going on in the dynamic tumor cell,” Dr. Pegram said. “But the problem with proteomics is size: the proteome is much larger than the genome, due to alternative splicing and protein modification.” The information desired from proteomics includes all protein-to-protein interactions, protein functions and their regulation, protein modifications, subcellular location, and protein concentrations. Current approaches do not provide all this information. While polymerase chain reaction (PCR) testing determines gene amplification, there is no PCR equivalent for proteomics. Sequencing tools are robust in genomics, but mass spectrometry is still emerging in proteomics. Furthermore, proteomic data is “big data,” and huge servers are needed just to store the data. Novel approaches are currently being pioneered to address these issues, he said. In summary, Dr. Pegram said, “Mutational events in cancer can yield complex and deranged pathways, but they are still highly functional and they can take the lives of our patients. We need to understand them.” Disclosure: Dr. Pegram reported no potential conflicts of interest. The ASCO Post, September 1, 2014, Volume 5, Issue 14 http://www.ascopost.com/issues/septembe ... locks.aspx

United States vs Caris Life Sciences, Caris Diagnostics, Miraca Life Sciences It is familiar and makes me somewhat feel slightly ill, but it was refreshing to see that action is being taken. Just a note, however, that the fraud was not discovered by CMS but instead the case derived its impetus from the persistence of two (justifiably) disgruntled former employees. I suspect that many companies are just one disgruntled employee away from facing a similar experience. In this case, except for the monumental stupidity that these complainants' superiors evinced in ineptly dealing with their employees, the violations likely would never have been detected by CMS. http://pathologyblawg.com/wp-content/up ... plaint.pdf Wild West of Molecular Testing? Lawsuit Alleges Caris Engaged in Aggressive Marketing http://www.cancerletter.com/articles/20140808_1 http://www.cancerletter.com/articles/20140808_3 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. Batch Processing of tumor biopsies for cell markers viewtopic.php?f=8&t=50687

Published Studies Often Conflict With Results Reported to ClinicalTrials.gov Joseph S. Ross, M.D., MHS. Yale University School of Medicine Study results published in major medical journals often conflict with the data its authors have submitted to ClinicalTrials.gov, according to an analysis published in JAMA March 11, 2014. The ClinicalTrials.gov registry, maintained by the National Library of Medicine, was created to help improve transparency in the medical literature by ensuring that all results of clinical trials, whether published or not, are archived in a single repository. A 2007 law mandated that researchers post results of studies on all products regulated by the US Food and Drug Administration (FDA) within 12 months. Many journals have also pledged to require their authors to report their findings in the registry. But numerous problems with the registry have been documented since its creation, including a failure of many researchers to report their results and sloppy data entry by investigators. A new analysis by Joseph S. Ross, MD, MHS, an assistant professor of medicine at Yale University School of Medicine, and his colleagues raise questions about the accuracy of what is reported in the registry and in the medical literature. The team compared the results of 96 trials published in top-tier medical journals, including JAMA, the New England Journal of Medicine, and the Lancet, with the results of those trials reported in ClinicalTrials.gov. They found at least 1 discrepency in the results reported for 93 of the trials. Results matched in both the registry and journal article in only about half the cases. Ross discussed the findings with news@JAMA. news@JAMA: Why did you choose to do this study? Dr Ross: Our research group is interested in thinking of ways to improve the quality of clinical research. When the Food and Drug Administration amendments were passed requiring results reporting [to the ClinicalTrials.gov registry], we were interested in how that would play out. There have been studies about how compliant researchers are with this requirement. We wanted to look at how accurate the reported findings are. By comparing the reported results to published trials, we wanted to see how well it was working. What we found was a surprise. news@JAMA: Why were the results surprising? Dr Ross: We found important discrepancies between the results reported in ClinicalTrials.gov and the published results. We don’t know which is right. There were lots of end points reported in 1 source that weren’t reported in the other. news@JAMA: Can you give an example? Dr Ross: We started by looking at the primary end points published in high-impact journals and what end points were reported in ClinicalTrials.gov. Of 90-some-odd trials, there were 150 to 160 primary end points; 85% were described in both sources, 9% only in ClinicalTrials.gov and 6% only in the publications. For the more than 2000 secondary end points, 20% were reported only in ClinicalTrials.gov and 50% only in publications. Only 30% were described in both sources. You see that only part of the information is available in 1 source. We need to make the sources as complete as possible. The publications need to link back to ClinicalTrials.gov because they often don’t include all the end points. news@JAMA:Why might there be such a difference? Dr Ross: There are a lot of potential explanations. More end points were reported in the published papers than in ClinicalTrials.gov. This suggests authors are reporting end points in the paper that make the results look better that weren’t predetermined. That can skew the literature. news@JAMA: Could edits made by the journals, such as requests for more information or new analyses, or typographical errors account for some discrepancies? Dr Ross: It could be editing. An authorship team submits the results and these are publications that have strong editorial staffs. There could be slightly different approaches in analysis submitted to the 2 sources. Some are typographical errors. For example, 1 study reported a hazard ratio of 4 in ClinicalTrials.gov instead of the hazard ratio of 2 in the study [the hazard ratio and standard deviation were transposed]. That perverts the study result. news@JAMA: What can be done to improve the accuracy results in reporting? Dr Ross: These results are increasingly being used by researchers and in meta-analyses; we want them to be accurate. The journals pay a large staff of full-time editors to make sure these studies don’t have errors, but ClinicalTrials.gov has a relatively small staff. We may need a larger endeavor than what the National Library of Medicine originally envisioned. A third of the discordant results led to a different interpretation of the trial. This a problem we need to be attending to. We studied the highest-tier journals, so this is likely the best-case scenario. These are likely the highest-achieving researchers. Who knows what’s happening with lower-tier journals? http://newsatjama.jama.com/2014/03/11/a ... rials-gov/ Note: Different results from the same study reported in different publications. This is sort of mind boggling. It shows that a whole lot of the time medical research authors are massaging and/or cherry picking their own data and they can't even keep their own stories straight!

Personalized Chemotherapy: Understanding Clinical Trials - the Kaplan Meier Graph In this video of Personalized Cancer Chemotherapy, Dr. Larry M. Weisenthal explains the Kaplan-Meier graph. The graph is often used in clinical trials to compare survival times among patients with the same type of cancer who received different chemotherapy treatments. Understanding the graph is easy and also very useful as it will enable you to cut through the clutter in published clinical trial manuscripts and see at glance if any chemotherapy regimen provided a superior survival benefit. Big Data Meets Cancer: Neil Hunt at TEDxBeaconStreet The consistent and specific cure or control of cancer will require multiple drugs administered in combination targeted to abnormal patterns of normal cellular machinery that effect or reflect malignant behavior, according to Dr. Arnold Glazier, former Oncology Fellow at Johns Hopkins. It is finding the patterns of malignant cells and developing a set of 5 to 10 drugs in order to cure or control cancer that classical clinical trials are not going to solve. In clinical research, studies are deemed reportable when they achieve statistical significance. The so-called power analysis is the purview of the biostatistician who examines the desired outcome and explores the number of patients (subjects) required to achieve significance. The term “N” is this number. The most famous clinical trials are those large, cooperative group studies that, when successful, are considered practice-changing. That is, a new paradigm for a disease is described. To achieve this level of significance it is generally necessary to accrue hundreds, even thousands of patients. This is the “N” that satisfies the power analysis and fulfills the investigators expectations. So what about Trials of N=1? This disrupts every tenet of cancer research, upends every power analysis and completely rewrites the book of developmental therapeutics, according to Laboratory Oncologist Dr. Robert A. Nagourney. Every patient is his or her own control. Their good outcome reflects the success or failure of "the trial." There is no power analysis. It is an "N" of 1. This “breakthrough” concept however, has been the underpinning of the work of investigators like Drs. Larry Weisenthal, Andrew Bosanquet, Ian Cree, Robert Nagourney and all the other dedicated researchers who pioneered the concept of advancing cancer outcomes one patient at a time. These intrepid scientists described the use of each patient’s tissue to guide therapy selection. They wrote papers, conducted trials and reported their successful results in the peer-reviewed literature. These results have provided statistically significant improvements in clinical responses, times to progression, even survival. By incorporating the contribution of the cellular milieu into clinical response prediction, these functional platforms have consistently outperformed their genomic counterparts in therapy selection. With Cancer, Don’t Ask the Experts http://robertanagourney.wordpress.com/2 ... e-experts/

Robert A. Nagourney, M.D. The New York Yankees catcher Yogi Berra famous quote, “Déjà vu all over again,” reminds me of the growing focus on the concept of “N- of-1.” For those of you unfamiliar with the catchphrase, it refers to a clinical trial of one subject. In clinical research, studies are deemed reportable when they achieve statistical significance. The so-called power analysis is the purview of the biostatistician who examines the desired outcome and explores the number of patients (subjects) required to achieve significance. The term “N” is this number. The most famous clinical trials are those large, cooperative group studies that, when successful, are considered practice-changing. That is, a new paradigm for a disease is described. To achieve this level of significance it is generally necessary to accrue hundreds, even thousands of patients. This is the “N” that satisfies the power analysis and fulfills the investigators expectations. So what about an N-of-1? This disrupts every tenet of cancer research, upends every power analysis, and completely rewrites the book of developmental therapeutics. Every patient is his or her own control. Their good outcome reflects the success or failure of “the trial.” There is no power analysis. It is an “N” of 1. This “breakthrough” concept however, has been the underpinning of the work of investigators like Drs. Larry Weisenthal, Andrew Bosanquet, Ian Cree, myself and all the other dedicated researchers who pioneered the concept of advancing cancer outcomes one patient at a time. These intrepid scientists described the use of each patient’s tissue to guide therapy selection. They wrote papers, conducted trials and reported their successful results in the peer-reviewed literature. These results I might add have provided statistically significant improvements in clinical responses, times to progression, even survival. By incorporating the contribution of the cellular milieu into clinical response prediction, these functional platforms have consistently outperformed their genomic counterparts in therapy selection So why, one might ask, have the efforts of these dedicated investigators fallen on deaf ears? I think that the explanation lies in the fact that we live in a technocracy. In this environment, science has replaced religion and medical doctors have abdicated control of clinical development to the basic scientists and basic scientists love genomics. It is no longer enough to have good results; you have to get the results the right way. And so, meaningful advances in therapeutics based on functional platforms have been passed over in favor of marginal advances based on genomic platforms. There is nothing new about N-of-1. It has been the subject of these investigators compelling observations for more than two decades. Though functional platforms (such as our EVA-PCD) are not perfect, they provide a 2.04 (1.62 to 2.57, P < 0.001) fold improvement in clinical response for virtually all forms of cancer – as we will be reporting (Apfel C, et al Proc ASCO, 2013). It seems that in the field of cancer therapeutics “perfect is the enemy of good.” By this reasoning, good tests should not be used until perfect tests are available. Unfortunately, for the thousands of Americans who confront cancer each day there are no perfect tests. Perhaps we should be more willing to use good ones while we await the arrival of perfect ones. After all, it was Yogi Berra who said, “If the world was perfect, it wouldn’t be.”

An illustrated executive summary to explain what MCED is all about. They made another discovery, a refinement to the elusive mechanism of arterial inflammation, which is in turn the triggering event in atherosclerosis. It's not simply massive calcium accumulation death (MCAD), but massively calcified endosomal death (MCED). It's not simply generalized increased calcium uptake; it's formation of massively calcified endosomes, which are extruded as massively calcified exosomes, which phagocytic immune cells try to ingest and around which lymphocytes form rosettes. In his most recent research, Dr. Weisenthal has drilled-down even deeper into the mechanism that causes blood vessels to become inflamed and blocked. Previously, Dr. Weisenthal’s understood the newly-discovered process of endothelial cell death somehow involved a massive accumulation of calcium. However, Dr. Weisenthal has discovered that the calcium focuses on tiny structures called endosomes and exosomes - and he now understands how that produces the inflammation. MCED Discovery http://www.vasocell.com/MCED_Discovery.html A two minute animated video that explains the new finding. http://vimeo.com/100928488

The 16th International Symposium on Anti-Angiogenic Therapy: Recent Advances and Future Directions in Basic and Clinical Cancer Research. Date:February 6-8, 2014 Location:San Diego, California 92122, United States Description: The field of anti-angiogenic therapy is quite complicated with various results with individual agents in different disease types. In fact, the efficacy of such agents in the advanced setting is different from that of an early stage in the adjuvant setting. In addition to learning more about the efficacy and appropriate use of these agents, it is also important for health care providers to understand new toxicities that have been recognized in association with the use of anti-angiogenic agents. This symposium will provide a comprehensive overview of the appropriate use of anti-angiogenic therapy in patients with solid malignancies. In addition, this symposium will review the appropriate use of anti-angiogenic agents and allow the learner to recognize toxicity and potential biomarkers. The main area of feedback focused on biomarkers and the appropriate use of therapy. This is an inherent challenge as we do not have any biomarkers that are validated. However we will continue to scan the literature and seek speakers who can address the issue of biomarkers and patient selection. We will also seek speakers and discuss resistance pathways, which overlap with the above issues. One Laboratory Oncologist has the answer; will they listen? Poster Presentation: Massive calcium accumulation death (MCAD) of endothelial cells as a putative mechanism for Avastin (bevacizumab) anti-angiogenesis and acquired resistance to bevacizumab. Larry Weisenthal, Summer Williamson, Cindy Brunschweiler, and Constance Rueff-Weiesnthal We have discovered that human endothelial cells undergo two forms of cell death. 1. A non-specific form of cell death, similar to that of other normal and neoplastic cells. 2. A unique form of cell death, seen only in endothelial cells, associated with massive accumulation of calcium. We call this massive calcium accumulation death or MCAD. MCAD may be identified by cytochemical staining with: a. Fast Green alone b. Fast Green/Hematozylin c. Fast Green/Wright-Giemsa d. Alizarin red S (most advantageous) Sera from different patients is variably inhibitory of MCAD; circulating pro-angiogenic factors may be the mechanism of bevacizumab failure and a test called AngioRx assay may identify sera with such factors. Here is what's new this year: We have discovered that MCAD occurs when endothelial cells deprived of VEGF begin to form massively calcified endosomes, which result in a unique form of cell death, specific to endothelial cells, and triggered only by pharmaceuticals known to have anti-angiogenic effects. It is not triggered by traditional cytotoxic agents. When many calcified endosomes are formed, the cells die and release massively calcified exosomes into the cellular millieu. We isolated calcified exosomes produced by incubating circulating endothelial cells from a normal blood donor for 3 days in the presence of bevacizumab. We then incubated freshly drawn buffy coat leukocytes from the same normal donor and found that (1) neutrophils, monocytes, and lymphocytes clustered around these calcified exosomes and appeared to interact with them and (2) this resulted in the release of TNF into the culture medium. Besides being an inflammatory mediator, TNF has been shown to promote retinal vasculogenesis in various occular models and TNF inhibition has been shown to inhibit retinal vasculogenesis, in a manner similar to VEGF depletion by bevacizumab. We think that this provides a mechanism for bevacizumab resistance, to wit: VEGF depletion --> MCAD, with formation of massively calcified endosomes and exosomes --> provoke phagocytosis and other direct interactions with inflammatory cells which result in the release of TNF (and probably other pro-angiogenic mediators; studies in progress) --> rescue of microcapillaries from the vasculotoxic effect of VEGF depletion.

Endothelial Massive Calcium Accumulation Death (MCAD): Mechanism, Target, and Predictive Biomarker for Anti-Angiogenic Therapy Presented at the 13th international symposium on anti-angiogenic therapy: recent advances and future directions in basic and clinical cancer research. LaJolla, CA. Sponsor: MD Anderson Cancer Center; planning committee Robert S. Kerbel, Lee M Ellis, et al., 03 February 2011 Weisenthal Cancer Group Abstract We cultured human umbilical vein endothelial cells with bevacizumab, with tyrosine kinase inhibitors known to be AA, and with traditional cytotoxic drugs. The images below show that, in the presence of physiological saline and non-favorable culture conditions, the vast majority of the endothelial cells undergo a “non-specific” type of cell death (NSCD), not associated with calcium accumulation, but with loss of cell membrane integrity, allowing uptake of the Fast Green dye, staining these dead dells a pale blue green. In the presence of known AA agents (e.g. bevacizumab, some TK inhibitors) a large percentage of the endothelial cells undergo death associated with massive calcium accumulation (MCAD), with these cells staining hyperchromatic, refractile, blue-black, precisely as reported in http://www.ncbi.nlm.nih.gov/pubmed/18793333 and http://meeting.ascopubs.org/cgi/content ... ppl/e13617 and http://tinyurl.com/weisenthal-breast-lapatinib MCAD is strikingly demonstrated by Fast Green/Alizarin staining as reported in http://precedings.nature.com/documents/4499/version/1 Traditional cytotoxic drugs (e.g. cisplatin) produce only NSCD and inhibit MCAD. We propose that MCAD is a cell death mechanism unique to endothelial cells and provides a practical biomarker to predict for AA activity in clinical oncology and drug development, as well as a potential drug target. http://precedings.nature.com/documents/ ... 6647-1.pdf Nature Precedings doi:10.1038/npre.2011.6647.1

MCED: A newly-discovered mechanism of endothelial cell death Using a patented laboratory test, we have discovered a new cell death pathway in endothelial cells which we refer to as Massively Calcified Endosomal Death (MCED). Exploitation of this pathway may afford an effective approach to the treatment of cancer and to the prevention and treatment of atherosclerotic vascular disease, heart attack, and stroke. MCED in Coronary and Vascular Disease “Cholesterol levels might not matter. The most important cause of heart attack, stroke, and arterial blockage might be the presence or absence of circulating MCED factors.” The underlying mechanisms of atherosclerotic vascular disease are not well-understood by scientists. It is not known, for example, precisely how pathogenic lipids trigger an inflammatory response in arterial walls. Neither is it well-established how atherosclerotic arteries become calcified. Likewise, the mechanisms underlying the cause of calcific cardiac valvular disease (e.g. mitral and aortic stenosis) remain largely unknown. And why are cholesterol and other lipid levels only imperfect predictors of coronary artery disease? We have discovered a previously unknown biological mechanism that explains much of what is not understood about arterial blockage and heart disease. Understanding the mechanism will allow for development of drugs that control it. We postulate that massively calcified endosomal death is the triggering event for both vascular inflammation and vascular calcium accumulation. Interruption of the MCED pathway, using MCED-targeted drugs, may offer the most potent and specific approach to the prevention and treatment of coronary vascular disease. MCED in Cancer The highly-promising treatment paradigm of anti-angiogenic therapy has so far achieved only moderate success. Anti-angiogenic research is severely hindered by the inadequacy of disease models and predictive biomarkers. Acquired resistance to anti-angiogenic agents is poorly understood, as is the mechanism through which treatment with bevacizumab (Avastin) increases the risk of cardiovascular disease MCED induction - precisely the opposite of the goal in heart disease - may be effective in treating cancer. “Activation of the MCED pathway may enhance effectiveness of anticancer drugs that work by destroying blood vessels necessary to feed the growth of the cancer.” http://mcadvasocell.com/MCED_Home.html

"These measures are 'the most important aims of treatment' in these patients because improvements in overall and progression-free survival (OS and PFS) have hit a ceiling in trial after trial of chemotherapies," I personally think this is scandalous. What they are saying is that they are coming up with any drugs which are any better than the drugs that they have been using all along. So big Pharma has no new drugs to sell. So they change the goal line. Or, more accurately, they lower the bar. Progression free survival and overall survival are hard objective endpoints. Quality of life is totally squishy and unobjective. So future clinical trials in cancer are going to be designed and scored by psychologists. Patients should never ever let them get away with this. Their feet should be held close to the fire. Shame on them.

Quality of life (QoL) and symptom benefit should be accepted by clinicians and regulators as additional coprimary endpoints in clinical trials of chemotherapies for platinum-resistant and refractory ovarian cancer, according to a group of experts. These measures are "the most important aims of treatment" in these patients because improvements in overall and progression-free survival (OS and PFS) have hit a ceiling in trial after trial of chemotherapies, say Michael Friedlander, MD, and colleagues from the Prince of Wales Hospital, in New South Wales, Australia. The group published a letter on May 13 in the Journal of Clinical Oncology. The conventions of OS and PFS should remain in place in trials but be supplemented by these "other meaningful ways to measure treatment benefit," they say. The letter writers are responding to a recent study and accompanying editorial published in the journal that related to 2 chemotherapies being compared in a phase 3 trial in this patient population (J Clin Oncol. 2013;30:3841-3847). In the trial, patupilone and liposomal doxorubicin produce the same PFS (3.7 months median) and a comparable OS (13.2 vs 12.7 months). In short, Dr. Friedlander and his colleagues believe that palliative chemotherapy should be also evaluated for improvement in quality of life and symptoms and that those measures should count in the drug approval process. There needs to more than one "route to registration of new agents," they say about the need for regulatory changes. Agreed, said David Spriggs, MD, of Memorial Sloan-Kettering Cancer Center, in New York City. He served as the editorialist on the study of patupilone vs liposomal doxorubicin and, in turn, responded to the Australian letter about his essay and the trial. "It is essential," Dr. Spriggs writes, that "comfort, function and quality of life have a place in the commercialization pathway." Both he and the Australians believe that "today's development process seems excessively focused on duration of life." Patients with recurrent ovarian cancer…are incurable with today's therapies. "Patients with recurrent ovarian cancer (platinum-sensitive or resistant) are incurable with today's therapies," he writes, adding that life expectancy is 12 to 18 months. The problem of poor prognosis is not limited to ovarian cancer, adds Dr. Spriggs. "In these settings…a patient's goal is to enjoy a comfortable and highly enjoyable life for as long as possible," he says. But accommodating QoL as a primary endpoint in a drug approval trial "has historically been quite difficult," Dr. Spriggs adds. "This is a reflection of the fact that QoL can be slippery when reduced to practice." Dr. Friedlander and colleagues report that there is a major clinical trial under way (Gynecologic Cancer Intergroup Symptom Benefit Trial) that is seeking to "validate an instrument to measure symptom benefit that can be applied to clinical trials." The trial is also seeking to identify subsets of patients who are most likely to benefit from palliative chemotherapy. The authors have disclosed no relevant financial relationships. Citation: New Endpoints Proposed for Chemotherapy in Ovarian Cancer. Medscape. Jun 18, 2013.