Rallying the Troops to Confront Cancer

The recent blog “Stand Up To Cancer Research!” described some of the pitfalls of modern cancer research and the clinical trial process. It has engendered an active discussion. It may be helpful to address some of issues raised. For those of you who did not have the opportunity to read that blog, it defined the difficulty that many patients encounter when they seek experimental treatments. Clinical trials are often only available at select centers, sometimes at great distances from patient’s homes. There can be rigid inclusionary and exclusionary criteria, and the pre-entry evaluations e.g. re-biopsy, CT/PET, etc. can be daunting, time consuming and inconvenient. Travel and accommodations may come at great personal expense.

I penned the blog, in part, to remind patients that they are ultimately in control of the process. One patient asked how can “we stand up to the system” describing herself a consumer while “they’ve got the goods.” This is the frustration many people feel. It should be remembered, however, that a substantial portion of research support comes from tax dollars and charitable donations. These are your dollars. If the system is not working, then those responsible must be held accountable. The American public has the power of the vote. Patient advocates can approach and lobby their representatives and demand improvements in the clinical trial process. To wit, the level of scrutiny and restriction upon access to new drugs must be re-examined. There is an army of well-trained clinical oncologists capable of delivering experimental drugs today. Not just the fully vetted, just-about-ready-for-prime-time agents currently found in phase III trials, but the really new exciting drugs. Once a drug has passed Phase I and found to be safe in patients, open up the accrual process. “Compassionate use” has virtually disappeared from the lexicon of cancer research. Twenty years ago I made a discovery in the laboratory. Working with the pharmaceutical company and the FDA, we were almost immediately granted access to a yet-to-be approved agent. The combination proved so effective that today it is one of the most widely used regimens in the world. That would not happen today. We simply cannot get access to the best drugs for our patients.

Microscope Detail2-lo resWith the industrialization of medical care, growth of mega-medical systems and the increasing role of government, medicine must be viewed through a different lens. Changes in cancer research will require changes in cancer policy, and policy comes from political power. Cancer patients will need to identify legitimate spokespeople to take their concerns forward to their elected officials. While the current clinical trial process slowly grinds out new development, even the smartest, fastest trials take years to change practice. Every day, more than 1,500 cancer patients die in the United States alone. Cancer patients do not have time for clever doctors to pose interesting questions while they suffer the slings and arrows of ignoble, ineffective therapy. It is time for a change in cancer research, and patients must be the instrument for that change.

Does Chemotherapy Work? Yes and No.

A doctor goes through many stages in the course of his or her career. Like Jacques’ famous soliloquy in Shakespeare’s “As you Like It,” the “Seven Ages of Man,” there are similar stages in oncologic practice.

In the beginning, fresh out of fellowship, you are sure that your treatments will have an important impact on every patient’s life. As you mature, you must accept the failures as you cling to your successes. Later still, even some of your best successes become failures. That is, patients who achieve complete remissions and return year after year for follow-up with no evidence of disease, suddenly present with, a pleural effusion, an enlarged liver or a new mass in their breast and the whole process begins again.

I met with just such a patient this week. Indeed when she arrived for an appointment, I only vaguely remembered her name. After all, it had been 13 years since we met. When she reintroduced herself I realized that I had studied her breast cancer and had found a very favorable profile for several chemotherapy drugs. As the patient resided in Orange County, CA, she went on to receive our recommended treatment under the care of one of my close colleagues, achieving an excellent response to neo-adjuvant therapy, followed by surgery, additional adjuvant chemotherapy, and radiation. Her decade long remission reflected the accuracy of the assay drug selection. She was a success story, just not a perfect success story. After all, her large tumor had melted away using the drugs we recommended and her 10 year disease-free interval was a victory for such an aggressive cancer.

A dying leukemia cell

A dying leukemia cell

So what went wrong? Nothing, or more likely, everything. Cancer chemotherapy drugs were designed to do one thing very well, stop cancer cells from dividing. They target DNA synthesis and utilization, damage the double helix or disrupt cell division at the level of mitosis. All of these assaults upon normal cellular physiology target proliferation. Our century long belief that cancer was a disease of cell growth had provided us a wealth of growth-inhibiting drugs. However, in the context of our modern understanding of cancer as a disease of abnormal cell survival (and the need to kill cells outright to achieve remissions), the fact that these drugs worked at all can now be viewed as little more than an accident. Despite chemotherapy’s impact on cell division, it is these drugs unintended capacity to injure cells in ways they cannot easily repair, (resulting in programmed cell death) that correlates with response. Cancer, as a disease, is relatively impervious to growth inhibition, but can in select patients be quite sensitive to lethal injury. While cancer drugs may have been devised as birth control devices, they work, when they do work at all, as bullets.

There is an old joke about aspirin for birth control. It seems that aspirin is an effective contraceptive. When you ask how this simple headache remedy might serve the purpose, the explanation is that an aspirin tablet held firmly between the knees of a young woman can prevent conception. The joke is emblematic of chemotherapy’s effect on cancer as a drug designed for one purpose, but can prove effective through some other unanticipated mechanism.

Chemotherapy does work. It just does not work in a manner reflective of its conceptualization or design. Not surprisingly it does not work very well and rarely provides curative outcomes. Furthermore, its efficacy comes at a high price in toxicity with that toxicity reflecting exactly what the chemotherapy drugs were designed to do; stop cells from growing.  It seems that the hair follicles, bone marrow, immune system, gastrointestinal mucosa and reproductive tissues are all highly proliferative cells in their own right. Not surprisingly, chemotherapy extracts a heavy price on these normal (proliferative) tissues. It is the cancer cells, relatively quiescent throughout much of their lives that escape the harmful effects.

As a medical oncologist in the modern era, I have recognized only too well the shortcomings of conventional cytotoxic drugs. It is for this reason that I use a laboratory platform to select the most effective drugs from among the many badly designed agents. Culling from the herd those few good drugs capable of inducing lethal injury these are the ones that the EVA-PCD assay selects for our patients. Applying this approach, we have doubled responses and prolonged survivals.

Over the past decade we have focused increasingly on the new signal transduction inhibitors and growth factor down regulators. If we can double the response rates and improve survivals using our laboratory assay to select among bad drugs, just imagine what our response rates will be when we apply this approach to good drugs.

Garlic – The Common Man’s Cure All

Garlic_3A recent study published in the Journal of Cancer Prevention Research by investigators in China compared the outcome of patients with lung cancer who consumed fresh garlic against those who did not. In the study of 1,424 lung cancer patients there was a 44 percent reduction of the risk of lung cancer for non-smokers.  Even among smoking patients the risk of lung cancer was reduced by 30 percent.

The findings of the study are consistent with a treatise that I published several years ago on garlic (Garlic: Medicinal Food or Nutritious Medicine? Robert A. Nagourney, Journal of Medicinal Food, 1998). In this study, I examined the history of garlic, as well as its chemistry and its medicinal properties. In addition to its anti-cancer properties, garlic is antibacterial, antiviral, antifungal, lowers blood pressure, reduces the risk of blood clots, lowers cholesterol and may serve as an anti-aging nutrient.

Where the recent study struck chord was its concordance with my strong recommendation from that 1998 article that we consume fresh garlic over the other preparations. The aged garlic extracts, dried garlic and garlic oil preparations lack the most important chemical constituent of all – allicin. Allicin, also known diallyl disulphide oxide (2-propanethiol sufinate) imparts the characteristic odor to garlic. It is only formed when the precursor alliin is enzymatically converted to the allicin via the action of the enzyme alliinase. Once allicin is exposed to excess heat or oxygen it undergoes a variety of conversions that lead to diallyl sulfone as well the diallyl di, tri, and tetra sulfides.

These compounds, though biologically active, do not carry the potency of allicin. It is for this reason that I have, over the past two decades, urged my patients, family and friends to consume fresh garlic as a foodstuff. Indeed as I write in my book, Outliving Cancer, our family consumes the equivalent 2 – 3 liters of fresh garlic a month.

The history of garlic as a medicinal is indeed rich. And it was Gallen, in 130 AD, who described it as “Theriacum rusticorum” (the common man’s cure all). I am pleased that two millennia later Chinese cancer researchers have provided additional data to support his prescient observation.

The Angelina Jolie Effect

stock-photo-16854195-premiere-of-sony-pictures-salt-arrivalsAngelina Jolie’s willingness to bravely publish her saga, as she confronts the risk of a nefarious form of cancer, has focused international attention upon the phenomenon of genetic predisposition to cancer.

The term BRCA, coined by investigators from Berkeley and Europe, described an inherited predisposition to cancer. Years of research came to fruition in the early 90s when geneticist Mary-Claire King and her collaborators recognized that patients who lacked DNA repair capacity tended to accumulate chromosomal damage that ultimately lead to cancer.

The BRCA genes (1 and 2) are part of the genomic fidelity system. Just like it sounds, these DNA repair enzymes maintain the “fidelity” or trueness of your makeup. Everyday our bodies are exposed to mutations. Radon in the atmosphere, carcinogens, even our dietary intake regularly injures our chromosomes. In response, we marshal defenses that recognize, remove and repair the damaged elements before they can be transmitted to the next generation of cells. When these BRCA genes are absent or silenced, the normal wear and tear goes unrepaired. Over a lifetime, this leads to a triggering mutation and cancer.

BRCA genes are like the Zamboni machines that clean up the ice in-between periods in a hockey game. Imagine what a Stanley cup game would be like if the ice was so dug up that the players took a tumble every time they skated toward the goal.

Ms. Jolie’s chromosomes are like the ice, but her Zamboni machine isn’t working.

So why would someone like Angelina Jolie get cancer? The reason why patients with the BRCA1 or 2 mutation get breast and ovarian cancer over other types of cancer remains somewhat of a mystery. But the reason that they get cancer at all is quiet clear. They accumulate damage they can’t repair.

That being the case, what happens when we introduce DNA damage intentionally? The answer is: patients with BRCA1 and BRCA2 respond to chemotherapy often quite dramatically. It is the very fact that cells cannot repair damage, which makes them hypersensitive to DNA damaging drugs like alkylators (cytoxan), platins (cisplatin and carboplatin) and ionizing radiation (x-rays). Indeed, tumors that carry DNA repair deficiencies are among the easiest to treat with conventional cytotoxics. Thus, the very reason that these patients develop cancers is the Achilles heel that makes their tumors drug sensitive.
Interestingly, BRCA1 and BRCA2 positive patients don’t only get breast and ovarian cancer; they can also develop melanoma, lymphoma and other tumors.

The importance of the BRCA discovery has been important on many levels. First and foremost it has enabled us to develop screening techniques to identify the at-risk populations that allows them to undertake preventative measures. Second, it has granted an insight into carcinogenesis and the concept of genomic fidelity. Third, it has provided new therapeutic options for these patients and all patients with cancers that have “BRCA-ness.”

Finally, it has opened up a field of investigation that connects cancer with other long-recognized disease states like the pediatric condition Fanconi’s anemia. It only reminds us again that cancer is but one part of a continuum of human diseases.

Cancer Explained – The Role of Cell Death

Following a recent blog, I received an inquiry from one of our readers. The individual asked whether I could better explain my oft repeated statement that “cancer doesn’t grow too much, it dies too little.” The questioner was puzzled by my assertion that chemotherapy drugs acted to stop cells from growing, while she had come to believe that this was synonymous with killing them. This dichotomy is at the crux of our modern understanding of cancer.

In response, I would like to examine the very basis of what is known as carcinogenesis, the process by which cancer comes to exist.

For more than a century, scientists believed that cancer cells were growing more rapidly than normal cells. They based this on serial measurements of patient’s tumors, which revealed that tumor dimensions increased. A small lump in the breast measuring one-half inch in diameter would be found six months later to be one inch in diameter. And six months after that it was two inches in diameter. This was growth, plain and simple, and so it was reasoned that cancer cells must be growing too much. As such, cancer therapies, per force of necessity, would need to stop cancer cells from growing if they were to work at all.

Dying Cell - lo resAnd then, in 1972, a paper was published in the British Journal of Cancer that described the phenomenon of apoptosis, a form of programmed cell death. Although it would be almost a decade before cancer researchers fully grasped the implications of this paper, it represented a sea change in our understanding of human tumor biology.

Let’s use the example of a simple mathematical equation. Every child would recognize the principles of the following formula:
Tumor mass = growth rate – death rate
This simple equation represents the principle of modern cancer biology. Where cancer researchers went wrong was that they mistakenly posited that the only way a tumor mass could increase was through an increase in the growth rate. However, as any child will tell you, a negative of a negative is a positive. That is, at a given growth rate, the tumor mass can also increase if you reduce the death rate. Thus, the “growth” so obvious to earlier investigators did not reflect an increase in proliferation but instead a decrease in cell attrition. Cancer didn’t grow too much it died too little, but the end result was exactly the same.

It should now be abundantly clear exactly why chemotherapy drugs, designed to stop cells from growing, didn’t work. Yes, the drugs stopped cells from growing, and yes any population of “growing cells” would suffer the effect. But they didn’t cure cancers because the cancers weren’t growing particularly fast. Indeed, the fact that chemotherapy works at all is almost an accident. Contrary to our long held belief that we were inhibiting cell proliferation, chemotherapy drugs designed to damage DNA and disrupt mitosis, were actually working (when they did at all) by forcing the cells to take inventory and decide whether they could continue to survive. If the injury were too extreme, the cells would commit suicide through the process of cell death. If the cells were not severely damaged or could repair the damage, then they carried on to fight another day. None of this, however, had anything to do with cell growth.

What is Cancer?

This is a question that has vexed scientific investigators for  centuries, and for the last century, our belief was predicated upon physical observation that cancer reflected altered  cell growth. After all, to the untrained eye, or even to the rather sophisticated eye, the mass in the pelvis or the lymph node under the arm, or the abnormality on a chest x-ray, continued to expand upon serial observation. This was “growth” (at least since the time of Rudolph Virchow); and growth it was reasoned represented cell division.

Based upon the cell growth model, cancer therapists devised drugs and treatments that would stanch cellular proliferation. If cells were growing, then cells needed to reproduce the genetic elements found in chromosomes leading to the duplication of the cell through mitosis. If chromosomes were made of DNA, then DNA would be the target of therapy. From radiation to cytotoxic chemotherapy, one mantra rang through the halls of academia, “Stop cancer cells from dividing and you stop cancer.”

As in many scientific disciplines, nothing spoils a lovely theory more than a little fact. And, the fact turned out to be that cancer does not grow too much, it dies too little. Cancer doesn’t “grow” its way into becoming a measurable tumor, it “accumulates” its way to that end.

In 1972, we realized that the most basic understanding of cancer biology up to that point was absolutely, positively wrong.

Working in a laboratory during my fellowships, I began to realize that something was wrong with the principles that guided cancer therapeutics. My first inkling came from the rather poor outcomes that many of my patients experienced despite high-dose, aggressive drug combinations.

Then, it was the failure of the clonogenic assay to predict clinical outcomes that further raised my suspicions. I began to ponder cell growth – cell death, cell growth – cell death. With each passing day the laboratory analysis that I conducted identified active treatments that worked.  Using short-term measures of cell death (not cell growth),. I could predict which of my patients would get better.  All of the complicated and inefficient clonogenic assay investigations could not. Cell growth – cell death – what was I missing?

It would be years before I would attend a special symposium on the topic of cell death that it all became abundantly clear.

My “eureka” moment is captured in Chapter 6 of my soon-to-be-released book, Outliving Cancer.FINAL book cover-lo res

Phar Lap and the Treatment of Leukemia

250px-Phar_LapPhar Lap (1926-1932) was a thoroughbred horse bred in New Zealand. After winning the Melbourne Cup and 37 other races, his victory at the Agua Caliente racecourse in Tijuana, Mexico, established the track record in 1932.

With each victory, his detractors became more strident. He was even the target of an assassination attempt. To prevent him from winning (and thereby disrupting the betting odds) officials would add lead bricks to his saddle. On the occasion of the Melbourne cup of 1930 he carried 138 pounds of lead, yet won the race. A quote from the Sydney Morning Herald dated Wednesday, November 5, 1930, read, “The question was not which horse could win, but could Phar Lap carry the weight. Could he do what no other horse before him had done?”

It appeared that the one thing that race officialdom feared above all else, was a horse that could consistently beat the field and win the race.

The tale of Phar Lap was brought to mind after a colleague forwarded a paper published in the journal Leukemia on August 10, 2012: “The use of individualized tumor response testing in treatment selection: second randomization results from the LRF CLL4 trial and the predictive value of the test at trial entry.” (E Matutes, AG Bosanquet et al, Leukemia, Letter to the Editor.)

Published as a letter to the editor, the paper describes correlations between the TRAC (tumor response to antineoplastic compounds) assay, a short-term suspension culture cell death laboratory assay (very similar to our work) and clinical response, time to progression and overall survival in patients with chronic lymphocytic leukemia (CLL) who received chemotherapy as part of the LRF CLL4 trial conducted in England between 1999 and 2004.

The initial trial was a blinded correlation between laboratory assay results and patient response to one of three treatment regimens. An examination of the data reveals a clear and statistically significant correlation between drug sensitivity and overall survival (p = .0001). The 10-year survival of drug sensitive patients was 28 percent, while the 10-year survival for drug resistant patients was 12 percent.

Significant correlations with survival were observed for known prognostic factors like 17p and 11q deletion, as well as IGHV mutational status. Correlations were also observed between the TRAC assay results and these prognostic factors.

The report goes on to describe a second randomization that took place at the time of disease progression, either failure of first-line therapy or reoccurrence within 12 months. In this part of the study, 84 relapsed patients were allocated to standard therapy and their outcomes were compared with 84 patients allocated to treatment guided by the TRAC assay. The drugs tested in the assay-directed arm included chlorambucil, cytoxan, methylprednisolone, prednisolone, vincristine, doxorubicin, mitoxantrone, 2CDA, fludarabine and pentostatin. In vitro resistance for combinations was defined as resistance to all constituent drugs in the combination, while drug sensitivity was defined as TRAC-assay sensitivity for any of the drugs used in combination. No discussion of synergy analysis was included.

In examining this study, I cannot help but be reminded of Phar Lap. First, marshaling a study of 777 CLL patients, and conducting 544 TRAC analyses, is a phenomenal undertaking for which these authors should be commended.

Second, the observation of a significant correlation between laboratory assay results and overall survival, as well as the biological implications of this platform’s capacity to correlate with molecular markers is a demonstrable and noteworthy success, however unheralded.

Where the analogy with poor Phar Lap’s struggles, weighted down with lead, becomes most poignant is the final portion of the study wherein 84 patients received assay-directed therapy. To wit, we must remember that in 2012, drug refractory CLL remains an incurable malignancy (with the exception of a small subset of successfully transplanted patients) and that no chemotherapy-alone trial has provided a survival advantage in this group. But this only begins to explain this trial’s results.

Among the virtually insurmountable hurdles that these investigators were forced to confront was the fact that fully 52 percent of the standard treatment arm group were destined to receive fludarabine. This drug, the current gold standard for previously treated patients who fail chlorambucil (constituting 73 percent of the patients in this part of the trial), has an objective response rate of 48 – 52 percent in this population. As the drug would likely be identified as active in vitro as well, this had the impact of pitting the assay arm and the standard arm against one another, frequently using exactly the same treatment.

While this does not mean that the assay arm could not succeed, it does have an enormous impact upon the sample size calculations used to determine the number of patients required to achieve significance.  No pharmaceutical company would ever allow a registration trial to be conducted against an “unknown” control arm, particularly one using the same therapy as the study arm – not ever! Despite these burdens, the assay-directed arm had a superior one-year survival, while virtually all other trends favored the group who received assay-selected therapy. The results of this study are worthy of recognition and further support the clinical relevance, predictive validity and importance of functional analyses. Yet, this interesting study in CLL is unceremoniously relegated to the status of a Letter to the Editor in Leukemia. Perhaps, like Phar Lap, no one really wants to upset the odds.

The Tumor Micro Environment

As I was reading the October 1 issue of the Journal of Clinical Oncology, past the pages of advertisement by gene profiling companies, I came upon an article of very real interest.

While most scientists continue to focus on cancer-gene analyses, a report in this issue from a collaboration between American and European investigators provided compelling evidence for the role of tumor associated inflammatory cells in metastatic human cancer. (Asgharzadeh, S J Clin Oncol 30 (28)3525–3532 Oct 1, 2012) Through the analysis of children with metastatic neuroblastoma, they found that the degree of infiltration into the tumor environment by macrophages had a profound effect upon clinical outcome. This study confirmed earlier reports that macrophage infiltration is an integral part and potential driver of the malignant process.

Using immunohistochemistry and light microscopy the investigators scored patients for the number of CD163(+) macrophages, representing the alternatively activated (M2) subset within the tumor tissue. They then examined inflammation related gene expressions to develop a “high” risk, “low” risk algorithm and applied it to the progression free survival in these children.

Highly significant differences were observed between the two groups. This report adds to a growing body of literature that describes the interplay between cancer cells and their microenvironment. Similar studies in breast cancer, melanoma and multiple myeloma have shown that tumor cells “co-opt” their non-malignant counterparts as they drive transformation from benign to malignant, from in-situ to invasive and from localized disease to metastatic. These same forces have the potential to strongly influence cellular responses to stressors like chemotherapy and growth factor withdrawal. While we may now be on the verge of identifying these tumor attributes and characterizing their impact upon survival, these analyses represent little more than increasingly sophisticated prognostics.

The task at hand remains the elucidation of those attributes and features that characterize each patient’s tumor response to injury toward ultimate therapeutic response. To address this level of complexity, we need the guidance of more global measures of human tumor biology, measures that incorporate the dynamic interplay between tumors cells, their stroma, vasculature and the inflammatory environment.  These are the “real-time” insights that can only be achieved using human tissue in its native state. Ex vivo analyses offer these insights. Their information moves us from the realm of prognostics to one of predictives, and it is after all predictive measures that our patients are most desperately in need of today.

Systems Biology Comes of Age: Metastatic Lung Cancer in the Crosshairs

Cancer therapists have long sought mechanisms to match patients to available therapies. Current fashion revolves around DNA mutations, gene copy and rearrangements to select drugs. While every cancer patient may be as unique as their fingerprints, all of the fingerprints on file with the federal AFIS (automated fingerprint identification system) database don’t add up to a hill of genes (pun intended), if you can’t connect them to the criminal.

To continue the analogy, it doesn’t matter why the individual chose a life of crime, his upbringing, childhood traumas or personal tragedies. What matters is that you capture him in the flesh and incarcerate him (or her, to be politically correct).

The term we apply to the study of cancer, as a biological phenomenon is “systems biology.” This discipline strikes fear into the heart of molecular biologists, for it complicates their tidy algorithms and undermines the artificial linearity of their cancer pathways. We frequently allude to the catchphrase, genotype ≠ phenotype, yet it is the cancer phenotype that we must confront if we are to cure this disease.

Using a systems biology approach, we applied the ex-vivo analysis of programmed cell death (EVA-PCD®) to the study of previously untreated patients with non-small cell lung cancer. Tissue aggregates isolated from their surgical specimens were studied in their native state against drugs and signal transduction inhibitors. This methodology captures all of the interacting “systems,” as they respond to cytotoxic agents and growth factor withdrawal. The trial was powered to achieve a two-fold improvement in response.

At interim analysis, we had more than accomplished our goal. The results speak for themselves.

First: a two-fold improvement in clinical response – from the national average of 30 percent we achieved 64.5 percent (p – 0.00015).

Second: The median time to progression was improved from 6.4 to 8.5 months.

Third: And most importantly the median overall survival was improved from an average of 10 – 12 months to 21.3 months, a near doubling.

These results, from a prospective clinical trial in which previously untreated lung cancer patients were provided assay directed therapy, reflects the first real time application of systems biology to chemotherapeutics. The closest comparison for improved clinical outcome with chemotherapeutic drugs chosen from among all active agents by a molecular platform in a prospective clinical trial is . . .

Oh, that’s right there isn’t any.

Type I Error

Scientific proof is rarely proof, but instead our best approximation. Beyond death and taxes, there are few certainties in life. That is why investigators rely so heavily on statistics.

Statistical analyses enable researchers to establish “levels” of certainty. Reported as “p-values,” these metrics offer the reader levels of statistical significance indicating that a given finding is not simply the result of chance. To wit, a p-value equal to 0.1 (1 in 10) means that the findings are 90 percent likely to be true with a 10 percent error. A p-value of 0.05 (1 in 20) tells the reader that the findings are 95 percent likely to be true. While a p-value equal to 0.01 (1 in 100) tells the reader that the results are 99 percent likely to be true. For an example in real time, we are just reporting a paper in the lung cancer literature that doubled the response rate for metastatic disease compared with the national standard. The results achieved statistical significance where p = 0.00015.  That is to say, that there is only 15 chances out of 100,000 that this finding is the result of chance.

Today, many laboratories offer tests that claim to select candidates for treatment. Almost all of these laboratories are conducting gene-based analysis. While there are no good prospective studies that prove that these genomic analyses accurately predict response, this has not prevented these companies from marketing their tests aggressively. Indeed, many insurers are covering these services despite the lack of proof.

So let’s examine why these tests may encounter difficulties now and in the future. The answer to put it succinctly is Type I errors. In the statistical literature, a Type I error occurs when a premise cannot be rejected.  The statistical term for this is to reject the “null” hypothesis. Type II errors occur when the null hypothesis is falsely rejected.

Example: The scientific community is asked to test the hypothesis that Up is Down. Dedicated investigators conduct exhaustive analyses to test this provocative hypothesis but cannot refute the premise that Up is Down. They are left with no alternative but to report according to their carefully conducted studies that Up is Down.

The unsuspecting recipient of this report takes it to their physician and demands to be treated based on the finding. The physician explains that, to his best recollection, Up is not Down.  Unfazed the patient, armed with this august laboratory’s result, demands to be treated accordingly. What is wrong with this scenario? Type I error.

The human genome is comprised of more than 23,000 genes: Splice variants, duplications, mutations, SNPs, non-coding DNA, small interfering RNAs and a wealth of downstream events, which make the interpretation of genomic data highly problematic. The fact that a laboratory can identify a gene does not confer a certainty that the gene or mutation or splice variant will confer an outcome. To put it simply, the input of possibilities overwhelms the capacity of the test to rule in or out, the answer.

Yes, we can measure the gene finding, and yes we have found some interesting mutations. But no we can’t reject the null hypothesis. Thus, other than a small number of discreet events for which the performance characteristics of these genomic analyses have been established and rigorously tested, Type I errors undermine and corrupt the predictions of even the best laboratories. You would think with all of the brainpower dedicated to contemporary genomic analyses that these smart guys would remember some basic statistics.