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.

Beyond Our Borders

I recently returned from Brazil where I participated in a cancer symposium. During my visit I encountered many highly skilled physicians with expertise in breast, thoracic, gastrointestinal and orthopedic oncology. The degree of collegiality and enthusiasm was palpable. The most exciting aspect of my visit was the warm reception and extremely high level of interest in the clinical application of our laboratory platform. It was a refreshing reminder that the parochial thinking of the American oncology community is not the norm throughout the world.

Upon my return, I had the pleasure of meeting a charming 61-year-old woman from New Delhi, India. In review of her chart I recognized her name as a patient for whom we had conducted a study in February of 2012. Her husband, an accomplished businessman, had learned of our laboratory and worked diligently to obtain, process and transport a portion of his wife’s tumor from the surgical suite to our lab. Despite multiply recurrent disease and numerous prior treatments, this patient’s ovarian cancer cells revealed exquisite sensitivity to a drug combination in the laboratory. Her physicians at the Apollo Hospital of New Delhi delivered the treatment exactly as outlined by our lab, and here sitting across from me was the patient in complete remission six months later. The family had traveled from India to meet me and express their thanks.

Each of these experiences speaks volumes for the globalization of cancer care. Cancer patients, whether from Brazil, India or China are more alike than different. Each confronts a seemingly insurmountable adversary. Each in their own way seeks out the best information and advice. And each can be best managed with those treatments found uniquely effective for their tumor. Perhaps once we have conquered cancer in India and Brazil, the EVA-PCD® assay will be ultimately accepted in the United States of America.