Is There a Role for PI3k Inhibitors in Breast Cancer? Maybe.

Over the past decades oncologists have learned that cancer is driven by circuits known as signal transduction pathways. Signal_transduction_pathways.svgThe first breakthroughs were in chronic myelogenous leukemia (CML) where a short circuit in the gene as c-Abl caused the overgrowth of malignant blasts. The development of Imatinib (Gleevec) a c-Abl inhibitor yielded brilliant responses and durable remissions with a pill a day.

The next breakthrough came with the epidermal growth factor pathway and the development of Gefitinib (Iressa) and shortly thereafter Erlotinib (Tarceva). Good responses in lung cancers, many durable were observed and the field of targeted therapy seemed to be upon us.

220px-PI3kinaseAmong the other signal pathways that captured the imagination of the pharmaceutical industry as a potential target was phospho-inositol-kinase (PI3K). Following experimental work by Lew Cantley, PhD, who first described this pathway in 1992, more than a dozen small molecules were developed to inhibit this cell signal system.

The PI3K pathway is important for cell survival and regulates metabolic activities like glucose uptake and protein synthesis. It is associated with insulin signaling and many bio-energetic phenomena. The earliest inhibitors functioned downstream at a protein known as mTOR, and two have been approved for breast, neuroendocrine and kidney cancers. Based on these early successes, PI3K, which functions upstream and seemed to have much broader appeal, became a favored target for developmental clinical trials.

The San Antonio Breast Cancer Symposium is one of the most important forums for breast cancer research. The December 2014 meeting featured a study that combined one of the most potent PI3K inhibitors, known as Pictilisib, with a standard anti-estrogen drug, Fulvestrant, in women with recurrent breast cancer. The FERGI Trial only included ER positive patients who had failed prior treatment with an aromatase inhibitor (Aromasin, Arimidex or Femara). The patients were randomized to receive the ER blocker Fulvestrant with or without Pictilisib.

With seventeen months of follow-up there was some improvement in time to progressive disease, but this was not large enough to achieve significance and the benefit remains unproven. A subset analysis did find that for patients who were both ER (+) and PR (+) a significant improvement did occur. The ER & PR (+) patients benefitted for 7.4 months on the combination while those on single agent Fulvestrant for only for 3.7 months.

The FERGI trial is more interesting for what it did not show. And that is that patients who carried the PI3K mutation, the target of Pictilisib, did not do better than those without mutation (known as wild type). To the dismay of those who tout the use of genomic biomarkers like PI3K mutation for patient drug selection, the stunning failure to identify responders at a genetic level should send a chill down the spine of every investor who has lavished money upon the current generation of genetic testing companies.

It should also raise concerns for the new federal programs that have designated hundreds of millions of dollars on the new “Personalized Cancer Therapy Initiatives” based entirely on genomic analyses. The contemporary concept of personalized cancer care is explicitly predicated upon the belief that genomic patient selection will improve response rates, reduce costs and limit exposure to toxic drugs in patients unlikely to respond.

This unanticipated failure is only the most recent reminder that genomic analyses can only suggest the likelihood of response and are not determinants of clinical outcome even in the most enriched and carefully selected individuals. It is evident from these findings that PI3K mutation alone doesn’t define the many bioenergetic pathways associated with the phenotype. This strongly supports phenotypic analyses like EVA-PCD as better predictors of response to agents of this type, as we have shown in preclinical and clinical analyses.

Investigators in Boston Re-Invent the Wheel

A report published in Cell from Dana-Farber Cancer Institute describes a technique to measure drug cov150hinduced cell death in cell lines and human cancer cells. The method “Dynamic BH3 profiling” uses an oligopeptidic BIM to gauge the degree to which cancer cells are “primed” to die following exposure to drugs and signal transduction inhibitors. The results are provocative and suggest that in cell lines and some human primary tissues, the method may select for sensitivity and resistance.

We applaud these investigators’ recognition of the importance of phenotypic measures in drug discovery and drug selection and agree with the points that they raise regarding the superiority of functional platforms over static (omic) measures performed on paraffin fixed tissues. It is heartening that scientists from so august an institution as Dana-Farber should come to the same understanding of human cancer biology that many dedicated researchers had pioneered over the preceding five decades.

Several points bear consideration. The first, as these investigators so correctly point out: “DBP should only be predictive if the mitochondrial apoptosis pathway is being engaged.” This underscores the limitation of this methodology in that it only measures one form of programmed cell death – apoptosis. It well known that apoptosis is but one of many pathways of programmed cell death, which include necroptosis, autophagy and others.

While leukemias are highly apoptosis driven, the same cannot so easily be said of many solid tumors like colon, pancreas and lung. That is, apoptosis may be a great predictor of response except when it is not. The limited results with ovarian cancers (also apoptosis driven) are far from definitive and may better reflect unique features of epithelial ovarian cancers among solid tumors than the broad generalizability of the technique.

A second point is that these “single cell suspensions” do not recreate the microenvironment of human tumors replete with stroma, vasculature, effector immune cells and cytokines. As Rakesh Jain, a member of the same faculty, and others have so eloquently shown, cancer is not a cell but a system. Gauging the system by only one component may grossly underestimate the systems’ complexity, bringing to mind the allegory of elephant and the blind man. Continuing this line of reasoning, how might these investigators apply their technique to newer classes of drugs that influence vasculature, fibroblasts or stroma as their principal modes of action? It is now recognized that micro environmental factors may contribute greatly to cell survival in many solid tumors. Assay systems must be capable of capturing human tumors in their “native state” to accurately measure these complex contributions.

Thirdly, the ROC analyses consistently show that this 16-hour endpoint highly correlates with 72- and 96-hour measures of cell death. The authors state, “that there is a significant correlation between ∆% priming and ∆% cell death” and return to this finding repeatedly. Given that existing short term (72 – 96 hour) assays that measure global drug induced cell death (apoptotic and non-apoptotic) in human tumor primary cultures have already established high degrees of predictive validity with an ROC of 0.89, a 2.04 fold higher objective response rate (p =0.0015) and a 1.44 fold higher one-year survival (p = 0.02) are we to assume that the key contribution of this technique is 56 hour time advantage? If so, is this of any clinical relevance? The report further notes that 7/24 (29%) of ovarian cancer and 5/30 (16%) CML samples could not be evaluated, rendering the efficiency of this platform demonstrably lower than that of many existing techniques that provide actionable results in over 90% of samples.

Most concerning however, is the authors’ lack of recognition of the seminal influence of previous investigators in this arena. One is left with the impression that this entire field of investigation began in 2008. It may be instructive for these researchers to read the first paper of this type in the literature published in in the JNCI in 1954 by Black and Spear. They might also benefit by examining the contributions of dedicated scientists like Larry Weisenthal, Andrew Bosanquet and Ian Cree, all of whom published similar studies with similar predictive validities many years earlier.

If this paper serves to finally alert the academic community of the importance of human tumor primary culture analyses for drug discovery and individual patient drug selection then it will have served an important purpose for a field that has been grossly underappreciated and underutilized for decades. Mankind’s earliest use of the wheel dates to Mesopotamia in 3500 BC. No one today would argue with the utility of this tool. Claiming to have invented it anew however is something quite different.

Bevacizumab In Colon Cancer – “A Shot Across The Bowel”

Colon2 130320.01 lo resAn E-Publication article in the February Journal of Clinical Oncology analyzes the cost efficacy of Bevacizumab for colon cancer. Bevacizumab, sold commercially as Avastin, has become a standard in the treatment of patients with advanced colorectal cancer. Indeed, Bevacizumab plus FOLFOX or FOLFIRI, are supported by NCCN guidelines and patients who receive one of these regimens are usually switched to the other at progression.

A Markov computer model explored the cost and efficacy of Bevacizumab in the first and second line setting using a well-established metric known as a Quality-Adjusted Life Year (QALY). In today’s dollars $100,000 per QALY is considered a threshold for utility of any treatment. To put this bluntly, the medical system values a year of yavastinour life at $100,000. The authors confirmed that Bevacizumab prolongs survival but that it does so at significantly increased costs. By their most optimistic projections, Bevacizumab + FOLFOX come in at more than $200,000 per QALY. Similar results were reported for Canadian, British and Japanese costs. Though more favorable, the results with FOLFIRI + Bevacizumab still came in above the $100,000 threshold.

No one doubts that Bevacizumab provides improved outcomes. It’s the incremental costs that remain an issue. Society is now confronting an era where the majority of new cancer agents come in at a cost in excess of $10,000 per month. Where and how will we draw the line that designates some treatments unaffordable? On the one hand, clinical therapies could be made available only to the “highest bidder.” However, this is contrary to the western societal ethic that holds that medical care should be available to all regardless of ability to pay. Alternatively, increasingly narrow definitions could be applied to new drugs making these treatments available to a shrinking minority of those who might actually benefit; a form of “evidence-based” rationing. A much more appealing option would be to apply validated drug predication assays for the intelligent selection of treatment candidates.
In support of the latter, the authors state, “Bevacizumab potentially could be improved with the use of an effective biomarker to select patients most likely to benefit.” This is something that genomic (DNA) profiling has long sought to achieve but, so far, has been unable to do. This conceptual approach however is demonstrably more attractive in that all patients have equal access, futile care is avoided and the costs saved would immediately provide highly favorable QALY’s as the percentage of responders improved.

Similar to the recent reports from the National Health Service of England, the American public now confronts the challenge of meeting the needs of a growing population of cancer patients at ever-higher costs. It is only a matter of time before these same metrics described for colon cancer are applied to lung, ovarian and other cancers for which Avastin is currently approved.

At what point will the American medical system recognize the need for validated predictive platforms, like EVA-PCD analyses, that have the proven capacity to save both money and lives? We can only wonder.