Toward A 100% Response Rate in Human Cancer
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 (American Society of Clinical Oncology) 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 functional profiling 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.
Metastatic Colorectal Cancer Treatment Success
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.