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Genomic vs functional analyses: Lesson from recurrent colon cancer

Updated: Oct 25, 2021

With the growing interest in personalized medicine, many physicians and patients have come to equate personalized medicine with genomic medicine. This is an error. Personalized medicine is a medical discipline that uses all available data to guide individualized therapy. It is not a technique. It is a philosophy that has the capacity to change the future of cancer medicine, but only if it is done right.

A 63-year-old gentleman presented to my office in 2013 with a complicated history that included a kidney transplant 10 years earlier, a subsequent diagnosis of prostate cancer and finally lymph-node-positive colon cancer.

{{cta('81d5c984-b7b5-4347-86f9-281162ffa9a3','justifyleft')}}Though platinum based regimens like FOLFOX are often recommended, they carry some risk of kidney damage and he opted against it. In 2015, new symptoms revealed recurrent colon cancer involving the brain and neurosurgery was performed to remove the metastasis. This solved the immediate problem and also provided living tumor cells for our

The results were extremely favorable with numerous drug combinations found effective in vitro. I felt optimistic as we met to discuss the findings. Based on our results, he could avoid platinum-based combinations like FOLFOX, in favor of irinotecan-based therapy that carried less risk to the kidney. He was visibly relieved and left my office with a plan to follow up with his medical oncologist in Los Angeles to discuss treatment.

All of this changed suddenly when numerous painful lumps under the skin and onset of hip pain led to a biopsy that confirmed metastatic colon cancer. A portion of that tissue was submitted to a commercial laboratory in Arizona for gene testing.

When he returned for follow up, I explained that our original EVA-PCD® assay results found his tumor highly sensitive to chemotherapy and that I anticipated a good response. I further explained that I felt he could the avoid platinum-based treatments with the potential risk of kidney damage in favor of the Irinotecan-based therapy. His physician agreed and the patient began the exact combination that we had identified in the laboratory, Irinotecan plus Capecitabine known as XELIRI.

Two weeks later, after just one cycle of XELIRI, the lumps under the skin and nagging hip pain had disappeared. His CEA tumor marker fell by 40% and he was on his way to an excellent response. That was until he received the genomic test results.

Much to everyone’s surprise the gene profile recommended against capecitabine and irinotecan (XELIRI), the very combination that had provided his dramatic response. Instead, the gene profile recommended Oxaliplatin, a drug that carried risk of kidney damage. All of this was based upon gene findings of an enzyme called TS and another called TOPO-1.

The patient contacted me alarmed and confused by the gene results. According to his gene profile, he was on the wrong drugs!! “Should I stop the treatment” he asked? I paused for a moment and then inquired: “Aren’t you on the drugs that our functional analysis recommended?” “Yes” he replied. “And aren’t you showing an excellent response” I continued. “Yes” he replied again. “Do you think we should stop?” I asked.

Here, in this patient was the embodiment of the difference between genomic studies and functional analyses. Here was a responding patient prepared to throw over the very drugs that were working so well for his cancer all because his “genes didn’t fit”.

It was abundantly clear that our functional analysis had captured the true biology of this patient's cancer. I went on to explain that we have painstakingly conducted years of study to prove the predictive validity of our functional techniques. The fact that the patient expressed a gene for one or another enzyme has not been proven to be predictive of clinical response. In fact, those who have attempted clinical trials using gene mutations to predict drug response have virtually all failed. Strangely this has not influenced physicians’ willingness to order gene tests or insurers’ willingness to pay for them.

All tests function within their performance characteristics. No test is perfect. However, functional analyses have established their performance characteristics in patients, where it matters the most. This patient is but one example of the demonstrable superiority of functional analyses over genomics. Fortunately this patient was treated before his genomic results were available or the outcome might have been very different indeed.

I welcome your thoughts and comments.

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