Uh-oh. (New cancer biology understanding to negate targeted RX?)

A NEJM article coming in 3 days is reported to prove that cancerous tumors are not monolithic in their genomic profile - to the extreme that multiple samples of the same patient's tumor express different genetic mutations, with only limited commonalities among samples from the same tumor. (Early news coverage here (WSJ), here (Bloomberg), and here (FierceBiotech).)

Implications: this is going to turn some worlds on their heads Here's my quick guesses at implications:

• Cancer just became even harder to solve. Think chess is complex? How about 3-D chess? That's pretty much the leap in complexity that cancer researchers just experienced. 

• Possible boon for DNA sequencing: demand for multiple sequences per patient may make DNA sequencing a bigger market faster. The NEJM study suggests that sequencing a tumor longitudinally (i.e. at a regular schedule, during treatment) will guide multiple treatment decisions, which may differ based on new expression patterns or mutations. Does this also mean that each tumor will be sampled many times in different sites at diagnosis? If so, we may move from 1 sequence/patient to dozens of sequences per patient. (Good gosh that's ALOT of data. 1 terabyte per sequence is hard enough to handle. 20 TB/patient? Wow.)

• Anyone working on hazily predictive PGX might be wise to give up. The idea of a single analyte for a single disease, or a number of analytes against a number of diseases probably only targets one portion of a given disease. In other words, if your predictive test isn't 100% predictive, you are only explaining a fraction of the disease, and therefore of negligible utility.

• This might be the death of Affymetrix.  I can't see much value from a highly variable 1-dimensional expression profile, unless they invent a way to generate multiple expression profiles from a single sample at roughly the sample price point.

• This news might actually boost sales of targeted therapies in the near term. Why test for HER-2 status? Even if a patient is tested HER-2 negative, an oncologist wouldn't be out of line to still treat with Herceptin, knowing that the HER-2 negative status may only apply to a portion of the tumor.

• Along the same lines, the NEJM finding may give a boost to combination therapies. For example, Sutent, Nexavar (VEGFR and PDGFR) and Raf kinases), Torisel (mTor), Votrient (VEGFR-1, VEGFR-2, VEGFR-3, PDGFR-a/β, and c-kit), and Inlyta ( VEGFR-1, VEGFR-2, VEGFR-3,platelet derived growth factor receptor (PDGFR), and cKIT (CD117).) are each sold for RCC (kidney cancer) but each seem to be only marginally effective. They have different inhibitory profiles though - could combining them produce a better outcome? Roche would seem to be in the best position to gain if combinations are successful.

• Would a need for combination therapies be the straw that breaks the camels's back on the American health care system or even the USA? If treatment with a single targeted therapeutic may be $30,000 per month, would a combination therapy be $100k/month? I can't imagine Medicare and private insurers are ready to pay these sums. If they are, Medicare is already a multi-trillion dollar liability. Wanna go for quadrillion dollar liability?

• This may be a boon to systems biology researchers, like Lee Hood and his team at the ISB. Hood has for a long time seen cancer not as a product of a single mutation, but rather a cascade of biological signals which in total result in cancer. 

• Expect more attention to early detection and treatment. Cancers are FAR less complex in their earlier stages of development.

• Just a guess on my part: more surgical biopsies, less needle biopsies if more tumor material is needed?

• The result: we need more different cancer meds to mix and match patient profiles. Could the FDA loosen up a little on approval requirements, or would this make it even more difficult, as any new targeted drug for a given disease would need to succeed or fail in combination with other targeted therapies? (i.e. more false negatives a false positives from combinatorial effects.)

• Good news/bad news. Bad news: every single clinical-stage targeted therapy just became less valuable. That drug targeting gene "X" is less valuable, now that gene "X" explains less of the disease. Good news: targeted drugs that narrowly failed late stage trials might be resurrected. Maybe the drug didn't fail because it wasn't effective against the target, but rather because the target explains less of the disease than previously believed.

Ultimately, this news dampens the optimism for personalized medicine, but because of cancer's proven ability to mutate, most of us knew that cancer would not be beaten by single silver bullets.