Dr Daniel Haber’s cancer circulating tumor cell blood test CTC chip used to track cancer progress in realtime

The blood test requires a 10 milliliter blood sample — just two teaspoons. It takes about eight hours to send the blood across the 80,000 tiny columns so a specially designed antibody glue can latch onto passing cancer cells. It was used to detect cancer in 27 people and helped to track the progress of cancer in near real time. This will help determine what treatments are working and a genetic fingerprint of the current state of any tumor. This should lead to better cancer treatment and earlier detection and better disease monitoring

Haber and his colleagues analyzed blood samples from 27 patients with non-small cell lung cancer, 23 who had EGFR gene mutations and four who did not. CTCs were identified in all samples and in genetic analyses from mutations 92 percent of the time.

Mutations in EGFR, a protein, can help predict whether these tumors will respond to a family of drugs called tyrosine kinase inhibitors.

“Even in the three to four months that we followed patients, the genetic make-up of the tumors changed. Resistant mutations appear and other mutations appear, obviously because we’re doing things [with drug therapy] to the cancer,” Haber said. “But the way we practice oncology we don’t typically test for that. We do one biopsy which takes a tiny, tiny amount and assume that for the rest of the course, the tumor is the same.”

“It’s important to know in real time what you’re treating,” he continued. “We need to be able to follow the patient without needing to re-biopsy the tumor every time.”

A previous study published in Nature used the CTC (Circulating Tumor Cells) chip technology to look at CTCs in lung, pancreatic, prostate, breast and colon cancers. The CTC chip successfully found such cells in 99 percent of the samples.

Schiller, of the University of Texas Southwestern Medical Center in Dallas, said there are practical questions about whether enough cells can be extracted to make the technique effective and whether it will work for other types of tumors.

Haber said he believes it will.

The CTC chip, licensed to the privately held CellPoint Diagnostics in Mountain View, California, is 100 times more sensitive than a U.S. Food and Drug Administration-approved technique that uses magnetic beads to try to extract cancer cells, according to Haber.

“I think this is key to personalized medicine,” said Dr. Daniel Haber, senior author of a paper detailing the technology, to be published in the July 24 issue of the New England Journal of Medicine but released early online Wednesday. “As we get to targeted therapies in increasing numbers, and increasing understanding about the genetics that guide targeted therapies, we need a way to know what we’re treating.”

The technology is in its infancy, however. “This is still in a very, very early stage where it takes a long time to handle every sample, to flow the blood through the chip,” Haber said. “This is a proof of principle that we can do this. We need a much more automated system for larger clinical trials.”

Dr. Len Horovitz, a pulmonary specialist at Lenox Hill Hospital in New York City, said that “you have to have some circulating cells to do this test, but it’s very exciting because they’re getting a genetic fingerprint of a tumor which will tell an oncologist what therapy the tumor might respond to or not respond to.

“It’s expensive, but it may well be that if we can identify patients who can have a personalized regimen that works, we will be saving the cost of treating all those patients with regimens that don’t work,” he added.

Megpagetoday coverage

Web MD coverage of the CTC chip

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Now procesors made with 65nm technology and have ruoghly 100GFLOPS perfomance. Atom size is roughly 0.1nm. So I can predict, what processors making technology can reach 0.65nm (5 atoms size). Now single processor consist of roughly 500M tranzistors. One chip is size roughly 100mm^2. 65nm/0.65nm=100 times shorter. Becouse tranzsistors is in two dimension chip, then after processors making technology reach 0.65nm, single procesor will be 100^2=10000 times faster than "Intel Core2 quad". So processors, which will be maked with 0.65nm technology will has 100GFLOPS*10000=1PFLOPS=10^15FLOPS. To simulate human brain need at least 100PFLOPS. This single chip (which maybe be made at 2020) will needs power of roughly 100W. Our brains needed power is 20W. So conclusion is if chips will be very small and fast and will has tranzistors size of few atoms, they still not will be so eficient like effiecent like human brain (maybe only processors can be effiecent like human brain if chips will be maked with more tranzistors but will less frequently).


Loki thanks for the CPS/$1K calculator. Very nice.

For the sensing, see the work with flourescent markers. They can take real time snapshots. they use a rat and replace a section of skull with clear plastic. they they can do detailed tracking of activity. Recent cycling of flourescent markers lets them look at a larger area past the diffraction limit of visible light (200nm) down to 20nm.

Snake oil baron. Your discussion of an AI saving intelligence resources by not needing to think about sex etc... reminds of the Seinfeld episode where George becomes a genius when he stops trying to get sex.


I suppose that a lot depends on whether the behavior of individual neurons needs to be simulated or if the a simulation of neocortical columns could be achieved while cutting out much of the complexity needed to manifest them in cell form.

From skimming the Kurzweil article that the CPS/$1K calculator page used I could not tell if the brain computation estimate was for the whole brain or just the neocortex but even if it was the later I could imagine a lot of savings on things like knowing how to make bread and cross the street. A colour-blind AI with no sense of smell or taste and no interest in food or sex would still be extremely intelligent with far less computational demands.


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My feeling is that the critical piece is sensors for mapping the brain at a high resolution in real-time. I think such sensors are required to obtain the dynamic behavior of the neurons with only rudimentary understanding of the biochemistry of each neuron type.

Advances in computation power seem a given, but the sensing and modeling process needs directed research.