Here is a tweetorial from our latest publication in @Annals_Oncology about longitudinal tracking of esophageal adenocarcinoma. #OesophagealCancer #EsophagealCancer https://t.co/QWvrzBXmK7 1/8

We sequenced 245 plasma samples from 97 patients with oesophageal adenocarcinoma using a 77 gene pan-cancer ctDNA panel. 2/8
Variants derived from previously characterised driver oesophageal adenocarcinoma genes had a significantly higher VAF than variants from other genes, indicating selection. 3/8
Peripheral blood cell samples were also sequenced for 78/97 patients. CHIP mutations were identified in 23% of cases, longitudinal tracking of CHIP variants suggested these variants were dynamic over time. 4/8
We found patients that were ctDNA positive post-surgery had a significantly poorer survival than ctDNA negative patients, and the elimination of CHIP variants improved the positive predictive value. 5/8
In summary, we demonstrate in a large, national, prospectively-collected dataset that ctDNA in plasma following surgery for EAC is prognostic for relapse. Inclusion of peripheral blood cell samples can reduce or eliminate false positives from CHIP. 6/8
In the future, post-operative ctDNA could be used to risk stratify patients into high- and low-risk groups for intensification or de-escalation of adjuvant chemotherapy. 7/8
Many thanks to our founders and all patients who participated within the OCCAMS consortium framework. The study was carried out by our brilliant PhD student Emma Ococks, medical oncologist @LizzySmyth1, postdoc @AFrankell, and postdoc @neus_snows among others @MRC_CU. 8/8
@threadreaderapp unroll please

More from Health

You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below 👇

The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through


You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.

👇


Here is what we know:

- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative

Assuming 1 million people take the test (including you):

- 100 of them are sick
- 999,900 of them are healthy

👇

Let's now test both groups, starting with the 100 people sick:

▫️ 99 of them will be diagnosed (correctly) as sick (99%)

▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)

👇

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