Abstract 3149: Enumeration and mutational profiling of CTCs and comparison to ctDNA and colorectal cancer liver metastases
Bibliographic record
Abstract
Abstract Background Colorectal cancer (CRC) is the 3rd most common cancer diagnosed worldwide in both men and women. Only 39% of cancers are diagnosed at a localized stage, and 5-year survival rates decrease rapidly for patients with advanced and metastasized disease (stage III 61%, stage IV 8%). Better markers for detection of disease progression, therapeutic resistance and minimal residual disease are still needed. Liquid biopsies, such as CTCs and ctDNA, are emerging biomarkers shed by the tumor into the blood stream. Both markers currently are attracting growing interest for their use in disease prognosis, early detection of recurrence and are promising candidates for guiding cancer therapy in real-time. Method For rapid label-free isolation of CTCs from peripheral blood we used the Vortex technology, a microfluidic device using inertia and laminar microvortices. From 15 patients with metastatic CRC to the liver that underwent liver metastatectomy with curative intent, we collected CTCs preoperatively, at the 5th postoperative day and during follow-up visits. Cells collected were immunostained for EpCAM, CD45 and DAPI, enumerated using standardized classification criteria, and subjected to Sanger sequencing. CTC enumeration and mutational patterns were compared to the primary tumor, liver metastases and ctDNA (detected by a multiplexed PCR and enrichment technology; Kidess E et al., 2015) as well as CEA levels when available. Results 41 blood samples from 15 patients were collected at different time points prior to and after surgical resection of liver metastases. More CTCs were found in preoperatively collected CRC patient samples (2.4 CTCs/mL, 0.1 - 5.5/mL) than in age-matched healthy controls (0.1 CTCs/mL, 0 - 0.4/mL). 80% of all CRC samples were identified as positive for CTCs (based on a calculated threshold from healthy controls), with varying levels of EpCAM expression (81.4% of CTCs being EpCAM+). The number of CTCs for each patient, showed a close correlation to clinical parameters and ctDNA levels: detection of CTCs, CTC mutational profiles as well as ctDNA revealed minimal residual disease and anticipated tumor recurrence earlier than carcinoembryonic antigen (CEA) value or imaging. For example, for P006, postoperative imaging surveillance revealed progressive disease, which was accompanied by rising levels of CTCs (up to 29 CTCs/mL at the last time point) and PIK3CA mutant DNA in both plasma ctDNA and CTC DNA, while CEA remained in the normal range. Conclusion Our data illustrate that CTCs as well as ctDNA can efficiently reveal disease recurrence as well as disease progression earlier than imaging and far more reliable compared to CEA, the currently standard biomarker for CRC. Beyond enumeration, CTC molecular analysis gives additional information and will potentially help to promote the development of tailored therapies for every individual patient. Citation Format: Evelyn Kidess-Sigal, Haiyan E. Liu, Melanie Triboulet, James Che, Georges A. Poultsides, Brendan C. Visser, Andre Marziali, Marc Lee, Valentina Vysotskaia, Matthew Wiggin, Vishnu C. Ramani, Ulrich Keilholz, Ingeborg Tinhofer, Amin Zia, John Coller, Jeffrey A. Norton, Elodie Sollier, Stefanie S. Jeffrey. Enumeration and mutational profiling of CTCs and comparison to ctDNA and colorectal cancer liver metastases. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3149.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".