Abstract 4595: The VAR2CSA malaria protein efficiently retrieves CTCs from a broad spectrum of cancers
Bibliographic record
Abstract
Abstract Metastasis is the leading cause of cancer-related deaths. The metastatic process requires malignant cells to break out of the primary tumor and travel to distant sites through the vasculatory system. Studies have shown that blood circulating tumor cells (CTCs) can be used for patient management as CTC enumeration provides a non-invasive tool for stratification and monitoring disease status. Furthermore, there is a great interest in fully characterizing this highly metastatic subfraction of the tumor cells. Various assays have been developed to enable magnetic enrichment of CTCs from patient blood. Most depend on antibodies targeting the epithelial marker, EpCAM, and are therefore limited to tumors of epithelial origin. Consequently, there is a need for technologies that enable broad capture of CTCs from all types of cancers. We have recently shown that placental and cancer cells share a unique form of oncofetal chondroitin sulfate (ofCS), not present on normal tissue. We have further shown that ofCS is an acquired trait for cancer cells to enable migration and to potentiate the metastatic potential of cancer cells. Most importantly, we showed that VAR2CSA, a malaria protein involved in placental malaria, binds specifically to ofCS on a wide range of cancer patient-derived cell lines and tissues of both epithelial and mesenchymal origin. Since ofCS seems to be universally expressed across most cancer types and is heavily involved in cancer cell migration, we hypothesized that the recombinant VAR2CSA (rVAR2) protein could enable an efficient isolation of CTCs from blood samples from a broad spectrum of cancer patients. Here, we show that we can use rVAR2-coated magnetic beads to capture ofCS-positive CTCs. The method was validated using blood samples spiked with cancer cells, followed by clinical feasibility testing in diverse types of cancers to demonstrate versatility. We show the presence of CTCs in blood from more than a hundred cancer patients having pancreatic, hepatic, lung, colorectal, melanoma or prostate cancer. CTCs were even found at very early stage disease. No CTCs were detected in samples from healthy subjects. This method not only enabled the efficient isolation of CTC from non-epithelial cancers, but also showed a dramatic increase in CTC capture compared to EpCAM-based CTC isolation. Furthermore, the method allows for downstream single cell analyses as well as for culturing of the CTCs. This method could potentially prove beneficial in cancer diagnosis, patient stratification, monitoring response to therapy, and in future molecular characterization of this highly metastatic subpopulation of cancer cells. Citation Format: Mette Ø. Agerbæk, Sara R. Bang-Christensen, Ming-Hsin Yang, Thomas M. Clausen, Sisse B. Ditlev, Marina A. Pereira, Morten A. Nielsen, Swati Choudhary, Tobias Gustavsson, Poul H. Sorensen, Tim Meyer, David Propper, Jonathan Shamash, Thor G. Theander, Alexandra Aicher, Mads Daugaard, Christopher Heeschen, Ali Salanti. The VAR2CSA malaria protein efficiently retrieves CTCs from a broad spectrum of cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4595.
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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 | 0.000 | 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) | 0.002 | 0.001 |
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".