Abstract 1695: Vascularization of colorectal cancer liver metastasis: correlation with growth patterns
Bibliographic record
Abstract
Abstract Colorectal cancer (CRC) is the third leading cause of cancer-related death in North America. Approximately 50% of patients will be diagnosed with CRC liver metastasis (CRLM) during the course of their disease. Untreated, patients will survive for only a few months, but with chemotherapy, a median survival of 20 months can be achieved. At present, no reliable indicators exist to predict outcome or prognosis in treatable patients. Moreover, no biological parameters are currently being considered for patient stratification into different treatment groups. We examined CRCLM resected from patients and have identified two major histologic growth patterns (HGP): a desmoplastic (DHGP) pattern and a replacement (RHGP) pattern where tumor cells replace parenchymal cells in the liver plates. These HGP involve distinct modes of angiogenesis and host cell responses. Namely, liver metastases with a RHGP grow by co-opting the stroma, without hypoxia-induced angiogenesis and with little perturbation of the liver architecture. In contrast, metastases with a DHGP show characteristics of ongoing, hypoxia-driven angiogenesis including increased fibrin deposition at the tumour-liver interface and increased endothelial cell proliferation. In addition RHGP is associated with poor clinical response to bevacizumab chemotherapy. These differences suggest that the two patterns trigger distinct microenvironment responses and as a consequence, may initiate and utilize different modes of vascularization and expansion. Our hypothesis is that there are distinct gene expression signatures in the tumor and/or host compartments of different HGPs, which will shed light on the biological mechanisms underlying this diversity of HGPs. To test this hypothesis we: i) have extracted high quality RNA from lesions of chemonaïve patients and through RNAseq analysis identified gene expression differences between DHGP, RHGP lesions ii.) to further understand the role of tumor associated vascularity, we have stained different lesions (chemonaïve, chemo only and chemo + bevacizumab) with vascular markers to look at tumor associated blood vessels (immature, intermediate and mature: aSMA1 & CD31) and the rate of endothelial cell proliferation (Ki67/CD34). Our preliminary data demonstrates the expression of neo-vessels in the DHGP desmoplastic ring and in the peripheral tumor along the parenchyma tissue have lower vascularity with fewer branches that are supplying the tumor. The RHGP has similar vasculature to the adjacent normal tissue with a sinusoidal network of blood supply and a higher vascularity than DHGP. We are now correlating these findings with RNAseq expression data. This work will result in the identification of targets in the HGPs that will help stratify patients in terms of treatment. We currently have less treatment options for the RHGP patients and it would be important to utilize the data from this study to develop new treatment strategies for those patients. Citation Format: Anthoula Lazaris, Abdellatif Amri, Pablo Zoroquiain, Stephanie K. Petrillo, Rafif Mattar, Zu-Hua Gao, Peter Vermeulen, Peter Metrakos. Vascularization of colorectal cancer liver metastasis: correlation with growth patterns. [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 1695.
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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) | 0.004 | 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".