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Abstract 5151: Deciphering the molecular mechanisms driving infiltrative histopathological type of colorectal cancer liver metastases

2019· article· en· W4247262312 on OpenAlexaff
Miran Rada, Anthoula Lazaris, Stephanie Petrillo, Abdellatif Amri, Peter Metrakos

Bibliographic record

VenueMolecular and Cellular Biology / Genetics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsColorectal cancerMedicineCancerPathologyOncologyCancer researchInternal medicine

Abstract

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Colorectal carcinoma (CRC) remains the second leading cause of cancer death in the western world. Over 50% of CRC patients develop liver metastases (LM) and 90% will succumb to their disease. Liver resection of the LMs provides the only possibility of cure, but only 20% of colorectal cancer liver metastases (CRCLM) patients are resectable. The combination of angiogenic inhibitors (AI: anti-VEGF) with chemotherapy is the current form of treatment. Unfortunately, 65-70% of the patients continue on chemotherapy until resistance develops and then are treated with second, third and some times a fourth line of treatment, with an expected median overall survival of 24-28 months. We have no way of identifying those CRCLM patients that would respond/benefit to the addition of anti-angiogenic therapies (e.g. Bevacizumab). Recently we have identified two CRCLM histologic growth patterns (HGP) that predict treatment response and survival: 1) Desmoplastic (DHGP), a desmoplastic ring separating cancer cells from the liver parenchyma and lesions grow by angiogenesis; 2) Replacement or infiltrative (RHGP), tumor cells infiltrate the parenchymal cells in the liver as the lesions grow by co-opting the sinusoidal blood vessels between the liver cell plates. We showed that CRCLM patients with predominantly desmoplastic HGP metastasis receiving AIs plus chemotherapy have more than double the 5-year overall survival compared to patients with replacement HGP who have received the same treatment. In addition, our clinical data revealed that Angiogenic Inhibitors could negatively affect outcomes in patients with replacement HGPs. These non-angiogenic lesions do not respond to angiogenic inhibitors. To further our understanding of the molecular differences between the two HGPs we demonstrated by knocking out ARPC3 (Actin-related protein 2/3 complex subunit 3, involved in actin polymerization) in the human colon cancer cell line, HT-29, that cancer cell motility is a crucial process that regulates histological growth pattern in CRCLM. HT29 CRC cells injected directly into the mouse liver grow into replacement HGPs, while HT29s silenced for ARPC3 grow into desmoplastic HGPs lesions. However, the molecular mechanisms that regulate ARPC3 in CRCLM remain unknown. To further dissect the molecular mechanisms differentiating desmoplastic from replacement HGPs, we performed RNA-seq analysis of CRCLM lesions from chemonaïve patients. Our data revealed that both TGFβ1 and RUNX1 were upregulated in RHGP comparing to DHGP lesions. This has been further validated by immunoblotting and immunohistochemistry. Consistently, RUNX1 has been reported as a downstream of TGFβ1 and transcriptional factor for ARPC3. Collectively, our data suggests that TGFβ1 and RUNX1 contribute to the formation of infiltrative type of colorectal cancer liver metastases possibly through upregulation of ARPC3.Note: This abstract was not presented at the meeting.Citation Format: Miran Rada, Anthoula Lazaris, Stephanie Petrillo, Abdellatif Amri, Peter Metrakos. Deciphering the molecular mechanisms driving infiltrative histopathological type of colorectal cancer liver metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5151.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.014
GPT teacher head0.278
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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