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Record W7161981877 · doi:10.82308/47156

Colorectal cancer liver metastasis histological growth patterns; the role of the immune system

2017· dissertation· en· W7161981877 on OpenAlexaboutno aff
Rafif Mattar

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsnot available
Fundersnot available
KeywordsColorectal cancerImmune systemMetastasisInfiltration (HVAC)Liver cancerGeneDisease

Abstract

fetched live from OpenAlex

Background: Colorectal cancer (CRC) is amongst the three most common cancers worldwide. The majority of patients presenting with liver metastases (LM). Resection of liver lesions is the only curative measure. Unfortunately, less than 30% of patients are eligible for resection. Three major histological growth patterns (HGPs) have been identified in patients with colorectal cancer liver metastasis (CRCLM); desmoplastic, pushing and replacement. Patients with replacement lesions have a poor overall survival rate compared to those with desmoplastic when treated. A number of studies have reported that the magnitude of T-lymphocyte infiltration in CRCLM is significantly correlated with prognosis. High infiltration of macrophages has also been associated with better outcome. Furthermore, the role of these immune components when LM are segregated by HGP has not yet been evaluated thoroughly. Defining this is important in light of the fact that new therapies are based on manipulation of various components of the immune system. Methodology Gene Expression Analysis: Samples were obtained through the McGill University Hospital Centre Liver Disease Biobank; 9 Desmoplastic and 7 Replacement lesions. To minimize heterogeneity in the data, we selected chemonaïve samples. Samples were scored with our pathologist collaborators. Tumors were macrodissected, along with adjacent normal liver and processed Sequencing was performed by our collaborator Dr. Woong-Yang Park at the Samsung Genome Centre in South Korea, using the illumine HISeq sequencers. R© software was used to identify differentially expressed genes, and to generate heatmaps. From the output data, we studyied genes and pathways using Ingenuity Pathway Analysis © software.Immune Cell Quantification: Serial sections of formalin fixed paraffin embedded biopsies from CRCLM of both desmoplastic and replacement HGPs were stained via immunofluorescence, to assess the presence and distribution of various subtypes of T-lymphocytes and macrophages. Antibodies used were: CD68 (all macrophages), IRF5 (M1 macrophages), CMAF (M2 macrophages). Granzyme B (activated cytotoxic T-lymphocytes), CD4 (helper T-lymphocytes), and FOXP3 (regulatory T-lymphocytes). Tiled images of the whole tissue sections were obtained using a florescent microscope, followed by manual counting of each of the immune cell populations.ResultsGene Expression Analysis: Significant biological functions between the patterns consisted of those mostly related to cellular processes such as survival and invasion, suggesting that genes differentially expressed are those related to the tumor formation and metastases cascade. Furthermore, various genes that are related to immunological functions are mostly activated in the replacement pattern and conversely undetected in the desmoplastic subtypes.Immune Cell Quantification: The majority of immune cell populations concentrated at the tumor/liver interface in both growth patterns, whether that be the tumor peripheries, the desmoplastic ring, or the adjacent normal livers. Furthermore, when comparing desmoplastic and replacement patterns, we found that immune cells are mostly higher at the tumor/liver interface of the replacement pattern. We have also identified a rare subtype of immune cells; CD4- regulatory T-lymphocytes. This population has not yet been described in patients with CRC or with CRCLM.Conclusions: Different components of the immune system play diverse roles in the desmoplastic and replacement patterns of CRCLM. The findings described in this paper are new and will help increase our understanding of the diversity of CRC liver metastases. Such findings can direct future work to identity new prognostic markers and to help future therapeutics development towards therapies that can manipulate various components of the immune system.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.267
Teacher spread0.252 · 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
Published2017
Admission routes1
Has abstractyes

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