Identification of surrogate biomarkers for the replacement histopathological growth pattern in colorectal cancer liver metastasis
Bibliographic record
Abstract
Introduction: Colorectal cancer (CRC) is the third most common cancer in both males and females in North America. It is the second leading cause of cancer-related deaths due in large part to CRC liver metastasis (CRCLM), with which approximately 50% of patients will be diagnosed during the course of their disease. Three major histopathological growth patterns (HGP) have been identified in CRCLM, and evidence suggests that the tumour's predominant HGP has prognostic implications. Specifically, CRCLMs that present with the replacement growth pattern are resistant to anti-angiogenic therapy, which is frequently used alongside neoadjuvant chemotherapy for the treatment of metastatic CRC. The HGPs in CRCLM may have a promising role as predictive biomarkers of response to angiogenesis inhibitors, where no such marker has yet been validated. However, a CRCLM's growth pattern must be evaluated by a pathologist from resected tumour tissue, implying that preoperative treatment precedes HGP scoring. Therefore, surrogate molecular markers for the CRCLM HGPs that can be assessed prior to surgery would be instrumental in determining whether a patient may benefit from anti-angiogenic treatment. Objectives: It is hypothesized that there are products of differentially expressed genes (DEG) in replacement HGP CRCLMs that may serve as potential diagnostic biomarkers and/or targets for anti-metastatic therapy. The objective of this study is to characterize and compare the global gene expression profiles of chemonaïve CRCLMs presenting with the replacement or desmoplastic HGPs via RNA sequencing (RNA-Seq) and immunohistochemical staining. The secondary aim is to isolate extracellular vesicles (EV) from the plasma of CRCLM patients and verify whether they contain the gene products of select DEGs. Methods: RNA-Seq was performed using the total RNA extracted from liver metastases and adjacent normal liver tissues that had been resected from 18 patients with CRCLM who did not receive preoperative chemotherapy. Immunohistochemical staining of select DEGs identified from the RNA-Seq data, among other protein targets, was then performed on formalin-fixed, paraffin-embedded (FFPE) CRCLM samples. The visualization and analysis of immunohistochemistry results were done using the Aperio ImageScope software program. Lastly, EVs were isolated from patient plasma samples by differential centrifugation and subsequently lysed for protein detection via Western blot. Results: A gene expression signature comprised of genes whose transcription was upregulated in chemonaïve replacement HGP CRCLMs compared to chemonaïve desmoplastic HGP CRCLMs and normal liver tissues was generated. The protein levels of one of these genes, LOXL4, were found to be significantly elevated at the tumour-liver interface and in areas of inflammation in replacement HGP metastases. LOXL4 protein was also detectable in EVs isolated from the plasma of patients with CRCLM or benign liver disease, though the quantities were comparable between the growth patterns. Conclusion: Our study showed that the replacement and desmoplastic HGP CRCLMs have distinguishable gene expression profiles, with several genes related to the extracellular matrix and the immune system being upregulated in replacement HGP metastases. The expression levels of LOXL4 mRNA and protein were significantly elevated in replacement HGP CRCLMs relative to desmoplastic HGP CRCLMs, and LOXL4 protein is also detectable in EVs isolated from patient plasma. Therefore, LOXL4 may potentially serve as a surrogate biomarker for the replacement HGP in CRCLM.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".