Aberrant NOTUM+ Program Induced in LGR5+ Crypt Base Columnar Cells Maintains an Immunosuppressive Niche in Colorectal Cancer
Bibliographic record
Abstract
Abstract Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, with treatment failure largely driven by cancer stem-like cells that resist conventional chemoradiation and subsequently initiate tumor recurrence. While immune checkpoint blockade is effective in microsatellite instability-high (MSI-H) CRCs, the majority of CRCs are microsatellite stable (MSS) and exhibit immune exclusion, rendering them refractory to immunotherapy. Here, we identify a previously uncharacterized cancer cell subtype, which we term cancerous Crypt Base Columnar (canCBC) cells. These cells transcriptionally resemble normal LGR5+ CBC cells but activate an aberrant WNT/β-catenin signalling inhibitory program, marked by NOTUM expression. We show that canCBC cells are specifically enriched in MSS tumors, where their presence correlates with reduced CD8⁺ T cell infiltration, broader immune exclusion, and a propensity for regional lymphatic dissemination. Consistently, targeted ablation of canCBCs enhances the tumor-clearing potential of CD8⁺ T cells. This study identifies a novel therapeutic target for overcoming immune exclusion and improving immunotherapy responses in MSS CRCs.
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 | 0.000 |
| 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".