Abstract 67: Predicting colorectal patient prognoses by functional characterisation of heterogeneous cell types and their spatial interaction using a new technique: Whole slide imaging mass cytometry
Bibliographic record
Abstract
Abstract Immunotherapies stimulate T cell function in the tumor microenvironment (TME). CD3+ and CD8+ T cell infiltration can predict disease recurrence in colorectal cancer (CRC) patients and has been validated as Immunoscore® (IS). The TME comprises multiple heterogeneous populations of immune cells, including T cells and cancer-associated fibroblasts (CAFs); CRC tumors are also morphologically diverse. Standard BioTools™ (SBI) has developed a novel whole slide Imaging Mass Cytometry™ technique that allows complete analysis of cell population heterogeneity in the TME using >40 markers. We hypothesised there are multiple heterogeneous subtypes of CAFs within CRC tumors and that they modulate T cell infiltration and patient outcome. In collaboration with SBI, we studied a cohort of CRC patients stratified based on T cell infiltrate and disease recurrence. We identified multiple CAF populations that interact with tumor-infiltrating T cell populations. Slides from 10 CRC patients with high or low CD3+CD8+ T cell infiltrate (IS) were stained with a panel encompassing markers for CAF and T cell heterogeneity, function, and metabolism. Slides were imaged using the Hyperion XTi™ Imaging System at SBI. Using a scan mode called Preview Mode, the whole slide was imaged in minutes to identify CAF and T cell phenotypic regions of interest. Next, the same slide was imaged at 1 μm resolution using Cell Mode (CM). Finally, for each sample, an additional serial section was imaged using a slightly lower resolution called Tissue Mode (TM). TM allowed us to image the samples in several hours instead of days while capturing the full heterogeneity of the entire tissue sample. We used clustering to identify phenotype clusters and neighbourhood analysis to study spatial enrichment. We also compared results between CM and TM. We report: Identification of heterogeneous CAF subtypes in CRC patients with high or low IS; determination of CAF populations that spatially interact with T cells in patients with high or low IS; and examination of consistent spatial interactions between CAF and T cells in CRC, and whether these interactions differ in patients with high or low IS. TM revealed several heterogeneous cellular populations that differed between patients with high or low IS; importantly, low IS tumors had more podoplanin+αSMA+ CAF populations and fewer podoplanin+αSMA- cell populations compared with high IS tumors. High IS tumors had more PD-1+CD8+ T cells compared with low IS tumors; these PD-1+CD8+ T cells spatially interacted with podoplanin+αSMA- CAF populations in high IS tumors. CAF-PD-1+CD8+ T cell interactions were absent from low IS tumors. Thus, CAF-PD-1+CD8+ T cell interactions are important in CRC patients with high IS, and these cellular interactions require further investigation. Citation Format: Rory M. Costello, Sonya Fenton, Helen McGuire, Liang Lim, David Howell, Qanber Raza, Jyh Yun Chwee, Roslyn Kemp. Predicting colorectal patient prognoses by functional characterisation of heterogeneous cell types and their spatial interaction using a new technique: Whole slide imaging mass cytometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 67.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".