Abstract 4550: Correlations between tumor mutation burden, inflammatory profile and histological characteristics of tumor microenvironment in early-stage squamous cell lung carcinoma
Bibliographic record
Abstract
Abstract Background: Anti-PD1/PD-L1 immunotherapy has demonstrated success in the treatment of advanced non-small cell lung cancer (NSCLC). Clinical data have shown that both the expression of PD-L1 in patient tumors and high tumor mutation burden (TMB) predicts the likelihood of a positive response to anti-PD-1/PD-L1 immunotherapy. Also, tumor microenvironment (TME) is the constitutive element in cancer immunity, in which analysis of characteristics reflects the potential existing immune reaction. Method: Histologic sections from 150 squamous cell lung carcinoma (SqCLC) were evaluated by two pathologists independently for percentage and character of intratumoral inflammatory cells and percentage and character of para-tumoral infiltrate. The ratios of infiltrating inflammatory cells to tumor cells were estimated in 10% increments by microscopic inspection. The proportions of immune cell populations were deconvulated using the CIBERSORT method based on Affymetrix gene expression profiles. PD-L1 protein expression by IHC was evaluated using the Dako PD-L1 22C3 pharmDx kit and scoring was determined according to the Dako tumor proportion score (TPS). Tumor Mutation Burden (TMB) was calculated based on data from targeted genome sequencing. CD4 and CD8 mRNA levels were determined from Affymetrix gene expression data from frozen specimens. Results: The infiltrates could be divided into intratumoral and paratumoral patterns according to their location in relation to microscopic tumor cell nests. Using the CIBERSORT assay, we confirmed our histological findings by microscopic examination that the SqCLC cohort can be subtyped into plasma cell dominant (74.8%) or other immune infiltrates dominant (such as macrophages), based on the proportions of immune cell populations. We found by regression analysis that TMB had a negative correlation with the percentage of intratumoral inflammatory cells (P=0.014), but did not significantly correlate with paratumoral infiltrates. The TMB demonstrated a significant negative correlation with CD4 mRNA level (P=0.017), but not with CD8 mRNA level. No correlation was determined for TMB and the immune cells dominant subgroup. Interestingly, we didn’t find any association for PD-L1 protein expression with the percentage of intra- or para-tumoral infiltrates, plasma cells dominant group and CD4 and CD8 mRNA levels. Conclusions: TMB was negatively correlated with the percentage of intratumoral inflammatory cells and CD4 mRNA level, which indicate that high TMB may promote an immune suppression environment. In addition, we did not find any association of PD-L1 expression with characteristics of TME in this early-stage SqCLC cohort. Further studies are needed to verify these interesting results. Citation Format: Hui Yu, Daniel T. Merrick, Ming-Sound Tsao, William G. Richards, Lucian R. Chirieac, Mark A. Watson, Christopher J. Rivard, David H. Harpole, Raphael Bueno, Adrie van Bokhoven, Aik-Choon Tan, Fred R. Hirsch, Wilbur A. Franklin. Correlations between tumor mutation burden, inflammatory profile and histological characteristics of tumor microenvironment in early-stage squamous cell lung carcinoma [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 4550.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".