Abstract 1711: A multi-modal analysis approach leveraging multiplexed spatial phenotyping and multi-omics analysis to better understand the prognostic value of tertiary lymphoid structures in non-small cell lung cancer
Bibliographic record
Abstract
Abstract Introduction: Tertiary Lymphoid Structures (TLS) are highly organized ectopic lymphoid structures found in inflamed or tumor tissues, acting as sites of lymphoid recruitment and immune activation. A high TLS density within the tumor is commonly associated with an increased prognostic effect of TILs and with an improved disease free survival and overall survival for patients. However, the existence of conflicting studies suggest that multiple TLS features should be taken into account when assessing their prognostic value, such as their location, cellular composition, maturation stage and spatial organisation, as those may affect their functionalities. Methods: With the aim of gaining insights into TLS biology and evaluating the prognostic role of TLS in Non-Small Cell Lung Carcinoma (NSCLC) according to their multiple features, we developed a TLS multiplex immunofluorescent (mIF) panel that includes T cell (CD3, CD8), B cell (CD20), Follicular Dendritic cell (CD21, CD23) and mature dendritic cell (DC-LAMP) markers. We deployed this panel across a cohort of primary tumors from NSCLC patients (n=408) and established a mIF image analysis workstream to assess the status and spatial location of each cell within the tissue. A H&E staining of the same tissue section was performed to evaluate mIF spatial data in relation to the tumor context. Additional multi-omics assessments were conducted across the same cohort including; whole exome sequencing, NanoString transcriptomics, and immunohistochemistry (e.g. PD-L1, FOXP3). We have leveraged clinical metadata, including demographics (e.g. age, sex, smoking status) and clinical risk factors (e.g. stage, grade, Standard of Care treatment) with clinical follow up (e.g. OS, PFS) for prevalence analysis, novel biomarker identification, and survival association. Results: Assessment of the prevalence of each cell phenotype within the tumor tissue and TLS (tumor centre vs invasive margin; tumor epithelium vs stroma), the distance between each cell type, and the distance of non-TLS immune cells to the closest TLS will be described, demonstrating the different types of lymphoid aggregates and TLS and their functional status. An integrative analysis combining these spatial biology data with multi-omics and clinical data will be presented evaluating the prognostic value of TLS composition, maturation status and spatial organization, in correlation with additional biomarkers and clinical characteristics. Conclusion: This exploratory study using cutting-edge technologies enables us to better understand how TLS orchestrate an organised anti-tumour response, defining TLS spatial biomarker signatures, TLS gene signatures, and TLS features associated with NSCLC patient outcomes to evaluate in the clinic. Citation Format: Julie Berthe, Felix Segerer, Emily C. Jennings, Alma Andoni, Marco Testori, Megha Saraiya, Miljenka Vuko, Harald Hessel, Andreas Spitzmüller, Mari Heininen-Brown, Jorge Blando, Felicia Ng, Emma Jones, Sophie Willis, Michael Surace, Rieneke van de Ven, Tanja De Gruijl, Helen Angell. A multi-modal analysis approach leveraging multiplexed spatial phenotyping and multi-omics analysis to better understand the prognostic value of tertiary lymphoid structures in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1711.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".