Abstract B32: Lymphocytes and immune checkpoint biomarker expression in sarcomas
Bibliographic record
Abstract
Abstract Recent clinical trials of immune checkpoint inhibitors have raised hopes for sarcoma therapy, but they have not taken into account the variability of the immune microenvironment in their design or interpretation. Here, we present a comprehensive analysis of tumor-infiltrating lymphocytes (TILs) across a spectrum of sarcoma types, in order to better identify sarcoma populations that are more likely to respond to specific immunotherapies. Methods: From a tissue microarray database of 1918 formalin-fixed, paraffin-embedded sarcoma tissue samples spanning 24 subtypes, immunohistochemistry was performed to quantify TILs, to characterize major T-cell subsets (CD4, CD8, FOXP3), and to evaluate expression of targetable immune checkpoint ligand PDL1 (programmed death ligand 1) and its immune cell receptor PD1. PDL1 was scored by percentage of tumor cells exhibiting positive membranous staining; all other markers were quantified by counting positive-staining lymphocytes. Results: Lymphocytes were present in 93% of samples (ranging from 1-1300 TILS/mm2), of which 63% were CD8+. Dedifferentiated liposarcoma (DDLPS) showed the greatest enrichment, with a median count of 103 TILs/mm2. Other subtypes with notably high TIL counts include undifferentiated pleomorphic sarcoma (UPS; 38 TILs/mm2), myxofibrosarcoma (35 TILs/mm2), well-differentiated liposarcoma (26 TILs/mm2), osteosarcoma (21 TILs/mm2), and leiomyosarcoma (19 TILs/mm2). Without exception, translocation-associated sarcoma subtypes had very low levels of infiltrating lymphocytes (median 9 TILs/mm2). The subtype with the lowest levels of TILs was chondrosarcoma (0 TILs/mm2). PDL1 was present (≥1% tumor cell staining cutpoint) in 7% of samples, and stained ≥10% of tumor cells in 5.3% of samples. The subtypes that account for the majority of this PDL1 positivity are UPS (15/72), osteosarcoma (12/224), malignant peripheral nerve sheath tumor (9/74), myxofibrosarcoma (8/52), and DDLPS (6/60). Angiosarcoma had the highest proportion of positive cases (60%), but this was based on a small number of samples (n=5). PD1 lymphocyte staining was also low overall, with 14% of samples having at least one PD1+ TIL per mm2. The subtypes with the highest proportions of PD1+ cases were DDLPS (36/79), UPS (23/80), leiomyosarcoma (8/19), and epithelioid sarcoma (4/8). Conclusions: Lymphocyte infiltration is predominantly limited to complex karyotype sarcomas and is rarely seen in translocation-associated sarcomas. The very highest levels of infiltrates are observed in DDLPS and the very lowest in chondrosarcoma. CD8+ effector T cells are the predominant lymphocyte subset present in sarcomas. PDL1 and PD1 expression are low overall for sarcomas, but some subtypes (UPS, DDLPS) appear to employ PDL1 immune blockade in up to 20% of cases. Further studies are pending to determine the significance of these findings for immunotherapy response. Citation Format: Amanda R. Dancsok, David Thomas, Jean-Yves Blay, Robert G. Maki, Torsten O. Nielsen, Elizabeth G. Demicco. Lymphocytes and immune checkpoint biomarker expression in sarcomas [abstract]. In: Proceedings of the AACR Conference on Advances in Sarcomas: From Basic Science to Clinical Translation; May 16-19, 2017; Philadelphia, PA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(2_Suppl):Abstract nr B32.
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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 | 0.001 |
| 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) | 0.003 | 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".