Immune Biomarkers on Tissue Microarray Cores Support the Presence of Adjacent Tertiary Lymphoid Structures in Soft Tissue Sarcoma
Bibliographic record
Abstract
Immunotherapy has emerged as a new treatment modality in some soft tissue sarcomas, particularly for tumors associated with tertiary lymphoid structures (TLSs). These structures are functional lymphoid aggregates, and their presence is indicative of an active anticancer immune response in the tumor microenvironment. The assessment of TLS as a predictive biomarker at scale on patient specimens remains challenging. Although tissue microarrays (TMAs) could facilitate this assessment, it is unclear whether small microarray cores can represent and identify associated TLS responses. We sought to use multiplex immunohistochemistry to identify key components of TLS: T cells, B cells, and dendritic cells. The multiplex panels (CD3, CD20, CD208, and PNAd) were applied to 80 cases both on TMAs and on their cognate available full-faced sections from epithelioid sarcoma and dedifferentiated/well-differentiated liposarcoma case series. TMAs were digitally scored for the number of immune cells using the HALO image analysis platform, and cognate full-faced sections were visually evaluated for the presence of TLS. An independent validation set of soft tissue sarcomas (N = 49) was stained with the CD3, CD20, and CD208, and scored by QuPath. A combined immune marker (defined as the presence of more than 24% CD3+ T cells, or 0.51% CD20+ B cells, or >0.14% CD208+ mature dendritic cells on tissue microarray cores) is highly specific (100%) and moderately sensitive (61%) to predict the existence of TLS on full-faced sections. The combined immune marker showed a sensitivity of 25% and specificity of 91% on the validation set. The combined immune marker assessed on tissue microarrays is highly specific in inferring the presence of TLS on cognate full-faced sections. Therefore, despite the small area sampled, tissue microarrays may be utilized to assess the clinical value of TLS on data sets where specificity is critical and large sample size can mitigate low-to-moderate sensitivity.
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 | 0.001 | 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) | 0.002 | 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".