Abstract A018: Characterization of macrophage population in head and neck squamous cell carcinoma and renal cell carcinoma and their role in modulating immune checkpoint blockade response
Bibliographic record
Abstract
Abstract Tumor-associated macrophages (TAMs) and monocytes are an integral part of tumor microenvironment (TME) which modulates disease progression. M1 and M2 are the two well-defined subtypes of TAMs whose polarization influence response to immune checkpoint inhibitors (ICIs). Unavailability of data from complex models poses limitations to extensive characterization of these immune subpopulations. In this study, we have evaluated the role of monocytes and TAMs in modulating response to ICI, using the FarcastTM TruTumor histoculture platform. Two different cancer indications, Head and Neck Squamous Cell Carcinoma (HNSCC) and Renal Cell Carcinoma (RCC) were used in the study. HNSCC (n = 25) and RCC (n = 24) tissue samples were collected along with matched blood from the consented patients, post-surgery. Tissue explants were generated and allotted to arms and cultured for 72 hours. Fifteen samples from both indication were treated with anti-PD1 ICI, Nivolumab at a concentration of 132 µg/ml. Macrophage and monocyte sub-populations were characterized by performing flow cytometry, and cytokine (tumor necrosis factor-α (TNF-α), and interferon gamma (IFN-γ)) analysis. In HNSCC, a higher proportion of monocytes compared to RCC (p = 0.02) was observed. Though total TAM proportions in the two indications exhibited no significant difference, a significantly higher proportion of M1/M2 was observed in RCC (p = 0.006) as compared to HNSCC. In addition, RCC also exhibited higher secretion of TNF-α (p = 0.08) as compared to HNSCC. Eleven out of fifteen HNSCC samples (73%) exhibited more than 1.2-fold increase in IFN-γ secretion as opposed to only 6/13 RCC samples (46%), on treatment with Nivolumab. However, the correlation between fold change, with respect to control, in IFN- γ secretion and tumor content was much stronger in RCC (ρ=-0.82; p=0.0009) as compared to HNSCC (ρ=-0.07; p=0.81). This observation could be explained by a comparatively higher immunosuppressive microenvironment in HNSCC, potentially mediated by a higher monocyte subpopulation. On the other hand, the relatively higher M1/M2 ratio in RCC seemed to potentially enhance Nivolumab treatment efficacy as compared to HNSCC. Spatial orientation of macrophage sub-population could give further insights into the role they play in TME. Thus, FarcastTM TruTumor is a relevant platform to characterize the monocyte and TAM population in TME across different cancer indications and to investigate their role in modulating ICI response. Citation Format: Moumita Nath, Nandini Pal Basak, Kowshik Jaganathan, Oliyarasi M, Rajashekar M, Saurabh Bhargava, Amritha Suresh, Lalitha Laxhmi, Jayaprakash Chandra Reddy, Ganesh Mandakulutur Subramanya, Amritha Prabha, Prakash BV, Biswajit Das, Syamkumar V, Chandan Bhowal, Mouniss M, Dharanidharan M, Ritu Malhotra, Govindraj K, Mohit Malhotra, Satish Sankaran. Characterization of macrophage population in head and neck squamous cell carcinoma and renal cell carcinoma and their role in modulating immune checkpoint blockade response [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A018.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".