Abstract IA006: Immunobiology of sarcomas and therapeutic implications
Bibliographic record
Abstract
Abstract Undoubtedly, the greatest breakthrough in cancer treatment over the past decade has been immunotherapy. However, the immunosuppressive nature of the tumor microenvironment (TME) remains the biggest obstacle to increasing the number of patients that respond to immunotherapy. Unfortunately, initial trials of immune checkpoint inhibitors in patients with soft tissue sarcoma (STS) have achieved very limited success, and the reasons for this are crudely unappreciated. Hence, it is critical to move beyond the current focus on targeting individual cell types of interest and rather adopt a more comprehensive systems-level approach in which we analyze and integrate all the TME components to identify and disable the critical nodes. Our team has characterized the immunobiology of STS via cutting–edge high-dimensional mapping platforms that we have established and standardized for analyzing specimens from patients with STS. We have discovered that tumor infiltrating myeloid cells (TIM) penetrate and dominate the TME of STS provoking the suppression of effective antitumoral T cell immune responses. Using a set of sophisticated multiscale techniques, we deconvoluted the TME of various STS creating a comprehensive map of all immune cell populations present and dissected out chemokine gradients of specific histological regions relevant for the recruitment and development of TIM in the TME of STS. STS TIM are regulated by tumor-derived cytokines to acquire a polarized immunosuppressive phenotype, which in turn de-activates the T cell compartment in the TME. In this way, we elucidated unique myeloid populations as key facilitators of STS progression and mediators of local tumor immunosuppression. Furthermore, we defined metabolic pathways utilized by TIM that contribute to their immunosuppressive properties in STS and manipulated them via glutamine antagonism in our mouse model of STS. Our intervention generated a TME that was less acidic, less hypoxic and more replete with nutrients leading to an intratumoral influx of T cells, both CD4+ and CD8+ cells, along with B cells. These modifications in the TME in combination with anti PD-1 therapy induced better endogenous anti-tumor responses and repurposed TIM markedly thereby reducing the tumor size and improving the survival of mice. Thus, we propose a metabolic strategy to therapeutically target the TME of STS and reprogram their immunosuppressive myeloid niche as a means of enhancing immunotherapy. Citation Format: Nicolas J. Llosa. Immunobiology of sarcomas and therapeutic implications [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA006.
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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 | 0.000 |
| 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) | 0.001 | 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 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".