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Abstract B033: Multiplex analysis of the tumor immune microenvironment during treatment with atezolizumab/pelareorep/letrozole reveals novel immune-tumor interactions

2023· article· en· W4389239901 on OpenAlexaffabout
Homa Dadrastoussi, Julian Olea, Eduardo Fernandez Hernandez, Hugo Lara Martinez, Kaijin Wu, Houra Loghmani, Thomas Heineman, Richard Trauger, Matt Coffey, Akil Merchant, Kevin R. Kelly

Bibliographic record

VenueCancer Immunology Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Research and Treatments
Canadian institutionsOncolytics Biotech (Canada)
Fundersnot available
KeywordsAtezolizumabTumor microenvironmentImmune systemCancer researchImmunotherapyMedicineCytotoxic T cellImmunologyBiologyNivolumab

Abstract

fetched live from OpenAlex

Abstract Background: Pelareorep (pela) is a non-modified intravenously administered oncolytic reovirus exhibiting effective tumor suppression through both innate and adaptive immune responses, as well as direct tumor lysis. Findings from the AWARE-1 window of opportunity study previously demonstrated the synergistic potential of combining pela with atezolizumab (atezo), showcasing promising immunological reactions within tumors of early breast cancer (eBC) patients. To gain deeper insights into the complex tumor immune microenvironment (TiME) pre- and post-treatment, we employed imaging mass cytometry (IMC) to conduct high-dimensional, single-cell analysis of tissue samples. Methods: Patients (n=10) received pela (days 1, 2 and 8, 9), atezolizumab (day 3), and letrozole (day 1 to 21). Tumor biopsies (FFPE samples) were collected pre-treatment on day 3 (prior to atezolizumab administration) and on day ~21 (surgical excision). Samples from 8 out of 10 patients were subjected to IMC with a comprehensive panel of 37 antibodies for single cell investigation of TiME changes. Tumor, proliferation, T cell, macrophage, NK, and immunoregulatory markers were included in the panel. Results: Our first pass analysis of IMC images, showed an increase in proliferating (Ki67+) cytotoxic T cells adjacent to apoptotic (caspase 3+) tumor cells post-treatment (both D3 and D21) samples indicating immunogenic cell death. Conversely, we observed a decrease in Ki67+ and ER-positive tumor cells after treatment indicating decreased tumor cell proliferation and hormone expression. In 3 out of 8 patients, treatment was associated with a shift in monocyte/macrophages from an M1 (CD68+/CD163-) to M2 (CD68+/CD163+) phenotype associated with significant tumor infiltration. Surprisingly, in these patients we also noted a significant increase in apoptotic M2 macrophages on D21. Consistent with the known immune priming effects of pela, both PD-1 and PD-L1 increased on D3. Subsequently, on day 21, post atezo, tumor PD-L1 decreased on tumor cells while PD-1 expression persisted. Pela induced IDO expression on both tumor and monocyte/macrophage cells. Unlike its effect on PD-L1, atezo did not appear to attenuate IDO expression on day 21. We were also able to detect a treatment related increase in immune cell perivascular localization on D3 providing further evidence of TiME priming. Conclusions: The immune system plays a crucial role in regulating cancer progression; however, our understanding of immune interactions with tumors remains limited. Our study utilizing IMC revealed complex immune-tumor interactions changes during treatment with atezo/pela/letrozole. These findings provide valuable insights into the intricate dynamics of the TiME and may inform new therapeutic combination approaches in breast cancer treatment. Citation Format: Homa Dadrastoussi, Julian Olea, Eduardo Fernandez Hernandez, Hugo Lara Martinez, Kaijin Wu, Houra Loghmani, Thomas Heineman, Richard Trauger, Matt Coffey, Akil Merchant, Kevin Kelly. Multiplex analysis of the tumor immune microenvironment during treatment with atezolizumab/pelareorep/letrozole reveals novel immune-tumor interactions [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 B033.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.354
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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