Abstract PD10-09: Comparison of early modulation of biological pathways and immune microenvironment by anthracyclines- or taxane-based treatment
Bibliographic record
Abstract
Abstract Background - Chemotherapy is the current backbone of immune checkpoint inhibitors (ICIs) for approved indications in triple negative breast cancer (TNBC). While it is expected that different chemotherapeutic agents might have distinct immunomodulatory effects, data in patients is scarce. Here we compared the early modulation of molecular pathways and immune-related features in TNBC patients receiving neoadjuvant doxorubicin/cyclophosphamide (AC) or nab-paclitaxel/carboplatin (nab/C). Methods - Two cohorts of TNBC patients with available RNA-seq for paired core biopsies obtained before (baseline) and after the first cycle of neoadjuvant chemotherapy (day 21, D1C2) were selected: i) 84 patients treated with nab/C from the control arm of the NeoTRIPaPDL1 trial (Gianni L SABCS 2019, Bianchini G ESMO 2021); ii) 22 patients treated with AC from a publicly available dataset (Park Nat Comms 2020). Presence of immune cell populations was estimated by gene expression profile deconvolution using ConsensusTME R package (n=19). Hallmark gene set collection and custom signature activation status were estimated in each sample using singscore R package (n=63). We also assessed modulation of PD-L1 gene expression and selected immune-related genes. To minimize batch effects between the two cohorts, in each dataset, median score expression at baseline was subtracted from all samples. Continuous scores were compared by 2-sided t-test. Results - Thirty-nine pathways were significantly and similarly modulated in both cohorts (all p<0.01) at D1C2 compared to baseline. They included upregulation of all immune cell related signatures (e.g. T cells, dendritic cells, monocytes and macrophages, B cells and NK), immune function-related signatures and apoptosis, and downregulation of proliferation related signatures. PD-L1 gene expression was upregulated in both cohorts (p<0.001). A comparison between D1C2 of the two cohorts was performed to evaluate whether chemotherapies differently modulated gene signatures. Nab/C more strongly downregulated signatures of G2M checkpoint, mitotic spindle, DNA repair, MYC targets and oxidative phosphorylation. AC more strongly upregulated some immune-related signatures (e.g. cytotoxic cells, CD4+ T-cells, regulatory T cells, IL2/STAT5 and JAK/STAT3) and apoptosis. PD-L1 was more upregulated in AC cohort (p=0.006). For each signature, we identified, in each cohort, patients ‘signature high’ and ‘signature low’ using the median value at baseline as a threshold. We then investigated whether signature modulation at D1C2 was different in the two groups. In ‘signature high’ patients, immune-related signatures were similarly upregulated, whereas nab/C showed downregulation of proliferation related markers. In ‘signature low’ patients, a more robust upregulation of all immune-related signatures was observed in both cohorts (all p<0.0001), but it was quantitatively stronger in AC arm for several immune signatures (e.g. cytotoxic cells, gamma/delta T cells, CD4+ T-cells, regulatory T cells, NK) (p<0.01). PD-L1 was significantly upregulated at D1C2 only in PD-L1 low group in both cohorts (p<0.0001), but the effect was stronger in AC (p=0.014). Chemotherapy induced modulation of other individual genes will be presented at the meeting. Conclusions - The two investigated chemotherapy regimens had a strong early immunomodulatory and chemoattractant effect, which confirms the rationale for combination with ICIs. Anthracyclines elicit a quantitatively stronger immune modulatory effect compared to nab/C, which is particularly evident in. “immune low” tumors. These observations can have clinical implications for the selection of the ideal chemotherapy partner to ICIs in not-inflamed/PD-L1 negative tumors. Citation Format: Maurizio Callari, Marco Barreca, Matteo Dugo, Barbara Galbardi, Lucia Viganò, Alberta Locatelli, Luca Licata, Giulia Viale, Pinuccia Valagussa, Giuseppe Viale, Luca Gianni, Giampaolo Bianchini. Comparison of early modulation of biological pathways and immune microenvironment by anthracyclines- or taxane-based treatment [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD10-09.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".