Abstract PD9-06: Histopathological and molecular immune landscape and DNA damage response signatures to predict response to carboplatin and docetaxel in TNT trial TNBC cohort
Bibliographic record
Abstract
Abstract Background The TNT trial (NCT00532727) showed no evidence of carboplatin (C) superiority over docetaxel (D) overall in metastatic triple negative breast cancers (TNBC), but a C benefit was observed in the pre-specified sub-group analysis in patients with a gBRCA1/2 mutation (Tutt et al, Nat Med 2018). Given only ~30% of patients have a gBRCA1/2 mutation, broader predictive biomarkers of response are needed. In this cohort we previously found that DNA Damage Response (DDR) signatures were associated with improved C response in chemotherapy (CT) naïve patients only (Tovey et al, ASCO 2020). Since DDR activities influence tumour immune-microenvironment, we explored the predictive ability of immune cell markers and performed integrative analyses on multi-omics features to identify novel TNBC subgroups. Patients and Methods Tumour infiltrating lymphocytes (TILs) were evaluated on haematoxylin and eosin stained primary tumour (PT) slides for 222/376 TNT patients. Formalin-fixed paraffin-embedded PT tissues from 186/376 TNT patients were successfully profiled using total RNA-sequencing. Matched recurrence (REC) was also sequenced for 13 patients. Twenty-five immune signatures were assessed. Logistic regression and restricted mean progression free survival (PFS) were applied to delineate the relationship of these features with treatment outcomes. Random forest clustering of multi-omics DDR and immune biology markers, including gene expression signatures and mutation/methylation status, was applied to identify subgroups. We further molecularly characterised these clusters through supervised clustering of 693 gene expression “modules” (sets of co-expressed genes), immune cell deconvolution and genomic scars. Results Immune gene expression signatures and TILs were highly correlated. Average immune infiltration based on ConsensusTME was lower in mutated/methylated tumours compared with BRCA1 wildtype tumours (p=0.04). Immune signature score markers decreased from PT to REC, demonstrating a dynamic immune microenvironment. In the overall population and when restricting to prior CT treated patients, high immune infiltration (gene expression based & TILs) was associated with response to D while C response rates were not associated with immune scores (interaction p-values< 0.05). This did not translate to a PFS benefit. Multi-omics clustering identified 6 biological subgroups including immune enriched, immune depleted, DDR deficient and proficient clusters as well as 2 small clusters with no obvious distinguishing features. Immune enriched TNBC were predominantly basal-like immune activated with high B-cell/T-cell diversity. Immune depleted TNBC showed higher activity of proliferation and DDR pathway modules. DDR proficient tumours had low expression of immune markers and enrichment for ESR1/PGR expression, markers of extra cellular formation, cell structure, lipid metabolism and proliferation. The DDR deficient cluster was enriched for proliferation and demonstrated high number of TILs despite no apparent enrichment for gene expression-based immune modules. In the prior CT treated cohort, the immune enriched cluster had preferential response to D (62.5% (D) vs. 29.4% (C); p=0.02). The immune depleted cluster had preferential response to C (8.0% (D) vs. 40.0% (C); p=0.01). Numbers were too small to assess differential response within the other clusters or in the CT naïve cohort. Conclusions Tumours with high immune features have high response to D while those with low immune features have preferential response to C in advanced TNBC. Combining multi-omics markers of DDR deficiency and immune biology can identify clusters of patients with distinct biological profiles and differential treatment specific response rates. Citation Format: Holly Tovey, Orsolya Sipos, Katherine A Hoadley, Joel S Parker, Jelmar Quist, Sarah Kernaghan, Lucy Kilburn, Roberto Salgado, Sherene Loi, Richard D Kennedy, Ioannis Roxanis, Patrycja Gazinska, Sarah E. Pinder, Judith Bliss, Charles M. Perou, Syed Haider, Andrew Tutt, Anita Grigoriadis, Maggie Chon U Cheang. Histopathological and molecular immune landscape and DNA damage response signatures to predict response to carboplatin and docetaxel in TNT trial TNBC cohort [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD9-06.
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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 | 0.000 |
| 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) | 0.002 | 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".