Abstract A88: Identification of factors modulating sensitivity to T cell infiltration in the breast cancer setting via next-generation sequencing.
Bibliographic record
Abstract
Abstract Clinical and pre-clinical trials have established that killer (CD8+) T cells can recognize and destroy tumors. The challenge of our time is to address barriers to T cell infiltration into solid tumors and thereby make cancer immunotherapy a powerful, broadly applicable treatment modality. Bilateral breast cancer represents a unique opportunity to identify those proteins that modulate immune cell entry into solid tumors. Bilateral synchronous breast cancer is when tumors arise independently in separate breasts of the same patient at the same time. Similar to the powerful use of “twin studies” in epidemiology, bilateral tumors provide an opportunity to explore two independent tumorigenic events against a common genetic (and immunological) background. We hypothesise that the sensitivity of breast cancers to T cell infiltration can be predicted on the basis of their molecular profiles. To assess this question, a cohort of bilateral and unilateral breast cancer tissues has been assembled in collaboration with CTRNet-affiliated biobanks. Our objective was to identify 10 tumor pairs with differential T cell infiltration. Here we present the novel protocol for multi-colour brightfield immunohistochemistry (IHC) which was used to quantitate multiple lymphocyte subsets in these precious specimens at the same time. A workflow for automated image collection is presented, which is geared to the analysis of whole-tumour sections. Our results demonstrate that bilateral breast tumors often show differential lymphocyte infiltration despite being exposed to the same level of systemic immunity. Paired tumors will be subjected to whole transcriptome sequencing and lymphocyte receptor spectratyping to identify proteins that distinguish infiltrated from non-infiltrated tumors. Thus, patients with bilateral breast cancer have provided a powerful, translationally-relevant opportunity to study local barriers to T cell infiltration in solid tumors. Citation Format: Sally Amos, Ron de Leeuw, Nikita Kuklev, Juzer Kakal, Sindy Babinsky, Katy Milne, Sara Kost, Doug Freeman, Rob Holt, Peter Watson, Brad Nelson. Identification of factors modulating sensitivity to T cell infiltration in the breast cancer setting via next-generation sequencing. [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology: Multidisciplinary Science Driving Basic and Clinical Advances; Dec 2-5, 2012; Miami, FL. Philadelphia (PA): AACR; Cancer Res 2013;73(1 Suppl):Abstract nr A88.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".