Abstract P1-06-11: A targetable EGFR-driven tumor-initiating program in breast cancer
Bibliographic record
Abstract
Abstract Background: Inter- and intra-tumour heterogeneity underlies variability in therapeutic response. Although targeting of the epidermal growth factor receptor (EGFR) in breast cancer has failed to demonstrate clinical efficacy at the population level, complete and durable responses have been reported at low frequencies. The molecular determinants of these responses are unknown, but are of importance in the era of precision medicine. Results: We performed a patient-derived xenograft (PDX) clinical trial with gefitinib in a breast cancer PDX cohort. Consistent with clinical trial data, gefitinib exhibited limited efficacy across most models. One PDX, however, demonstrated a complete and durable (>6 months) clinical response, and was subject to deep molecular profiling to identify determinants of response. Exome sequencing revealed no single nucleotide variants or copy number alterations in EGFR pathway members. EGFR was differentially expressed between the two major cellular subpopulations identified by single-cell RNAseq and this cellular heterogeneity in EGFR expression was validated immunohistochemically. Fluorescence-activated cell sorting of the EGFRhi subpopulation revealed cells with enhanced stem-like properties, including ALDH activity, sphere-forming capacity in vitro, ability to form tumours in vivo and seeding lung micrometastases from orthotopically transplanted tumours. Tumourspheres derived from EGFRhi cells developed into mixed EGFRhi and EGFRlo subpopulations, as did macrometastases, supporting that EGFRhi subpopulation can self-renew and re-populate. Analysis of expressed SNVs in the single-cell RNAseq data, filtered by variants identified from exome sequencing, showed no clonal segregation, supporting a non-clonal origin of the functionally distinct EGFRhi and EGFRlo subpopulations. This EGFR-driven tumour initiating cell program was observed in independent PDX models, some which showed growth inhibition in response to gefitinib. Conclusions: Using bulk and single-cell genomic profiling, we identified and functionally validated an EGFR-driven tumour-initiating program in a subset of aggressive breast tumours, which may be predictive of gefitinib sensitivity. This contradicts traditional beliefs that good therapeutic targets are homogenously expressed, in that we show that a target displaying intra-tumour heterogeneity can be effective so long that it is expressed in the tumour-initiating population. Citation Format: Savage P, Saleh SMI, Wang Y-C, Revil T, Badescu D, Liu L, Iacucci E, Zuo D, Bertos N, Munoz-Ramos V, Asselah J, Meterissian S, Omeroglu A, Hébert S, Kleinman C, Park M, Ragoussis J. A targetable EGFR-driven tumor-initiating program in breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-06-11.
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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 | 0.000 | 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 | 0.001 |
| 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".