Abstract 2015: A transcriptionally enhanced biosensor to detect and monitor biopsy-dissociated primary prostate cancer single cell drug responses by bioluminescence microscopy
Bibliographic record
Abstract
Abstract Background: In the past decade, the therapeutic landscape of metastatic castration resistant prostate cancer (mCRPC) has been transformed with the introduction of novel pharmaceutical agents including novel antiandrogens (nAA) such as darolutamide, enzalutamide or apalutamide. Currently, the management of mCRPC is mainly based on biomarkers obtained from the patient's blood or bulk tumor samples, which do not take into account for prostate cancer (PCa) cell heterogeneity. Objectives: To develop a bioluminescence imaging technology enabling: 1) the detection of PCa single cells from dissociated biopsies and 2) single cell nAA sensitivity assessment. Methods: The PCA3 promoter (PCa specific) and PSEBC promoter (androgen receptor-driven to monitor response to nAA therapy) have been incorporated in the non-replicating adenoviral multi-promoter integrated two-step transcriptional amplification system (MP-ITSTA) that allows imaging of complementary activities of two different promoters by a single output reporter gene. Thus, PCA3/PSEBC-ITSTA system was designed to monitor androgen receptor (AR) activity specifically within each single PCa cell by bioluminescence microscopy. Prostatic biopsies were dissociated in the presence of collagenase II and DNase for 18h. PCa cell lines or fresh biopsies-dissociated cancer cells were transduced for 72h and then covered with an extracellular matrix gel. Single cell bioluminescence microscopy images were done before and after exposure to DHT or DHT+AA to specifically monitoring therapy response. Changes in cell number and luminescence intensity after AA were normalized to the DHT group to exclude non-specific cell-death due to infection or time spent in culture. Results: PCA3/PSEBC-ITSTA was specific to PCa cell lines. By determining the ratio of AR active cells before and after DHT or DHT+AA treatment in culture, PCA3-Cre-PSEBC-ITSTA was able to determine the AA sensitivity of LNCaP (sensitive), LAPC4 (moderately resistant) and 22Rv1 (resistant) PCa cell lines. Also, our method could detect and monitor AA sensitivity of primary PCa cells harvested from eight radical prostatectomy specimens. PCA3/PSEBC-ITSTA could dynamically quantify AR transcriptional activity during enzalutamide or bicalutamide treatments, in a dose dependent manner, and could unveil tumor AA response heterogeneity. As expected, inhibition of AR activity was stronger with enzalutamide than with bicalutamide. Conclusions: Our bioluminescence microscopy-based biosensor could detect primary PCa cells in culture and it allows dynamic evaluation of AA sensitivity at a single-cell level in a heterogeneous cell population harvested from radical prostatectomy biopsies. We believe our approach opens to a novel field of treatment response predictive tools for cancer treatment decision-making. Citation Format: Audrey Champagne, Pallavi Jain, Bertrand Neveu, Frédéric Pouliot. A transcriptionally enhanced biosensor to detect and monitor biopsy-dissociated primary prostate cancer single cell drug responses by bioluminescence microscopy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2015.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".