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Abstract A26: Immune profiling and organoids generation of a rare case of prostate cancer liver metastasis

2020· article· en· W3118944735 on OpenAlexaff
Aurélie Y. Le Page, Anna de Polo, K.-P. Guérard, Anthoula Lazaris, Stephanie Petrillo, Walead Ebrahimizadeh, Sébastien Tabariès, Surashri Shinde-Jadhav, Andrei Feldiorean, N. Boufaeid, Wassim Kassouf, Ciriaco A. Piccirillo, Peter M. Siegel, Armen Aprikian, Alex Gregorieff, Peter Metrakos, David P. Labbé

Bibliographic record

VenueCancer Immunology Research · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineProstate cancerMetastasisImmunotherapyCancerPathologyImmunophenotypingOncologyInternal medicineImmunologyAntigen

Abstract

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Abstract Introduction: Prostate cancer (PCa) is the second most frequent cancer in men and a leading cause of cancer-related mortality. Despite major advances in immunotherapy, PCa remains a poor responder. Metastatic PCa is responsible for the majority of PCa-associated mortality. Most PCa metastases are multifocal and display a strong bones tropism (91.1% of cases), but PCa metastases can also spread to the lymph nodes (8.7%), lungs (5.7%), liver (4.5%) and brain (1.8%). Liver metastases are associated with worse prognosis but due to their multifocal nature and frequent spreading to other sites, PCa metastases are rarely resected. Therefore, immunologic characterization of these lesions concomitant with generation of research tools derived from these lesions are urgently needed to understand how to intercept disease progression. Methods: A 62-year-old male who previously underwent radical prostatectomy in 2016 was diagnosed in July 2018 with a single liver metastasis (5.3 cm) by MRI. The tumor was surgically resected and tumor tissue along with peripheral blood was collected and processed for in-depth immunologic/molecular characterization and generation of tumor models. The study was done in accordance with the guidelines approved by MUHC IRB. Prior written informed consent was obtained from the subject to participate in the study (protocol: SDR-11-066). Results: The prostatic origin of the tumor mass was confirmed by positivity for PSMA and NKX3.1 expression. Patient-derived xenografts, 2D cell and organoid cultures were generated and immunophenotyping of the innate and adaptive peripheral and tumor-infiltrating immune cells subsets was performed. Genomic alterations are currently being characterized by multiplex ligation-dependent probe amplification (MLPA). Additionally, chromatin accessibility-based characterization of the gene regulatory network of tumor luminal cells (CD49-CD26+) using the assay for transposase-accessible chromatin using sequencing (ATAC-seq) together with RNA-seq is presently under way. Conclusions: Our collaborative effort will provide the much-needed research tools required to model and understand the processes leading to the rare, but lethal, progression from a localized PCa lesion to liver metastases. Combined with other ongoing research efforts, we believe this case will help us understand the molecular basis to the liver tropism of a subset of PCa metastases and ultimately provide biomarkers for early identification of patients with increased metastatic potential as well as a basis to determine the appropriate immunotherapy modality for metastatic patients. Citation Format: Aurélie Y. Le Page, Anna de Polo, K-P Guérard, A. Lazaris, S.K. Petrillo, W. Ebrahimizadeh, S. Tabariès, S. Shinde-Jadhav, A. Feldiorean, N. Boufaeid, W. Kassouf, C. Piccirillo, P.M. Siegel, A. Aprikian, A. Gregorieff, J. Lapointe, P. Metrakos, D.P. Labbé. Immune profiling and organoids generation of a rare case of prostate cancer liver metastasis [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2018 Nov 27-30; Miami Beach, FL. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(4 Suppl):Abstract nr A26.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.731

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.157
GPT teacher head0.415
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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