Abstract 1044: Establishment and characterization of rare breast patient-derived xenograft models as a potential resource for personalized medicine
Bibliographic record
Abstract
Abstract Breast cancer (BC) exhibits a wide range of morphologic phenotypes and gene expression profiles. Most of the studies that led to the identification of intrinsic molecular subtypes in BC were limited to invasive ductal carcinomas of the breast and did not take rare histopathologic subtypes into account. Rare histopathologic BC subtypes (collectively less than 2% of all breast cancer) have particular prognostic and clinical characteristics. There is no current established treatment that takes into account the specificity of rare BC subtypes. This is mainly due to the absence of clinical trials to determine the optimal management of these rare pathologies. The establishment of relevant preclinical models and molecular characterization of rare BC subtypes is essential for identifying directed and suitable therapeutic regimens for BC patients diagnosed with these rare histopathologic variants. Patient-derived xenograft (PDX) has been recognized as a valuable method to evaluate the clinical diversity of breast cancer. These models were shown to be predictive of clinical outcomes and are being used for preclinical drug evaluation, biomarker identification, and personalized medicine strategies. We developed a cohort of eight BC rare histopathologic subtypes, including four metaplastic, one adenoid cystic, one IDC pleomorphic, one neuroendocrine, and one mucinous BC subtype. These PDXs and their primary tumors were submitted to whole-genome sequencing (WGS) and RNA sequencing. We also evaluated a total of 255 proteins by reverse phase protein array (RPPA) in these PDX samples. We are currently performing conditional reprogramming experiments to generate cell lines from these rare BC PDXs. Our preliminary results indicate that pathways related to PI3K/AKT, ERK/MAPK, mTOR, HGF, ERBB, AMPK and IL3 signaling are disrupted in rare BC subtypes. Several genes belonging to these pathways are dysregulated in rare BC tumors, and therefore represent potential therapeutic targets for personalized treatment. Citation Format: Hellen Kuasne, Paul Savage, Constanza Martinez Ramirez, Leah Liu, Valentina Muñoz-Ramos, Virginie Pilon, Anie Monast, Radia Johnson, Nicholas Bertos, Jamil Asselah, Nathaniel Bouganim, Kevin Petrecca, Sarkis Meterissian, Atilla Omeroglu, Mark Basik, Morag Park. Establishment and characterization of rare breast patient-derived xenograft models as a potential resource for personalized medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1044.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".