Abstract PS4-48: Development of a multiplexed protein panel using a targeted proteomics approach for the study of CDK4/6 inhibitors resistance in hormone receptor positive breast cancer
Bibliographic record
Abstract
Abstract Background Breast cancer is the most common malignancy in woman and hormone receptor positive breast cancer represents approximately 70% of all cases. These patients are treated with endocrine therapies aimed at disrupting hormone receptor signaling and estrogen production which improves survival and allows a cure in early stages. However, recurrent disease, metastatic dissemination and drug resistance limit the survival of patients. The limitations regarding endocrine therapy have prompted the search for new therapeutic targets, such as CDK4/6 Inhibitors. Despite the improved disease control that CDK4/6 Inhibitors offer to patients, not all patients respond to these drugs and some patients whose tumors respond to CDK4/6 Inhibitors eventually develop acquired resistance. No proven biomarkers of CDK4/6 Inhibitors efficacy exist to date, and there is a need for diagnostic tools that could stratify patients to save costs and the burden of unnecessary therapy. Our aim is to perform a quantitative evaluation of marker proteins with a developed multiplexed panel using targeted mass spectrometry (MS)-based proteomics for 25 proteins from the CDK/RB/E2F-pathway which have been shown in the literature to be central to CDK4/6I resistance. Material and Methods We developed Multiple Reaction Monitoring (MRM) MS methods for the 25 target proteins from the CDK/RB/E2F-pathway using synthetic heavy-isotope-labeled standards with the aim of creating MRM assays to enable specific, sensitive and precise quantitation of these proteins in small amounts of cell line and tissue samples. Moreover, we developed a high resolution peptide fractionation system using high-pH micro-flow liquid chromatography (LC) which is required to overcome the problem of small samples amounts while improving analytical assay sensitivity in the analysis of complex biological matrices such as breast cancer biopsies. The MCF-7 human breast cancer cell line was used as model during method development. Proteins from cell lysates were isolated, reduced, alkylated and digested with trypsin. The resulting tryptic peptides were micro-flow fractionated into 70 fractions and the developed nano-LC-MS MRM assays were used for peptide detection and quantification. Data were analyzed using Skyline. Results Our developed micro-flow fractionation method allowed us to work on limited amounts of samples (60ug), and increased the possibility to detecting low abundance proteins such as cell cycle components. Using the MCF-7 cell model, we are able to identify and quantify 17 proteins out of the 25 from our panel: CDK1, CDK2, CDK4, Cyclin B1, Cyclin D1, Cyclin D3, Cyclin E1, RB1, E2F-3, E2F-4, E2F-5, ESR1, TOP2A, TYMS, EZH2, MKI67, BIRC5. Conclusion We have developed a highly specific MS-based multiplexed assay with peptide standards targeting 25 proteins relevant to CDK4/6 Inhibitors breast cancer treatment. Our micro-flow fractionation method increased assay sensitivity and allows for the analysis of small sample amounts. In the future we will apply this workflow to samples such as Patient Derived Xenografts models, breast cancer tissues and FFPE samples in order to identify the predictive value of these potential biomarkers for responsiveness to CDK4/6 Inhibitors. Citation Format: Marta Zurawska, Adriana Aguilar-Mahecha, Mark Basik, Michal Dadlez, Dominik Domanski. Development of a multiplexed protein panel using a targeted proteomics approach for the study of CDK4/6 inhibitors resistance in hormone receptor positive breast cancer [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-48.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".