Abstract 4250: Insights on genomic status and biomarker modulation using OncoPanel™ and BioMAP®platforms
Bibliographic record
Abstract
Abstract Trastuzumab (Herceptin®), a humanized monoclonal antibody (mAb) targeted against the ErbB2 protein, was approved by the FDA in 1998 and the EMA in 2000 to treat Her2-positive breast cancer, and in 2010 received FDA approval for the treatment of Her2-positive gastric cancer. As of April 2021 there were five biosimilars for trastuzumab with regulatory approval. Trastuzumab’s efficacy, and success, comes from its phenotypic impact on multiple cellular mechanisms. These include induction of Her2 receptor internalization and degradation following cell surface binding; activation of antibody-dependent cellular cytotoxicity (ADCC) following recruitment of cytotoxic, innate immune cells to the tumor microenvironment; and suppression of cell growth and proliferation signaling via inhibition of RAS-MAPK and PI3K-AKT pathways. Using trastuzumab as a case study in cellular functional assays, we show trastuzumab is antiproliferative to head and neck, and lung cancer cell lines, suggesting a role for Her2 in other tumor types beyond breast cancer. Receptor occupancy, ADCC, and univariate genomic analysis indicate the importance of genomic status to drug sensitivity even in the presence of moderate cell surface receptor expression. OncoPanel genomic analysis indicated ERBB2 amplification as the most significant feature correlating with sensitivity to trastuzumab, almost 30-fold above the next most significant marker EIF4A2. Using the BioMAP® Oncology Panel modeling tumor microenvironment (TME) biology, we profiled trastuzumab for impacts on protein biomarkers relevant for immune responses, inflammation, and matrix remodeling. Trastuzumab modulated immune and angiogenesis-related biomarkers, including increased IFNγ and decreased VEGF, both important for an anticancer response in patients. Profiling of trastuzumab in BioMAP Diversity PLUS®, which provides insights on broad tissue and disease coverage as might be seen in a patient, indicted that the mAb trastuzumab, the Her2-EGFR small molecule kinase inhibitor lapatinib, and the broad-spectrum cytotoxic chemotherapeutic agent paclitaxel differentially modified clinically-relevant protein biomarkers, as might be expected for these three different therapeutic classes. Interestingly, at concentrations near their Cmax, lapatinib and trastuzumab differentially modulated biomarkers associated with tissue remodeling and inflammation, supporting the potential for combination therapies of these anticancer agents. Combinations of the agents are in clinical trial. This current work demonstrates that multipronged assessment of candidate therapeutics using human cell-based in vitro models are needed to understand 1) inhibition of tumor cell proliferation, 2) differential TME responses, 3) genetic status before and after treatment as reported in the literature, and 4) impact on broader biology for safety. Citation Format: Sheryl P. Denker, Jennifer I. Drake, Natiya E. Robinson, Elsa Liu, Alastair J. King. Insights on genomic status and biomarker modulation using OncoPanel™ and BioMAP®platforms [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4250.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".