Abstract A24: Identifying the genomic and clinical features of AKT1/PIK3CA mutant metastatic prostate cancer using circulating tumor DNA
Bibliographic record
Abstract
Abstract Background: Hotspot activating mutations in AKT1 and PIK3CA represent a rare but potentially unique subset of metastatic prostate cancers (mPCa). The accompanying genomic and clinical features of these patients are currently unknown. Preclinical evidence suggests that unconstrained PI3K signaling via PTEN loss may render androgen receptor (AR)-targeted therapy less effective. Given the availability of agents that target nodes within the PI3K pathway, elucidating the genomic properties of AKT1/PIK3CA mutant patients and their response to AR-targeted therapy will be critical for therapeutic selection. Methods: We performed deep targeted sequencing on 1,381 cell-free DNA samples from 608 patients with mPCa. Analysis was restricted to patients with hotspot AKT1 or PI3KCA mutations of presumed clonal origin, requiring a variant allele frequency >25% of a sample’s ctDNA fraction. Activating AKT1 and PIK3CA mutations were defined as recurrent hotspot mutations with a cBioPortal annotation, as well as functionally equivalent variants within 5bp of these canonical hotspot positions. Patient records were reviewed for baseline clinical characteristics, as well as time from androgen deprivation therapy (ADT) initiation to castration resistance and overall survival (OS). Results: 5.9% (36/608) of patients harbored at least 1 clonal hotspot mutation in either AKT1 or PIK3CA, of which p.E17K and p.E545K/Q/A were most common. This population had a significantly higher ctDNA fraction compared to a control cohort of AKT1/PIK3CA wild-type mPCa patients (Mann-Whitney U test, 0.48 vs. 0.21, p < 0.001). There were no differences in PTEN copy number or mutation frequency compared to the control cohort. Although AR mutations and copy number amplifications were observed at similar frequencies, patients harboring AKT1 or PI3KCA mutations had fewer additional copies of AR (median 4.71 vs. 10.33, p=0.011, Mann-Whitney U Test). 30 patients with activating AKT1/PIK3CA mutations had clinical outcomes available. Interestingly, there were no significant differences in time to castration resistance or OS compared to ctDNA-positive patients without activating PI3K defects. Conclusions: AKT1/PIK3CA mutant mPCa is defined by a genomic landscape featuring high ctDNA-fraction and lower levels of AR amplification. Response to standard-of-care treatment among these patients is typical for ctDNA-positive mPCa. These findings may nominate patients who may benefit from PI3K-targeted therapeutics following resistance on AR-targeted therapy. Citation Format: Cameron M. Herberts, Andrew J. Murtha, Simon Fu, Gang Wang, Elena Schönlau, Anna Gleave, Steven Yip, Arkhjamil Angeles, Sebastien Hotte, Abdulraheem Alshangiti, Ben Tran, Scott North, Sinja Taavitsainen, Kevin Beja, Gillian Vandekerkhove1, Elie Ritch, Fred Saad, Nayyer Iqbal, Martin E. Gleave, Matti Annala, Kim N. Chi, Alexander W. Wyatt. Identifying the genomic and clinical features of AKT1/PIK3CA mutant metastatic prostate cancer using circulating tumor DNA [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr A24.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".