Biology subjects

Aguilar-Mahecha, A.

Publications and source records attributed to Aguilar-Mahecha, A..

3 recordsLinked to original sources

bioRxiv2025

ATR and PKMYT1 inhibition re-sensitize a subset of TNBC patient-derived models to carboplatin inducing mitotic catastrophe

Triple negative breast cancer (TNBC) is associated with poor prognosis and is mainly treated with chemotherapy-based regimens, often including carboplatin. Resistance to carboplatin is a common clinical issue that is either initially present or develops with treatment. Overcoming this resistance is a significant clinical challenge, which highlights the need for novel therapeutic strategies. We used a pooled shRNA screening approach with a chemoresistant TNBC patient-derived xenograft (PDX) cell (PDXC) line to identify targets whose knockdown would enhance the efficacy of carboplatin. This screening led to the identification of the ATR (ataxia telangiectasia and Rad3-related) gene as a key therapeutic vulnerability. Inhibiting ATR with BAY1895344 or AZD6738 re-sensitized carboplatin-resistant PDXCs and PDXs to carboplatin, resulting in an increase in DNA damage, and apoptosis. ATR inhibition disrupts the dependence of carboplatin-resistant cells on the S and G2/M checkpoints for DNA repair, leading to mitotic catastrophe. We further found that the addition of ATR inhibitors to carboplatin reversed a FOXM1-targeted gene program enabling premature passage into mitosis. Moreover, targeting PKMYT1, a regulator of cyclin-dependent kinase 1 (CDK1) controlling the G2/M checkpoint, through knockdown or with the novel PKMYT1 inhibitor RP-6306, also enhanced carboplatin efficacy in our TNBC PDXC. Molecular factors associated with response to the ATR inhibitor/carboplatin combination included low RNA levels of PKMYT1. These results underscore the pivotal roles of ATR and PKMYT1 in mediating resistance to carboplatin in TNBC and support targeting these pathways to overcome carboplatin resistance in this disease.

cancer biology↗

bioRxiv2024

Federated deep learning enables cancer subtyping by proteomics

Artificial intelligence applications in biomedicine face major challenges from data privacy requirements. To address this issue for clinically annotated tissue proteomic data, we developed a Federated Deep Learning (FDL) approach (ProCanFDL), training local models on simulated sites containing data from a pan-cancer cohort (n=1,260) and 29 cohorts held behind private firewalls (n=6,265), representing 19,930 replicate data-independent acquisition mass spectrometry (DIA-MS) runs. Local parameter updates were aggregated to build the global model, achieving a 43% performance gain on the hold-out test set (n=625) in 14 cancer subtyping tasks compared to local models, and matching centralized model performance. The approachs generalizability was demonstrated by retraining the global model with data from two external DIA-MS cohorts (n=55) and eight acquired by tandem mass tag (TMT) proteomics (n=832). ProCanFDL presents a solution for internationally collaborative machine learning initiatives using proteomic data, e.g., for discovering predictive biomarkers or treatment targets, while maintaining data privacy. Statement of SignificanceA federated deep learning approach applied to human proteomic data, acquired using two distinct proteomic technologies from 40 tumor cohorts from eight countries, enabled accurate cancer histopathological subtyping while preserving data privacy. This approach will enable privacy-compliant development of large-scale proteomic AI models, including foundation models, across institutions globally.

cancer biology↗

bioRxiv2023

Clinical proteomics reveals vulnerabilities in non-invasive breast ductal carcinoma and drives personalized treatment strategies.

Ductal carcinoma in situ (DCIS) is the most common type (80%) of noninvasive breast lesions. The lack of validated prognostic markers, limited patient numbers and variable tissue quality significantly impact diagnosis, risk stratification, patient enrolment, and results of clinical studies. We performed label-free quantitative proteomics on 50 clinical formalin-fixed, paraffin embedded biopsies, validating 22 putative biomarkers from independent genetic studies. Our comprehensive proteomic phenotyping reveals more than 380 differentially expressed proteins and metabolic vulnerabilities, that can inform new therapeutic strategies for DCIS and IDC. Due to the readily druggable nature of proteins and metabolites, this study is of high interest for clinical research and pharmaceutical industry. To further evaluate our findings, and to promote the clinical translation of our study, we developed a highly multiplexed targeted proteomics assay for 90 proteins associated with cancer metabolism, RNA regulation and signature cancer pathways, such as Pi3K/AKT/mTOR and EGFR/RAS/RAF.

cancer biology↗