Abstract B013: Precision medicine platform to guide the treatment of NSCLC
Notice bibliographique
Résumé
Abstract Lung cancer (LC) remains the top cause of cancer-associated mortality worldwide, with a 10-year overall survival rate of only 5%. While most LCs are smoking related, 25% of non-small cell LC (NSCLC) are diagnosed in patients with little or no smoking history. Fusions involving anaplastic lymphoma kinase (ALK) are the oncogenic driver in ∼3–7% of NSCLC. While inhibitors targeting the kinase domain of ALK have proven effective, inevitably, resistance develops with limited subsequent efficacious options. We aimed to integrate multiomic characterization and drug sensitivity testing of minimally cultured NSCLC samples to provide a ranked list of most effective drugs for each patient. We developed a precision medicine platform (PMP) to screen patient-derived material (PDM) directly from the operating room with curated panels of drugs. PDM collected during clinically indicated procedures is plated in 3D-culture to generate patient-derived organoids (PDOs). PDOs are screened at therapeutically relevant doses, drawing from pharmacokinetic data for each drug. We have optimized an assay to rapidly screen for EML4-ALK fusions and can perform next-generation sequencing in ∼7 days to integrate with drug screening results. To date, we have screened 80+ NSCLC tissue/fluid collections and molecularly characterized 77 of these PDMs. Our cohort prioritized collection of tissue from patients with EML4-ALK LC, resulting in the collection of 14 distinct cases from patients, with 13 patients having progressed to 2nd line therapy or beyond. While our dataset is enriched in non-smokers (32/80 screened models), we observed only 4 cases of LC with mutations in EGFR. Our cohort included 12 models with mutations in KRAS, with ¼ of those occurring in never-smokers. We have demonstrated an ability to produce high quality drug screening data from low input samples (biopsies). In one case of EML4-ALK NSCLC, we were able to collect PDM from two distinct anatomic spaces (pleural effusion and peritoneal fluid) and screen with the same panel of drugs, with nearly identical results, highlighting the consistency of our assay. Our results recapitulate known resistance in samples previously exposed to therapy, demonstrating a strong negative predictive value. Sequencing identified diverse driver mutations in populations of both smokers and non-smokers. Our PMP captures robust results that are consistent with known clinical pathogenesis. Prioritization of drug screening using compounds with diverse inhibition mechanisms is critical when driver mutations are unknown to ensure we screened the most clinically relevant drugs for each individual tumor. We are currently collecting longitudinal data from enrolled patients in parallel with clinical trials to demonstrate the positive predictive value of our PMP. We additionally strive to fully demonstrate reproducibility to obtain Clinical Laboratory Improvement Amendments approval. Citation Format: Nathan M Merrill, Aaron M Udager, Angel Qin, Kiran Lagisetty, Liwei Bao, Xu Cheng, Hamadi Madhi, Ananya Banerjee, Marziyeh Salehi Jahromi, Laura Goo, Varun Kathawate, Bryce Vandenburg, Mary Horn, Derek Nancarrow, Tusharika Rastogi, Albert Liu, Ning Gu, Zhaoping Qin, Habib Serhan, Marisa Aikins, Vishal Navani, John Jefferies, Muhammad Sajawal Ali, Michael Monument, Johannes Kratz, Amber Smith, Andrew Chang, Gregory Kalemkerian, Stacy Fry, Peter Ulintz, Sunitha Nagrath, Peggy Hsu, Matthew B Soellner, Sofia D Merajver. Precision medicine platform to guide the treatment of NSCLC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B013.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».