Abstract A044: Immune profiling tools for adult and pediatric cancer: From whole genome and transcriptome sequencing to immunotherapy clinical trial design
Notice bibliographique
Résumé
Abstract The emerging use of immunotherapy for cancer treatment has led to a greater need for characterizing markers of response. Incorporating immune profiling tools into whole genome and transcriptome sequencing and analysis (WGTA) pipelines facilitates the identification of biomarkers of response that may be used to inform clinical trial design. The Personalized OncoGenomics (POG) program has enrolled >1,200 patients with advanced/metastatic cancers of diverse histologies and underwent WGTA, which allowed for a retrospective investigation of biomarkers for immune checkpoint inhibitor (ICI) therapy from 194 adult patients. We demonstrated that a combination of biomarkers, including tumour mutation burden (TMB), mutations in SWI/SNF genes, presence of viruses, and inferred RNA-derived immune cell expression scores could predict response to therapy more effectively than individual biomarkers alone. This analysis led to the design of a combined biomarkers score termed the immune burden variant (IBV) score, informing the Canadian Atezolizumab Precision Targeting for Immunotherapy Intervention (CAPTIV-8) trial (NCT04273061). Despite of its utility in adult cancers, the IBV score has limited application to pediatric cancers as biomarkers such as high TMB or the presence of virus are rarely present. Based on previous work, we constructed a scoring system using three biomarkers from transcriptome: (a) CD8+ T-cell score: RNA-based deconvolution to estimate CD8+ T cell abundance using CIBERSORT; (b) M1/M2 score: a ten-gene signature to assess expression patterns of M1:M2 macrophages, and (c) IPASS score: a 15-gene signature that predicts T-cell infiltration. For each biomarker, we calculated percentiles with reference to a pediatric pan-cancer cohort with 222 samples from the Canadian PROFYLE (PRecision Oncology For Young peopLE) program and validated using 600 samples from the Australian ZERO (Zero Childhood Cancer precision medicine) program. Outlier high scores were defined as two scores >80th percentile, excluding samples of lymph node origin and hematologic malignancies. Within the PROFYLE + ZERO combined cohorts, we observed outlier high scores across many pediatric cancer types such as sarcoma, rhabdoid tumor, glioma, neuroblastoma, chordoma, melanoma, mesothelioma, and carcinoma. We constructed an immune profiling bioinformatic workflow container called RICO (Rna-seq Immune COntainer), which takes RNA sequencing data as input, and calculates scores and percentiles of these three biomarkers. RICO will be used to assess patient eligibility for the ICI combination therapy arm in the international pediatric precision oncology basket trial OPTIMISE “Optimal Precision Therapies to CustoMISE Care in Childhood and Adolescent Cancer” (NCT06208657). The development of the CAPTIV-8 and OPTIMISE trials demonstrates the utility of WGTA analysis to aid in clinical trial design and investigate key factors of cancer treatment response. In addition, a cohort of over 2000 samples with WGTA and immune profiling has been generated as a resource for ongoing investigation. Citation Format: Yaoqing Shen, Chelsea Mayoh, Kathleen Wee, Erin Pleasance, Emma Titmuss, Richard Corbett, Laura Williamson, Zakhar Krekhno, Arash Nabbi, Raoul Santiago, Stephanie Bianco, Scott Davidson, Adam Shlien, Kyoko E Yuki, Denise Connolly, Daniel Morgenstern, Melika Bonakdar, Greg Taylor, Veronika Csizmok, Cameron J Grisdale, Melissa McConechy, Jing Xu, John H Dupuis, Karen Mungall, Eric Chuah, Andrew Mungall, Jessica Nelson, Stephen Yip, Sophie Sun, Howard Lim, Daniel Renouf, Janessa Laskin, Sarah Cohen-Gogo, Shahrad R Rassekh, Rebecca J Deyell, Marco A Marra, Paul G Ekert, Steven JM Jones. Immune profiling tools for adult and pediatric cancer: From whole genome and transcriptome sequencing to immunotherapy clinical trial design [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 A044.
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 | 0,031 | 0,026 |
| 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 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».