Abstract A044: Immune profiling tools for adult and pediatric cancer: From whole genome and transcriptome sequencing to immunotherapy clinical trial design
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".