Abstract 1647: CAPTIV-8: A prospective trial of atezolizumab using a multivariate model incorporating whole genome and transcriptome analysis
Bibliographic record
Abstract
Abstract Dramatic and durable responses to immune checkpoint inhibitors (ICIs) have been observed across multiple tumor types, but identifying the patients most likely to respond to these drugs remains challenging, particularly in the context of metastatic and pre-treated disease. Recent clinically approved biomarkers for patient selection are tumor agnostic, but currently all approved markers are evaluated independently. Through the Personalized Onco Genomics (POG) program at BC Cancer, we aimed to study the impact of evaluating multiple biomarkers in a tumor agnostic cohort. We performed whole genome and transcriptome analysis (WGTA) on fresh tumor biopsies from a heterogeneous pan-cancer cohort of 82 patients with advanced metastatic disease subsequently treated with ICIs. Established biomarkers, including tumor mutation burden (TMB) and CD8+ T cell scores, were able to distinguish responders in our advanced and pre-treated cohort. Additionally, we discovered that combining multiple biomarkers provided the best stratification of patients, suggesting a multifaceted approach, such as WGTA, may be suitable for more accurate identification of patients that may benefit from ICIs. As such, we have initiated a Phase II clinical trial, CAPTIV-8 (NCT04273061), which is distinctive in its use of WGTA to evaluate multiple markers including TMB, CD8+ T cell scores, an M1-M2 macrophage score and viral integration to select patients most likely to respond to atezolizumab. Clinical and genomic data prospectively collected from two hundred patients will be evaluated to test the efficacy of combining these biomarkers and identify additional biomarkers of response which can be used to guide treatment with ICIs. Citation Format: Emma Titmuss, Alexandra Pender, Erin Pleasance, Scott Brown, Cameron J. Grisdale, James Topham, Yaoqing Shen, Melika Bonakdar, Gregory A. Taylor, Laura Williamson, Karen Mungall, Eric Chuah, Andrew J. Mungall, Richard A. Moore, Jean-Michel Lavoie, Stephen Yip, Howard Lim, Daniel J. Renouf, Sophie Sun, Steven J. Jones, Robert Holt, Marco A. Marra, Janessa Laskin. CAPTIV-8: A prospective trial of atezolizumab using a multivariate model incorporating whole genome and transcriptome analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1647.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".