Abstract IA11: Clinical applications of liquid biopsy in multiple myeloma
Bibliographic record
Abstract
Abstract Multiple myeloma (MM) is characterized by recurrent cytogenetic and molecular abnormalities that have the potential to profoundly impact therapeutic decisions. Conventional karyotyping and/or FISH cytogenetics studies have identified translocations and copy number abnormalities (CNAs) that predict clinical outcomes. More recently, MM whole-exome sequencing studies have uncovered recurrent mutations in genes associated with prognosis and others that are potentially actionable and/or implicated in drug resistance. These observations highlight the need for advanced genomic platforms to inform prognosis and therapeutic decisions. Currently, molecular profiling of MM is performed on bone marrow (BM) aspirates; however, the genetic information obtained from aspirates is confounded by spatial and temporal intratumor genetic heterogeneity. Genomic studies of MM have confirmed the bias introduced by single-site biopsies, demonstrating that not all mutations are shared between targeted biopsies of extramedullary sites and BM. Further, longitudinal whole-genome sequencing studies have not only uncovered tumor heterogeneity at diagnosis but also subclonal competition with a shifting dominance of tumor clones and accumulation of genetic events during progression that are particularly affected by therapeutic pressures. Thus, information acquired from a single biopsy provides a spatially and temporally limited snapshot of a tumor that likely underestimates the complexity of the tumor genomic landscape. Analysis of circulating tumor DNA (ctDNA) and in blood plasma has the potential to overcome these barriers, providing access to the genetic landscape of all cancerous lesions and allowing for noninvasive tracking of genomic evolution over time. Circulating biomarkers can provide a “liquid biopsy” alternative to tissue biopsy, providing new opportunities for treatment tailoring based on real-time monitoring from a simple blood test. Here I will discuss the current liquid biopsy technologies under evaluation for genomic characterization of multiple myeloma. I will also describe the potential clinical applications for liquid biopsies to identify patients for targeted therapies, to track genomic changes, and to follow disease burden in patients with multiple myeloma. Citation Format: Suzanne Trudel, Trevor Pugh. Clinical applications of liquid biopsy in multiple myeloma [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr IA11.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".