Single-cell profiling of multiple myeloma reveals molecular response to FGFR3 inhibitor despite clinical progression
Bibliographic record
Abstract
Genomic characterization of cancer has enabled identification of numerous molecular targets, which has led to significant advances in personalized medicine. However, with few exceptions, precision medicine approaches in the plasma cell malignancy multiple myeloma (MM) have had limited success, likely owing to the subclonal nature of molecular targets in this disease. Targeted therapies against FGFR3 have been under development for the past decade in the hopes of targeting aberrant FGFR3 activity in MM. FGFR3 activation results from the recurrent transforming event of t(4;14) found in ∼15% of MM patients, as well as secondaryFGFR3mutations in this subgroup. To evaluate the effectiveness of targeting FGFR3 in MM, we undertook a phase 2 clinical trial evaluating the small-molecule FGFR1–4 inhibitor, erdafitinib, in relapsed/refractory myeloma patients with or withoutFGFR3mutations ( NCT02952573 ). Herein, we report on a single t(4;14) patient enrolled on this study who was identified to have a subclonalFGFR3stop-loss deletion. Although this individual eventually progressed on study and succumbed to their disease, the intended molecular response was revealed through an extensive molecular characterization of the patient's tumor at baseline and on treatment using single-cell genomics. We identified elimination of theFGFR3-mutant subclone after treatment and expansion of a preexisting clone with loss of Chromosome 17p. Altogether, our study highlights the utility of single-cell genomics in targeted trials as they can reveal molecular mechanisms that underlie sensitivity and resistance. This in turn can guide more personalized and targeted therapeutic approaches, including those that involve FGFR3-targeting therapies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".