A Multi-Modal Analysis of Acquired Resistance to Acalabrutinib and Pirtobrutinib Provides Potential Strategies to Augment Treatment Outcome with BTKi Drugs
Bibliographic record
Abstract
Introduction: Acalabrutinib (acala), a covalent BTK inhibitor (BTKi), exhibits excellent efficacy and safety in CLL and other B cell malignancies. However, long term efficacy is often compromised by the acquisition of mutations at the acala-binding residue C481, the most prevalent being C481S. Pirtobrutinib (pirto), a reversible BTKi, is active clinically in patients who have progressed on a covalent BTKi with or without C481S mutations. Besides BTK mutations, several additional pathways are hypothesized to contribute to BTKi resistance and may be highly relevant to the sequencing and combining of CLL-directed therapies. To understand acquired resistance to both acala and pirto, we employed multiple approaches, including a genome-wide CRISPR knockout (KO) screen and the generation of BTK mutant drug resistant cell lines. Methods: Genome-wide CRISPR KO screen was performed in the BTKi-sensitive CD79B-MYD88 double mutant ABC-DLBCL cell line TMD8 with 3 guides/gene (best Vienna BioScore). Pooled library-transduced cells at 750x target coverage were cultured for 14 days with sublethal acala or pirto concentrations to achieve ~60% growth inhibition over DMSO. NGS/BAGAL/MAGeCK analyses identified enriched/depleted guides and corresponding resistance/sensitizing genes (FDR<0.1). Hits were validated by single gene KO (Cas9 RNP + 3-plex gRNA nucleofection) and 10-day growth assays with BTKi treatment. BTK mutant cell lines resistant to acala or pirto were derived in TMD8 and the BTKi-sensitive Karpas1718 MZL cell line by culturing through dose escalation. Exome/RNAseq was used to identify BTK mutations. TMD8 BTK C481R cell line was generated by CRISPR-HDR. BTK kinase and pathway activities were assayed by Western/Flow. Results: CRISPR screening results correlated highly between acala and pirto (Pearson's r=0.66), indicating shared pathways that impact response to both BTKi's. Collectively we identified 133 resistance and 57 sensitizing genes that span both known pathways (BCR/NFκB signaling) and novel modulators (autophagy, g-secretase, PBAF complex) of BTKi response. Systematic hit validation by single gene KO confirmed resistance phenotype for 6 genes: TNFAIP3 and TRAF3 negative regulators of canonical/non-canonical NFκB pathways; GRB2 adaptor protein for BCR signaling; PSEN1 in γ-secretase, PHF10 in PBAF complex; and SPOP substrate recognition protein for ubiquitin ligase. Interrogating the Project GENIE patient database identified loss-of-function mutations in these genes (8.6% CLL, 19.3% MCL, 25% MZL), supporting the hypothesis that they may be biomarkers for resistance to both BTKis. Sensitizing genes included CBL adaptor protein, PTPRCAP a CD45 tyrosine phosphatase, and RRAS2 small GTPase; KO of these genes did not alter TMD8 cell growth but enhanced the potency of both BTKis. Genomic analysis of BTKi-adaptive resistant cell lines identified causal BTK mutations, including nonclassical binding site mutation C481F for acala resistance and 2 novel mutations F442L and L460S for pirto resistance. C481F and C481R are kinase-inactive but cells still retain BCR signaling, pointing to BTK scaffolding activity. HCK is a proposed mediator of BTK scaffolding activity (Dhami et al., 2022; Yuan et al., 2022). However, while HCK activation was detected in C481R cells, HCK KO did not impair cell growth. In addition, no HCK activation was detected in C481F cells. Thus, scaffolding activity by C481F/R mutants does not depend on HCK. For the 2 pirto-resistant BTK mutations, F442L is kinase active but has reduced response to pirto, whereas L460S is kinase impaired. All our BTK mutant cell lines were still sensitive to the other BTKi, supporting a rationale for treatment sequencing between BTKi drugs to overcome resistance due to BTK mutations. Conclusions: We uncovered known and novel BTK resistance mutations and demonstrated BTK scaffolding activity independent of HCK, highlighting the need for other strategies to disrupt scaffolding-mediated BCR signaling. Beyond BTK mutations, our CRISPR KO screens illustrate a map of genetic modifiers of BTKi response and point to several potential resistance biomarkers for both acala and pirto. A better understanding of resistance mechanisms in the presence and absence of BTK mutations will help augment the use of BTKi treatments in patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".