Marr, Alexander

Background: Treatment of chronic lymphocytic leukemia (CLL) has been transformed with targeted therapies including inhibitors of Bruton's tyrosine kinase (BTKi). Currently three covalent BTK inhibitors (cBTKi) are approved for CLL, but most patients eventually relapse, commonly through acquisition of the C481S BTK mutation (Woyach et al. 2014). Pirtobrutinib and nemtabrutinib are non-covalent BTK inhibitors (ncBTKi) developed to target and inhibit C481S mutant BTK. However novel secondary site mutations in BTK, namely T474I and L528W, have been found to confer resistance to both cBTKi and ncBTKi (Wang et al. 2022). Regardless of these mutations, BCR signaling remains intact, suggesting that inhibition of BTK maintains its therapeutic importance. LP-168 is a novel ultra-selective 4 th generation BTKi with an active warhead capable of covalent interaction with WT BTK or non-covalent binding when a BTK C481 mutation is present. Methods: To determine the target selectivity of LP-168 we screened the compound against 468 kinases in the scanMAX Kinase Assay. For validation of covalent binding, dialysis against WT BTK was performed. Primary CLL B cells were isolated by negative selection and treated with LP-168 for all experiments. BCR signaling alterations were assessed via immunoblot to observe changes in target protein phosphorylation. Changes in CLL migration towards CXCL12 or CXCL13 were interrogated by transwell migration. Cytotoxicity towards primary CLL cells was measured via Annexin V/PI flow cytometry with and without HS-5 stromal cell support. Experiments using TMD8 cells were performed following CRISPR modification to insert WT, C481S, or T474I BTK. In vivo survival studies were performed using both the Eμ-TCL1 and Eμ-MTCP1 mouse engraftment models. Results: LP-168 demonstrated selectivity towards BTK with roughly 700-fold selectivity for BTK vs its next off-target. In an enzymatic assay, LP-168 displayed nanomolar potency towards both WT BTK (IC 50=0.11 nM) and C481S BTK (IC 50=1.0 nM). Following 2-hour drugging, LP-168 inhibited BCR signaling in primary CLL B cells at increasing concentrations, demonstrated by reduced phosphorylation of BTK (92%, p<0.0001, n=8) and its immediate downstream target PLCγ2 (41%, p=0.0013, n=8). Migration capacity of primary CLL cells towards CXCL12 and CXCL14 was also found to decrease after 2-hours of treatment with LP-168 (52%, p<0.001, n=10 and 51%, p<0.001, n=10, respectively). After 48 hours of exposure, LP-168 was able to induce cytotoxicity of primary CLL cells alone and in co-culture with HS-5 stromal cells in a dose-dependent manner, with similar or improved efficacy to other BTKi. LP-168 decreased CLL cell production of CCL3 and CCL4 chemokines (CCL3: 93%, p<0.001, n=8; CCL4: 93%, p<0.001, n=8). LP-168 also demonstrated modest cytotoxicity towards TMD8 cells harboring either C481S BTK (24%, p=0.0008 , n=3) or T474I BTK (13%, p=0.0690, n=3), as well as decreased production of CCL3 (C481S: 95%, p<0.0001, n=3; T474I: 99%, p<0.0001, n=3) and CCL4 (C481S: 97%, p<0.0001, n=3; T474I: 98%, p<0.0001, n=3) (Figure 1A). Further, we observed inhibition of downstream BCR signaling in primary CLL B cells harboring C481S via immunoblotting and cytokine production. Finally, to evaluate the in vivo efficacy of LP-168 we tested this compound in both the Eµ-TCL1 and Eµ-MTCP1 models. We treated mice daily via oral gavage and found that 50 mg/kg of LP-168 significantly improved survival in the Eµ-TCL1 model when compared to vehicle (median 51 vs 44 days; p=0.0018) or ibrutinib at 50 mg/kg (median 51 vs 45 days; p=0.0098) and the Eµ-MTCP1 model when compared with vehicle (median 122 vs 62 days; p<0.0001) or ibrutinib at 50 mg/kg (median 122 vs 96 days; p=0.0162) (Figure 1B). Conclusions: Collectively, our data demonstrate that LP-168 is a potent and selective inhibitor of BTK with activity against CLL, even in the presence of mutations that mediate resistance to cBTKi and ncBTKi. These data support the continued preclinical and clinical investigation of LP-168, which is currently being studied in the phase 1 setting of CLL (NCT04775745) and NHL (NCT04993690).

Abstract CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC’s functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.

Abstract The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase (civicdb.org) is an open access, centralized hub for structured, community curated and expertly moderated relationships between genomic variants and cancer. Evidence is curated from peer-reviewed, published literature and is classified into one of five Types: Predisposing, Diagnostic, Prognostic, Predictive (therapeutic), or Functional. The robustness of the Evidence is conveyed through the assignment of Levels with the first three derived from patient studies (Validated, Clinical, Case Study), Preclinical, generated from in vivo or in vitro data, and Inferential, which describes indirect associations. Each Evidence Item requires an Evidence Statement written in the curator's own words summarizing the source's results regarding the variant's clinical impact. Collaborations with groups like ClinGen have generated a significant influx of new curators, increasing the demand for detailed principles regarding data prioritization in the Evidence Statement in order to streamline the curation process. The curation community would benefit from simpler, visual guides through the complex decisions needed to appropriately and consistently curate Evidence Items. We are devoting significant effort to continue the development of straightforward Evidence curation algorithms (decision trees) similar to those used in clinical molecular testing labs to aid CIViC curators. Previously published guidelines on development of these statements are the basis of our Evidence algorithms. Obvious inflection points for curators are clearly identified with specific details noted for each to optimize decision efficiency. As the predominant Evidence Type comprising 57% of all CIViC submissions, 58% of referenced patient trials, and 92% of Preclinical submissions, Predictive Evidence is the initial focus of our pilot guidelines with Diagnostic and Prognostic to follow. Within the Predictive Evidence Type, clinical trials, case studies, and preclinical Levels each require vastly different Evidence Statement details and ultimately the creation of three separate, uniquely modeled algorithms. The implementation of these algorithms will assist in streamlining both curation and the expert review process. Notably, a template is not being created, as the preservation of curator style and voice is important to maintain the community feel of the database. To ensure the highest level of clarity, our team is utilizing specific novice and experienced curators to assist with the development process. As these algorithms pass the pilot phase, they are being tested as curator training tools. Ultimately, these guidelines will be used to encourage independence in curators and to enhance the Evidence already contained in CIViC. Citation Format: Jason Saliba, Lana Sheta, Kilannin Krysiak, Arpad Danos, Alex Marr, Erica Barnell, Shahil Pema, Wan-Hsin Lin, Panieh Terraf, Joshua F. McMichael, Cameron J. Grisdale, Shruti Rao, Susanna Kiwala, Adam Coffman, Alex Wagner, Obi L. Griffith, Malachi Griffith. Development of Evidence Statement curation algorithms to aid cancer variant interpretation [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 208.