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Prior to the early 2000s, patients with advanced gastrointestinal stromal tumors (GIST) had very poor prognoses owing to a lack of effective therapies. The development of tyrosine kinase inhibitors at the turn of the century significantly improved the overall survival for patients with GIST. The resounding success of imatinib in the first clinical trial of a tyrosine kinase inhibitor to treat GIST led to its approval for first-line therapy for advanced GIST; this study was open to all comers and not restricted to any GIST subtype(s). The trials that led to the approvals of second-, third-, and fourth-line therapy for advanced GIST were also open to all patients with advanced/metastatic GIST. Only in retrospect do we realize the role that the molecular subtypes played in the results observed in these studies. In this review, we discuss the studies that led to the US Food and Drug Administration approval of imatinib (first line), sunitinib (second line), regorafenib (third line), and ripretinib (fourth line) for advanced KIT-mutant GIST. In addition, we review how information about GIST molecular subtypes has been used to accelerate the approval of other targeted therapies for non-KIT mutant GIST, leading to the approval of five additional drugs indicated for the treatment of specific GIST molecular subtypes. We also discuss how our understanding of the molecular subtypes will play a role in the next generation of therapeutic approaches for treating advanced GIST.

Figure S1: HMC1.2 cell line has constitutively actived NFAT2, NFAT2 and NFAT4 species; Figure S2: RT-PCR of transcripts modulated by 24 hour treatment with CSA; Figure S3: CNPIs do not modulate KIT signaling and KIT inhibitors do not affect NFAT localization; Figure S4: Combination therapy does not enhance the effect of KIT inhibitors on KIT or downstream signaling; Table S1: Summary of KIT-mutant mast cell characteristics; Table S2: Summary of antibodies used in immunoblotting experiments; Table S3: qRT-PCR analysis of NFAT expression in KIT-mutant mast cell lines.

Gastrointestinal stromal tumors (GIST) harboring activating mutations of PDGFRA respond to imatinib, with the notable exception of the most common mutation, D842V. Avapritinib is a novel, potent KIT/PDGFRA inhibitor with substantial clinical activity in patients with the D842V genotype. To date, only a minority of PDGFRA-mutant patients treated with avapritinib have developed secondary resistance. Tumor and plasma biopsies in 6 of 7 patients with PDGFRA primary mutations who progressed on avapritinib or imatinib had secondary resistance mutations within PDGFRA exons 13, 14, and 15 that interfere with avapritinib binding. Secondary PDGFRA mutations causing V658A, N659K, Y676C, and G680R substitutions were found in 2 or more patients each, representing recurrent mechanisms of PDGFRA GIST drug resistance. Notably, most PDGFRA-mutant GISTs refractory to avapritinib remain dependent on the PDGFRA oncogenic signal. Inhibitors that target PDGFRA protein stability or inhibition of PDGFRA-dependent signaling pathways may overcome avapritinib resistance.Significance:Here, we provide the first description of avapritinib resistance mechanisms in PDGFRA-mutant GIST.This article is highlighted in the In This Issue feature, p. 1

Contains: Supplemental Figures 1-7; Supplemental Tables 1 -3; Supplemental Methods

Resistant KIT mutations have hindered the development of KIT kinase inhibitors for treatment of patients with systemic mastocytosis. The goal of this research was to characterize the synergistic effects of a novel combination therapy involving inhibition of KIT and calcineurin phosphatase, a nuclear factor of activated T cells (NFAT) regulator, using a panel of KIT-mutant mast cell lines. The effects of monotherapy or combination therapy on the cellular viability/survival of KIT-mutant mast cells were evaluated. In addition, NFAT-dependent transcriptional activity was monitored in a representative cell line to evaluate the mechanisms responsible for the efficacy of combination therapy. Finally, shRNA was used to stably knockdown calcineurin expression to confirm the role of calcineurin in the observed synergy. The combination of a KIT inhibitor and a calcineurin phosphatase inhibitor (CNPI) synergized to reduce cell viability and induce apoptosis in six distinct KIT-mutant mast cell lines. Both KIT inhibitors and CNPIs were found to decrease NFAT-dependent transcriptional activity. NFAT-specific inhibitors induced similar synergistic apoptosis induction as CNPIs when combined with a KIT inhibitor. Notably, NFAT was constitutively active in each KIT-mutant cell line tested. Knockdown of calcineurin subunit PPP3R1 sensitized cells to KIT inhibition and increased NFAT phosphorylation and cytoplasmic localization. Constitutive activation of NFAT appears to represent a novel and targetable characteristic of KIT-mutant mast cell disease. Our studies suggest that combining KIT inhibition with NFAT inhibition might represent a new treatment strategy for mast cell disease. Mol Cancer Ther; 13(12); 2840–51. ©2014 AACR.

Figure and table legends for supplemental figures 1-4 and supplemental tables 1-3.

Purpose: Patients who inherit a pathogenic loss-of-function genetic variant involving one of the four succinate dehydrogenase (SDH) subunit genes have up to an 86% chance of developing one or more cancers by the age of 50. If tumors are identified and removed early in these high-risk patients, they have a higher potential for cure. Unfortunately, many alterations identified in these genes are variants of unknown significance (VUS), confounding the identification of high-risk patients. If we could identify misclassified SDH VUS as benign or pathogenic SDH mutations, we could better select patients for cancer screening procedures and remove tumors at earlier stages.Experimental Design: In this study, we combine data from clinical observations, a functional yeast model, and a computational model to determine the pathogenicity of 22 SDHA VUS. We gathered SDHA VUS from two primary sources: The OHSU Knight Diagnostics Laboratory and the literature. We used a yeast model to identify the functional effect of a VUS on mitochondrial function with a variety of biochemical assays. The computational model was used to visualize variants' effect on protein structure.Results: We were able to draw conclusions on functional effects of variants using our three-prong approach to understanding VUS. We determined that 16 (73%) of the alterations are actually pathogenic, causing loss of SDH function, and six (27%) have no effect upon SDH function.Conclusions: We thus report the reclassification of the majority of the VUS tested as pathogenic, and highlight the need for more thorough functional assessment of inherited SDH variants. Clin Cancer Res; 23(21); 6733–43. ©2017 AACR.

TIFF file for supplemental figure 1

TIFF file for supplemental figure 1

Video for Biochemical, molecular, and clinical characterization of SDHA VUS from Biochemical, Molecular, and Clinical Characterization of Succinate Dehydrogenase Subunit A Variants of Unknown Significance

Supplemental tables 1 and 2, supplemental figure legends, supplemental video legend

Generative artificial intelligence (AI) has the potential to greatly increase the speed, quality and controllability of antibody design. Traditionalde novoantibody discovery requires time and resource intensive screening of large immune or synthetic libraries. These methods also offer little control over the output sequences, which can result in lead candidates with sub-optimal binding and poor developability attributes. Several groups have introduced models for generative antibody design with promisingin silicoevidence [1–10], however, no such method has demonstrated generative AI-basedde novoantibody design with experimental validation. Here we use generative deep learning models tode novodesign antibodies against three distinct targets, in azero-shotfashion, where all designs are the result of a single round of model generations with no follow-up optimization. In particular, we screen over 1 million antibody variants designed for binding to human epidermal growth factor receptor 2 (HER2) using our high-throughput wet lab capabilities. Our models successfully design all CDRs in the heavy chain of the antibody and compute likelihoods that are calibrated with binding. We achieve binding rates of 10.6% and 1.8% for heavy chain CDR3 (HCDR3) and HCDR123 designs respectively, which is four and eleven times higher than HCDR3s and HCDR123s randomly sampled from the Observed Antibody Space (OAS) [11]. We further characterize 421 AI-designed binders using surface plasmon resonance (SPR), finding three that bind tighter than the therapeutic antibody trastuzumab. The binders are highly diverse, have low sequence identity to known antibodies, and adopt variable structural conformations. Additionally, the binders score highly on our previously introducedNaturalnessmetric [12], indicating they are likely to possess desirable developability profiles and low immunogenicity. We open source1the HER2 binders and report the measured binding affinities. These results unlock a path to accelerated drug creation for novel therapeutic targets using generative AI and high-throughput experimentation.

When gastrointestinal stromal tumour (GIST), the most common form of sarcoma, was first recognized as a distinct pathological entity in the 1990s, patients with advanced-stage disease had a very poor prognosis owing to a lack of effective medical therapies. The discovery of KIT mutations as the first and most prevalent drivers of GIST and the subsequent development of the first KIT tyrosine kinase inhibitor (TKI), imatinib, revolutionized the treatment of patients with this disease. We can now identify the driver mutation in 99% of patients with GIST via molecular diagnostic testing, and therapies have been developed to treat many, but not all, molecular subtypes of the disease. At present, seven drugs are approved by the FDA for the treatment of advanced-stage GIST (imatinib, sunitinib, regorafenib, ripretinib, avapritinib, larotrectinib and entrectinib), all of which are TKIs. Although these agents can be very effective for treating certain GIST subtypes, challenges remain and new therapeutic approaches are needed. In this Review, we discuss the molecular subtypes of GIST and the evolution of current treatments, as well as their therapeutic limitations. We also highlight emerging therapeutic approaches that might overcome clinical challenges through novel strategies predicated on the biological features of the distinct GIST molecular subtypes.

Gastrointestinal stromal tumor (GIST) is the most common GI sarcoma and frequently harbor oncogenic mutations in KIT or platelet derived growth factor receptor alpha (PDGFRA) receptor tyrosine kinases. While type II tyrosine kinase inhibitors (TKIs) revolutionized the treatment of KIT-mutant GIST, they are thought to be ineffective for many PDGFRA-mutant GIST cases. Over 80% of PDGFRA mutations in GIST occur in exon 18 and patients with the most common mutation, D842V, are resistant to imatinib and other type II TKIs, leading to poor outcomes. However, beyond D842V, numerous other PDGFRA exon 18 mutations have been observed. From curated data of a large cohort of PDGFRA-mutant GISTs, we have identified nearly 50 unique PDGFRA exon 18 mutations, including both point mutations and complex indels. Although it is largely unknown how these mutations respond to imatinib, currently most, if not all, PDGFRA exon 18 mutations are assumed to be imatinib resistant. Using cell models, we tested some of the most commonly observed mutations in this cohort. Contrary to dogma, we observed that some exon 18 mutations tested were imatinib sensitive. We also observed that imatinib sensitivity seemed to be dependent on the properties of the residue in the 842-codon position, which encodes a key auto-inhibitory residue. We then hypothesized that imatinib sensitivity of every known or future reported PDGFRA exon 18 mutation might be predicted based on the 842 residue. To test the predictive potential of the 842 residue, we created mutagenic libraries that included every amino acid substitution at the 842 position. We expressed each mutation in Ba/F3 cells and assessed imatinib sensitivity via immunoblotting for phosphorylated-PDGFRA. Overall, 40% of tested mutations were sensitive to imatinib, further debunking the belief that all PDGFRA exon 18 mutations are imatinib resistant. In agreement with our hypothesis, we found that any hydrophobic residue at the 842 position conferred imatinib resistance (IC50 > 100nM), and all other amino acids tested to date were imatinib sensitive (IC50 < 100nM). Based on these data alone, over 55% of the 50 unique mutations identified from our PDGFRA-mutant GIST patient cohort would be predicted to be imatinib sensitive, demonstrating the importance of understanding the biochemical properties of various exon 18 mutations to predict treatment outcomes. Our predictive model would help identify patients with PDGFRA exon 18 mutations that could benefit from using front-line imatinib as an alternative to avapritinib, which has been recently approved for first-line treatment for all PDGFRA exon 18 mutant GIST. Imatinib, which has been used clinically for over 20 years, has a superior safety and tolerability profile compared to avapritinib. This paradigm shifting approach has the potential to transform patient care and outcomes by optimizing the sequencing of TKIs for PDGFRA-mutant GIST patients. Citation Format: Homma M. Khosroyani, Lillian R. Klug, Johanna Falkenhorst, Sebastian Bauer, Michael C. Heinrich. Predicting imatinib responses in exon 18 PDGFRA-mutant GIST [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 660.