Li, Hu

TRAIL is a TNF family ligand that trimerizes TRAIL-R1 (DR4) or TRAIL-R2 (DR5) to induce apoptosis, necroptosis, and/or NF-κB activation in receptor-bearing cells. We previously identified TRAILshort as a splice variant of TRAIL that lacks cysteine 230, cannot trimerize, and acts as a dominant-negative ligand that blocks TRAIL-mediated apoptosis. TRAILshort is expressed on cell surfaces and within extracellular vesicles, enabling it to confer TRAIL resistance to both producing and bystander cells. In this study, we showed that elevated TRAILshort levels were associated with chronic viral infections, cancer, and autoimmune diseases, suggesting a link to impaired immune regulation. Using unbiased phosphoproteomics and mechanistic studies, we demonstrated that TRAILshort binding to DR5 recruited and activated the phosphatase Src homology region 2 domain–containing phosphatase 1 (SHP-1), leading to zeta-chain-associated protein kinase 70 (ZAP-70) dephosphorylation, disruption of ZAP-70–CD3ζ interactions, and impaired T cell receptor signaling, thereby reducing T cell activation, proliferation, and cytokine production in response to antigen or CD3/CD28 ligation. Genetic or pharmacologic SHP-1 inhibition reverses these effects. In humanized mouse models, TRAILshort promoted the persistence of transformed mouse embryonic fibroblasts (MEFs) and L428 and antagonized CD19-directed CAR T cell activity, revealing TRAILshort as an immunomodulator of T cell function with therapeutic implications, including blocking TRAILshort to restore T cell immunity or delivering TRAILshort to enforce tolerance. TRAILshort, a variant of TRAIL, suppresses T cell function and immune responses in cancer and infectious diseases, highlighting its potential as a therapeutic target.

Clinical trial enrichment is the targeted recruitment of prospective individual patients with defined clinical characteristics who are likely to benefit from newly developed or repurposed drugs. This process is central to the success of clinical trials together with patient management and regulatory compliance. A main challenge in clinical trial enrichment lies in the recognition of a priori clinical parameters and information that informs drug efficacy or toxicity, particularly when intended for a broader unseen population. Although Artificial Intelligence (AI) approaches, especially large language models (LLMs), have been employed in many aspects of clinical trials, to our knowledge, there is no AI method that has been developed which offers a prospective prediction and assesses the extent to which a given therapeutic intervention benefits an unseen population. Here, we offer an outlook on how to build Artificial Clinic Intelligence (ACI), a generative AI (GAI)-powered modeling platform for modeling clinical trial enrichment. ACI generates synthetic patient data and models clinical trial enrichment to inform clinicians on key clinical parameters that are enriched in prospective patients prior to accrual.

Abstract BRCA2 mutations contribute to the pathogenesis and treatment sensitivity of a subset of ovarian, breast, prostate, and pancreatic cancers. When these cancers become therapy resistant, secondary mutations that restore the BRCA2 open reading frame are found in half the cases, but other causes of resistance remain incompletely understood. Here, we identified translational readthrough of a premature termination codon (PTC) as a cause of resistance to poly(ADP-ribose) polymerase inhibitors (PARPis) and cisplatin in cells derived from the BRCA2-mutated ovarian cancer line PEO1 by PARPi selection. Despite persistence of the signature 4965C > G (p.Y1655X) BRCA2 mutation, low-level expression of full-length BRCA2 protein was detectable in these cells by immunoblotting and tandem mass spectrometry. Either BRCA2 knockdown or gene interruption 5′ or 3′ to the PTC restored treatment sensitivity, implicating BRCA2 in the resistance. Reporter assays demonstrated UAG-selective readthrough in the resistant clones but not parental cells. Moreover, custom searching of global proteomic data indicated readthrough of stop codons, particularly UAGs, in additional proteins in the resistant clones. Finally, multi-omic analysis identified multiple changes in the nonsense-mediated decay and termination machineries that favor readthrough. Accordingly, the present results identify PTC readthrough as a potential mechanism of drug resistance in cells with BRCA2 nonsense mutations.

Abstract Background: Replication checkpoint modulators (RCMs), including inhibitors of CHK1, ATR, and WEE1, have been widely studied for their ability to inhibit cell cycle checkpoints and enhance the toxicity of agents that stall replication forks. In preclinical studies each of these inhibitors also demonstrates notable activity as a single agent against relapsed/refractory acute myeloid leukemia (R/R AML). In particular, our prior study (PMID: 33414171) demonstrated that several myeloid cell lines underwent cell death at nanomolar concentrations, with a median IC50 of 9 nM irrespective of the TP53 status. Examination of pathways involved in RCM-induced leukemia cell killing demonstrated that CHK1 inhibition induces CDC25A upregulation accompanied by activation of a death program that involves a CDK2-initiated 10- to 50-fold upregulation of TNFα and downstream engagement of the extrinsic apoptotic pathway. Here we have comprehensively characterized the processes that occur between the interruption of replication checkpoints and leukemic cell death in these sensitive cells versus cells selected for RCM resistance. Methods: Using the CHK1 inhibitors MK8776 and prexasertib, ATR inhibitors berzosertib and ceralasertib, and WEE1 inhibitor adavosertib as paradigm drugs, we examined signaling in two AML cell lines, U937 and THP.1, that were selected for CHK1 inhibitor resistance, examined cross-resistance patterns, and assessed the biochemical basis for leukemic cell death. Changes in gene expression and signaling induced by RCMs were analyzed in isogenic pairs using RNA sequencing, flow cytometry and immunoblotting. Results: In contrast to parental U937 and THP.1 cells, CHK1 inhibitor-selected U937 and THP.1 cell exhibited a lack of TNFα mRNA induction and did not undergo cell death in response to multiple CHK1 inhibitors. RNA sequencing of CHK1 inhibitor-selected AML lines revealed an almost complete absence of induction of CDK2-activated genes following prexasertib treatment. Interestingly, unlike solid tumors, AML cell lines that were resistant to CHK1 inhibitors exhibited hypersensitivity to WEE1 or ATR inhibition. Further studies demonstrated that CHK1 inhibitor response was varied in primary AML samples, with 50% of primary AML specimens resistant to CHK1i monotherapy, again without any relationship to TP53 status. The common features among sensitive vs. resistant leukemias was the successful inhibition of CDK2 phosphorylation and the associated induction of TNFα. Conclusions: Our findings reveals critical new insights into the factors that influence the response to CHK1 inhibitors in AML. Importantly, we found no evidence of cross-resistance to inhibitors of other replication checkpoint proteins, enabling alternative options. Citation Format: Annapoorna Venkatachalam, Husheng Ding, Nicole Vincelette, Cordelia McGehee, Kevin Peterson, Paula Schneider, Cristina Correia, Hu Li, Scott Kaufmann. CDK2 activation mediates response of acute myeloid leukemia to CHK1, ATR and WEE1 inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 423.

e16331 Background: Elraglusib (9-ING-41), a potent and selective GSK-3 inhibitor, is a targeted anticancer agent with clinical activity in phase 1 trial and undergoing clinical development for the treatment of metastatic pancreatic ductal adenocarcinoma (mPDAC). Elraglusib (9-ING-41) displayed immune-modulatory activity in preclinical models and modulation of serum cytokine profile in clinical specimens. Here, we investigated the correlation between plasma levels of cytokines/chemokines/soluble cell receptors/growth factors (CCSG) and clinical outcomes of patients with mPDAC treated with elraglusib and gemcitabine/nab-paclitaxel (GnP) in an exploratory study. Methods: CCSGs were evaluated in the pre-dose plasma samples of patients with newly diagnosed mPDAC enrolled in the 1801 phase 2 study (NCT03678883) treated with GnP (n = 13) or elraglusib/GnP (n = 32). Using a Luminex immunoassay, 40 CCSGs were screened as predictive biomarkers of median overall survival (mOS) in the elraglusib/GnP arm using 5-fold cross-validation and maximally selected rank statistic cutpoint determination. A subset of CCSGs (n = 10) was remeasured using an MSD immunoassay with greater sensitivity. Maximally selected rank statistic cutpoint determinations were conducted using MSD results for elraglusib/GnP samples with mOS and median progression free survival (mPFS). P values < 0.05 were considered statistically significant. Results: Luminex screening revealed several CCSGs significantly stratified elraglusib/GnP patients by mOS. IL-6 and IL-8 were identified as top candidates with stable and significant results on cross-validation. Using the MSD assay, both were confirmed to stratify elraglusib/GnP patients by OS and extended to analyze PFS. Low level of IL-8 conferred a better OS prognosis (HR: 0.0919, 95%, CI: 0.0205-0.411, P < 0.001) than low IL-6 (HR: 0.205, 95%, CI: 0.063-0.665, P = 0.005) in the elraglusib/GnP treated patients. Correlations with mPFS were similar in the elraglusib/GnP patients for IL-6 (HR: 0.248, 95% CI: 0.072-0.848, P = 0.02) and IL-8 (HR: 0.297, 95%, CI: 0.111-0.743, P = 0.01). Conclusions: Preliminary results of our exploratory study identified low plasma levels of IL-6 and IL-8 prior to elraglusib/GnP treatment as potential predictive biomarkers associated with improved outcomes in previously untreated patients with mPDAC. The 1801 Part 3 Arm B clinical trial recruitment has been completed. We plan to analyze additional pre-dose plasma samples obtained from approximately 200 patients by April 2024. These will be analyzed using interim OS data available by April 2024 and included in the final presentation. Clinical trial information: NCT03678883 .

e16306 Background: Glycogen Synthase Kinase-3beta has been well-credentialed as a therapeutic target in patients with advanced cancer including pancreatic cancer. Elraglusib (9-ING-41), a novel best-in-class GSK-3 inhibitor, is being developed in combination with gemcitabine/nab-paclitaxel (GnP) as first-line therapy for patients with metastatic pancreatic cancer (mPDAC). Using liquid biopsy samples from an ongoing randomized phase 2 trial (1801 Part 3B; NCT03678883), we examined whether gene mutations in cfDNA correlated with clinical outcomes in mPDAC patients treated with GnP or elraglusib/GnP. Methods: Pre-dose plasma samples were obtained from 37 (GnP arm) and 89 (elraglusib/GnP arm) mPDAC patients previously untreated for advanced disease. Mutational analysis of cfDNA extracted from plasma samples was performed by Tempus (Chicago, IL) using the xF+ platform. The Tempus xF+ assay is an NGS cfDNA liquid biopsy tumor profiling assay for identifying genomic alterations in 523 genes. Tempus xF+ reports were used for analysis of gene mutations. Results: In 126 pancreatic cancer patients, frequently mutated genes ( > 10%) consisted of KRAS (93/126, 74%), TP53 (79/126, 63%), CDKN2A (26/126, 21%), DNMT3A (25/126, 20%), NOTCH1/2 (25/126, 20%), and MLL3 (23/126, 18%). Gain-of-function mutations in KRAS, and loss-of-function mutations of TP53, and CDKN2A genes were associated with worse overall survival (OS) in patients treated with elraglusib/GnP (P < 0.05) but not in GnP-treated patients. Multivariate analysis of frequently mutated genes showed that patients with double (KRAS+TP53) and triple (KRAS+TP53+CDKN2A) mutations had significantly worse OS only in the elraglusib/GnP arm. The presence of DNMT3A, NOTCH1/2 or MLL3 gene mutations did not correlate with any clinical outcomes. Conclusions: Our preliminary analysis of gene mutations in liquid biopsy samples obtained from patients in the ongoing 1801 Part 3B phase 2 trial identified potential predictive biomarkers of clinical outcome in mPDAC patients treated with combination of elraglusib/GnP. Clinical trial information: NCT03678883 .

Abstract Background: The kinases ataxia-telangiectasia-mutated-and-Rad3-related kinase (ATR), checkpoint kinase 1 (CHK1) and WEE1 participate in the response to replication stress. Inhibitors of ATR, WEE1, and CHK1 are being tested for antineoplastic activity as monotherapies and in combination with chemotherapy. Although these agents impair cell cycle checkpoints, fork stabilization, origin firing, and HR, little is known about the apoptotic pathways that are engaged and the critical mediators activating cell death mechanisms. The present study utilizes high-grade serous ovarian cancer (HGSOC) cell lines and patient-derived xenografts (PDXs) to understand the predominant apoptotic players and assess changes involved in the resistance setting after treatment with these inhibitors. Methods: HGSOC cell lines were examined for responses to ceralasertib (ATRi), prexasertib (CHK1i), or adavosertib (WEE1i) using colony forming assays, immunoblotting, and assays for apoptosis. To characterize the cell death networks after prexasertib treatment, RNA-seq analysis was performed in two representative cell lines. Mechanisms involved in acquired resistance and downstream changes in cell fate were evaluated by generating prexasertib- resistant HGSOC cell lines. Results: Irrespective of the homologous recombination (HR) status, ovarian cell lines, and PDXs underwent cell death during prolonged exposure at clinically achievable concentrations. CDC25A-CDK2 signaling was predictive of responsiveness in vitro and in vivo. The subsequent apoptotic response was c-Jun N-terminal kinases (JNK) dependent and involved engagement of mitochondrial pathways rather than the TNFa-induced death receptor-mediated apoptosis we recently described in acute leukemia (H. Ding, N. Vincelette, et al., Cancer Res. 81:2666-2678, 2021). Additionally, ovarian lines selected for CHK1i resistance failed to die in response to either ATRi or WEE1i but were sensitive to front-line therapy options, including carboplatin and PARP inhibitor. Conclusions: Unlike AML, death receptor-mediated apoptosis was not observed; instead, the primary mechanism of cell killing in HGSOC lines involves activation of the mitochondrial apoptotic pathway. Our findings identify the key cell death-initiating events that serve as measures of response to replication checkpoint inhibitors in ovarian cancer. Acquired resistance to these therapies is multifactorial and mainly involves failure to exacerbate the replication stress response. Citation Format: Annapoorna Venkatachalam, Cristina Correia, Kevin L. Peterson, Xiaonan Hou, Annabella R. Strathman, Paula A Schneider, Karen S. Flatten, Laura N. Duffield, Cordelia D. McGehee, Melissa C. Larson, Xue Meng, Nicole D Vincelette, Ann L. Oberg, Hu Li, John Weroha, Scott H Kaufmann. Transcriptomic insights reveal role of BCL2 proteins in replication checkpoint inhibitor response [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B012.

Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.

Background: Cellular senescence has been associated with neurodegenerative disease and clearance of senescent cells using genetic or pharmaceutical strategies (senolytics) has demonstrated beneficial effects in mouse models investigating individual disease etiologies of Alzheimer’s disease (AD). However, it has remained unclear if senescent cell clearance in a mouse model exhibiting both plaque and tau pathologies modifies the disease state (3xTg). Objective: To investigate the effects of senescent cell clearance in the 3xTg mouse model. Methods: 3xTg mice were treated with senolytics (ABT263 (navitoclax; NAVI), a combination of dasatinib and quercetin (D+Q)), or subjected to transgene-mediated removal of p16-expressing cells (via INK-ATTAC). Results: Senolytic treatments consistently reduced microgliosis and ameliorated both amyloid and tau pathology in 3xTg mice. Using RNA sequencing, we found evidence that synaptic dysfunction and neuroinflammation were attenuated with treatment. These beneficial effects were not observed with short-term senolytic treatment in mice with more advanced disease. Conclusions: Overall, our results further corroborate the beneficial effects senescent cell clearance could have on AD and highlight the importance of early intervention for the treatment of this debilitating disease.

AbstractBackgroundContext‐specific networks can capture functionally important genes that may not be differentially expressed. Leveraging seven different gene expression datasets of Alzheimer’s disease (AD) and healthy control brains, we constructed a novel pipeline that includes such network analysis, augmented by in vivo validations and in silico drug repurposing to facilitate prioritization of AD therapeutic targets.MethodWe utilized publicly available gene expression data from 733 AD and 454 control brains across 7 datasets and the NetDecoder (PMID: 26975659) network biology platform to build context‐specific networks. Based on the known protein‐protein interaction and gene expression correlations, information flow from the top differentially expressed genes (DEG) to transcriptional regulator genes was calculated using a process‐guided flow algorithm. Genes with the greatest informational flow changes across AD cohorts were systematically prioritized. We next validated these genes in a medium‐throughput screen of a tau‐transgenic Drosophila model (PMID: 11408621). Further, we identified existing small‐molecule drugs to target the candidate genes utilizing curated databases.ResultsA total of 14 context‐specific networks were constructed across 7 datasets. We found AD‐related genes, such as APP, to have high flow differences between AD and control networks and high connectivity. Notably, many such genes were not DEGs between AD and control, suggesting that the information flow captures novel aspects of AD pathophysiology. We nominated 224 unique genes with the greatest flow differences for functional validation. We screened 111 of the 224 candidate genes with readily available, high‐confidence orthologs in Drosophila. We identified genes that suppress (N = 68) or enhance (N = 27) tau‐related neurodegeneration in this model. We confirmed the effects of previously nominated genes, such as HDAC1, which enhances tau‐neurodegeneration if overexpressed and suppresses it if knocked down. We also identified and validated additional therapeutic targets. Lastly, we applied a network‐proximity‐based algorithm and identified small molecule drugs for the candidate genes.ConclusionWe present a novel pipeline that integrates analytic approaches, experimental validation, and drug repurposing to facilitate therapeutic discovery in AD. We applied this pipeline and nominated previously known and novel genes implicated in AD pathogenesis. Our pipeline demonstrates a cross‐species, systems‐level approach to therapeutic nomination in AD and related neurodegenerative diseases.