Citations

Abstract PARP inhibitors (PARPis) are now a standard of care for high-grade serous ovarian cancer (HGSOC), but resistance to these drugs poses a significant concern, necessitating the exploration of novel treatment strategies. Here we investigated the mechanism underlying enhanced PARPi activity in HGSOC cells following treatment with brigatinib—a second-generation anaplastic lymphoma kinase (ALK) inhibitor approved for treating lung cancer. Our previous work has shown that brigatinib is a dual inhibitor of tyrosine kinases FAK and EPHA2 with subsequent additive reduction in downstream PI3K/AKT and MAPK/ERK cell survival signaling. Suppression of these pathways conferred PARPi sensitivity to HGSOC cell lines and patient-derived xenograft (PDX) models, particularly in those with high FAK and EPHA2. To dissect the mechanism linking the FAK/EPHA2 – AKT/ERK pathway to PARPi synergy, we focused on proteomics approaches which revealed that FRA1—an Activator Protein 1 (AP-1) transcription factor known to be downstream of AKT/ERK—and its targets were decreased by brigatinib treatment. In subsequent phosphoproteomics approaches and cell-based confirmatory studies, a phosphorylation crucial for FRA1 stability was found to be disrupted, leading to reduction in total FRA1 protein levels and synergy with PARPis. To explore how FRA1 mediates response to PARPis, immunoblotting analysis revealed that PARPis increased the abundance of FRA1 in HGSOC cells at early timepoints, indicating potential involvement in an adaptive survival response. Follow-up pharmacologic and gene silencing techniques demonstrated that brigatinib still reduces FRA1 in PARPi-treated cells, and FRA1 depletion induces apoptosis when combined with PARPi to levels comparable to brigatinib treatment. Overall, our studies unveil a beneficial ALK-independent effect of brigatinib that may offer new avenues for considering its repurposing as a HGSOC therapy through disruption of a PARPi-induced adaptive survival mechanism. Citation Format: Julie R. Duffield, Xiaonan Hou, Benjamin W. Wilson, Anjali Prasad, Iman K. McKeon-Makki, Amelia M. Huehls, Xinyan Wu, Cristina Correia, Melissa C. Larson, Fergus J. Couch, Ann L. Oberg, Scott H. Kaufmann, Larry M. Karnitz, Arun Kanakkanthara. A PARP-inhibitor-induced early adaptive survival response is tackled through depletion of FRA1 by brigatinib in high-grade serous ovarian carcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr A024.

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.

ABSTRACT Symmetry refers to properties that remain invariant upon mathematical transformations. The principles of symmetry have guided numerous important discoveries in physics and chemistry but not in biology and medicine. Here, we aim to explore the presence of symmetry relationships at the gene expression level as a mean to distinguish between healthy and disease states. We deployed Learning-Based Invariant Feature Engineering - LIFE, a hybrid machine learning approach implemented with two symmetric invariant feature functions (IFFs) to identify Invariant Feature Genes (IFGs), which are gene pairs whose IFF single-value outputs remain invariant across individual samples in a given biological phenotype. Our multiclass classification results across the transcriptomes of 25 normal organs, 25 cancer types, and blood samples obtained from 4 different types of neurodegenerative diseases revealed the presence of unique phenotype-specific IFGs. We constructed networks using these IFGs (IF-Nets) and intriguingly, we demonstrated that the hubs could serve as information encoders, capable of reconstructing sample-wise expression values in relation to their counterpart genes. More importantly, we found that hubs of cancer IF-Nets were enriched with both approved and clinical trial drugs, highlighting “symmetry breaking” as a novel approach for treating diseases.

Abstract Background: Ovarian cancer ranks fifth in cancer deaths among women. The gold standard of care for advanced disease includes cytoreductive surgery along with adjuvant or neoadjuvant platinum/taxane-based chemotherapy. Although response to this treatment is high, chemotherapy-induced nausea and vomiting due to platinum agents and allergic reactions due to both agents are common. For nearly half a century, the glucocorticoid (GC) dexamethasone (DEX) has been administered as part of supportive care. However, there have periodically been questions about the possible deleterious effects of DEX on chemotherapy response, especially in solid tumors. Methods: Platinum-sensitive, glucocorticoid receptor (GRa) expressing ovarian cancer cells were exposed to DEX prior to chemotherapy and clonogenic survival was assessed. The effects of DEX on cell cycle and apoptosis were assessed by flow cytometry. Changes in the tumor cell transcriptome after acute DEX treatment were determined by RNA sequencing. Mice bearing orthotopic ovarian cancer patient-derived xenografts (PDXs) were treated with chemotherapy ± DEX to assess changes in chemotherapy response in vivo. Findings: DEX pre-treatment decreased platinum cytotoxicity in ovarian cancer cell lines in vitro and diminished platinum response in some PDXs analyzed for chemotherapy response ex vivo. RNA-seq analysis revealed changes in transcripts encoding proteins involved in platinum detoxification and TNFa pro-survival signaling. In ovarian cancer PDXs, DEX decreased platinum response in one model and had limited impact on both platinum and platinum/taxane chemotherapy in three distinct studies. Interpretation: DEX appears to modulate chemotherapy response in some settings in vitro and ex-vivo. The impact of DEX on chemotherapy response in vivo appears to be limited. However, further investigation of this question in vivo in non-immunosuppressed murine models appears warranted. Citation Format: Annapoorna Venkatachalam, Cristina Correia, Cordelia Mcgehee, Sarah Finstuen-Magro, Paula A. Schneider, Rachel Hurley, Kevin L. Peterson, Ethan Heinzen, Melissa C. Larson, Xiaonon Hou, Ann L. Oberg, Saravut Weroha, Scott H. Kaufmann. Impact of dexamethasone on chemotherapy response in ovarian cancer [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 B098.

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.

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.

Abstract Despite intensive characterization of immune responses after COVID-19 infection and vaccination, research examining protective correlates of vertical transmission in pregnancy are limited. Herein, we profiled humoral and cellular characteristics in pregnant women infected or vaccinated at different trimesters and in their corresponding newborns. We noted a significant correlation between spike S1–specific IgG antibody and its RBD-ACE2 blocking activity (receptor-binding domain–human angiotensin-converting enzyme 2) in maternal and cord plasma (P < .001, R > 0.90). Blocking activity of spike S1–specific IgG was significantly higher in pregnant women infected during the third trimester than the first and second trimesters. Elevated levels of 28 cytokines/chemokines, mainly proinflammatory, were noted in maternal plasma with infection at delivery, while cord plasma with maternal infection 2 weeks before delivery exhibited the emergence of anti-inflammatory cytokines. Our data support vertical transmission of protective SARS-CoV-2–specific antibodies. This vertical antibody transmission and the presence of anti-inflammatory cytokines in cord blood may offset adverse outcomes of inflammation in exposed newborns.

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.