Correia, Cristina

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.

Anticipating and understanding cancers’ need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors’ dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

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 (GRα) 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 TNFα 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 D. Mcgehee, Sarah Finstuen-Magro, Jamison VanBlaricom, Paula A. Schneider, Kevin L. Peterson, Ethan P. Heinzen, Melissa C. Larson, Xiaonon Hou, Ann L. Oberg, Saravut John Weroha, Scott H. Kaufmann. Impact of dexamethasone on chemotherapy response in ovarian cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5058.

Abstract Glioblastoma is the most common and lethal primary brain tumor in adults, with a median survival less than 15 months despite current standard of care. Oncolytic immunovirotherapies (OVs) have shown promise in early clinical trials. OVs are engineered to specifically target cancer cells, trigger autophagic programmed cell death, and release tumor antigens in the context of T-cell activating cytokines. Cellular communication network factor 1 (CCN1) is an extracellular matrix protein expressed in 70% of glioblastomas that has been shown to drastically reduce OV efficacy, particularly herpes simplex virus type 1 (HSV-1). Our objectives were to decode protein-protein interaction (PPI) networks activated by CCN1, identify critical nodes in resistance networks, and use this analysis to design OVs that overcome CCN1-mediated resistance. We used NetDecoder to elucidate phenotype-specific PPI subnetworks in LN229 human glioblastoma cells with tetracycline-inducible CCN1 expression. Publicly available microarray data on CCN1-induced and control samples established differentially expressed genes, which served as sources in our PPI network to derive prioritized context-specific subnetworks. We found that 11 source genes collaborate via 39 deep nodes to confer OV resistance to CCN1-expressing glioblastomas. Of these, a router (IKBKE) and a sink (YBX1) have been previously implicated in glioblastoma pathogenesis. We conclude that autophagy regulator HSP90AA1, a critical node, may be targeted to improve OV efficacy in CCN1-expressing glioblastomas. Furthermore, WebGestalt overrepresentation analysis of subnetwork nodes suggests that oncolytic adenovirus may be more effective than HSV-1 as a synthetic biology chassis.