Abstract Background: The kinases ataxia-telangiectasia-mutated-and-Rad3-related (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 homologous recombination (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 identify the primary mechanism of cell death in ovarian cancer and assess changes involved in the resistance setting after treatment with these inhibitors. Methods: A panel of HGSOC cell lines was examined for responses to ceralasertib (ATRi), prexasertib (CHK1i) or adavosertib (WEE1i) using colony forming assays, immunoblotting and assays for apoptosis. Mechanisms involved in acquired resistance and downstream changes in cell fate were evaluated by generating prexasertib-resistant HGSOC cell lines. Subsequent NGS analysis was performed in the sensitive and resistant pairs. Results: Irrespective of the homologous recombination (HR) status, ovarian cell lines and PDXs underwent cell death during prolonged exposure at clinically achievable concentrations. Treatment of ovarian cancer cell lines with ATRi, CHK1i or WEE1i activated the replication checkpoint, consistent with previously published reports. The subsequent apoptotic response involved PMAIP1 and BCL2L11 upregulation rather than the TNFa-induced death receptor-mediated apoptosis we recently described in acute leukemia (Cancer Res. PMID: 33414171). Additionally, ovarian lines selected for CHK1i resistance failed to die in response to either ATRi or WEE1i. Conclusions: Prexasertib, ceralasertib, and adavosertib exhibit monotherapy activity in both HR deficient and proficient HGSOC cell lines and PDX models. 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. Cross-resistance of these DNA damage repair modulators is likely multifactorial and mainly involves diminished replication stress response. Citation Format: Annapoorna Venkatachalam, Kevin L. Peterson, Cristina Correia, Karen S. Flatten, Xianon Hou, Paula A. Schneider, Emily Balczewski, Cordelia McGehee, Rachel M. Hurley, Xue W. Meng, Chance Sine, Rameen Shah, Nicole Vincelettte, Husheng Ding, Hu Li, Saravut (John) Weroha, Scott H. Kaufmann. CHK1 inhibitor prexasertib induces NOXA-dependent apoptosis in ovarian cancer [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 1832.
Neuroblastoma, a Paradigm for Big Data Science in Pediatric Oncology
Pediatric cancers rarely exhibit recurrent mutational events when compared to most adult cancers. This poses a challenge in understanding how cancers initiate, progress, and metastasize in early childhood. Also, due to limited detected driver mutations, it is difficult to benchmark key genes for drug development. In this review, we use neuroblastoma, a pediatric solid tumor of neural crest origin, as a paradigm for exploring “big data” applications in pediatric oncology. Computational strategies derived from big data science–network- and machine learning-based modeling and drug repositioning—hold the promise of shedding new light on the molecular mechanisms driving neuroblastoma pathogenesis and identifying potential therapeutics to combat this devastating disease. These strategies integrate robust data input, from genomic and transcriptomic studies, clinical data, and in vivo and in vitro experimental models specific to neuroblastoma and other types of cancers that closely mimic its biological characteristics. We discuss contexts in which “big data” and computational approaches, especially network-based modeling, may advance neuroblastoma research, describe currently available data and resources, and propose future models of strategic data collection and analyses for neuroblastoma and other related diseases.