Correia, Cristina

Accurate estimation of Alzheimer’s disease (AD) severity from structural magnetic resonance imaging (MRI) remains difficult, as disease-associated anatomical alterations are often subtle and publicly available datasets are typically too small to support robust deep learning model training. This feasibility study sought to determine how much Alzheimer’s disease spectrum-related information could be extracted from a small structural MRI cohort using a deliberately lightweight two-dimensional convolutional neural network (2D CNN), and whether transfer learning improves model performance. This study was intended as a methodological proof of concept rather than the development of a clinically deployable diagnostic tool. Structural scans and Clinical Dementia Rating (CDR) labels from the OASIS-1 dataset were filtered to 214 subjects: 124 cognitively normal (CN), 65 with mild cognitive impairment (MCI; CDR = 0.5), and 25 with AD-level impairment (CDR ≥ 1). A compact 2D CNN trained from scratch and a transfer learning model (frozen ImageNet MobileNetV2 features) were evaluated on four binary tasks (CN vs. AD, MCI vs. AD, CN vs. MCI, and CN vs. any impairment) under identical pre-processing and subject-level repeated 5-fold cross-validation (10 repeats), with the decision threshold tuned only on an inner split. Discrimination was summarized by ROC-AUC with 95% confidence intervals (CIs), permutation tests against chance, and per-task sensitivity and specificity. The from-scratch CNN recovered only a broad normal-versus-impaired signal (CN vs. any impairment AUC 0.59) and was at chance on adjacent-stage tasks (MCI vs. AD 0.41; CN vs. MCI 0.51). Transfer learning improved every task: CN vs. AD AUC 0.745 (95% CI 0.730–0.763), CN vs. any impairment 0.642, CN vs. MCI 0.601, and MCI vs. AD 0.599. On an independent OASIS-2 cohort, the transfer learning CN vs. AD model retained AUC 0.748. In this small-data regime, transfer learning recovers substantially more Alzheimer-spectrum signals than a from-scratch CNN, but performance remains modest because it is bounded by CDR-based, non-biomarker-confirmed labels, suggesting the model separates CDR-defined cognitive-status groups rather than detecting AD pathology.

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.

Abstract Despite long-standing interest in combining topoisomerase I (TOP1) poisons with PARP inhibitors (PARPis), the mechanistic basis for the variable and often unpredictable response to this strategy remains unclear. Early preclinical studies suggested that PARP inhibition amplifies TOP1 poison-induced DNA damage, implying broad therapeutic potential; however, emerging translational and clinical data—particularly in ovarian cancer—indicate that synergy is context-dependent and governed by tumor-intrinsic features that have not been fully defined.Here we assessed determinants of response in five HR proficient ovarian cancer (OC) patient-derived xenografts (PDXs) previously treated with the SN-38 prodrug etirinotecan pegol (NKTR-102) in combination with the PARPi rucaparib (C. Nitschmann, Clin. Cancer Res. 22(2) Suppl. B50, 2016). Although all models were refractory to single-agent rucaparib, 50% exhibited enhancement of NKTR-102-induced tumor regression with the addition of rucaparib. Neither PARP1 abundance nor extent of rucaparib-mediated poly(ADP-ribose) suppression correlated with therapeutic benefit. Instead, sensitization was restricted to PDXs intrinsically responsive to NKTR-102 and was strongly associated with high levels of NKTR-102-induced TOP1-DNA covalent complexes. In further analysis, 2 of 3 resistant PDXs exhibited high levels of the ABC transporter ABCG2 despite lack of previous therapy, revealing a previously underappreciated mechanism of de novo resistance that limits effective TOP1 inhibitor activity independent of homologous recombination status. Notably, NKTR-102 response did not track with platinum/taxane sensitivity, highlighting the distinct molecular circuitry governing TOP1 poison susceptibility.Collectively, these findings demonstrate that rucaparib can potentiate NKTR-102-induced regression in tumors capable of forming abundant TOP1-DNA covalent complexes. This work identifies TOP1-DNA complex trapping and lack of ABCG2 expression as mechanistic predictors of benefit, providing an updated framework for selecting patients most likely to respond to TOP1 poison/PARPi combinations and challenging the long-standing assumption that PARP inhibition can universally resensitize tumors to TOP1 poisons. Citation Format: Annapoorna Venkatachalam, Caroline C. Nitschmann, Karen S. Flatten, Rachel Hurley, Xiaonan Hou, Cristina Correia, Paula A. Schenider, Krista Goergen, Matthew Maurer, Ethan P. Heinzen, Ann L. Oberg, S John Weroha, Paul Haluska, Scott H. Kaufmann, Elizabeth M. Swisher. TOP1-DNA protein complex as a predictive biomarker for NKTR-102/Rucaparib combination therapy in PARP-inhibitor resistant ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB364.

Poly(ADP-ribose) polymerase (PARP) inhibitors (PARPis) are an important therapy for high-grade serous ovarian cancer (HGSOC). However, PARPi resistance frequently emerges, necessitating previously unrecognized approaches to improve HGSOC responses. Here, we showed that the anaplastic lymphoma kinase (ALK) inhibitor brigatinib enhances PARPi activity in HGSOC cells by disrupting an adaptive survival mechanism orchestrated by Fos-related antigen 1 (FRA1) in response to PARPi. This effect of brigatinib occurred through an ALK-independent pathway, wherein brigatinib induced a dual blockade of focal adhesion kinase (FAK) and EPH receptor A2 (EPHA2) tyrosine kinases, leading to the suppression of protein kinase B (Akt) and extracellular-regulated kinase (ERK) signaling accompanied by disruption of a phosphorylation event crucial for FRA1 protein stability. Moreover, in HGSOC patient-derived xenograft (PDX) models, brigatinib and PARPi combination therapy induced tumor regression and improved overall survival compared with PARPi alone, particularly in models with high FAK and EPHA2. These findings support dual targeting of FAK and EPHA2 as a strategy to achieve effective and durable PARPi responses and identify a promising biomarker-based combinatorial approach using brigatinib and PARPi for HGSOC, particularly the subset characterized by high FAK and EPHA2.

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 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.