Goetz, Matthew P.

Abstract Despite therapeutic advances, ovarian cancer (OC) recurrence remains common and outcomes for advanced disease are poor, highlighting the need for novel therapeutic targets. To this end, we identified CTPS1 as a highly expressed and essential gene for the survival of many OC cell lines, including models resistant to chemotherapy and PARP inhibitors. CTPS1 is one of two enzymes, alongside CTPS2, responsible for the de novo synthesis of cytidine triphosphate (CTP), a critical nucleotide required for DNA and phospholipid biosynthesis. We discovered that CTPS1 is the predominant isoform expressed in OC cells, with significantly higher levels of expression in advanced and resistant disease compared to normal tissue, benign lesions and treatment naïve cancers. Intriguingly, 25% of all OC samples were found to completely lack CTPS2 protein expression suggesting their near complete reliance on CTPS1 for survival. Using CTPS1 siRNAs and genetically engineered OC cells with a degradation tag (dTAG) fused to the C-terminus of endogenous CTPS1 alleles, we showed that CTPS1 knockdown or proteasomal degradation induces an S-phase cell cycle arrest followed by apoptosis. Leveraging a first-in-class, highly selective, and orally bioavailable CTPS1 inhibitor (STP938) developed by Step Pharma, we identified nanomolar-range IC50 values across a panel of over 20 OC cell lines and validated its anti-neoplastic activity in ex vivo patient-derived xenograft (PDX) models. STP938 synergized with standard-of-care chemotherapy and PARP inhibitors, even in resistant models, further supporting its clinical potential. To elucidate STP938’s mechanism of action, we performed multi-omic profiling (bulk RNAseq, phospho- and total proteomics) on sensitive and isogenic resistant OC lines, as well as single-cell RNAseq of an OC PDX tumor following in vivo treatment with vehicle or STP938, revealed dysregulation of DNA replication, cell cycle, and metabolic pathways. As expected, STP938 rapidly depleted CTP levels, which remain suppressed for 72 hours, suggesting insufficient salvage pathway compensation which ultimately compromised cancer cell viability. As CTP is also a direct precursor for activated intermediates in phospholipid biosynthesis. Mass-spectrometry based lipidomics and metabolomics were employed to interrogate the impact of STP938 in these processes where numerous changes were found. To uncover mechanisms of resistance to STP938 and reveal therapeutic vulnerabilities, we conducted genome-wide CRISPR knockout screens in multiple treatment naïve and STP938 resistant models and analysis of results are ongoing. In collaboration with Step Pharma, a Phase 1a/b clinical trial of STP938 is currently enrolling patients with advanced solid tumors, including an expansion cohort for CTPS2 null OC patients (NCT06297525). These laboratory discoveries and ongoing clinical efforts aim to enable broader uptake of this novel therapeutic approach to improve long-term outcomes in patients with CTPS1-dependent disease. Citation Format: Xiyin Wang, Michael J. Emch, Lauren A. Voll, Rebecca Epp, Esther P. Rodman, Noa Odell, Nicole A. Pearson, Xiaonan Hou, Matthew P. Goetz, Scott H. Kaufmann, S. John Weroha, Philip Beer, John R. Hawse. Exploiting CTPS1 dependency for the treatment of ovarian cancerPR005: Exploiting CTPS1 dependency for the treatment of ovarian cancer [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 PR005.

Abstract Deep learning has advanced digital pathology and multi-omics data analysis. However, there is still a critical need to integrate these modalities to better understand complex diseases. Deep learning models, such as Prov-Gigapath, a state-of-the-art pathology foundation model, have been developed for analyzing whole-slide images (WSI). Our previously published deep-learning framework, OmicsFootPrint (OFP), transforms high-dimensional multi-omics data into intuitive genomically-arranged circular images and utilizes transfer learning to overcome the challenges of high-dimensional, low-sample-size data. In this study, we aimed to combine WSI and multi-omics data to develop a framework capable of distinguishing between invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC), which is clinically significant as it may help explain differential responses to therapies, such as tamoxifen versus letrozole. We enhanced the performance of our OFP model by dividing each multi-omics circular image (1024x1024x3 pixels) into 16 equal-sized patches (256x256x3 pixels). Each patch was trained using a separate EfficientNetV2 architecture with pre-trained ImageNet weights. The probability outcomes from these 16 models were subsequently combined through a meta-learner model using the AutoGluon framework. Concurrently, WSI images corresponding to the same patients were independently trained, maintaining consistent train/validation/test splits. The Prov-Gigapath model, which was trained using a dataset from the Providence Health System, was employed to extract patch-level embeddings from the WSI data. These embeddings were fine-tuned using a LongNet-based masked autoencoder to predict outcomes. Finally, predictions from the OFP model and the WSI-based Prov-Gigapath model were integrated through a meta-learner, preserving the original train/validation/test split. Model performances (AUC) were evaluated using the held-out test set. The gold standard used for the analysis was histological identification of IDC versus ILC, against which the performance of omics data alone, WSI data alone, and the multi-modal integration approach were compared. We evaluated our integrated model as a proof-of-concept using The Cancer Genome Atlas (TCGA) breast cancer cohort, comprising 92 ILC and 391 IDC samples. Multi-omics data, including gene expression, microRNA, copy number variation, and RPPA, were analyzed using the OFP framework, while WSI data were processed using the Prov-Gigapath method. The results demonstrated that the multi-modal integration approach significantly outperformed individual modalities, achieving an AUC of 0.98 compared to 0.87 for OFP (original), 0.95 for OFP (16-patches), and 0.92 for WSI. Integrating WSI and multi-omics data improves predictive accuracy, significantly improving the classification of breast cancer subtypes over histology alone. Citation Format: Naresh Prodduturi, Xiaojia Tang, Hamid R. Tizhoosh, Kevin J. Thompson, Richard Weinshilboum, Karthik V. Giridhar, Judy C. Boughey, Eric W. Klee, Liewei Wang, Matthew P. Goetz, Vera Suman, Krishna R. Kalari. A multi-modal learning framework to integrate digital pathology image and multi-omics data in breast cancer [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 2429.

Purpose The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and prioritize drug targets and cancer therapy regimens. Materials and Methods The PANOPLY approach integrates clinical data with germline and somatic features obtained from multiomics platforms and applies machine learning and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow uses the following four steps: selection of matched controls to the patient of interest; identification of patient-specific genomic events; identification of suitable drugs using the driver-gene network and random forest analyses; and provision of an integrated multiomics case report of the patient with prioritization of anticancer drugs. Results The PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study that collected clinical and genomic data as well as patient-derived xenografts to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant patient-derived xenograft model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor ( P < .05). We also applied PANOPLY to in-house and publicly accessible multiomics tumor data sets with therapeutic response or survival data available. Conclusion PANOPLY shows promise as a means to prioritize drugs on the basis of clinical and multiomics data for an individual patient with cancer. Additional studies are needed to confirm this approach.