Cell Reports Medicine

Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models

The determination of endometrial carcinoma histological subtypes, molecular subtypes, and mutation status is critical for the diagnostic process, and directly affects patients' prognosis and treatment. Sequencing, albeit slower and more expensive, can provide additional information on molecular subtypes and mutations that can be used to better select treatments. Here, we implement a customized multi-resolution deep convolutional neural network, Panoptes, that predicts not only the histological subtypes but also the molecular subtypes and 18 common gene mutations based on digitized H&E-stained pathological images. The model achieves high accuracy and generalizes well on independent datasets. Our results suggest that Panoptes, with further refinement, has the potential for clinical application to help pathologists determine molecular subtypes and mutations of endometrial carcinoma without sequencing.

Updated efficacy and predictive biomarkers of QL1706, a bifunctional PD-1/CTLA-4 dual blocker in advanced solid tumors—A phase 1/1b study

QL1706 has shown promising efficacy in solid tumors in a phase 1/1b study. Here, we report updated long-term survival outcomes and biomarker analyses. Among 468 patients treated with QL1706 (5 mg/kg), median progression-free survival (mPFS) and overall survival (mOS) are 1.5 and 14.2 months for non-small cell lung cancer (NSCLC), 1.9 and 20.2 months for nasopharyngeal carcinoma (NPC), and 4.2 and 18.6 months for cervical cancer (CC), respectively. Liver metastasis is correlated with poor progression-free survival (PFS) and overall survival (OS) in NSCLC and poor OS in CC, while elevated lactate dehydrogenase is linked to shorter PFS and OS in NPC. CDK4/11q13 diploid or the expression of GZMK

Minimally invasive diagnosis of precancerous cervical lesions using single-cell peripheral immune atlas

Cervical cancer remains a major global health concern for women. Current screening methods are either invasive or lead to low participation and over-referral for colposcopy, particularly among high-risk human papillomavirus (HPV)-positive women. This study analyzes 613 participants with varying cervical lesions using mass cytometry by time-of-flight (CyTOF) to identify disease-specific peripheral immune signatures. A diagnostic model based on 23 immune features achieves ∼91% sensitivity and specificity for detecting precancerous and cancerous lesions. A separate model for HPV-positive women shows even higher accuracy (∼93% sensitivity, ∼95% specificity), especially in HPV16/18-positive cases (99% sensitivity, 100% specificity). In an independent validation cohort (n = 105), the model distinguishes cervical intraepithelial neoplasia (CIN) 2+ from ≤CIN1 with 86.5% sensitivity and 85.3% specificity (area under the curve [AUC] = 0.89). These findings support peripheral immune profiling as a minimally invasive and accurate biomarker strategy for early cervical cancer screening, particularly in HPV16/18-positive women.

Transformer-based AI technology improves early ovarian cancer diagnosis using cfDNA methylation markers

Epithelial ovarian cancer (EOC) is the deadliest women's cancer and has a poor prognosis. Early detection is the key for improving survival (a 5-year survival rate in stage I/II is over 70% compared to that of 25% in stage III/IV) and can be achieved through methylation markers from circulating cell-free DNA (cfDNA) using a liquid biopsy. In this study, we first identify top 500 EOC markers differentiating EOC from healthy female controls from 3.3 million methylome-wide CpG sites and validated them in 1,800 independent cfDNA samples. We then utilize a pretrained AI transformer system called MethylBERT to develop an EOC diagnostic model which achieves 80% sensitivity and 95% specificity in early-stage EOC diagnosis. We next develop a simple digital droplet PCR (ddPCR) assay which archives good performance, facilitating early EOC detection.

Targeting PDGF signaling of cancer-associated fibroblasts blocks feedback activation of HIF-1α and tumor progression of clear cell ovarian cancer

Ovarian clear cell carcinoma (OCCC) is a gynecological cancer with a dismal prognosis; however, the mechanism underlying OCCC chemoresistance is not well understood. To explore the intracellular networks associated with the chemoresistance, we analyze surgical specimens by performing integrative analyses that combine single-cell analyses and spatial transcriptomics. We find that a chemoresistant OCCC subpopulation with elevated HIF activity localizes mainly in areas populated by cancer-associated fibroblasts (CAFs) with a myofibroblastic phenotype, which is corroborated by quantitative immunostaining. CAF-enhanced chemoresistance and HIF-1α induction are recapitulated in co-culture assays, which show that cancer-derived platelet-derived growth factor (PDGF) contributes to the chemoresistance and HIF-1α induction via PDGF receptor signaling in CAFs. Ripretinib is identified as an effective receptor tyrosine kinase inhibitor against CAF survival. In the co-culture system and xenograft tumors, ripretinib prevents CAF survival and suppresses OCCC proliferation in the presence of carboplatin, indicating that combination of conventional chemotherapy and CAF-targeted agents is effective against OCCC.

Anlotinib combined with TQB2450 in patients with platinum-resistant or -refractory ovarian cancer: A multi-center, single-arm, phase 1b trial

This is a phase Ib study of anlotinib plus a programmed death-ligand 1 (PD-L1) inhibitor TQB2450 for platinum-resistant or -refractory ovarian cancer. Thirty-four patients are enrolled and receive treatment. The objective response rate (ORR) is 47.1%, and the disease control rate is 97.1%. The median duration of response (DOR) has not been reached, and 61.3% of patients have a DOR of at least 8 months. The median progression-free survival (PFS) is 7.8 months, and the median overall survival (OS) has not been reached. The PD-L1-positive group has an ORR of 25.0%, whereas the PD-L1-negative group has an ORR of 92.9%. Treatment-related grade 3 or 4 adverse events (AEs) occur in 70.6% of patients, with the most common being hypertension (29.4%) and palmar-plantar erythrodysesthesia syndrome (29.4%). Anlotinib plus TQB2450 show promising antitumor activity and manageable toxicities in patients with platinum-resistant or -refractory ovarian cancer. A phase 3 randomized controlled trial to further validate our findings is ongoing.

Spatial heterogeneity of infiltrating T cells in high-grade serous ovarian cancer revealed by multi-omics analysis

Tumor-infiltrating lymphocytes (TILs), especially CD8

UniCAS: A foundation model for cervical cytology screening

Cervical abnormality screening is pivotal for prevention and treatment. However, the substantial size of whole slide images (WSIs) makes examination labor-intensive and time-consuming. Current deep learning-based approaches struggle with the morphological diversity of cervical cytology and require specialized models for distinct diagnostic tasks, leading to fragmented workflows. Here, we present UniCAS, a cytology foundation model pre-trained on 48,532 cervical WSIs encompassing diverse patient demographics and pathological conditions. UniCAS enables various clinical analysis tasks, achieving state-of-the-art performance in slide-level diagnosis, region-level analysis, and pixel-level image enhancement. In particular, by integrating a multi-task aggregator for slide-level diagnosis, UniCAS achieves area under the curve (AUC) values of 92.60%, 92.58%, and 98.39% for cancer screening, candidiasis testing, and clue cell diagnosis, respectively, while reducing diagnostic time by 70% compared with conventional approaches. This work establishes a paradigm for efficient multi-scale analysis in automated cervical cytology, bridging the gap between computational pathology and clinical diagnostic workflows.

Chemoradiotherapy-induced ACKR2+ tumor cells drive CD8+ T cell senescence and cervical cancer recurrence

Tumor recurrence after chemoradiotherapy is challenging to overcome, and approaches to predict the recurrence remain elusive. Here, human cervical cancer tissues before and after concurrent chemoradiotherapy (CCRT) analyzed by single-cell RNA sequencing reveal that CCRT specifically promotes CD8

Therapeutic targeting of interleukin-17C signaling in carcinogenesis of endometriosis

Endometriosis-associated ovarian carcinoma (EAOC) predominantly arises from the malignant transformation of endometriomas, yet the mechanism is incompletely defined. Spatial transcriptomic analysis of human specimens of normal endometrium, endometriomas, and EAOC identified interleukin-17C (IL-17C) signaling activation, with higher IL-17 receptor E (IL-17RE) expression in EAOC. Additionally, the IL-17C concentration was significantly increased in the peritoneal fluid of women with ovarian cancer. Using an endometriosis mouse model overexpressing IL-17RE, IL-17C levels were elevated in the peritoneal fluid. Furthermore, IL-17C knockout reduced the peritoneal fluid IL-17C concentration and inhibited ectopic lesion growth in endometriosis mice. In addition, the role of IL-17C signaling in promoting endometriosis carcinogenesis was investigated by blocking and modulating the IL-17C/IL-17RE pathway in human endometriotic epithelial cells, endometrial organoids, and ovarian endometriosis mice. These data identified IL-17C signaling as a driver of endometriosis carcinogenesis and propose IL-17C/IL-17RE as promising therapeutic targets, particularly for EAOC cases characterized by high IL-17C expression.

Unlocking the therapeutic potential of rigosertib as a selective therapy for ovarian cancer

Precision oncology seeks to exploit tumor-specific drug sensitivities. Traditionally, this is accomplished through the identification and targeting of highly recurrent mutations. This paradigm falls short in ovarian cancer where the oncogenic alterations are more diverse, necessitating an alternate approach for the identification of tumor-specific vulnerabilities. To address this, we have used a functional modeling approach, integrating drug screening with a Kinome Atlas-based assessment of signaling, to nominate a therapeutic regimen for ovarian tumors. This approach identifies a small-molecule RAS mimetic, rigosertib, as a tumor-selective agent and leads us to identify the combination of rigosertib with phosphoinositide 3-kinase (PI3K) or mammalian target of rapamycin (mTOR) inhibition as effective combinations that prevent rigosertib-induced survival signaling while inducing regressions in ovarian cancer xenografts. These data support further exploration of these combinations for the treatment of ovarian cancer.

Genomic profiling of a multi-lineage and multi-passage patient-derived xenograft biobank reflects heterogeneity of ovarian cancer

Ovarian cancer (OC) manifests as a complex disease characterized by inter- and intra-patient heterogeneity. Despite enhanced biological and genetic insights, OC remains a recalcitrant malignancy with minimal survival improvement. Based on multi-site sampling and a multi-lineage patient-derived xenograft (PDX) establishment strategy, we present herein the establishment of a comprehensive PDX biobank from histologically and molecularly heterogeneous OC patients. Comprehensive profiling of matched PDX and patient samples demonstrates that PDXs closely recapitulate parental tumors. By leveraging multi-lineage models, we reveal that the previously reported genomic disparities of PDX could be mainly attributed to intra-patient spatial heterogeneity instead of substantial model-independent genomic evolution. Moreover, DNA damage response pathway inhibitor (DDRi) screening uncovers heterogeneous responses across models. Prolonged iterative drug exposure recapitulates acquired drug resistance in initially sensitive models. Meanwhile, interrogation of induced drug-resistant (IDR) models reveals that suppressed interferon (IFN) response and activated Wnt/β-catenin signaling contribute to acquired DDRi drug resistance.

Epigenetic modulation of BARD1 to enhance anti-VEGF therapy

Despite the clinical use of anti-vascular endothelial growth factor (VEGF) antibodies (AVAs) in cancer therapy, resistance frequently develops, leading to disease progression. To address this, we identify a previously unknown role for breast cancer type 1 susceptibility protein (BRCA1)-associated RING domain 1 (BARD1) in modulating AVA sensitivity. Epigenetic modulation-via global and targeted DNA methylation-reveals BARD1 as a key regulator of angiogenesis. Sequential treatment with azacytidine overcomes AVA resistance in vivo. To enable precise epigenetic reactivation, we develop a liposomal CRISPR-deactivated Cas9 (dCas9)-TET1 system guided by BARD1-targeting single-guide RNAs (sgRNAs). This platform achieves CpG-specific demethylation of the BARD1 promoter, restores expression, and enhances AVA response. Additionally, BARD1 restoration, through either dCas9-TET1 or small interfering RNA (siRNA), significantly reduces tumor growth in combination with AVA in ovarian cancer models. These findings uncover a previously unrecognized function of BARD1 in tumor angiogenesis and demonstrate the potential of gene-specific epigenetic targeting to overcome AVA resistance.

In-depth proteomics analysis of sentinel lymph nodes from individuals with endometrial cancer

Endometrial cancer (EC) is one of the most common gynecological cancers worldwide. Sentinel lymph node (SLN) status could be a major prognostic factor in evaluation of EC, but several prospective studies need to be performed. Here we report an in-depth proteomics analysis showing significant variations in the SLN protein landscape in EC. We show that SLNs are correlated to each tumor grade, which strengthens evidence of SLN involvement in EC. A few proteins are overexpressed specifically at each EC tumor grade and in the corresponding SLN. These proteins, which are significantly variable in both locations, should be considered potential markers of overall survival. Five major proteins for EC and SLN (PRSS3, PTX3, ASS1, ALDH2, and ANXA1) were identified in large-scale proteomics and validated by immunohistochemistry. This study improves stratification and diagnosis of individuals with EC as a result of proteomics profiling of SLNs.

Spatial tumor evolution panorama of ovarian cancer

Ovarian cancer is an aggressive disease characterized by intraperitoneal dissemination and a distinctive microenvironment. By generating metastatic cohorts encompassing approximately 60 pairs of whole-genome and RNA sequencing, 100 single-cell samples, and 2.5 million spatial transcriptomics (ST) spots, we delineate site-specific tumor-host colocalization patterns. Utilizing our STARLETS framework, we elucidate a Darwinian evolutionary trajectory in which hypoxia and immune pressures select for clones that eventually metastasize. High-resolution ST and ultimate dimensional imaging of solvent-cleared organs (uDISCO) imaging further identify a tripartite ensemble comprising MMP11

Spatial and temporal intra-tumoral heterogeneity in advanced HGSOC: Implications for surgical and clinical outcomes

Limited evidence exists on the impact of spatial and temporal heterogeneity of high-grade serous ovarian cancer (HGSOC) on tumor evolution, clinical outcomes, and surgical operability. We perform systematic multi-site tumor mapping at presentation and matched relapse from 49 high-tumor-burden patients, operated up front. From SNP array-derived copy-number data, we categorize dendrograms representing tumor clonal evolution as sympodial or dichotomous, noting most chemo-resistant patients favor simpler sympodial evolution. Three distinct tumor evolutionary patterns from primary to relapse are identified, demonstrating recurrent disease may emerge from pre-existing or newly detected clones. Crucially, we identify spatial heterogeneity for clinically actionable homologous recombination deficiency scores and for poor prognosis biomarkers CCNE1 and MYC. Copy-number signature, phenotypic, proteomic, and proliferative-index heterogeneity further highlight HGSOC complexity. This study explores HGSOC evolution and dissemination across space and time, its impact on optimal surgical cytoreductive effort and clinical outcomes, and its consequences for clinical decision-making.

iPSC-derived hypoimmunogenic tissue resident memory T cells mediate robust anti-tumor activity against cervical cancer

Functionally rejuvenated human papilloma virus-specific cytotoxic T lymphocytes (HPV-rejTs) generated from induced pluripotent stem cells robustly suppress cervical cancer. However, autologous rejT generation is time consuming, leading to difficulty in treating patients with advanced cancer. Although use of allogeneic HPV-rejTs can obviate this, the major obstacle is rejection by the patient immune system. To overcome this, we develop HLA-A24&-E dual integrated HPV-rejTs after erasing HLA class I antigens. These rejTs effectively suppress recipient immune rejection while maintaining more robust cytotoxicity than original cytotoxic T lymphocytes. Single-cell RNA sequencing performed to gain deeper insights reveal that HPV-rejTs are highly enriched with tissue resident memory T cells, which enhance cytotoxicity against cervical cancer through TGFβR signaling, with increased CD103 expression. Genes associated with the immunological synapse also are upregulated, suggesting that these features promote stronger activation of T cell receptor (TCR) and increased TCR-mediated target cell death. We believe that our work will contribute to feasible "off-the-shelf" T cell therapy with robust anti-cervical cancer effects.

ZNF671 methylation test in cervical scrapings for cervical intraepithelial neoplasia grade 3 and cervical cancer detection

Effective triage of high-risk human papillomavirus (hrHPV)+ women is warranted to avoid unnecessary referral and overtreatment. Molecular triage tests have recently begun to impact cervical intraepithelial neoplasia grade 3 (CIN3) or cervical cancer (CC), termed CIN3+, detection. We find that zinc finger protein 671 methylation (ZNF671

Screening a living biobank identifies cabazitaxel as a strategy to combat acquired taxol resistance in high-grade serous ovarian cancer

The anti-mitotic agent taxol (paclitaxel) remains a cornerstone of ovarian cancer treatment. To tackle drug resistance and toxicity, second-generation targeted anti-mitotic agents and combination strategies are being explored but have yet to demonstrate meaningful clinical benefits. A limitation is the lack of a platform to compare strategies in models that capture disease heterogeneity. To overcome this, we screen 83 patient-derived ex vivo ovarian cancer models that exhibit extensive intra- and inter-patient heterogeneity, testing four distinct approaches to enhance taxol sensitivity. Inhibitors of the HSET kinesin or the Mps1 spindle assembly checkpoint kinase show minimal impact on the taxol sensitivity landscape. By contrast, Bcl-xL inhibition exerts a global anti-proliferative effect. Inhibition of the MDR1 drug efflux pump restores taxol sensitivity in models characterized by ABCB1 overexpression. These MDR1-driven resistant models also respond to cabazitaxel, which is a poor MDR1 substrate, highlighting a potential therapeutic option for ovarian cancers with acquired taxol resistance.

Quantifiable TCR repertoire changes in prediagnostic blood specimens among patients with high-grade ovarian cancer

High-grade ovarian cancer (HGOC) is a major cause of death in women. Early detection of HGOC usually leads to a cure, yet it remains a clinical challenge with over 90% HGOCs diagnosed at advanced stages. This is mainly because conventional biomarkers are not sensitive enough to detect the microscopic yet metastatic early lesions. In this study, we sequence the blood T cell receptor (TCR) repertoires of 466 patients with ovarian cancer and controls and systematically investigate the immune repertoire signatures in HGOCs. We observe quantifiable changes of selected TCRs in HGOCs that are reproducible in multiple independent cohorts. Importantly, these changes are stronger during stage I. Using pre-diagnostic patient blood samples from the Nurses' Health Study, we confirm that HGOC signals can be detected in the blood TCR repertoire up to 4 years preceding conventional diagnosis. Our findings may provide the basis for future immune-based HGOC early detection criteria.

WEE1i-ATRi combination therapy: a promising low-dose treatment for CCNE1-amplified gynecologic cancers

Spatially resolved proteomics identify biomarkers from endometrial sentinel lymph nodes

In this issue, Aboulouard et al. (2021) use spatially resolved shot-gun proteomics to characterize the sentinel lymph nodes (SLNs) of endometrial cancer patients at different pathological grades. They identify biomarkers that predict prognosis as well as tumor grade.

Profiling the metabolome of uterine fluid for early detection of ovarian cancer

Ovarian cancer (OC) causes high mortality in women because of ineffective biomarkers for early diagnosis. Here, we perform metabolomics analysis on an initial training set of uterine fluid from 96 gynecological patients. A seven-metabolite-marker panel consisting of vanillylmandelic acid, norepinephrine, phenylalanine, beta-alanine, tyrosine, 12-S-hydroxy-5,8,10-heptadecatrienoic acid, and crithmumdiol is established for detecting early-stage OC. The panel is further validated in an independent sample set from 123 patients, discriminating early OC from controls with an area under the curve (AUC) of 0.957 (95% confidence interval [CI], 0.894-1). Interestingly, we find elevated norepinephrine and decreased vanillylmandelic acid in most OC cells, resulting from excess 4-hydroxyestradiol that antagonizes the catabolism of norepinephrine by catechol-O-methyltransferase. Moreover, exposure to 4-hydroxyestradiol induces cellular DNA damage and genomic instability that could lead to tumorigenesis. Thus, this study not only reveals metabolic features in uterine fluid of gynecological patients but also establishes a noninvasive approach for the early diagnosis of OC.

MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images

Combining molecular classification with clinicopathologic methods improves risk assessment and chooses therapies for endometrial cancer (EC). Detecting mismatch repair (MMR) deficiencies in EC is crucial for screening Lynch syndrome and identifying immunotherapy candidates. An affordable and accessible tool is urgently needed to determine MMR status in EC patients. We introduce MMRNet, a deep convolutional neural network designed to predict MMR-deficient EC from whole-slide images stained with hematoxylin and eosin. MMRNet demonstrates strong performance, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.897, with a sensitivity of 0.628 and a specificity of 0.949 in internal cross-validation. External validation using three additional datasets results in AUROCs of 0.790, 0.807, and 0.863. Employing a human-machine fusion approach notably improves diagnostic accuracy. MMRNet presents an effective method for identifying EC cases for confirmatory MMR testing and may assist in selecting candidates for immunotherapy.

Multi-omics analysis reveals the attenuation of the interferon pathway as a driver of chemo-refractory ovarian cancer

Ovarian high-grade serous carcinoma (HGSC) is one of the deadliest gynecological malignancies, with 10%-15% of patients exhibiting primary resistance to first-line chemotherapy. To characterize the molecular drivers of chemo-refractoriness, we perform multi-omics profiling of treatment-naive biopsies from patients with refractory HGSC enrolled in the DECIDER observational trial. We demonstrate that chemo-refractory HGSC is characterized by diminished interferon type I (IFN-I) and enhanced hypoxia pathway activity, and baseline IFN-I activity in chemo-naive cancer is an independent prognostic factor. Single-cell RNA sequencing and spatial protein profiling analyses corroborate the importance of elevated IFN-I activity in response to chemotherapy. Importantly, in vitro experiments demonstrate that high levels of IFN-I signaling increase cell chemosensitivity to platinum in a cell-autonomous manner. Together, these findings indicate that the IFN-I pathway activity in HGSC cancer cells predicts response to first-line chemotherapy in HGSC, proposing the stimulation of the IFN-I response as a therapeutic strategy. The study is registered at ClinicalTrials.gov (NCT04846933).

Pan-carcinoma sialyl-Tn-targeting expands CAR therapy to solid tumors

Coordinated protein modules define DNA damage responses to carboplatin at single-cell resolution in human ovarian carcinoma models

Tubo-ovarian high-grade serous carcinoma (HGSC), the most lethal gynecologic malignancy, initially responds to platinum-based chemotherapy, but due to frequent defects in the DNA damage response (DDR), most tumors develop resistance. The molecular mechanisms underlying clinical platinum resistance remain poorly defined with no biomarkers or targeted therapies to improve outcomes. Here, applying mass cytometry, we quantify phosphorylation and abundance of DDR proteins in carboplatin-treated HGSC cell line models. Despite similar levels of intranuclear platinum, a proxy for carboplatin uptake, cells follow divergent fates, reflecting DDR heterogeneity. Unsupervised analysis reveals a continuum of DDR states, and matrix factorization identifies eight protein modules. The activity of one module, containing canonical DDR proteins, increases in carboplatin-sensitive cells. Resistant cells engage a broader DDR protein module. These findings demonstrate the ability of single-cell proteomics to identify functional DDR states and reveal a DDR sensitivity module as a promising biomarker for clinical stratification and therapeutic decisions in HGSC.

Tumor and local lymphoid tissue interaction determines prognosis in high-grade serous ovarian cancer

Tertiary lymphoid structure (TLS) is associated with prognosis in copy-number-driven tumors, including high-grade serous ovarian cancer (HGSOC), although the function of TLS and its interaction with copy-number alterations in HGSOC are not fully understood. In the current study, we confirm that TLS-high HGSOC patients show significantly better progression-free survival (PFS). We show that the presence of TLS in HGSOC tumors is associated with B cell maturation and cytotoxic tumor-specific T cell activation and proliferation. In addition, the copy-number loss of IL15 and CXCL10 may limit TLS formation in HGSOC; a list of genes that may dysregulate TLS function is also proposed. Last, a radiomics-based signature is developed to predict the presence of TLS, which independently predicts PFS in both HGSOC patients and immune checkpoint inhibitor (ICI)-treated non-small cell lung cancer (NSCLC) patients. Overall, we reveal that TLS coordinates intratumoral B cell and T cell response to HGSOC tumor, while the cancer genome evolves to counteract TLS formation and function.

UBA1 inhibition sensitizes cancer cells to PARP inhibitors

Therapeutic strategies targeting the DNA damage response, such as poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi), have revolutionized cancer treatment in tumors deficient in homologous recombination (HR). However, overcoming innate and acquired resistance to PARPi remains a significant challenge. Here, we employ a genome-wide CRISPR knockout screen and discover that the depletion of ubiquitin-activating enzyme E1 (UBA1) enhances sensitivity to PARPi in HR-proficient ovarian cancer cells. We show that silencing or pharmacological inhibition of UBA1 sensitizes multiple cell lines and organoid models to PARPi. Mechanistic studies uncover that UBA1 inhibition not only impedes HR repair to sensitize cells to PARP inhibition but also increases PARylation, which may subsequently be targeted by PARP inhibition. In vivo experiments using patient-derived xenografts demonstrate that combining PARP and UBA1 inhibition provided significant survival benefit compared to individual therapies with no detectable signs of toxicity, establishing this combination approach as a promising strategy to extend PARPi benefit.

GIInger predicts homologous recombination deficiency and patient response to PARPi treatment from shallow genomic profiles

Homologous recombination deficiency (HRD) is a predictive biomarker for poly(ADP-ribose) polymerase 1 inhibitor (PARPi) sensitivity. Routine HRD testing relies on identifying BRCA mutations, but additional HRD-positive patients can be identified by measuring genomic instability (GI), a consequence of HRD. However, the cost and complexity of available solutions hamper GI testing. We introduce a deep learning framework, GIInger, that identifies GI from HRD-induced scarring observed in low-pass whole-genome sequencing data. GIInger seamlessly integrates into standard BRCA testing workflows and yields reproducible results concordant with a reference method in a multisite study of 327 ovarian cancer samples. Applied to a BRCA wild-type enriched subgroup of 195 PAOLA-1 clinical trial patients, GIInger identified HRD-positive patients who experienced significantly extended progression-free survival when treated with PARPi. GIInger is, therefore, a cost-effective and easy-to-implement method for accurately stratifying patients with ovarian cancer for first-line PARPi treatment.

An activity-based functional test for identifying homologous recombination deficiencies across cancer types in real time

Homologous recombination (HR)-mediated DNA repair is a prerequisite for maintaining genome stability. Cancer cells displaying HR deficiency (HRD) are selectively eliminated by poly(ADP-ribose) polymerase inhibitors (PARPis). To date, sequencing of HR-associated genes and analyzing genome instability have been used as clinical predictions for PARPi therapy. However, these genetic tests cannot reflect dynamic changes in the HR status. Here, we have developed a virus- and activity-based functional assay to quantify real-time HR activity directly. Instead of focusing on a few HR-associated genes, our functional assay detects endpoint HR activity and establishes an activity threshold for identifying HRD across cancer types, validated by PARPi sensitivity and BRCA status. Notably, this fluorescence-based assay can be applied to primary ovarian cancer cells from patients to reflect their level of HRD, which is associated with survival benefits. Thus, our work provides a functional test to predict the response of primary cancer cells to PARPis.

zAvatar-test—A functional precision model to personalize ovarian cancer treatments: Results from a co-clinical study

In ovarian cancer, 80% of patients relapse after first-line therapy. In recurrent cases, oncologists lack reliable tests to guide chemotherapy choices, creating an unmet clinical need. Here, we develop the ovarian cancer zebrafish Avatar-test, a functional in vivo model using patient tumor cells implanted in zebrafish embryos to predict treatment responses. We present the largest observational study (32 patients), where the zAvatar-test achieves 91% accuracy in predicting patient outcomes. Patients with a zAvatar-sensitive-test correlate with longer progression-free survival (17 vs. 6 months). Tumors in zAvatars are dynamic, with human-host cell interactions, and higher metastatic potential in poor-prognosis cases. Finally, as a proof of concept, we demonstrate that venetoclax has the potential to sensitize multidrug-resistant tumors. Altogether, this clinical study demonstrates that the zAvatar-test may help clinicians personalize treatments for ovarian cancer patients. We are now conducting a multicentric randomized clinical trial to evaluate the zAvatar-test as a companion tool in clinical oncology.