Durvalumab and cediranib with and without olaparib in recurrent ovarian cancer: a phase II proof-of-concept study

Domain: biology
Published: 2026-03-16
Editorial outcome: major_revision

Abstract
Here we report the efficacy and translational findings of durvalumab, olaparib, and cediranib (D+O+C) and of durvalumab plus cediranib (D+C) from the recurrent ovarian cancer cohort within a single-center, multi-arm, non-randomized, multi-cohort phase I/II trial (NCT02484404). Sixty-eight patients were enrolled (39 in D+O+C, 29 in D+C). The primary endpoint was objective response rate (ORR); secondary endpoints included progression-free survival (PFS) and safety. ORR was 19.4% (95% CI, 9.5-43.5) for D+O+C and 29.6% (95% CI, 13.8-46.9) for D+C; D+C met the primary endpoint while D+O+C did not. Median PFS was 4.5 months in both arms, with four exceptional responders (PFS ≥ 12 months) per arm. Toxicity was manageable. Pre- and on-treatment biopsies and blood samples were collected for prespecified transcriptomic and immunophenotypic profiling; signature analyses and preclinical studies were conducted post hoc and were exploratory. Baseline tumors from exceptional responders and patients with clinical benefit (partial response or stable disease with PFS ≥ 4 months) demonstrated enrichment of immune activation and metabolic pathways, whereas tumors with no clinical benefit (NCB; progressive disease or stable disease with PFS < 4 months) exhibited upregulation of vascular adaptation and cytoskeletal remodeling pathways. These findings support the proof-of-concept clinical activity of D+O+C and D+C and identify molecular signatures with potential predictive value in subsets of recurrent ovarian cancer.

Introduction
Epithelial ovarian carcinoma (EOC) represents the most lethal gynecologic malignancy worldwide1. Nearly 75% of patients present with advanced disease, and more than two-thirds (~70%) experience disease recurrence within 3 years2. Standard of care (SOC) therapy for relapsed EOC involves platinum-based combination treatment (with paclitaxel, gemcitabine, or pegylated liposomal doxorubicin [PLD])3,4, with or without bevacizumab for platinum-sensitive disease5,6. In the platinum-resistant setting, bevacizumab in combination with paclitaxel, PLD, or topotecan has demonstrated improved progression-free survival (PFS) but limited overall survival (OS) benefit7. Mirvetuximab soravtansine (MIRV), a folate receptor α (FRα)-targeting antibody-drug conjugate (ADC), was the first ADC to show an OS advantage compared to SOC in this setting, but its activity is restricted to tumors with high FRα expression and high-grade serous histology8. As such, additional therapeutic options are needed for the remaining platinum-resistant EOC population. Nab-paclitaxel plus relacorilant has demonstrated PFS improvement; however, this regimen was evaluated only in bevacizumab-pretreated platinum-resistant disease and excluded patients with primary platinum-refractory EOC9. These restrictions highlight the ongoing need for alternative therapeutic strategies in relapsed EOC.

Poly(ADP-ribose) polymerase inhibitors (PARPis) have been approved by the United States Food and Drug Administration (FDA) and the European Medicines Agency (EMA) as maintenance therapy for newly diagnosed EOC responding to platinum-based chemotherapy10,11. However, their use in relapsed settings has diminished12, as regulatory agencies have restricted later-line use due to the lack of long-term survival benefit observed in the SOLO-3, ARIEL4, and QUADRA trials13. Although these studies demonstrated improvements in PFS, their inability to generate durable responses highlights the limitations of PARPi monotherapy in recurrent EOC and reinforces the need for rational combination strategies.

Immune checkpoint inhibitors (ICIs) targeting the programmed death-ligand 1 (PD-L1)/programmed cell death protein 1 (PD-1) axis have been evaluated in EOC, yet monotherapy has not demonstrated superiority over SOC treatments14,15. The limited activity reflects the profoundly immunosuppressive ovarian tumor microenvironment16 and suggests the need for combination approaches. Recently, data from the ENGOT-ov65/KEYNOTE-B96 phase III study showed that adding pembrolizumab to weekly paclitaxel, with or without bevacizumab, significantly improved both PFS and OS in patients with platinum-resistant EOC17, providing clinical evidence that ICIs can be effective when paired with an appropriate therapeutic backbone.

Preclinical studies indicate that PARPis may enhance tumor immunogenicity by increasing cytotoxic T-cell infiltration18, activating the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway19, and promoting the accumulation of unrepaired DNA damage that recruits antigen-presenting cells20. Anti-angiogenic therapy further contributes to immune remodeling by reducing immunosuppressive cell populations within the EOC microenvironment21. Together, these mechanisms suggest that combining PARPi and anti-angiogenic therapy may create a tumor milieu more permissive to immune checkpoint blockade and thereby augment the antitumor activity of ICIs.

Early-phase clinical trials combining the vascular endothelial growth factor receptor 1-3 (VEGFR1-3) inhibitor cediranib with the PARPi olaparib demonstrated activity in a subset of patients with platinum-resistant EOC22. A subsequent phase I study incorporating durvalumab into olaparib and cediranib combination confirmed the tolerability of the triplet regimen and suggested preliminary clinical activity in recurrent EOC23. However, the multicenter phase II trial (NRG-GY023; NCT04739800) did not demonstrate superior activity of the triplet regimen compared with SOC (median PFS, 8.3 months versus 7.5 months)24. Notably, a subset of patients receiving ICI plus cediranib, with or without olaparib, achieved exceptional responses (PFS ≥ 12 months), including three patients (6.8%) in the durvalumab, olaparib, and cediranib (D+O+C) arm and two (4.8%) in the durvalumab plus cediranib (D+C) arm. However, this trial lacked translational analyses to characterize these responders24.

In this study, we present clinical outcomes and translational findings from the recurrent EOC cohort within a single-center, multi-arm, non-randomized, multi-cohort phase I/II trial (NCT02484404). We performed transcriptomic profiling of pretreatment biopsies and immunophenotypic analysis of serial blood samples, integrated with preclinical validation. Using parallel, treatment arm-specific analyses, we identified molecular signatures associated with treatment response (immune-primed and immunometabolic axes) versus resistance (alternative vascularization and cytoskeletal plasticity) in anti-VEGF/ICI ± PARPi regimens. These exploratory, hypothesis-generating findings suggest the potential of integrating immune, metabolic, and cytoskeletal profiling to guide biomarker-driven treatment strategies in recurrent EOC.

Results
Patient enrollment and baseline characteristics
Between September 2016 and August 2024, 68 recurrent ovarian cancer patients were enrolled and received at least one dose of treatment (D+O+C arm: 39 patients enrolled between November 2017 and August 2024; D+C arm: 29 patients enrolled between September 2016 and May 2023) (Fig. 1a-b). Baseline characteristics of each arm are detailed in Supplementary Data 1. Briefly, the D+O+C arm enrolled patients with EOC histology (high-grade serous [87.2%] and clear cell [12.8%]). The majority of patients (~85%) had BRCA wild-type and platinum-resistant or primary platinum-refractory disease. Also, a majority (~80%) had received prior bevacizumab. Previously, we reported the findings of the durvalumab plus olaparib (D+O) arm, which had similar baseline clinical characteristics, except for a lower rate of prior bevacizumab exposure (46%)25.

The D+C arm also enrolled patients with mostly EOC histology (high-grade serous [72.4%], clear cell [6.9%], and high-grade endometrioid [3.4%]). 86.2% of patients were BRCA wild-type, and 69% of participants presented with platinum-resistant or primary platinum-refractory disease. About 59% of patients had received prior bevacizumab. The median number of prior treatments was three for both the D+O+C and D+C arms, representing a heavily pretreated population.

Efficacy and safety
For the D+O+C arm, 7 of 36 (19.4%, 95% confidence interval [CI]: 9.5-43.5) Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1)-evaluable patients achieved partial response (PR), including one unconfirmed PR, and 26 patients (72.2%) had stable disease (SD) as their best overall response (Fig. 1b). In the D+C arm, 8 of 27 (29.6%, 95% CI, 13.8-46.9) RECIST-evaluable patients achieved PR, including two unconfirmed PRs, and 13 (48.1%) had SD (Fig. 1b). Per the prespecified Simon two-stage design26, the 7 PRs observed in D+O+C did not meet the ≥ 11-response threshold, whereas the 8 PRs observed in D+C exceeded the ≥ 6-response requirement, indicating promising activity despite under-enrollment.

The clinical benefit (CB, PR or SD ≥ 4 months) rate was 66.7 % (7 PRs and 17 SD ≥ 4 months; 24/36, 95 % CI: 49.0-81.4) in the D+O+C arm, and 59.3% (8 PRs and 8 SD ≥ 4 months; 16/27, 95% CI, 38.8-77.6) in the D+C arm (Fig. 2a). Waterfall plots summarizing best tumor response in RECIST-evaluable patients are shown in Fig. 2a.

PRs were observed in both treatment arms irrespective of platinum sensitivity or prior bevacizumab exposure. Of the seven participants with PRs in the D+O+C arm, four had primary platinum-resistant disease and three were platinum-sensitive. All seven PRs occurred in bevacizumab-pretreated patients. In the D+C arm, four were platinum-resistant (primary platinum-resistant [n = 1] and secondary platinum-resistant [n = 3]), one was primary platinum-refractory, and three were platinum-sensitive. Two PRs were observed in bevacizumab-pretreated patients.

In the intention-to-treat population, the median PFS was 4.5 months (CI: 3.8-6.1) for the D+O+C arm (n = 39), and 4.5 months (CI: 3.6-8.0) for the D+C arm (n = 29) (Fig. 2b). At the time of data cut-off (May 13, 2025), there were two patients with ongoing responses (17.9+ months [PR] in the D+O+C arm and 24.0+ months [PR] in the D+C arm). Notably, there were four exceptional responders (defined as those with PFS ≥ 12 months) in the D+O+C arm (45.4 months [PR], 18.4 months [SD], 17.9 months [PR], and 13.6 months [SD]) and four in the D+C arm (24.0 months [PR], 23.5 months [PR], 13.2 months [SD], and 12.9 months [PR]). PFS for each patient is illustrated in Fig. 2b.

Treatment-related adverse events (TRAEs) occurring in ≥ 10% of patients are presented in Supplementary Data 2. Consistent with prior reports24, the most common TRAEs observed in both treatment arms were hematologic and gastrointestinal. The most frequent all-grade TRAEs were anemia (79.5%, 31/39) in the D+O+C arm and diarrhea (58.6%, 17/29) in the D+C arm.

Upregulation of immune and metabolic pathways in patients with clinical benefit
To identify transcriptomic characteristics of patient tumors with CB, we first analyzed prespecified RNA sequencing (RNAseq) data from pretreatment fresh biopsy samples. Post-hoc exploratory analyses using Gene Set Enrichment Analysis (GSEA) were performed to investigate pathways that may have contributed to treatment response. In both treatment arms, tumors from the CB group (D+O+C [n = 21], D+C [n = 14]) exhibited significant upregulation of interferon-alpha (IFN-α) response compared with tumors from no clinical benefit (NCB; PD or SD < 4 months) group (adjusted p [padj] = 0.002 in D+O+C and 0.014 in D+C; Fig. 3a and Supplementary Data 3-4). The CB group from both treatment arms also showed enrichment of multiple metabolism-related pathways (all padj < 0.01, Fig. 3a). Of note, no individual gene was significantly associated with clinical outcome in either treatment arm based on the differential gene expression analysis after multiple testing adjustment (padj = 0.77-1; Supplementary Data 5-6).

Distinct immune and metabolic signatures in exceptional responders
In a post-hoc exploratory analysis, we investigated whether this pattern was also found in tumors of exceptional responders. Compared to baseline transcriptomes of the NCB group (n = 10), D+O+C exceptional responders (n = 4) showed enrichment of immune pathways (all padj < 0.05) but without accompanying metabolic activation (Fig. 3b and Supplementary Data 7). In contrast, tumors from D+C exceptional responders (n = 3) had concurrent enrichment of immune (all padj < 0.05) and metabolic (all padj < 0.01) pathways relative to NCB tumors (n = 10; Fig. 3b and Supplementary Data 8).

Next, we used a post-hoc exploratory single-sample scoring analysis (singscore27) to quantify immune (Supplementary Data 9-10) or metabolic activity (Supplementary Data 11-12) in individual tumors. In the D+O+C arm, tumors from CB patients, including exceptional responders, had significantly higher immune scores than NCB tumors (Wilcoxon p = 0.02; Fig. 3c), and immune scores showed a trend and modest correlation with PFS (Spearman ρ = 0.35, p = 0.05; Fig. 3d). Exceptional responder tended to exhibit higher immune scores within the CB group. Metabolic scores showed no association with benefit or PFS in D+O+C (Fig. 3e-f). In contrast, in the D+C arm, CB tumors had higher metabolic scores than NCB tumors (Wilcoxon p = 0.018; Fig. 3e), and metabolic scores showed a modest correlation with PFS (Spearman ρ = 0.40, p = 0.05; Fig. 3f). Immune scores were not associated with benefit in D+C (Fig. 3c-d). While exceptional responders again had the highest metabolic scores, these correlations appear to be driven in part by these top-performing cases.

Immune phenotype is also identified in an independent clinical trial dataset
In a post-hoc exploratory analysis of an independent dataset from Rosario et al.28, we examined pretreatment RNAseq from a phase II trial of ICI (pembrolizumab), VEGF blockade (bevacizumab), and DNA damage response inhibitor (metronomic cyclophosphamide) combination therapy for ovarian cancer (GSE206422; NCT0285331828). Using the same CB (n = 21)/NCB (n = 6) definition as in this study, we found CB tumors had higher immune scores (Wilcoxon p = 0.03; Fig. 3c) which positively correlated with PFS (Spearman ρ = 0.45, p = 0.017; Fig. 3d) in response to pembrolizumab+bevacizumab+metronomic cyclophosphamide, suggesting that the immune signature may have predictive relevance across the datasets. In contrast, metabolic scores showed no significant correlation with CB or PFS in this external cohort (Fig. 3e-f), consistent with our findings from the inhouse data.

Genomic profiling reveals no association between somatic alterations and treatment response
We next asked whether genomic alterations at baseline were associated with treatment outcomes. In a prespecified whole-exome sequencing (WES) correlative analysis of pretreatment tumor biopsies, TP53 mutations were identified in the majority of cases in both D+O+C and D+C arms (Supplementary Fig. 1a-b). We examined DNA repair-related pathways and tumor mutational burden (TMB) because homologous recombination deficiency (HRD) is linked to elevated immune activity29, and high TMB is also associated with improved survival and increased CD8⁺ T-cell infiltration in ovarian cancer30. TMB was uniformly low (< 10 mutations/Mb) for all patients across the treatment arms (Supplementary Fig. 1). In D+O+C, a CB patient carried a pathogenic MSH2 nonsense mutation (Supplementary Fig. 1, top). One NCB patient in D+O+C carried a pathogenic PIK3CA missense mutation and one NCB patient had a KRAS nonsense mutation in D+C (Supplementary Fig. 1). Overall, no consistent associations between baseline genomic alterations and CB were observed.

Treatment-induced immune activation in patients with clinical benefit
Given the distinct baseline transcriptional patterns, we performed a prespecified analysis of paired pre- and on-treatment RNAseq data to assess treatment-induced transcriptional changes in the D+O+C (n = 11) and D+C (n = 9) cohorts. In the D+O+C arm, post-hoc exploratory GSEA revealed that both CB (n = 9) and NCB (n = 2) tumors exhibited enrichment of immune activation pathways after treatment (Fig. 4a, Supplementary Data 13). However, only CB tumors demonstrated coordinated upregulation of specific immune activation genes, such as complement components, Fc receptors, and phagocytosis-related genes, at the transcriptional level (Fig. 4b, Supplementary Data 14). Notably, post-treatment IFN-α pathway enrichment was observed in both CB and NCB groups (Fig. 4a), whereas elevated baseline IFN-α signaling was associated with CB (Fig. 3a-b). Conversely, NCB tumors failed to show corresponding increases in individual immune effector transcripts despite pathway-level enrichment (Fig. 4b). Instead, these tumors shifted toward developmental and stromal programs, characterized by enrichment in angiogenesis and extracellular matrix (ECM) pathways (Fig. 4a, Supplementary Data 15).

In the D+C arm, CB tumors (n = 6) showed concurrent upregulation of immune and stromal remodeling pathways alongside downregulation of mitotic spindle and DNA repair programs, reflecting immune engagement coupled with suppressed proliferation (Fig. 4c, Supplementary Data 16). At the gene level, CB tumors exhibited increased expression of immune, angiogenesis, and ECM-related genes, while cell cycle and DNA repair genes were downregulated (Fig. 4d, Supplementary Data 17). While certain metabolic and immune pathway shifts occurred in both groups, NCB tumors (n = 3) uniquely displayed increased glycolysis alongside reduced fatty acid metabolism and adipogenesis (Fig. 4c). Similar to the D+O+C arm, NCB tumors in the D+C arm exhibited minimal gene-level changes in immune-related transcriptomic data despite pathway-level enrichment (Fig. 4d, Supplementary Data 18). Collectively, these data suggest that while immune pathway enrichment may be a general treatment effect, the lack of coordinated, gene-specific activation in NCB tumors may have contributed to their lack of clinical response.

Peripheral immune cells and circulating tumor cells (CTCs)
As part of the prespecified peripheral immune monitoring, we evaluated the dynamic changes of circulating immune cells and CTCs following treatment with D+O+C (n = 36) or D+C (n = 26). Monocytic myeloid-derived suppressor cells (M-MDSC) significantly decreased by cycle 1 day 15 (C1D15) in CB groups of both arms (p < 0.001 in D+O+C and 0.04 in D+C), while a non-significant downward trend was observed in NCB groups (p = 0.1055 in D+O+C and 0.2656 in D+C; Fig. 5a). Polymorphonuclear MDSC (PMN-MDSC) levels were comparable across groups, although baseline levels were higher in NCB versus CB groups in the D+O+C arm (p = 0.02; Fig. 5b). Treatment with D+O+C induced systemic reduction in activated and proliferating CD8⁺ and CD4⁺ T cells across CB and NCB groups (p < 0.05; Fig. 5c-d) and increase in CD56+CD16+ natural killer (NK) cells in the CB group (p = 0.04; Fig. 5e). Lastly, CTC levels did not demonstrate significant changes over time and were not associated with PFS (Supplementary Fig. 2a-b).

An 18-gene NCB signature shared across treatment arms reflects vascular remodeling and cytoskeletal plasticity and is associated with clinical outcome
To develop biomarkers identifying patients at risk of treatment failure, we compared baseline transcriptomes of NCB versus CB tumors. In NCB tumors, post-hoc exploratory GSEA revealed upregulation of developmental morphogenesis and vascular adaptation signatures (all padj < 0.05; Fig. 6a and Supplementary Data 3-4). These included VEGF-driven angiogenic processes alongside non-VEGF morphogenic programs, including WNT/β-catenin, TGF-β, Notch, and Hedgehog signaling and epithelial-mesenchymal transition (padj < 0.0001).

NCB tumors also showed upregulation of cytoskeletal organization and microtubule dynamics pathways (Fig. 6a and Supplementary Data 3-4), indicating enhanced motility and cellular plasticity31. Arm-specific enrichment included DNA repair, cell cycle, and replication stress response pathways in D+O+C, and angiogenesis and actin dynamics pathways in D+C (all padj < 0.05, Fig. 6a and Supplementary Data 3-4).

In a post-hoc exploratory effort to derive a compact resistance biomarker, we intersected the leading-edge genes from the cell morphogenesis involved in differentiation and microtubule cytoskeleton organization pathway recurrently enriched in NCB tumors across both the D+O+C and D+C arms (Fig. 6a and Supplementary Data 19). This yielded an 18-gene NCB signature (Supplementary Data 20). NCB scores were significantly higher in NCB than in CB tumors within each arm (all p < 0.05; Fig. 6b), and a similar trend was observed in an external dataset from patients treated with pembrolizumab, bevacizumab, and metronomic cyclophosphamide (GSE206422, Fig. 6b). Higher NCB scores correlated with shorter PFS in both arms and the external dataset (ρ ≤ –0.4, p < 0.05; Fig. 6c), and patients with high NCB scores consistently experienced worse outcomes (Fig. 6d).

We also performed post-hoc stratified analysis within the platinum-resistant subpopulation to ensure independence from prior platinum response. The predictive power of all signatures was sustained in this subgroup (Supplementary Data 21 and Supplementary Fig. 3a-c). The NCB signature achieved high accuracy in identifying NCB in platinum-resistant D+C (AUC = 0.900, p = 0.005) and D+O+C (AUC = 0.806, p = 0.009), with a consistent trend in GSE206422 (AUC = 0.714, p = 0.207). The immune signature maintained robust performance in predicting CB in D+O+C (AUC = 0.756, p = 0.031) and GSE206422 (AUC = 0.810, p = 0.033), while the metabolic signature showed strong predictive value in D+C (AUC = 0.843, p = 0.019), suggesting that these signatures may retain predictive potential in platinum-resistant disease.

Microtubule-Associated Protein 2 (MAP2) emerges as a recurrent NCB-associated gene and exploratory marker of resistance
MAP2 was the only gene consistently upregulated in NCB tumors across both our cohort and the GSE206422 dataset (p < 0.05, Fig. 7a). In post-hoc exploratory analysis, high MAP2 expression was associated with shorter PFS in the D+O+C arm (median PFS 4.0 vs. 6.1 months; log-rank p = 0.01) and the GSE206422 dataset (median PFS 4.1 vs. 8.8 months; log-rank p = 0.04; Fig. 7b). MAP2 encodes a microtubule-associated protein involved in cytoskeleton organization32 and has been linked to drug resistance in glioma cells33. Its recurrent upregulation in NCB tumors suggests that MAP2 may contribute to cytoskeletal remodeling and cellular plasticity to bypass treatment-induced stress.

To examine MAP2-associated phenotypes, we evaluated protein levels and performed siRNA-mediated knockdown (Fig. 7c-d) using ovarian cancer cell lines with varying MAP2 expression, i.e., OVCAR3 and OVCAR8 (MAP2-high) and PEO1 (MAP2-low). In cancer cell-only cultures, MAP2 depletion substantially reduced tumor cell growth across all treatment conditions in MAP2-high cell lines, with the most pronounced effects observed under D+O+C treatment (Fig. 7e). In contrast, MAP2 knockdown had minimal impact on MAP2-low PEO1 growth, which remained primarily responsive to drug exposure (Fig. 7e).

In CD8⁺ T-cell co-cultures, T cells contributed additional cytotoxicity beyond the tumor-intrinsic effects (Fig. 7f). MAP2 knockdown enhanced T-cell–mediated killing in MAP2-high cell lines treated with D+O+C, but not in MAP2-low PEO1 (Fig. 7f). Collectively, these results suggest that MAP2 may contribute to resistance through both tumor-intrinsic mechanisms and modulation of immune susceptibility, with the former representing the dominant effect in our in vitro models.

Methods
Clinical trial
Study design and participants
Between September 2016 and August 2024, 68 patients were enrolled in this study. The study was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines and was approved by the Institutional Review Board of the Center for Cancer Research, National Cancer Institute. All participants provided written informed consent for treatment and research use of clinical samples. This study describes the phase II EOC cohort of an open-label, multi-cohort, multi-arm, single-center phase I/II study (NCT02484404, https://clinicaltrials.gov/study/NCT02484404?tab=study, date of study registration on clinicalTrials.gov: June 29, 2015)23,47,48. Patients enrolled were  18 years of age with histologically or cytologically confirmed recurrent ovarian, fallopian tube, or primary peritoneal cancer able to undergo fresh pre-treatment core biopsies. Other eligibility criteria are detailed in the study protocol (Supplementary Information). Patient were assigned to the D+O+C or D+C arm, as per investigator’s discretion based on prior treatment history.

Clinical trial procedures
Patients in the D+O+C and D+C arms received durvalumab 1,500 mg intravenously every 4 weeks, in combination with cediranib 20 mg orally once daily, administered on a 5-days-on/2-days-off schedule with or without olaparib 300 mg orally twice daily. Treatment was given in 28-day cycles and continued until disease progression, unacceptable toxicity, or withdrawal of consent. Serial blood samples were collected at baseline, C1D15, cycle 3 day 1 (C3D1), and at disease progression. Mandatory fresh core biopsies were obtained at baseline (within 24-48 hours prior to C1D1), and optional on-treatment tumor biopsies prior to C1D15, C3D1 and at progression. Tumor core biopsies collected during treatment were obtained from the same lesion as the baseline biopsy whenever feasible, or otherwise from the same organ if re-sampling of the original lesion was not possible. Radiologic assessments were conducted at baseline and every 2 cycles (+/- 1 week) using CT or MRI. Tumor responses were evaluated according to investigator-assessed RECIST v1.1 criteria. Patients were considered RECIST-evaluable for treatment response if they completed at least one post-treatment imaging assessment. Adverse events were recorded at each study visit and graded per Common Terminology Criteria for Adverse Events version 4.0 (CTCAE v4.0). Safety analyses included all patients who received at least one dose of study treatment.

Study objectives and endpoints
The primary objective was to evaluate efficacy, with the primary endpoint defined as objective response rate (ORR). ORR was the proportion of patients achieving a complete response (CR) or a PR per investigator-assessed RECIST v1.1; both confirmed and unconfirmed PRs were included, with confirmation requiring a second scan performed at least four weeks after the initial PR. Secondary objectives included PFS, safety, and tolerability, accessed using NCI CTCAE v4.0. PFS was defined as the time from enrollment to the first documentation of disease progression (radiographic or clinical, as assessed by investigators) or death.

Prespecified translational endpoints included WES and RNAseq of tumor biopsy samples, immune cell subset profiling, and CTC analysis from peripheral blood. Post-hoc exploratory analyses and mechanistic studies, including signature scoring, external dataset evaluation (GSE20642228), and MAP2 functional studies, were performed. Each arm was analyzed independently to identify biological correlates of response and resistance.

CB was defined as PR or SD lasting ≥ 4 months, aligning with the median PFS of approximately 4 months observed in this study. NCB was defined as PD or SD lasting < 4 months. Exceptional responders (defined as PFS ≥ 12 months) was not prespecified in the clinical protocol and were defined retrospectively to highlight patients with markedly prolonged benefit, using the ≥12-month benchmark consistent with the NRG-GY023 trial34.

Samples collection
Pre-treatment biopsies for genomic and transcriptomic profiling were available for 31 patients in the D+O+C arm (21 CB including 4 exceptional responders, and 10 NCB) and 24 in the D+C arm (14 CB including 3 exceptional responders, and 10 NCB); one additional exceptional responder in the D+C arm was not biopsied due to safety concerns. Paired pre- and on-treatment biopsies were available for 11 and 9 patients in the D+O+C and D+C arms, respectively. Biopsies were not obtained from five patients in the D+O+C arm for the following reasons: three patients were unable to undergo biopsy due to the restriction on research biopsy during COVID-19, one had tissue of insufficient quality for DNA/RNA extraction, and one withdrew consent prior to the scheduled procedure. In the D+C arm, biopsies were not obtained from four patients: one was unable to undergo biopsy due to elevated partial thromboplastin time, two were unable to undergo biopsy due to the restriction on research biopsy during COVID-19 and one had the procedure cancelled with no safely accessible target lesions on imaging.

WES analysis
WES was performed on genomic DNA extracted from tumor biopsy specimens and matched normal samples. DNA libraries were prepared using the Agilent SureSelect XT-Low Human All Exon V8 kit (Agilent Technologies, CA, USA) and sequenced on the Illumina NovaSeq X plus platform in 150-bp paired-end mode. Raw sequencing data were processed using the Illumina DRAGEN Bio-IT Platform (v4.0). Reads were aligned to the human reference genome GRCh38 (hg38). The sequencing achieved a high mean target coverage ranging from 95x to 599x, with more than 90% of the target regions covered at  20 x. For all samples, the Q30 score remained above 87%, and approximately 98% of reads were successfully mapped to the reference genome.

Somatic single nucleotide variants and small insertions/deletions (Indels) were identified using the DRAGEN somatic pipeline in tumor-normal matched mode for 55 cases. Germline variants were identified for 55 normal samples using the DRAGEN joint genotyping mode. Tumor mutational burden (TMB) was defined as the number of non-synonymous somatic mutations per megabase of the captured exome. To achieve maximum precision in TMB normalization, the effective capture size was empirically determined by calculating the total length of the genomic intervals defined in the S33266340_Covered_hg38_v8.bed file. The precise capture size was determined to be 41.62 Mb. TMB was calculated by dividing the total count of filtered non-synonymous somatic mutations by this effective capture size.

RNAseq analysis
RNAseq was performed on tumor biopsies to characterize gene expression profiles. RNA samples were pooled and sequenced on NovaSeq Xplus 1.5B using Illumina® Stranded Total RNA Prep with Ribo-Zero Plus and paired-end sequencing. The samples have 122 to 190 million pass filter reads with more than 92% of bases above the quality score of Q30. Reads of the samples were trimmed for adapters and low-quality bases using Cutadapt before alignment with the reference genome (hg38) and the annotated transcripts using STAR. The average mapping rate of all samples is 93%. Unique alignment is above 47%. There are 3.55 to 49.58% unmapped reads. The mapping statistics are calculated using Picard software. The samples have 4.29% ribosomal bases. Percent coding bases are between 8-50%. Percent UTR bases are 13-31%, and mRNA bases are between 21-77% for all the samples. Library complexity is measured in terms of unique fragments in the mapped reads using Picard’s MarkDuplicate utility. The samples have 7-76% non-duplicate reads. In addition, the gene expression quantification analysis was performed for all samples using STAR/RSEM tools. Differential gene expression (DEG) analysis was conducted using the limma-voom pipeline implemented in NIH Integrated Data Analysis Platform (NIDAP). GSEA analysis using the Molecular Signature Database (MSigDB) hallmark and gene ontology biological process (GoBP) gene set collections49 was done by GSEA software (v4.4.0). Functional enrichment of DEG lists (Supplementary Data 10 and 12) was performed using STRING v12.0 (https://string-db.org/).

Single-sample gene set scoring
Single-sample gene set scoring was performed using pre-treatment RNAseq data. In D+O+C, 20 genes annotated to the GO term positive regulation of immune response were selected as the immune signature (Supplementary Data 10), while in D+C, 13 genes annotated to sterol metabolic process were used as the metabolic signature (Supplementary Data 12). Shared pathways enriched in NCB tumors across both treatment arms were identified by intersecting significant gene sets. Leading-edge subsets from each arm were extracted for the overlapping pathways in cell morphogenesis and microtubule cytoskeleton organization. Intersection of leading-edge genes across arms yielded an 18-gene NCB core signature (Supplementary Data 20). We quantified the relative activity of immune and metabolic gene signatures using the singscore27 R/Bioconductor package (v1.29.0). Resulting signature scores were merged with clinical metadata, including CB/NCB status and PFS.

In the external dataset (GSE20642228), raw read counts were downloaded from the Gene Expression Omnibus (GEO) and normalized using the variance-stabilizing transformation in DESeq2. Clinical data were extracted from the associated publication, including PFS and best response per RECIST. Variance-stabilized expression values for each signature gene were z-scored across samples and averaged to compute a per-sample immune signature, metabolism signature, and NCB signature scores. Samples were stratified by CB and NCB using the same thresholds as in our in-house analysis.

Immune cell subset analysis
For immune cell subset analysis, peripheral blood samples were collected in BD Vacutainer® CPT™ Sodium Citrate tubes at baseline (pre-treatment), C1D15, C3D1, and at disease progression. PBMCs were isolated from whole blood by centrifugation and viably frozen until analysis. On the day of analysis, frozen PBMCs were thawed and washed with PBS containing 0.5% BSA and 2.5 mM EDTA, followed by incubation with Fc receptor blocking reagent (#130-059-901, Miltenyi Biotec, Gaithersburg, MD, USA) used at 20 µL per 1 × 10⁷ cells (or fewer), according to the manufacturer’s instructions, and 1.0 l LIVE/DEAD Fixable Aqua viability dye (#L34966, Life Technologies). Thereafter, samples were immunostained with monoclonal antibodies against immune subset specific surface markers (Supplementary Data 22) for 20 minutes at 4 °C in the dark. Intracellular staining was performed following fixing and permeabilization using Invitrogen Fix-perm buffer (#Cat-00-5523-00). All analyses were performed using multiparametric flow cytometry (MACSQuant; Miltenyi Biotec). Data were analyzed using FlowJo software v.10.6.1 (FlowJo, LLC, OR, USA). Cells were sequentially gated on predefined immune cell subsets (Supplementary Figs. 4-6) and further characterized based on functional marker expression (Supplementary Data 23).

CTC analysis
8 mL peripheral blood samples were collected at baseline (pretreatment), C1D15, C3D1, and at progression in BD Vacutainer® K2 EDTA tubes. Blood was centrifuged at 900 x g for 7 minutes and plasma was discarded. Red blood cells were lysed by incubating blood with ACK Lysing buffer (#A1049201, Thermofisher Scientific) for 7 minutes at room temperature, followed by centrifugation at 500 x g for 5 minutes. Supernatant was discarded and cells were washed with flow buffer (PBS with 0.5% BSA and 2.5 mM EDTA). Cell pellets were then incubated with 500 l Hoechst 33342 (1:3000 dilution in flow buffer) (#H3570, Hoechst 33342, Life Technologies, DC, USA) for 10 minutes at 37°C. After washing cells with flow buffer by centrifuging at 500 xg for 5 minutes, cells were incubated with 1.0 l LIVE/DEAD Fixable Aqua viability dye (#L34966, Life Technologies) and antibodies as listed in Supplementary Data 22 for 20 minutes at 4°C in the dark. Following washing with flow buffer, cells were then incubated with 20 l anti-PE magnetic beads (#130-048-801, Miltenyi Biotec) for 15 minutes at 4°C in the dark to enrich EpCAM-positive cells. Cell quantification was calculated by multiparameter flow cytometry. Viable, nucleated, EpCAM-positive, CD45-negative cells were considered as CTCs.

In vitro study
Ovarian cancer cell lines
PEO1 (BRCA2 mutation 5193C>G , #10032308–1VL) and PEO4 (BRCA2 reversion mutation, #10032309-1VL) were purchased from MilliporeSigma (Rockville, MD, USA). OVCAR3 and OVCAR8 (platinum-resistant BRCA wild-type HGSOC) were received from NCI-60 collection at the NCI Frederick (Frederick, MD, USA). PARPi-resistant derivatives included PEO1-olaR (gift from Dr. Benjamin Bitler, University of Colorado) and in-house generated PEO1-olaJR50. All cell lines were cultured in RPMI1640 with medium L-glutamine (#11875119, Life Technologies, Frederick, MD, USA) and supplemented with 10% fetal bovine serum (FBS), 1% penicillin/ streptomycin, 1 mM sodium pyruvate and 5µg/ml of insulin from bovine pancreas (#I0516, MilliporeSigma). PEO1-olaR was routinely maintained at 2 µM of olaparib while PEO1-olaJR at 20 µM of olaparib. Cells were cultured without olaparib for at least 3 days prior to experiments. All cell lines were routinely tested for Mycoplasma using MycoAlert Mycoplasma Detection Kit (#LT-07-318, Lonza, Portsmouth, NH, USA).

CD8⁺ T cell activation for in vitro T cell co-culture experiments
Primary human CD8⁺ T cells were purchased from ATCC (#PCS-800-017, Manassas, VA, USA), and cultured in RPMI-1640 supplemented with 10% FBS and recombinant human IL-2 (50 IU/mL, #200-02-50UG, ThermoFisher Scientific, Rockville, MD, USA). Cell density was adjusted to 1 × 106 cells/mL, and the culture medium was refreshed every 2 days. On day 10, the CD8+ T cells were restimulated with CD3/CD28 Dynabeads (#11161D, Thermo Fisher Scientific) at a 1:1 bead-to-cell ratio and incubated at 37°C, 5% CO2. On day 15 post-activation, the cells were used for co-culture assays.

siRNA transfection
ON-TARGETplus SMARTpool-Human of MAP2 (#L-007299-00-0005) siRNAs and Dharmafect 1 reagent (#T-2001-02) were used for gene knockdown experiments as per manufacturer’s protocol (Horizon Discovery, Lafayette, CO, USA). Non-targeting control siRNAs (#D-001810-10-20, Horizon Discovery) were used as negative control. Cells transfected with siRNA targeting MAP2 were seeded at 5×104 cells per well in 24-well plates for trypan blue cell counting. Knockdown efficiency was confirmed by immunoblotting 48 hours post-transfection. The sequences of siRNAs used in this study are listed in Supplementary Data 24.

Immunoblotting
Cells were collected for protein extraction and subjected to immunoblotting. Blots were visualized using the Licor Odyssey Imaging System. MAP2 (#4542), ECL goat anti-rabbit IgG HRP (#7074) and GAPDH (#5174) antibodies were purchased from Cell Signaling Technology (Danvers, MA, USA). Antibody dilution details are provided in Supplementary Data 22.

Drug preparation
For in vitro assays, PARPi olaparib (#S1060) was purchased from Selleck Chemicals (Houston, TX, USA). Durvalumab (#HY-P9919), cediranib (#HY-10205), human IgG1 kappa isotype control (#HY-P99001) were from MedCahemExpress (Monmouth Junction, NJ, USA). 100 mM of olaparib as well as 10 mM of cediranib were prepared as stocks in dimethyl sulfoxide (DMSO; #S-002-M, MilliporeSigma) and stored in aliquots at -80°C until use.

Cell growth assay
Cells transfected with siRNA targeting MAP2 (siMAP2) or non-targeting control siRNA (siControl) were seeded at 5 x 104 cells per well in 24-well plates. After 24-hour transfection, cells were pretreated for 24 hours with cediranib (10 µM), olaparib (10 µM), durvalumab (10 µg/mL), or their combinations. DMSO (0.01% v/v) together with human IgG1 isotype (10 µg/mL) control was used as the vehicle control. For cancer cell-only assays, cell numbers were quantified after 48 hours of drug exposure by trypan blue staining and manual cell counting to assess baseline drug responses.

For T-cell co-culture assays, activated CD8⁺ T cells were added following the 24-hour drug pretreatment at an effector-to-target (E:T) ratio of 3:1 and co-cultured for 48 hours in drug-containing medium without IL-2. After co-culture, non-adherent CD8⁺ T cells were removed by gently washing twice with PBS and collected for trypan blue staining and cell counting. T-cell–specific killing was calculated by normalizing viability in wells with T cells to the matched wells without T wells for each siRNA and drug condition.

Statistical analyses
Each arm’s design followed a Simon optimal two-stage approach26. The D+O+C arm was designed to test an ORR improvement from 20% to 40% (p0=0.20, p1=0.40) with α=0.10 and β = 0.10. In the first stage, 17 patients were to be enrolled. If ≥ 4 responses in the first 17 patients, the second stage would enroll additional 20 patients, with 11 or more responders of 37 patients (29.7%) would be considered positive for further development. Similarly, the D+C arm, the design aimed to rule out an ORR of 10% in favor of a target ORR of 30%, with a one-sided α=0.10 and β=0.10. In the first stage, 12 patients were to be enrolled. If 2 or more responses occurred in the first 12 patients, accrual would continue to a total of 35 patients. 6 or more responses out of 35 (17.1%) would be considered positive. Safety analyses included all patients. Unfortunately, the trial was stopped early due to COVID-19, slow accrual and drug supply issues, therefore approximately 90% of planned enrollment was achieved.

Given this under-recruitment, we recalculated the final decision boundaries using the conditional-error approach51, which preserved the type I error of the original two-stage design. Using the observed stage 1 responses (4/17 in D+O+C; 4/12 in D+C), we determined the minimum total number of responses required to reject the null hypothesis under the recalibrated design while maintaining the original conditional type I error. All computations were performed in R (v4.5.1) using base binomial tail probability functions (pbinom).

Descriptive statistics (median, frequency, range) were used to summarize baseline characteristics, AEs, and efficacy outcomes (ORR, DCR). ORR 95% CIs were estimated using the design-consistent method of Koyama and Chen, implemented via twostage.inference in the clinfun R package52. Median PFS was estimated by the Kaplan-Meier method, with patients without progression censored at the last follow-up on May 13, 2025.

For RNAseq data, DEG analysis using two-sided p values were derived from the moderated t distribution and adjusted for multiple testing using the Benjamini–Hochberg method (padj < 0.05 considered significant). GSEA results with padj < 0.05 and |normalized enrichment score (NES)| > 1 were deemed significant. Group means of immune, metabolic, and NCB signature scores were compared using the two-sided Wilcoxon signed rank-sum test. Spearman’s rank correlation was used to assess the association among immune, metabolism and NCB signature scores across patients. To ensure the signatures were independent of previous treatment history, we performed a stratified analysis by platinum sensitivity status. Predictive accuracy was assessed via the AUC. For the NCB signature, AUC was reported as 1 – AUC to reflect its performance in predicting lack of response. Independent predictive value was further validated using multivariate logistic regression, adjusting for platinum status. AUC values > 0.7 were interpreted as strong and > 0.8 as excellent predictive accuracy. PFS by NCB score, MAP2 level, or CTC change was estimated using the Kaplan–Meier method, with comparisons between arms analyzed by the log-rank test to derive hazard ratios and 95% CIs. Changes in immune cell subsets and CTCs were assessed using the two-sided Wilcoxon rank-sum test for unpaired samples and the two-sided Wilcoxon matched-pairs test for paired samples.

For in vitro studies, all experiments were performed in triplicate. Data were analyzed using one-way ANOVA for multiple comparison and are shown as mean ± standard deviation. The p values < 0.05 were considered significant. Statistical analyses for preclinical studies were done using GraphPad Prism v10.6.1.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Tabata and colleagues reports on a Phase II proof-of-concept study investigating the efficacy and mechanisms of two treatment regimens for recurrent ovarian cancer: durvalumab plus cediranib (D+C) and durvalumab, cediranib, and olaparib (D+O+C). The study enrolled 68 patients with platinum-resistant epithelial ovarian cancer (EOC) and analyzed clinical outcomes, WES, and transcriptomic data to understand treatment response and resistance mechanisms. ORR of 19.4% and 29.6% was observed in the DOC and DC arms, respectively. Based on transcriptomic analyses, the authors conclude that response is dependent on baseline immune activation in both arms and on metabolic activity in the DC arm.\nOverall, the manuscript is well written and I congratulate the authors on the nicely conducted study and biomarker efforts. There are several potential concerns about the mechanistic conclusions and overinterpretation of some of the data, as outlined below.\n1. Conclusions regarding correlations with PFS may be somewhat overreaching, given that correlations are not strong and appear to be driven by a small number of outliers (Fig. 3d,f)\n2. The authors note that one exceptional responder in D+C group was noted to have multiple mutations in DDR genes. However, this patient appears to have ultra-mutated tumor (possibly due to POLD1/POLE mutation), where the response is more likely to be driven by high TMB. This should be noted.\n3. Figure 4a,c: It doesn’t appear that immune upregulation in response to therapy was any different between the CB vs. NCB groups, though the text seems to suggest that CB patients were the ones that exhibited immune upregulation. This should be reconciled. Same goes for the majority of metabolic pathways described, which seem to be going in the same direction for both CB and NCB patients (with exception of NCB patients in the DOC treatment group).\n4. The conclusion that therapeutic efficacy in DC arm has a different biomarker (metabolic activity) than that in the DOC arm may be somewhat overstated. The conclusion is based on very small number of samples and is likely due to random effect rather than true biology related to the specific treatment arm (particularly since metabolic pathways were upregulated in both arms when comparing CB vs. non-CB patients and the fact that DC arm actually performed better than DOC arm). The language in abstract and manuscript claiming this should be softened. Furthermore, discussion regarding potential reasons for metabolism correlation with response is warranted. Is this biologically related to response to immunotherapy, cediranib, or both?\n5. Figure 7e: The data with MAP2 knockdown are compelling, but how much is it T-cell related vs. just the intrinsic effects of drugs on the cancer cells? It doesn’t appear that addition of T cells contributes much. Also, since the T cells were added to culture with drugs, one cannot exclude the possibility that cediranib directly impacts T cell functon(rather than making tumor more susceptible to T cell mediated killing). This should be mentioned.", "reviewer_2": "Manuscript: NCOMMS-25-76416\nIn their manuscript, “Phase II Proof-of-Concept Study of Durvalumab and Cediranib With and Without Olaparib in Recurrent Ovarian Cancer,” Tabata et al. present a single-center, multi-arm clinical and translational study that evaluates the efficacy of durvalumab-based combination therapies and investigates immune, metabolic, and cytoskeletal mechanisms associated with treatment response in platinum-resistant epithelial ovarian cancer.\nThe patient cohorts are well characterized, and the authors have assembled a unique collection of biological samples. The datasets are extensive and generally sufficient presented. However, the text is difficult to read due to poor scientific English, which limits accessibility and clarity. Although results’ significance is obvious, the manuscript needs revision before it can be recommended for publication.\nThe following concerns should be addressed:\nGeneral: The scientific English needs substantial refinement. Word choice—particularly verbs and adverbs—is often suboptimal, resulting in awkward phrasing and reduced readability.\n1. Abstract\na. Given that the study is positioned as a proof-of-concept, phase II investigation, the conclusion should clearly articulate whether proof of concept was achieved. As written, it highlights only the translational results, leaving the reader uncertain about the primary clinical outcome or validation of the study hypothesis.\nb. It is not immediately clear from the abstract that the data are based on an ovarian cancer cohort within a larger multi-cancer study; this should be more explicitly stated\n2. Introduction\na. After submission of this manuscript (NCOMMS-25-76416), Colombo et al. presented results from the KEYNOTE-B96 study during the Presidential Symposium at this year’s ESMO meeting, demonstrating that the addition of pembrolizumab to weekly paclitaxel, with or without bevacizumab, improved both progression-free and overall survival in patients with platinum-resistant ovarian cancer. Paragraph 3 on page 4 should be revised to contextualize the introduction in light of these new data.\nb. In paragraph 3 on page 4, “proof-of-concept” is used in a different context than in the Abstract, which may be confusing to the reader. A different term should be used to maintain consistency and clarity.\n3. Methods\na. Approvals:\nAll necessary approvals seem to be in place.\nb. Design:\nThe clinical protocol is well described. However, the authors do not mention the translational research endpoints, nor whether the analyses performed were preplanned or decided retrospectively.\nc. Materials:\ni. Description of the cohort: This paragraph includes considerable detail that is reiterated in the Results section. Condensing this section would improve clarity and avoid redundancy.\nii. The following information is missing from the main document\n– What types of blood samples were collected?\n– Were the different biopsies obtained from the same lesion in each patient?\nd. Methods:\ni. The section would benefit from improved clarity and completeness. Many abbreviations are introduced without definition, which interrupts readability and makes it difficult to follow the methodological flow. All abbreviations (e.g., CB, NCB) should be written out in full at first mention in both the main text and figure legends.\nii. Definition of response groups: The categories “clinical benefit (CB),” “no clinical benefit (NCB),” and “exceptional responders” should be explicitly defined in the Materials and Methods section. There is also an inconsistency in the reported number of exceptional responders (three in line 215 versus four in line 188) that should be clarified.\niii. In the paragraph “Single-sample gene set scoring,” essential information about the analyzed samples and the analytical setup (with reference to the Supplementary Material) is missing. In the current version, it is difficult to understand which data were included in the analysis and how they were selected.\niv. Multiple analytical approaches were applied to the genomic data. Given that these analyses form the core of the Results section, a more detailed description should be provided here to ensure clarity and context. E.g. Gene set scoring: In the paragraph describing single-sample gene set scoring, please provide details on the genes annotated to the immune and metabolic signatures (20 genes for DOC and 15 for DC, respectively; line 473). These gene lists should also be made available in the main text or shown in Figure 3 to improve transparency.\nv. The manuscript should include details of the flow cytometry panels used for immune profiling (antibody list and definition of immune subsets) and for the identification of circulating tumor cells (CTCs), to ensure methodological transparency and reproducibility.\nvi. Immune cell subset analysis: Handling of peripheral blood mononuclear cells (PBMCs) should be described in more detail. Include information on the concentrations of Fc block and live/dead cell markers, and provide a complete antibody list (clone, fluorochrome, and concentration) to ensure reproducibility.\nvii. In vitro cell growth and drug sensitivity assays (Figure 7e): Please clarify whether dimethyl sulfoxide (DMSO) was used as a vehicle control and specify its final concentration in the culture medium. The reported growth inhibition appears to be primarily associated with the MAP2 knockout rather than the treatment effect. It would also strengthen the analysis to include PEO1 cells as a positive control.\n4. Results\nThis section should be limited to the presentation of results; interpretation should be reserved for the Discussion.\na. The manuscript would benefit from a more balanced presentation of the data. While the genomic analyses are comprehensively detailed, the phenotypic characterization of peripheral immune cells and circulating tumor cells (CTCs), as well as the functional studies, receive minimal attention and should be expanded for completeness.\nb. Currently, some important translational observations are not fully contextualized, and key comparisons are underdeveloped.\ni. IFN-α expression: The reported upregulation of interferon-alpha (IFN-α) in patients with clinical benefit before treatment requires further clarification. The post-treatment increase of IFN-α observed in both clinical benefit (CB) and no clinical benefit (NCB) groups suggests that this may represent a treatment-related rather than response-specific effect. The authors should reconcile this observation with the relative pretreatment elevation of IFN-α in the CB group (Figure 3a).\nii. Cytoskeletal and microtubule pathway activation: NCB tumors reportedly show enrichment of cytoskeletal organization and microtubule dynamics pathways (lines 311–312). The biological and morphological relevance of these findings is not talked over (in the Discussion). Were there observable histological or phenotypic differences between these tumors? Since the abstract proposes cytoskeletal profiling as a potential predictive marker for lack of clinical benefit, validation using non-transcriptional methods (e.g., morphological or immunohistochemical assessment) would strengthen this conclusion.\niii. MAP2-related growth effects: The analysis of microtubule-associated protein 2 (MAP2) expression in NCB tumors indicates a role in modulating microtubule dynamics (line 335), yet the rationale for the experimental design is unclear. Given that MAP2 expression is high in OVCAR3 and OVCAR8 cells, why was PEO1 not included as a positive control in the drug-response assays? The observed growth reduction across all knockout models, including untreated and vehicle-treated (dimethyl sulfoxide, DMSO) controls, suggests that the inhibitory effect may derive primarily from the knockout rather than from drug treatment. Clarification is needed to distinguish treatment-specific effects from MAP2-dependent growth suppression.\n5. Figure legends and Figures\na. Most figure legends require rephrasing for clarity and completeness. Each legend should provide a concise description of the experimental setup, sample size, and main findings to enable understanding without referring to the main text.\nb. Most of the figures are well designed and effectively illustrate the findings.\ni. Figure 1: The number of analyzed samples (blood and biopsies) should be clearly indicated within the figure itself, not only in the legend.\nii. Figure 2: Panels (a) and (b) appear to display identical results using different visualization methods. Please clarify the rationale for presenting both or consider consolidating them.\niii. Figure 3: Panels (g) and (h) are missing—see the figure legend. The Spearman correlation analysis is not convincing (e.g., line 222 p = 0.35; line 227 p = 0.40), indicating only weak correlations, both for immune and metabolic scores.\niv. Figure 4: The observed upregulation appears to reflect a treatment effect rather than a response-associated effect. Please clarify how the post-treatment upregulation of IFN-α in both CB and NCB groups aligns with the relative pretreatment increase of IFN-α in the CB group (Figure 3a).\nv. Figure 5: Violin plots are best suited for large datasets. Given the limited sample size, boxplots are recommended for improved clarity and transparency. The number of CB and NCB samples included should be explicitly indicated. Expression markers for m-MDSCs and PMN-MDSCs (panels a and b) should be defined in the figure legend.\nvi. Figure 7: The comparison between MAP2A-expressing and knockout (KO) cells across treatment groups would be more intuitive if shown side by side, rather than comparing treatment effects independently. This arrangement would better highlight differences attributable to MAP2A expression status.\n6. Discussion\nThe Discussion should be more focused and structured. The first paragraph should concisely summarize the main findings. Thereafter, three to four major results should be interpreted individually and in relation to the existing literature before being placed in a broader clinical perspective. Each section should end with a clear point of interpretation rather than a preliminary conclusion. The authors should also integrate the clinical outcomes, immune profiling, and functional data into a more overarching biological and clinical context. As noted in the comments on the Introduction, the recently presented results from the KEYNOTE-B96 study provide important new clinical data that should be discussed in relation to the present findings. In addition, the translational data require validation, which should be addressed explicitly in this section.", "reviewer_3": "I have focused on evaluation of trial design/methodology, reporting of outcomes, and statistical analyses methods.\nAbstract\nTranslational results are highlighted without any data reported - which of these analyses were pre-specified in the protocol/Analysis plan? In the discussion, the exploratory nature of the biomarker analyses is highlighted.\nMethods - Study objectives and endpoints:\nSection states (clinical) endpoints rather than objectives. [ie Primary objective as per clintrials.gov: To determine how effective this combination is in treating ovarian cancer. Then primary endpoint is ORR as measure of antitumour activity].\nThis section would benefit from specifying pre-specified secondary objectives for translational analyses (and which ones were post-hoc/exploratory).\nDefinition of ORR: complete (CR) or confirmed/unconfirmed PR per investigator-assessed RECIST v1.1. (how was this confirmed? Central review? Second scan?\nPFS - does it include clinical progression or was it just imaging-defined progression?\nIn many of the results, and also in the abstract, Clinical benefit (CB: PR or SD>4m) or exceptional responders (PFS>12m) are the key endpoints analysed, but these are not defined in objectives/endpoints (nor are in clintrials.gov). Was this endpoint/objective pre-specified in the trial? Is the duration of SD 'standard' definition for CB?\nMethods - Statistical analyses\nExact 95%confidence intervals are used for the ORR - but these are not consistent with the 2-stage design. Better inference methods have been advocated: https://pmc.ncbi.nlm.nih.gov/articles/PMC6047527/\nAnd are include din R packages, such as clinfun (function twostage.inference).\nSimilarly, there are methods for proper handling of under-recruitment in phase 2 studies, which allow to 'recalculate' decision thresholds in the 2-stage design: https://pubmed.ncbi.nlm.nih.gov/25781860/ (I think also implemented in the above R package).\nI am unsure by the attempt to demonstrate statistical equivalence testing between the two treatment arms. This is not an aim/stated in the protocol. I understand the aim of this analysis was to enable the \"pooled\" analysis of the two cohorts for biomarker analyses. But as designed, it was never aimed at comparing these two treatment groups, patients were not randomised to one or the other, patients had, a priori, different histologies (and selection bias to be allocated into each arm).\nWas this a pre-specified analysis? Importantly, was the equivalence margin pre-specified a priory? FYI, there are equivalence methods for censored data (but maybe not in the TOSTER package) although I accept with the low %censoring it does not make a big difference. I'd argue that normality test for PFs is 'redundant' as time to event endpoints are usually skewed. In short, I'd remove this whole bit from methods (no linked results) and state all results are presented by cohort as different populations in any case.\nFor the translational analyses, largely non-parametric tests were used (page 20, lines 584-589). Results indicate that time-to-event analysis were performed (figures 6d and 7b) but this is not explained in the methods. Results clearly show adjustment of multiplicity was implemented (adjusted p-values) but this is not explained in the methods. There is mention to \"one-way AMOVA for multiple comparison\" in line 591, but I interpreted that this corresponds to the in-vitro studies, is this correct? A reference for the AMOVA method may be useful.\nLines 591-592 \"The p < 0.05 were considered significant. All statistical analyses were done using GraphPad Prism v10.\"\nDoes this refer to the whole analysis or just the in-vitro analysis?\nResults - Patient enrollment and baseline characteristics\nPlease report recruitment start/stop dates for each cohort separately.\nResults - Efficacy and safety\nPlease state how many of the PR responses were unconfirmed.\nWhen refer to patients having SD, needs to be specified this is 'best overall response as per RECIST - I think only stated in figure 1b legend. Is this something that should be more clear in the endpoint definition?\nThis is the main results for cohorts D+C and D+C+O but there is not a statement regarding the primary outcome of the trial - Although it under-recruited, the number of responses observed permit to conclude according to the proposed design: 7PR in 36 D+O+C (planned >=11/37) --> based on ORR, D+O+C did not reach its threshold to investigate further; 8PR in 27 D+C (planned >=6/35), so even if recruitment had continued, conclusion would have been that D+C is promising to investigate further (based on ORR).\nResults - Upregulation of immune and metabolic pathways\nI would be explicit in stating \"In both treatment arms, tumors from CB group (D+O+C [n = 21], D+C [n = 14]) exhibited significant upregulation of interferon alpha response than tumours in NCB group (all adjusted p…)\"", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We agree with the reviewer that the correlations with PFS may have been driven by the\nlimited sample size and exceptional responders, requiring interpretation with caution and\nprospective validation in large cohorts. We revised the Results (page 8) and Discussion (page 14)\nto reflect this concern.\n2. The authors note that one exceptional responder in D+C group was noted to have multiple\nmutations in DDR genes. However, this patient appears to have ultra-mutated tumor\n(possibly due to POLD1/POLE mutation), where the response is more likely to be driven by\nhigh TMB. This should be noted.\nResponse: We thank the reviewer for raising this important point. To address this, we first cross-\nchecked a high TMB from our inhouse data (non-CLIA-certified lab) with a CLIA-certified NGS\nreport, performed by Caris Life Sciences, which showed a low TMB of 6 mutations/Mb. This\ndiscrepancy led us to re-investigate the whole datasets including WES to recheck the accuracy.\nmatched to the normal PBMC samples for each patient, although we were initially informed by\nthe NCI Frederick Sequencing and Genomics Core and Bioinformatics Core that they were tumor-\nnormal sample matched WES analyses.\nWe apologize for the oversight and believe that our initial report on POLE/POLD1 may\nhave been overcalled polymerase gene variants given the absence of matched normal sequencing.\nAfter re-analysis using tumor-normal matched WES, we confirmed that this exceptional responder\nin the D+C arm didn’t harbor pathogenic POLE or POLD1 mutations and TMB was 1.8\nmutations/Mb.\nWe have updated the data (Extended Data Fig. 1) and revised the manuscript accordingly\n(page 9).\n3. Figure 4a,c: It doesn’t appear that immune upregulation in response to therapy was any\ndifferent between the CB vs. NCB groups, though the text seems to suggest that CB patients\nwere the ones that exhibited immune upregulation. This should be reconciled. Same goes for\nthe majority of metabolic pathways described, which seem to be going in the same direction\nfor both CB and NCB patients (with exception of NCB patients in the DOC treatment group).\nResponse: We thank the reviewer for identifying this inconsistency and have updated the\nmanuscript (pages 9-10). Briefly, we specified that while pathway-level enrichment was observed\nin both groups, only CB tumors demonstrated coordinated upregulation of immune effector genes\nat the individual gene level. In contrast, NCB tumors lacked meaningful gene-level immune\nactivation and instead shifted toward developmental and stromal programs, marked by increased\nangiogenesis and extracellular matrix gene expression. Also, we clarified that enhanced glycolytic\nprograms were uniquely present in NCB tumors within the D+C arm.\n4a. The conclusion that therapeutic efficacy in DC arm has a different biomarker (metabolic\nactivity) than that in the DOC arm may be somewhat overstated. The conclusion is based on\nvery small number of samples and is likely due to random effect rather than true biology\nrelated to the specific treatment arm (particularly since metabolic pathways were\nupregulated in both arms when comparing CB vs. non-CB patients and the fact that DC arm\n2\nactually performed better than DOC arm). The language in abstract and manuscript\nclaiming this should be softened.\nResponse: We have updated the Abstract (page 3) and Results (page 8) to describe these metabolic\nassociations as exploratory findings, warranting further investigation.\n4b. Furthermore, discussion regarding potential reasons for metabolism correlation with\nresponse is warranted. Is this biologically related to response to immunotherapy, cediranib,\nor both?\nResponse: Thank you for your comment. We initially hypothesized that improved tumor\noxygenation and nutrient delivery by VEGFR inhibition1,2 would activate mitochondrial and\ncholesterol-linked metabolic programs3 which are essential for T-cell functional fitness. Therefore,\na metabolically active tumor environment may facilitate immune engagement by combined\ndurvalumab and cediranib. However, we agree with the reviewer that this metabolism correlation\nwith response could be related to both drugs or either alone. To test this hypothesis, proof-of-\nconcept clinical trial and translational studies should be conducted to study this important question.\nWe have expanded the Discussion (page 13) to acknowledge that further clinical and translational\nstudies are needed to address the biological basis.\n5. Figure 7e: The data with MAP2 knockdown are compelling, but how much is it T-cell\nrelated vs. just the intrinsic effects of drugs on the cancer cells? It doesn’t appear that\naddition of T cells contributes much. Also, since the T cells were added to culture with drugs,\none cannot exclude the possibility that cediranib directly impacts T cell function (rather than\nmaking tumor more susceptible to T cell mediated killing). This should be mentioned.\nResponse: We appreciate this important observation. To dissect the relative contributions of\ntumor-intrinsic versus T-cell–mediated effects, we have performed new parallel experiments\nacross MAP2-high and MAP2-low cell lines, comparing cancer cell-only cultures with CD8⁺ T-\ncell co-cultures.\nBriefly, in cancer cell-only cultures, MAP2 knockdown substantially reduced tumor cell\ngrowth across all treatment conditions in MAP2-high models (OVCAR3 and OVCAR8), with the\nmost pronounced effects under D+O+C treatment (new Fig. 7e). In contrast, MAP2 depletion had\n3\nminimal impact on MAP2-low cell line (PEO1), where growth inhibition remained primarily drug-\ndriven (new Fig. 7e). These data indicate a substantial tumor-intrinsic component of MAP2-\nmediated resistance.\nAdditionally, further cytotoxicity was observed upon addition of CD8⁺ T cells (new Fig.\n7f). MAP2 knockdown in MAP2-high cell lines treated with D+O+C, enhanced T-cell–mediated\nkilling (e.g., ~45% in OVCAR3 siMAP2 vs. ~27% in siControl), whereas MAP2-low PEO1\nremained predominantly drug-responsive with negligible MAP2-dependent enhancement (new\nFig. 7f). These findings indicate that while tumor-intrinsic mechanisms dominate, T cells provide\nmeasurable additional killing in MAP2-high contexts.\nLastly, we agree with the reviewer that our experimental design wouldn’t exclude the\npossibility of cediranib’s direct effects on T-cell function. Our intention was to mirror the clinical\nscenario for the preclinical experimental design where immune cells and therapeutics coexist. This\nlimitation is now acknowledged in the Discussion (page 14).\nReviewer #2\nManuscript: NCOMMS-25-76416\nIn their manuscript, “Phase II Proof-of-Concept Study of Durvalumab and Cediranib With\nand Without Olaparib in Recurrent Ovarian Cancer,” Tabata et al. present a single-center,\nmulti-arm clinical and translational study that evaluates the efficacy of durvalumab-based\ncombination therapies and investigates immune, metabolic, and cytoskeletal mechanisms\nassociated with treatment response in platinum-resistant epithelial ovarian cancer.\nThe patient cohorts are well characterized, and the authors have assembled a unique\ncollection of biological samples. The datasets are extensive and generally sufficient presented.\nHowever, the text is difficult to read due to poor scientific English, which limits accessibility\nand clarity. Although results’ significance is obvious, the manuscript needs revision before it\ncan be recommended for publication.\nThe following concerns should be addressed:\nGeneral: The scientific English needs substantial refinement. Word choice—particularly\nverbs and adverbs—is often suboptimal, resulting in awkward phrasing and reduced\nreadability.\n1. Abstract\n1a. Given that the study is positioned as a proof-of-concept, phase II investigation, the\nconclusion should clearly articulate whether proof of concept was achieved. As written, it\nhighlights only the translational results, leaving the reader uncertain about the primary\nclinical outcome or validation of the study hypothesis.\nResponse: We agree and have revised the Abstract to explicitly state the proof-of-concept outcome.\n1b. It is not immediately clear from the abstract that the data are based on an ovarian cancer\ncohort within a larger multi-cancer study; this should be more explicitly stated\nResponse: We have stated that this is a recurrent ovarian cancer cohort in a phase I/II multi-\ntreatment arm, multi-disease cohort trial.\n2. Introduction\n2a. After submission of this manuscript (NCOMMS-25-76416), Colombo et al. presented\nresults from the KEYNOTE-B96 study during the Presidential Symposium at this year’s\nESMO meeting, demonstrating that the addition of pembrolizumab to weekly paclitaxel,\nwith or without bevacizumab, improved both progression-free and overall survival in\npatients with platinum-resistant ovarian cancer. Paragraph 3 on page 4 should be revised to\ncontextualize the introduction in light of these new data.\nResponse: We thank the reviewer for highlighting the newly presented KEYNOTE-B96 results4.\nWe have incorporated the KEYNOTE-B96/ENGOT-ov65 results into the Introduction (pages 4-\n5), highlighting the improvement in PFS and OS with pembrolizumab plus weekly paclitaxel ±\nbevacizumab in platinum-resistant EOC. We contextualize our study as exploring non-\nchemotherapy-based ICI combinations that may provide alternative treatment options\ncomplementary to this chemotherapy-based strategy.\n5\n2b. In paragraph 3 on page 4, “proof-of-concept” is used in a different context than in the\nAbstract, which may be confusing to the reader. A different term should be used to maintain\nconsistency and clarity.\nResponse: We removed the term “proof-of-concept” when referring to preclinical studies to keep\nits usage consistent with the clinical context.\n3. Methods\n3a. Approvals: All necessary approvals seem to be in place.\nResponse: Thank you.\n3b. Design: The clinical protocol is well described. However, the authors do not mention the\ntranslational research endpoints, nor whether the analyses performed were preplanned or\ndecided retrospectively.\nResponse: We have specified in the Methods (page 16) that RNAseq, WES, immune subset\nprofiling, and CTC analyses were preplanned post-hoc translational endpoints in the original study\nprotocol, whereas signature scoring and MAP2-related functional studies were conducted\nretrospectively as exploratory analyses to generate hypotheses for future validation.\n3c. Materials:\n(i) Description of the cohort: This paragraph includes considerable detail that is reiterated\nin the Results section. Condensing this section would improve clarity and avoid redundancy.\nResponse: We have condensed the cohort description in the Methods (page 15) by removing\nredundant clinical characteristics that are detailed in Table 1 and focusing on essential eligibility\ncriteria and study design elements.\n(ii) The following information is missing from the main document\n6\n– What types of blood samples were collected?\n– Were the different biopsies obtained from the same lesion in each patient?\nResponse: For both immune subset and CTC analysis, 8 mL peripheral blood samples were\ncollected at baseline, cycle 1 day 15, cycle 3 day 1, and at progression. We have added this\ninformation to the Methods (page 17) and Extended data file (pages 3-4).\n– Were the different biopsies obtained from the same lesion in each patient?\nResponse: We have clarified that on-treatment biopsies were obtained from the same lesion\nwhenever feasible, or from the same organ when re-biopsy of the original lesion was not possible\n(page 15).\n3d. Methods:\n(i) The section would benefit from improved clarity and completeness. Many abbreviations\nare introduced without definition, which interrupts readability and makes it difficult to\nfollow the methodological flow. All abbreviations (e.g., CB, NCB) should be written out in\nfull at first mention in both the main text and figure legends.\nResponse: All abbreviations are now defined at first use in the main text and figure legends.\n(ii) Definition of response groups: The categories “clinical benefit (CB),” “no clinical benefit\n(NCB),” and “exceptional responders” should be explicitly defined in the Materials and\nMethods section. There is also an inconsistency in the reported number of exceptional\nresponders (three in line 215 versus four in line 188) that should be clarified.\nResponse: We have added the definitions of the response groups in the Methods section (page 16)\nand clarified the sample numbers: pre-treatment biopsies for genomic and transcriptomic profiling\nwere available for 31 patients in the D+O+C arm (21 CB including 4 exceptional responders, and\nadditional exceptional responder in the D+C arm was not biopsied due to the absence of\nmeasurable lesions on imaging.\n(iii) In the paragraph “Single-sample gene set scoring,” essential information about the\nanalyzed samples and the analytical setup (with reference to the Supplementary Material) is\n7\nmissing. In the current version, it is difficult to understand which data were included in the\nanalysis and how they were selected.\nResponse: We have revised the Methods section (pages 16-17) to specify the number and type of\nsamples analyzed and referenced the Supplementary Materials.\n(iv) Multiple analytical approaches were applied to the genomic data. Given that these\nanalyses form the core of the Results section, a more detailed description should be provided\nhere to ensure clarity and context. E.g. Gene set scoring: In the paragraph describing single-\nsample gene set scoring, please provide details on the genes annotated to the immune and\nmetabolic signatures (20 genes for DOC and 15 for DC, respectively; line 473). These gene\nlists should also be made available in the main text or shown in Figure 3 to improve\ntransparency.\nResponse: The gene lists used for the immune and metabolic signatures have been added to the\nupdated Fig. 3. Additional methodological details describing these analyses have also been\nincluded in the main Methods section (pages 16-17) and Extended data file (page 3).\n(v) The manuscript should include details of the flow cytometry panels used for immune\nprofiling (antibody list and definition of immune subsets) and for the identification of\ncirculating tumor cells (CTCs), to ensure methodological transparency and reproducibility.\nResponse: We have added the antibody panels for immune profiling and CTC analyses to a new\nSupplementary Table 21, and the definitions of all immune subsets to a new Supplementary\nTable 22. The Supplementary Methods (Extended data file, page 4) now specifies that CTCs\nwere defined as viable, nucleated, EpCAM-positive, CD45-negative cells.\n(vi) Immune cell subset analysis: Handling of peripheral blood mononuclear cells (PBMCs)\nshould be described in more detail. Include information on the concentrations of Fc block\nand live/dead cell markers, and provide a complete antibody list (clone, fluorochrome, and\nconcentration) to ensure reproducibility.\nantibody list in a new Supplementary Table 21.\n(vii) In vitro cell growth and drug sensitivity assays (Figure 7e): Please clarify whether\ndimethyl sulfoxide (DMSO) was used as a vehicle control and specify its final concentration\nin the culture medium. The reported growth inhibition appears to be primarily associated\nwith the MAP2 knockout rather than the treatment effect. It would also strengthen the\nanalysis to include PEO1 cells as a positive control.\nResponse: We have included PEO1 (new Fig. 7d-f) and clarified that DMSO (0.01% v/v) together\nwith human isotype IgG (10 µg/mL) was used as the vehicle control (page 19).\n4. Results\n4a. This section should be limited to the presentation of results; interpretation should be\nreserved for the Discussion.\nResponse: We have moved interpretative statements to the Discussion where appropriate to\nmaintain a primarily descriptive Results section.\n4b. The manuscript would benefit from a more balanced presentation of the data. While the\ngenomic analyses are comprehensively detailed, the phenotypic characterization of\nperipheral immune cells and circulating tumor cells (CTCs), as well as the functional studies,\nreceive minimal attention and should be expanded for completeness.\nResponse: We have expanded the Results to ensure a more balanced presentation of our multi-\nlayered data. Specifically, we added detailed descriptions of the systemic reduction in activated\nand proliferating CD8⁺ and CD4⁺ T-cell subsets and the induction of CD56+CD16+ NK cells (page\n10), along with the expanded functional validation of MAP2 (page 12).\n4c. Currently, some important translational observations are not fully contextualized, and\nkey comparisons are underdeveloped.\n(i) IFN-α expression: The reported upregulation of interferon-alpha (IFN-α) in patients with\nclinical benefit before treatment requires further clarification. The post-treatment increase\n9\nof IFN-α observed in both clinical benefit (CB) and no clinical benefit (NCB) groups suggests\nthat this may represent a treatment-related rather than response-specific effect. The authors\nshould reconcile this observation with the relative pretreatment elevation of IFN-α in the CB\ngroup (Figure 3a).\nResponse: We thank the reviewer for highlighting this observation. We have revised the Results\n(page 9) to clearly distinguish these patterns: post-treatment IFN-α pathway enrichment occurred\nin both CB and NCB groups, whereas elevated baseline IFN-α signaling was specifically\nassociated with a CB group.\nWe have expanded the Discussion (page 13) to interpret this finding. We propose that\ntreatment-induced IFN-α upregulation represents a broad pharmacodynamic effect of checkpoint\nblockade, while baseline IFN-α reflects pre-existing immune priming that may predict response.\nOnly tumors with baseline immune activation possess the microenvironmental features necessary\nto translate treatment-induced signals into sustained anti-tumor immunity.\n(ii) Cytoskeletal and microtubule pathway activation: NCB tumors reportedly show\nenrichment of cytoskeletal organization and microtubule dynamics pathways (lines 311–312).\nThe biological and morphological relevance of these findings is not talked over (in the\nDiscussion). Were there observable histological or phenotypic differences between these\ntumors? Since the abstract proposes cytoskeletal profiling as a potential predictive marker\nfor lack of clinical benefit, validation using non-transcriptional methods (e.g., morphological\nor immunohistochemical assessment) would strengthen this conclusion.\nResponse: We agree that morphological and immunohistochemical validation would strengthen\nour conclusions. Unfortunately, residual tissue samples were insufficient for such assessments.\nWe have expanded the Discussion (page 14) to address the biological relevance of\ncytoskeletal enrichment, specifically noting that microtubule and cytoskeletal dynamics facilitate\nstructural plasticity and mechanical remodeling that enable tumor cell survival and immune\nevasion under therapeutic stress5-8.\nTo provide non-transcriptional validation, we identified microtubule-associated protein 2\n(MAP2) as the only gene consistently upregulated in NCB tumors across both our cohort and an\nindependent dataset (GSE2064229). Functional studies using siRNA-mediated MAP2 knockdown\n10\nin ovarian cancer cell lines with varying MAP2 expression (new Fig. 7d-f) demonstrate that MAP2\ndepletion sensitizes MAP2-high ovarian cancer cells to treatment through both tumor-intrinsic and\nimmune-modulatory mechanisms, providing functional evidence that these cytoskeletal programs\ndrive resistance.\nWe acknowledge that future studies incorporating spatial transcriptomics and\nimmunohistochemistry will be essential to validate the morphological correlates of these\ntranscriptional signatures and assess MAP2 protein expression in clinical specimens (page 14).\n(iii) MAP2-related growth effects: The analysis of microtubule-associated protein 2 (MAP2)\nexpression in NCB tumors indicates a role in modulating microtubule dynamics (line 335),\nyet the rationale for the experimental design is unclear. Given that MAP2 expression is high\nin OVCAR3 and OVCAR8 cells, why was PEO1 not included as a positive control in the\ndrug-response assays? The observed growth reduction across all knockout models, including\nuntreated and vehicle-treated (dimethyl sulfoxide, DMSO) controls, suggests that the\ninhibitory effect may derive primarily from the knockout rather than from drug treatment.\nClarification is needed to distinguish treatment-specific effects from MAP2-dependent\ngrowth suppression.\nResponse: To distinguish MAP2-dependent baseline effects from treatment-related effects, we\nperformed additional assays in MAP2-high (OVCAR3, OVCAR8) and MAP2-low (PEO1) cell\nlines. As requested, PEO1 was included as a low-MAP2 control to determine whether the observed\nphenotypes were baseline-dependent.\nWe found that MAP2 knockdown reduced baseline proliferation in all three models, with\na pronounced effect in MAP2-high cells and a minimal effect in PEO1 (new Fig. 7e) indicating\nthe intrinsic growth inhibition is proportional to baseline MAP2 levels. Regarding treatment-\nspecific effects, MAP2 depletion sensitized MAP2-high cells to D+C and D+O+C, yielding\nadditional suppression beyond the baseline knockout effect. In contrast, drug treatment produced\nno further inhibition in MAP2-depleted PEO1. These results indicate that both intrinsic growth\nsuppression and drug sensitization contribute to the overall phenotype, with the relative\ncontribution of each determined by the cell's baseline MAP2 status. We have revised the Results\n(page 12) to reflect these findings.\n11\n5. Figure legends and Figures\n5a. Most figure legends require rephrasing for clarity and completeness. Each legend should\nprovide a concise description of the experimental setup, sample size, and main findings to\nenable understanding without referring to the main text.\nResponse: Done.\n5b. Most of the figures are well designed and effectively illustrate the findings.\n(i) Figure 1: The number of analyzed samples (blood and biopsies) should be clearly indicated\nwithin the figure itself, not only in the legend.\nResponse: Fig. 1b now includes the numbers of pre- and on-treatment biopsies and blood samples\nfor each arm directly in the diagram.\n(ii) Figure 2: Panels (a) and (b) appear to display identical results using different visualization\nmethods. Please clarify the rationale for presenting both or consider consolidating them.\nResponse:We retain both panels and now clarify their complementary roles: the waterfall plots\ndepict best tumor response and ORR in RECIST-evaluable patients (Fig. 2a), whereas the swimmer\nplots depict treatment duration and exceptional responders in the intention-to-treat population (Fig.\n2b).\n(iii) Figure 3: Panels (g) and (h) are missing—see the figure legend. The Spearman\ncorrelation analysis is not convincing (e.g., line 222 p = 0.35; line 227 p = 0.40), indicating\nonly weak correlations, both for immune and metabolic scores.\nResponse:We have removed references to panels (g) and (h) from the legend to match the current\nfigure layout. We have also clarified that the correlations between signature scores and PFS are\nmodest and exploratory (page 8).\n(iv) Figure 4: The observed upregulation appears to reflect a treatment effect rather than a\nresponse-associated effect. Please clarify how the post-treatment upregulation of 14-α in both\n(Figure 3a).\nResponse: We have updated the Results (page 9) to indicate that post-treatment IFN-α\nupregulation appears to be a treatment effect seen in both CB and NCB tumors, whereas higher\nbaseline IFN-α signaling is associated with clinical benefit.\n(v) Figure 5: Violin plots are best suited for large datasets. Given the limited sample size,\nboxplots are recommended for improved clarity and transparency. The number of CB and\nNCB samples included should be explicitly indicated. Expression markers for m-MDSCs and\nPMN-MDSCs (panels a and b) should be defined in the figure legend.\nResponse: We have replaced violin plots with boxplots, added the number of CB and NCB\nsamples to each panel, and defined M-MDSCs (CD11b⁺ CD14⁺ HLA-DR^low/– CD15⁻) and\nPMN-MDSCs (CD14⁻ CD11b⁺ CD15⁺) in the legend.\n(vi) Figure 7: The comparison between MAP2A-expressing and knockout (KO) cells across\ntreatment groups would be more intuitive if shown side by side, rather than comparing\ntreatment effects independently. This arrangement would better highlight differences\nattributable to MAP2A expression status.\nResponse: New Fig. 7e-f has been reformatted to present MAP2-expressing (siControl) and\nMAP2-knockdown (siMAP2) cells side by side.\n6. Discussion\nThe Discussion should be more focused and structured. The first paragraph should concisely\nsummarize the main findings. Thereafter, three to four major results should be interpreted\nindividually and in relation to the existing literature before being placed in a broader clinical\nperspective. Each section should end with a clear point of interpretation rather than a\npreliminary conclusion. The authors should also integrate the clinical outcomes, immune\nprofiling, and functional data into a more overarching biological and clinical context. As\nnoted in the comments on the Introduction, the recently presented results from the\nrelation to the present findings. In addition, the translational data require validation, which\nshould be addressed explicitly in this section.\nResponse: We have restructured the Discussion to improve focus and clarity as suggested.\nReviewer #3\nI have focused on evaluation of trial design/methodology, reporting of outcomes, and\nstatistical analyses methods.\n1. Abstract\nTranslational results are highlighted without any data reported - which of these analyses\nwere pre-specified in the protocol/Analysis plan? In the discussion, the exploratory nature of\nthe biomarker analyses is highlighted.\nResponse: We have revised the Abstract to clarify that transcriptomic profiling was a pre-specified\ntranslational endpoint, while additional biomarker analyses were exploratory, aligning with the\nDiscussion.\n2. Methods\n2a. Study objectives and endpoints:Section states (clinical) endpoints rather than objectives.\n[ie Primary objective as perclintrials.gov: To determine how effective this combination is in\ntreating ovarian cancer. Then primary endpoint is ORR as measure of antitumour activity].\nThis section would benefit from specifying pre-specified secondary objectives for\ntranslational analyses (and which ones were post-hoc/exploratory).\nResponse: We have revised the Methods (page 16) to distinguish the primary objective from the\nprimary endpoint, specify secondary clinical objectives, and list the pre-specified translational\nendpoints (WES, RNAseq, immune subset profiling, and CTC analysis). We also now clarify that\nadditional biomarker analyses, including signature scoring and MAP2-related functional studies,\nwere performed as exploratory analyses.\n14\n2b. Definition of ORR: complete (CR) or confirmed/unconfirmed PR per investigator-\nassessed RECIST v1.1. (how was this confirmed? Central review? Second scan?)\nResponse: We have clarified in the Methods section (page 16) that ORR as CR or PR per\ninvestigator-assessed RECIST v1.1, including both confirmed and unconfirmed PRs, with\nconfirmation requiring a follow-up scan.\n2c. PFS - does it include clinical progression or was it just imaging-defined progression?\nResponse: We have clarified in the Methods (page 16) that PFS included both radiographic and\nclinical progression, as assessed by the investigators.\n2d. In many of the results, and also in the abstract, Clinical benefit (CB: PR or SD>4m) or\nexceptional responders (PFS>12m) are the key endpoints analysed, but these are not defined\nin objectives/endpoints (nor are in clintrials.gov). Was this endpoint/objective pre-specified\nin the trial? Is the duration of SD 'standard' definition for CB?\nResponse: Thank you for this helpful comment. We have stated that CB (PR or SD ≥ 4 months)\nand exceptional responders (PFS ≥ 12 months) were defined retrospectively, explain the rationale\n(e.g., median PFS and consistency with NRG-GY02310), and acknowledge that these are post-hoc\nexploratory endpoints in the Methods (page 16).\n3. Methods - Statistical analyses\n3a. Exact 95%confidence intervals are used for the ORR - but these are not consistent with\nthe 2-stage design. Better inference methods have been\nadvocated:https://pmc.ncbi.nlm.nih.gov/articles/PMC6047527/\nAnd are include din R packages, such as clinfun (function twostage.inference).\nSimilarly, there are methods for proper handling of under-recruitment in phase 2 studies,\nwhich allow to 'recalculate' decision thresholds in the 2-stage\ndesign:https://pubmed.ncbi.nlm.nih.gov/25781860/ (I think also implemented in the above\nR package).\nSimon two-stage trials11, and applied a conditional-error approach to account for under-\nrecruitment12, as suggested. The Methods now describe these procedures and the resulting decision\nthresholds (page 20).\n3b. I am unsure by the attempt to demonstrate statistical equivalence testing between the two\ntreatment arms. This is not an aim/stated in the protocol. I understand the aim of this\nanalysis was to enable the \"pooled\" analysis of the two cohorts for biomarker analyses. But\nas designed, it was never aimed at comparing these two treatment groups, patients were not\nrandomised to one or the other, patients had, a priori, different histologies (and selection bias\nto be allocated into each arm). Was this a pre-specified analysis? Importantly, was the\nequivalence margin pre-specified a priory? FYI, there are equivalence methods for censored\ndata (but maybe not in the TOSTER package) although I accept with the low %censoring it\ndoes not make a big difference. I'd argue that normality test for PFs is 'redundant' as time\nto event endpoints are usually skewed. In short, I'd remove this whole bit from methods (no\nlinked results) and state all results are presented by cohort as different populations in any\ncase.\nResponse: We agree that equivalence testing was neither pre-specified nor appropriate for this\nnon-randomized design. All equivalence analyses have been removed, and we present results\nseparately by treatment arm.\n3c. For the translational analyses, largely non-parametric tests were used (page 20, lines 584-\n589). Results indicate that time-to-event analysis were performed (figures 6d and 7b) but this\nis not explained in the methods. Results clearly show adjustment of multiplicity was\nimplemented (adjusted p-values) but this is not explained in the methods. There is mention\nto \"one-way AMOVA for multiple comparison\" in line 591, but I interpreted that this\ncorresponds to the in-vitro studies, is this correct? A reference for the AMOVA method may\nbe useful.\nResponse: We have added a dedicated paragraph describing the use of Kaplan–Meier and log-\nrank tests for time-to-event analyses, and we specify that multiple testing for RNAseq analyses\n16\nwas controlled using the Benjamini–Hochberg method. We also corrected “AMOVA” to “ANOVA”\nfor in vitro analyses (page 21).\n3d. Lines 591-592 \"The p < 0.05 were considered significant. All statistical analyses were\ndone using GraphPad Prism v10.\" Does this refer to the whole analysis or just the in-vitro\nanalysis?\nResponse: We have clarified that GraphPad Prism v10 was used only for in vitro experiments and\nassociated analyses (page 21).\n4. Results - Patient enrollment and baseline characteristics\n4a. Please report recruitment start/stop dates for each cohort separately.\nResponse: We have reported it as suggested (page 6).\n4b. Results - Efficacy and safety\n(i) Please state how many of the PR responses were unconfirmed.\nWhen refer to patients having SD, needs to be specified this is 'best overall response as per\nRECIST - I think only stated in figure 1b legend. Is this something that should be more clear\nin the endpoint definition?\nResponse: We appreciate the reviewer’s comment and have revised the Results (page 6). Briefly,\nin the D+O+C arm, 7 PRs included 1 unconfirmed PR; in the D+C arm, 8 PRs included 2\nunconfirmed PRs. We have also clarified in the endpoint definition that SD refers to best overall\nresponse per RECIST v1.1.\n(ii) This is the main results for cohorts D+C and D+C+O but there is not a statement\nregarding the primary outcome of the trial - Although it under-recruited, the number of\nresponses observed permit to conclude according to the proposed design: 7PR in 36 D+O+C\n(planned >=11/37) --> based on ORR, D+O+C did not reach its threshold to investigate\n17\nfurther; 8PR in 27 D+C (planned >=6/35), so even if recruitment had continued, conclusion\nwould have been that D+C is promising to investigate further (based on ORR).\nResponse: We thank the reviewer for this helpful clarification. This information has been\nincorporated into the Results to clearly contextualize the clinical activity of each arm (page 6).\n5. Results - Upregulation of immune and metabolic pathways\n5a. I would be explicit in stating \"In both treatment arms, tumors from CB group (D+O+C\n[n = 21], D+C [n = 14]) exhibited significant upregulation of interferon alpha response than\ntumours in NCB group (all adjusted p…)\"\nResponse: We have revised the text as suggested (page 7).\n5b. [Editorial note: please note that the reviewer has confidentially highlighted how it would\nbe important to clarify whether the translational analyses - and the signals identified in the\npaper - are influenced by the patients being platinum sensitive vs platinum refractory.\nPlease also ensure to address/discuss this point].\nResponse: We have expanded our analysis to ensure that the identified signals are not confounded\nby prior platinum sensitivity. By restricting our analysis to the platinum-resistant subpopulation,\nwe demonstrate that our signatures maintain high discriminatory power with the Area Under the\nReceiver Operating Characteristic curve (AUC) values > 0.7 interpreted as strong and > 0.8 as\nexcellent predictive accuracy (new Supplementary Table 20 and Extended Data Fig. 3a-c):\n Immune signature maintained strong predictive accuracy in identifying CB within the\nplatinum-resistant D+O+C arm (AUC = 0.756, p = 0.031) and the external GSE2064229\ndataset (AUC = 0.81, p = 0.033).\n NCB signature showed excellent performance in predicting NCB within platinum-resistant\nsubgroups of D+C (AUC = 0.90, p = 0.005) and D+O+C arms (AUC = 0.806, p = 0.009),\nwith a consistent trend observed in the GSE206422 dataset (AUC = 0.714, p = 0.207).\n Metabolic signature specifically preserved excellent predictive value for identifying CB in\nthe platinum-resistant D+C subgroup (AUC = 0.843, p = 0.019).\nsignatures represent intrinsic therapeutic vulnerabilities unlikely driven by platinum sensitivity.\nWe have updated the Results (page 11) to highlight these findings.\nReviewer #4\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse: Thank you.\nReferences\nspecific T-cell migration in metastatic renal cell carcinoma. Nat Commun7, 12624 (2016).\nhttps://doi.org/10.1038/ncomms12624\nefficient infiltration into solid tumors. Nat Commun 15, 2203 (2024).\nhttps://doi.org/10.1038/s41467-024-46377-7\nmechanisms and interventions. Signal Transduct Target Ther 9, 268 (2024).\nhttps://doi.org/10.1038/s41392-024-01954-6\nbevacizumab in platinum-resistant recurrent ovarian cancer: Results from the randomized\ndouble-blind phase III ENGOT-ov65/KEYNOTE-B96 study. Annals of Oncology 36,\nS1697 (2025). https://doi.org/10.1016/j.annonc.2025.09.049\nNature473, 298-307 (2011). https://doi.org/10.1038/nature10144\nangiogenesis therapy. Biochim Biophys Acta Rev Cancer 1875, 188446 (2021).\nhttps://doi.org/10.1016/j.bbcan.2020.188446\n19\ninduced myeloid-derived suppressor cell recruitment. Br J Cancer 122, 778-788 (2020).\nhttps://doi.org/10.1038/s41416-019-0725-x\nreciprocity. Cell 147, 992-1009 (2011). https://doi.org/10.1016/j.cell.2011.11.016\ninfluencing immunotherapy outcomes in ovarian cancer. Nat Commun 15, 10609 (2024).\nhttps://doi.org/10.1038/s41467-024-54565-8\nCombination Therapy, or Chemotherapy in Patients with Platinum-Resistant Ovarian\nCancer with Prior Bevacizumab: The Phase II NRG-GY023 Trial. Clin Cancer Res 31,\n2370-2378 (2025). https://doi.org/10.1158/1078-0432.CCR-24-3877\n3145-3154 (2008). https://doi.org/10.1002/sim.3123\nphase II designs for oncology trials. Stat Med 34, 2128-2137 (2015).\nhttps://doi.org/10.1002/sim.6479\n20"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I'm satisfied with author responses to my comments. I have no further comments.", "reviewer_2": "NCOMMS-25-76416A\nThe manuscript has improved substantially following revision. The majority of our previous comments have been addressed, and the necessary modifications have been implemented. Overall, the revisions have improved the clarity, coherence, and scientific presentation of the study.\nMajor comment:\nIt remains unclear why the authors chose to compare only the D+O+C arm with the D+C arm, while excluding the D+O arm—which, based on the rationale presented in the Introduction, would appear to be a more logical comparator—or why all three treatment arms were not included in the comparative analyses. This issue requires clearer justification and should be addressed consistently throughout the different sections.\nMinor comments:\nAbstract\n- The cohorts should be presented in the same order throughout the paragraph.\n- The last sentence should be rephrased. Suggested revision: “These findings support proof-of-concept clinical activity of D+O+C and D+C and identify molecular signatures with potential predictive value in subsets of recurrent EOC.“\nIntroduction\nOK\nMethods\n- Please rephrase the description of the translational research end-points to better reflect the results from analyses performed and the results obtained (page 16, lines 454–457).\n- Given that only 13 genes are annotated to the metabolic signature (Supplementary Table 11 and Figure 3e), the current description in the Methods section requires revision\n- Immune cell subset analysis: When describing the handling of peripheral blood mononuclear cells (PBMCs), the concentration of the Fc-blocking agent could be reported rather than the volume used (page 17).\nResults\n- Main document: The Figures have been included twice.\n- The following statement “The concomitant upregulation of angiogenesis and ECM genes, alongside downregulation of chromatin and endothelial transcripts, suggests a state of immune exclusion with enhanced tissue plasticity” (page 10) could be removed and eventually moved to the Discussion.\nFigure legends and Figures\n- Figure 6a: Add “NCB” to improve clarity.\n- Significant IFN-α upregulation: Include the p-values to the text (page 7).\n- Figure 5: The p-values for the NCB group in the M-MDSC analyses should be reported in the text, as a trend toward M-MDSC downregulation at C1D15 is also observed in this group. In addition, Figure 5f is not described in the Results section; if this panel is redundant, it should be removed from Figure 5 (for example, replaced with the CTC results shown in Extended Figure 2a/b).\nDiscussion\n- The relevance of the following statement is unclear: “Our data showing that MAP2 knockdown sensitizes cells to treatment may be clinically relevant given the KEYNOTE-B96/ENGOT-ov65 trial4 (pembrolizumab, bevacizumab plus paclitaxel) reported improved PFS and OS in patients with recurrent EOC, although this requires further mechanistic studies and prospective clinical validation.” The mechanistic link between the presented preclinical findings and the clinical outcomes reported in this trial is not sufficiently explained and requires clearer justification.\nOthers\n- Clinical benefit (CB) and no clinical benefit (NCB) should be defined at first mention.", "reviewer_3": "Thank you - the authors have addressed my comments and concerns.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We thank the reviewer for this comment and appreciate the opportunity to clarify the study design and analytical framework.\nThis study was designed as a multi-treatment arm pilot study (like an umbrella study) with a Simon’s two-stage design for signal-finding, rather than a randomized study of comparing 3 different treatment arms with prespecified statistical endpoints. As a result, each arm enrolled distinct patient populations, making direct comparative analyses methodologically inappropriate. Therefore, all analyses were conducted in an arm-specific, non-comparative manner. We clarified the current study as a non-randomized study in pages 3 and 5.\nWe also evaluated the technical feasibility of joint translational analyses and consulted with the institutional sequencing core facility. To avoid batch effects, DNA and RNA samples from the D+C and D+O+C arms were prepared using the same methods and within the same time frame, and whole-exome sequencing (WES) and RNA sequencing were processed in parallel using the same platforms (NovaSeq Xplus), library preparation kits (exome V8 and RNA Ribo-Zero Plus) and workflows. Immune subset profiling and circulating tumor cell (CTC) analyses for these two arms were also performed in parallel using the same antibody lots.\nIn contrast, DNA/RNA sample preparation, WES, RNA sequencing, and immune/CTC analyses for the D+O arm1 were performed before COVID19 (2018-2019), using different sequencing platform (NovaSeq 6000), library preparation kits (exome V7 and RNA TruSeq), target capture designs, data processing pipelines (including software versions and reference annotations), and antibody lots. Although Illumina sequencing platforms themselves are not expected to introduce major quality-related batch effects, the use of non-overlapping workflows, software, and reagents, together with the distinct processing periods (2018-2019 vs. 2025), would introduce compound batch effects that cannot be reliably separated from true biological signals.\nFor these reasons, inclusion of the D+O arm in joint or comparative translational analyses would be methodologically inappropriate. Accordingly, all analyses were performed in an arm-specific, non-comparative manner, as clarified in the Methods (page 16).\nMinor comments:\n1. Abstract\n1a. The cohorts should be presented in the same order throughout the paragraph.\nResponse: The cohorts have been reordered throughout the Abstract to ensure consistent presentation (D+O+C followed by D+C).\n1b. The last sentence should be rephrased. Suggested revision: “These findings support proof-of-concept clinical activity of D+O+C and D+C and identify molecular signatures with potential predictive value in subsets of recurrent EOC.“\nResponse: We have revised it as suggested.\n2. Introduction OK\nResponse: Thank you.\n3. Methods\n3a. Please rephrase the description of the translational research end-points to better reflect the results from analyses performed and the results obtained (page 16, lines 454–457).\nResponse:The Methods section has been revised to better align with the analyses performed and results presented (page 16).\n3b. Given that only 13 genes are annotated to the metabolic signature (Supplementary Table 11 and Figure 3e), the current description in the Methods section requires revision\nResponse: It has been corrected.\n3c. Immune cell subset analysis: When describing the handling of peripheral blood mononuclear cells (PBMCs), the concentration of the Fc-blocking agent could be reported rather than the volume used (page 17)\nResponse: The Fc receptor blocking reagent used (Miltenyi Biotec, #130-059-901) is supplied without a specified concentration. As recommended by the manufacturer, the reagent was used at 20 µL per 1 × 107cells (or fewer). We have clarified this in the Methods section (page 17).\n4. Results\n4a. Main document: The Figures have been included twice.\nResponse: The Figures are now uploaded separately in the submission system to avoid duplication.\n4b. The following statement “The concomitant upregulation of angiogenesis and ECM genes, alongside downregulation of chromatin and endothelial transcripts, suggests a state of immune exclusion with enhanced tissue plasticity” (page 10) could be removed and eventually moved to the Discussion.\nResponse: This statement has been removed from the Results section, and its conceptual implications are addressed in the Discussion (paragraph 3, page 13).\n5. Figure legends and Figures\n5a. Figure 6a: Add “NCB” to improve clarity.\nResponse: Done.\n5b. Significant IFN-α upregulation: Include the p-values to the text (page 7).\nResponse: The specific adjusted p-values have been added (page 7).\n5c. Figure 5: The p-values for the NCB group in the M-MDSC analyses should be reported in the text, as a trend toward M-MDSC downregulation at C1D15 is also observed in this group.\nIn addition, Figure 5f is not described in the Results section; if this panel is redundant, it should be removed from Figure 5 (for example, replaced with the CTC results shown in Extended Figure 2a/b).\nResponse: We have reported the p-values for M-MDSC changes in the NCB groups in the Results (page 10), showing a non-significant downward trend (p = 0.1055 in D+O+C and p = 0.2656 in D+C).\nFig. 5f has been removed to avoid redundancy, and the CTC analyses are retained in Extended Data Fig. 2.\n6. Discussion\nThe relevance of the following statement is unclear: “Our data showing that MAP2 knockdown sensitizes cells to treatment may be clinically relevant given the KEYNOTE-B96/ENGOT-ov65 trial4 (pembrolizumab, bevacizumab plus paclitaxel) reported improved PFS and OS in patients with recurrent EOC, although this requires further mechanistic studies and prospective clinical validation.” The mechanistic link between the presented preclinical findings and the clinical outcomes reported in this trial is not sufficiently explained and requires clearer justification.\nResponse: We agree and have revised the Discussion to remove speculative statements regarding clinical relevance (page 14). The trial is now cited as a hypothesis-generating clinical context, and the clinical relevance of the MAP2 findings requires further mechanistic validation.\n7. Others\nClinical benefit (CB) and no clinical benefit (NCB) should be defined at first mention.\nResponse: Done.\nReferences"}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors have addressed all comments and concerns.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": ""}]

Decision letter