Deciphering brain metastasis in epithelial ovarian cancer: multimodal analysis and potential biomarkers R. Trozzi, M. Salvi, M. Karimi, A. Minucci, G. Raspaglio, M. De Donato, M. Buttarelli, A. Piermattei, L. Vaccaro, A. Grimaldi, R. De Santis, M. Massa, F. Sillano, L. Giacò, L. Mastrantoni, V. Iacobelli, F. Camarda, M. Cesana, S. Duranti, M. C. Sassu, P. Mattogno, A. Fagotti, C. Marchetti, G. Scambia, C. Nero, D. Cacchiarelli Npj Precision Oncology, 2026 Epithelial ovarian cancer (EOC) remains the most lethal gynaecological malignancy in developed countries, with recurrence and drug resistance posing significant clinical challenges. Brain metastases (BM) from epithelial ovarian cancer, once rare, are an increasing phenomenon and are characterised by a dismal prognosis. To explore the molecular underpinnings of BM in EOC, we conducted a multimodal genomics and transcriptomics analysis of matched primary tumour and brain metastases samples from a retrospective cohort. Our findings revealed high genomic concordance between primary tumour (PT) and BM, with alterations in key pathways such as MYC (MYC Proto-Oncogene, bHLH Transcription Factor) targets, extracellular matrix remodelling, and inflammatory signalling characterizing the BM. AFP (Alpha-fetoprotein) and GFAP (Glial Fibrillary Acidic Protein) emerged as potential biomarkers from the primary lesion for BM onset, while network analysis identified MET (MET Proto-Oncogene, Receptor Tyrosine Kinase), GDF15 (Growth Differentiation Factor 15), and S100A9 (S100 Calcium Binding Protein A9) as candidate mediators of tumour-brain crosstalk. These results offer new insights into EOC brain tropism, highlighting potential targets for therapeutic intervention and personalized patient management in the precision oncology era.
Co-targeting hallmarks of cancer for therapeutic benefit in ovarian cancer: a scoping review Valentina Iacobelli, Floriana Camarda, Gloria Anderson, Marianna Buttarelli, Luca Mastrantoni, Miriam Grazia Ferrara, Rita Trozzi, Simona Duranti, Vanda Salutari, Giulia Sabetta, Giovanni Scambia, Anna Fagotti, Camilla Nero International Journal of Gynecological Cancer, 2026 The conceptualization of cancer characteristics into 14 hallmarks is now widely adopted. Simultaneous targeting of multiple cancer hallmarks represents a promising strategy to overcome therapeutic resistance and improve clinical outcomes. This approach is particularly relevant in epithelial ovarian cancer, which remains highly lethal despite significant advances in first-line treatment. This scoping review was conducted according to Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines and included searches of Medline, Scopus, the Cochrane Library, and ClinicalTrials.gov for studies published up to September 30, 2024. To be eligible, trials were required to be phase I to III clinical trials (both completed and ongoing) enrolling patients with any histotype of epithelial ovarian cancer and to target ≥2 of the 14 cancer hallmarks. Studies were screened and data extracted independently by 3 reviewers. Out of 1461 records screened at the title and abstract level (data cutoff: September 30, 2024), 225 studies were identified, comprising 111 completed and 114 ongoing trials. The adoption of co-targeting strategies has notably increased, with a 3-fold increase in trials between 2007-2013 and 2021-2024. Among 94 trials reporting clinically meaningful benefits, the most frequent combination involved targeting "sustaining proliferative signaling" and "genome instability and mutations." In ongoing trials, 40 are focused on modulating "avoiding immune destruction." However, no clinical trials were identified for 4 of the 14 hallmarks: "unlocking phenotypic plasticity," "senescent cells," "non-mutational epigenetic re-programming," and "polymorphic microbiomes." These hallmarks remain underexplored, highlighting critical gaps and potential areas for future research. In conclusion, co-targeting approaches in epithelial ovarian cancer rely on combining genomic instability and angiogenesis inhibition with chemotherapy. Current research trends are shifting toward chemotherapy-free regimens and novel therapeutic targets, aiming to address resistance mechanisms and improve long-term outcomes.
Predicting progression-free survival in hormone-receptor positive (HR+/HER2–) metastatic breast cancer (MBC) treated with CDK4/6 inhibitors: A machine learning approach Sergio Pannunzio, Luca Mastrantoni, Noemi Maliziola, Letizia Pontolillo, Giovanna Garufi, Elena Di Monte, Alessandra Emiliani, Margherita Sgambato, Anna Cardillo, Antonella Palazzo, Armando Orlandi, Giampaolo Tortora, Emilio Bria Breast, 2026 <h2>ABSTRACT</h2><h3>Background</h3> In HR+/HER2– metastatic breast cancer (MBC), CDK4/6 inhibitors combined with endocrine therapy (ET) significantly improve progression-free survival (PFS). Machine learning (ML) approaches may improve individualized progression risk estimation. <h3>Methods</h3> We retrospectively analysed HR+/HER2– MBC patients treated with first-line CDK4/6i plus ET to develop CoxNet regression and Gradient Boosting Machine (GBM) models from baseline clinicopathological features. The primary endpoint was PFS prediction. The dataset was split into a 70/30 train/validation set. Performance was assessed by Harrell's C-index (1,000 bootstrap replicates). Risk stratification was performed using Gaussian Mixture Modeling (GMM) to define high- and low-risk groups. Cox regression estimated the corresponding hazard ratios (HR). Early progression at 6 months (EP) prediction was evaluated using the area under the receiver operating characteristic curve (AUROC). <h3>Results</h3> 459 patients were included, with a median follow-up of 43.7 months (95% CI 39.6-48.3). Median PFS was 29.3 months (95% CI 24.0-33.7). Both ML models achieved strong predictive performance, with a Harrell's C-index of 0.74 (95% CI 0.67-0.80) in the validation set. The main predictors were liver metastases, Ki67 expression, and primary endocrine resistance. Stratification defined two risk groups with significantly different PFS in the validation set (HR 2.58, 95% CI 1.65-4.03, p=3.3×10<sup>-5</sup>). Median PFS was 34.8 (95%CI 24.0-52.4) in the low-risk and 10.6 (95%CI 7.7-14.6) in the high-risk group. For EP prediction, the model achieved an AUROC of 0.77 (95% CI 0.61-0.89). <h3>Conclusions</h3> This study supports the clinical applicability ML models using baseline clinicopathological variables for individualized risk stratification in HR+/HER2– MBC.
Next Frontier in HER2+/HR+ Breast Cancer: Leveraging Cell Cycle Control with CDK4/6 Inhibitors Ilaria Poli, Gaia Rachele Oliva, Ginevra Mongelli, Angelachiara Rotondi, Valentina Frescura, Giorgia Arcuri, Giovanna Garufi, Letizia Pontolillo, Luca Mastrantoni, Elena Di Monte, Noemi Maliziola, Maria Antonia Fucile, Francesca Salvatori, Rita Mondello, Antonella Palazzo, Alessandra Fabi, Emilio Bria, Giampaolo Tortora, Armando Orlandi Journal of Personalized Medicine, 2026 HER2-positive/hormone-receptor-positive breast cancer represents approximately 10% of all breast cancer cases and constitutes a distinct biological entity with unique therapeutic challenges. The complex crosstalk between HER2 and estrogen receptor signaling pathways contributes to both primary and acquired resistance to anti-HER2 therapies, and the convergence of these pathways on cell cycle regulation, particularly through the cyclin D1-CDK4/6-Rb axis, has provided a compelling rationale for combining CDK4/6 inhibitors with anti-HER2 therapy. This scoping review aimed to map preclinical and clinical evidence evaluating combinations of CDK4/6 inhibitors with HER2-targeted therapy in HER2+/HR+ disease. Eligible sources included preclinical models and clinical studies assessing CDK4/6 inhibitor-based combinations with anti-HER2 therapy, identified through searches of PubMed, Embase, Cochrane Library, Web of Science and ClinicalTrials.gov. Data were charted and synthesized descriptively according to PRISMA-ScR guidelines. Preclinical studies have demonstrated synergistic antitumor activity when CDK4/6 inhibitors are combined with trastuzumab, pertuzumab, or newer HER2-targeted agents across multiple HER2+ breast cancer models. In the metastatic setting, phase II trials including MonarcHER and PATRICIA II have shown encouraging efficacy signals, while the phase III PATINA trial demonstrated a clinically meaningful 15.2-month progression-free survival benefit with palbociclib plus anti-HER2 therapy and endocrine therapy. In the neoadjuvant setting, trials including NA-PHER2 and MUKDEN-01 demonstrated marked Ki67 suppression and promising pathologic responses, supporting the exploration of chemotherapy de-escalation strategies. Despite these advances, key challenges remain including the identification of predictive biomarkers, optimal treatment sequencing, and the integration of emerging HER2-targeted agents such as trastuzumab deruxtecan. Novel CDK4/6 inhibitors including dalpiciclib and next-generation agents are expanding therapeutic options, while combination strategies incorporating CDK7 inhibition represent future therapeutic frontiers. The evolving landscape of HER2+/HR+ breast cancer treatment increasingly emphasizes precision medicine approaches that leverage cell cycle control mechanisms to overcome resistance and improve patient outcomes across all disease stages.
Concordance Analysis of Microsatellite Instability via NGS and Mismatch Repair Deficiency via IHC in Endometrial and Colorectal Cancer Camilla Nero, Lisa Salvatore, Simona Duranti, Gloria Anderson, Luca Mastrantoni, Mina Karimi, Giulia Mantini, Angelo Minucci, Giulia Maneri, Luciano Giacò, Angela Santoro, Arianna Panfili, Alessia Piermattei, Ilenia Marino, Giulia Caira, Maria Alessandra Calegari, Giovanni Trovato, Valentina Iacobelli, Vanda Salutari, Nicola Normanno, Francesco Fanfani, Giovanni Scambia, Giampaolo Tortora Targeted Oncology, 2026 BACKGROUND: Assessment of mismatch repair (MMR) function provides critical guidance for diagnosis, prognosis, and therapeutic decision making in colorectal and endometrial cancers. Mismatch repair immunohistochemistry (IHC) is the routine clinical test for identifying MMR deficiency, while microsatellite instability (MSI) serves as its surrogate, detected by polymerase chain reaction or next-generation sequencing (NGS). Available data indicate a high concordance rate between these approaches in colon cancer, whereas a lower concordance has been reported in endometrial cancer. OBJECTIVE: We aimed to assess the concordance rate between MMR-IHC and MSI-NGS from patients with colorectal or endometrial cancer, using IHC as the gold standard. METHODS: A cohort of 520 patients (352 with endometrial cancer and 168 with colorectal cancer) were included. MMR‑IHC assessed MLH1, MSH2, MSH6, and PMS2 expression, while MSI‑NGS was determined by profiling 130 homopolymer repeat loci using the TruSight Oncology 500 panel from Illumina. RESULTS: While concordance was high in the colorectal cancer cohort (99%, 95% confidence interval 96-100), a lower level of agreement was observed in endometrial cancer cases (85%, 95% confidence interval 81-89). Fifty-two of 53 discordant cases exhibited MMR deficiency by IHC in the absence of detectable MSI. Forty percent of discordant cases could be explained by factors previously associated with reduced MSI levels, including mutations in DNA polymerase genes (n = 5), isolated MSH6 loss (n = 10), atypical IHC staining patterns (n = 8), and germline variants (n = 6). Additionally, the presence of genetic and epigenetic alterations (specifically, 19 cases with MLH1 promoter hypermethylation and ten with somatic or germline MMR variants) supports the interpretation that MSI calls were missed in a subset of cases. Finally, optimizing the MSI threshold enhanced detection accuracy in endometrial tumors. CONCLUSIONS: These findings confirm the lower concordance between MMR-IHC and MSI-NGS in endometrial cancer compared with colorectal cancer when broad panels are used, underscoring the importance of tumor-specific interpretation even within tumor-agnostic assays. Although cut-off optimization improved agreement, the evidence remains insufficient for clinical implementation, and further validation studies are needed.
Impact of ECOG performance status 2 participants on outcomes of pivotal cancer clinical trials: a meta-analysis and meta-regression G.M. Iannantuono, T. Giovagnoli, L. Mastrantoni, B. Gyawali, C.S. Floudas, S. Sganga, D. Giannarelli, M. Filetti, A. Spinazzola, F. Lo Bianco, E. Giudice, A. Vitale, J.L. Gulley, P. Navarra, E. Bria, G. Daniele ESMO Open, 2026 BACKGROUND: Although patients with Eastern Cooperative Oncology Group performance status (PS) of 2 constitute a significant proportion of the cancer population, they are often excluded from pivotal clinical trials owing to presumed higher risks of treatment effect dilution, toxicity, and lower compliance. Here, we conducted a systematic review and meta-analysis to evaluate the impact of including PS 2 participants on efficacy and safety outcomes in pivotal cancer clinical trials. MATERIALS AND METHODS: We searched the 'Oncology/Hematologic Malignancies Approval Notifications' and 'Drugs@FDA' databases for clinical trials supporting 'Food and Drug Administration' anticancer drug approvals from 1 January 2009 to 31 December 2024. Eligible studies were randomized phase III clinical trials of systemic therapies for metastatic solid tumors permitting the inclusion of PS 2 participants. We assessed efficacy outcomes [progression-free survival (PFS) and overall survival (OS)] and safety outcomes [occurrence of any-grade adverse events (AEs), high-grade AEs, serious AE (SAEs), AE-related deaths, and treatment modifications] in the included studies. RESULTS: Thirty-six trials were included. In subgroup analyses, no statistically significant differences were found between PS 2 and PS ≤1 participants for PFS [hazard ratio (HR) 0.45, 95% confidence interval (CI) 0.30-0.69 versus HR 0.52, 95% CI 0.41-0.66, P = 0.59] and OS (HR 0.81, 95% CI 0.68-0.97 versus HR 0.71, 95% CI 0.66-0.77, P = 0.18). In meta-regression analyses, no significant associations were found for efficacy outcomes. However, a higher proportion of PS 2 participants was significantly associated with an increased risk of SAEs, AE-related deaths, and treatment discontinuations. CONCLUSIONS: Although PS 2 participants showed a greater propensity to serious toxicity, no significant differences in efficacy outcomes were observed compared with those with PS ≤1. Our results support the inclusion of PS 2 participants in clinical trials, as their exclusion limits the generalizability of results.
Development and Validation of Multivariable Machine-Learning Models for the Prediction of Multisystemic Inflammatory Syndrome Outcomes in Latin American Children Danilo Buonsenso, Luca Mastrantoni, Rolando Ulloa‐Gutierrez, Jimena García‐Silva, Gabriela Ivankovich‐Escoto, Marco A. Yamazaki‐Nakashimada, Enrique Faugier‐Fuentes, Olguita del Águila, German Camacho‐Moreno, Dora Estripeaut, Iván F. Gutiérrez‐Tobar, Adriana H. Tremoulet, and Acta Paediatrica International Journal of Paediatrics, 2026 AimWe aimed to develop and test machine learning algorithms for the prediction of severe outcomes associated with MIS‐C.MethodAn observational ambispective cohort study was conducted including children aged from 1 month to 18 years old in 84 hospitals from the REKAMLATINA (Red de la Enfermedad de Kawasaki en America Latina) network diagnosed with MIS‐C from 1st January 2020 to 31st June 2022. Multiple models were developed to predict four main outcomes: paediatric intensive care unit (PICU) admission, need for inotropes, need for mechanical ventilation, and death. Performance measures were accuracy for PICU admission, inotropes use and mechanical ventilation, and the area under the receiver operating characteristic curve (AUROC) for death. Variable contribution was analysed using Shapley Additive Explanations (SHAP) values.ResultsWe included 1303 children with a diagnosis of MIS‐C. The model for the prediction of PICU admission (random forest [RF]) reached an accuracy of 0.80 (95% CI: 0.76–0.84), the model for inotrope use (RF) an accuracy of 0.86 (95% CI: 0.82–0.90), the model for mechanical ventilation (histogram‐based gradient boosting [HBGB]) an accuracy of 0.84 (95% CI 0.80–0.88), and the model for death (RF) reached an AUROC of 0.85 (95% CI 0.77–0.93).ConclusionsWe developed and validated machine learning models for the prediction of MIS‐C related outcomes that can help clinicians risk stratify patients to identify those most likely to have a severe outcome from MIS‐C.
Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy Luca Mastrantoni, Giovanna Garufi, Giulia Giordano, Noemi Maliziola, Elena Di Monte, Giorgia Arcuri, Valentina Frescura, Angelachiara Rotondi, Armando Orlandi, Luisa Carbognin, Antonella Palazzo, Federica Miglietta, Letizia Pontolillo, Alessandra Fabi, Lorenzo Gerratana, Sergio Pannunzio, Ida Paris, Sara Pilotto, Fabio Marazzi, Antonio Franco, Gianluca Franceschini, Maria Vittoria Dieci, Roberta Mazzeo, Fabio Puglisi, Valentina Guarneri, Michele Milella, Giovanni Scambia, Diana Giannarelli, Giampaolo Tortora, Emilio Bria Npj Breast Cancer, 2025 Hormone receptor-positive/HER2-negative breast cancer (BC) is the most common subtype of BC and typically occurs as an early, operable disease. In patients receiving neoadjuvant chemotherapy (NACT), pathological complete response (pCR) is rare and multiple efforts have been made to predict disease recurrence. We developed a framework to predict pCR using clinicopathological characteristics widely available at diagnosis. The machine learning (ML) models were trained to predict pCR (n = 463), evaluated in an internal validation cohort (n = 109) and validated in an external validation cohort (n = 151). The best model was an Elastic Net, which achieved an area under the curve (AUC) of respectively 0.86 and 0.81. Our results highlight how simpler models using few input variables can be as valuable as more complex ML architectures. Our model is freely available and can be used to enhance the stratification of BC patients receiving NACT, providing a framework for the development of risk-adapted clinical trials.
Nutritional Challenges in Older Cancer Patients: A Narrative Review of Assessment Tools and Management Strategies Giulia Giordano, Roberta Terranova, Luca Mastrantoni, Francesco Landi Nutrients, 2025 Background/Objectives: Malnutrition, sarcopenia, cachexia, and frailty often coexist in older cancer patients and are associated with worse treatment tolerance, reduced quality of life, and increased mortality. These syndromes can be underrecognized, and the therapeutic approach is often fragmented. In light of this, the aim of this review was to synthesize current evidence on the screening and clinical management of nutritional aspects and the related tools, favoring multidimensional and personalized nutritional care. Methods: This narrative review was conducted according to the SANRA guidelines. A comprehensive literature search was performed on PubMed for studies published between January 2000 and June 2025, with no language restrictions. Eligible studies included adults aged ≥65 with cancer, addressing malnutrition, sarcopenia, cachexia, frailty, or nutrition-related interventions. Results: Malnutrition affects 30–80% of older cancer patients and is strongly associated with reduced survival, impaired treatment tolerance, and poorer quality of life. Tools such as PG-SGA, G8, GNRI, and CONUT offer practical options for early risk identification. Nutritional interventions, including oral supplements, dietary counseling, symptom management, and multimodal strategies (nutrition plus exercise), are associated with improved clinical outcomes. Evidence also supports the prognostic value of early screening and individualized nutrition care pathways. Conclusions: Malnutrition represents a modifiable risk factor in geriatric oncology and should be assessed considering other related conditions such as sarcopenia, cachexia, and frailty. Systematic screening and targeted interventions should be integrated into standard cancer care to improve outcomes in older adults. Future research should prioritize personalized nutrition strategies and multicenter trials focused on survival, function, and quality of life.
