Murine double minute 2 (MDM2) inhibitors have shown promising activity in TP53-wild type tumors and are under active investigation across a spectrum of malignancies. Herein, we report a 51-year-old female with MDM2-amplified, TP53-wild type adenoid cystic carcinoma who was treated with a MDM2 inhibitor and developed persistent pancytopenia despite drug discontinuation. Her pancytopenia was associated with 20 distinct pathogenic TP53 mutations in peripheral blood and bone marrow not present in drug-resistant tumor tissue. Plasma TP53 mutations were similarly detected among 4 other patients treated at our institution, with the number of mutations correlating strongly with duration of treatment. This case suggests that MDM2 inhibitors are associated with TP53 clonal hematopoiesis, which may confer a risk of subsequent myeloid malignancy. As multiple MDM2 inhibitor trials are ongoing, our findings underscore the need for further investigation into the potential long-term deleterious effects of these inhibitors in the hematopoietic stem and progenitor compartment.

The rapid development of deep learning has revolutionized medical image processing, including analyzing whole slide images (WSIs). Despite the demonstrated potential for characterizing gene mutations directly from WSIs in certain cancers, challenges remain due to image resolution and reliance on manual annotations for acute myeloid leukemia (AML). We, therefore, propose a deep learning model based on multiple instance learning (MIL) with ensemble techniques to predict gene mutations from AML WSIs. Our model predicts NPM1 mutations and FLT3-ITD without requiring patch-level or cell-level annotations. Using a dataset of 572 WSIs, the largest database with both WSI and genetic mutation information, our model achieved an AUC of 0.90 ± 0.08 for NPM1 and 0.80 ± 0.10 for FLT3-ITD in the testing cohort. Additionally, we found that blasts are pivotal indicators for gene mutation predictions, with their proportions varying between mutated and standard WSIs, highlighting the clinical potential of AML WSI analysis.

This study developed and validated a deep learning network using baseline magnetic resonance imaging (MRI) to predict Ki-67 status in meningioma patients. A total of 1239 patients were retrospectively recruited from three hospitals between January 2010 and December 2023, forming training, internal validation, and two external validation cohorts. A representation learning framework was utilized for modeling, and performance was assessed against existing methods. Furthermore, Kaplan-Meier survival analysis was conducted to investigate whether the model could be used for tumor growth prediction. The model achieved superior results, with areas under the curve (AUCs) of 0.797 for internal testing and 0.808 for generalization, alongside 0.756 and 0.727 for 3- and 5-year tumor growth predictions, respectively. The prediction was significantly associated with the growth of asymptomatic small meningiomas. Overall, the model provides an effective tool for early prediction of Ki-67 and tumor volume growth, aiding in individualized patient management.

Tumor dissemination to the central nervous system (CNS) is almost a rule in the treatment journey of advanced HER2+ breast cancer (BC). Recent results demonstrated high intracranial efficacy with Trastuzumab Deruxtecan (T-DXd). However, a real-world evidence is lacking in literature. We conducted a multicenter, observational, retrospective real-world analysis on 39 cases collected at 12 Italian Oncological Units. Patients with brain metastases (BMs) from HER2 + BC treated with T-DXd in various treatment lines were enrolled. Primary endpoint was the intracranial overall response rate (iORR). Secondary endpoints were intra- and global progression free survival (iPFS - gPFS); other secondary objectives were the intracranial disease control rate (iDCR), duration of response (iDoR), clinical benefit rate at 6 and 12 months (iCBr), overall survival, and safety. iORR was 59%, iPFS was 15.6 months, gPFS was 11.8 months. iDCR was 94.9%, iDoR was 11.9 months, and iCBr at 6 and 12 months were 69.2% and 59%, respectively. OS was not reached, with an overall rate of 77.9% of patients alive at 12 months. This study confirmed the high intracranial efficacy and manageable safety profile of T-DXd in this first-ever real world analysis.

We report the basal cell cancer (BCC) cohort of the SWOG/NCI 1609 Dual Anti-CTLA-4 & Anti-PD-1 blockade in Rare Tumors (DART), a phase II prospective, multicenter basket trial of nivolumab and ipilimumab. The primary endpoint was objective response rate (ORR) (RECIST v1.1). Overall survival (OS), progression-free survival (PFS), and toxicity were secondary endpoints. Sixteen patients with advanced/metastatic BCC were evaluable. The ORR was 31% (95% CI, 19-50%), and the 12-month OS, 75% (95% CI, 57-100%). Median PFS was 9.3 months (95% CI, 3.3-NA). Of 15 patients evaluable for clinical benefit, five partial responses (PRs) and five stable disease >6 months (total = 10/15 (66.7%)) were seen. The most common toxicities included fatigue (37.5%), pruritis (31.3%), and diarrhea (25%). In patients with advanced/metastatic BCC, ipilimumab and nivolumab produced an ORR of 31% and prolonged (>6 months) PFS in 73% of patients, with seven PFS/iPFS of >1 year, including one with prior anti-PD-1. ClinicalTrials.gov ID: NCT02834013 (Registered 7/15/2016; https://clinicaltrials.gov/ct2/show/NCT02834013 ).

Previous studies on serum albumin-to-globulin ratio (AGR) in human cancers were limited to its preoperative level, with postoperative serial AGR remeasurements ignored. In this study, 2844 CRC patients, 2267 NSCLC patients and 507 HCC patients who underwent curative resection were included. Postoperative AGR was a prognostic factor independent to preoperative AGR, performing a L shaped relation with OS. The 5-year OS rates for the persistently normal, normalized, lowered and persistently low perioperative AGR groups were 84.0%, 80.7%, 78.5% and 70.2%. Three longitudinal AGR trajectory groups were identified within 12 months after surgery. Compared with the normal-stable group, the adjusted HRs on OS for the rising-decreasing and decreasing-rising groups were 1.38 (95% CI: 1.13-1.68, P = 0.001) and 2.78 (95% CI: 2.30-3.36, P < 0.001). Similar results were observed for RFS. In conclusion, a routine follow-up of AGR in the postoperative surveillance will improve prognosis risk stratification of cancer patients.

Next-generation sequencing (NGS) offers a promising approach for differentiating multiple primary lung cancers (MPLC) from intrapulmonary metastasis (IPM), though panel selection and clonal interpretation remain challenging. Whole-exome sequencing (WES) data from 80 lung cancer samples were utilized to simulate MPLC and IPM, with various sequenced panels constructed through gene subsampling. Two clonal interpretation approaches primarily applied in clinical practice, MoleA (based on shared mutation comparison) and MoleB (based on probability calculation), were subsequently evaluated. ROC analysis highlighted MoleB's superior performance, especially with the NCCNplus panel (AUC = 0.950 ± 0.002) and pancancer MoleA (AUC = 0.792 ± 0.004). In two independent cohorts (WES cohort, N = 42 and non-WES cohort, N = 94), NGS-based methodologies effectively stratified disease-free survival, with NCCNplus MoleB further predicting prognosis. Phylogenetic analysis further revealed evolutionary distinctions between MPLC and IPM, establishing an optimized NGS-based framework for differentiating multiple lung cancers.

Pancreatic ductal adenocarcinoma (PDAC) is notably resistant to conventional chemotherapy and radiation treatment. However, clinical trials indicate that carbon ion radiotherapy (CIRT) with concurrent gemcitabine is effective for unresectable locally advanced PDAC. This study aimed to identify patient characteristics predictive of CIRT response. We utilized a panel of human PDAC cell lines with diverse genetic profiles to determine their sensitivity to CIRT compared to γ-rays, assessing relative biological effectiveness (RBE) at 10% survival, which ranged from 1.96 to 3.04. Increased radiosensitivity was linked to impaired DNA double-strand break (DSB) repair, particularly in cell lines with deficiencies in the homologous recombination (HR) repair pathway and/or elevated genomic instability from replication stress. Furthermore, pretreatment with the HR inhibitor B02 significantly enhanced CIRT sensitivity in a radioresistant PDAC cell line when irradiated in the spread-out Bragg peak but not at the entry position of the beam. These findings suggest that PDAC tumors with HR pathway mutations or high replication stress are more likely to benefit from CIRT while minimizing normal tissue toxicity.

Macrophage plasticity is critical for maintaining immune function and developing solid tumors; however, the macrophage polarization mechanism remains incompletely understood. Our findings reveal that Mg entry through distinct plasma membrane channels is critical to macrophage plasticity. Naïve macrophages displayed a previously unidentified Mg dependent current, and TRPM7-like activity, which modulates its survival. Significantly, in M1 macrophages, Mg entry is facilitated by a novel Mg²-dependent current that relies on extracellular Mg, which was crucial for activating iNOS/NFκB pathways and cellular bioenergetics, which drives pro-inflammatory cytokines. Conversely, in M2 macrophages, Mg entry occurs primarily through TRPM7 channels, pivotal for IL-4 and IL-10-mediated anti-inflammatory cytokine secretion. Notably, the Mg deficient diet or addition of TRPM7 agonist Naltriben suppresses the M1 phenotype while promoting angiogenic factors and fostering tumor growth. These findings suggest that Mg flux via specific channels is indispensable for macrophage polarization, with its dysregulation playing a pivotal role in tumor progression.

Ductal carcinoma in situ (DCIS) may progress to ipsilateral invasive breast cancer (iIBC), but often never will. Because DCIS is treated as early breast cancer, many women with harmless DCIS face overtreatment. To identify features associated with progression, we developed an artificial intelligence-based DCIS morphometric analysis pipeline (AIDmap) on hematoxylin-eosin-stained (H&E) tissue sections. We analyzed 689 digitized H&Es of pure primary DCIS of which 226 were diagnosed with subsequent iIBC and 463 were not. The distribution of 15 duct morphological measurements was summarized in 55 morphometric variables. A ridge regression classifier with cross validation predicted 5-years-free of iIBC with an area-under the curve of 0.67 (95% CI 0.57-0.77). A combined clinical-morphometric signature, characterized by small-sized ducts, a low number of cells and a low DCIS/stroma ratio, was associated with outcome (HR = 0.56; 95% CI 0.28-0.78). AIDmap has potential to identify harmless DCIS that may not need treatment.

Brain metastasis leads to poor outcomes and CNS injury, significantly reducing quality of life and survival rates. Advances in understanding the tumor immune microenvironment have revealed the promise of immunotherapies, which, alongside surgery, chemotherapy, and radiation, offer improved survival for some patients. However, resistance to immunotherapy remains a critical challenge. This review explores the immune landscape of brain metastases, current therapies, clinical trials, and the need for personalized, biomarker-driven approaches to optimize outcomes.

In the context of the global increase in early-onset tumours, investigating the global disease burden caused by early-onset pancreatic cancer (EOPC) is imperative. Data on the burden of EOPC were obtained from the Global Burden of Disease Study 2021. A joinpoint regression model was used to analyse the temporal trend of the EOPC burden, and an age‒period‒cohort (APC) model was used to analyse the influence of age, period, and birth cohort on burden trends. Globally, the number of EOPC cases increased from 24,480 to 42,254, and the number of deaths increased from 17,193 to 26,996 between 1990 and 2021. The results of the APC model showed that the burden of EOPC increases with increasing age, whereas the variations in period and cohort effects exhibited a complex pattern across different sociodemographic index regions. Consequently, the disease burden of EOPC is increasing worldwide, highlighting the need for effective interventions.

Black men suffer disproportionately from prostate cancer (PCa) compared to men of other races and ethnicities. Comparing the molecular landscape of PCa among Black and White patients has the potential to identify targets for development of new precision medicine interventions. Herein, we conducted transcriptomic analysis of prostate tumors and paired tumor-adjacent normals from self-reported Black and White PCa patients and estimated patient genetic ancestry. Clinical follow-up revealed increased biochemical recurrence (BCR) among Black patients compared to White patients with high-grade PCa. Transcriptomic analysis identified differential alternative RNA splicing events (ARSs) between Black and White PCa patients. Genes undergoing genetic ancestry-concordant ARSs in high-grade or low-grade tumors involved cancer promoting genes. Most genes undergoing genetic ancestry-concordant ARSs did not exhibit differential aggregate gene expression or alternative polyadenylation. A number of the genetic ancestry-concordant ARSs associated with BCR; thus, genetic ancestry-concordant RNA splice variants may represent unique targets for PCa precision oncology.

Histopathology foundation models show great promise across many tasks, but analyses have been limited by arbitrary hyperparameters. We report the most rigorous single-task validation study to date, specifically in the context of ovarian carcinoma morphological subtyping. Attention-based multiple instance learning classifiers were compared using three ImageNet-pretrained encoders and fourteen foundation models, each trained with 1864 whole slide images and validated through hold-out testing and two external validations (the Transcanadian Study and OCEAN Challenge). The best-performing classifier used the H-optimus-0 foundation model, with balanced accuracies of 89%, 97%, and 74%, though UNI achieved similar results at a quarter of the computational cost. Hyperparameter tuning the classifiers improved performance by a median 1.9% balanced accuracy, with many improvements being statistically significant. Foundation models improve classification performance and may allow for clinical utility, with models providing a second opinion in challenging cases and potentially improving the accuracy and efficiency of diagnoses.

Osteosarcoma represents 20% of primary malignant bone tumors globally. Assessing its prognosis is challenging due to the complex roles of integrins in tumor development and metastasis. This study utilized 209,268 osteosarcoma cells from the GEO database to identify integrin-associated genes using advanced analysis methods. A novel machine learning framework combining 10 algorithms was developed to construct an Integrin-related Signature (IRS), which demonstrated robust predictive power across multiple datasets. The IRS's utility in predicting overall survival was confirmed using data from The Cancer Genome Atlas, underscoring its potential in personalized cancer management.

Predicting long-term recurrence of disease in breast cancer (BC) patients remains a significant challenge for patients with early stage disease who are at low to intermediate risk of relapse as determined using current clinical tools. Prognostic assays which utilize bulk transcriptomics ignore the spatial context of the cellular material and are, therefore, of limited value in the development of mechanistic models. In this study, Fourier-transform infrared (FTIR) chemical images of BC tissue were used to train deep learning models to predict future disease recurrence. A number of deep learning models were employed, with champion models employing two-dimensional and two-dimensional-separable convolutional networks found to have predictive performance of a ROC AUC of approximately 0.64, which compares well to other clinically used prognostic assays in this space. All-digital chemical imaging may therefore provide a label-free platform for histopathological prognosis in breast cancer, opening new horizons for future deployment of these technologies.

Publicly available trial matching tools can improve the access to therapeutic innovations, but errors may expose to over-solicitation and disappointment. We performed a pragmatic non-interventional prospective evaluation on sequential patients at the Molecular Tumor Board of Centre Leon Berard. During 10 weeks in 2024, we analysed 157 patients with four clinical trial matching tools from the 19 screened: Klineo, ScreenAct, Trialing and DigitalECMT. Each patient had 2.19 trials proposed on average, and 38% had no trials suggested. The mean performances were precision = 0.33, recall = 0.32, AP@3 = 0.45, and NDCG@3 = 0.34. Using all the tools can increase to 26% the clinical trial options. The most frequent error concerned the type of gene variants required by the selection criteria. We showed that using a Large Language Model on the patients' molecular reports could improve the performance by up to 5%. We recommend that experts supervise the results and we advocate for improved technologies.

Chemoradiotherapy (CRT) followed by durvalumab is standard for unresectable locally advanced non-small-cell lung cancer (LA-NSCLC). This study assesses how CRT alters the T-cell receptor (TCR) repertoire in CD8 + PD-1 + T-cells and its impact on clinical outcomes. This prospective study, conducted from November 2019 to May 2021 at three institutions in Japan, evaluated the diversity of TCR repertoire (DE50) in PD-1 + CD8 + T-cells and CD8 + T-cell phenotypes in peripheral blood before and after CRT. Forty patients treated with CRT were included. The diversity and usage of TCR beta variable chains (TRBV) and 14 junctional chains (TRBJ) were significantly and positively correlated before and after CRT. Regarding the DE50, the progression-free survival (PFS) of patients with DE50High before CRT was significantly greater than that of those with DE50Low (NR vs. NR months, HR 0.17, p = 0.01). The diversity of TCR repertoire might more accurately predict the efficacy of CRT followed by durvalumab therapy.

Rare cancers present significant challenges in diagnosis, treatment, and research, accounting for up to 25% of global cancer cases. Due to their rarity and atypical presentations, they are often misdiagnosed, resulting in late-stage detection and poor outcomes. Here, we describe a patient case with advanced metastatic nasopharynx NUT carcinoma, one of the rarest and most aggressive cancers. We conducted a comprehensive analysis of this patient's tumor, including Tumor Mutation Burden, Microsatellite Instability, and genetic profiling to explore further putative druggable targets. The tumor exhibited high PD-L1 expression and showed a notable response to immune checkpoint inhibitors when combined with platinum-based radio-chemotherapy. Our findings indicate that checkpoint inhibitors could play a critical role in treating NUT carcinoma, offering new therapeutic avenues and hope for patients with this challenging diagnosis. Whether PD-L1 expression may be a useful predictor of immune checkpoint inhibitor efficacy warrants further research.

Despite advances in various chemotherapy regimens, current therapeutic options are limited for ovarian cancer patients. Oxidative stress-induced growth inhibitor 1 (OSGIN1), which is a tumor suppressor gene known to regulate the cellular stress response and apoptosis, is associated with ovarian cancer development. However, the underlying mechanisms involved in ferroptosis regulation have not been elucidated. Thus, this study aimed to investigate the effect and underlying regulatory mechanism of the OSGIN1 gene on ovarian cancer cells. Our results demonstrated that loss of the OSGIN1 gene promoted ovarian cancer growth and conferred resistance to drug-induced ferroptosis. Mechanistically, the loss of OSGIN1 activates AMPK signaling through ATM, leading to the upregulation of SLC2A3, which protects cells from ferroptosis and renders them insensitive to ferroptosis inducers. Notably, an SLC2A3-neutralizing antibody enhances the ferroptosis-inducing and anticancer effects of sorafenib on ovarian cancer patient-derived xenograft tumors. Overall, anti-SLC2A3 therapy is a promising method to improve ovarian cancer treatment by targeting ferroptosis.

Patients with High-Grade Serous Ovarian Cancer (HGSOC) exhibit varied responses to treatment, with 20-30% showing de novo resistance to platinum-based chemotherapy. While hematoxylin-eosin (H&E)-stained pathological slides are used for routine diagnosis of cancer type, they may also contain diagnostically useful information about treatment response. Our study demonstrates that combining H&E-stained whole slide images (WSIs) with proteomic signatures using a multimodal deep learning framework significantly improves the prediction of platinum response in both discovery and validation cohorts. This method outperforms the Homologous Recombination Deficiency (HRD) score in predicting platinum response and overall patient survival. Our study suggests that histology and proteomics contain complementary information about biological processes determining response to first line platinum treatment in HGSOC. This integrative approach has the potential to improve personalized treatment and provide insights into the therapeutic vulnerabilities of HGSOC.