Predict Tumor Progression Research Articles

DTI analysis of the peritumoral zone of diffuse low-grade gliomas in progressing patients

DLGGs are rare brain tumors transforming to higher grade even with surgery, chemotherapy and radiotherapy. Their preferential infiltration of WM tracts, beyond tumor boundaries on FLAIR, make difficult to plan focal treatment such as surgery, radiotherapy and monitor response to chemotherapy. DTI might reflect this infiltration of WM tracts. The aim of our study is to assess how DTI signal in the peritumoral zone might be modified before FLAIR tumor progression appears at one-year follow-up. The study retrospectively enrolled five patients who met inclusion criteria: DTI with 25 directions, T1 and FLAIR at initial imaging; FLAIR at one-year follow-up. Patients with surgery, radiotherapy and chemotherapy completed less than 2 years before initial imaging were excluded. FLAIR tumor progression, named progression mask, was assessed by subtracting tumor masks between initial imaging and one-year follow-up. Initial DTI signal was analyzed within this progression mask and compared with the healthy contralateral side. Tumor progression was confirmed for the five patients at one year. All patients showed pre-existing DTI signal abnormalities within the progression mask. Mean FA (p = 0.03) was lower in the progression mask, whereas MD, AD and RD mean (p = 0.03) was higher in the progression mask, compared to healthy side. This study shows pre-existing DTI signal abnormalities in regions with tumor progression at one year. Such abnormalities could correspond to a tumor infiltration not yet visible on FLAIR. This might be helpful to predict tumor progression and allow to adapt the therapeutic strategy.

Altered immunophenotypic expression in the peripheral bladder cancer immune landscape.

Treatments targeting the immune system only benefit a subset of patients with bladder cancer (BC). Biomarkers predictive of BC progression and response to specific therapeutic interventions are required. We evaluated whether peripheral blood immune subsets and expression of clinically relevant immune checkpoint markers are associated with clinicopathologic features of BC. Peripheral blood mononuclear cells isolated from blood collected from 23 patients with BC and 9 age-matched unaffected-by-cancer control donors were assessed using a 21-parameter flow cytometry panel composed of markers of T, B, natural killer and myeloid populations and immune checkpoint markers. Patients with BC had significantly lower numbers of circulating CD19+ B cells and elevated circulating CD4+CD8+ Tcells compared with the control cohort. Immune checkpoint markers programmed cell death protein 1 (PD-1) and T-cell immunoglobulin and mucin-domain containing-3 (TIM-3) were elevated in the total peripheral immune cell population in patients with BC. Within the BC cohort, PD-1 expression in T and myeloid cells was elevated in muscle-invasive compared with non-muscle-invasive disease. In addition, elevated T, B and myeloid PD-1 cell surface expression was significantly associated with tumor stage, suggesting that measures of peripheral immune cell exhaustion may be a predictor of tumor progression in BC. Finally, positive correlations between expression levels of the various immune checkpoints both overall and within key peripheral blood immune subsets collected from patients with BC were observed, highlighting likely coregulation of peripheral immune checkpoint expression. The peripheral blood immunophenotype in patients with BC is altered compared with cancer-free individuals. Understanding this dysregulated immune profile will contribute to the identification of diagnostic and prognostic indicators to guide effective immune-targeted, personalized treatments.

Non-glycanated ΔDCN isoform in muscle invasive bladder cancer mediates cancer stemness and gemcitabine resistance.

The small leucine-rich proteoglycan decorin (DCN) is recognized for its diverse roles in tissue homeostasis and malignant progression. Nevertheless, the regulatory effects of DCN on bladder cancer stem cells (BCSCs) and the underlying mechanisms in muscle-invasive bladder cancer (MIBC) remain to be elucidated. The study obtained data (including scRNA-seq, clinicopathological characteristics, and survival) were acquired from TCGA and GEO. The BCSCs were cultured by enriching the suspension culture in a serum-free medium, followed by flow cytometry sorting. Overexpression/knockdown was constructed by utilizing lentivirus. The surface biomarkers of cancer stem cells were identified via flow cytometry. Cell proliferation and self-renewal were evaluated by CCK8 and Sphere formation assays, and in vivo tumor growth was evaluated with subcutaneous xenografts. Total DCN expression was significantly elevated in muscle-invasive bladder cancer (MIBC) and was associated with poor prognosis. The ΔDCN isoform, which lacks glycosylation sites, was identified in bladder cancer stem cells (BCSCs) derived from clinical tissue samples and bladder cancer cell lines. Suppression of ΔDCN expression resulted in a reduction of BCSC stemness. Both in vitro and in vivo experiments indicated that overexpression of full-length DCN inhibited stemness within the extracellular matrix. Conversely, overexpression of ΔDCN and the introduction of exogenous recombinant decorin protein in ΔDCN-knockdown BCSC-SW780 cell lines enhanced stemness within the cytoplasm. The ΔDCN isoform exhibited resistance to gemcitabine chemotherapy in vitro. Non-glycanated ΔDCN isoforms were identified in bladder cancer stem cells (BCSCs), where they exhibited differential cytoplasmic localization and promoted oncogenic effects by inducing a stemness phenotype and conferring resistance to gemcitabine chemotherapy. These oncogenic effects are in stark contrast to the anti-tumor functions of glycosylated DCN in the extracellular matrix. The ratio of ΔDCN isoforms to glycosylated DCN is pivotal in predicting tumor progression and therapeutic resistance.

Development and Validation of Machine Learning Models for Predicting Tumor Progression in OSCC.

Development of a prediction model using machine learning (ML) method for tumor progression in oral squamous cell carcinoma (OSCC) patients would provide risk estimation for individual patient outcomes. This predictive modeling study was conducted of 1163 patients with OSCC from Hospital of Stomatology, SYSU and SYSU Cancer Center from March 2009 to October 2021. Clinical, pathological, and hematological features of the patients were collected. Six ML algorithms were explored, and model performance was assessed by accuracy, sensitivity, specificity, f1 score, and AUC. SHAP values were used to identify the variables with the greatest contribution to the model. Among the 1163 patients (mean [SD] age, 55.36 [12.91] years), 563 are from development cohort and 600 are from validation cohort. The Logistic Regression algorithm outperformed all other models, with a sensitivity of 94.7% (68.2%), a specificity of 55.3% (63.7%), and the AUC of 0.76 ± 0.09 (0.723) in the development (validation) cohort. The most predictive feature was neutrophil count. This study demonstrated ML models can improve clinical prediction of oral squamous cell carcinoma progression through basic information of patients. These tools could be used to provide individual risk estimation and may help direct intervention.

Prognostic Value of PlGF Upregulation in Prostate Cancer.

Prostate cancer (PCa) is the second most commonly diagnosed cancer in men worldwide, with metastasis, particularly to bone, being the primary cause of mortality. Currently, prognostic markers like PSA levels and Gleason classification are limited in predicting metastasis, emphasizing the need for novel clinical biomarkers. New molecules predicting tumor progression have been identified over time. Some, such as the immune checkpoint inhibitors (ICIs) PD-1/PD-L1, have become valid markers as theranostic tools essential for prognosis and drug target therapy. However, despite the success of ICIs as an anti-cancer therapy for solid tumors, their efficacy in treating bone metastases has mainly proven ineffective, suggesting intrinsic resistance to this therapy in the bone microenvironment. This study explores the potential of immunological intratumoral biomarkers, focusing on placental growth factor (PlGF), Vascular Endothelial Growth Factor Receptor 1 (VEGFR1), and Programmed Cell Death Protein 1 (PD-1), in predicting bone metastasis formation. we analyzed PCa samples from patients with and without metastasis by immunohistochemical analysis. Results revealed that PlGF expression is significantly higher in primary tumors of patients that developed metastasis within five years from the histological diagnosis. Additionally, PlGF expression correlates with increased VEGFR1 and PD-1 levels, as well as the presence of intratumoral M2 macrophages. These findings suggest that PlGF contributes to an immunosuppressive environment, thus favoring tumor progression and metastatic process. Results here highlight the potential of integrating these molecular markers with existing prognostic tools to enhance the accuracy of metastasis prediction in PCa. By identifying patients at risk for metastasis, clinicians can tailor treatment strategies more effectively, potentially improving survival outcomes and quality of life. This study underscores the importance of further research into the role of intratumoral biomarkers in PCa management.

Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics.

Introduction: Accurate prediction of tumor dynamics following Gamma Knife radiosurgery (GKRS) is critical for optimizing treatment strategies for patients with brain metastases (BMs). Traditional machine learning (ML) algorithms have been widely used for this purpose; however, recent advancements in deep learning, such as autoencoders, offer the potential to enhance predictive accuracy. This study aims to evaluate the efficacy of autoencoders compared to traditional ML models in predicting tumor progression or regression after GKRS. Objectives: The primary objective of this study is to assess whether integrating autoencoder-derived features into traditional ML models can improve their performance in predicting tumor dynamics three months post-GKRS in patients with brain metastases. Methods: This retrospective analysis utilized clinical data from 77 patients treated at the "Prof. Dr. Nicolae Oblu" Emergency Clinic Hospital-Iasi. Twelve variables, including socio-demographic, clinical, treatment, and radiosurgery-related factors, were considered. Tumor progression or regression within three months post-GKRS was the primary outcome, with 71 cases of regression and 6 cases of progression. Traditional ML models, such as Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extra Trees, Random Forest, and XGBoost, were trained and evaluated. The study further explored the impact of incorporating features derived from autoencoders, particularly focusing on the effect of compression in the bottleneck layer on model performance. Results: Traditional ML models achieved accuracy rates ranging from 0.91 (KNN) to 1.00 (Extra Trees). Integrating autoencoder-derived features generally enhanced model performance. Logistic Regression saw an accuracy increase from 0.91 to 0.94, and SVM improved from 0.85 to 0.96. XGBoost maintained consistent performance with an accuracy of 0.94 and an AUC of 0.98, regardless of the feature set used. These results demonstrate that hybrid models combining deep learning and traditional ML techniques can improve predictive accuracy. Conclusion: The study highlights the potential of hybrid models incorporating autoencoder-derived features to enhance the predictive accuracy and robustness of traditional ML models in forecasting tumor dynamics post-GKRS. These advancements could significantly contribute to personalized medicine, enabling more precise and individualized treatment planning based on refined predictive insights, ultimately improving patient outcomes.

Neural networks in oncourology

In recent decades, neural networks have been widely applied in many fields of science and medicine. Accurate and early diagnosis of malignancies is a key challenge in oncology. Neural networks can analyse a wide range of medical data and identify relationships between qualitative and quantitative features. This allows for more precise and timely diagnoses. Moreover, they can be used to predict tumour progression, evaluate treatment effectiveness, and optimise treatment plans for each patientIn oncourology, the use of neural networks offers new perspectives for the diagnosis, prognosis, and treatment of various cancer conditions related to the urinary tract and male reproductive system. This review article explores how neural networks are being used in this field and present research into the use of neural networks for diagnosing, predicting the course and treating urological oncological diseases. The advantages and limitations of using neural networks in this field are demonstrated, and possible directions for future research are suggested. The application of neural networks in oncourology opens new horizons for the development of a personalised approach to diagnosing and treating oncological diseases. Artificial intelligence has the potential to become a powerful tool for improving the accuracy of patient outcome predictions and reducing undesirable side effects of therapy. Introducing neural networks into oncourological practice creates new opportunities for enhancing the work of healthcare organisations and improving the quality of care provided to patients. This can lead to better treatment outcomes and improved patient satisfaction.

Advanced tumor growth modeling: A numerical study integrating phase plane analysis with finite volume method

This study investigates tumor density dynamics through computational modeling, focusing on key parameters: proliferation rate m, diffusion coefficient τ, and additional factors c and κ. Systematic exploration reveals nuanced interactions shaping tumor growth, regression, and dispersal. Higher m values accelerate proliferation, while increased τ facilitates dispersal. Parameters c and κ further refine our understanding of tumor behavior. These insights inform the development of computational models for oncological research, with implications for targeted therapeutic interventions. The aim can be realized by utilizing the propose Finite Volume Method to develop a representation of cancer models and improve the efficiency of their simulations. Furthermore, the numerical results will be validated by comparing them with the Tanh-Coth method formulation to assess the model's accuracy and efficiency. The findings suggest that accurate modeling of tumor dynamics can aid in predicting tumor progression and response to treatments, ultimately contributing to personalized medicine and more effective therapeutic strategies. Phase plane analysis shows the schemes are conditionally stable and accurate to the fourth order in time. The method demonstrates exceptional agreement and error-free findings compared to 3D plots, underscoring its potential utility in clinical and research settings.

Hyperpolarized Magnetic Resonance Imaging, Nuclear Magnetic Resonance Metabolomics, and Artificial Intelligence to Interrogate the Metabolic Evolution of Glioblastoma.

Glioblastoma (GBM) is a malignant Grade VI cancer type with a median survival duration of only 8-16 months. Earlier detection of GBM could enable more effective treatment. Hyperpolarized magnetic resonance spectroscopy (HPMRS) could detect GBM earlier than conventional anatomical MRI in glioblastoma murine models. We further investigated whether artificial intelligence (A.I.) could detect GBM earlier than HPMRS. We developed a deep learning model that combines multiple modalities of cancer data to predict tumor progression, assess treatment effects, and to reconstruct in vivo metabolomic information from ex vivo data. Our model can detect GBM progression two weeks earlier than conventional MRIs and a week earlier than HPMRS alone. Our model accurately predicted in vivo biomarkers from HPMRS, and the results inferred biological relevance. Additionally, the model showed potential for examining treatment effects. Our model successfully detected tumor progression two weeks earlier than conventional MRIs and accurately predicted in vivo biomarkers using ex vivo information such as conventional MRIs, HPMRS, and tumor size data. The accuracy of these predictions is consistent with biological relevance.

Far-Red Aggregation-Induced Emission Hydrogel-Reinforced Tissue Clearing for 3D Vasculature Imaging of Whole Lung and Whole Tumor.

Understanding the vascular formation and distribution in metastatic lung tumors is a significant challenge due to autofluorescence, antibody/dye diffusion in dense tumor, and fluorophore stability when exposed to solvent-based clearing agents. Here, an approach is presented that redefines 3D vasculature imaging within metastatic tumor, peritumoral lung tissue, and normal lung. Specifically, a far-red aggregation-induced emission nanoparticle with surface amino groups (termed as TSCN nanoparticle, TSCNNP) is designed for in situ formation of hydrogel (TSCNNP@Gel) inside vasculatures to provide structural support and enhance the fluorescence in solvent-based tissue clearing method. Using this TSCNNP@Gel-reinforced tissue clearing imaging approach, the critical challenges are successfully overcome and comprehensive visualization of the whole pulmonary vasculature up to 2µm resolution is enabled, including its detailed examination in metastatic tumors. Importantly, features of tumor-associated vasculature in 3D panoramic views are unveiled, providing the potential to determine tumor stages, predict tumor progression, and facilitate the histopathological diagnosis of various tumor types.

An 18-gene signature of recurrence-associated endothelial cells predicts tumor progression and castration resistance in prostate cancer

BackgroundThe prognostic and therapeutic implications of endothelial cells (ECs) heterogeneity in prostate cancer (PCa) are poorly understood.MethodsWe investigated associations of EC heterogeneity with PCa recurrence and castration resistance in 8 bulk transcriptomic and 4 single-cell RNA-seq cohorts. A recurrence-associated EC (RAEC) signature was constructed by comparing 11 machine learning algorithms through nested cross-validation. Functional relevances of RAEC-specific genes were also tested.ResultsA subset of ECs was significantly associated with recurrence in primary PCa and named RAECs. RAECs were characteristic of tip and immature cells and were enriched in migration, angiogenesis, and collagen-related pathways. We then developed an 18-gene RAEC signature (RAECsig) representative of RAECs. Higher RAECsig scores independently predicted tumor recurrence and performed better or comparably compared to clinicopathological factors and commercial gene signatures in multiple PCa cohorts. Of the 18 RAECsig genes, FSCN1 was upregulated in ECs from PCa with higher Gleason scores; and the silencing of FSCN1, TMEME255B, or GABRD in ECs either attenuated tube formation or inhibited PCa cell proliferation. Finally, higher RAECsig scores predicted castration resistance in both primary and castration-resistant PCa.ConclusionThis study establishes an endothelial signature that links a subset of ECs to prostate cancer recurrence and castration resistance.

Lasso-Cox interpretable model of AFP-negative hepatocellular carcinoma.

In AFP-negative hepatocellular carcinoma patients, markers for predicting tumor progression or prognosis are limited. Therefore, our objective is to establish an optimal predicet model for this subset of patients, utilizing interpretable methods to enhance the accuracy of HCC prognosis prediction. We recruited a total of 508 AFP-negative HCC patients in this study, modeling with randomly divided training set and validated with validation set. At the same time, 86 patients treated in different time periods were used as internal validation. After comparing the cox model with the random forest model based on Lasso regression, we have chosen the former to build our model. This model has been interpreted with SHAP values and validated using ROC, DCA. Additionally, we have reconfirmed the model's effectiveness by employing an internal validation set of independent periods. Subsequently, we have established a risk stratification system. The AUC values of the Lasso-Cox model at 1, 2, and 3years were 0.807, 0.846, and 0.803, and the AUC values of the Lasso-RSF model at 1, 2, and 3years were 0.783, 0.829, and 0.776. Lasso-Cox model was finally used to predict the prognosis of AFP-negative HCC patients in this study. And BCLC stage, gamma-glutamyl transferase (GGT), diameter of tumor, lung metastases (LM), albumin (ALB), alkaline phosphatase (ALP), and the number of tumors were included in the model. The validation set and the separate internal validation set both indicate that the model is stable and accurate. Using risk factors to establish risk stratification, we observed that the survival time of the low-risk group, the middle-risk group, and the high-risk group decreased gradually, with significant differences among the three groups. The Lasso-Cox model based on AFP-negative HCC showed good predictive performance for liver cancer. SHAP explained the model for further clinical application.

Prediction of Efficacy for Atezolizumab/Bevacizumab in Unresectable Hepatocellular Carcinoma with Hepatobiliary-Phase Gadolinium Ethoxybenzyl-Diethylenetriaminepentaacetic Acid MRI.

This study aimed to examine whether the coefficient of variation (CV) in the hepatobiliary-phase (HBP) of Gd-EOB-DTPA-MRI could be an independent predictive factor for tumor progression. Patients who underwent Gd-EOB-DTPA-MRI before Atezolizumab/bevacizumab therapy at six affiliated institutions between 2018 and 2022 were included. CV for each patient was calculated as the mean value for up to five tumors larger than 10 mm, and CV of the whole tumor was calculated using LIFEx software. The tumor response was evaluated within 6-10 weeks. The primary endpoint was to investigate the predictive factors, including CV, related to tumor progression using logistic regression analysis. The secondary endpoints were tumor response rate and progression-free survival (PFS) based on CV. Of the 46 enrolled patients, 13 (28.3%) underwent early progressive disease. Multivariate analysis revealed that a high CV (≥0.22) was an independent predictive factor for tumor progression (p = 0.043). Patients with a high CV had significantly frequent PD than those with a low CV (43.5 vs. 13.0%, p = 0.047). Patients with a high CV tended to have shorter PFS than those with a low CV (3.5 vs. 6.7 months, p = 0.071). Quantitative analysis using CV in the HBP of Gd-EOB-DTPA-MRI may be useful for predicting tumor progression for atezolizumab/bevacizumab therapy.

IMG-11. RADIOGENOMIC ANALYSIS INFORMS ON IMMUNOLOGICAL PROFILES, PROGNOSIS, AND TREATMENT RESPONSE IN PEDIATRIC LOW-GRADE GLIOMA

Abstract BACKGROUND Pediatric low-grade gliomas (pLGGs) have variable prognosis and treatment responses. Complete resection cannot be achieved for all tumors, especially for highly infiltrative or deep-seated tumors, necessitating additional therapy, from chemotherapy to targeted inhibitors. We integrated imaging-derived phenotypes with genotypic traits from transcriptional analysis, offering an in-depth characterization of pLGG immune microenvironment, progression risk, and likelihood of multiple treatments. METHODS Analyzing 549 treatment-naïve pLGGs with multiparametric MRI and RNA sequencing, we identified distinct immunological groups using XCell scores based on immune cell infiltration. We developed a radiomic signature using conventional MRI and machine learning techniques (support vector machines with a linear kernel and nested cross-validation) to distinguish the ‘immune-hot’ group, and incorporated diffusion MRI to improve signature accuracy. Additionally, a clinicoradiomic model predicting tumor progression risk and treatment response was trained, integrating clinical and radiomic data. Transcriptomic analysis was conducted to identify pathways correlated with clinicoradiomic risk, predictive of pLGG progression. RESULTS Three immunological groups were revealed, the ‘immune-hot’ group characterized by poor prognosis due to a high concentration of pro-tumorigenic M2-polarized macrophages, despite a higher preponderance of T-lymphocytes. The radiomic signature effectively distinguished the ‘immune-hot’ group with balanced accuracies of 76.8%/86.0% in discovery/replication sets, improved by diffusion MRI to 81.5%/84.4%. The clinicoradiomic model showed concordance indices of 0.71 (discovery) and 0.77 (replication), predicting patient progression risk. Significant differences (p=0.0010) were found in clinicoradiomic risk scores between patients undergoing one versus multiple treatments post-diagnosis, linking higher scores to a likelihood of multiple treatments. Transcriptomic pathways associated with higher clinicoradiomic risk highlighted the importance of fatty acid oxidation, a tumor-promoting mechanism that drives adaptive resistance to cytolytic immune cell effectors. CONCLUSIONS This first large-scale radiogenomic analysis in pLGGs aids in prognostication, assessing progression risk, predicting treatment response to standard-of-care therapies, and stratification of patients to identify potential candidates for novel therapies targeting aberrantly regulated pathways.

IMG-12. CBTN BRAIN TUMOR SEGMENTATION INITIATIVE: UPDATES ON MODEL RELEASE, HGG SEGMENTATION, AND SURVIVAL ANALYSIS

Abstract BACKGROUND Assessment of treatment responses in pediatric brain tumors requires accurate tumor segmentation, particularly important for nonresectable tumors like diffuse midline glioma (DMG), including diffuse intrinsic pontine glioma (DIPGs). Evaluating tumor progression in these tumors relies on monitoring tumor size changes in longitudinal MRI exams which is challenged by their infiltrative growth patterns. Our team developed a comprehensive multi-institutional, multi-histology autosegmentation deep learning (DL) model leveraging data from the Children’s Brain Tumor Network (CBTN), involving continuous enhancements and enriched by an expanding dataset including post-surgical/post-treatment studies. METHODS Recently, we trained and validated this DL model (publicly available) on a multi-institutional dataset of 644 PBTs, including 273 pediatric-type diffuse high-grade gliomas (HGGs, including DMG/DIPGs), using pre-operative and post-operative/post-treatment multiparametric MRI scans. We tested the model in 23 longitudinal MRI exams of 8 DMGs from the PNOC003 and PNOC007 clinical trials and applied Cox regression for predicting overall survival (OS) using tumor volume, computed with our autosegmentation model, as a time-varying variable in 18 patients. RESULTS For HGGs in treatment-naïve sessions, our model demonstrated strong performance with median Dice scores of 0.89 for whole tumor and tumor core (encompassing all regions, except for edema), and 0.73 for enhancing tumor segmentation. In longitudinal MRI exams of DMGs from the aforementioned clinical trials, the model achieved median Dice scores of 0.79 for whole tumor, and 0.78 for enhancing tumor and tumor core segmentation. Cox regression showed the significance of longitudinal volumetric measurements (p=0.08) for predicting OS in DMGs. CONCLUSIONS Our approach provides reliable tumor segmentation in HGGs across diverse datasets, crucial for treatment response assessment. Looking ahead, we plan to validate our model on a larger set of longitudinal exams and use volumetric measurements to predict tumor progression. This ongoing effort aims to improve model performance, extend its generalizability, and mitigate potential model decay.

A phase II trial of olaparib and durvalumab in patients with recurrent IDH-mutated gliomas.

2013 Background: Isocitrate dehydrogenase mutations (IDHmt) define astrocytomas and oligodendrogliomas. IDHmt results in accumulation of R-2-hydroxyglutarate (2HG), leading to epigenetic dysregulation and defective homologous recombination repair, providing a rationale for poly (adenosine 5’-diphophate-ribose) polymerase (PARP) inhibitors. PARP inhibition upregulates PD-L1 so the combination with immune checkpoint inhibition is potentially synergistic. Methods: Patients (pts) with recurrent high-grade IDHmt gliomas were enrolled in this phase II open-label study (NCT03991832). Eligibility included progressive disease with up to 2 prior lines of systemic therapies and ECOG 0–1. Pts received olaparib 300 mg twice daily continuously and durvalumab 1500 mg IV every 4 weeks. Simon’s optimal two-stage design was used. The primary objective was overall response rate (ORR) and disease control rate (DCR) by RANO criteria. Secondary objectives included overall survival (OS), progression free survival (PFS) and safety.Exploratory biomarkers of response and resistance were assessed with serial blood samples using cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) as well as multiplex-immunohistochemistry. Results: In the 29 pts enrolled between January 2020–February 2023, median age was 40.5 (range 23–66) and 41% were female. Initial tumor grade was 2 in 9, 3 in 8 and 4 in 12 pts, respectively. Median time to enrollment from tumour diagnosis was 5.9 years. All had prior resection and median number of prior systemic therapies was 2. One patient clinically deteriorated before starting treatment. ORR was 10%, 95% CI 2.2–27%, with responses in 3 pts. One pt with grade 4 astrocytoma had a complete response and remains on treatment after 33 months. The other 2 responders with grade 4 astrocytoma had response durations of 4.1 and 9.8 months. DCR was 28% (95% CI 12.7–47.2%) with 5 additional pts demonstrating stable disease. With a median follow-up of 33 months, mOS was 9.5 months (95% CI 4.3–19.3) and mPFS was 1.9 months (95% CI 1.8–3.0). There was no treatment-related grade 3–4 toxicities. Any grade toxicities included fatigue (48%), nausea (17%), diarrhea (10%), and cytopenias (3%). Using serial cfMeDIP-seq, cell free DNA methylomes comparing responders versus progressors using the top differentially methylated regions can predict treatment response with high accuracy (AUC 0.833). Post progression, differentially methylated genes converge on TGF-β and signal transduction pathways, suggesting possible mechanisms of resistance. Multi-modal analysis of long-term responders will additionally be presented. Conclusions: Combination treatment with olaparib and durvalumab for pts with IDHmt glioma is well tolerated but has limited efficacy in unselected pts. cfMeDIP-seq can reliably predict tumour progression providing a blood-based biomarker of treatment response. Clinical trial information: NCT03991832 .

Advancements in breast cancer progression monitoring: Analysis of methylated circulating cell- free DNA.

e12548 Background: Breast cancer is a leading cause of mortality among women, with a significant number developing metastasis. Existing markers like CA-153 and CEA have limited sensitivity. Circulating cell-free DNA (ccfDNA) in plasma can be used for the noninvasive sampling of cancer cells obtained from patients with breast cancer. Aberrant circulating methylated GCM2 and TMEM240 have been reported in patients with breast cancer. The study aims to evaluate the potential of simultaneous detection of methylated GCM2 and TMEM240 in plasma for enhanced prediction of residual disease and tumor progression. Methods: In a prospective study conducted in Taipei, Taiwan, we enrolled a total of 396 patients between 2016 and 2023. The retrospective study involving plasma samples from Western populations included a total of 134 samples. Utilizing the Monte Carlo replicates module within the R software, is employed to automate the random partitioning of 70% of the entire patient dataset into a training set, while the remaining 30% is assigned to the validation set. The model's performance, including sensitivity, specificity, accuracy, false positives, and false negatives, will be thoroughly assessed to ensure its robustness. Results: The test demonstrated an accuracy of 91.5%, a sensitivity of 95.7%, and a specificity of 90.3% for prediction of residual disease and tumor progression. CA15-3 and CEA markers show limited sensitivity (53.3% and 33.3%, respectively). The tests also assess the tumor burden dynamically through monitoring circulating methylated GCM2 and TMEM240 in plasma of patients with breast cancer following their treatment. Conclusions: The simultaneous detection of circulating methylated GCM2 and TMEM240 in plasma presents a novel and accurate noninvasive tool for predicting tumor progression. Clinical trial information: N201708049, N202105091, N202202061.

The prognostic marker KRT81 is involved in suppressing CD8 + T cells and predicts immunotherapy response for triple-negative breast cancer

ABSTRACT Triple-negative breast Cancer (TNBC) is an aggressive subtype lacking estrogen, progesterone, and HER2 receptors. Known for limited targeted therapies, it poses challenges and requires personalized treatment strategies. Differential analysis revealed a significant decrease in keratin 81 (KRT81) expression in non-TNBC samples and an increase in TNBC samples, lower KRT81 expression correlated with better TNBC patient outcomes. It emerged as an independent predictive factor for TNBC, with associations found between its expression and clinically relevant features. We further developed a nomogram for survival probability assessment based on Cox regression results, demonstrating its accuracy through calibration curves. Gene annotation analysis indicated that KRT81 is involved in immune-related pathways and tumor cell adhesion. KRT81 is associated with immune cell infiltration of Follicular helper T cells (Tfh) and CD8 + T cells, suggesting its potential impact on the immunological microenvironment. The study delved into KRT81‘s predictive value for immunotherapy responses, high expression of KRT81 was associated with greater potential for immune evasion. Single-cell RNA sequencing analysis pinpointed KRT81 expression within a specific malignant subtype which was a risk factor for TNBC. Furthermore, KRT81 promoted TNBC cell proliferation, migration, invasion, and adhesion was confirmed by gene knockout or overexpression assay. Co-culture experiments further indicated KRT81‘s potential role in inhibiting CD8 + T cells, and correlation analysis implied KRT81 was highly correlated with immune checkpoint CD276, providing insights into its involvement in the immune microenvironment via CD276. In conclusion, this comprehensive study positions KRT81 as a promising prognostic marker for predicting tumor progression and immunotherapy responses in TNBC.

Proton versus photon adjuvant radiotherapy: a multicenter comparative evaluation of recurrence following spinal chordoma resection.

Chordomas are rare tumors of the skull base and spine believed to arise from the vestiges of the embryonic notochord. These tumors are locally aggressive and frequently recur following resection and adjuvant radiotherapy. Proton therapy has been introduced as a tissue-sparing option because of the higher level of precision that proton-beam techniques offer compared with traditional photon radiotherapy. This study aimed to compare recurrence in patients with chordomas receiving proton versus photon radiotherapy following resection by applying tree-based machine learning models. The clinical records of all patients treated with resection followed by adjuvant proton or photon radiotherapy for chordoma at Mayo Clinic were reviewed. Patient demographics, type of surgery and radiotherapy, tumor recurrence, and other variables were extracted. Decision tree classifiers were trained and tested to predict long-term recurrence based on unseen data using an 80/20 split. Fifty-three patients with a mean ± SD age of 55.2 ± 13.4 years receiving surgery and adjuvant proton or photon therapy to treat chordoma were identified; most patients were male. Gross-total resection was achieved in 54.7% of cases. Proton therapy was the most common adjuvant radiotherapy (84.9%), followed by conventional or external-beam radiation therapy (9.4%) and stereotactic radiosurgery (5.7%). Patients receiving proton therapy exhibited a 40% likelihood of having recurrence, significantly lower than the 88% likelihood observed in those treated with nonproton therapy. This was confirmed on logistic regression analysis adjusted for extent of tumor resection and tumor location, which revealed that proton adjuvant radiotherapy was associated with a decreased risk of recurrence (OR 0.1, 95% CI 0.01-0.71; p = 0.047) compared with photon therapy. The decision tree algorithm predicted recurrence with an accuracy of 90% (95% CI 55.5%-99.8%), with the lowest risk of recurrence observed in patients receiving gross-total resection with adjuvant proton therapy (23%). Following resection, adjuvant proton therapy was associated with a lower risk of chordoma recurrence compared with photon therapy. The described machine learning models were able to predict tumor progression based on the extent of tumor resection and adjuvant radiotherapy modality used.

Is It Possible to Predict Tumor Progression Through Genomic Characterization of Monoclonal Gammopathy and Smoldering Multiple Myeloma?

The study of monoclonal serum proteins has led to the generation of two major theories: one proposing that individuals who had monoclonal proteins without any symptoms or evidence of end-organ damage have a benign condition, the other one suggesting that some individuals with asymptomatic monoclonal proteins may progress to multiple myeloma and thus are affected by a monoclonal gammopathy of undetermined significance (MGUS). Longitudinal studies of subjects with MGUS have supported the second theory. Subsequent studies have characterized and defined the existence of another precursor of multiple myeloma, smoldering multiple myeloma (SMM), intermediate between MGUS and multiple myeloma. Primary molecular events, chromosome translocations, and chromosome number alterations resulting in hyperploidy, required for multiple myeloma development, are already observed in myeloma precursors. MGUS and SMM are heterogeneous conditions with the presence of tumors with distinct pathogenic phenotypes and clinical outcomes. The identification of MGUS and SMM patients with a molecularly defined high risk of progression to MM offers the unique opportunity of early intervention with a therapeutic approach on a low tumor burden.