Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

V. De Falco, P.P. Vitiello, D. Ciardiello et al.

Background: Angiogenesis is a key mechanism in metastatic colorectal cancer (mCRC). Novel agents targeting this pathway, like cabozantinib, are of great therapeutic need. Patients and methods: We conducted an open-label, single-arm phase II trial to assess the antitumor activity of cabozantinib in patients with pretreated mCRC who had progressed following at least two prior lines of therapy. The primary endpoint was the progression-free survival (PFS) rate at 16 weeks, whereas secondary endpoints included median PFS, overall survival (OS), response rate, disease control rate, and safety. DNA- and RNA-based translational analyses were carried out on biological samples. Results: From October 2019 to January 2023, 33 patients were treated with oral cabozantinib, 60 mg daily. The primary endpoint was met: 11/33 assessable patients (33%) were progression-free at 16 weeks. Median PFS was 2.27 months [95% confidence interval (CI) 1.71-3.65 months], median OS was 6.25 months (95% CI 3.81-10.26 months). Disease control rate was 45.5%. Cabozantinib was generally fairly tolerated. Exploratory analyses investigating the effect of clinical disease features on PFS showed no significant correlation. Comprehensive genomic profiling on 30 patients (tissues and plasma) suggested that absence of TP53 mutations and tumor mutational burden (TMB) ≥4 mutations/Mb positively correlated with response (PFS >16 weeks). Additionally, for a subset of 18 (54.5%) patients, RNA sequencing from archival formalin-fixed paraffin-embedded samples was carried out. Molecular subtypes 4 (CMS4) was the most represented transcriptional subtype (10/18 cases). To verify if other transcriptional features were associated with treatment benefit, for each sample we computed gene set variation analysis. For epithelial-mesenchymal transition (EMT) and angiogenesis gene sets, we identified a trend toward higher scores in tumors from patients with longer PFS. Conclusion: Within the limitations of a single-arm phase II trial, cabozantinib demonstrates both safety and antitumor activity in mCRC. The observed correlation between specific molecular features, such as EMT activation and angiogenesis, and cabozantinib activity is hypothesis-generating and warrants further investigation.

Xiaohuan Zhang, Junqing Wang, Wenjuan Wu et al.

Abstract Background To develop a predictive nomogram for differentiating pancreatic cancer from periampullary non-pancreatic cancers based on computed tomography (CT) imaging features. Methods This retrospective study included 171 patients diagnosed with periampullary carcinoma (90 pancreatic cancer and 81 non-pancreatic cancer). Variables assessed included CT imaging features along with relevant clinical data. Statistically significant variables were identified through multivariable logistic regression analysis, and a predictive nomogram was developed and internally validated based on these factors. Results Multivariable analysis identified the following independent risk factors: the distance from the distal end of the dilated pancreatic duct to the medial wall of the papilla (DPDP) (odds ratio [OR] 8.76, P < 0.05), the distance from the distal end of the dilated bile duct to the medial wall of the papilla (DBDP) (OR 31.83, P < 0.05), papillary enlargement (OR 0.03, P < 0.05), and visibility of pancreatic and/or bile ducts between the tumor and the papilla (VPBD) (OR 3.97, P < 0.05). A nomogram was constructed based on these four significant features. In both the development and validation cohorts, the nomogram demonstrated robust predictive performance, with areas under the receiver operating characteristic curve (AUCs) of 0.84 (95% CI, 0.77–0.91) and 0.81 (95% CI, 0.67–0.96), respectively. Conclusions This study underscores the value of CT imaging features in distinguishing pancreatic cancer from periampullary non-pancreatic cancers. The identification of key imaging markers with significant diagnostic value facilitated the development and validation of a nomogram that integrates these features, providing a more reliable tool for clinical decision-making.

Li Y, Li C, Fan J et al.

Yanyun Li,1 Changying Li,2 Junzi Fan,3 Yutong Liu,1 Yincong Xu,4 Guowei Pang1 1School of Acupuncture and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, People’s Republic of China; 2School of Ophthalmology and optometry, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, People’s Republic of China; 3School of Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, People’s Republic of China; 4College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, People’s Republic of ChinaCorrespondence: Yincong Xu; Guowei Pang, Email onion521@sina.com; pgw@sdutcm.edu.cnBackground: Cancer immunotherapy is an advanced therapeutic approach that harnesses the body’s immune system to target and eliminate tumor cells. Traditional Chinese medicine (TCM), with a history rooted in centuries of clinical practice, plays a crucial role in enhancing immune responses, alleviating cancer-related symptoms, and reducing the risks of infections and complications in cancer patients.Methodology: This review systematically examines the current literature on TCM-based formulations in cancer immunotherapy. It explores the mechanisms by which TCM augments immune responses, particularly focusing on how these formulations influence both innate and adaptive immunity. Various TCM herbs and compounds, their active ingredients, and their application in cancer prevention and treatment were analyzed through an integrated review of preclinical studies, clinical trials, and molecular mechanistic investigations.Results: TCM formulations contribute to cancer therapy by modulating the body’s internal environment to improve immune defense. They enhance the immune system’s ability to detect and destroy cancer cells, promote apoptosis in tumor cells, inhibit tumor growth and metastasis, and augment the effectiveness of conventional cancer treatments. The review highlights specific TCM herbs and formulations that have demonstrated significant anti-cancer properties, including their ability to strengthen immune responses and provide synergistic effects with existing cancer therapies.Conclusion: TCM-derived formulations represent a promising addition to cancer immunotherapy. The mechanisms through which these formulations enhance anti-tumor immunity are multifaceted, involving modulation of immune cell activity, apoptosis induction, and suppression of tumor progression. As cancer immunotherapy evolves, the integration of TCM into conventional treatment regimens may offer enhanced therapeutic efficacy, reduced side effects, and improved overall outcomes for cancer patients. Further clinical research is needed to fully elucidate the therapeutic potential and safety of TCM-based immunotherapeutic strategies in cancer care.Keywords: traditional Chinese medicine, TCM, cancer, nanocarriers, immune ability, anti-tumor therapy, tumor microenvironment, drug delivery systems

Douglas I. Munga, Nancy A. Okinda, Geoffrey A. Omuse et al.

PURPOSEAcute leukemia is a group of hematologic malignancies categorized according to the immature cells that proliferate and replace the normal bone marrow. Flow cytometry has emerged as a cornerstone in the diagnosis of hematologic malignancies. Staged analysis with a screening tube containing specific lineage markers determines the need for subsequent testing if there is an abnormal population (blasts). The specific lineage panels to be analyzed are determined depending on the positive markers in the screening tube. This study aimed to determine the agreement of diagnosis using the acute leukemia screening tube (ALST) and the lineage-specific panel.METHODSThis was an analytical cross-sectional study performed at the Aga Khan University Hospital, Nairobi. It included 256 cases diagnosed as acute leukemia. The diagnosis after the screening tube was compared with the final diagnosis from the lineage-specific panels, and Cohen's Kappa was used to determine the level of agreement.RESULTSOf the 256 cases, the overall agreement was 94%, with 14 cases being discordant. Myeloperoxidase interpretation in the screening tube accounted for 64% (9 of 14) of the discrepant results.CONCLUSIONThe study demonstrated that the ALST is almost as good as lineage-specific panels in the diagnosis and classification of acute leukemias. The screening tube can be an added diagnostic tool to complement morphology in resource-limited centers with the capacity to interpret flow cytometry analysis.

J. T. Korley

We conducted a proof-of-concept evaluation of individualized treatment effect (ITE) estimation using survival data from a randomized trial of 475 men with advanced prostate cancer treated with high- versus low-dose diethylstilbestrol (DES). A Weibull accelerated failure time (AFT) model with interaction terms for treatment-by-age and treatment-by-log tumor size was used to capture subgroup-specific treatment effects. The estimated main effect of high-dose DES indicated a time ratio of 0.582 (95% CI: [0.306, 1.110]), reflecting reduced survival at the reference levels of age and tumor size. However, interaction-adjusted ITEs revealed marked effect modification: younger patients (e.g., age 50 years) had over fourfold expected survival gains (time ratio 4.09), whereas older patients (e.g., age 80 years) experienced reduced benefit (time ratio 0.71). Similarly, patients with larger tumors (log size $\sim$4.25, $\sim$70 $cm^2$) derived a stronger benefit (time ratio 1.89) than those with smaller tumors. To evaluate the reliability of these individualized estimates, both the delta method and bootstrap resampling were applied for uncertainty quantification, producing closely aligned intervals across the risk spectrum. This analysis illustrates how parametric survival models with clinically motivated interactions and robust inference procedures can yield interpretable patient-level treatment effect estimates, even in moderately sized oncology trials.

Santiago Cepeda, Olga Esteban-Sinovas, Roberto Romero et al.

Intraoperative ultrasound (ioUS) is a valuable tool in brain tumor surgery due to its versatility, affordability, and seamless integration into the surgical workflow. However, its adoption remains limited, primarily because of the challenges associated with image interpretation and the steep learning curve required for effective use. This study aimed to enhance the interpretability of ioUS images by developing a real-time brain tumor detection system deployable in the operating room. We collected 2D ioUS images from the Brain Tumor Intraoperative Database (BraTioUS) and the public ReMIND dataset, annotated with expert-refined tumor labels. Using the YOLO11 architecture and its variants, we trained object detection models to identify brain tumors. The dataset included 1,732 images from 192 patients, divided into training, validation, and test sets. Data augmentation expanded the training set to 11,570 images. In the test dataset, YOLO11s achieved the best balance of precision and computational efficiency, with a mAP@50 of 0.95, mAP@50-95 of 0.65, and a processing speed of 34.16 frames per second. The proposed solution was prospectively validated in a cohort of 15 consecutively operated patients diagnosed with brain tumors. Neurosurgeons confirmed its seamless integration into the surgical workflow, with real-time predictions accurately delineating tumor regions. These findings highlight the potential of real-time object detection algorithms to enhance ioUS-guided brain tumor surgery, addressing key challenges in interpretation and providing a foundation for future development of computer vision-based tools for neuro-oncological surgery.

Yuhong Zhang, Chenghang Li, Boya Wang et al.

Tumor-immune interactions are central to cancer progression and treatment outcomes. In this study, we present a stochastic agent-based model that integrates cellular heterogeneity, spatial cell-cell interactions, and drug resistance evolution to simulate tumor growth and immune response in a two-dimensional microenvironment. The model captures dynamic behaviors of four major cell types--tumor cells, cytotoxic T lymphocytes, helper T cells, and regulatory T cells--and incorporates key biological processes such as proliferation, apoptosis, migration, and immune regulation. Using this framework, we simulate tumor progression under different therapeutic interventions, including radiotherapy, targeted therapy, and immune checkpoint blockade. Our simulations reproduce emergent phenomena such as immune privilege and spatial immune exclusion. Quantitative analyses show that all therapies suppress tumor growth to varying degrees and reshape the tumor microenvironment. Notably, combination therapies--especially targeted therapy with immunotherapy--achieve the most effective tumor control and delay the emergence of resistance. Additionally, sensitivity analyses reveal a nonlinear relationship between treatment intensity and therapeutic efficacy, highlighting the existence of optimal dosing thresholds. This work demonstrates the utility of agent-based modeling in capturing complex tumor-immune dynamics and provides a computational platform for optimizing cancer treatment strategies. The model is extensible, biologically interpretable, and well-suited for future integration with experimental or clinical data.

Molly Brennan, Andrew L. Krause, Edgardo Villar-Sepúlveda et al.

Mathematical and computational modelling in oncology has played an increasingly important role in not only understanding the impact of various approaches to treatment on tumour growth, but in optimizing dosing regimens and aiding the development of treatment strategies. However, as with all modelling, only an approximation is made in the description of the biological and physical system. Here we show that tissue-scale spatial structure can have a profound impact on the resilience of tumours to immunotherapy using a classical model incorporating IL-2 compounds and effector cells as treatment parameters. Using linear stability analysis, numerical continuation, and direct simulations, we show that diffusing cancer cell populations can undergo pattern-forming (Turing) instabilities, leading to spatially-structured states that persist far into treatment regimes where the corresponding spatially homogeneous systems would uniformly predict a cancer-free state. These spatially-patterned states persist in a wide range of parameters, as well as under time-dependent treatment regimes. Incorporating treatment via domain boundaries can increase this resistance to treatment in the interior of the domain, further highlighting the importance of spatial modelling when designing treatment protocols informed by mathematical models. Counter-intuitively, this mechanism shows that increased effector cell mobility can increase the resilience of tumours to treatment. We conclude by discussing practical and theoretical considerations for understanding this kind of spatial resilience in other models of cancer treatment, in particular those incorporating more realistic spatial transport.

Md. Arafat Alam Khandaker, Ziyan Shirin Raha, Salehin Bin Iqbal et al.

Brain cancer represents a major challenge in medical diagnostics, requisite precise and timely detection for effective treatment. Diagnosis initially relies on the proficiency of radiologists, which can cause difficulties and threats when the expertise is sparse. Despite the use of imaging resources, brain cancer remains often difficult, time-consuming, and vulnerable to intraclass variability. This study conveys the Bangladesh Brain Cancer MRI Dataset, containing 6,056 MRI images organized into three categories: Brain Tumor, Brain Glioma, and Brain Menin. The dataset was collected from several hospitals in Bangladesh, providing a diverse and realistic sample for research. We implemented advanced deep learning models, and DenseNet169 achieved exceptional results, with accuracy, precision, recall, and F1-Score all reaching 0.9983. In addition, Explainable AI (XAI) methods including GradCAM, GradCAM++, ScoreCAM, and LayerCAM were employed to provide visual representations of the decision-making processes of the models. In the context of brain cancer, these techniques highlight DenseNet169's potential to enhance diagnostic accuracy while simultaneously offering transparency, facilitating early diagnosis and better patient outcomes.

Yichuan Song, Rui Zhao, Wenxuan Fu et al.

ABSTRACT Introduction This study aimed to evaluate the prognostic value of the Mayo additive staging system (MASS) and minimal residual disease (MRD) in newly diagnosed multiple myeloma (NDMM) patients. Methods A total of 238 NDMM patients were enrolled in Beijing Chaoyang Hospital. Fluorescence in‐situ hybridization and next‐generation flow cytometry were used to examine cytogenetic abnormalities and MRD, respectively. The patients were classified into three groups to compare the effects on progression‐free survival (PFS). Univariate and multivariate analyses were applied to identify the survival‐related factors. Results For MASS group, the PFS was significant difference in MASS I, II, and III patients (p = 0.0006); the patients with sustained MRD‐negative, non‐sustained MRD‐negative, sustained MRD‐positive, and non‐sustained MRD‐positive were divided into Groups 1, 2, 3, and 4, respectively. The Group 1 patients had superior PFS than Groups 2 and 3 patients (p < 0.05), but no difference in PFS was observed for Group 2 versus Group 3, Group 2 versus Group 4, and Group 3 versus Group 4 patients. For the MASS and MRD groups, among Groups 2, 3, and 4, MASS I patients had a superior PFS, while III patients showed the opposite result. Strikingly, no difference in PFS for Group 1 patients regardless of the MASS stage was observed. Despite being in MASS II and III, the PFS of Group 1 patients was longer than those with the other three groups. Response to treatment was an independent prognostic factor for MM patients. Conclusion Patients with an accumulation of high‐risk factors and MRD‐positive have poor outcomes. Sustained MRD‐negative may improve high‐risk patients' prognoses.

Christopher J. Ferreri, Manisha Bhutani

Chimeric antigen receptor (CAR) T cell therapies have dramatically improved treatment outcomes for patients with relapsed or refractory B-cell acute lymphoblastic leukemia, large B-cell lymphoma, follicular lymphoma, mantle cell lymphoma, and multiple myeloma. Despite unprecedented efficacy, treatment with CAR T cell therapies can cause a multitude of adverse effects which require monitoring and management at specialized centers and contribute to morbidity and non-relapse mortality. Such toxicities include cytokine release syndrome, immune effector cell-associated neurotoxicity syndrome, neurotoxicity distinct from ICANS, immune effector cell-associated hemophagocytic lymphohistiocytosis-like syndrome, and immune effector cell-associated hematotoxicity that can lead to prolonged cytopenias and infectious complications. This review will discuss the current understanding of the underlying pathophysiologic mechanisms and provide guidelines for the grading and management of such toxicities.

Gong Xiaochuan, Zhao Wei, Yuan Chaoyong et al.

Undifferentiated pleomorphic sarcoma (UPS) is a rare type of tumor, and UPS originating in the adrenal gland is even rarer. Up to now, there have been no reports in English literature of UPS originating from the adrenal gland. This case report presents a 44-year-old female patient with UPS of the adrenal gland, who has shown no signs of recurrence or metastasis half a year after undergoing resection of a left adrenal tumor. A retrospective analysis of the patient’s diagnosis and treatment process is conducted, with the aim of providing a reference for the diagnosis and treatment of adrenal UPS.

Michael Adeniyi

Infrared (IR) dyes, especially those within the near-infrared (NIR) spectrum, offer substantial advantages for in vivo imaging, owing to their deep tissue penetration and minimal background autofluorescence. Nanoparticles incorporating these IR dyes, such as indocyanine green (ICG) have undergone extensive investigation regarding their pharmacokinetic behavior, including biodistribution patterns and clearance mechanisms. These nanoparticles often preferentially accumulate in tumor tissues, primarily through the enhanced permeability and retention (EPR) effect, thereby establishing them as potent instruments for sophisticated tumor imaging and precision-targeted drug delivery systems. The development of near-infrared fluorescence (NIRF) for large-scale tissue imaging is attributed to its ability to generate highly specific, targeted visualization of organs. The distinctive properties of the near-infrared spectrum, including its selective absorption by biomolecules that bind to specific sites within tissues, minimal autofluorescence, and reduced light scattering, make it an appealing option for cancer imaging. Unlike the conventional structural imaging system, molecular imaging leverages these properties to differentiate between malignant and normal tissues at the molecular level, offering a more refined and precise diagnostic capability. This review offers a timely and comprehensive overview of the latest advancements in Nanoparticle-based NIRF probes and multifunctional agents for cancer molecular imaging. These advances will extend the current concepts of cancer theranostics by NIRF imaging.

Ishmael Nii Amartei Amartey

Breast Cancer is a major disease affecting women's health in the United States with incidence and prevalence dominant among younger women and the Black race. We analyzed the association between breast cancer characteristics with age and race and how survival months and age differ in racial groups. Using the Surveillance, Epidemiology, and End Results (SEER) datasets we performed a logistic regression to examine significant predictors that affect survival month. There were 3414 whites, 291 Blacks, and 320 Others (American Indian/AK Native, Asian/Pacific Islander) in the sample. We found significant associations between racial groups and ages with significant differences in age between Blacks and Whites, and Whites and Others. Patients with a breast cancer tumor in grades 1 and 2 have higher survival months (by 1.49% and 0.49%) respectively.

Xubin Wang, Yunhe Wang, Zhiqing Ma et al.

Accurate screening of cancer types is crucial for effective cancer detection and precise treatment selection. However, the association between gene expression profiles and tumors is often limited to a small number of biomarker genes. While computational methods using nature-inspired algorithms have shown promise in selecting predictive genes, existing techniques are limited by inefficient search and poor generalization across diverse datasets. This study presents a framework termed Evolutionary Optimized Diverse Ensemble Learning (EODE) to improve ensemble learning for cancer classification from gene expression data. The EODE methodology combines an intelligent grey wolf optimization algorithm for selective feature space reduction, guided random injection modeling for ensemble diversity enhancement, and subset model optimization for synergistic classifier combinations. Extensive experiments were conducted across 35 gene expression benchmark datasets encompassing varied cancer types. Results demonstrated that EODE obtained significantly improved screening accuracy over individual and conventionally aggregated models. The integrated optimization of advanced feature selection, directed specialized modeling, and cooperative classifier ensembles helps address key challenges in current nature-inspired approaches. This provides an effective framework for robust and generalized ensemble learning with gene expression biomarkers. Specifically, we have opened EODE source code on Github at https://github.com/wangxb96/EODE.

Yibing Wei, Dingka Song, Ran Wang et al.

Immunotherapies are recently emerged as a new strategy in treating various kinds of cancers which are insensitive to standard therapies, while the clinical application of immunotherapy is largely compromised by the low efficiency and serious side effects. Gut microbiota has been shown critical for the development of different cancer types, and the potential of gut microbiota manipulation through direct implantation or antibiotic-based depletion in regulating the overall efficacy of cancer immunotherapies has also been evaluated. However, the role of dietary supplementations, especially fungal products, in gut microbiota regulation and the enhancement of cancer immunotherapy remains elusive. In the present review, we comprehensively illustrated the limitations of current cancer immunotherapies, the biological functions as well as underlying mechanisms of gut microbiota manipulation in regulating cancer immunotherapies, and the benefits of dietary fungal supplementation in promoting cancer immunotherapies through gut microbiota modulation.

Steven A Rosenberg, Noam Levin, Sanghyun Kim et al.

GenYi Qu, Guang Yang, Dan Chen et al.

Background: Renal cell carcinoma (RCC) is a common malignant tumor of the urinary system with high mortality and morbidity. Although E2F2, a classical transcription factor implicated in cell cycle, has been shown to foster tumorigenesis in several human cancers, it could not draw a satisfy answer referring to precise downstream signaling axis in RCC development yet. Methods: Based on the publicly available data from TCGA database, expression patterns of E2F2, SPTLC1 and miR-16–5p were identified, either with the ability to predict the prognosis of patients with RCC, which was further validated in 38 paired RCC tissues and matched adjacent tissues by RT-qPCR and Western blot, respectively. Their cellular biofunctions were evaluated using MTT, EdU, Colony formation and transwell assays. Chromatin immunoprecipitation (ChIP) and luciferase reporter assay were employed to certain the exquisite core transcription regulatory circuitry of E2F2/miR-16–5p/SPTLC1 in RCC progression, which was also determined in xenograft tumor model. Results: Consistent with the public TCGA database, E2F2 was significantly increased in RCC tissues and cells, indicating shorter overall survival. Mechanistically, E2F2 served as a transcriptional activator of miR-16–5p, thus accounting for its negative regulation on SPTLC1 expression. E2F2 knockdown-mediated suppressive biofunctions on RCC cells were rescued by miR-16–5p mimics, while this effect was abolished again by SPTLC1 overexpression. Role of E2F2 on RCC tumorigenesis via the miR-16–5p/SPTLC1 axis was verified both in vitro and in vivo. Conclusion: E2F2 promoted RCC progression via the miR-16–5p/SPTLC1 axis, which may represent a novel prognostic and therapeutic biomarker for RCC.

Ming Lu, Zhongxin Zhou, Wei Chen et al.

BackgroundEn bloc resection of spinal tumors provides better local control and survival outcomes than intralesional resection. Safe margins during en bloc resection of primary spinal tumors with epidural involvement are required for improved outcomes. The present study describes a “rotation–reversion” technique that has been used for en bloc resection of huge primary tumors in the mobile spine with epidural involvement and reported the clinical outcomes in these patients.MethodsAll patients with primary spinal tumors who were treated with the rotation–reversion technique at our institution between 2015 and 2021 were evaluated retrospectively. Of the patients identified, those with both huge extraosseous soft-tissue masses and epidural involvement were selected for a case review. Clinical and radiological characteristics, pathologic findings, operative procedures, complications, and oncological and functional outcomes of these patients were reviewed.ResultsOf the 86 patients identified with primary spinal tumors who underwent en bloc resection using the rotation–reversion technique between 2015 and 2021, 11 had huge extraosseous soft-tissue masses with epidural involvement in the mobile spine. The average maximum size of these 11 tumors was 8.1 × 7.5 × 9.7 cm. Median follow-up time was 28.1 months, mean operation time was 849.1 min (range 465–1,340 min), and mean blood loss was 6,972.7 ml (range 2,500–17,700 ml), with 10 (91%) of the 11 patients experiencing perioperative complications. The negative margin rate was 91%, with only one patient (9%) experiencing local recurrence. Ten patients were able to walk normally or with a crutch at the last follow-up, whereas one was completely paralyzed preoperatively.ConclusionThe rotation–reversion technique is an effective procedure for the en bloc resection of huge primary spinal tumors, with the extension of invasion in selected patients including not only the vertebral body but also the pedicle and part of the posterior arch.

Mohammad Mahdi Behzadi, Mohammad Madani, Hanzhang Wang et al.

Prostate cancer is the most common cancer in men worldwide and the second leading cause of cancer death in the United States. One of the prognostic features in prostate cancer is the Gleason grading of histopathology images. The Gleason grade is assigned based on tumor architecture on Hematoxylin and Eosin (H&E) stained whole slide images (WSI) by the pathologists. This process is time-consuming and has known interobserver variability. In the past few years, deep learning algorithms have been used to analyze histopathology images, delivering promising results for grading prostate cancer. However, most of the algorithms rely on the fully annotated datasets which are expensive to generate. In this work, we proposed a novel weakly-supervised algorithm to classify prostate cancer grades. The proposed algorithm consists of three steps: (1) extracting discriminative areas in a histopathology image by employing the Multiple Instance Learning (MIL) algorithm based on Transformers, (2) representing the image by constructing a graph using the discriminative patches, and (3) classifying the image into its Gleason grades by developing a Graph Convolutional Neural Network (GCN) based on the gated attention mechanism. We evaluated our algorithm using publicly available datasets, including TCGAPRAD, PANDA, and Gleason 2019 challenge datasets. We also cross validated the algorithm on an independent dataset. Results show that the proposed model achieved state-of-the-art performance in the Gleason grading task in terms of accuracy, F1 score, and cohen-kappa. The code is available at https://github.com/NabaviLab/Prostate-Cancer.