Comprehensive pharmacogenomics characterization of temozolomide response in gliomas

IDH1-mutant metabolite D-2-hydroxyglutarate inhibits proliferation and sensitizes glioma to temozolomide via down-regulating ITGB4/PI3K/AKT

The heterogeneous molecular subtypes of gliomas demonstrate varied responses to chemotherapy and distinct prognostic outcomes. Gliomas with Isocitrate dehydrogenase 1 (IDH1) mutation are associated with better outcomes and are more responsive to temozolomide (TMZ) compared to those without IDH1 mutation. IDH1-mutant gliomas elevate D-2-hydroxyglutarate (D-2HG) levels, with potential dual effects on tumor progression. Limited research has explored the potential anti-glioma effects of D-2HG in combination with TMZ. Clinical data from over 2500 glioma patients in our study confirms that those with IDH1 mutations exhibit enhanced responsiveness to TMZ chemotherapy and a significantly better prognosis compared to IDH1 wild-type patients. In subsequent cellular experiments, we found that the IDH1-mutant metabolite D-2HG suppresses Integrin subunit beta 4 (ITGB4) expression, and down-regulate the phosphorylation levels of PI3K and AKT, ultimately inhibiting cell proliferation while promoting apoptosis, thereby improving glioma prognosis. Additionally, we have demonstrated the synergistic effect of D-2HG and TMZ in anti-glioma therapy involved inhibiting the proliferation of glioma cells and promoting apoptosis. Finally, by integrating data from the CGGA and TCGA databases, it was validated that ITGB4 expression was lower in IDH1-mutant gliomas, and patients with lower ITGB4 expression were associated with better prognosis. These findings indicate that ITGB4 may be a promising therapeutic target for gliomas and D-2HG inhibits proliferation and sensitizes glioma to temozolomide via down-regulating ITGB4/PI3K/AKT. These findings drive theoretical innovation and research progress in glioma therapy.

Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model

OBJECTIVES

As a few types of glioma, young high-risk low-grade gliomas (HRLGGs) have higher requirements for postoperative quality of life. Although adjuvant chemotherapy with delayed radiotherapy is the first treatment strategy for HRLGGs, not all HRLGGs benefit from it. Accurate assessment of chemosensitivity in HRLGGs is vital for making treatment choices. This study developed a multimodal fusion radiomics (MFR) model to support radiochemotherapy decision-making for HRLGGs.

METHODS

A MFR model combining macroscopic MRI and microscopic pathological images was proposed. Multiscale features including macroscopic tumor structure and microscopic histological layer and nuclear information were grabbed by unique paradigm, respectively. Then, these features were adaptively incorporated into the MFR model through attention mechanism to predict the chemosensitivity of temozolomide (TMZ) by means of objective response rate and progression free survival (PFS).

RESULTS

Macroscopic tumor texture complexity and microscopic nuclear size showed significant statistical differences (p < 0.001) between sensitivity and insensitivity groups. The MFR model achieved stable prediction results, with an area under the curve of 0.950 (95% CI: 0.942-0.958), sensitivity of 0.833 (95% CI: 0.780-0.848), specificity of 0.929 (95% CI: 0.914-0.936), positive predictive value of 0.833 (95% CI: 0.811-0.860), and negative predictive value of 0.929 (95% CI: 0.914-0.934). The predictive efficacy of MFR was significantly higher than that of the reported molecular markers (p < 0.001). MFR was also demonstrated to be a predictor of PFS.

CONCLUSIONS

A MFR model including radiomics and pathological features predicts accurately the response postoperative TMZ treatment.

CLINICAL RELEVANCE STATEMENT

Our MFR model could identify young high-risk low-grade glioma patients who can have the most benefit from postoperative upfront temozolomide (TMZ) treatment.

KEY POINTS

• Multimodal radiomics is proposed to support the radiochemotherapy of glioma. • Some macro and micro image markers related to tumor chemotherapy sensitivity are revealed. • The proposed model surpasses reported molecular markers, with a promising area under the curve (AUC) of 0.95.

Emerging roles of m6A RNA modification in cancer therapeutic resistance

Marvelous advancements have been made in cancer therapies to improve clinical outcomes over the years. However, therapeutic resistance has always been a major difficulty in cancer therapy, with extremely complicated mechanisms remain elusive. N6-methyladenosine (m6A) RNA modification, a hotspot in epigenetics, has gained growing attention as a potential determinant of therapeutic resistance. As the most prevalent RNA modification, m6A is involved in every links of RNA metabolism, including RNA splicing, nuclear export, translation and stability. Three kinds of regulators, "writer" (methyltransferase), "eraser" (demethylase) and "reader" (m6A binding proteins), together orchestrate the dynamic and reversible process of m6A modification. Herein, we primarily reviewed the regulatory mechanisms of m6A in therapeutic resistance, including chemotherapy, targeted therapy, radiotherapy and immunotherapy. Then we discussed the clinical potential of m6A modification to overcome resistance and optimize cancer therapy. Additionally, we proposed existing problems in current research and prospects for future research.

Small Extracellular Vesicles and Their Involvement in Cancer Resistance: An Up-to-Date Review

In recent years, tremendous progress has been made in understanding the roles of extracellular vesicles (EVs) in cancer. Thanks to advancements in molecular biology, it has been found that the fraction of EVs called exosomes or small EVs (sEVs) modulates the sensitivity of cancer cells to chemotherapeutic agents by delivering molecularly active non-coding RNAs (ncRNAs). An in-depth analysis shows that two main molecular mechanisms are involved in exosomal modified chemoresistance: (1) translational repression of anti-oncogenes by exosomal microRNAs (miRs) and (2) lack of translational repression of oncogenes by sponging of miRs through long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). At the cellular level, these processes increase the proliferation and survival of cancer cells and improve their ability to metastasize and resist apoptosis. In addition, studies in animal models have shown enhancing tumor size under the influence of exosomal ncRNAs. Ultimately, exosomal ncRNAs are responsible for clinically significant chemotherapy failures in patients with different types of cancer. Preliminary data have also revealed that exosomal ncRNAs can overcome chemotherapeutic agent resistance, but the results are thoroughly fragmented. This review presents how exosomes modulate the response of cancer cells to chemotherapeutic agents. Understanding how exosomes interfere with chemoresistance may become a milestone in developing new therapeutic options, but more data are still required.

Pharmacogenomic landscape of head and neck squamous cell carcinoma informs precision oncology therapy

Head and neck squamous cell carcinoma (HNSCC) is a common and frequently lethal cancer with few therapeutic options. In particular, there are few effective targeted therapies. Development of highly effective therapeutic strategies tailored to patients with HNSCC remains a pressing challenge. To address this, we present a pharmacogenomic study to facilitate precision treatments for patients with HNSCC. We established a large collection of 56 HNSCC patient-derived cells (PDCs), which recapitulated the molecular features of the original tumors. Pharmacological assessment of HNSCCs was conducted using a three-tiered high-throughput drug screening using 2248 compounds across these PDC models and an additional 18 immortalized cell lines. We integrated genomic, transcriptomic, and pharmacological analysis to predict biomarkers, gene-drug associations, and validated biomarkers. These results supported drug repurposing for multiple HNSCC subtypes, including the JAK2 inhibitor fedratinib, for low KRT18-expressing HNSCC cases, and the topoisomerase inhibitor mitoxantrone, for IL6R-activated HNSCC cases. Our results demonstrated concordance between susceptibility predictions from the PDCs and the matched patients' responses to standard clinical medication. Moreover, we identified and experimentally confirmed that high expression of ITGB1 elicited therapeutic resistance to docetaxel and high SOD1 expression conferred resistance to afatinib. We further validated ITGB1 as a predictive biomarker for the efficacy of docetaxel therapy in a phase 2 clinical trial. In summary, our study shows that this HNSCC cell resource, as well as the resulting pharmacogenomic profiles, is effective for biomarker discovery and for guiding precision oncology therapies in HNSCCs.

Marine biome-derived secondary metabolites, a class of promising antineoplastic agents: A systematic review on their classification, mechanism of action and future perspectives

Cancer is one of the most deadly diseases on the planet. Over the past decades, numerous antineoplastic compounds have been discovered from natural resources such as medicinal plants and marine species as part of multiple drug discovery initiatives. Notably, several marine flora (e.g. Ascophyllum nodosum, Sargassum thunbergii) have been identified as a rich source for novel cytotoxic compounds of different chemical forms. Despite the availability of enormous chemically enhanced new resources, the anticancer potential of marine flora and fauna has received little attention. Interestingly, numerous marine-derived secondary metabolites (e.g., Cytarabine, Trabectedin) have exhibited anticancer effects in preclinical cancer models. Most of the anticancer drugs obtained from marine sources stimulated apoptotic signal transduction pathways in cancer cells, such as the intrinsic and extrinsic pathways. This review highlights the sources of different cytotoxic secondary metabolites obtained from marine bacteria, algae, fungi, invertebrates, and vertebrates. Furthermore, this review provides a comprehensive overview of the utilisation of numerous marine-derived cytotoxic compounds as anticancer drugs, as well as their modes of action (e.g., molecular target). Finally, it also discusses the future prospects of marine-derived drug developments and their constraints.

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks

Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care systems. Recent advances in deep learning for medical imaging have shown remarkable results, especially in the automatic and instant diagnosis of various cancers. However, we need a large amount of data (images) to train the deep learning models in order to obtain good results. Large public datasets are rare in medicine. This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs). We swap the encoder-decoder network after initially training it on the training set of available MR images. The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead of random Gaussian noise. The proposed method helps the GAN to avoid mode collapse and generate realistic-looking brain tumor magnetic resonance images. These artificially generated images could solve the limitation of small medical datasets up to a reasonable extent and help the deep learning models perform acceptably. We used the ResNet50 as a classifier, and the artificially generated brain tumor images are used to augment the real and available images during the classifier training. We compared the classification results with several existing studies and state-of-the-art machine learning models. Our proposed methodology noticeably achieved better results. By using brain tumor images generated artificially by our proposed method, the classification average accuracy improved from 72.63% to 96.25%. For the most severe class of brain tumor, glioma, we achieved 0.769, 0.837, 0.833, and 0.80 values for recall, specificity, precision, and F1-score, respectively. The proposed generative model framework could be used to generate medical images in any domain, including PET (positron emission tomography) and MRI scans of various parts of the body, and the results show that it could be a useful clinical tool for medical experts.