Successes and challenges in modeling heterogeneous BRAFV600E mutated central nervous system neoplasms

Central nervous system (CNS) neoplasms are difficult to treat due to their sensitive location. Over the past two decades, the availability of patient tumor materials facilitated large scale genomic and epigenomic profiling studies, which have resulted in detailed insights into the molecular underpinnings of CNS tumorigenesis. Based on results from these studies, CNS tumors have high molecular and cellular intra-tumoral and inter-tumoral heterogeneity. CNS cancer models have yet to reflect the broad diversity of CNS tumors and patients and the lack of such faithful cancer models represents a major bottleneck to urgently needed innovations in CNS cancer treatment. Pediatric cancer model development is lagging behind adult tumor model development, which is why we focus this review on CNS tumors mutated for BRAFV600E which are more prevalent in the pediatric patient population. BRAFV600E-mutated CNS tumors exhibit high inter-tumoral heterogeneity, encompassing clinically and histopathological diverse tumor types. Moreover, BRAFV600E is the second most common alteration in pediatric low-grade CNS tumors, and low-grade tumors are notoriously difficult to recapitulate in vitro and in vivo. Although the mutation predominates in low-grade CNS tumors, when combined with other mutations, most commonly CDKN2A deletion, BRAFV600E-mutated CNS tumors are prone to develop high-grade features, and therefore BRAFV600E-mutated CNS are a paradigm for tumor progression. Here, we describe existing in vitro and in vivo models of BRAFV600E-mutated CNS tumors, including patient-derived cell lines, patient-derived xenografts, syngeneic models, and genetically engineered mouse models, along with their advantages and shortcomings. We discuss which research gaps each model might be best suited to answer, and identify those areas in model development that need to be strengthened further. We highlight areas of potential research focus that will lead to the heightened predictive capacity of preclinical studies, allow for appropriate validation, and ultimately improve the success of "bench to bedside" translational research.

Human APOBEC3B promotes tumor development in vivo including signature mutations and metastases

The antiviral DNA cytosine deaminase APOBEC3B has been implicated as a source of mutation in many cancers. However, despite years of work, a causal relationship has yet to be established in vivo. Here, we report a murine model that expresses tumor-like levels of human APOBEC3B. Animals expressing full-body APOBEC3B appear to develop normally. However, adult males manifest infertility, and older animals of both sexes show accelerated rates of carcinogenesis, visual and molecular tumor heterogeneity, and metastasis. Both primary and metastatic tumors exhibit increased frequencies of C-to-T mutations in TC dinucleotide motifs consistent with the established biochemical activity of APOBEC3B. Enrichment for APOBEC3B-attributable single base substitution mutations also associates with elevated levels of insertion-deletion mutations and structural variations. APOBEC3B catalytic activity is required for all of these phenotypes. Together, these studies provide a cause-and-effect demonstration that human APOBEC3B is capable of driving both tumor initiation and evolution in vivo.

Single-cell analysis technologies for cancer research: from tumor-specific single cell discovery to cancer therapy

Single-cell sequencing (SCS) technology is changing our understanding of cellular components, functions, and interactions across organisms, because of its inherent advantage of avoiding noise resulting from genotypic and phenotypic heterogeneity across numerous samples. By directly and individually measuring multiple molecular characteristics of thousands to millions of single cells, SCS technology can characterize multiple cell types and uncover the mechanisms of gene regulatory networks, the dynamics of transcription, and the functional state of proteomic profiling. In this context, we conducted systematic research on SCS techniques, including the fundamental concepts, procedural steps, and applications of scDNA, scRNA, scATAC, scCITE, and scSNARE methods, focusing on the unique clinical advantages of SCS, particularly in cancer therapy. We have explored challenging but critical areas such as circulating tumor cells (CTCs), lineage tracing, tumor heterogeneity, drug resistance, and tumor immunotherapy. Despite challenges in managing and analyzing the large amounts of data that result from SCS, this technique is expected to reveal new horizons in cancer research. This review aims to emphasize the key role of SCS in cancer research and promote the application of single-cell technologies to cancer therapy.

Aptamer/Peptide-Functionalized Nanoprobe for Detecting Multiple miRNAs in Circulating Malignant Cells to Study Tumor Heterogeneity

Identification of diverse biomarkers in heterogenic circulating malignant cells (CMCs) such as circulating tumor cells (CTCs) and circulating tumor endothelial cells (CTECs) has crucial significance in tumor diagnosis. However, it remains a substantial challenge to achieve in situ detection of multiple miRNA markers in living cells in blood. Herein, we demonstrate that an aptamer/peptide-functionalized vector can deliver molecular beacons into targeted living CMCs in peripheral blood of patients for in situ detection of multiple cancer biomarkers, including miRNA-21 (miR-21) and miRNA-221 (miR-221). Based on miR-21 and miR-221 levels, heterogenic CMCs are identified for both nondistant metastatic and distant metastatic cancer patients. CMCs from nondistant metastatic and distant metastatic cancer patients exhibit similar miR-21 levels, while the miR-221 level in CMCs of the distant metastatic cancer patient is higher than that of the nondistant metastatic cancer patient. With the capability to realize precise probing of multiple intracellular biomarkers in living CMCs at the single-cell resolution, the nanoprobe can reveal the tumor heterogeneity and provide useful information for diagnosis and prognosis. The nanoprobe we developed would accelerate the progress toward noninvasive precise cancer diagnosis.

CAR T cells ignite antitumor immunity

Broadening immune responses through antigen spreading remains the 'Holy Grail' of cancer immunotherapy. A study by Ma and colleagues reveals that vaccine boosting of chimeric antigen receptor (CAR)-T cells in mice promotes endogenous immunity and elicits antigen spread to eliminate antigenically heterogenous solid tumors through a mechanism crucially dependent on interferon (IFN)γ.

Spatial Heterogeneity of Integrins and Their Ligands in Primary Breast Tumors

BACKGROUND

The diversity of cell-cell interactions in different regions of a tumor reflects the functional heterogeneity of cancer, which poses challenges in early diagnosis, selection of treatment strategies, and prognosis of breast cancer. Cancer cells interact with each other to form different morphological structures in the tumor and stromal host cells via integrins. The objective of this study was to characterize the morphological and spatial heterogeneity of primary breast tumors in the context of expression profiles of integrins and their ligands.

METHODS

We studied spatial transcriptomics using the 10X Visium approach and the Niche Interactions and Communication Heterogeneity in Extracellular Signaling (NICHES) algorithm to map ligand-receptor signaling pathways and visualize the heterogeneity of signaling archetypes in tumor clusters.

RESULTS

Cluster analysis of the expression profiles of tumor spots from the samples indicated pronounced inter-tumoral heterogeneity. Integrin-ligand functional clusters were associated with intratumoral heterogeneity, which was manifested by the presence of several morphological loci as observed in histological tumor samples. Inter-tumoral heterogeneity was manifested by a different number of functional clusters, ranging from 2 to 9 for each tumor sample. The main characteristic of these clusters was the significant predominance of non-complementary integrin subunits. Of the 42 functional integrin-ligand pairs in 21 clusters of five samples, 41 pairs occurred only once. The exception was the laminin subunit alpha-5 (LAMA5)-integrin beta 4 (ITGB4) pair, which was detected in two clusters of different samples.

CONCLUSIONS

The spatial heterogeneity of integrin-ligand expression clusters in breast cancer contributes significantly to the functional heterogeneity of the tumor, which sets the stage for many scenarios of parenchymatous-stromal relationships, some of which may be effective in the emergence of metastasizing tumor seed cells. The intra- and inter-tumoral spatio-functional heterogeneity of the tumor tissue that we discovered may largely explain why it is difficult to achieve success in most patients with breast cancer using any therapeutic strategy targeting one molecule of the vast array, regardless of the importance of its pathogenetic significance.

Spatially Resolved Transcriptomics Technology Facilitates Cancer Research

Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.

Role of Epithelial to Mesenchymal Transition in Colorectal Cancer

The epithelial-mesenchymal transition (EMT) is a cellular reprogramming process that occurs during embryonic development and adult tissue homeostasis. This process involves epithelial cells acquiring a mesenchymal phenotype. Through EMT, cancer cells acquire properties associated with a more aggressive phenotype. EMT and its opposite, mesenchymal-epithelial transition (MET), have been described in more tumors over the past ten years, including colorectal cancer (CRC). When EMT is activated, the expression of the epithelial marker E-cadherin is decreased and the expression of the mesenchymal marker vimentin is raised. As a result, cells temporarily take on a mesenchymal phenotype, becoming motile and promoting the spread of tumor cells. Epithelial-mesenchymal plasticity (EMP) has become a hot issue in CRC because strong inducers of EMT (such as transforming growth factor β, TGF-β) can initiate EMT and regulate metastasis, microenvironment, and immune system resistance in CRC. In this review, we take into account the significance of EMT-MET in CRC and the impact of the epithelial cells' plasticity on the prognosis of CRC. The analysis of connection between EMT and colorectal cancer stem cells (CCSCs) will help to further clarify the current meager understandings of EMT. Recent advances affecting important EMT transcription factors and EMT and CCSCs are highlighted. We come to the conclusion that the regulatory network for EMT in CRC is complicated, with a great deal of crosstalk and alternate paths. More thorough research is required to more effectively connect the clinical management of CRC with biomarkers and targeted treatments associated with EMT.

Exploring New Dimensions of Tumor Heterogeneity: The Application of Single Cell Analysis to Organoid-Based 3D In Vitro Models

Modeling the heterogeneity of the tumor microenvironment (TME) in vitro is essential to investigating fundamental cancer biology and developing novel treatment strategies that holistically address the factors affecting tumor progression and therapeutic response. Thus, the development of new tools for both in vitro modeling, such as patient-derived organoids (PDOs) and complex 3D in vitro models, and single cell omics analysis, such as single-cell RNA-sequencing, represents a new frontier for investigating tumor heterogeneity. Specifically, the integration of PDO-based 3D in vitro models and single cell analysis offers a unique opportunity to explore the intersecting effects of interpatient, microenvironmental, and tumor cell heterogeneity on cell phenotypes in the TME. In this review, the current use of PDOs in complex 3D in vitro models of the TME is discussed and the emerging directions in the development of these models are highlighted. Next, work that has successfully applied single cell analysis to PDO-based models is examined and important experimental considerations are identified for this approach. Finally, open questions are highlighted that may be amenable to exploration using the integration of PDO-based models and single cell analysis. Ultimately, such investigations may facilitate the identification of novel therapeutic targets for cancer that address the significant influence of tumor-TME interactions.

Single-cell RNA-sequencing analysis reveals divergent transcriptome events between platinum-sensitive and platinum-resistant high-grade serous ovarian carcinoma

BACKGROUND

Tumor resistance is one of the main reasons leading to the failure of ovarian cancer treatment. Overcoming platinum resistance remains the greatest challenge in the management of high-grade serous ovarian carcinoma (HGSC).

METHODS

Small conditional RNA-sequencing is a powerful method for exploring the complexity of the cellular components and their interactions in the tumor microenvironment. We profiled the transcriptomes of 35,042 cells from two platinum-sensitive and three platinum resistance HGSC clinical cases downloaded from Gene Expression Omnibus (GSE154600) and annotated tumor cells as platinum-resistant or sensitive based on the clinical trait. The study systematically investigated the inter-tumoral (using differential expression analysis, CellChat, and SCENIC) and intra-tumoral heterogeneity (using enrichment analysis such as gene set enrichment analysis, as well as gene set variation analysis, weighted gene correlation network analysis, and Pseudo-time analysis) of HGSC.

RESULTS

A cellular map of HGSC generated by profiling 30,780 cells was revisualized using Uniform Manifold Approximation and Projection. The inter-tumoral heterogeneity was demonstrated with intercellular ligand-receptor interactions of major cell types and regulons networks. FN1, SPP1, and COLLAGEN play important roles in the cross-talk between tumor cells and the tumor microenvironment. HOXA7, HOXA9_extended, TBL1XR1_extended, KLF5, SOX17, and CTCFL regulons consistent with the distribution of platinum-resistant HGSC cells were the high activity regions. The intra-tumoral heterogeneity of HGSC was presented with corresponding functional pathway characteristics, tumor stemness features, and the cellular lineage transition from platinum-sensitive to resistant condition. Epithelial-mesenchymal transition played an important role in platinum resistance, whereas oxidative phosphorylation was the opposite. There was a small subset of cells in platinum-sensitive samples that had transcriptomic characteristics similar to platinum-resistant cells, suggesting that the progression of platinum resistance in ovarian cancer is inevitable.

CONCLUSIONS

The present study describes a view of HGSC at single-cell resolution that reveals the characteristics of the HGSC heterogeneity and provides a useful framework for future investigation of platinum-resistant.

Decoding tumor heterogeneity in uveal melanoma: basement membrane genes as novel biomarkers and therapeutic targets revealed by multi-omics approaches for cancer immunotherapy

Background: Uveal melanoma (UVM) is a primary intraocular malignancy that poses a significant threat to patients' visual function and life. The basement membrane (BM) is critical for establishing and maintaining cell polarity, adult function, embryonic and organ morphogenesis, and many other biological processes. Some basement membrane protein genes have been proven to be prognostic biomarkers for various cancers. This research aimed to develop a novel risk assessment system based on BMRGs that would serve as a theoretical foundation for tailored and accurate treatment. Methods: We used gene expression profiles and clinical data from the TCGA-UVM cohort of 80 UVM patients as a training set. 56 UVM patients from the combined cohort of GSE84976 and GSE22138 were employed as an external validation dataset. Prognostic characteristics of basement membrane protein-related genes (BMRGs) were characterized by Lasso, stepwise multifactorial Cox. Multivariate analysis revealed BMRGs to be independent predictors of UVM. The TISCH database probes the crosstalk of BMEGs in the tumor microenvironment at the single-cell level. Finally, we investigated the function of ITGA5 in UVM using multiple experimental techniques, including CCK8, transwell, wound healing assay, and colony formation assay. Results: There are three genes in the prognostic risk model (ADAMTS10, ADAMTS14, and ITGA5). After validation, we determined that the model is quite reliable and accurately forecasts the prognosis of UVM patients. Immunotherapy is more likely to be beneficial for UVM patients in the high-risk group, whereas the survival advantage may be greater for UVM patients in the low-risk group. Knockdown of ITGA5 expression was shown to inhibit the proliferation, migration, and invasive ability of UVM cells in vitro experiments. Conclusion: The 3-BMRGs feature model we constructed has excellent predictive performance which plays a key role in the prognosis, informing the individualized treatment of UVM patients. It also provides a new perspective for assessing pre-immune efficacy.

A glycosylation risk score comprehensively assists the treatment of bladder neoplasm in the real-world cohort, including the tumor microenvironment, molecular and clinical prognosis

Background: Bladder cancer is a common urological cancer associated high significant morbidity and mortality rates. Immunotherapy has emerged as a promising treatment option, although response rates vary among patients. Glycosylation has been implicated in tumorigenesis and immune regulation. However, our current comprehensive understanding of the role of glycosylation in bladder cancer and its clinical implications is limited. Methods: We constructed a training cohort based on the downloaded TCGA-BLCA dataset, while additional datasets (Xiangya cohort, GSE32894, GSE48075, GSE31684, GSE69795 and E-MTAB-1803) from Xiangya hospital, GEO and ArrayExpress database were obtained and used as validation cohorts. To identify glycosylation-related genes associated with prognosis, univariate Cox regression and LASSO regression were performed. A Cox proportional hazards regression model was then constructed to develop a risk score model. The performance of the risk score was assessed in the training cohort using Kaplan-Meier survival curves and ROC curves, and further validated in multiple validation cohorts. Results: We classified patients in the training cohort into two groups based on glycosylation-related gene expression patterns: Cluster 1 and Cluster 2. Prognostic analysis revealed that Cluster 2 had poorer survival outcomes. Cluster 2 also showed higher levels of immune cell presence in the tumor microenvironment and increased activation in key steps of the cancer immune response cycle. We developed an independent prognostic risk score (p < 0.001) and used it to construct an accurate prognostic prediction nomogram. The high glycosylation risk score group exhibited higher tumor immune cell infiltration, enrichment scores in immune therapy-related pathways, and a tendency towards a basal subtype. Conversely, the low-risk score group had minimal immune cell infiltration and tended to have a luminal subtype. These findings were consistent in our real-world Xiangya cohort. Conclusion: This multi-omics glycosylation score based on these genes reliably confirmed the heterogeneity of bladder cancer tumors, predicted the efficacy of immunotherapy and molecular subtypes, optimizing individual treatment decisions.

Identification of alternative splicing associated with clinical features: from pan-cancers to genitourinary tumors

Background

Alternative splicing events (ASEs) are vital causes of tumor heterogeneity in genitourinary tumors and many other cancers. However, the clinicopathological relevance of ASEs in cancers has not yet been comprehensively characterized.

Methods

By analyzing splicing data from the TCGA SpliceSeq database and phenotype data for all TCGA samples from the UCSC Xena database, we identified differential clinical feature-related ASEs in 33 tumors. CIBERSORT immune cell infiltration data from the TIMER2.0 database were used for differential clinical feature-related immune cell infiltration analysis. Gene function enrichment analysis was used to analyze the gene function of ASEs related to different clinical features in tumors. To reveal the regulatory mechanisms of ASEs, we integrated race-related ASEs and splicing quantitative trait loci (sQTLs) data in kidney renal clear cell carcinoma (KIRC) to comprehensively assess the impact of SNPs on ASEs. In addition, we predicted regulatory RNA binding proteins in bladder urothelial carcinoma (BLCA) based on the enrichment of motifs around alternative exons for ASEs.

Results

Alternative splicing differences were systematically analyzed between different groups of 58 clinical features in 33 cancers, and 30 clinical features in 24 cancer types were identified to be associated with more than 50 ASEs individually. The types of immune cell infiltration were found to be significantly different between subgroups of primary diagnosis and disease type. After integrating ASEs with sQTLs data, we found that 63 (58.9%) of the race-related ASEs were significantly SNP-correlated ASEs in KIRC. Gene function enrichment analyses showed that metastasis-related ASEs in KIRC mainly enriched Rho GTPase signaling pathways. Among those ASEs associated with metastasis, alternative splicing of GIT2 and TUBB3 might play key roles in tumor metastasis in KIRC patients. Finally, we identified several RNA binding proteins such as PCBP2, SNRNP70, and HuR, which might contribute to splicing differences between different groups of neoplasm grade in BLCA.

Conclusion

We demonstrated the significant clinical relevance of ASEs in multiple cancer types. Furthermore, we identified and validated alternative splicing of TUBB3 and RNA binding proteins such as PCBP2 as critical regulators in the progression of urogenital cancers.

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states

Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). However, the joint tumor-immune states that mediate ICI response remain elusive. We develop spatially aware deep-learning models of tumor and immune features to learn representations of ccRCC tumors using diagnostic whole-slide images (WSIs) in untreated and treated contexts (n = 1,102 patients). We identify patterns of grade heterogeneity in WSIs not achievable through human pathologist analysis, and these graph-based "microheterogeneity" structures associate with PBRM1 loss of function and with patient outcomes. Joint analysis of tumor phenotypes and immune infiltration identifies a subpopulation of highly infiltrated, microheterogeneous tumors responsive to ICI. In paired multiplex immunofluorescence images of ccRCC, microheterogeneity associates with greater PD1 activation in CD8+ lymphocytes and increased tumor-immune interactions. Our work reveals spatially interacting tumor-immune structures underlying ccRCC biology that may also inform selective response to ICI.

Homogeneity in immune features between colorectal liver metastases better identifies patients with good prognosis compared to pathological response to preoperative chemotherapy

In colorectal cancer liver metastases (CRLM), the density of tumor-infiltrating lymphocytes, the expression of class I major histocompatibility complex (MHC-I), and the pathological response to preoperative chemotherapy have been associated with oncological outcomes after complete resection. However, the prognostic significance of the heterogeneity of these features in patients with multiple CRLMs remains under investigation. We used a tissue microarray of 220 mismatch repair-gene proficient CRLMs resected in 97 patients followed prospectively to quantify CD3+ T cells and MHC-I by immunohistochemistry. Histopathological response to preoperative chemotherapy was assessed using standard scoring systems. We tested associations between clinical, immunological, and pathological features with oncologic outcomes. Overall, 29 patients (30.2%) had CRLMs homogeneous for CD3+ T cell infiltration and MHC-I. Patients with immune homogeneous compared to heterogeneous CRLMs had longer median time to recurrence (TTR) (30 vs. 12 months, p = .0018) and disease-specific survival (DSS) (not reached vs. 48 months, p = .0009). At 6 years, 80% of the patients with immune homogeneous CRLMs were still alive. Homogeneity of response to preoperative chemotherapy was seen in 60 (61.9%) and 69 (80.2%) patients according to different grading systems and was not associated with TTR or DSS. CD3 and MHC-I heterogeneity was independent of response to pre-operative chemotherapy and of other clinicopathological variables for their association with oncological outcomes. In patients with multiple CRLMs resected with curative intent, similar adaptive immune features seen across metastases could be more informative than pathological response to pre-operative chemotherapy in predicting oncological outcomes.

Rules-based Volumetric Segmentation of Multiparametric MRI for Response Assessment in Recurrent High-Grade Glioma

We report domain knowledge-based rules for assigning voxels in brain multiparametric MRI (mpMRI) to distinct tissuetypes based on their appearance on Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced and contrast-enhanced, T2-weighted, and Fluid-Attenuated Inversion Recovery images. The development dataset comprised mpMRI of 18 participants with preoperative high-grade glioma (HGG), recurrent HGG (rHGG), and brain metastases. External validation was performed on mpMRI of 235 HGG participants in the BraTS 2020 training dataset. The treatment dataset comprised serial mpMRI of 32 participants (total 231 scan dates) in a clinical trial of immunoradiotherapy in rHGG (NCT02313272). Pixel intensity-based rules for segmenting contrast-enhancing tumor (CE), hemorrhage, Fluid, non-enhancing tumor (Edema1), and leukoaraiosis (Edema2) were identified on calibrated, co-registered mpMRI images in the development dataset. On validation, rule-based CE and High FLAIR (Edema1 + Edema2) volumes were significantly correlated with ground truth volumes of enhancing tumor (R = 0.85;p < 0.001) and peritumoral edema (R = 0.87;p < 0.001), respectively. In the treatment dataset, a model combining time-on-treatment and rule-based volumes of CE and intratumoral Fluid was 82.5% accurate for predicting progression within 30 days of the scan date. An explainable decision tree applied to brain mpMRI yields validated, consistent, intratumoral tissuetype volumes suitable for quantitative response assessment in clinical trials of rHGG.

Unveiling novel insights in prostate cancer through single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technology that provides insights at the individual cell level. In contrast to traditional bulk RNA-seq, which captures gene expression at an average level and may overlook important details, scRNA-seq examines each individual cell as a fundamental unit and is particularly well-suited for identifying rare cell populations. Analogous to a microscope that distinguishes various cell types within a tissue sample, scRNA-seq unravels the heterogeneity and diversity within a single cell species, offering great potential as a leading sequencing method in the future. In the context of prostate cancer (PCa), a disease characterized by significant heterogeneity and multiple stages of progression, scRNA-seq emerges as a powerful tool for uncovering its intricate secrets.

Cell-based therapies for glioblastoma: Promising tools against tumor heterogeneity

Glioblastoma (GBM) is a highly aggressive tumor with a devastating impact on quality-of-life and abysmal survivorship. Patients have very limited effective treatment options. The successes of targeted small molecule drugs and immune checkpoint inhibitors seen in various solid tumors have not translated to GBM, despite significant advances in our understanding of its molecular, immune, and microenvironment landscapes. These discoveries, however, have unveiled GBM's incredible heterogeneity and its role in treatment failure and survival. Novel cellular therapy technologies are finding successes in oncology and harbor characteristics that make them uniquely suited to overcome challenges posed by GBM, such as increased resistance to tumor heterogeneity, modularity, localized delivery, and safety. Considering these advantages, we compiled this review article on cellular therapies for GBM, focusing on cellular immunotherapies and stem cell-based therapies, to evaluate their utility. We categorize them based on their specificity, review their preclinical and clinical data, and extract valuable insights to help guide future cellular therapy development.

Novel insights from spatial transcriptome analysis in solid tumors

Since its first application in 2016, spatial transcriptomics has become a rapidly evolving technology in recent years. Spatial transcriptomics enables transcriptomic data to be acquired from intact tissue sections and provides spatial distribution information and remedies the disadvantage of single-cell RNA sequencing (scRNA-seq), whose data lack spatially resolved information. Presently, spatial transcriptomics has been widely applied to various tissue types, especially for the study of tumor heterogeneity. In this review, we provide a summary of the research progress in utilizing spatial transcriptomics to investigate tumor heterogeneity and the microenvironment with a focus on solid tumors. We summarize the research breakthroughs in various fields and perspectives due to the application of spatial transcriptomics, including cell clustering and interaction, cellular metabolism, gene expression, immune cell programs and combination with other techniques. As a combination of multiple transcriptomics, single-cell multiomics shows its superiority and validity in single-cell analysis. We also discuss the application prospect of single-cell multiomics, and we believe that with the progress of data integration from various transcriptomics, a multilayered subcellular landscape will be revealed.

New horizon of radiopharmaceuticals in management of neuroendocrine tumors

Neuroendocrine neoplasms are rare and heterogenous group of tumors with varying degrees of clinical presentations and involvement of multiple organ systems in the body. In the modern clinical practice somatostatin receptor molecular imaging and targeted radioligand therapy plays a vital role in the diagnosis and management of the disease. Several new and promising radiotracers for NET imaging and theranostics, belonging to various groups and classes are being studied and investigated. This exponential growth of radiotracers poses concerns about the indication, clinical benefit, and safety profile of the agents. We discuss the basis behind these radiotracers clinical use, receptor targeting and intra and inter tumor heterogeneity. Furthermore, role of dual tracer imaging, combination therapy and potential applications of dosimetry in predicting treatment outcome and safety profile is reviewed. Individualized precision medicine with better tumor characterization, maximum therapeutic benefit and minimum toxicity is the way forward for future medicine.

Identification of a ferroptosis-related gene signature predicting recurrence in stage II/III colorectal cancer based on machine learning algorithms

Background: Colorectal cancer (CRC) is one of the most prevalent cancer types globally. A survival paradox exists due to the inherent heterogeneity in stage II/III CRC tumor biology. Ferroptosis is closely related to the progression of tumors, and ferroptosis-related genes can be used as a novel biomarker in predicting cancer prognosis. Methods: Ferroptosis-related genes were retrieved from the FerrDb and KEGG databases. A total of 1,397 samples were enrolled in our study from nine independent datasets, four of which were integrated as the training dataset to train and construct the model, and validated in the remaining datasets. We developed a machine learning framework with 83 combinations of 10 algorithms based on 10-fold cross-validation (CV) or bootstrap resampling algorithm to identify the most robust and stable model. C-indice and ROC analysis were performed to gauge its predictive accuracy and discrimination capabilities. Survival analysis was conducted followed by univariate and multivariate Cox regression analyses to evaluate the performance of identified signature. Results: The ferroptosis-related gene (FRG) signature was identified by the combination of Lasso and plsRcox and composed of 23 genes. The FRG signature presented better performance than common clinicopathological features (e.g., age and stage), molecular characteristics (e.g., BRAF mutation and microsatellite instability) and several published signatures in predicting the prognosis of the CRC. The signature was further stratified into a high-risk group and low-risk subgroup, where a high FRG signature indicated poor prognosis among all collected datasets. Sensitivity analysis showed the FRG signature remained a significant prognostic factor. Finally, we have developed a nomogram and a decision tree to enhance prognosis evaluation. Conclusion: The FRG signature enabled the accurate selection of high-risk stage II/III CRC population and helped optimize precision treatment to improve their clinical outcomes.

Coexisting cancer stem cells with heterogeneous gene amplifications, transcriptional profiles, and malignancy are isolated from single glioblastomas

Glioblastoma (GBM) is known as an intractable, highly heterogeneous tumor encompassing multiple subclones, each supported by a distinct glioblastoma stem cell (GSC). The contribution of GSC genetic and transcriptional heterogeneity to tumor subclonal properties is debated. In this study, we describe the systematic derivation, propagation, and characterization of multiple distinct GSCs from single, treatment-naive GBMs (GSC families). The tumorigenic potential of each GSC better correlates with its transcriptional profile than its genetic make-up, with classical GSCs being inherently more aggressive and mesenchymal more dependent on exogenous growth factors across multiple GBMs. These GSCs can segregate and recapitulate different histopathological aspects of the same GBM, as shown in a paradigmatic tumor with two histopathologically distinct components, including a conventional GBM and a more aggressive primitive neuronal component. This study provides a resource for investigating how GSCs with distinct genetic and/or phenotypic features contribute to individual GBM heterogeneity and malignant escalation.