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Cell-based immunotherapies (CBIs), notably exemplified by chimeric antigen receptor (CAR)-engineered T (CAR-T) cell therapy, have emerged as groundbreaking approaches for cancer therapy. Nevertheless, akin to various other therapeutic modalities, tumor cells employ counterstrategies to manifest immune evasion, thereby circumventing the impact of CBIs. This phenomenon is facilitated by an intricately immunosuppression entrenched within the tumor microenvironment (TME). Principal mechanisms underpinning tumor immune evasion from CBIs encompass loss of antigens, downregulation of antigen presentation, activation of immune checkpoint pathways, initiation of anti-apoptotic cascades, and induction of immune dysfunction and exhaustion. In this review, we delve into the intrinsic mechanisms underlying the capacity of tumor cells to resist CBIs and proffer prospective stratagems to navigate around these challenges.

OBJECTIVES: To determine the dysregulated signaling pathways of head and neck squamous cell carcinoma associated with circulating tumor cells (CTCs) via single-cell molecular characterization. INTRODUCTION: Head and neck squamous cell carcinoma (HNSCC) has a significant global burden and is a disease with poor survival. Despite trials exploring new treatment modalities to improve disease control rates, the 5 year survival rate remains low at only 60%. Most cancer malignancies are reported to progress to a fatal phase due to the metastatic activity derived from treatment-resistant cancer cells, regarded as one of the most significant obstacles to develope effective cancer treatment options. However, the molecular profiles of cancer cells have not been thoroughly studied. METHODS: Here, we examined in-situ HNSCC tumors and pairwisely followed up with the downstream circulating tumor cells (CTCs)-based on the surrogate biomarkers to detect metastasis that is established in other cancers - not yet being fully adopted in HNSCC treatment algorithms. RESULTS: Specifically, we revealed metastatic HNSCC patients have complex CTCs that could be defined through gene expression and mutational gene profiling derived from completed single-cell RNASeq (scRNASeq) that served to confirm molecular pathways inherent in these CTCs. To enhance the reliability of our findings, we cross-validated those molecular profiles with results from previously published studies. CONCLUSION: Thus, we identified 5 dysregulated signaling pathways in CTCs to derive HNSCC biomarker panels for screening HNSCC in situ tumors.

Breast cancer is a complex disease with significant phenotypic heterogeneity of cells, even within a single breast tumor. Emerging evidence underscores the significance of intratumoral competition, which can serve as a key contributor to cancer drug resistance, imparting substantial clinical implications. Understanding the competitive dynamics is paramount as it can significantly influence disease progression and treatment outcomes. In the present work, a mathematical model was developed using a system of differential equations to describe the dynamic interactions between two cancer subtypes (each further classified into cancer stem cells and tumor cells) and innate immune cells. The purpose of the model is to comprehensively understand the competitive interactions between the heterogeneous subpopulations. The equilibrium points and stability analysis for each equilibrium point were established. Model simulations showed that the competition between two cancer subtypes directly affects the number of both species. When competition between two cancer subtypes is strong, increasing the immune response rate specific to the more competitive species effectively reduces the tumor size. However, if the competition is relatively weak, an optimal immune response rate is required to minimize the total number of tumor cells. Rates below the optimal level fail to reduce the population of the stronger species, whereas rates above the optimal level can lead to the recurrence of the weaker species. Overall, this model provides insights into breast cancer dynamics and guides the development of effective treatment strategies. [ABSTRACT FROM AUTHOR]

Cellular plasticity is enhanced by dedifferentiation processes such as epithelial-mesenchymal transition (EMT). The dynamic and transient nature of EMT-like processes challenges the investigation of cell plasticity in patient-derived breast cancer models. Here, we utilized patient-derived organoids (PDOs) as a model to study the susceptibility of primary breast cancer cells to EMT. Upon induction with TGF-b, PDOs exhibited EMT-like features, including morphological changes, E-cadherin downregulation and cytoskeletal reorganization, leading to an invasive phenotype. Image analysis and the integration of deep learning algorithms enabled the implantation of microscopy-based quantifications demonstrating repetitive results between organoid lines from different breast cancer patients. Interestingly, epithelial plasticity was also expressed in terms of alterations in luminal and myoepithelial distribution upon TGF-b induction. The effective modeling of dynamic processes such as EMT in organoids and their characteristic spatial diversity highlight their potential to advance research on cancer cell plasticity in cancer patients. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most prevalent cancers in women globally. Its treatment and prognosis are significantly influenced by the tumor microenvironment and tumor heterogeneity. Precision therapy enhances treatment efficacy, reduces unwanted side effects, and maximizes patients' survival duration while improving their quality of life. Spatial transcriptomics is of significant importance for the precise treatment of breast cancer, playing a critical role in revealing the internal structural differences of tumors and the composition of the tumor microenvironment. It offers a novel perspective in studying the spatial structure and cell interactions within tumors, facilitating more effective personalized treatments for breast cancer. This article will summarize the latest findings in the diagnosis and treatment of breast cancer from the perspective of spatial transcriptomics, focusing on the revelation of the tumor microenvironment, identification of new therapeutic targets, enhancement of disease diagnostic accuracy, comprehension of tumor progression and metastasis, assessment of drug responses, creation of highresolution maps of tumor cells, representation of tumor heterogeneity, and support for clinical decision-making, particularly in elucidating the tumor microenvironment, tumor heterogeneity, immunotherapy and their correlation with clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: Tumor genomic profiling has a significant impact on the selection of targeted therapy. Circulating tumor DNA (ctDNA) has emerged as a noninvasive, and reproducible assay compared with tissue biopsy. We aimed to evaluate its utility in identifying mutations and guiding targeted therapy for lung cancer. Methods: A total of 173 lung cancer patients underwent next-generation sequencing (NGS) using a targeted enrichment panel covering 20 lung cancer-related genes. The performance of the ctDNA NGS assay in identifying genetic mutations or alterations was compared with tissue biopsy and droplet digital PCR (ddPCR). The treatment response to epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) therapies based on the ctDNA assay results was also assessed. Results: The ctDNA was detected in 61.85% of patients. Tissue mutations were detected in paired ctDNA in 38.57% of cases, while ctDNA mutations were detected in paired tissues in 89.1% of cases. The ctDNA increased the number of advanced non-small cell lung cancer (NSCLC) patients who received NCCN-recommended genetic testing by 12%. The concordance between ddPCR and ctDNA was relatively high reaching 99.43%. EGFR T790M/C797S c.G2390C and EGFR T790M/C797S c.T2389A were detected in tissue and ctDNA, respectively, in patient 01015. Moreover, ctDNA assay identified the EGFR T790M mutation, which was missed by tissue biopsy in patient 01149, who developed drug resistance after 1 year of EGFR-TKI therapy. Of the 17 patients who received EGFR-TKI targeted therapies based on the ctDNA NGS results, 12 patients achieved a partial response and two patients had stable disease. Conclusions: The results demonstrated that the ctDNA assay could partially overcome tumor heterogeneity in detecting mutations and provide complementary information on tumor genomic profiles. Moreover, the presence of EGFR mutations in ctDNA could offer valuable guidance for selecting appropriate EGFR-TKI treatment for advanced lung cancer patients. However, it is important to note that the ctDNA NGS assay has certain limitations in fully identifying all genomic alterations present in the tumor. [ABSTRACT FROM AUTHOR]

Purpose: Standardized reporting of treatment response in oncology patients has traditionally relied on methods like RECIST, PERCIST and Deauville score. These endpoints assess only a few lesions, potentially overlooking the response heterogeneity of all disease. This study hypothesizes that comprehensive spatial-temporal evaluation of all individual lesions is necessary for superior prognostication of clinical outcome. Methods: [18F]FDG PET/CT scans from 241 patients (127 diffuse large B-cell lymphoma (DLBCL) and 114 non-small cell lung cancer (NSCLC)) were retrospectively obtained at baseline and either during chemotherapy or post-chemoradiotherapy. An automated TRAQinform IQ software (AIQ Solutions) analyzed the images, performing quantification of change in regions of interest suspicious of cancer (lesion-ROI). Multivariable Cox proportional hazards (CoxPH) models were trained to predict overall survival (OS) with varied sets of quantitative features and lesion-ROI, compared by bootstrapping with C-index and t-tests. The best-fit model was compared to automated versions of previously established methods like RECIST, PERCIST and Deauville score. Results: Multivariable CoxPH models demonstrated superior prognostic power when trained with features quantifying response heterogeneity in all individual lesion-ROI in DLBCL (C-index = 0.84, p < 0.001) and NSCLC (C-index = 0.71, p < 0.001). Prognostic power significantly deteriorated (p < 0.001) when using subsets of lesion-ROI (C-index = 0.78 and 0.67 for DLBCL and NSCLC, respectively) or excluding response heterogeneity (C-index = 0.67 and 0.70). RECIST, PERCIST, and Deauville score could not significantly associate with OS (C-index < 0.65 and p > 0.1), performing significantly worse than the multivariable models (p < 0.001). Conclusions: Quantitative evaluation of response heterogeneity of all individual lesions is necessary for the superior prognostication of clinical outcome. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) technology enables the precise analysis of individual cell transcripts with high sensitivity and throughput. When integrated with multiomics technologies, scRNA-seq significantly enhances the understanding of cellular diversity, particularly within the tumor microenvironment. Similarly, single-cell DNA sequencing has emerged as a powerful tool in cancer research, offering unparalleled insights into the genetic heterogeneity and evolution of tumors. In the context of breast cancer, this technology holds substantial promise for decoding the intricate genomic landscape that drives disease progression, treatment resistance, and metastasis. By unraveling the complexities of tumor biology at a granular level, single-cell DNA sequencing provides a pathway to advancing our comprehension of breast cancer and improving patient outcomes through personalized therapeutic interventions. As single-cell sequencing technology continues to evolve and integrate into clinical practice, its application is poised to revolutionize the diagnosis, prognosis, and treatment strategies for breast cancer. This review explores the potential of single-cell sequencing technology to deepen our understanding of breast cancer, highlighting key approaches, recent advancements, and the role of the tumor microenvironment in disease plasticity. Additionally, the review discusses the impact of single-cell sequencing in paving the way for the development of personalized therapies. [ABSTRACT FROM AUTHOR]

In this case report, we present the treatment outcomes of the first patient enrolled in the LuDO-N trial. The patient is a 21-month-old girl diagnosed with high-risk neuroblastoma (NB) and widespread skeletal metastasis. The patient initially underwent first-line therapy according to SIOPEN HRNBL-1 but was switched to second-line treatments due to disease progression, and she was finally screened for enrollment in the LuDO-N trial due to refractory disease. Upon enrollment, the patient received two rounds of the radiolabeled somatostatin analogue lutetium-177 octreotate (177Lu-DOTATATE), which was well tolerated. A dosimetry analysis revealed a heterogeneous uptake across tumor lesions, resulting in a significant absorbed dose of 54 Gy in the primary tumor, but only 2 Gy at one of the metastatic sites in the distal femur. While the initial treatment response showed disease stabilization, the distal femoral metastasis continued to progress, leading to the eventual death of the patient. A tissue analysis of the biopsies collected throughout the course of the disease revealed heterogeneous drug target expression of somatostatin receptor 2 (SSTR2) across and within tumor lesions. Furthermore, genomic profiling revealed a novel KIAA1549::BRAF fusion oncogene amplification in the distal femoral metastasis at recurrence that might be related with resistance to radiation, possibly through the downregulation of SSTR2. This case report demonstrates a mixed response to molecular radiotherapy (MRT) with 177Lu- DOTATATE. The observed variation in SSTR2 expression between tumor lesions suggests that heterogeneous target expression may have been the reason for treatment failure in this patient's case. Further investigation within the LuDO-N trial will give a more comprehensive understanding of the correlation between SSTR2 expression levels and treatment outcomes, which will be important to advance treatment strategies based on MRT for children with high-risk NB. [ABSTRACT FROM AUTHOR]

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer. [ABSTRACT FROM AUTHOR]

The clinical outcome of patients with muscle-invasive bladder cancer (MIBC) is poor despite the approval of neoadjuvant chemotherapy or immunotherapy to improve overall survival after cystectomy. MIBC subtypes, immune, transcriptome, metabolomic signatures, and mutation burden have the potential to predict treatment response but none have been incorporated into clinical practice, as tumor heterogeneity and lineage plasticity influence their efficacy. Using the PRISMA statement, we conducted a systematic review of the literature, involving 135 studies published within the last five years, to identify studies reporting on the prognostic value of protein-based biomarkers for response to neoadjuvant therapy in patients with MIBC. The studies were grouped based on biomarkers related to molecular subtypes, cancer stem cell, actin-cytoskeleton, epithelial–mesenchymal transition, apoptosis, and tumor-infiltrating immune cells. These studies show the potential of protein-based biomarkers, especially in the spatial context, to reduce the influence of tumor heterogeneity on a biomarker's prognostic capability. Nevertheless, currently, there is little consensus on the methodology, reagents, and the scoring systems to allow reliable assessment of the biomarkers of interest. Furthermore, the small sample size of several studies necessitates the validation of potential prognostic biomarkers in larger multicenter cohorts before their use for individualizing neoadjuvant therapy regimens for patients with MIBC. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer (CC) is the fourthmost commonmalignancy among women globally and serves as the main cause of cancer-related deaths among women in developing countries. The early symptoms of CC are often not apparent, with diagnoses typically made at advanced stages, which lead to poor clinical prognoses. In recent years, numerous studies have shown that there is a close relationship betweenmast cells (MCs) and tumor development. However, research on the role MCs played in CC is still very limited at that time. Thus, the study conducted a single-cellmulti-omics analysis on human CC cells, aiming to explore the mechanisms by which MCs interact with the tumor microenvironment in CC. The goal was to provide a scientific basis for the prevention, diagnosis, and treatment of CC, with the hope of improving patients’ prognoses and quality of life. Method: The present study acquired single-cell RNA sequencing data from ten CC tumor samples in the ArrayExpress database. Slingshot and AUCcell were utilized to infer and assess the differentiation trajectory and cell plasticity of MCs subpopulations. Differential expression analysis of MCs subpopulations in CC was performed, employing Gene Ontology, gene set enrichment analysis, and gene set variation analysis. CellChat software package was applied to predict cell communication between MCs subpopulations and CC cells. Cellular functional experiments validated the functionality of TNFRSF12A in HeLa and Caski cell lines. Additionally, a risk scoring model was constructed to evaluate the differences in clinical features, prognosis, immune infiltration, immune checkpoint, and functional enrichment across various risk scores. Copy number variation levels were computed using inference of copy number variations. Result: The obtained 93,524 high-quality cells were classified into ten cell types, including T_NK cells, endothelial cells, fibroblasts, smooth muscle cells, epithelial cells, B cells, plasma cells, MCs, neutrophils, and myeloid cells. Furthermore, a total of 1,392 MCs were subdivided into seven subpopulations: C0 CTSG+ MCs, C1 CALR+ MCs, C2 ALOX5+ MCs, C3 ANXA2+ MCs, C4 MGP+ MCs, C5 IL32+ MCs, and C6 ADGRL4+ MCs. Notably, the C2 subpopulation showed close associations with tumor-related MCs, with Slingshot results indicating that C2 subpopulation resided at the intermediate-to-late stage of differentiation, potentially representing a crucial transition point in the benign-to-malignant transformation of CC. CNVscore and bulk analysis results further confirmed the transforming state of the C2 subpopulation. CellChat analysis revealed TNFRSF12A as a key receptor involved in the actions of C2 ALOX5+ MCs. Moreover, in vitro experiments indicated that downregulating the TNFRSF12A gene may partially inhibit the development of CC. Additionally, a prognosis model and immune infiltration analysis based on the marker genes of the C2 subpopulation provided valuable guidance for patient prognosis and clinical intervention strategies. Conclusions: We first identified the transformative tumor-associated MCs subpopulation C2 ALOX5+ MCs within CC, which was at a critical stage of tumor differentiation and impacted the progression of CC. In vitro experiments confirmed the inhibitory effect of knocking down the TNFRSF12A gene on the development of CC. The prognostic model constructed based on the C2 ALOX5 +MCs subset demonstrated excellent predictive value. These findings offer a fresh perspective for clinical decision-making in CC. [ABSTRACT FROM AUTHOR]

Background: Gastrointestinal cancer cells display both morphology and physiology diversity, thus posing a significant challenge for precise representation by a single data model. We conducted an in-depth study of gastrointestinal cancer heterogeneity by integrating and analyzing data from multiple modalities. Methods: We used a modified Canny algorithm to identify edges from tumor images, capturing intricate nonlinear interactions between pixels. These edge features were then combined with differentially expressed mRNA, miRNA, and immune cell data. Before data integration, we used the K-medoids algorithm to pre-cluster individual data types. The results of pre-clustering were used to construct the kernel matrix. Finally, we applied spectral clustering to the fusion matrix to identify different tumor subtypes. Furthermore, we identified hub genes linked to these subtypes and their biological roles through the application of Weighted Gene Co-expression Network Analysis (WGCNA) and Gene Ontology (GO) enrichment analysis. Results: Our investigation categorized patients into three distinct tumor subtypes and pinpointed hub genes associated with each. Genes MAGI2-AS3, MALAT1, and SPARC were identified as having a differential impact on the metastatic and invasive capabilities of cancer cells. Conclusion: By harnessing multimodal features, our study enhances the understanding of gastrointestinal tumor heterogeneity and identifies biomarkers for personalized medicine and targeted treatments. [ABSTRACT FROM AUTHOR]

Introduction: The ability of PET/CT imaging to delineate neuroendocrine neoplasms (NENs) and predict prognosis in affected patients is often compromised by substantial uptake heterogeneity. We hereby proposed a hybrid standardized uptake value (SUV) thresholding algorithm to extract volumetric parameters from somatostatin receptor (SSTR) PET/CT imaging and investigate their prognostic performance in patients with 68Ga-DOTATATE-avid stage IV NENs. Methods: For 38 retrospectively enrolled patients, we used either fixed SUV thresholding of normal liver parenchyma (method A), 41% of the SUVmax for each lesion (method B), or a hybrid method (method A for liver metastases; fixed SUV threshold of normal bone for bone metastases; method B for primary tumors and other metastases) to quantify the whole-body SSTR-expressing tumor volume (SRETVwb) and total lesion SSTR expression (TLSREwb). Patient survival was also recorded and analyzed. Results: PET/CT images revealed heterogeneous uptake of 68Ga-DOTATATE at primary and metastatic sites. Progression-free survival (PFS) and overall survival (OS) were negatively correlated with the extent of liver or bone metastases (p < 0.05), but not significantly correlated with tumor grade or 18F-FDG PET/CT positivity. By the hybrid method, PFS was significantly shorter in patients with high SRETVwb, and OS was significantly shorter in those with high SRETVwb and TLSREwb (p < 0.05). However, when derived from method A or method B, neither SRETVwb nor TLSREwb could predict patient outcomes. Conclusion: Compared with other methods used in 68Ga-DOTATATE-avid stage IV NENs, our hybrid SUV thresholding method demonstrated robustness, with greater precision, reliability, and prognostic power. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) presents a significant global health burden, with alarming statistics revealing its rising incidence and high mortality rates. Despite advances in medical care, HCC treatment remains challenging due to late-stage diagnosis, limited effective therapeutic options, tumor heterogeneity, and drug resistance. MicroRNAs (miRNAs) have attracted substantial attention as key regulators of HCC pathogenesis. These small non-coding RNA molecules play pivotal roles in modulating gene expression, implicated in various cellular processes relevant to cancer development. Understanding the intricate network of miRNA-mediated molecular pathways in HCC is essential for unraveling the complex mechanisms underlying hepatocarcinogenesis and developing novel therapeutic approaches. This manuscript aims to provide a comprehensive review of recent experimental and clinical discoveries regarding the complex role of miRNAs in influencing the key hallmarks of HCC, as well as their promising clinical utility as potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Abstract Biomarkers are not only of significant importance for cancer diagnosis and selection of treatment plans but also recently increasingly used for the evaluation of malignancy development and tumor heterogeneity. Large-size tumors from clinical patients can be unique and valuable sources for the study of cancer progression, particularly to the extent of intratumoral heterogeneity. In the present study, we obtained a series of post-surgery puncture samples from a breast cancer patient with a 4 × 3.5 × 2 cm tumor in its original size. Immunohistochemistry for Ki-67, COX-2, and CA IX was performed and the expression levels within the breast cancer tumor mass were evaluated in the reconstructed 3D models. To further evaluate the intratumoral heterogeneity, we performed high throughput whole transcriptome sequencing of 12 samples from different spatial positions within the tumor tissue. Comparing the reconstructed 3D distribution of biomarkers with projected tumor growth models, asymmetric and heterogeneous expansion of tumor mass was found to be possibly influenced by factors such as blood supply, inflammation and/or hypoxia stimulations, as suggested from the correlation between the results of Ki-67 and CA IX or COX-2 staining. Furthermore, high-throughput RNA sequencing data provided additional information for profiling the intratumoral heterogeneity and expanded the understanding of cancer progression. Digital technology for medical imaging once properly integrated with molecular pathology examinations will become particularly helpful in dissecting out in-depth information for precision medicine. We prospect that this approach, facilitated by rapidly advancing artificial intelligence, could provide new insights for clinical decision-making in the future. Strategies for the continuous development from the present study for better performance and application were discussed.

Abstract As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

Abstract Background Intrahepatic cholangiocarcinoma (iCCA) is one of the most lethal malignancies and highly heterogeneous. We thus aimed to identify and characterize iCCA cell subpopulations with severe malignant features. Methods Transcriptomic datasets from three independent iCCA cohorts (iCCA cohorts 1–3, n = 382) and formalin-fixed and paraffin-embedded tissues from iCCA cohort 4 (n = 31) were used. An unbiased global screening strategy was established, including the transcriptome analysis with the activated malignancy/stemness (MS) signature in iCCA cohorts 1–3 and the mass spectrometry analysis of the sorted stemness reporter-positive iCCA cells. A group of cellular assays and subcutaneous tumor xenograft assay were performed to investigate functional roles of the candidate. Immunohistochemistry was performed in iCCA cohort 4 to examine the expression and localization of the candidate. Molecular and biochemical assays were used to evaluate the membrane localization and functional protein domains of the candidate. Cell sorting was performed and the corresponding cellular molecular assays were utilized to examine cancer stem cell features of the sorted cells. Results The unbiased global screening identified RRM2 as the top candidate, with a significantly higher level in iCCA patients with the MS signature activation and in iCCA cells positive for the stemness reporter. Consistently, silencing RRM2 significantly suppressed iCCA malignancy phenotypes both in vitro and in vivo. Moreover, immunohistochemistry in tumor tissues of iCCA patients revealed an unreported cell membrane localization of RRM2, in contrast to its usual cytoplasmic localization. RRM2 cell membrane localization was then confirmed in iCCA cells via immunofluorescence with or without cell membrane permeabilization, cell fractionation assay and cell surface biotinylation assay. Meanwhile, an unclassical signal peptide and a transmembrane domain of RRM2 were revealed experimentally. They were essential for RRM2 trafficking to cell membrane via the conventional endoplasmic reticulum (ER)–Golgi secretory pathway. Furthermore, the membrane RRM2-positive iCCA cells were successfully sorted. These cells possessed significant cancer stem cell malignant features including cell differentiation ability, self-renewal ability, tumor initiation ability, and stemness/malignancy gene signatures. Patients with membrane RRM2-positive iCCA cells had poor prognosis. Conclusions RRM2 had an alternative cell membrane localization. The membrane RRM2-positive iCCA cells represented a malignant subpopulation with cancer stem cell features.

Abstract Background Neuroblastoma (NB) is a heterogeneous embryonal malignancy and the deadliest tumor of infancy. It is a complex disease that can result in diverse clinical outcomes. In some children, tumors regress spontaneously. Others respond well to existing treatments. But for the high-risk group, which constitutes approximately 40% of all patients, the prognosis remains dire despite collaborative efforts in basic and clinical research. While its exact cellular origin is still under debate, NB is assumed to arise from the neural crest cell lineage including multipotent Schwann cell precursors (SCPs), which differentiate into sympatho-adrenal cell states eventually producing chromaffin cells and sympathoblasts. Methods To investigate clonal development of neuroblastoma cell states, we performed haplotype-specific analysis of human tumor samples using single-cell multi-omics, including joint DNA/RNA sequencing of sorted single cells (DNTR-seq). Samples were also assessed using immunofluorescence stainings and fluorescence in-situ hybridization (FISH). Results Beyond adrenergic tumor cells, we identify subpopulations of aneuploid SCP-like cells, characterized by clonal expansion, whole-chromosome 17 gains, as well as expression programs of proliferation, apoptosis, and a non-immunomodulatory phenotype. Conclusion Aneuploid pre-malignant SCP-like cells represent a novel feature of NB. Genetic evidence and tumor phylogeny suggest that these clones and malignant adrenergic populations originate from aneuploidy-prone cells of migrating neural crest or SCP origin, before lineage commitment to sympatho-adrenal cell states. Our findings expand the phenotypic spectrum of NB cell states. Considering the multipotency of SCPs in development, we suggest that the transformation of fetal SCPs may represent one possible mechanism of tumor initiation in NB with chromosome 17 aberrations as a characteristic element.

Abstract Osteosarcomas are intricate cellular ecosystems, where heterotypic interactions significantly influence disease progression and therapeutic outcomes. Despite their importance, a detailed understanding of their cellular composition and organizational structure remains elusive. In this study, we provide a comprehensive single-cell and spatially resolved transcriptomics analysis of human osteosarcomas. We construct a cellular meta-map to dissect spatial transcriptomic data, unveiling a detailed atlas of osteosarcoma compositional subgroups. We meticulously characterize the unique gene signatures and functional states of each subgroup and investigate the impact of chemotherapy on these cellular subpopulations. Additionally, our spatial transcriptomics analysis identifies a distinct spatial niche, located at the forefront of tumor necrotic zones, potentially associated with chemotherapy resistance. We also delve into the crosstalk between different cellular subgroups. This study furnishes a comprehensive transcriptional atlas of osteosarcoma's cellular architecture, enriching our comprehension of its complexity and laying the groundwork for more targeted therapeutic approaches.

Abstract Extensive efforts have been dedicated to exploring the impact of tumor heterogeneity on cancer treatment at both histological and genetic levels. To accurately measure intra-tumoral heterogeneity, a non-invasive imaging technique, known as habitat imaging, was developed. The technique quantifies intra-tumoral heterogeneity by dividing complex tumors into distinct sub- regions, called habitats. This article reviews the following aspects of habitat imaging in cancer treatment, with a focus on radiotherapy: (1) Habitat imaging biomarkers for assessing tumor physiology; (2) Methods for habitat generation; (3) Efforts to combine radiomics, another imaging quantification method, with habitat imaging; (4) Technical challenges and potential solutions related to habitat imaging; (5) Pathological validation of habitat imaging and how it can be utilized to evaluate cancer treatment by predicting treatment response including survival rate, recurrence, and pathological response as well as ongoing open clinical trials.

Abstract Background Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear. Methods Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed. Results We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis. Conclusion By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.

Aim: This article presents an investigation into various mathematical models for cell population growth, including tumor cells, and their dynamics. Methods: We classify the models into five categories: exponential, logistic, time-tested, heterogeneous, and immunology. Mathematical modeling provides insights into the development of tumors over time and how their proliferation rate becomes more dangerous. To explore the impact of immune response on tumor heterogeneity, we develop a reaction-diffusion model of tumor growth that incorporates tumor-immune interactions and a mechanism for tumor mutation and clonal expansion. We use numerical simulations to investigate how variation in immune response affects tumor heterogeneity. Results: Our findings show that a stronger immune response leads to greater homogeneity in the tumor population, which suggests that enhancing immune response could reduce tumor heterogeneity and improve treatment outcomes. Conclusions: These results have important implications for the development of therapeutic strategies targeting the immune system to combat tumor heterogeneity.

The classification of breast cancer (BC) by immunohistochemical phenotypes is widely used in routine clinical practice. However, the genetic profile of the tumor does not always correspond to the pathomorphological one, which can significantly affect the prognosis and predict the effectiveness of therapy in BC. This literature review examines the effectiveness of endocrine therapy depending on the internal subtype of BC, and also presents data on the effectiveness of CDK4/6 inhibitors in these subgroups. It has been shown that during metastasis, the tumor acquires a more aggressive subtype (for example, it switches from luminal to HER2-E or basal-like), which can be stopped when using CDK4/6 inhibitors: the change of the internal subtype passes into a more favorable group.

The immune microenvironment plays a key role in the development and progression of tumors. In recent years, with the rapid advancement of high-throughput sequencing technologies, researchers have gained a deeper understanding of the composition and function of immune cells in the tumor microenvironment. However, traditional bulk sequencing technologies are limited in resolving heterogeneity at the single-cell level, constraining a comprehensive understanding of the complexity of the tumor microenvironment. The advent of single-cell RNA sequencing technology has brought new opportunities to uncover the heterogeneity of the immune microenvironment in lung cancer. Currently, T-cell-centered immunotherapy in clinical settings is prone to side effects affecting prognosis, such as immunogenic drug resistance or immune-related pneumonia, with the key factor being changes in the interactions between immune cells and tumor cells in the tumor microenvironment. Single-cell RNA sequencing technology can reveal the origins and functions of different subgroups within the tumor microenvironment from perspectives such as intercellular interactions and pseudotime analysis, thereby discovering new cell subgroups or novel biomarkers, providing new avenues for uncovering resistance to immunotherapy and monitoring therapeutic efficacy. This review comprehensively discusses the newest research techniques and advancements in single-cell RNA sequencing technology for unveiling the heterogeneity of the tumor microenvironment after lung cancer immunotherapy, offering insights for enhancing the precision and personalization of immunotherapy.

Abstract Background HER2-targeted therapies have recently emerged as an option in the management of metastatic colorectal cancer (mCRC) overexpressing HER2. However, data regarding HER2 status in primary CRC and its corresponding liver metastases are limited, potentially influencing clinical decisions. Therefore, the aim of this study was to compare the HER2 status in primary CRC and paired liver metastases. Methods Patients with mCRC who were operated from their primary colorectal cancer and their corresponding synchronous or metachronous liver metastases, in the digestive surgery department of Besançon University Hospital, between April 1999 and October 2021, were included. Tissue microarrays were constructed from matched primary CRC and liver metastastic tissue samples. HER2 status was assessed by immunohistochemistry and in situ hybridization according to Valtorta’s criteria. Results A series of 108 paired primary CRC and liver metastases, including a series of multiple liver metastases originating from the same patients (n = 24), were assessed. Among the primary CRC, 89 (82.4%), 17 (15.8%) and 2 (1.8%) cases were scored 0, 1 + and 2 + respectively. In liver metastases, 99 (91.7%), 7 (6.5%) and 2 (1.8%) were scored 0, 1 + and 2, respectively. Overall, there was a 19% discrepancy rate in HER2 status between primary CRC and metastases, which increased to 21% in cases with multiple synchronous or metachronous liver metastases in a given patient. No significant difference was found between metachronous and synchronous metastases regarding the HER2 status (p = 0.237). Conclusions Our study highlights the temporal and spatial heterogeneity of HER2 status between primary CRC and corresponding liver metastases. These findings raise the question of a sequential evaluation of the HER2 status during disease progression, to provide the most suitable treatment strategy.

Abstract Background Glioblastoma (GBM) is a highly heterogeneous, recurrent and aggressively invasive primary malignant brain tumor. The heterogeneity of GBM results in poor targeted therapy. Therefore, the aim of this study is to depict the cellular landscape of GBM and its peritumor from a single-cell perspective. Discovering new cell subtypes and biomarkers, and providing a theoretical basis for precision therapy. Methods We collected 8 tissue samples from 4 GBM patients to perform 10 × single-cell transcriptome sequencing. Quality control and filtering of data by Seurat package for clustering. Inferring copy number variations to identify malignant cells via the infercnv package. Functional enrichment analysis was performed by GSVA and clusterProfiler packages. STRING database and Cytoscape software were used to construct protein interaction networks. Inferring transcription factors by pySCENIC. Building cell differentiation trajectories via the monocle package. To infer intercellular communication networks by CellPhoneDB software. Results We observed that the tumor microenvironment (TME) varies among different locations and different GBM patients. We identified a proliferative cluster of oligodendrocytes with high expression of mitochondrial genes. We also identified two clusters of myeloid cells, one primarily located in the peritumor exhibiting an M1 phenotype with elevated TNFAIP8L3 expression, and another in the tumor and peritumor showing a proliferative tendency towards an M2 phenotype with increased DTL expression. We identified XIST, KCNH7, SYT1 and DIAPH3 as potential factors associated with the proliferation of malignant cells in GBM. Conclusions These biomarkers and cell clusters we discovered may serve as targets for treatment. Targeted drugs developed against these biomarkers and cell clusters may enhance treatment efficacy, optimize immune therapy strategies, and improve the response rates of GBM patients to immunotherapy. Our findings provide a theoretical basis for the development of individualized treatment and precision medicine for GBM, which may be used to improve the survival of GBM patients.

Background: Neuroblastoma (NB) is a heterogeneous embryonal malignancy and the deadliest tumor of infancy. It is a complex disease that can result in diverse clinical outcomes. In some children, tumors regress spontaneously. Others respond well to existing treatments. But for the high-risk group, which constitutes approximately 40% of all patients, the prognosis remains dire despite collaborative efforts in basic and clinical research. While its exact cellular origin is still under debate, NB is assumed to arise from the neural crest cell lineage including multipotent Schwann cell precursors (SCPs), which differentiate into sympatho-adrenal cell states eventually producing chromaffin cells and sympathoblasts. Methods: To investigate clonal development of neuroblastoma cell states, we performed haplotype-specific analysis of human tumor samples using single-cell multi-omics, including joint DNA/RNA sequencing of sorted single cells (DNTR-seq). Samples were also assessed using immunofluorescence stainings and fluorescence in-situ hybridization (FISH). Results: Beyond adrenergic tumor cells, we identify subpopulations of aneuploid SCP-like cells, characterized by clonal expansion, whole-chromosome 17 gains, as well as expression programs of proliferation, apoptosis, and a non-immunomodulatory phenotype. Conclusion: Aneuploid pre-malignant SCP-like cells represent a novel feature of NB. Genetic evidence and tumor phylogeny suggest that these clones and malignant adrenergic populations originate from aneuploidy-prone cells of migrating neural crest or SCP origin, before lineage commitment to sympatho-adrenal cell states. Our findings expand the phenotypic spectrum of NB cell states. Considering the multipotency of SCPs in development, we suggest that the transformation of fetal SCPs may represent one possible mechanism of tumor initiation in NB with chromosome 17 aberrations as a characteristic element. [ABSTRACT FROM AUTHOR]

Osteosarcomas are intricate cellular ecosystems, where heterotypic interactions significantly influence disease progression and therapeutic outcomes. Despite their importance, a detailed understanding of their cellular composition and organizational structure remains elusive. In this study, we provide a comprehensive single-cell and spatially resolved transcriptomics analysis of human osteosarcomas. We construct a cellular meta-map to dissect spatial transcriptomic data, unveiling a detailed atlas of osteosarcoma compositional subgroups. We meticulously characterize the unique gene signatures and functional states of each subgroup and investigate the impact of chemotherapy on these cellular subpopulations. Additionally, our spatial transcriptomics analysis identifies a distinct spatial niche, located at the forefront of tumor necrotic zones, potentially associated with chemotherapy resistance. We also delve into the crosstalk between different cellular subgroups. This study furnishes a comprehensive transcriptional atlas of osteosarcoma's cellular architecture, enriching our comprehension of its complexity and laying the groundwork for more targeted therapeutic approaches. [ABSTRACT FROM AUTHOR]

Simple Summary: Intrahepatic cholangiocarcinoma (iCCA) is a highly aggressive primary liver cancer leading to death in 10–15% of cases. In recent years, the iCCA worldwide incidence has been increasing, and research is currently focused on identifying valuable diagnostic biomarkers and developing specific therapies to counteract the disease despite its high heterogeneity. The use of cutting-edge and broad-spectrum applications like omics represents a successful approach to studying the intricate pathobiology of iCCA. In this review, we discuss how both single- and multi-omics studies can pave the way for identifying potential biomarkers, and more importantly, for stratifying iCCA patients. This stratification aims to enhance the therapeutic intervention through personalized medicine (PM). Intrahepatic cholangiocarcinoma (iCCA) is recognized worldwide as the second leading cause of morbidity and mortality among primary liver cancers, showing a continuously increasing incidence rate in recent years. iCCA aggressiveness is revealed through its rapid and silent intrahepatic expansion and spread through the lymphatic system leading to late diagnosis and poor prognoses. Multi-omics studies have aggregated information derived from single-omics data, providing a more comprehensive understanding of the phenomena being studied. These approaches are gradually becoming powerful tools for investigating the intricate pathobiology of iCCA, facilitating the correlation between molecular signature and phenotypic manifestation. Consequently, preliminary stratifications of iCCA patients have been proposed according to their "omics" features opening the possibility of identifying potential biomarkers for early diagnosis and developing new therapies based on personalized medicine (PM). The focus of this review is to provide new and advanced insight into the molecular pathobiology of the iCCA, starting from single- to the latest multi-omics approaches, paving the way for translating new basic research into therapeutic practices. [ABSTRACT FROM AUTHOR]

The use of chimeric antigen receptor (CAR) in natural killer (NK) cells for cancer therapy is gaining momentum, marking a significant shift in cancer treatment. This review aims to explore the potential of CAR-NK cell therapy in cancer immunotherapy, providing a fresh perspective. It discusses the innovative approaches in CAR-NK cell design and engineering, particularly targeting refractory or recurrent cancers. By comparing CAR-NK cells with traditional therapies, the review highlights their unique ability to tackle tumor heterogeneity and immune system suppression. Additionally, it explains how novel cytokines and receptors can enhance CAR-NK cell efficacy, specificity, and functionality. This review underscores the advantages of CAR-NK cells, including reduced toxicity, lower cost, and broader accessibility compared to CAR-T cells, along with their potential in treating both blood cancers and solid tumors. [ABSTRACT FROM AUTHOR]

Background: Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear. Methods: Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed. Results: We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis. Conclusion: By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy. [ABSTRACT FROM AUTHOR]

Purpose: Single-cell transcriptional profiling reveals cell heterogeneity and clinically relevant traits in intra-operatively collected patient-derived tissue. So far, single-cell studies have been constrained by the requirement for prospectively collected fresh or cryopreserved tissue. This limitation might be overcome by recent technical developments enabling single-cell analysis of FFPE tissue. Methods: We benchmark single-cell profiles from patient-matched fresh, cryopreserved and archival FFPE cancer tissue. Results: We find that fresh tissue and FFPE routine blocks can be employed for the robust detection of clinically relevant traits on the single-cell level. Specifically, single-cell maps of fresh patient tissues and corresponding FFPE tissue blocks could be integrated into common low-dimensional representations, and cell subtype clusters showed highly correlated transcriptional strengths of signaling pathway, hallmark, and clinically useful signatures, although expression of single genes varied due to technological differences. FFPE tissue blocks revealed higher cell diversity compared to fresh tissue. In contrast, single-cell profiling of cryopreserved tissue was prone to artifacts in the clinical setting. Conclusion: Our analysis highlights the potential of single-cell profiling in the analysis of retrospectively and prospectively collected archival pathology cohorts and increases the applicability in translational research. [ABSTRACT FROM AUTHOR]

Copy number alterations (CNAs) are prevalent across breast cancer (BCa) subtypes, with triple-negative BCa exhibiting the most heterogeneous CN profile, while luminal A/estrogen-receptor-positive tumors demonstrate the least heterogeneity. While chromosome arm-level CNAs impact 25% of the typical cancer genome, only 10% of the genome is affected by focal CNAs. The most frequent CNAs in BCa include losses of 8p, 13q, 16q, and 17p and gains of 1q and 8q. CNAs influence tumor biology, including cell proliferation, invasion, and metastasis. They can also serve as prognostic markers, with specific CNAs associated with disease aggressiveness and poor clinical outcomes. While CNAs offer promise as biomarkers for personalized treatments, challenges such as tumor heterogeneity and clonal evolution must be addressed. Technological advances and bioinformatics present opportunities for refining our understanding of CNAs and translating this knowledge into clinical practice. Breast cancer (BCa) is a prevalent malignancy that predominantly affects women around the world. Somatic copy number alterations (CNAs) are tumor-specific amplifications or deletions of DNA segments that often drive BCa development and therapy resistance. Hence, the complex patterns of CNAs complement BCa classification systems. In addition, understanding the precise contributions of CNAs is essential for tailoring personalized treatment approaches. This review highlights how tumor evolution drives the acquisition of CNAs, which in turn shape the genomic landscapes of BCas. It also discusses advanced methodologies for identifying recurrent CNAs, studying CNAs in BCa and their clinical impact. [ABSTRACT FROM AUTHOR]

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Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity - e.g., MDLC with triple-negative breast cancer (TNBC) or basal ductal and estrogen receptor positive (ER+) luminal lobular regions, distinct enrichment of cell cycle arrest/senescence and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular but not ductal regions, and single-cell ductal and lobular subpopulations with unique oncogenic signatures further highlighting intraregional heterogeneity. Altogether, we demonstrated that the intratumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma. [ABSTRACT FROM AUTHOR]

Background: Glycosyltransferase-associated genes play a crucial role in hepatocellular carcinoma (HCC) pathogenesis. This study investigates their impact on the tumor microenvironment and molecular mechanisms, offering insights into innovative immunotherapeutic strategies for HCC. Methods: We utilized cutting-edge single-cell and spatial transcriptomics to examine HCC heterogeneity. Four single-cell scoring techniques were employed to evaluate glycosyltransferase genes. Spatial transcriptomic findings were validated, and bulk RNA-seq analysis was conducted to identify prognostic glycosyltransferase-related genes and potential immunotherapeutic targets. MGAT1's role was further explored through various functional assays. Results: Our analysis revealed diverse cell subpopulations in HCC with distinct glycosyltransferase gene activities, particularly in macrophages. Key glycosyltransferase genes specific to macrophages were identified. Temporal analysis illustrated macrophage evolution during tumor progression, while spatial transcriptomics highlighted reduced expression of these genes in core tumor macrophages. Integrating scRNA-seq, bulk RNA-seq, and spatial transcriptomics, MGAT1 emerged as a promising therapeutic target, showing significant potential in HCC immunotherapy. Conclusion: This comprehensive study delves into glycosyltransferaseassociated genes in HCC, elucidating their critical roles in cellular dynamics and immune cell interactions. Our findings open new avenues for immunotherapeutic interventions and personalized HCC management, pushing the boundaries of HCC immunotherapy. [ABSTRACT FROM AUTHOR]

The coordinated migration of invasive tumor cells is a complex and dynamic mechanism driven by diverse cellular and molecular events. Unfortunately, the inherent heterogeneity within tumors raises multiple challenges in deciphering key biomarkers and novel therapeutic approaches to prevent tumor metastasis. Here, a microengineered tumor‐on‐a‐chip system incorporating human platelet lysate hydrogels is proposed to recreate the early metastatic process of tumor invasion and drug response. By co‐culturing human bone marrow mesenchymal stem cells with two tumor cell lines with distinct metastatic capability, the developed model can emulate the 3D tumor microarchitecture and inter‐heterogeneity regarding its intrinsic metastatic ability. The recreated microenvironment supports tumor and stromal cell movement, evidencing the synergistic tumor‐stromal cell and cell‐extracellular matrix interactions of an invading tumor. Through gene and protein expression analysis and exometabolomic profiling, this tumor‐on‐a‐chip platform provides evidence for the role of a dynamic environment as a key regulator of tumor metastatic ability. Additionally, the effect of doxorubicin treatment on tumor invasiveness and biomarker profile highlights the suitability of the established models for therapy assessment. Overall, this study presents a tumor‐on‐a‐chip model useful to pursue mechanistic studies on early metastatic events in a fully human‐derived microenvironment, while contributing with fundamental insights into biomolecular profiling. [ABSTRACT FROM AUTHOR]

Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level. [ABSTRACT FROM AUTHOR]

Background: HER2-targeted therapies have recently emerged as an option in the management of metastatic colorectal cancer (mCRC) overexpressing HER2. However, data regarding HER2 status in primary CRC and its corresponding liver metastases are limited, potentially influencing clinical decisions. Therefore, the aim of this study was to compare the HER2 status in primary CRC and paired liver metastases. Methods: Patients with mCRC who were operated from their primary colorectal cancer and their corresponding synchronous or metachronous liver metastases, in the digestive surgery department of Besançon University Hospital, between April 1999 and October 2021, were included. Tissue microarrays were constructed from matched primary CRC and liver metastastic tissue samples. HER2 status was assessed by immunohistochemistry and in situ hybridization according to Valtorta's criteria. Results: A series of 108 paired primary CRC and liver metastases, including a series of multiple liver metastases originating from the same patients (n = 24), were assessed. Among the primary CRC, 89 (82.4%), 17 (15.8%) and 2 (1.8%) cases were scored 0, 1 + and 2 + respectively. In liver metastases, 99 (91.7%), 7 (6.5%) and 2 (1.8%) were scored 0, 1 + and 2, respectively. Overall, there was a 19% discrepancy rate in HER2 status between primary CRC and metastases, which increased to 21% in cases with multiple synchronous or metachronous liver metastases in a given patient. No significant difference was found between metachronous and synchronous metastases regarding the HER2 status (p = 0.237). Conclusions: Our study highlights the temporal and spatial heterogeneity of HER2 status between primary CRC and corresponding liver metastases. These findings raise the question of a sequential evaluation of the HER2 status during disease progression, to provide the most suitable treatment strategy. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is a highly heterogeneous, recurrent and aggressively invasive primary malignant brain tumor. The heterogeneity of GBM results in poor targeted therapy. Therefore, the aim of this study is to depict the cellular landscape of GBM and its peritumor from a single-cell perspective. Discovering new cell subtypes and biomarkers, and providing a theoretical basis for precision therapy. Methods: We collected 8 tissue samples from 4 GBM patients to perform 10 × single-cell transcriptome sequencing. Quality control and filtering of data by Seurat package for clustering. Inferring copy number variations to identify malignant cells via the infercnv package. Functional enrichment analysis was performed by GSVA and clusterProfiler packages. STRING database and Cytoscape software were used to construct protein interaction networks. Inferring transcription factors by pySCENIC. Building cell differentiation trajectories via the monocle package. To infer intercellular communication networks by CellPhoneDB software. Results: We observed that the tumor microenvironment (TME) varies among different locations and different GBM patients. We identified a proliferative cluster of oligodendrocytes with high expression of mitochondrial genes. We also identified two clusters of myeloid cells, one primarily located in the peritumor exhibiting an M1 phenotype with elevated TNFAIP8L3 expression, and another in the tumor and peritumor showing a proliferative tendency towards an M2 phenotype with increased DTL expression. We identified XIST, KCNH7, SYT1 and DIAPH3 as potential factors associated with the proliferation of malignant cells in GBM. Conclusions: These biomarkers and cell clusters we discovered may serve as targets for treatment. Targeted drugs developed against these biomarkers and cell clusters may enhance treatment efficacy, optimize immune therapy strategies, and improve the response rates of GBM patients to immunotherapy. Our findings provide a theoretical basis for the development of individualized treatment and precision medicine for GBM, which may be used to improve the survival of GBM patients. [ABSTRACT FROM AUTHOR]

Cancer is a disease that stems from a fundamental liability inherent to multicellular life forms in which an individual cell is capable of reneging on the interests of the collective organism. Although cancer is commonly described as an evolutionary process, a less appreciated aspect of tumorigenesis may be the constraints imposed by the organism's developmental programs. Recent work from single-cell transcriptomic analyses across a range of cancer types has revealed the recurrence, plasticity, and co-option of distinct cellular states among cancer cell populations. Here, we note that across diverse cancer types, the observed cell states are proximate within the developmental hierarchy of the cell of origin. We thus posit a model by which cancer cell states are directly constrained by the organism's "developmental map." According to this model, a population of cancer cells traverses the developmental map, thereby generating a heterogeneous set of states whose interactions underpin emergent tumor behavior. Single-cell transcriptomic analyses in diverse cancer types have revealed the recurrence, plasticity, and co-option of distinct cellular states, leading to a "developmental constraint" model whereby cancer cell state plasticity is restricted by the organism's developmental map to access progenitor-like states and differentiated-like states of adjacent lineages. [ABSTRACT FROM AUTHOR]

Simple Summary: Despite recent advances in diagnosing and treating glioma, the prognosis remains poor. It is crucial to investigate its clinical characteristics and prognostic factors to provide a basis for treating and managing patients with glioma. We examined how clinical variables and molecular profiles may have affected overall survival (OS) over the past 10 years. A correlation between IDH status, TERT mutations, MGMT methylation, and tumor location characteristics was also studied. Our study demonstrates that patients with higher tumor histological grades who had received adjuvant radiotherapy exhibited isocitrate dehydrogenase 1 (IDH1) mutations or presented with wildtype telomerase reverse transcriptase promoter (TERTp) experienced improved OS. Gliomas are primary brain lesions involving cerebral structures without well-defined boundaries and constitute the most prevalent central nervous system (CNS) neoplasms. Among gliomas, glioblastoma (GB) is a glioma of the highest grade and is associated with a grim prognosis. We examined how clinical variables and molecular profiles may have affected overall survival (OS) over the past ten years. A retrospective study was conducted at Sina Hospital in Tehran, Iran and examined patients with confirmed glioma diagnoses between 2012 and 2020. We evaluated the correlation between OS in GB patients and sociodemographic as well as clinical factors and molecular profiling based on IDH1, O-6-Methylguanine-DNA Methyltransferase (MGMT), TERTp, and epidermal growth factor receptor (EGFR) amplification (EGFR-amp) status. Kaplan–Meier and multivariate Cox regression models were used to assess patient survival. A total of 178 patients were enrolled in the study. The median OS was 20 months, with a 2-year survival rate of 61.0%. Among the 127 patients with available IDH measurements, 100 (78.7%) exhibited mutated IDH1 (IDH1-mut) tumors. Of the 127 patients with assessed MGMT promoter methylation (MGMTp-met), 89 (70.1%) had MGMT methylated tumors. Mutant TERTp (TERTp-mut) was detected in 20 out of 127 cases (15.7%), while wildtype TERTp (wildtype TERTp-wt) was observed in 107 cases (84.3%). Analyses using multivariable models revealed that age at histological grade (p < 0.0001), adjuvant radiotherapy (p < 0.018), IDH1 status (p < 0.043), and TERT-p status (p < 0.014) were independently associated with OS. Our study demonstrates that patients with higher tumor histological grades who had received adjuvant radiotherapy exhibited IDH1-mut or presented with TERTp-wt experienced improved OS. Besides, an interesting finding showed an association between methylation of MGMTp and TERTp status with tumor location. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) poses a significant challenge in clinical oncology due to its aggressive nature, heterogeneity, and resistance to therapies. Cancer stem cells (CSCs) play a critical role in GBM, particularly in treatment resistance and tumor relapse, emphasizing the need to comprehend the mechanisms regulating these cells. Also, their multifaceted contributions to the tumor microenvironment (TME) underline their significance, driven by their unique properties. This study aimed to characterize glioblastoma stem cells (GSCs), specifically slow-cycling cells (SCCs), in an immunocompetent murine GBM model to explore their similarities with their human counterparts. Using the KR158 mouse model, we confirmed that SCCs isolated from this model exhibited key traits and functional properties akin to human SCCs. KR158 murine SCCs, expanded in the gliomasphere assay, demonstrated sphere forming ability, self-renewing capacity, positive tumorigenicity, enhanced stemness and resistance to chemotherapy. Together, our findings validate the KR158 murine model as a framework to investigate GSCs and SCCs in GBM pathology, and explore specifically the SCC–immune system communications, understand their role in disease progression, and evaluate the effect of therapeutic strategies targeting these specific connections. [ABSTRACT FROM AUTHOR]

Purpose: To study the capability of diffusion-relaxation correlation spectroscopic imaging (DR-CSI) on subtype classification and grade differentiation for small renal cell carcinoma (RCC). Histogram analysis for apparent diffusion coefficient (ADC) was studied for comparison. Materials and methods: A total of 61 patients with small RCC (< 4 cm) were included in the retrospective study. MRI data were reviewed, including a multi-b (0–1500 s/mm2) multi-TE (51–200 ms) diffusion weighted imaging (DWI) sequence. Region of interest (ROI) was delineated manually on DWI to include solid tumor. For each patient, a D-T2 spectrum was fitted and segmented into 5 compartments, and the volume fractions VA, VB, VC, VD, VE were obtained. ADC mapping was calculated, and histogram parameters ADC 90th, 10th, median, standard deviation, skewness and kurtosis were obtained. All MRI metrices were compared between clear cell RCC (ccRCC) and non-ccRCC group, and between high-grade and low-grade group. Receiver operator curve analysis was used to assess the corresponding diagnostic performance. Results: Significantly higher ADC 90th, ADC 10th and ADC median, and significantly lower DR-CSI VB was found for ccRCC compared to non-ccRCC. Significantly lower ADC 90th, ADC median and significantly higher VB was found for high-grade RCC compared to low-grade. For identifying ccRCC from non-ccRCC, VB showed the highest area under curve (AUC, 0.861) and specificity (0.882). For differentiating high- from low-grade, ADC 90th showed the highest AUC (0.726) and specificity (0.786), while VB also displayed a moderate AUC (0.715). Conclusion: DR-CSI may offer improved accuracy in subtype identification for small RCC, while do not show better performance for small RCC grading compared to ADC histogram. [ABSTRACT FROM AUTHOR]

Cellular plasticity is enhanced by dedifferentiation processes such as epithelial-mesenchymal transition (EMT). The dynamic and transient nature of EMT-like processes challenges the investigation of cell plasticity in patient-derived breast cancer models. Here, we utilized patient-derived organoids (PDOs) as a model to study the susceptibility of primary breast cancer cells to EMT. Upon induction with TGF-β, PDOs exhibited EMT-like features, including morphological changes, E-cadherin downregulation and cytoskeletal reorganization, leading to an invasive phenotype. Image analysis and the integration of deep learning algorithms enabled the implantation of microscopy-based quantifications demonstrating repetitive results between organoid lines from different breast cancer patients. Interestingly, epithelial plasticity was also expressed in terms of alterations in luminal and myoepithelial distribution upon TGF-β induction. The effective modeling of dynamic processes such as EMT in organoids and their characteristic spatial diversity highlight their potential to advance research on cancer cell plasticity in cancer patients.

Single-cell RNA sequencing (scRNA-seq) technology enables the precise analysis of individual cell transcripts with high sensitivity and throughput. When integrated with multiomics technologies, scRNA-seq significantly enhances the understanding of cellular diversity, particularly within the tumor microenvironment. Similarly, single-cell DNA sequencing has emerged as a powerful tool in cancer research, offering unparalleled insights into the genetic heterogeneity and evolution of tumors. In the context of breast cancer, this technology holds substantial promise for decoding the intricate genomic landscape that drives disease progression, treatment resistance, and metastasis. By unraveling the complexities of tumor biology at a granular level, single-cell DNA sequencing provides a pathway to advancing our comprehension of breast cancer and improving patient outcomes through personalized therapeutic interventions. As single-cell sequencing technology continues to evolve and integrate into clinical practice, its application is poised to revolutionize the diagnosis, prognosis, and treatment strategies for breast cancer. This review explores the potential of single-cell sequencing technology to deepen our understanding of breast cancer, highlighting key approaches, recent advancements, and the role of the tumor microenvironment in disease plasticity. Additionally, the review discusses the impact of single-cell sequencing in paving the way for the development of personalized therapies.

Background: Spatial biology is an emerging concept to interrogate tumor heterogeneity. The NanoString GeoMx® Digital Spatial Profiling (DSP) platform has become increasingly available. It combines high-plex analysis of protein or messenger RNA expression using barcoded antibodies or oligonucleotide probes with investigator-driven selection of regions of interest. Cell populations, e.g. immune cells, can be selectively analyzed via segmentation. A key advantage is the use of archived formalin-fixed, paraffin-embedded tissue, however, begging the question whether and to what extent tissue fixation and storage time affect the results. Materials and methods: Antibody binding density (ABD), i.e. the number of barcodes/μm2, is a key quality control measure for DSP spatial proteomics. To assess whether regional differences in tissue fixation have an influence on ABD, we compared 652 regions of interest selected from tumor center and periphery of 49 prostate cancer and 25 renal cell carcinoma (RCC) specimens. Moreover, the effect of tissue storage time on ABD was examined. Finally, we tested whether regional differences have an influence on ABD of segmented CD45+ or CD8+ cells. Results: No significant differences in ABD between tumor center and periphery were found in prostate cancer or RCC. However, ABD was significantly higher in recent specimens (≤5 years) when compared with those that were older (>5 years; P = 0.027). There was a trend towards higher ABD in the tumor periphery of RCC specimens after segmentation for immune cells, albeit without reaching statistical significance. Conclusions: The NanoString GeoMx® DSP platform delivers robust data to interrogate tumor heterogeneity, but tissue storage time should be considered when interpreting the results.

In this case report, we present the treatment outcomes of the first patient enrolled in the LuDO-N trial. The patient is a 21-month-old girl diagnosed with high-risk neuroblastoma (NB) and widespread skeletal metastasis. The patient initially underwent first-line therapy according to SIOPEN HRNBL-1 but was switched to second-line treatments due to disease progression, and she was finally screened for enrollment in the LuDO-N trial due to refractory disease. Upon enrollment, the patient received two rounds of the radiolabeled somatostatin analogue lutetium-177 octreotate (177Lu-DOTATATE), which was well tolerated. A dosimetry analysis revealed a heterogeneous uptake across tumor lesions, resulting in a significant absorbed dose of 54 Gy in the primary tumor, but only 2 Gy at one of the metastatic sites in the distal femur. While the initial treatment response showed disease stabilization, the distal femoral metastasis continued to progress, leading to the eventual death of the patient. A tissue analysis of the biopsies collected throughout the course of the disease revealed heterogeneous drug target expression of somatostatin receptor 2 (SSTR2) across and within tumor lesions. Furthermore, genomic profiling revealed a novel KIAA1549::BRAF fusion oncogene amplification in the distal femoral metastasis at recurrence that might be related with resistance to radiation, possibly through the downregulation of SSTR2. This case report demonstrates a mixed response to molecular radiotherapy (MRT) with 177Lu-DOTATATE. The observed variation in SSTR2 expression between tumor lesions suggests that heterogeneous target expression may have been the reason for treatment failure in this patient’s case. Further investigation within the LuDO-N trial will give a more comprehensive understanding of the correlation between SSTR2 expression levels and treatment outcomes, which will be important to advance treatment strategies based on MRT for children with high-risk NB.

The heterogeneity of immune cells and metabolic pathways in hepatocellular carcinoma (HCC) patients has not been fully elucidated, leading to diverse clinical outcomes. Accurately distinguishing different HCC subtypes and recommending appropriate treatments is are highly important. In this study, we conducted a comprehensive analysis of 28 immune cells and 85 metabolic pathways in the TCGA-LIHC and GSE14520 datasets. Metabolism-related first principal component (MRPC1) and cytotoxic T lymphocyte (CTL) infiltration were used to assess the metabolic and immune infiltration levels of HCC patients, respectively. These two quantifiable indicators were then used to construct an immune‒metabolic classifier, which categorized HCC patients into three distinct groups. The potential biological mechanisms were explored through multiomics analysis, revealing that group S1 exhibited high metabolic activity and a high level of immune infiltration, that group S2 presented a low level of immune infiltration, and that group S3 presented low metabolic activity. This new immune‒metabolic classifier was well validated in a pancancer cohort of 9296 patients. The efficacy of multiple treatment approaches was assessed in relation to different immune‒metabolic groups, indicating that group S1 patients may benefit from immunotherapy, that group S2 patients are suitable for transcatheter arterial chemoembolization (TACE), and that group S3 patients are appropriate candidates for tyrosine kinase inhibitors. In conclusion, this immune‒metabolic classifier is anticipated to address the differences in treatment efficacy among HCC patients due to the heterogeneity of the tumor microenvironment, and to help refine the individualized treatment choices for clinical patients.