Single-cell Spatial Proteomic Revelations on the Multiparametric MRI Heterogeneity of Clinically Significant Prostate Cancer

NIR laser-activated phthalocyanine loaded lipid nanoparticles targeting M2 macrophage for improved photoacoustic imaging-guided photothermal therapy

The development of novel phototheranostic agents with significant potential in bioimaging-guided therapy is highly desirable for precise tumor therapy. Herein, NIR laser-activated ruthenium phthalocyanine (PcRu) loaded sub-30 nm targeting lipid nanoparticles (α-PcRu-NPs) were fabricated for photoacoustic imaging (PAI)-guided photothermal therapy (PTT). Due to the formation of J-type aggregation of PcRu in the core of the nanostructure, the α-PcRu-NPs exhibited high stability, efficient NIR absorption, reduced singlet oxygen generation, high photothermal activity, and intense photoacoustic signal. With the M2 macrophage target peptide (M2pep) modification and small size of α-PcRu-NPs, in vivo evaluations reveal that α-PcRu-NPs can specifically target and deeply penetrate the tumor foci. Under a high contrast PAI guidance with α-PcRu-NPs (744 nm, 0.35 μW), it also realizes superior photothermal therapy (PTT) for breast cancer under 670 nm laser irradiation (0.5 W/cm2). The prominent therapeutic efficacy of α-PcRu-NP-based PTT not only directly kills tumor cells, but also enhances the immune response by promoting dendritic cell maturation and increasing cytotoxic T cell infiltration. Thus, this work broadens the applications of phthalocyanine derivatives as phototheranostics in the PAI-guided PTT field.

The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy

In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.

Genomics and Artificial Intelligence: Prostate Cancer

Artificial intelligence (AI) is revolutionizing prostate cancer genomics research. By leveraging machine learning and deep learning algorithms, researchers can rapidly analyze vast genomic datasets to identify patterns and correlations that may be missed by traditional methods. These AI-driven insights can lead to the discovery of novel biomarkers, enhance the accuracy of diagnosis, and predict disease progression and treatment response. As such, AI is becoming an indispensable tool in the pursuit of personalized medicine for prostate cancer.

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis

Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics, and spatial transcriptomics allow recording of patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multiscale dynamics of its evolution. Here, we review the latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed burgeoning growth in the recent past in terms of mapping heterogeneity within tumor cell types as well as the stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor and a more systematic investigation of the implications of heterogeneity for patient outcomes.

CHRDL1, NEFH, TAGLN and SYNM as novel diagnostic biomarkers of benign prostatic hyperplasia and prostate cancer

BACKGROUND

Prostate cancer (PCa) and benign prostatic hyperplasia (BPH) are common male diseases whose incidence rates gradually increase with age. They seriously affect men's physical health and quality of life. This study aimed to identify new biomarkers for the diagnosis of BPH and PCa.

METHODS

Two datasets, GSE28204 and GSE134051 (including human PCa and BPH), were downloaded from the GEO database. The batch effect was removed for merging, and then differential gene expression analysis was conducted to identify BPH and PCa cases. The diagnostic biomarkers of BPH and PCa were further screened using machine learning and bioinformatics. ROC curves were drawn to evaluate the diagnostic accuracy of the selected biomarkers. An online website and qPCR were used to preliminarily explore the expression levels of PCa biomarkers. The correlations between the expression of biomarkers and the tumor microenvironment, tumor mutation load and immunotherapy drugs were evaluated.

RESULTS

We identified fifteen genes (CHRDL1, DES, FLNC, GSTP1, MYL9, TGFB3, NEFH, TAGLN, SPARCL1, SYNM, TRPM8, HPN, PLA2G7, ENTPD5 and GPR160) as critical diagnostic biomarkers. After reviewing the literature on all selected biomarkers, we found few studies on the four genes CHRDL1, NEFH, TAGLN and SYNM in BPH or PCa. We defined these four genes as new potential diagnostic biomarkers (NPDBs) of BPH and PCa. All NPDBs were downregulated in PCa patients and PCa cell lines and upregulated in BPH patients and cell lines. When the immune landscape and mutation frequencies were analyzed, the results showed that the tumor microenvironment (TME), immune landscape, tumor mutation burden, and drug response were significantly correlated with NPDB expressions.

CONCLUSIONS

We found four new diagnostic markers of BPH and PCa, which may facilitate the early diagnosis, treatment, and immunotherapeutic responses assessment and may be of major value in guiding clinical practice.

Multiregional single-cell proteogenomic analysis of ccRCC reveals cytokine drivers of intratumor spatial heterogeneity

Intratumor heterogeneity (ITH) represents a major challenge for anticancer therapies. An integrated, multidimensional, multiregional approach dissecting ITH of the clear cell renal cell carcinoma (ccRCC) tumor microenvironment (TME) is employed at the single-cell level with mass cytometry (CyTOF), multiplex immunofluorescence (MxIF), and single-nucleus RNA sequencing (snRNA-seq) and at the bulk level with whole-exome sequencing (WES), RNA-seq, and methylation profiling. Multiregional analyses reveal unexpected conservation of immune composition within each individual patient, with profound differences among patients, presenting patient-specific tumor immune microenvironment signatures despite underlying genetic heterogeneity from clonal evolution. Spatial proteogenomic TME analysis using MxIF identifies 14 distinct cellular neighborhoods and, conversely, demonstrated architectural heterogeneity among different tumor regions. Tumor-expressed cytokines are identified as key determinants of the TME and correlate with clinical outcome. Overall, this work signifies that spatial ITH occurs in ccRCC, which may drive clinical heterogeneity and warrants further interrogation to improve patient outcomes.

Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes

Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. We present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of >9,400 tissue and blood sorted cell RNA profiles incorporated into millions of artificial transcriptomes to accurately reconstruct the tumor microenvironment (TME). Bioinformatics correction for technical and biological variability, aberrant cancer cell expression inclusion, and accurate quantification and normalization of transcript expression increased Kassandra stability and robustness. Performance was validated on 4,000 H&E slides and 1,000 tissues by comparison with cytometric, immunohistochemical, or single-cell RNA-seq measurements. Kassandra accurately deconvolved TME elements, showing the role of these populations in tumor pathogenesis and other biological processes. Digital TME reconstruction revealed that the presence of PD-1-positive CD8⁺ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

Prostate cancer multiparametric magnetic resonance imaging visibility is a tumor-intrinsic phenomena

Multiparametric magnetic resonance imaging (mpMRI) is an emerging standard for diagnosing and prognosing prostate cancer, but ~ 20% of clinically significant tumors are invisible to mpMRI, as defined by the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) score of one or two. To understand the biological underpinnings of tumor visibility on mpMRI, we examined the proteomes of forty clinically significant tumors (i.e., International Society of Urological Pathology (ISUP) Grade Group 2)—twenty mpMRI-visible and twenty mpMRI-invisible, with matched histologically normal prostate. Normal prostate tissue was indistinguishable between patients with visible and invisible tumors, and invisible tumors closely resembled the normal prostate. These data indicate that mpMRI-visibility arises when tumor evolution leads to large-magnitude proteomic divergences from histologically normal prostate.

Harnessing the Utility of Ex Vivo Patient Prostate Tissue Slice Cultures

Patient-derived prostate tissue explant cultures are powerful research tools that offer the potential for personalized medicine. These cultures preserve the local microenvironment of the surrounding stroma but are not without limitations and challenges. There are several methods and processing techniques to culture tissue ex vivo, that include explant tissue chunks and precision-cut tissue slices. Precision-cut tissue slices provide a consistent distribution of nutrients and gases to the explant. Herein we summarize the prostate tissue slice method, its limitations and discuss the utility of this model, to investigate prostate biology and therapeutic treatment responses.

Stromal FAP Expression is Associated with MRI Visibility and Patient Survival in Prostate Cancer

Some clinically significant prostate cancers are missed by MRI. We asked whether the tumor stroma in surgically treated localized prostate cancer lesions positive or negative with MRI are different in their cellular and molecular properties, and whether the differences are reflected to the clinical course of the disease. We profiled the stromal and immune cell composition of MRI-classified tumor lesions by applying multiplexed fluorescence IHC (mfIHC) and automated image analysis in a clinical cohort of 343 patients (cohort I). We compared stromal variables between MRI-visible lesions, invisible lesions, and benign tissue and assessed the predictive significance for biochemical recurrence (BCR) and disease-specific survival (DSS) using Cox regression and log-rank analysis. Subsequently, we carried out a prognostic validation of the identified biomarkers in a population-based cohort of 319 patients (cohort II). MRI true-positive lesions are different from benign tissue and MRI false-negative lesions in their stromal composition. CD163⁺ cells (macrophages) and fibroblast activation protein (FAP)⁺ cells were more abundant in MRI true-positive than in MRI false-negative lesions or benign areas. In MRI true-visible lesions, a high proportion of stromal FAP⁺ cells was associated with PTEN status and increased immune infiltration (CD8⁺, CD163⁺), and predicted elevated risk for BCR. High FAP phenotype was confirmed to be a strong indicator of poor prognosis in two independent patient cohorts using also conventional IHC. The molecular composition of the tumor stroma may determine whether early prostate lesions are detectable by MRI and associates with survival after surgical treatment.

Significance

These findings may have a significant impact on clinical decision making as more radical treatments may be recommended for men with a combination of MRI-visible primary tumors and FAP⁺ tumor stroma.

Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity

Tumor heterogeneity, a hallmark of cancer, impairs the efficacy of cancer therapy and drives tumor progression. Exploring inter- and intra-tumoral heterogeneity not only provides insights into tumor development and progression, but also guides the design of personalized therapies. Previously, high-throughput sequencing techniques have been used to investigate the heterogeneity of tumor ecosystems. However, they could not provide a high-resolution landscape of cellular components in tumor ecosystem. Recently, advance in single-cell technologies has provided an unprecedented resolution to uncover the intra-tumoral heterogeneity by profiling the transcriptomes, genomes, proteomes and epigenomes of the cellular components and also their spatial distribution, which greatly accelerated the process of basic and translational cancer research. Importantly, it has been demonstrated that some cancer cells are able to transit between different states in order to adapt to the changing tumor microenvironment, which led to increased cellular plasticity and tumor heterogeneity. Understanding the molecular mechanisms driving cancer cell plasticity is critical for developing precision therapies. In this review, we summarize the recent progress in dissecting the cancer cell plasticity and tumor heterogeneity by use of single-cell multi-omics techniques.