Single-cell sequencing reveals effects of chemotherapy on the immune landscape and TCR/BCR clonal expansion in a relapsed ovarian cancer patient

Molecular characterization of ovarian mesonephric-like adenocarcinoma: Insights from single-cell RNA sequencing and mitochondrial metabolism

Objectives

Ovarian mesonephric-like adenocarcinoma (MLA) is a rare malignancy with limited understanding of its molecular features and therapeutic vulnerabilities. Although similar to uterine MLA, its unique characteristics remain undefined. This study aimed to characterize ovarian MLA using single-cell RNA sequencing (scRNA-seq) and compare it with high-grade serous ovarian cancer (HGSOC).

Methods

We analyzed the cellular and molecular heterogeneity of an ovarian MLA sample using scRNA-seq. Differential gene expression and pathway analyses were performed to identify unique molecular signatures and therapeutic targets. HGSOC scRNA-seq datasets were used for comparative analysis.

Results

Ovarian MLA demonstrated reduced heterogeneity, with a predominance of epithelial cells compared to HGSOC. Transcriptomic profiling revealed an upregulation of mitochondrial metabolism and lipid biosynthesis genes, indicating a metabolic shift toward oxidative phosphorylation. Gene enrichment and protein-protein interaction analyses identified distinct pathways, including mitochondrial biogenesis and dynamics, suggesting mitochondrial reprogramming.

Conclusions

This study provides the first scRNA-seq-based molecular characterization of ovarian MLA, differentiating it from HGSOC. Findings suggest potential therapeutic avenues, with a proposed combination therapy targeting MAP kinase and PI3K/AKT/mTOR pathways. Validation in larger cohorts is necessary for clinical application.

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study

BACKGROUND

Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes.

METHODS

We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model.

RESULTS

The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients.

CONCLUSIONS

The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Decoding senescence of aging single cells at the nexus of biomaterials, microfluidics, and spatial omics

Aging has profound effects on the body, most notably an increase in the prevalence of several diseases. An important aging hallmark is the presence of senescent cells that no longer multiply nor die off properly. Another characteristic is an altered immune system that fails to properly self-surveil. In this multi-player aging process, cellular senescence induces a change in the secretory phenotype, known as senescence-associated secretory phenotype (SASP), of many cells with the intention of recruiting immune cells to accelerate the clearance of these damaged senescent cells. However, the SASP phenotype results in inducing secondary senescence of nearby cells, resulting in those cells becoming senescent, and improper immune activation resulting in a state of chronic inflammation, called inflammaging, in many diseases. Senescence in immune cells, termed immunosenescence, results in further dysregulation of the immune system. An interdisciplinary approach is needed to physiologically assess aging changes of the immune system at the cellular and tissue level. Thus, the intersection of biomaterials, microfluidics, and spatial omics has great potential to collectively model aging and immunosenescence. Each of these approaches mimics unique aspects of the body undergoes as a part of aging. This perspective highlights the key aspects of how biomaterials provide non-cellular cues to cell aging, microfluidics recapitulate flow-induced and multi-cellular dynamics, and spatial omics analyses dissect the coordination of several biomarkers of senescence as a function of cell interactions in distinct tissue environments. An overview of how senescence and immune dysregulation play a role in organ aging, cancer, wound healing, Alzheimer's, and osteoporosis is included. To illuminate the societal impact of aging, an increasing trend in anti-senescence and anti-aging interventions, including pharmacological interventions, medical procedures, and lifestyle changes is discussed, including further context of senescence.

Platinum-based chemotherapy promotes antigen presenting potential in monocytes of patients with high-grade serous ovarian carcinoma

Ovarian cancer (OC) is the most lethal gynecologic malignancy worldwide. The major clinical challenge includes the asymptomatic state of the disease, making diagnosis possible only at advanced stages. Another OC complication is the high relapse rate and poor prognosis following the standard first-line treatment with platinum-based chemotherapy. At present, numerous clinical trials are being conducted focusing on immunotherapy in OC; nevertheless, there are still no FDA-approved indications. Personalized decision regarding the immunotherapy, including immune checkpoint blockade and immune cell-based immunotherapies, can depend on the effective antigen presentation required for the cytotoxic immune response. The major aim of our study was to uncover tumor-specific transcriptional and epigenetic changes in peripheral blood monocytes in patients with high-grade serous ovarian cancer (HGSOC). Another key point was to elucidate how chemotherapy can reprogram monocytes and how that relates to changes in other immune subpopulations in the blood. To this end, we performed single-cell RNA sequencing of peripheral blood mononuclear cells (PBMCs) from patients with HGSOC who underwent neoadjuvant chemotherapeutic treatment (NACT) and in treatment-naïve patients. Monocyte cluster was significantly affected by tumor-derived factors as well as by chemotherapeutic treatment. Bioinformatical analysis revealed three distinct monocyte subpopulations within PBMCs based on feature gene expression - CD14.Mn.S100A8.9hi, CD14.Mn.MHC2hi and CD16.Mn subsets. The intriguing result was that NACT induced antigen presentation in monocytes by the transcriptional upregulation of MHC class II molecules, but not by epigenetic changes. Increased MHC class II gene expression was a feature observed across all three monocyte subpopulations after chemotherapy. Our data also demonstrated that chemotherapy inhibited interferon-dependent signaling pathways, but activated some TGFb-related genes. Our results can enable personalized decision regarding the necessity to systemically re-educate immune cells to prime ovarian cancer to respond to anti-cancer therapy or to improve personalized prescription of existing immunotherapy in either combination with chemotherapy or a monotherapy regimen.

Identification of a G-protein coupled receptor-related gene signature through bioinformatics analysis to construct a risk model for ovarian cancer prognosis

BACKGROUND

Ovarian cancer (OV) is a common malignant tumor of the female reproductive system with a 5-year survival rate of ∼30 %. Inefficient early diagnosis and prognosis leads to poor survival in most patients. G protein-coupled receptors (GPCRs, the largest family of human cell surface receptors) are associated with OV. We aimed to identify GPCR-related gene (GPCRRG) signatures and develop a novel model to predict OV prognosis.

METHOD

We downloaded data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic GPCRRGs were screened using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and a prognostic model was constructed. The predictive ability of the model was evaluated by Kaplan-Meier (K-M) survival analysis. The levels of GPCRRGs were examined in normal and OV cell lines using quantitative reverse-Etranscription polymerase chain reaction. The immunological characteristics of the high- and low-risk groups were analyzed using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT.

RESULTS

Based on the risks scores, 17 GPCRRGs were associated with OV prognosis. CXCR4, GPR34, LGR6, LPAR3, and RGS2 were significantly expressed in three OV datasets and enabled accurate OV diagnosis. K-M analysis of the prognostic model showed that it could differentiate high- and low-risk patients, which correspond to poorer and better prognoses, respectively. GPCRRG expression was correlated with immune infiltration rates.

CONCLUSIONS

Our prognostic model elaborates on the roles of GPCRRGs in OV and provides a new tool for prognosis and immune response prediction in patients with OV.

Altered tumor signature and T-cell profile after chemotherapy reveal new therapeutic opportunities in high-grade serous ovarian carcinoma

Chemotherapy combined with debulking surgery is the standard treatment protocol for high-grade serous ovarian carcinoma (HGSOC). Nonetheless, a significant number of patients encounter relapse due to the development of chemotherapy resistance. To better understand and address this resistance, we conducted a comprehensive study investigating the transcriptional alterations at the single-cell resolution in tissue samples from patients with HGSOC, using single-cell RNA sequencing and T-cell receptor sequencing techniques. Our analyses unveiled notable changes in the tumor signatures after chemotherapy, including those associated with epithelial-mesenchymal transition and cell cycle arrest. Within the immune compartment, we observed alterations in the T-cell profiles, characterized by naïve or pre-exhausted populations following chemotherapy. This phenotypic change was further supported by the examination of adjoining T-cell receptor clonotypes in paired longitudinal samples. These findings underscore the profound impact of chemotherapy on reshaping the tumor landscape and the immune microenvironment. This knowledge may provide clues for the development of future therapeutic strategies to combat treatment resistance in HGSOC.

Neglected Anatomical Areas in Ovarian Cancer: Significance for Optimal Debulking Surgery

Ovarian cancer (OC), the most lethal gynecological malignancy, usually presents in advanced stages. Characterized by peritoneal and lymphatic dissemination, OC necessitates a complex surgical approach usually involving the upper abdomen with the aim of achieving optimal cytoreduction without visible macroscopic disease (R0). Failures in optimal cytoreduction, essential for prognosis, often stem from overlooking anatomical neglected sites that harbor residual tumor. Concealed OC metastases may be found in anatomical locations such as the omental bursa; Morison's pouch; the base of the round ligament and hepatic bridge; the splenic hilum; and suprarenal, retrocrural, cardiophrenic and inguinal lymph nodes. Hence, mastery of anatomy is crucial, given the necessity for maneuvers like liver mobilization, diaphragmatic peritonectomy and splenectomy, as well as dissection of suprarenal, celiac, and cardiophrenic lymph nodes in most cases. This article provides a meticulous anatomical description of neglected anatomical areas during OC surgery and describes surgical steps essential for the dissection of these "neglected" areas. This knowledge should equip clinicians with the tools needed for safe and complete cytoreduction in OC patients.

The role and function of autophagy through signaling and pathogenetic pathways and lncRNAs in ovarian cancer

Lysosomal-driven autophagy is a tightly controlled cellular catabolic process that breaks down and recycles broken or superfluous cell parts. It is involved in several illnesses, including cancer, and is essential in preserving cellular homeostasis. Autophagy prevents DNA mutation and cancer development by actively eliminating pro-oxidative mitochondria and protein aggregates from healthy cells. Oncosuppressor and oncogene gene mutations cause dysregulation of autophagy. Increased autophagy may offer cancer cells a pro-survival advantage when oxygen and nutrients are scarce and resistance to chemotherapy and radiation. This finding justifies the use of autophagy inhibitors in addition to anti-neoplastic treatments. Excessive autophagy levels can potentially kill cells. The diagnosis and treatment of ovarian cancer present many difficulties due to its complexity and heterogeneity. Understanding the role of autophagy, a cellular process involved in the breakdown and recycling of cellular components, in ovarian cancer has garnered increasing attention in recent years. Of particular note is the increasing amount of data indicating a close relationship between autophagy and ovarian cancer. Autophagy either promotes or restricts tumor growth in ovarian cancer. Dysregulation of autophagy signaling pathways in ovarian cancers can affect the development, metastasis, and response to tumor treatment. The precise mechanism underlying autophagy concerning ovarian cancer remains unclear, as does the role autophagy plays in ovarian carcinoma. In this review, we tried to encapsulate and evaluate current findings in investigating autophagy in ovarian cancer.

Identification of peripheral blood immune infiltration signatures and construction of monocyte-associated signatures in ovarian cancer and Alzheimer's disease using single-cell sequencing

BACKGROUND

Ovarian cancer (OC) is a common tumor of the female reproductive system, while Alzheimer's disease (AD) is a prevalent neurodegenerative disease that primarily affects cognitive function in the elderly. Monocytes are immune cells in the blood that can enter tissues and transform into macrophages, thus participating in immune and inflammatory responses. Overall, monocytes may play an important role in Alzheimer's disease and ovarian cancer.

METHODS

The CIBERSORT algorithm results indicate a potential crucial role of monocytes/macrophages in OC and AD. To identify monocyte marker genes, single-cell RNA-seq data of peripheral blood mononuclear cells (PBMCs) from OC and AD patients were analyzed. Enrichment analysis of various cell subpopulations was performed using the "irGSEA" R package. The estimation of cell cycle was conducted with the "tricycle" R package, and intercellular communication networks were analyzed using "CellChat". For 134 monocyte-associated genes (MRGs), bulk RNA-seq data from two diseased tissues were obtained. Cox regression analysis was employed to develop risk models, categorizing patients into high-risk (HR) and low-risk (LR) groups. The model's accuracy was validated using an external GEO cohort. The different risk groups were evaluated in terms of immune cell infiltration, mutational status, signaling pathways, immune checkpoint expression, and immunotherapy. To identify characteristic MRGs in AD, two machine learning algorithms, namely random forest and support vector machine (SVM), were utilized.

RESULTS

Based on Cox regression analysis, a risk model consisting of seven genes was developed in OC, indicating a better prognosis for patients in the LR group. The LR group had a higher tumor mutation burden, immune cell infiltration abundance, and immune checkpoint expression. The results of the TIDE algorithm and the IMvigor210 cohort showed that the LR group was more likely to benefit from immunotherapy. Finally, ZFP36L1 and AP1S2 were identified as characteristic MRGs affecting OC and AD progression.

CONCLUSION

The risk profile containing seven genes identified in this study may help further guide clinical management and targeted therapy for OC. ZFP36L1 and AP1S2 may serve as biomarkers and new therapeutic targets for patients with OC and AD.

Single-cell RNA-seq reveals intratumoral heterogeneity in osteosarcoma patients: A review

While primary bone malignancies make up just 0.2% of all cancers, osteosarcoma (OS) is the third most common cancer in adolescents. Due to its highly complex and heterogeneous tumor microenvironment (TME), OS has proven difficult to treat. There has been little to no improvement in therapy for this disease over the last 40 years. Even the recent success of immunotherapies in other blood-borne and solid malignancies has not translated to OS. With frequent recurrence and lung metastases continuing to pose a challenge in the clinic, recent advancements in molecular profiling, such as single-cell RNA sequencing (scRNA-seq), have proven useful in identifying novel biomarkers of OS tumors while providing new insight into this TME that could potentially lead to new therapeutic options. This review combines the analyses of over 150,000 cells from 18 lesions ranging from primary, recurrent, and metastatic OS lesions, revealing distinct cellular populations and gene signatures that exist between them. Here, we detail these previous findings and ultimately convey the intratumoral heterogeneity that exists within OS tumor specimens.

B cells in the tumor microenvironment: Multi-faceted organizers, regulators, and effectors of anti-tumor immunity

Our understanding of tumor-infiltrating lymphocytes (TILs) is rapidly expanding beyond T cell-centric perspectives to include B cells and plasma cells, collectively referred to as TIL-Bs. In many cancers, TIL-Bs carry strong prognostic significance and are emerging as key predictors of response to immune checkpoint inhibitors. TIL-Bs can perform multiple functions, including antigen presentation and antibody production, which allow them to focus immune responses on cognate antigen to support both T cell responses and innate mechanisms involving complement, macrophages, and natural killer cells. In the stroma of the most immunologically "hot" tumors, TIL-Bs are prominent components of tertiary lymphoid structures, which resemble lymph nodes structurally and functionally. Additionally, TIL-Bs participate in a variety of other lympho-myeloid aggregates and engage in dynamic interactions with the tumor stroma. Here, we summarize our current understanding of TIL-Bs in human cancer, highlighting the compelling therapeutic opportunities offered by their unique tumor recognition and effector mechanisms.