Decoding Cancer Biology One Cell at a Time

A single-cell atlas of Schwannoma across genetic backgrounds and anatomic locations

BACKGROUND

Schwannomas are nerve sheath tumors arising at cranial and peripheral nerves, either sporadically or in patients with a schwannomatosis-predisposition syndrome. There is limited understanding of the transcriptional heterogeneity of schwannomas across genetic backgrounds and anatomic locations.

METHODS

Here, we prospectively profile by single-cell full-length transcriptomics tumors from 22 patients with NF2-related schwannomatosis, non-NF2-related schwannomatosis, and sporadic schwannomas, resected from cranial and peripheral nerves. We profiled 11,373 cells (after QC), including neoplastic cells, fibroblasts, T cells, endothelial cells, myeloid cells, and pericytes.

RESULTS

We characterize the intra-tumoral genetic and transcriptional heterogeneity of schwannoma, identifying six distinct transcriptional metaprograms, with gene signatures related to stress, myelin production, antigen presentation, interferon signaling, glycolysis, and extracellular matrix. We demonstrate the robustness of our findings with analysis of an independent cohort.

CONCLUSIONS

Overall, our atlas describes the spectrum of gene expression across schwannoma entities at the single-cell level and will serve as an important resource for the community.

The Society for Immunotherapy of Cancer Perspective on Tissue-Based Technologies for Immuno-Oncology Biomarker Discovery and Application

With immuno-oncology becoming the standard of care for a variety of cancers, identifying biomarkers that reliably classify patient response, resistance, or toxicity becomes the next critical barrier toward improving care. Multiparametric, multi-omics, and computational platforms generating an unprecedented depth of data are poised to usher in the discovery of increasingly robust biomarkers for enhanced patient selection and personalized treatment approaches. Deciding which developing technologies to implement in clinical settings ultimately, applied either alone or in combination, relies on weighing pros and cons, from minimizing patient sampling to maximizing data outputs, and assessing the reproducibility and representativeness of findings, while lessening data fragmentation toward harmonization. These factors are all assessed while taking into consideration the shortest turnaround time. The Society for Immunotherapy of Cancer Biomarkers Committee convened to identify important advances in biomarker technologies and to address advances in biomarker discovery using multiplexed IHC and immunofluorescence, their coupling to single-cell transcriptomics, along with mass spectrometry-based quantitative and spatially resolved proteomics imaging technologies. We summarize key metrics obtained, ease of interpretation, limitations and dependencies, technical improvements, and outward comparisons of these technologies. By highlighting the most interesting recent data contributed by these technologies and by providing ways to improve their outputs, we hope to guide correlative research directions and assist in their evolution toward becoming clinically useful in immuno-oncology.

High Risk of Metabolic Dysfunction in Nonobese Breast Cancer Survivors

BACKGROUND

The aim of this study was to evaluate the metabolic profile of non-obese postmenopausal women with breast cancer (BC) compared to non-obese women without breast cancer.

METHODS

In this case-control study, 130 women with BC, aged 45-75years, body mass index < 30kg/m2 and without established cardiovascular disease were included. The control group consisted of 130 women with the same inclusion criteria, but without BC. The groups were matched by age and time since menopause. Clinical, anthropometric, and biochemical data were collected. Women who presented three or more diagnostic criteria were considered to have metabolic syndrome (MetS): waist circunference > 88cm; triglycerides ≥ 150mg/dL; HDL-cholesterol < 50mg/dL; blood pressure (BP) ≥ 130/85mmHg; glucose ≥ 100mg/dL.

RESULTS

Women with BC had a higher occurrence of MetS and elevated BP compared to the control (30.8% vs. 20.0% and 25.4% vs. 14.6%, respectively) (P < 0.05). A higher percentage of women with BC had values above the desirable range for total cholesterol and glucose compared to the control (56.2% vs. 43.1% and 29.2% vs. 15.4%, respectively) (P < 0.05). In the risk analysis of the metabolic profile, adjusted for age and menopausal status, women with BC had a significantly higher risk for MetS (OR =%2.76, 95% CI 1.48-5.15), elevated glucose (OR = 2.69, 95% CI 1.46-4.96), and hypertension (OR = 3.03, 95% CI 1.51-6.10).

CONCLUSION

Non-obese women with BC had a higher risk for MetS, hypertension, and diabetes, with a worse metabolic profile compared to non-obese women without BC. Prospective studies are needed to validate our results.

Single-Cell View of Tumor Microenvironment Gradients in Pleural Mesothelioma

Immunotherapies have shown great promise in pleural mesothelioma (PM), yet most patients still do not achieve significant clinical response, highlighting the importance of improving the understanding of the tumor microenvironment (TME). Here, we utilized high-throughput, single-cell RNA sequencing (scRNA-seq) to de novo identify 54 expression programs and construct a comprehensive cellular catalog of the PM TME. We found four cancer-intrinsic programs associated with poor disease outcome and a novel fetal-like, endothelial cell population that likely responds to VEGF signaling and promotes angiogenesis. Across cellular compartments, we observe substantial difference in the TME associated with a cancer-intrinsic sarcomatoid signature, including enrichment in fetal-like endothelial cells, CXCL9+ macrophages, and cytotoxic, exhausted, and regulatory T cells, which we validated using imaging and bulk deconvolution analyses on independent cohorts. Finally, we show, both computationally and experimentally, that NKG2A:HLA-E interaction between NK and tumor cells represents an important new therapeutic axis in PM, especially for epithelioid cases. Significance: This manuscript presents the first single-cell RNA sequencing atlas of PM tumor microenvironment. Findings of translational relevance, validated experimentally and using independent bulk cohorts, include identification of gene programs predictive of survival, a fetal-like endothelial cell population, and NKG2A blockade as a promising new immunotherapeutic intervention in PM.

Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD), a progressive inflammatory condition of the airways, emerges from the complex interplay between genetic predisposition and environmental factors. Notably, its incidence is on the rise, particularly among the elderly demographic. Current research increasingly highlights cellular senescence as a key driver in chronic lung pathologies. Despite this, the detailed mechanisms linking COPD with senescent genomic alterations remain elusive. To address this gap, there is a pressing need for comprehensive bioinformatics methodologies that can elucidate the molecular intricacies of this link. This approach is crucial for advancing our understanding of COPD and its association with cellular aging processes. Utilizing a spectrum of advanced bioinformatics techniques, this research delved into the potential mechanisms linking COPD with aging-related genes, identifying four key genes (EP300, MTOR, NFE2L1, TXN) through machine learning and weighted gene co-expression network analysis (WGCNA) analyses. Subsequently, a precise diagnostic model leveraging an artificial neural network was developed. The study further employed single-cell analysis and molecular docking to investigate senescence-related cell types in COPD tissues, particularly focusing on the interactions between COPD and NFE2L1, thereby enhancing the understanding of COPD's molecular underpinnings. Leveraging artificial neural networks, we developed a robust classification model centered on four genes-EP300, MTOR, NFE2L1, TXN-exhibiting significant predictive capability for COPD and offering novel avenues for its early diagnosis. Furthermore, employing various single-cell analysis techniques, the study intricately unraveled the characteristics of senescence-related cell types in COPD tissues, enriching our understanding of the disease's cellular landscape. This research anticipates offering novel biomarkers and therapeutic targets for early COPD intervention, potentially alleviating the disease's impact on individuals and healthcare systems, and contributing to a reduction in global COPD-related mortality. These findings carry significant clinical and public health ramifications, bolstering the foundation for future research and clinical strategies in managing and understanding COPD.

A single-cell atlas deconstructs heterogeneity across multiple models in murine traumatic brain injury and identifies novel cell-specific targets

Traumatic brain injury (TBI) heterogeneity remains a critical barrier to translating therapies. Identifying final common pathways/molecular signatures that integrate this heterogeneity informs biomarker and therapeutic-target development. We present the first large-scale murine single-cell atlas of the transcriptomic response to TBI (334,376 cells) across clinically relevant models, sex, brain region, and time as a foundational step in molecularly deconstructing TBI heterogeneity. Results were unique to cell populations, injury models, sex, brain regions, and time, highlighting the importance of cell-level resolution. We identify cell-specific targets and previously unrecognized roles for microglial and ependymal subtypes. Ependymal-4 was a hub of neuroinflammatory signaling. A distinct microglial lineage shared features with disease-associated microglia at 24 h, with persistent gene-expression changes in microglia-4 even 6 months after contusional TBI, contrasting all other cell types that mostly returned to naive levels. Regional and sexual dimorphism were noted. CEREBRI, our searchable atlas (https://shiny.crc.pitt.edu/cerebri/), identifies previously unrecognized cell subtypes/molecular targets and is a leverageable platform for future efforts in TBI and other diseases with overlapping pathophysiology.

Single-cell analysis of breast cancer metastasis reveals epithelial-mesenchymal plasticity signatures associated with poor outcomes

Metastasis is the leading cause of cancer-related deaths. It is unclear how intratumor heterogeneity (ITH) contributes to metastasis and how metastatic cells adapt to distant tissue environments. The study of these adaptations is challenged by the limited access to patient material and a lack of experimental models that appropriately recapitulate ITH. To investigate metastatic cell adaptations and the contribution of ITH to metastasis, we analyzed single-cell transcriptomes of matched primary tumors and metastases from patient-derived xenograft models of breast cancer. We found profound transcriptional differences between the primary tumor and metastatic cells. Primary tumors upregulated several metabolic genes, whereas motility pathway genes were upregulated in micrometastases, and stress response signaling was upregulated during progression. Additionally, we identified primary tumor gene signatures that were associated with increased metastatic potential and correlated with patient outcomes. Immune-regulatory control pathways were enriched in poorly metastatic primary tumors, whereas genes involved in epithelial-mesenchymal transition were upregulated in highly metastatic tumors. We found that ITH was dominated by epithelial-mesenchymal plasticity (EMP), which presented as a dynamic continuum with intermediate EMP cell states characterized by specific genes such as CRYAB and S100A2. Elevated expression of an intermediate EMP signature correlated with worse patient outcomes. Our findings identified inhibition of the intermediate EMP cell state as a potential therapeutic target to block metastasis.

Integrating frontiers: a holistic, quantum and evolutionary approach to conquering cancer through systems biology and multidisciplinary synergy

Cancer therapy is facing increasingly significant challenges, marked by a wide range of techniques and research efforts centered around somatic mutations, precision oncology, and the vast amount of big data. Despite this abundance of information, the quest to cure cancer often seems more elusive, with the "war on cancer" yet to deliver a definitive victory. A particularly pressing issue is the development of tumor treatment resistance, highlighting the urgent need for innovative approaches. Evolutionary, Quantum Biology and System Biology offer a promising framework for advancing experimental cancer research. By integrating theoretical studies, translational methods, and flexible multidisciplinary clinical research, there's potential to enhance current treatment strategies and improve outcomes for cancer patients. Establishing stronger links between evolutionary, quantum, entropy and chaos principles and oncology could lead to more effective treatments that leverage an understanding of the tumor's evolutionary dynamics, paving the way for novel methods to control and mitigate cancer. Achieving these objectives necessitates a commitment to multidisciplinary and interprofessional collaboration at the heart of both research and clinical endeavors in oncology. This entails dismantling silos between disciplines, encouraging open communication and data sharing, and integrating diverse viewpoints and expertise from the outset of research projects. Being receptive to new scientific discoveries and responsive to how patients react to treatments is also crucial. Such strategies are key to keeping the field of oncology at the forefront of effective cancer management, ensuring patients receive the most personalized and effective care. Ultimately, this approach aims to push the boundaries of cancer understanding, treating it as a manageable chronic condition, aiming to extend life expectancy and enhance patient quality of life.

Integrative single-cell RNA-seq and spatial transcriptomics analyses reveal diverse apoptosis-related gene expression profiles in EGFR-mutated lung cancer

In EGFR-mutated lung cancer, the duration of response to tyrosine kinase inhibitors (TKIs) is limited by the development of acquired drug resistance. Despite the crucial role played by apoptosis-related genes in tumor cell survival, how their expression changes as resistance to EGFR-TKIs emerges remains unclear. Here, we conduct a comprehensive analysis of apoptosis-related genes, including BCL-2 and IAP family members, using single-cell RNA sequence (scRNA-seq) and spatial transcriptomics (ST). scRNA-seq of EGFR-mutated lung cancer cell lines captures changes in apoptosis-related gene expression following EGFR-TKI treatment, most notably BCL2L1 upregulation. scRNA-seq of EGFR-mutated lung cancer patient samples also reveals high BCL2L1 expression, specifically in tumor cells, while MCL1 expression is lower in tumors compared to non-tumor cells. ST analysis of specimens from transgenic mice with EGFR-driven lung cancer indicates spatial heterogeneity of tumors and corroborates scRNA-seq findings. Genetic ablation and pharmacological inhibition of BCL2L1/BCL-XL overcome or delay EGFR-TKI resistance. Overall, our findings indicate that BCL2L1/BCL-XL expression is important for tumor cell survival as EGFR-TKI resistance emerges.

ROS induced pyroptosis in inflammatory disease and cancer

Pyroptosis, a form of caspase-1-dependent cell death, also known as inflammation-dependent death, plays a crucial role in diseases such as stroke, heart disease, or tumors. Since its elucidation, pyroptosis has attracted widespread attention from various sectors. Reactive oxygen species (ROS) can regulate numerous cellular signaling pathways. Through further research on ROS and pyroptosis, the level of ROS has been revealed to be pivotal for the occurrence of pyroptosis, establishing a close relationship between the two. This review primarily focuses on the molecular mechanisms of ROS and pyroptosis in tumors and inflammatory diseases, exploring key proteins that may serve as drug targets linking ROS and pyroptosis and emerging fields targeting pyroptosis. Additionally, the potential future development of compounds and proteins that influence ROS-regulated cell pyroptosis is anticipated, aiming to provide insights for the development of anti-tumor and anti-inflammatory drugs.

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.

Multicellular ecotypes shape progression of lung adenocarcinoma from ground-glass opacity toward advanced stages

Lung adenocarcinoma is a type of cancer that exhibits a wide range of clinical radiological manifestations, from ground-glass opacity (GGO) to pure solid nodules, which vary greatly in terms of their biological characteristics. Our current understanding of this heterogeneity is limited. To address this gap, we analyze 58 lung adenocarcinoma patients via machine learning, single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing, and we identify six lung multicellular ecotypes (LMEs) correlating with distinct radiological patterns and cancer cell states. Notably, GGO-associated neoantigens in early-stage cancers are recognized by CD8+ T cells, indicating an immune-active environment, while solid nodules feature an immune-suppressive LME with exhausted CD8+ T cells, driven by specific stromal cells such as CTHCR1+ fibroblasts. This study also highlights EGFR(L858R) neoantigens in GGO samples, suggesting potential CD8+ T cell activation. Our findings offer valuable insights into lung adenocarcinoma heterogeneity, suggesting avenues for targeted therapies in early-stage disease.

Lessons learned from phase 3 trials of immunotherapy for glioblastoma: Time for longitudinal sampling?

Glioblastoma (GBM)'s median overall survival is almost 21 months. Six phase 3 immunotherapy clinical trials have recently been published, yet 5/6 did not meet approval by regulatory bodies. For the sixth, approval is uncertain. Trial failures result from multiple factors, ranging from intrinsic tumor biology to clinical trial design. Understanding the clinical and basic science of these 6 trials is compelled by other immunotherapies reaching the point of advanced phase 3 clinical trial testing. We need to understand more of the science in human GBMs in early trials: the "window of opportunity" design may not be best to understand complex changes brought about by immunotherapeutic perturbations of the GBM microenvironment. The convergence of increased safety of image-guided biopsies with "multi-omics" of small cell numbers now permits longitudinal sampling of tumor and biofluids to dissect the complex temporal changes in the GBM microenvironment as a function of the immunotherapy.

Decoding leukemia at the single-cell level: clonal architecture, classification, microenvironment, and drug resistance

Leukemias are refractory hematological malignancies, characterized by marked intrinsic heterogeneity which poses significant obstacles to effective treatment. However, traditional bulk sequencing techniques have not been able to effectively unravel the heterogeneity among individual tumor cells. With the emergence of single-cell sequencing technology, it has bestowed upon us an unprecedented resolution to comprehend the mechanisms underlying leukemogenesis and drug resistance across various levels, including the genome, epigenome, transcriptome and proteome. Here, we provide an overview of the currently prevalent single-cell sequencing technologies and a detailed summary of single-cell studies conducted on leukemia, with a specific focus on four key aspects: (1) leukemia's clonal architecture, (2) frameworks to determine leukemia subtypes, (3) tumor microenvironment (TME) and (4) the drug-resistant mechanisms of leukemia. This review provides a comprehensive summary of current single-cell studies on leukemia and highlights the markers and mechanisms that show promising clinical implications for the diagnosis and treatment of leukemia.

Programmed Death-Ligand (PD-L1), Epidermal Growth Factor (EGF), Relaxin, and Matrix Metalloproteinase-3 (MMP3): Potential Biomarkers of Malignancy in Canine Mammary Neoplasia

Gene expression has been suggested as a putative tool for prognosis and diagnosis in canine mammary neoplasia (CMNs). In the present study, 58 formalin-fixed paraffin-embedded (FFPE) paraffined canine mammary neoplasias from 27 different bitches were included. Thirty-seven tumours were classified as benign, whereas thirty-one were classified as different types of canine carcinoma. In addition, mammary samples from three healthy bitches were also included. The gene expression for vascular endothelial growth factor-α (VEGFα), CD20, progesterone receptor (PGR), hyaluronidase-1 (HYAL-1), programmed death-ligand 1 (PD-L1), epidermal growth factor (EGF), relaxin (RLN2), and matrix metalloproteinase-3 (MMP3) was assessed through RT-qPCR. All the assessed genes yielded a higher expression in neoplastic mammary tissue than in healthy tissue. All the evaluated genes were overexpressed in neoplastic mammary tissue, suggesting a role in the process of tumorigenesis. Moreover, PD-L1, EGF, relaxin, and MMP3 were significantly overexpressed in malignant CMNs compared to benign CMNs, suggesting they may be useful as malignancy biomarkers.

Association Between Diverse Cell Death Patterns Related Gene Signature and Prognosis, Drug Sensitivity, and Immune Microenvironment in Glioblastoma

Glioblastoma (GBM) is the most invasive type of glioma and is difficult to treat. Diverse programmed cell death (PCD) patterns have a significant association with tumor initiation and progression. A novel prognostic model based on PCD genes may serve as an effective tool to predict the prognosis of GBM. The study incorporated 11 PCD patterns, namely apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, netotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, and oxeiptosis, to develop the model. To construct and validate the model, both bulk and single-cell transcriptome data, along with corresponding clinical data from GBM cases, were obtained from the TCGA-GBM, REMBRANDT, CGGA, and GSE162631 datasets. A cell death-related signature containing 14 genes was constructed with the TCGA-GBM cohort and validated in the REMBRANDT and CGGA datasets. GBM patients with a higher cell death index (CDI) were significantly associated with poorer survival outcomes. Two separate clusters associated with clinical outcomes emerged from unsupervised analysis. A multivariate Cox regression analysis was conducted to examine the association of CDI with clinical characteristics, and a prognostic nomogram was developed. Drug sensitivity analysis revealed high-CDI GBM patients might be resistant to carmustine while sensitive to 5-fluorouracil. Less abundance of natural killer cells was found in GBM cases with high CDI and bulk transcriptome data. A cell death-related prognostic model that could predict the prognosis of GBM patients with good performance was established, which could discriminate between the prognosis and drug sensitivity of GBM.

Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics

Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.

Single-cell transcriptome sequencing of B-cell heterogeneity and tertiary lymphoid structure predicts breast cancer prognosis and neoadjuvant therapy efficacy

BACKGROUND

Breast cancer (BC) is a highly heterogeneous disease, and although immunotherapy has recently increased patient survival in a number of solid and hematologic malignancies, most BC subtypes respond poorly to immune checkpoint blockade therapy (ICB). B cells, particularly those that congregate in tertiary lymphoid structures (TLS), play a significant role in antitumour immunity. However, B-cell heterogeneity at single-cell resolution and its clinical significance with TLS in BC need to be explored further.

METHODS

Primary tumour lesions and surrounding normal tissues were taken from 14 BC patients, totaling 124,587 cells, for single-cell transcriptome sequencing and bioinformatics analysis.

RESULTS

Based on the usual markers, the single-cell transcriptome profiles were classified into various clusters. A thorough single-cell study was conducted with a focus on tumour-infiltrating B cells (TIL-B) and tumour-associated neutrophils (TAN). TIL-B was divided into five clusters, and unusual cell types, such as follicular B cells, which are strongly related to immunotherapy efficacy, were identified. In BC, TAN and TIL-B infiltration are positively correlated, and at the same time, compared with TLS-high, TAN and TIL-B in TLS-low group are significantly positively correlated.

CONCLUSIONS

In conclusion, our study highlights the heterogeneity of B cells in BC, explains how B cells and TLS contribute significantly to antitumour immunity at both the single-cell and clinical level, and offers a straightforward marker for TLS called CD23. These results will offer more pertinent information on the applicability and effectiveness of tumour immunotherapy for BC.

Single-cell analysis of acute lymphoblastic and lineage-ambiguous leukemia: approaches and molecular insights

Despite recent progress in identifying the genetic drivers of acute lymphoblastic leukemia (ALL), prognosis remains poor for those individuals who experience disease recurrence. Moreover, acute leukemias of ambiguous lineage lack a biologically informed framework to guide classification and therapy. These needs have driven the adoption of multiple complementary single-cell sequencing approaches to explore key issues in the biology of these leukemias, including cell of origin, developmental hierarchy and ontogeny, and the molecular heterogeneity driving pathogenesis, progression, and therapeutic responsiveness. There are multiple single-cell techniques for profiling a specific modality, including RNA, DNA, chromatin accessibility and methylation; and an expanding range of approaches for simultaneous analysis of multiple modalities. Single-cell sequencing approaches have also enabled characterization of cell-intrinsic and -extrinsic features of ALL biology. In this review we describe these approaches and highlight the extensive heterogeneity that underpins ALL gene expression, cellular differentiation, and clonal architecture throughout disease pathogenesis and treatment resistance. In addition, we discuss the importance of the dynamic interactions that occur between leukemia cells and the nonleukemia microenvironment. We discuss potential opportunities and limitations of single-cell sequencing for the study of ALL biology and treatment responsiveness.

Spatial Transcriptomic Analysis of a Diverse Patient Cohort Reveals a Conserved Architecture in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive disease that disproportionately affects African American (AA) women. Limited targeted therapeutic options exist for patients with TNBC. Here, we employ spatial transcriptomics to interrogate tissue from a racially diverse TNBC cohort to comprehensively annotate the transcriptional states of spatially resolved cellular populations. A total of 38,706 spatial features from a cohort of 28 sections from 14 patients were analyzed. Intratumoral analysis of spatial features from individual sections revealed heterogeneous transcriptional substructures. However, integrated analysis of all samples resulted in nine transcriptionally distinct clusters that mapped across all individual sections. Furthermore, novel use of join count analysis demonstrated nonrandom directional spatial dependencies of the transcriptionally defined shared clusters, supporting a conserved spatio-transcriptional architecture in TNBC. These findings were substantiated in an independent validation cohort comprising 17,861 spatial features representing 15 samples from 8 patients. Stratification of samples by race revealed race-associated differences in hypoxic tumor content and regions of immune-rich infiltrate. Overall, this study combined spatial and functional molecular analyses to define the tumor architecture of TNBC, with potential implications in understanding TNBC disparities.

SIGNIFICANCE

Spatial transcriptomics profiling of a diverse cohort of triple-negative breast cancers and innovative informatics approaches reveal a conserved cellular architecture across cancers and identify proportional differences in tumor cell composition by race.

Perfusion-Based Fluorescent Dye Labeling to Sort Cancer Cells Based on Their Distance from Blood Vessels

Tumor vasculature is the major extrinsic factor that shapes Intra-tumoral heterogeneity (ITH). Non-uniform exposure of microenvironmental cues greatly impacts cancer cell phenotypes leading to ITH, which exacerbates therapy resistance. This raises a need to study the influence of non-uniform perfusion patterns and the resulting heterogeneity that persists within the tumor microenvironment (TME). A method was developed to identify cancer cells based on their proximity to functional blood vessels (BVs) called perfusion-based fluorescent dye labeling of cells (PFDLC). PFDLC works on the principle of perfusion, where a freely diffusible nuclear binding fluorescent dye (Hoechst 33342) is injected intravenously (i.v.) through a tail vein into atumor-bearing mice. The tumors are retrieved post dye perfusion, dissociated into single cells, and sorted based on their dye uptake proportional to their distance from the nearest blood capillary. This method is amenable to multi-omics as well as functional assays.

Challenges in glioblastoma research: focus on the tumor microenvironment

Glioblastoma (GBM) is the most deadly type of malignant brain tumor, despite extensive molecular analyses of GBM cells. In recent years, the tumor microenvironment (TME) has been recognized as an important player and therapeutic target in GBM. However, there is a need for a full and integrated understanding of the different cellular and molecular components involved in the GBM TME and their interactions for the development of more efficient therapies. In this review, we provide a comprehensive report of the GBM TME, which assembles the contributions of physicians and translational researchers working on brain tumor pathology and therapy in France. We propose a holistic view of the subject by delineating the specific features of the GBM TME at the cellular, molecular, and therapeutic levels.

Prescreening of tumor samples for tumor-centric transcriptome analyses of lung adenocarcinoma

Background

Single-cell RNA sequencing (scRNA-seq) enables the systemic assessment of intratumoral heterogeneity within tumor cell populations and in diverse stromal cells of the tumor microenvironment. Gain of treatment resistance during tumor progression or drug treatment are important subjects of tumor-centric scRNA-seq analyses, which are hampered by scarce tumor cell portions. To guarantee the inclusion of tumor cells in the data analysis, we developed a prescreening strategy for lung adenocarcinoma.

Methods

We obtained candidate genes that were differentially expressed between normal and tumor cells, excluding stromal cells, from the scRNA-seq data. Tumor cell-specific expression of the candidate genes was assessed via real-time reverse transcription-polymerase chain reaction (RT-PCR) using lung cancer cell lines, normal vs. lung cancer tissues, and lymph node biopsy samples with or without metastasis.

Results

We found that CEA cell adhesion molecule 5 (CEACAM5) and high mobility group box 3 (HMGB3) were reliable markers for RT-PCR-based prescreening of tumor cells in lung adenocarcinoma.

Conclusions

The prescreening strategy using CEACAM5 and HMGB3 expression facilitates tumor-centric scRNA-seq analyses of lung adenocarcinoma.

Characterizing the biology of primary brain tumors and their microenvironment via single-cell profiling methods

Genomic and transcriptional heterogeneity is prevalent among the most common and aggressive primary brain tumors in children and adults. Over the past 20 years, advances in bioengineering, biochemistry and bioinformatics have enabled the development of an array of techniques to study tumor biology at single-cell resolution. The application of these techniques to study primary brain tumors has helped advance our understanding of their intra-tumoral heterogeneity and uncover new insights regarding their co-option of developmental programs and signaling from their microenvironment to promote tumor proliferation and invasion. These insights are currently being harnessed to develop new therapeutic approaches. Here we provide an overview of current single-cell techniques and discuss relevant biology and therapeutic insights uncovered by their application to primary brain tumors in children and adults.

FOXM1 Inhibition Enhances the Therapeutic Outcome of Lung Cancer Immunotherapy by Modulating PD-L1 Expression and Cell Proliferation

Programmed death-ligand 1 (PD-L1) is a major target to cancer immunotherapy, and anti-PD-L1 and anti-PD-1 antibody-mediated immunotherapy are being increasingly used. However, immune checkpoint inhibitors (ICIs) are ineffective in treating large tumors and cause various immune-related adverse events in nontarget organs, including life-threatening cardiotoxicity. Therefore, the development of new therapeutic strategies to overcome these limitations is crucial. The focus of this study is the forkhead box protein M1 (FOXM1), which is identified as a potential therapeutic target for cancer immunotherapy and is associated with the modulation of PD-L1 expression. Selective small interfering RNA knockdown of FOXM1 or treatment with thiostrepton (TST) significantly reduces PD-L1 expression in non-small-cell lung cancer (NSCLC) cells and inhibits proliferation. Chromatin immunoprecipitation-PCR reveals that FOXM1 selectively upregulates PD-L1 expression by binding directly to the PD-L1 promoter. In vivo animal studies have shown that TST treatment significantly downregulates PD-L1 expression in human NSCLC tumors, while greatly reducing tumor size without side effects on normal tissues. Combined treatment with TST and anti-4-1BB antibody in the LLC-1 syngeneic tumor model induces synergistic therapeutic outcomes against immune resistant lung tumors as well as 2.72-folds higher CD3+ T cells in tumor tissues compared to that in the anti-4-1BB antibody treatment group.

Current Opportunities for Targeting Dysregulated Neurodevelopmental Signaling Pathways in Glioblastoma

Glioblastoma (GBM) is the most common and highly lethal type of brain tumor, with poor survival despite advances in understanding its complexity. After current standard therapeutic treatment, including tumor resection, radiotherapy and concomitant chemotherapy with temozolomide, the median overall survival of patients with this type of tumor is less than 15 months. Thus, there is an urgent need for new insights into GBM molecular characteristics and progress in targeted therapy in order to improve clinical outcomes. The literature data revealed that a number of different signaling pathways are dysregulated in GBM. In this review, we intended to summarize and discuss current literature data and therapeutic modalities focused on targeting dysregulated signaling pathways in GBM. A better understanding of opportunities for targeting signaling pathways that influences malignant behavior of GBM cells might open the way for the development of novel GBM-targeted therapies.

Functional Drug Screening in the Era of Precision Medicine

The focus of precision medicine is providing the right treatment to each unique patient. This scientific movement has incited monumental advances in oncology including the approval of effective, targeted agnostic therapies. Yet, precision oncology has focused largely on genomics in the treatment decision making process, and several recent clinical trials demonstrate that genomics is not the only variable to be considered. Drug screening in three dimensional (3D) models, including patient derived organoids, organs on a chip, xenografts, and 3D-bioprinted models provide a functional medicine perspective and necessary complement to genomic testing. In this review, we discuss the practicality of various 3D drug screening models and each model's ability to capture the patient's tumor microenvironment. We highlight the potential for enhancing precision medicine that personalized functional drug testing holds in combination with genomic testing and emerging mathematical models.

Integrative multi-omics approach to targeted therapy for glioblastoma

This review describes recent technological advances applied to glioblastoma (GBM), a brain tumor with dismal prognosis. International consortial efforts suggest the presence of molecular subtypes within histologically identical GBM tumors. This emphasizes that future treatment decisions should no longer be made based solely on morphological analyses, but must now take into consideration such molecular and cellular heterogeneity. The use of single-cell technologies has advanced our understanding and assignation of functional subtypes revealing therapeutic vulnerabilities. Our team has developed stratification approaches in the past few years, and we have been able to identify patient cohorts enriched for various signaling pathways. Importantly, our Glioportal brain tumor resource has been established under the National Neuroscience Institute Tissue Bank in 2021. This resource offers preclinical capability to validate working hypotheses established from patient clinical datasets. This review highlights recent developments with the ultimate goal of assigning functional meaning to molecular subtypes, revealing therapeutic vulnerabilities.

Somatic variant calling from single-cell DNA sequencing data

Single-cell sequencing has gained popularity in recent years. Despite its numerous applications, single-cell DNA sequencing data is highly error-prone due to technical biases arising from uneven sequencing coverage, allelic dropout, and amplification error. With these artifacts, the identification of somatic genomic variants becomes a challenging task, and over the years, several methods have been developed explicitly for this type of data. Single-cell variant callers implement distinct strategies, make different use of the data, and typically result in many discordant calls when applied to real data. Here, we review current approaches for single-cell variant calling, emphasizing single nucleotide variants. We highlight their potential benefits and shortcomings to help users choose a suitable tool for their data at hand.

Computational analysis of cancer genome sequencing data

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

Single-Cell Epigenomics Reveals Mechanisms of Cancer Progression

Cancer initiation is driven by the cooperation between genetic and epigenetic aberrations that disrupt gene regulatory programs critical to maintaining specialized cellular functions. After initiation, cells acquire additional genetic and epigenetic alterations influenced by tumor-intrinsic and -extrinsic mechanisms, which increase intratumoral heterogeneity, reshape the cell's underlying gene regulatory networks and promote cancer evolution. Furthermore, environmental or therapeutic insults drive the selection of heterogeneous cell states, with implications for cancer initiation, maintenance, and drug resistance. The advancement of single-cell genomics has begun to uncover the full repertoire of chromatin and gene expression states (cell states) that exist within individual tumors. These single-cell analyses suggest that cells diversify in their regulatory states upon transformation by co-opting damage-induced and nonlineage regulatory programs that can lead to epigenomic plasticity. Here, we review these recent studies related to regulatory state changes in cancer progression and highlight the growing single-cell epigenomics toolkit poised to address unresolved questions in the field.

Single-Cell Molecular Characterization to Partition the Human Glioblastoma Tumor Microenvironment Genetic Background

Background: Glioblastoma (GB) is a devastating primary brain malignancy. The recurrence of GB is inevitable despite the standard treatment of surgery, chemotherapy, and radiation, and the median survival is limited to around 15 months. The barriers to treatment include the complex interactions among the different cellular components inhabiting the tumor microenvironment. The complex heterogeneous nature of GB cells is helped by the local inflammatory tumor microenvironment, which mostly induces tumor aggressiveness and drug resistance. Methods: By using fluorescent multiple labeling and a DEPArray cell separator, we recovered several single cells or groups of single cells from populations of different origins from IDH-WT GB samples. From each GB sample, we collected astrocytes-like (GFAP+), microglia-like (IBA1+), stem-like cells (CD133+), and endothelial-like cells (CD105+) and performed Copy Number Aberration (CNA) analysis with a low sequencing depth. The same tumors were subjected to a bulk CNA analysis. Results: The tumor partition in its single components allowed single-cell molecular subtyping which revealed new aspects of the GB altered genetic background. Conclusions: Nowadays, single-cell approaches are leading to a new understanding of GB physiology and disease. Moreover, single-cell CNAs resource will permit new insights into genome heterogeneity, mutational processes, and clonal evolution in malignant tissues.

Tumor Cell Infiltration into the Brain in Glioblastoma: From Mechanisms to Clinical Perspectives

Glioblastoma is the most common and malignant primary brain tumor, defined by its highly aggressive nature. Despite the advances in diagnostic and surgical techniques, and the development of novel therapies in the last decade, the prognosis for glioblastoma is still extremely poor. One major factor for the failure of existing therapeutic approaches is the highly invasive nature of glioblastomas. The extreme infiltrating capacity of tumor cells into the brain parenchyma makes complete surgical removal difficult; glioblastomas almost inevitably recur in a more therapy-resistant state, sometimes at distant sites in the brain. Therefore, there are major efforts to understand the molecular mechanisms underpinning glioblastoma invasion; however, there is no approved therapy directed against the invasive phenotype as of now. Here, we review the major molecular mechanisms of glioblastoma cell invasion, including the routes followed by glioblastoma cells, the interaction of tumor cells within the brain environment and the extracellular matrix components, and the roles of tumor cell adhesion and extracellular matrix remodeling. We also include a perspective of high-throughput approaches utilized to discover novel players for invasion and clinical targeting of invasive glioblastoma cells.

[Molecular markers of neuro-oncogenesis in patients with glioblastoma]

The problem of current treatment approaches to brain gliomas is short-term life expectancy in these patients. Apparently, it is required to change treatment approach via analysis of glioma stem cells rather cells with overexpression of marker genes. This review is devoted to similarities and differences between neurogenesis and neuro-oncogenesis characterized with molecular markers (CD133 as an example). The role of tumor stem cells and their relationship with neural stem cells are considered regarding development of glioma. The authors analyzed CD133 as a marker of glioma stem cells. In the future, stem cells will be important target for eradication during target therapy. A single molecular marker cannot characterize tumor stem cells as supported by CD133 studies. A set of molecular markers specific for certain cell type is required, and their combination will provide more accurate establishment of tumor stem cells.

The Origins of Phenotypic Heterogeneity in Cancer

Heterogeneity is a pervasive feature of cancer, and understanding the sources and regulatory mechanisms underlying heterogeneity could provide key insights to help improve the diagnosis and treatment of cancer. In this review, we discuss the origin of heterogeneity in the phenotype of individual cancer cells. Genotype-phenotype (G-P) maps are widely used in evolutionary biology to represent the complex interactions of genes and the environment that lead to phenotypes that impact fitness. Here, we present the rationale of an extended G-P (eG-P) map with a cone structure in cancer. The eG-P cone is formed by cells that are similar at the genome layer but gradually increase variability in the epigenome, transcriptome, proteome, metabolome and signalome layers to produce large variability at the phenome layer. Experimental evidence from single-cell -omics analyses supporting the cancer eG-P cone concept is presented, and the impact of epimutations and the interaction of cancer and tumor microenvironmental eG-P cones are integrated with the current understanding of cancer biology. The eG-P cone concept uncovers potential therapeutic strategies to reduce cancer evolution and improve cancer treatment. More methods to study phenotypes in single cells will be key to better understand cancer cell fitness in tumor biology and therapeutics.

The Single-Cell Sequencing: A Dazzling Light Shining on the Dark Corner of Cancer

Tumorigenesis refers to the process of clonal dysplasia that occurs due to the collapse of normal growth regulation in cells caused by the action of various carcinogenic factors. These “successful” tumor cells pass on the genetic templates to their generations in evolutionary terms, but they also constantly adapt to ever-changing host environments. A unique peculiarity known as intratumor heterogeneity (ITH) is extensively involved in tumor development, metastasis, chemoresistance, and immune escape. An understanding of ITH is urgently required to identify the diversity and complexity of the tumor microenvironment (TME), but achieving this understanding has been a challenge. Single-cell sequencing (SCS) is a powerful tool that can gauge the distribution of genomic sequences in a single cell and the genetic variability among tumor cells, which can improve the understanding of ITH. SCS provides fundamental ideas about existing diversity in specific TMEs, thus improving cancer diagnosis and prognosis prediction, as well as improving the monitoring of therapeutic response. Herein, we will discuss advances in SCS and review SCS application in tumors based on current evidence.

Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions

In the past decades, transcriptomic studies have revolutionized cancer treatment and diagnosis. However, tumor sequencing strategies typically result in loss of spatial information, critical to understand cell interactions and their functional relevance. To address this, we investigate spatial gene expression in HER2-positive breast tumors using Spatial Transcriptomics technology. We show that expression-based clustering enables data-driven tumor annotation and assessment of intra- and interpatient heterogeneity; from which we discover shared gene signatures for immune and tumor processes. By integration with single cell data, we spatially map tumor-associated cell types to find tertiary lymphoid-like structures, and a type I interferon response overlapping with regions of T-cell and macrophage subset colocalization. We construct a predictive model to infer presence of tertiary lymphoid-like structures, applicable across tissue types and technical platforms. Taken together, we combine different data modalities to define a high resolution map of cellular interactions in tumors and provide tools generalizing across tissues and diseases.

Clinical Perspectives of Single-Cell RNA Sequencing

The ability of single-cell genomics to resolve cellular heterogeneity is highly appreciated in cancer and is being exploited for precision medicine. In the recent decade, we have witnessed the incorporation of cancer genomics into the clinical decision-making process for molecular-targeted therapies. Compared with conventional genomics, which primarily focuses on the specific and sensitive detection of the molecular targets, single-cell genomics addresses intratumoral heterogeneity and the microenvironmental components impacting the treatment response and resistance. As an exploratory tool, single-cell genomics provides an unprecedented opportunity to improve the diagnosis, monitoring, and treatment of cancer. The results obtained upon employing bulk cancer genomics indicate that single-cell genomics is at an early stage with respect to exploration of clinical relevance and requires further innovations to become a widely utilized technology in the clinic.

Applications of Single-Cell Omics in Tumor Immunology

The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem's complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques.

Markers and Reporters to Reveal the Hierarchy in Heterogeneous Cancer Stem Cells

A subpopulation within cancer, known as cancer stem cells (CSCs), regulates tumor initiation, chemoresistance, and metastasis. At a closer look, CSCs show functional heterogeneity and hierarchical organization. The present review is an attempt to assign marker profiles to define the functional heterogeneity and hierarchical organization of CSCs, based on a series of single-cell analyses. The evidences show that analogous to stem cell hierarchy, self-renewing Quiescent CSCs give rise to the Progenitor CSCs with limited proliferative capacity, and later to a Progenitor-like CSCs, which differentiates to Proliferating non-CSCs. Functionally, the CSCs can be tumor-initiating cells (TICs), drug-resistant CSCs, or metastasis initiating cells (MICs). Although there are certain marker profiles used to identify CSCs of different cancers, molecules like CD44, CD133, ALDH1A1, ABCG2, and pluripotency markers [Octamer binding transcriptional factor 4 (OCT4), SOX2, and NANOG] are used to mark CSCs of a wide range of cancers, ranging from hematological malignancies to solid tumors. Our analysis of the recent reports showed that a combination of these markers can demarcate the heterogeneous CSCs in solid tumors. Reporter constructs are widely used for easy identification and quantification of marker molecules. In this review, we discuss the suitability of reporters for the widely used CSC markers that can define the heterogeneous CSCs. Since the CSC-specific functions of CD44 and CD133 are regulated at the post-translational level, we do not recommend the reporters for these molecules for the detection of CSCs. A promoter-based reporter for ABCG2 may also be not relevant in CSCs, as the expression of the molecule in cancer is mainly regulated by promoter demethylation. In this context, a dual reporter consisting of one of the pluripotency markers and ALDH1A1 will be useful in marking the heterogeneous CSCs. This system can be easily adapted to high-throughput platforms to screen drugs for eliminating CSCs.