Single-cell sequencing reveals the landscape of the human brain metastatic microenvironment.

Brain metastases is the most common intracranial tumor and account for approximately 20% of all systematic cancer cases. It is a leading cause of death in advanced-stage cancer, resulting in a five-year overall survival rate below 10%. Therefore, there is a critical need to identify effective biomarkers that can support frequent surveillance and promote efficient drug guidance in brain metastasis. Recently, the remarkable breakthroughs in single-cell RNA-sequencing (scRNA-seq) technology have advanced our insights into the tumor microenvironment (TME) at single-cell resolution, which offers the potential to unravel the metastasis-related cellular crosstalk and provides the potential for improving therapeutic effects mediated by multifaceted cellular interactions within TME. In this study, we have applied scRNA-seq and profiled 10,896 cells collected from five brain tumor tissue samples originating from breast and lung cancers. Our analysis reveals the presence of various intratumoral components, including tumor cells, fibroblasts, myeloid cells, stromal cells expressing neural stem cell markers, as well as minor populations of oligodendrocytes and T cells. Interestingly, distinct cellular compositions are observed across different samples, indicating the influence of diverse cellular interactions on the infiltration patterns within the TME. Importantly, we identify tumor-associated fibroblasts in both our in-house dataset and external scRNA-seq datasets. These fibroblasts exhibit high expression of type I collagen genes, dominate cell-cell interactions within the TME via the type I collagen signaling axis, and facilitate the remodeling of the TME to a collagen-I-rich extracellular matrix similar to the original TME at primary sites. Additionally, we observe M1 activation in native microglial cells and infiltrated macrophages, which may contribute to a proinflammatory TME and the upregulation of collagen type I expression in fibroblasts. Furthermore, tumor cell-specific receptors exhibit a significant association with patient survival in both brain metastasis and native glioblastoma cases. Taken together, our comprehensive analyses identify type I collagen-secreting tumor-associated fibroblasts as key mediators in metastatic brain tumors and uncover tumor receptors that are potentially associated with patient survival. These discoveries provide potential biomarkers for effective therapeutic targets and intervention strategies.

Comprehensive and Computable Molecular Diagnostic Panel (C2Dx) From Small Volume Specimens for Precision Oncology: Molecular Subtyping of Non-Small Cell Lung Cancer From Fine Needle Aspirates.

The Comprehensive, Computable NanoString Diagnostic gene panel (C2Dx) is a promising solution to address the need for a molecular pathological research and diagnostic tool for precision oncology utilizing small volume tumor specimens. We translate subtyping-related gene expression patterns of Non-Small Cell Lung Cancer (NSCLC) derived from public transcriptomic data which establish a highly robust and accurate subtyping system. The C2Dx demonstrates supreme performance on the NanoString platform using microgram-level FNA samples and has excellent portability to frozen tissues and RNA-Seq transcriptomic data. This workflow shows great potential for research and the clinical practice of cancer molecular diagnosis.

Prognostic Molecular Classification of Appendiceal Mucinous Neoplasms Treated with Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy.

BACKGROUND: Appendiceal mucinous neoplasm (AMN) with peritoneal metastasis is a rare but deadly disease with few prognostic or therapy-predictive biomarkers to guide treatment decisions. Here, we investigated the prognostic and biological attributes of gene expression-based AMN molecular subtypes. METHODS: AMN specimens (n = 138) derived from a population-based subseries of patients treated at our institution with cytoreductive surgery and hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) between 05/2000 and 05/2013 were analyzed for gene expression using a custom-designed NanoString 148-gene panel. Signed non-negative matrix factorization (sNMF) was used to define a gene signature capable of delineating robustly-classified AMN molecular subtypes. The sNMF class assignments were evaluated by topology learning, reverse-graph embedding and cross-cohort performance analysis. RESULTS: Three molecular subtypes of AMN were discerned by the expression patterns of 17 genes with roles in cancer progression or anti-tumor immunity. Tumor subtype assignments were confirmed by topology learning. AMN subtypes were termed immune-enriched (IE), oncogene-enriched (OE) and mixed (M) as evidenced by their gene expression patterns, and exhibited significantly different post-treatment survival outcomes. Genes with specialized immune functions, including markers of T-cells, natural killer cells, B-cells, and cytolytic activity showed increased expression in the low-risk IE subtype, while genes implicated in the promotion of cancer growth and progression were more highly expressed in the high-risk OE subtype. In multivariate analysis, the subtypes demonstrated independent prediction power for post-treatment survival. CONCLUSIONS: Our findings suggest a greater role for the immune system in AMN than previously recognized. AMN subtypes may have clinical utility for predicting CRS/HIPEC treatment outcomes.

Dissecting intratumoral myeloid cell plasticity by single cell RNA-seq.

Tumor-infiltrating myeloid cells are the most abundant leukocyte population within tumors. Molecular cues from the tumor microenvironment promote the differentiation of immature myeloid cells toward an immunosuppressive phenotype. However, the in situ dynamics of the transcriptional reprogramming underlying this process are poorly understood. Therefore, we applied single cell RNA-seq (scRNA-seq) to computationally investigate the cellular composition and transcriptional dynamics of tumor and adjacent normal tissues from 4 early-stage non-small cell lung cancer (NSCLC) patients. Our scRNA-seq analyses identified 11 485 cells that varied in identity and gene expression traits between normal and tumor tissues. Among these, myeloid cell populations exhibited the most diverse changes between tumor and normal tissues, consistent with tumor-mediated reprogramming. Through trajectory analysis, we identified a differentiation path from CD14+ monocytes to M2 macrophages (monocyte-to-M2). This differentiation path was reproducible across patients, accompanied by increased expression of genes (eg, MRC1/CD206, MSR1/CD204, PPARG, TREM2) with significantly enriched functions (Oxidative phosphorylation and P53 pathway) and decreased expression of genes (eg, CXCL2, IL1B) with significantly enriched functions (TNF-α signaling via NF-κB and inflammatory response). Our analysis further identified a co-regulatory network implicating upstream transcription factors (JUN, NFKBIA) in monocyte-to-M2 differentiation, and activated ligand-receptor interactions (eg, SFTPA1-TLR2, ICAM1-ITGAM) suggesting intratumoral mechanisms whereby epithelial cells stimulate monocyte-to-M2 differentiation. Overall, our study identified the prevalent monocyte-to-M2 differentiation in NSCLC, accompanied by an intricate transcriptional reprogramming mediated by specific transcriptional activators and intercellular crosstalk involving ligand-receptor interactions.

Feasibility of lung cancer RNA acquisition from a single transbronchial or transthoracic needle pass (FASTT trial).

INTRODUCTION: RNA isolation from tumor tissue is used for biomarker analyses and validation. Limited diagnostic material from small volume biopsies combined with an increasing demand for standard histologic, molecular characterization, and next generation sequencing applications often leads to limited material for research. We sought to evaluate small volume sampling of lung cancer tissue collected from a single needle pass during a diagnostic procedure and determine if it can provide RNA of acceptable quantity and quality. METHODS: We enrolled 140 patients with probable primary bronchogenic carcinoma and collected RNA from a dedicated FNA aspiration. Total RNA (ηg), RNA integrity number (RIN), and %Mass in base pairs were evaluated from each patient sample. A customized nanoString nCounter® 95-gene panel was used to profile the expression patterns of feature NSCLC genes. We compared gene expression patterns that distinguish lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) in our cohort with a corresponding Cancer Genome Atlas (TCGA) NSCLC datasets. RESULTS: Of the 149 patients consented. RNA-extraction was performed in 101 eligible patients. A satisfactory total RNA mass and RIN was quantified for all samples with a similar distribution among cellular subtypes. Mean %-Mass over 300 base pairs was noted for all specimens and 96% of samples met criteria to perform genetic evaluation with our commercialized gene expression assay. The FNA-derived transcriptomic results showed excellent consistency with the TCGA counterparts, and the differential expression pattern of LUAD vs LUSC subtypes were highly similar. DISCUSSION: In this study, RNA retrieval from a single-pass FNA regardless of procedural approach showed equivalence and suitability for gene expression assessments. RNA extraction from small volume samples has the potential to provide valuable material for genetic profiling.