Integrative Analysis Identifies Candidate Tumor Microenvironment and Intracellular Signaling Pathways that Define Tumor Heterogeneity in NF1

Characterization of Immune Cell Populations of Cutaneous Neurofibromas in Neurofibromatosis 1

Cutaneous neurofibromas (cNFs) are characteristic of neurofibromatosis 1 (NF1), yet their immune microenvironment is incompletely known. A total of 61 cNFs from 10 patients with NF1 were immunolabeled for different types of T cells and macrophages, and the cell densities were correlated with clinical characteristics. Eight cNFs and their overlying skin were analyzed for T cell receptor CDR domain sequences, and mass spectrometry of 15 cNFs and the overlying skin was performed to study immune-related processes. Intratumoral T cells were detected in all cNFs. Tumors from individuals younger than the median age of the study participants (33 years), growing tumors, and tumors smaller than the data set median showed increased T cell density. Most samples displayed intratumoral or peritumoral aggregations of CD3-positive cells. T cell receptor sequencing demonstrated that the skin and cNFs host distinct T cell populations, whereas no dominant cNF-specific T cell clones were detected. Unique T cell clones were fewer in cNFs than in skin, and mass spectrometry suggested lower expression of proteins related to T cell-mediated immunity in cNFs than in skin. CD163-positive cells, suggestive of M2 macrophages, were abundant in cNFs. Human cNFs have substantial T cell and macrophage populations that may be tumor-specific.

The quest for effective immunotherapies against malignant peripheral nerve sheath tumors: Is there hope?

Immune-based therapies represent a new paradigm in the treatment of multiple cancers, where they have helped achieve durable and safe clinical responses in a growing subset of patients. While a wealth of information is available concerning the use of these agents in treating the more common malignancies, little has been reported about the use of immunotherapies against malignant peripheral nerve sheath tumors (MPNSTs), a rare form of soft tissue sarcoma that arises from the myelin sheaths that protect peripheral nerves. Surgical resection has been the mainstay of therapy in MPNSTs, but the recurrence rate is as high as 65%, and chemotherapy is generally ineffective. The immune contexture of MPNSTs, replete with macrophages and a varying degree of T cell infiltration, presents multiple opportunities to design meaningful therapeutic interventions. While preliminary results with macrophage-targeting strategies and oncolytic viruses are promising, identifying the subset of patients that respond to immune-based strategies will be a milestone. As part of our effort to help advance the use of immunotherapy for MPNSTs, here we describe recent insights regarding the immune contexture of MPNSTs, discuss emerging immune-based strategies, and provide a brief overview of potential biomarkers of response.

MousiPLIER: A Mouse Pathway-Level Information Extractor Model

High throughput gene expression profiling is a powerful approach to generate hypotheses on the underlying causes of biological function and disease. Yet this approach is limited by its ability to infer underlying biological pathways and burden of testing tens of thousands of individual genes. Machine learning models that incorporate prior biological knowledge are necessary to extract meaningful pathways and generate rational hypothesis from the vast amount of gene expression data generated to date. We adopted an unsupervised machine learning method, Pathway-level information extractor (PLIER), to train the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. mousiPLER converted gene expression data into a latent variables that align to known pathway or cell maker gene sets, substantially reducing data dimensionality and improving interpretability. To determine the utility of mousiPLIER, we applied it to a mouse brain aging study of microglia and astrocyte transcriptomic profiling. We found a specific set of latent variables that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. We next performed k-means clustering on the training data to identify studies that respond strongly to LV41, finding that the variable is relevant to striatum and aging across the scientific literature. Finally, we built a web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together this study provides proof of concept that mousiPLIER can uncover meaningful biological processes in mouse transcriptomic studies.

Machine learning in rare disease

High-throughput profiling methods (such as genomics or imaging) have accelerated basic research and made deep molecular characterization of patient samples routine. These approaches provide a rich portrait of genes, molecular pathways and cell types involved in disease phenotypes. Machine learning (ML) can be a useful tool for extracting disease-relevant patterns from high-dimensional datasets. However, depending upon the complexity of the biological question, machine learning often requires many samples to identify recurrent and biologically meaningful patterns. Rare diseases are inherently limited in clinical cases, leading to few samples to study. In this Perspective, we outline the challenges and emerging solutions for using ML for small sample sets, specifically in rare diseases. Advances in ML methods for rare diseases are likely to be informative for applications beyond rare diseases for which few samples exist with high-dimensional data. We propose that the method community prioritize the development of ML techniques for rare disease research.

Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives

Accurate transfer learning of clinical outcomes from one cellular context to another, between cell types, developmental stages, omics modalities or species, is considered tremendously useful. When transferring a prediction task from a source domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring states or processes common to both the source and the target that can be learned by the predictor reflected by shared denominators. These may form a compendium of knowledge that is learned in the source to enable predictions in the target, usually with few, if any, labeled target training samples to learn from. Transductive transfer learning refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive transfer learning considers cases where the target predictor is performing a different yet related task as compared with the source predictor. Often, there is also a need to first map the variables in the input/feature spaces and/or the variables in the output/outcome spaces. We here discuss and juxtapose various recently published transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in vivo) outcomes based on preclinical (mostly animal-based) molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.

Whole exome and transcriptome sequencing reveal clonal evolution and exhibit immune-related features in metastatic colorectal tumors

Liver is the most common site where metastatic lesions of colorectal cancer (CRC) arise. Although researches have shown mutations in driver genes, copy number variations (CNV) and alterations in relevant signaling pathways promoted the tumor evolution and immune escape during colorectal liver metastasis (CLM), the underlying mechanism remains largely elusive. Tumor and matched metastatic tissues were collected from 16 patients diagnosed with colorectal cancer and subjected to whole-exome sequencing (WES) and RNA sequencing (RNA-seq) for studying colorectal cancer clonal evolution and immune escape during CLM. Shared somatic mutations between primary and metastatic tissues with a commonly observed subclonal-clonal (S-C) changing pattern indicated a common clonal origin between two lesions. The recurrent mutations with S-C changing pattern included those in KRAS, SYNE1, CACNA1H, PCLO, FBXL2, and DNAH11. The main CNV events underwent clonal-clonal evolution (20q amplification (amp), 17p deletion (del), 18q del and 8p del), subclonal-clonal evolution (8q amp, 13q amp, 8p del) and metastasis-specific evolution (8q amp) during the process of CLM. In addition, we revealed a potential mechanism of tumor cell immune escape by analyzing human leukocytes antigens (HLA) related clonal neoantigens and immune cell components in CLM. Our study proposed a novel liver metastasis-related evolutionary process in colorectal cancer and emphasized the theory of neo-immune escape in colorectal liver metastasis.

Cell-free DNA ultra-low-pass whole genome sequencing to distinguish malignant peripheral nerve sheath tumor (MPNST) from its benign precursor lesion: A cross-sectional study

BACKGROUND

The leading cause of mortality for patients with the neurofibromatosis type 1 (NF1) cancer predisposition syndrome is the development of malignant peripheral nerve sheath tumor (MPNST), an aggressive soft tissue sarcoma. In the setting of NF1, this cancer type frequently arises from within its common and benign precursor, plexiform neurofibroma (PN). Transformation from PN to MPNST is challenging to diagnose due to difficulties in distinguishing cross-sectional imaging results and intralesional heterogeneity resulting in biopsy sampling errors.

METHODS AND FINDINGS

This multi-institutional study from the National Cancer Institute and Washington University in St. Louis used fragment size analysis and ultra-low-pass whole genome sequencing (ULP-WGS) of plasma cell-free DNA (cfDNA) to distinguish between MPNST and PN in patients with NF1. Following in silico enrichment for short cfDNA fragments and copy number analysis to estimate the fraction of plasma cfDNA originating from tumor (tumor fraction), we developed a noninvasive classifier that differentiates MPNST from PN with 86% pretreatment accuracy (91% specificity, 75% sensitivity) and 89% accuracy on serial analysis (91% specificity, 83% sensitivity). Healthy controls without NF1 (participants = 16, plasma samples = 16), PN (participants = 23, plasma samples = 23), and MPNST (participants = 14, plasma samples = 46) cohorts showed significant differences in tumor fraction in plasma (P = 0.001) as well as cfDNA fragment length (P < 0.001) with MPNST samples harboring shorter fragments and being enriched for tumor-derived cfDNA relative to PN and healthy controls. No other covariates were significant on multivariate logistic regression. Mutational analysis demonstrated focal NF1 copy number loss in PN and MPNST patient plasma but not in healthy controls. Greater genomic instability including alterations associated with malignant transformation (focal copy number gains in chromosome arms 1q, 7p, 8q, 9q, and 17q; focal copy number losses in SUZ12, SMARCA2, CDKN2A/B, and chromosome arms 6p and 9p) was more prominently observed in MPNST plasma. Furthermore, the sum of longest tumor diameters (SLD) visualized by cross-sectional imaging correlated significantly with paired tumor fractions in plasma from MPNST patients (r = 0.39, P = 0.024). On serial analysis, tumor fraction levels in plasma dynamically correlated with treatment response to therapy and minimal residual disease (MRD) detection before relapse. Study limitations include a modest MPNST sample size despite accrual from 2 major referral centers for this rare malignancy, and lack of uniform treatment and imaging protocols representing a real-world cohort.

CONCLUSIONS

Tumor fraction levels derived from cfDNA fragment size and copy number alteration analysis of plasma cfDNA using ULP-WGS significantly correlated with MPNST tumor burden, accurately distinguished MPNST from its benign PN precursor, and dynamically correlated with treatment response. In the future, our findings could form the basis for improved early cancer detection and monitoring in high-risk cancer-predisposed populations.

WNT5A inhibition alters the malignant peripheral nerve sheath tumor microenvironment and enhances tumor growth

Malignant peripheral nerve sheath tumors (MPNST) are aggressive soft-tissue sarcomas that cause significant mortality in adults with neurofibromatosis type 1. We compared gene expression of growth factors in normal human nerves to MPNST and normal human Schwann cells to MPNST cell lines. We identified WNT5A as the most significantly upregulated ligand-coding gene and verified its protein expression in MPNST cell lines and tumors. In many contexts WNT5A acts as an oncogene. However, inhibiting WNT5A expression using shRNA did not alter MPNST cell proliferation, invasion, migration, or survival in vitro. Rather, shWNT5A-treated MPNST cells upregulated mRNAs associated with the remodeling of extracellular matrix and with immune cell communication. In addition, these cells secreted increased amounts of the proinflammatory cytokines CXCL1, CCL2, IL6, CXCL8, and ICAM1. Versus controls, shWNT5A-expressing MPNST cells formed larger tumors in vivo. Grafted tumors contained elevated macrophage/stromal cells, larger and more numerous blood vessels, and increased levels of Mmp9, Cxcl13, Lipocalin-1, and Ccl12. In some MPNST settings, these effects were mimicked by targeting the WNT5A receptor ROR2. These data suggest that the non-canonical Wnt ligand WNT5A inhibits MPNST tumor formation by modulating the MPNST microenvironment, so that blocking WNT5A accelerates tumor growth in vivo.

Adrenergic signaling promotes the expansion of cancer stem-like cells of malignant peripheral nerve sheath tumors

Malignant peripheral nerve sheath tumor (MPNST), a highly malignant tumor that arises in peripheral nerve tissues, is known to be highly resistant to radiation and chemotherapy. Although there are several reports on genetic mutations and epigenetic changes that define the pathogenesis of MPNST, there is insufficient information regarding the microenvironment that contributes to the malignancy of MPNST. In the present study, we demonstrate that adrenaline increases the cancer stem cell population in MPNST. This effect is mediated by adrenaline stimulation of beta-2 adrenergic receptor (ADRB2), which activates the Hippo transducer, YAP/TAZ. Inhibition and RNAi experiments revealed that inhibition of ADRB2 attenuated the adrenaline-triggered activity of YAP/TAZ and subsequently attenuated MPNST cells stemness. Furthermore, ADRB2-YAP/TAZ axis was confirmed in the MPNST patients' specimens. The prognosis of patients with high levels of ADRB2 was found to be significantly worse. These data show that adrenaline exacerbates MPNST prognosis and may aid the development of new treatment strategies for MPNST.