A human lung tumor microenvironment interactome identifies clinically relevant cell-type cross-talk.

BackgroundTumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway.ResultTo develop a deeper understanding of the interactions between cells within human lung tumors, we perform RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We map the cell-specific differential expression of prognostically associated secreted factors and cell surface genes, and computationally reconstruct cross-talk between these cell types to generate a novel resource called the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identify and validate a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also find a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior.ConclusionThese results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance

CDX2 as a Prognostic Biomarker in Stage II and Stage III Colon Cancer

BACKGROUND: The identification of high-risk stage II colon cancers is key to the selection of patients who require adjuvant treatment after surgery. Microarray-based multigene-expression signatures derived from stem cells and progenitor cells hold promise, but they are difficult to use in clinical practice. METHODS: We used a new bioinformatics approach to search for biomarkers of colon epithelial differentiation across gene-expression arrays and then ranked candidate genes according to the availability of clinical-grade diagnostic assays. With the use of subgroup analysis involving independent and retrospective cohorts of patients with stage II or stage III colon cancer, the top candidate gene was tested for its association with disease-free survival and a benefit from adjuvant chemotherapy. RESULTS: The transcription factor CDX2 ranked first in our screening test. A group of 87 of 2115 tumor samples (4.1%) lacked CDX2 expression. In the discovery data set, which included 466 patients, the rate of 5-year disease-free survival was lower among the 32 patients (6.9%) with CDX2-negative colon cancers than among the 434 (93.1%) with CDX2-positive colon cancers (hazard ratio for disease recurrence, 3.44; 95% confidence interval [CI], 1.60 to 7.38; P = 0.002). In the validation data set, which included 314 patients, the rate of 5-year disease-free survival was lower among the 38 patients (12.1%) with CDX2 protein–negative colon cancers than among the 276 (87.9%) with CDX2 protein–positive colon cancers (hazard ratio, 2.42; 95% CI, 1.36 to 4.29; P = 0.003). In both these groups, these findings were independent of the patient's age, sex, and tumor stage and grade. Among patients with stage II cancer, the difference in 5-year disease-free survival was significant both in the discovery data set (49% among 15 patients with CDX2-negative tumors vs. 87% among 191 patients with CDX2-positive tumors, P = 0.003) and in the validation data set (51% among 15 patients with CDX2-negative tumors vs. 80% among 106 patients with CDX2-positive tumors, P = 0.004). In a pooled database of all patient cohorts, the rate of 5-year disease-free survival was higher among 23 patients with stage II CDX2-negative tumors who were treated with adjuvant chemotherapy than among 25 who were not treated with adjuvant chemotherapy (91% vs. 56%, P = 0.006). CONCLUSIONS: Lack of CDX2 expression identified a subgroup of patients with high-risk stage II colon cancer who appeared to benefit from adjuvant chemotherapy. (Funded by the National Comprehensive Cancer Network, the National Institutes of Health, and others.

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

DNA methylation-based classification of sinonasal tumors

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs

Molecular subtyping of leiomyosarcoma with 3′ end RNA sequencing

Leiomyosarcoma (LMS) is a malignant neoplasm with smooth muscle differentiation. Little is known about its molecular heterogeneity and no targeted therapy currently exists for LMS. We performed expression profiling on 99 cases of LMS with 3′ end RNA sequencing (3SEQ) and demonstrated the existence of 3 molecular subtypes in this cohort. We consequently showed that these molecular subtypes are reproducible using an independent cohort of 82 LMS cases from TCGA. Two new formalin-fixed, paraffin-embedded (FFPE) tissue-compatible diagnostic immunohistochemical markers were identified for two of the three subtypes: LMOD1 for subtype I LMS and ARL4C for subtype II LMS. Subtype I LMS and subtype II LMS were associated with good and poor prognosis, respectively. Here, we describe the details of LMS diagnosis, RNA isolation, 3SEQ library construction, 3SEQ sequencing data analysis and molecular subtype determination. The 3SEQ data produced in this study was deposited into Gene Expression Omnibus (GEO) under GSE45510

PAX7 Expression in Rhabdomyosarcoma, Related Soft Tissue Tumors, and Small Round Blue Cell Neoplasms

Rhabdomyosarcoma, the most common soft tissue malignancy of childhood, is a morphologically variable tumor defined by its phenotype of skeletal muscle differentiation. The diagnosis of rhabdomyosarcoma often relies in part on the identification of myogenic gene expression using immunohistochemical or molecular techniques. However, these techniques show imperfect sensitivity and specificity, particularly in scant tissue biopsies. Here, we expand the toolkit for rhabdomyosarcoma diagnosis by studying the expression of PAX7, a transcriptional regulator of mammalian muscle progenitor cells implicated in the pathogenesis of rhabdomyosarcoma. Immunohistochemical analysis of tissue microarrays using a monoclonal anti-PAX7 antibody was used to characterize PAX7 expression in 25 non-neoplastic tissues, 109 rhabdomyosarcomas, and 697 small round blue cell or other soft tissue tumors. Among non-neoplastic tissues, PAX7 was specifically expressed in adult muscle progenitor cells (satellite cells). In embryonal rhabdomyosarcoma, PAX7 expression was positive in 52 of 63 cases (83%), negative in 9 of 63 cases (14%), and focal in 2 of 63 cases (3%). PAX7-positive embryonal rhabdomyosarcoma cases included several showing focal or negative myogenin expression. PAX7 expression in alveolar rhabdomyosarcoma was positive in 6 of 31 cases (19%), negative in 14 of 31 cases (45%), and focal in 11 of 31 cases (36%). In addition, PAX7 was expressed in 5 of 7 pleomorphic rhabdomyosarcomas (71%) and 6 of 8 spindle cell rhabdomyosarcomas (75%). Among histologic mimics, only Ewing sarcoma showed PAX7 expression (7/7 cases, 100%). In contrast, expression of PAX7 was not seen in the large majority (688/690, 99.7%) of examined cases of other soft tissue tumors, small round blue cell neoplasms, and leukemias/lymphomas. In summary, immunohistochemical analysis of PAX7 expression may be a useful diagnostic tool in the assessment of skeletal muscle differentiation in human tumors

Combination approach for detecting different types of alterations in circulating tumor DNA in leiomyosarcoma

The clinical utility of circulating tumor DNA (ctDNA) monitoring has been shown in tumors that harbor highly recurrent mutations. Leiomyosarcoma (LMS) represents a type of tumor with a wide spectrum of heterogeneous genomic abnormalities; thus, targeting hotspot mutations or a narrow genomic region for ctDNA detection may not be practical. Here we demonstrate a combinatorial approach that integrates different sequencing protocols for the orthogonal detection of single nucleotide variants (SNVs), small indels and copy number alterations (CNAs) in ctDNA.status: publishe

Chromosomal copy number alterations for associations of ductal carcinoma in situ with invasive breast cancer

Introduction: Screening mammography has contributed to a significant increase in the diagnosis of ductal carcinoma in situ (DCIS), raising concerns about overdiagnosis and overtreatment. Building on prior observations from lineage evolution analysis, we examined whether measuring genomic features of DCIS would predict association with invasive breast carcinoma (IBC). The long-term goal is to enhance standard clinicopathologic measures of low- versus high-risk DCIS and to enable risk-appropriate treatment. Methods: We studied three common chromosomal copy number alterations (CNA) in IBC and designed fluorescence in situ hybridization-based assay to measure copy number at these loci in DCIS samples. Clinicopathologic data were extracted from the electronic medical records of Stanford Cancer Institute and linked to demographic data from the population-based California Cancer Registry; results were integrated with data from tissue microarrays of specimens containing DCIS that did not develop IBC versus DCIS with concurrent IBC. Multivariable logistic regression analysis was performed to describe associations of CNAs with these two groups of DCIS. Results: We examined 271 patients with DCIS (120 that did not develop IBC and 151 with concurrent IBC) for the presence of 1q, 8q24 and 11q13 copy number gains. Compared to DCIS-only patients, patients with concurrent IBC had higher frequencies of CNAs in their DCIS samples. On multivariable analysis with conventional clinicopathologic features, the copy number gains were significantly associated with concurrent IBC. The state of two of the three copy number gains in DCIS was associated with a risk of IBC that was 9.07 times that of no copy number gains, and the presence of gains at all three genomic loci in DCIS was associated with a more than 17-fold risk (P = 0.0013). Conclusions: CNAs have the potential to improve the identification of high-risk DCIS, defined by presence of concurrent IBC. Expanding and validating this approach in both additional cross-sectional and longitudinal cohorts may enable improved risk stratification and risk-appropriate treatment in DCIS. Electronic supplementary material The online version of this article (doi:10.1186/s13058-015-0623-y) contains supplementary material, which is available to authorized users

Table_1_A real-time GPU-accelerated parallelized image processor for large-scale multiplexed fluorescence microscopy data.xlsx

Highly multiplexed, single-cell imaging has revolutionized our understanding of spatial cellular interactions associated with health and disease. With ever-increasing numbers of antigens, region sizes, and sample sizes, multiplexed fluorescence imaging experiments routinely produce terabytes of data. Fast and accurate processing of these large-scale, high-dimensional imaging data is essential to ensure reliable segmentation and identification of cell types and for characterization of cellular neighborhoods and inference of mechanistic insights. Here, we describe RAPID, a Real-time, GPU-Accelerated Parallelized Image processing software for large-scale multiplexed fluorescence microscopy Data. RAPID deconvolves large-scale, high-dimensional fluorescence imaging data, stitches and registers images with axial and lateral drift correction, and minimizes tissue autofluorescence such as that introduced by erythrocytes. Incorporation of an open source CUDA-driven, GPU-assisted deconvolution produced results similar to fee-based commercial software. RAPID reduces data processing time and artifacts and improves image contrast and signal-to-noise compared to our previous image processing pipeline, thus providing a useful tool for accurate and robust analysis of large-scale, multiplexed, fluorescence imaging data.</p