Establishment of a Multi-Analyte Serum Biomarker Panel to Identify Lymph Node Metastases in Non-small Cell Lung Cancer

IntroductionIn non-small cell lung cancer (NSCLC), the presence of locoregional lymph node metastases remains the most important prognostic factor and significantly guides treatment regimens. Unfortunately, currently-available noninvasive staging modalities have limited accuracy. The objective of this study was to create a multianalyte blood test capable of discriminating a patient's true (pathologic) nodal status preoperatively.MethodsPretreatment serum specimens collected from 107 NSCLC patients with localized disease were screened with 47 biomarkers implicated in disease presence or progression. Multivariate statistical algorithms were then used to identify the optimal combination of biomarkers for accurately discerning each patient's nodal status.ResultsWe identified 15 candidate biomarkers that met our criteria for statistical relevance in discerning a patient's preoperative nodal status. A ‘random forest’ classification algorithm was used with these parameters to define a 6-analyte panel, consisting of macrophage inflammatory protein-1α, carcinoembryonic antigen, stem cell factor, tumor necrosis factor-receptor I, interferon-γ, and tumor necrosis factor-α, that was the optimum combination of biomarkers for identifying a patient's pathologic nodal status. A Classification and Regression Tree analysis was then created with this panel that was capable of correctly classifying 88% of the patients tested, relative to the pathologic assessments. This value is in contrast to our observed 85% classification rate using conventional clinical methods.ConclusionsThis study establishes a serum biomarker panel with efficacy in discerning preoperative nodal status. With further validation, this blood test may be useful for assessing nodal status (including occult disease) in NSCLC patients facing tumor resection therapy

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Cotranslational protein assembly imposes evolutionary constraints on homomeric proteins

Cotranslational protein folding can facilitate rapid formation of functional structures. However, it might also cause premature assembly of protein complexes, if two interacting nascent chains are in close proximity. By analyzing known protein structures, we show that homomeric protein contacts are enriched towards the C-termini of polypeptide chains across diverse proteomes. We hypothesize that this is the result of evolutionary constraints for folding to occur prior to assembly. Using high-throughput imaging of protein homomers in vivo in E. coli and engineered protein constructs with N- and C-terminal oligomerization domains, we show that, indeed, proteins with C-terminal homomeric interface residues consistently assemble more efficiently than those with N-terminal interface residues. Using in vivo, in vitro and in silico experiments, we identify features that govern successful assembly of homomers, which have implications for protein design and expression optimization

Refining colorectal cancer classification and clinical stratification through a single-cell atlas

Background Colorectal cancer (CRC) consensus molecular subtypes (CMS) have different immunological, stromal cell, and clinicopathological characteristics. Single-cell characterization of CMS subtype tumor microenvironments is required to elucidate mechanisms of tumor and stroma cell contributions to pathogenesis which may advance subtype-specific therapeutic development. We interrogate racially diverse human CRC samples and analyze multiple independent external cohorts for a total of 487,829 single cells enabling high-resolution depiction of the cellular diversity and heterogeneity within the tumor and microenvironmental cells. Results Tumor cells recapitulate individual CMS subgroups yet exhibit significant intratumoral CMS heterogeneity. Both CMS1 microsatellite instability (MSI-H) CRCs and microsatellite stable (MSS) CRC demonstrate similar pathway activations at the tumor epithelial level. However, CD8+ cytotoxic T cell phenotype infiltration in MSI-H CRCs may explain why these tumors respond to immune checkpoint inhibitors. Cellular transcriptomic profiles in CRC exist in a tumor immune stromal continuum in contrast to discrete subtypes proposed by studies utilizing bulk transcriptomics. We note a dichotomy in tumor microenvironments across CMS subgroups exists by which patients with high cancer-associated fibroblasts (CAFs) and C1Q+TAM content exhibit poor outcomes, providing a higher level of personalization and precision than would distinct subtypes. Additionally, we discover CAF subtypes known to be associated with immunotherapy resistance. Conclusions Distinct CAFs and C1Q+ TAMs are sufficient to explain CMS predictive ability and a simpler signature based on these cellular phenotypes could stratify CRC patient prognosis with greater precision. Therapeutically targeting specific CAF subtypes and C1Q + TAMs may promote immunotherapy responses in CRC patient

Cotranslational folding of spectrin domains via partially structured states.

How do the key features of protein folding, elucidated from studies on native, isolated proteins, manifest in cotranslational folding on the ribosome? Using a well-characterized family of homologous α-helical proteins with a range of biophysical properties, we show that spectrin domains can fold vectorially on the ribosome and may do so via a pathway different from that of the isolated domain. We use cryo-EM to reveal a folded or partially folded structure, formed in the vestibule of the ribosome. Our results reveal that it is not possible to predict which domains will fold within the ribosome on the basis of the folding behavior of isolated domains; instead, we propose that a complex balance of the rate of folding, the rate of translation and the lifetime of folded or partly folded states will determine whether folding occurs cotranslationally on actively translating ribosomes.Supported by grants from the Swedish Cancer Foundation, the Swedish Research Council and the Knut and Alice Wallenberg Foundation (to G.v.H.); the Wellcome Trust (WT095195 to J.C.) and the European Research Council (ERC-2008-AdG 232648, to R.B.). J.C. is a Wellcome Trust Senior Research Fellow