Intratumoral heterogeneity and clonal evolution in liver cancer

Clonal evolution of a tumor ecosystem depends on different selection pressures that are principally immune and treatment mediated. We integrate RNA-seq, DNA sequencing, TCR-seq and SNP array data across multiple regions of liver cancer specimens to map spatio-temporal interactions between cancer and immune cells. We investigate how these interactions reflect intra-tumor heterogeneity (ITH) by correlating regional neo-epitope and viral antigen burden with the regional adaptive immune response. Regional expression of passenger mutations dominantly recruits adaptive responses as opposed to hepatitis B virus and cancer-testis antigens. We detect different clonal expansion of the adaptive immune system in distant regions of the same tumor. An ITH-based gene signature improves single-biopsy patient survival predictions and an expression survey of 38,553 single cells across 7 regions of 2 patients further reveals heterogeneity in liver cancer. These data quantify transcriptomic ITH and how the different components of the HCC ecosystem interact during cancer evolution.

Immune-mediated selection pressures impact the clonal evolution of tumours. Here, in hepatocellular carcinoma the authors map spatio-temporal interactions between tumor and immune cells, highlighting the regulatory substrate of intra-tumoural heterogeneity that correlates with regional adaptive immune responses.

Assessment of network module identification across complex diseases

Many bioinformatics methods have been proposed for reducing the complexity of large gene or protein networks into relevant subnetworks or modules. Yet, how such methods compare to each other in terms of their ability to identify disease-relevant modules in different types of network remains poorly understood. We launched the 'Disease Module Identification DREAM Challenge', an open competition to comprehensively assess module identification methods across diverse protein-protein interaction, signaling, gene co-expression, homology and cancer-gene networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies. Our robust assessment of 75 module identification methods reveals top-performing algorithms, which recover complementary trait-associated modules. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets. This community challenge establishes biologically interpretable benchmarks, tools and guidelines for molecular network analysis to study human disease biology.

exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

Integrated nanoscale deterministic lateral displacement arrays for separation of extracellular vesicles from clinically-relevant volumes of biological samples

Extracellular vesicles (EVs) offer many opportunities in early-stage disease diagnosis, treatment monitoring, and precision therapy owing to their high abundance in bodily fluids, accessibility from liquid biopsy, and presence of nucleic acid and protein cargo from their cell of origin. Despite their growing promise, isolation of EVs for analysis remains a labor-intensive and time-consuming challenge given their nanoscale dimensions (30-200 nm) and low buoyant density. Here, we report a simple, size-based EV separation technology that integrates 1024 nanoscale deterministic lateral displacement (nanoDLD) arrays on a single chip capable of parallel processing sample fluids at rates of up to 900 μL h-1. Benchmarking the nanoDLD chip against commonly used EV isolation technologies, including ultracentrifugation (UC), UC plus density gradient, qEV size-exclusion chromatography (Izon Science), and the exoEasy Maxi Kit (QIAGEN), we demonstrate a superior yield of ∼50% for both serum and urine samples, representing the ability to use smaller input volumes to achieve the same number of isolated EVs, and a concentration factor enhancement of up to ∼3× for both sample types, adjustable to ∼60× for urine through judicious design. Further, RNA sequencing was carried out on nanoDLD- and UC-isolated EVs from prostate cancer (PCa) patient serum samples, resulting in a higher gene expression correlation between replicates for nanoDLD-isolated EVs with enriched miRNA, decreased rRNA, and the ability to detect previously reported RNA indicators of aggressive PCa. Taken together, these results suggest nanoDLD as a promising alternative technology for fast, reproducible, and automatable EV-isolation.

A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.

Estimating Waiting Time for Deceased Donor Renal Transplantion in the Era of New Kidney Allocation System

BACKGROUND

On December 4, 2014, a new deceased donor kidney allocation system (KAS) was implemented. The KAS was designed to improve organ equity and graft-recipient longevity matching. However, estimated wait-time to deceased donor transplantation is difficult to predict post-KAS.

METHODS

Using the Kidney-Pancreas Simulated Allocation Model software (KPSAM), a program that the Organ Procurement and Transplant Network uses to assess policy proposals, we compared the kidney allocations of both the new (post-KAS) and old policies (pre-KAS) (10 iterations for each group; total N = 204,148) and estimated wait-time based on blood type, duration of dialysis exposure, and calculated panel-reactive antibody (CPRA).

RESULTS

The simulations revealed that estimated median (25(th) and 75(th) percentile) waiting time in transplanted recipients decreased from 2.3 (1.2, 3.8) years in the old allocation to 1.8 (0.8, 3.4) years in the new allocation system. The rate of transplantations performed within the first year of wait-listing increased from 20.7% to 31.3%. The KPSAM resulted in more transplantations in recipients with more than 5 years of dialysis exposure (26.5% to 37.4%), longevity matching (12.2% to 17.5%), blood group B (12.6% to 17.2%), and high CPRA ≥98% (1.9% to 4.3%) in post-KAS compared with pre-KAS simulations.

CONCLUSIONS

Based on the KPSAM results, it was projected that post-KAS wait-time in transplanted recipients might decrease approximately 6 months (22%) across all CPRA categories. It might be related to the KAS awarding waiting time points for prelisting dialysis time and priority points awarded based on CPRA (bolus effect).

Abstract 2383: Multiregional RNA sequencing identifies intratumor transcriptomic heterogeneity in a subset of early-stage hepatocellular carcinoma

BACKGROUND: Hepatocellular carcinoma (HCC) is known to have histological intra-tumor heterogeneity. Little is known about the underlying molecular contributions to this observation, or its potential impact on resistance to therapies.

AIMS: 1) To evaluate intra-tumoral molecular heterogeneity of primary HCCs using multi-regional RNA-seq; 2) to correlate molecular alterations with histological features at distinct areas.

METHODS: We analyzed 55 fresh-frozen tissues from 10 patients with BCLC-A HCC treated with surgical resection. Multi-regional sampling included 38 HCCs and 16 adjacent non-tumoral tissues (average 5.4 samples per patient). We performed H&E staining and RNA-seq on all samples. Each sequenced section was evaluated for a panel of 11 histological features. Data analyses included clustering (consensus and multidimensional scaling (MDS)), sample ordering via linear graph modeling of expression, pathogen quantitation, gene set enrichment analysis (GSEA), and nearest template method (NTP) for gene signature prediction.

RESULTS: Patients enrolled were mostly males (70%), with an average age of 59, all with single-nodule HCC, and a median tumor size of 5.3 cm (range 3-16 cm). Background liver disease was HBV in 50% of cases without histological cirrhosis in 80%. MDS and consensus clustering identified heterogeneity in 40% (4/10) of patients as defined by at least one sample not clustering with their counterparts. Sample ordering uncovered dominant tumor heterogeneity evolution paths, including HBV specific ones. GSEA revealed differential enrichment (FDR<0.05) of Cancer Hallmark Gene Sets in the 4 heterogeneous tumors. Indeed, these 4 tumors showed concordant gene set enrichment (FDR<.05) in all regions only 19% of the time compared to 83% in homogenous tumors on average. NTP further confirmed heterogeneity in molecular classification predictions in 2/4 heterogeneous tumors (e.g., Proliferation class). There were no significant differences in size or regions sequenced between heterogeneous and homogenous tumors. Molecular outliers show distinct histological features such as high mitotic rate and poorer differentiation.

CONCLUSION: Transcriptomic intra-tumor heterogeneity is frequent in a subset of surgically resected HCC, and it correlates with histological features. Integrative analysis of CNVs, expressed mutations, allele specific expression, and gene fusions is ongoing.

Citation Format: Amanda Craig, Mehmet E. Ahsen, Ismail Labgaa, Ashley Stueck, Delia D’Avola, Stephen C. Ward, Maria Isabel Fiel, Ganesh Gunasekaran, Josep Llovet, Swan Thung, Myron Schwartz, Bojan Losic, Gustavo Stolovitzky, Augusto Villanueva. Multiregional RNA sequencing identifies intratumor transcriptomic heterogeneity in a subset of early-stage hepatocellular carcinoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2383.