Ex vivo liver resection and auto-transplantation as an alternative for the treatment of liver malignancies: Progress and challenges.

Hepatectomy is still the major curative treatment for patients with liver malignancies. However, it is still a big challenge to remove the tumors in the central posterior area, especially if their location involves the retrohepatic inferior vena cava and hepatic veins. Ex vivo liver resection and auto-transplantation (ELRA), a hybrid technique of the traditional liver resection and transplantation, has brought new hope to these patients and therefore becomes a valid alternative to liver transplantation. Due to its technical difficulty, ELRA is still concentrated in a few hepatobiliary centers that have experienced surgeons in both liver resection and liver transplantation. The efficacy and safety of this technique has already been demonstrated in the treatment of benign liver diseases, especially in the advanced alveolar echinococcosis. Recently, the application of ELRA for liver malignances has gained more attention. However, standardization of clinical practice norms and international consensus are still lacking. The prognostic impact in these oncologic patients also needs further evaluation. In this review, we summarized the principles and recent progresses on ELRA.

The Absence of Intra-Tumoral Tertiary Lymphoid Structures is Associated with a Worse Prognosis and mTOR Signaling Activation in Hepatocellular Carcinoma with Liver Transplantation: A Multicenter Retrospective Study.

Tertiary lymphoid structure (TLS) can predict the prognosis and sensitivity of tumors to immune checkpoint inhibitors (ICIs) therapy, whether it can be noninvasively predicted by radiomics in hepatocellular carcinoma with liver transplantation (HCC-LT) has not been explored. In this study, it is found that intra-tumoral TLS abundance is significantly correlated with recurrence-free survival (RFS) and overall survival (OS). Tumor tissues with TLS are characterized by inflammatory signatures and high infiltration of antitumor immune cells, while those without TLS exhibit uncontrolled cell cycle progression and activated mTOR signaling by bulk and single-cell RNA-seq analyses. The regulators involved in mTOR signaling (RHEB and LAMTOR4) and S-phase (RFC2, PSMC2, and ORC5) are highly expressed in HCC with low TLS. In addition, the largest cohort of HCC patients is studied with available radiomics data, and a classifier is built to detect the presence of TLS in a non-invasive manner. The classifier demonstrates remarkable performance in predicting intra-tumoral TLS abundance in both training and test sets, achieving areas under receiver operating characteristic curve (AUCs) of 92.9% and 90.2% respectively. In summary, the absence of intra-tumoral TLS abundance is associated with mTOR signaling activation and uncontrolled cell cycle progression in tumor cells, indicating unfavorable prognosis in HCC-LT.

The AGH score is a predictor of disease-free survival and targeted therapy efficacy after liver transplantation in patients with hepatocellular carcinoma.

BACKGROUND: Liver transplantation (LT) is the "cure" therapy for patients with hepatocellular carcinoma (HCC). However, some patients encounter HCC recurrence after LT. Unfortunately, there is no effective methods to identify the LT patients who have high risk of HCC recurrence and would benefit from adjuvant targeted therapy. The present study aimed to establish a scoring system to predict HCC recurrence of HCC patients after LT among the Chinese population, and to evaluate whether these patients are suitable for adjuvant targeted therapy. METHODS: Clinical data of HCC patients who underwent LT from March 2015 to June 2019 were retrospectively collected and analyzed. RESULTS: A total of 201 patients were included in the study. The multivariate Cox analysis suggested that preoperative alpha-fetoprotein (AFP) > 200 µg/L (HR = 2.666, 95% CI: 1.515-4.690; P = 0.001), glutamyl transferase (GGT) > 96 U/L (HR = 1.807, 95% CI: 1.012-3.224; P = 0.045), and exceeding the Hangzhou criteria (HR = 2.129, 95% CI: 1.158-3.914; P = 0.015) were independent risk factors for poor disease-free survival (DFS) in patients with HCC who underwent LT. We established an AFP-GGT-Hangzhou (AGH) scoring system based on these factors, and divided cases into high-, moderate-, and low-risk groups. The differences in overall survival (OS) and disease-free survival (DFS) rates among the three groups were significant (P < 0.05). The efficacy of the AGH scoring system to predict DFS was better than that of the Hangzhou criteria, UCSF criteria, Milan criteria, and TNM stage. Only in the high-risk group, we found that lenvatinib significantly improved prognosis compared with that of the control group (P < 0.05). CONCLUSIONS: The AGH scoring system provides a convenient and effective way to predict HCC recurrence after LT in HCC patients in China. Patients with a high-risk AGH score may benefit from lenvatinib adjuvant therapy after LT.

Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.

Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Interpreting somatic alterations within context-specific transcriptional programs will facilitate personalized therapeutic decisions but is a monumental task. Toward this goal, we develop a partially interpretable neural network model called Chromatin-informed Inference of Transcriptional Regulators Using Self-attention mechanism (CITRUS). CITRUS models the impact of somatic alterations on transcription factors and downstream transcriptional programs. Our approach employs a self-attention mechanism to model the contextual impact of somatic alterations. Furthermore, CITRUS uses a layer of hidden nodes to explicitly represent the state of transcription factors (TFs) to learn the relationships between TFs and their target genes based on TF binding motifs in the open chromatin regions of tumor samples. We apply CITRUS to genomic, transcriptomic, and epigenomic data from 17 cancer types profiled by The Cancer Genome Atlas. CITRUS predicts patient-specific TF activities and reveals transcriptional program variations between and within tumor types. We show that CITRUS yields biological insights into delineating TFs associated with somatic alterations in individual tumors. Thus, CITRUS is a promising tool for precision oncology.

Integrated multi-omics analysis identifies ENY2 as a predictor of recurrence and a regulator of telomere maintenance in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and has a high recurrence rate. Accurate prediction of recurrence risk is urgently required for tailoring personalized treatment programs for individual HCC patients in advance. In this study, we analyzed a gene expression dataset from an HCC cohort with 247 samples and identified five genes including ENY2, GPAA1, NDUFA4L2, NEDD9, and NRP1 as the variables for the prediction of HCC recurrence, especially the early recurrence. The Cox model and risks score were validated in two public HCC cohorts (GSE76427 and The Cancer Genome Atlas (TCGA)) and one cohort from Huashan Hospital, which included a total of 641 samples. Moreover, the multivariate Cox regression analysis revealed that the risk score could serve as an independent prognostic factor in the prediction of HCC recurrence. In addition, we found that ENY2, GPAA1, and NDUFA4L2 were significantly upregulated in HCC of the two validation cohorts, and ENY2 had significantly higher expression levels than another four genes in malignant cells, suggesting that ENY2 might play key roles in malignant cells. The cell line analysis revealed that ENY2 could promote cell cycle progression, cell proliferation, migration, and invasion. The functional analysis of the genes correlated with ENY2 revealed that ENY2 might be involved in telomere maintenance, one of the fundamental hallmarks of cancer. In conclusion, our data indicate that ENY2 may regulate the malignant phenotypes of HCC via activating telomere maintenance.

Effects of Tumor-Derived DNA on CXCL12-CXCR4 and CCL21-CCR7 Axes of Hepatocellular Carcinoma Cells and the Regulation of Sinomenine Hydrochloride.

Currently, chemokines and their receptors, CXCL12-CXCR4 and CCL21-CCR7 axes, are deemed vital factors in the modulation of angiogenesis and are crucial for the growth and development of liver cancer. Tumor-derived DNA can be recognized by immune cells to induce an autoimmune response. In this study, we demonstrated the mechanism of tumor-derived DNA on the CXCL12-CXCR4 and CCL21-CCR7 axes of hepatocellular carcinoma (HCC) cells and the regulatory effect of sinomenine hydrochloride. Tumor-derived DNA was separated from HCCLM cell lines. Tumor-derived DNA was transfected into SK-Hep1 cells by Lipofectamine 2000. We found that sinomenine hydrochloride reduced the expression of CXCR4, CXCR12, CCR7, and CCL21 in HCC cells, suppressed the growth and invasion of HCC cells, and increased apoptosis. In contrast to the controls, the protein expressions of CXCR4, CXCL12, CCR7, CCL21, P-ERK1/2, MMP-9, and MMP-2 in SK-Hep1 cells were significantly increased after transfection of tumor-derived DNA, while the increase was reversed by sinobine hydrochloride. Acid sinomenine interferes with tumor-derived DNA and affects ERK/MMP signaling via the CXCL12/CXCR4 axis in HCC cells. CXCR4 siRNA and CCR7 siRNA attenuated tumor-derived DNA activation of ERK1/2/MMP2/9 signaling pathways in HCC cells. CXCR4-oe and CCR7-OE enhance the stimulation of erK1/2/MMP2/9 signaling pathway by tumor-derived DNA in HCC cells. Tumor-derived DNA reduced apoptosis and increased invasion of SK-Hep1 cells by CXCL12-CXCR4 axis and CCL21-CCR7 axis, and sinobine hydrochloride reversed this regulation. These results strongly suggest that tumor-derived DNA can increase the growth and invasion of oncocytes via the upregulation of the expression of CXCL12-CXCR4 and CCL21-CCR7 axis and through ERK1/2/MMP2/9 signaling pathway in HCC cells, and sinobine hydrochloride can inhibit this signaling pathway, thus inhibiting HCC cells. These results provide new potential therapeutic targets for blocking the progression of HCC induced by CXCL12-CXCR4 axis and CCL21-CCR7.

Reconstructing tumor clonal lineage trees incorporating single-nucleotide variants, copy number alterations and structural variations.

MOTIVATION: Cancer develops through a process of clonal evolution in which an initially healthy cell gives rise to progeny gradually differentiating through the accumulation of genetic and epigenetic mutations. These mutations can take various forms, including single-nucleotide variants (SNVs), copy number alterations (CNAs) or structural variations (SVs), with each variant type providing complementary insights into tumor evolution as well as offering distinct challenges to phylogenetic inference. RESULTS: In this work, we develop a tumor phylogeny method, TUSV-ext, which incorporates SNVs, CNAs and SVs into a single inference framework. We demonstrate on simulated data that the method produces accurate tree inferences in the presence of all three variant types. We further demonstrate the method through application to real prostate tumor data, showing how our approach to coordinated phylogeny inference and clonal construction with all three variant types can reveal a more complicated clonal structure than is suggested by prior work, consistent with extensive polyclonal seeding or migration. AVAILABILITY AND IMPLEMENTATION: https://github.com/CMUSchwartzLab/TUSV-ext. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Semi-deconvolution of bulk and single-cell RNA-seq data with application to metastatic progression in breast cancer.

MOTIVATION: Identifying cell types and their abundances and how these evolve during tumor progression is critical to understanding the mechanisms of metastasis and identifying predictors of metastatic potential that can guide the development of new diagnostics or therapeutics. Single-cell RNA sequencing (scRNA-seq) has been especially promising in resolving heterogeneity of expression programs at the single-cell level, but is not always feasible, e.g. for large cohort studies or longitudinal analysis of archived samples. In such cases, clonal subpopulations may still be inferred via genomic deconvolution, but deconvolution methods have limited ability to resolve fine clonal structure and may require reference cell type profiles that are missing or imprecise. Prior methods can eliminate the need for reference profiles but show unstable performance when few bulk samples are available. RESULTS: In this work, we develop a new method using reference scRNA-seq to interpret sample collections for which only bulk RNA-seq is available for some samples, e.g. clonally resolving archived primary tissues using scRNA-seq from metastases. By integrating such information in a Quadratic Programming framework, our method can recover more accurate cell types and corresponding cell type abundances in bulk samples. Application to a breast tumor bone metastases dataset confirms the power of scRNA-seq data to improve cell type inference and quantification in same-patient bulk samples. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at https://github.com/CMUSchwartzLab/RADs.

LncRNA GASAL1 promotes hepatocellular carcinoma progression by up-regulating USP10-stabilized PCNA.

Hepatocellular carcinoma (HCC) is a fatal malignancy which has insufficient treatment options. Long non-coding RNA (lncRNA) GASAL1 was discovered to be conspicuously up-regulated in HCC. However, the study on the role of GASAL1 in HCC reamins limited. Our study aimed at exploring the role and mechanism of GASAL1 in HCC. RT-qPCR or Western blot was conducted to examine the expression of RNAs or proteins. Functional assays were carried out to investigate the impact of GASAL1, USP10, and PCNA on HCC cells. Mechanism assays were performed to fathom out the relationship among GASAL1, miR-193b-5p, USP10, and PCNA. In vivo assays were also employed to determine the role of GASAL1 in HCC tumor growth and metastases. According to the data collected, GASAL1 displayed a high expression in HCC cells and GASAL1 knockdown led to impeded cell proliferation and migration, as well as tumor progression. A series of mechanism analysis demonstrated GASAL1 could sponge miR-193b-5p to raise the expression of USP10. Moreover, USP10 could induce PCNA deubiquitination to promote HCC cell growth. To conclude, GASAL1 plays an oncogenic role in HCC. GASAL1 could up-regulate USP10 via competitively binding to miR-193b-5p. And USP10 could strengthen cell proliferative and migratory abilities through deubiquitinating PCNA.

De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization.

Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unseen by AI models) will respond to any of a set of existing anticancer drugs, based on responses of previous training cell samples to those drugs. To expand the repertoire of anticancer drugs, AI has also been used to repurpose drugs that have not been tested in an anticancer setting, i.e., predicting the anticancer effects of a new drug on previously unseen cancer cells de novo. Here, we report a computational model that addresses both of the above tasks in a unified AI framework. Our model, referred to as deep learning-based graph regularized matrix factorization (DeepGRMF), integrates neural networks, graph models, and matrix-factorization techniques to utilize diverse information from drug chemical structures, their impact on cellular signaling systems, and cancer cell cellular states to predict cell response to drugs. DeepGRMF learns embeddings of drugs so that drugs sharing similar structures and mechanisms of action (MOAs) are closely related in the embedding space. Similarly, DeepGRMF also learns representation embeddings of cells such that cells sharing similar cellular states and drug responses are closely related. Evaluation of DeepGRMF and competing models on Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets show its superiority in prediction performance. Finally, we show that the model is capable of predicting effectiveness of a chemotherapy regimen on patient outcomes for the lung cancer patients in The Cancer Genome Atlas (TCGA) dataset*.

Cancer Cell-Derived Exosomes Promote HCC Tumorigenesis Through Hedgehog Pathway.

PURPOSE: Hepatocellular carcinoma (HCC) accounts for more than 80% of primary liver cancers and is one of the leading causes of cancer-related death in many countries. Cancer cell-derived exosomes are shown to mediate communications between cancer cells and the microenvironment, promoting tumorigenesis. Hedgehog signaling pathway plays important roles in cancer development of HCC. METHODS: Exosomes were isolated from culture medium of HCC cell lines PLC/PRF/5 and MHCC-97H and were found to promote cancer cell growth measured with cell proliferation and colony formation assay. HCC cells cultured with cancer cell-derived exosome had increased cancer stem cell (CSC) population demonstrated by increased cell sphere formation CSC marker expressions. Hedgehog protein Shh was found to be highly expressed in these two HCC cell lines and preferably carried by exosomes. When Shh was knocked down with shRNA, the resulting exosomes had a reduced effect on promoting cancer cell growth or CSC population increase compared to normal cell-derived exosomes. RESULTS: The ability of PLC/PRF/5 cells to form tumor in a xenograft model was increased by the addition of the exosomes from control cancer cells but not the exosomes from Shh knocked down cancer cells. Finally, the higher plasma Exo-Shh levels were associated with later tumor stages, higher histological grades, multiple tumors, and higher recurrence rates. CONCLUSION: This study demonstrated that HCC cells secreted Shh via exosome and promote tumorigenesis through the activated Hedgehog pathway.

Joint Clustering of Single-Cell Sequencing and Fluorescence In Situ Hybridization Data for Reconstructing Clonal Heterogeneity in Cancers.

Aneuploidy and whole genome duplication (WGD) events are common features of cancers associated with poor outcomes, but the ways they influence trajectories of clonal evolution are poorly understood. Phylogenetic methods for reconstructing clonal evolution from genomic data have proven a powerful tool for understanding how clonal evolution occurs in the process of cancer progression, but extant methods so far have limited the ability to resolve tumor evolution via ploidy changes. This limitation exists in part because single-cell DNA-sequencing (scSeq), which has been crucial to developing detailed profiles of clonal evolution, has difficulty in resolving ploidy changes and WGD. Multiplex interphase fluorescence in situ hybridization (miFISH) provides a more unambiguous signal of single-cell ploidy changes but it is limited to profiling small numbers of single markers. Here, we develop a joint clustering method to combine these two data sources with the goal of better resolving ploidy changes in tumor evolution. We develop a probabilistic framework to maximize the probability of latent variables given the pre-clustered datasets, which we optimize via Markov chain Monte Carlo sampling combined with linear regression. We validate the method by using simulated data derived from a glioblastoma (GBM) case profiled by both scSeq and miFISH. We further apply the method to two GBM cases with scSeq and miFISH data by reconstructing a phylogenetic tree from the joint clustering results, demonstrating their synergistic value in understanding how focal copy number changes and WGD events can collectively contribute to tumor progression.

Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data.

MOTIVATION: Computational reconstruction of clonal evolution in cancers has become a crucial tool for understanding how tumors initiate and progress and how this process varies across patients. The field still struggles, however, with special challenges of applying phylogenetic methods to cancers, such as the prevalence and importance of copy number alteration (CNA) and structural variation events in tumor evolution, which are difficult to profile accurately by prevailing sequencing methods in such a way that subsequent reconstruction by phylogenetic inference algorithms is accurate. RESULTS: In this work, we develop computational methods to combine sequencing with multiplex interphase fluorescence in situ hybridization to exploit the complementary advantages of each technology in inferring accurate models of clonal CNA evolution accounting for both focal changes and aneuploidy at whole-genome scales. By integrating such information in an integer linear programming framework, we demonstrate on simulated data that incorporation of FISH data substantially improves accurate inference of focal CNA and ploidy changes in clonal evolution from deconvolving bulk sequence data. Analysis of real glioblastoma data for which FISH, bulk sequence and single cell sequence are all available confirms the power of FISH to enhance accurate reconstruction of clonal copy number evolution in conjunction with bulk and optionally single-cell sequence data. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at https://github.com/CMUSchwartzLab/FISH_deconvolution. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Development of a predictive nomogram for early recurrence of hepatocellular carcinoma in patients undergoing liver transplantation.

BACKGROUND: An individual prognostic model that includes inflammation caused by the delayed recovery of liver function after surgery for the early recurrence of hepatocellular carcinoma (HCC) following liver transplantation (LT) has not been well determined. Our aim was to develop a nomogram model for predicting individual survival and early recurrence following LT for patients. METHODS: Retrospective data, including clinical pathology and follow-up data, on HCC patients were collected between October 2016 and October 2019 at Huashan Hospital Affiliated to Fudan University. A nomogram estimating recurrence post-transplantation was constructed using multivariate Cox regression analysis. RESULTS: A total of 210 patients were included in the present study. The multivariate estimators of recurrence consisted of age, maximum tumor diameter, tumor thrombus, microvascular invasion (MVI), alanine aminotransferase and alpha-fetoprotein on postoperative day 7. Nomogram of recurrence-free survival was developed. The calibration and discrimination of the novel model were assessed with the calibration curves and concordance index (C-index). Its reliability and advantages were evaluated by comparing it with the conventional American Joint Committee on Cancer (AJCC) 8th edition staging system using integrated discrimination improvement (IDI) and net reclassification improvement (NRI). In comparison to the AJCC 8th edition staging system, the C-index (development set: 0.796 vs. 0.643, validation set: 0.741 vs. 0.563), the area under the receiver operating characteristic curve (AUC) of the validation set (1-year AUC: 0.732 vs. 0.586, 2-year AUC: 0.705 vs. 0.504), the development set (1-year AUC: 0.799 vs. 0.551, 2-year AUC: 0.801 vs. 0.512), and this model's calibration plots all showed improved performance. In addition, NRI and IDI verified that the nomogram is an accurate prognostic tool. Subsequently, a web calculator was generated to assess the risk of tumor recurrence post-LT. CONCLUSIONS: The nomogram, based on clinical and pathological factors, showed good accuracy in estimating prognostic recurrence and can be used to guide individual patient follow-up and treatment.

Assessing the contribution of tumor mutational phenotypes to cancer progression risk.

Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, which act in different combinations or degrees in different cancers. These variations in mutability phenotypes are predictive of progression outcomes independent of the specific mutations they have produced to date. Here we explore the question of how and to what degree these differences in mutational phenotypes act in a cancer to predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational phenotypes, followed by a machine learning framework to identify key features predictive of progression. Analyses of breast invasive carcinoma and lung carcinoma demonstrate that a large fraction of the risk of future clinical outcomes of cancer progression-overall survival and disease-free survival-can be explained solely from mutational phenotype features derived from the phylogenetic analysis. We further show that mutational phenotypes have additional predictive power even after accounting for traditional clinical and driver gene-centric genomic predictors of progression. These results confirm the importance of mutational phenotypes in contributing to cancer progression risk and suggest strategies for enhancing the predictive power of conventional clinical data or driver-centric biomarkers.

Left Hepatic Vein Preferential Approach Based on Anatomy Is Safe and Feasible for Laparoscopic Living Donor Left Lateral Sectionectomy.

We assess the safety and feasibility of the left hepatic vein preferential approach (LHVPA) based on left hepatic vein (LHV) anatomy for living donor laparoscopic left lateral sectionectomy (LLLS). Data from 50 donors who underwent LLLS in Huashan Hospital from October 2016 to November 2019 were analyzed retrospectively. On the basis of the classification of the LHV anatomy, the vein was defined as the direct import type, upper branch type, or indirect import type. A subgroup analysis was performed to compare the outcomes between the LHVPA and non-LHVPA groups. All 50 patients underwent pure LLLS. The mean operative duration was 157.5 ± 29.7 minutes. The intraoperative blood loss was 160.4 ± 97.5 mL. No complications more severe than grade 3 occurred. LHVPA was applied in 13 patients, whereas non-LHVPA was applied in 10 patients with the direct import type and upper branch type anatomy. The operative duration was shorter in the LHVPA group than the non-LHVPA group (142.7 ± 22.0 versus 173.0 ± 22.8 minutes; P = 0.01). Intraoperative blood loss was reduced in the LHVPA group compared with the non-LHVPA group (116.2 ± 45.6 versus 170.0 ± 63.3 mL; P = 0.02). The length of the LHV reserved extrahepatically in the LHVPA group was longer than in the non-LHVPA group (4.3 ± 0.2 versus 3.3 ± 0.3 mm; P = 0.01). Fewer reconstructions of the LHV in the direct import type anatomy were required for the LHVPA group than for the non-LHVPA group (0/8 versus 4/6). LHVPA based on the LHV anatomy is recommended in LLLS because it can further increase the safety and the efficiency of surgery for suitable donors.

Neural Network Deconvolution Method for Resolving Pathway-Level Progression of Tumor Clonal Expression Programs With Application to Breast Cancer Brain Metastases.

Metastasis is the primary mechanism by which cancer results in mortality and there are currently no reliable treatment options once it occurs, making the metastatic process a critical target for new diagnostics and therapeutics. Treating metastasis before it appears is challenging, however, in part because metastases may be quite distinct genomically from the primary tumors from which they presumably emerged. Phylogenetic studies of cancer development have suggested that changes in tumor genomics over stages of progression often result from shifts in the abundance of clonal cellular populations, as late stages of progression may derive from or select for clonal populations rare in the primary tumor. The present study develops computational methods to infer clonal heterogeneity and dynamics across progression stages via deconvolution and clonal phylogeny reconstruction of pathway-level expression signatures in order to reconstruct how these processes might influence average changes in genomic signatures over progression. We show, via application to a study of gene expression in a collection of matched breast primary tumor and metastatic samples, that the method can infer coarse-grained substructure and stromal infiltration across the metastatic transition. The results suggest that genomic changes observed in metastasis, such as gain of the ErbB signaling pathway, are likely caused by early events in clonal evolution followed by expansion of minor clonal populations in metastasis, a finding that may have translational implications for early detection or prevention of metastasis.

Robust and accurate deconvolution of tumor populations uncovers evolutionary mechanisms of breast cancer metastasis.

MOTIVATION: Cancer develops and progresses through a clonal evolutionary process. Understanding progression to metastasis is of particular clinical importance, but is not easily analyzed by recent methods because it generally requires studying samples gathered years apart, for which modern single-cell sequencing is rarely an option. Revealing the clonal evolution mechanisms in the metastatic transition thus still depends on unmixing tumor subpopulations from bulk genomic data. METHODS: We develop a novel toolkit called robust and accurate deconvolution (RAD) to deconvolve biologically meaningful tumor populations from multiple transcriptomic samples spanning the two progression states. RAD uses gene module compression to mitigate considerable noise in RNA, and a hybrid optimizer to achieve a robust and accurate solution. Finally, we apply a phylogenetic algorithm to infer how associated cell populations adapt across the metastatic transition via changes in expression programs and cell-type composition. RESULTS: We validated the superior robustness and accuracy of RAD over alternative algorithms on a real dataset, and validated the effectiveness of gene module compression on both simulated and real bulk RNA data. We further applied the methods to a breast cancer metastasis dataset, and discovered common early events that promote tumor progression and migration to different metastatic sites, such as dysregulation of ECM-receptor, focal adhesion and PI3k-Akt pathways. AVAILABILITY AND IMPLEMENTATION: The source code of the RAD package, models, experiments and technical details such as parameters, is available at https://github.com/CMUSchwartzLab/RAD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effect of lncRNA­BC200 on proliferation and migration of liver cancer cells in vitro and in vivo.

In recent years, the important role of long non­â€‹coding RNAs (lncRNAs) in the development of liver cancer has received increasing attention. The abnormal expression level of long non­coding RNAs has been associated with the occurrence and development of liver cancer. However, the role and molecular mechanisms of lncRNAs in the development and progression of liver cancer are not fully understood. The present study aimed to clarify the function and molecular mechanism of lncRNA brain cytoplasmic 200 (BC200) in liver cancer. In the present study, it was found that BC200 expression level was higher in hepatocellular carcinoma (HCC) tissues than that in adjacent tissues. Cell function was examined by constructing BC200 knockout (KO) and BC200­overexpression in vitro models. It was found that BC200 affected the proliferation and migration of HepG2 cells. Interestingly, it was found that BC200 affected the expression of c­Myc protein but did not affect the mRNA expression level of c­MYC. BC200 KO cells exhibited a reduced protein expression level of Bax protein and an increased protein expression level of Bcl­xL. Conversely, BC200 overexpression reduced the expression of Bcl­xL protein and increased the expression of Bax protein. Importantly, it was found that BC200 affected the formation of subcutaneous tumors in nude mice. In conclusion, the present results suggested that lncRNA BC200 may play an important role in liver cancer.