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.

Prospective population pharmacokinetic study of tacrolimus in adult recipients early after liver transplantation: A comparison of Michaelis-Menten and theory-based pharmacokinetic models.

Background and Objective: Tacrolimus, a calcineurin inhibitor widely used as a potent immunosuppressant to prevent graft rejection, exhibits nonlinear kinetics in patients with kidney transplantation and nephrotic syndrome. However, whether nonlinear drug metabolism occurs in adult patients undergoing liver transplantation remains unclear, as do the main underlying mechanisms. Therefore, here we aimed to further confirm the characteristics of nonlinearity through a large sample size, and determine the potential influence of nonlinearity and its possible mechanisms. Methods: In total, 906 trough concentrations from 176 adult patients (150 men/26 women; average age: 50.68 ± 9.71 years, average weight: 64.54 ± 11.85 kg after first liver transplantation) were included in this study. Population pharmacokinetic analysis was performed using NONMEM®. Two modeling strategies, theory-based linear compartmental and nonlinear Michaelis-Menten (MM) models, were evaluated and compared. Potential covariates were screened using a stepwise approach. Bootstrap, prediction-, and simulation-based diagnostics (prediction-corrected visual predictive checks) were performed to determine model stability and predictive performance. Finally, Monte Carlo simulations based on the superior model were conducted to design dosing regimens. Results: Postoperative days (POD), Aspartate aminotransferase (AST), daily tacrolimus dose, triazole antifungal agent (TAF) co-therapy, and recipient CYP3A5*3 genotype constituted the main factors in the theory-based compartmental final model, whereas POD, Total serum bilirubin (TBIL), Haematocrit (HCT), TAF co-therapy, and recipient CYP3A5*3 genotype were important in the nonlinear MM model. The theory-based final model exhibited 234 L h-1 apparent plasma clearance and 11,000 L plasma distribution volume. The maximum dose rate (V max ) of the nonlinear MM model was 6.62 mg day-1; the average concentration at steady state at half-V max (K m ) was 6.46 ng ml-1. The nonlinear MM final model was superior to the theory-based final model and used to propose dosing regimens based on simulations. Conclusion: Our findings demonstrate that saturated tacrolimus concentration-dependent binding to erythrocytes and the influence of daily tacrolimus dose on metabolism may partly contribute to nonlinearity. Further investigation is needed is need to explore the causes of nonlinear pharmacokinetic of tacrolimus. The nonlinear MM model can provide reliable support for tacrolimus dosing optimization and adjustment in adult patients undergoing liver transplantation.

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.

Itaconic acid facilitates inflammation abatement and alleviates liver ischemia-reperfusion injury by inhibiting NF-κB/NLRP3/caspase-1 inflammasome axis.

Background: Ischemia-reperfusion injury (IRI) severely limits the efficacy and donor source of liver transplantation, and the crucial step in alleviating it is to control inflammation. Itaconic acid is a metabolite produced by intrinsic immune cells (especially macrophages) in the inflammatory state and can promote inflammation subsidence. However, its role in liver ischemia-reperfusion is insufficiently clarified. Methods: A mouse liver ischemia-reperfusion model was constructed, and blood and liver tissue samples were collected by sequential euthanasia of mice at pre-set time points. Liver function and inflammatory factor concentrations were measured, and HE staining was conducted. In the hypoxia-reoxygenation model, proteins were collected at pre-set time points, and the expression of NF-κB pathway-associated protein and its downstream inflammation-associated protein NLRP3 and caspase-1 were detected by Western blot, immunohistochemistry, and immunofluorescence. The level of P-P65 in the nucleus was detected by immunofluorescence. Results: In the liver ischemia-reperfusion model, liver function and inflammatory factors were dynamically varied with reperfusion time in mice, and itaconic acid significantly modified liver function and inflammatory status during this process. NF-κB pathway activity was dynamically varied during hypoxia-reoxygenation, and itaconic acid significantly inhibited the activity of the pathway and significantly suppressed the expression of its downstream inflammation-related proteins. Conclusions: Itaconic acid inhibits NF-κB pathway activation and reduces the accumulation of P-P65 in the nucleus. In turn, this reduces NLRP3 and caspase-1 expression of downstream inflammation-related proteins, promotes inflammation regression, and attenuates liver IRI.

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.

Comparison of Two Donor Liver Procurement Methods for Treatment of Pediatric Acute Liver Failure.

Background: To evaluate the difference and efficacy of two donor liver procurement methods for treatment of pediatric acute liver failure (PALF) by living donor liver transplantation (LDLT). Methods: A total of 17 patients (12 men, 5 women) with PALF who underwent LDLT in our hospital between October 2016 and October 2020, and prognostic efficacy of donors and recipients using two donor liver procurement methods were analyzed. Results: The donors and recipients were both divided into laparoscopic (7 cases) and open (10 cases) donor liver procurement groups. In the recipients, two deaths occurred in the laparoscopic group and one in the open group, and there were three postoperative complications in the laparoscopic group and six in the open group. The cumulative 1-year and 3-year survival rates in the laparoscopic group and the open group were 80.0% and 85.7% separately. There was no difference in the postoperative survival and complications rates between the two groups. In the donors, the operation time, postoperative hospital stay, and blood loss of the laparoscopic group was significantly reduced compared with the open group (P ≤ 0.01). No death or serious complication occurred in either donor group. Conclusion: Laparoscopic donor liver procurement is worth recommending than open donor liver procurement for treatment of PALF combined with LDLT in qualified transplant centers.

CYP3A7, CYP3A4, and CYP3A5 genetic polymorphisms in recipients rather than donors influence tacrolimus concentrations in the early stages after liver transplantation.

AIM: The purpose of this study was to investigate the effect of CYP3A7, CYP3A4, and CYP3A5 genetic polymorphisms in liver transplant recipients and donors on tacrolimus concentrations in the early stages after liver transplantation. METHODS: One hundred and thirty-eight liver transplant recipients and matched donors were genotyped for CYP3A7 (rs10211 and rs2257401), CYP3A4 (rs4646437 and rs2242480), and CYP3A5*3 (rs776746) polymorphisms. The relationships between dose-adjusted trough concentrations (C0/D) of tacrolimus and corresponding genotypes were investigated. RESULTS: Recipient CYP3A polymorphisms were associated with tacrolimus concentrations. The CYP3A7 rs10211 AA carriers (186.2 vs 90.5, p < 0.001), CYP3A4 rs4646437 CC carriers (184.0 vs 88.8, p < 0.001), CYP3A4*1G rs2242480 CC carriers (189.8 vs 99.7, p < 0.001), and CYP3A5*3 rs776746 GG carriers (197.3 vs 86.0, p < 0.001) had an almost twofold increase in the tacrolimus C0/D compared to that of the non-carriers. We further investigated the effect of the combination of recipient (intestinal) and donor (hepatic) genotypes on tacrolimus concentrations. Regardless of the genotype of the matched donor, CYP3A7 rs10211, CYP3A4*1G (rs2242480), and CYP3A5*3 (rs776746) polymorphisms of recipients could affect tacrolimus concentrations. For the CYP3A4 rs4646437 polymorphisms, when the donor carried CYP3A4 rs4646437 CC, the recipient CYP3A4 rs4646437 polymorphism was associated with the C0/D of tacrolimus, and when the donor carried CYP3A4 rs4646437 CT/TT genotype, the recipient CYP3A4 rs4646437 polymorphism also affected on tacrolimus C0/D, although the effect was not significant. CONCLUSION: The large inter-individual variation in tacrolimus concentrations in the early stages after liver transplantation is influenced by genetic polymorphisms of CYP3A7, CYP3A4, and CYP3A5. Recipient (intestinal) CYP3A7, CYP3A4, and CYP3A5 polymorphisms seem to contribute more to such variation than donors. Therefore, the detection of CYP3A polymorphisms in recipients could help to predict the tacrolimus starting dose in the early stages after liver transplantation.

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.