Utility analyses of AVITI sequencing chemistry.

BACKGROUND: DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist. RESULTS: Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate. CONCLUSION: These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

Immuno-targeting the ectopic phosphorylation sites of PDGFRA generated by MAN2A1-FER fusion in HCC.

BACKGROUND: HCC is one of the most lethal cancers for humans. Mannosidase alpha class 2A member 1 (MAN2A1)-FER is one of the most frequent oncogenic fusion genes in HCC. In this report, we showed that MAN2A1-FER ectopically phosphorylated the extracellular domains of PDGFRA, MET, AXL, and N-cadherin. The ectopic phosphorylation of these transmembrane proteins led to the activation of their kinase activities and initiated the activation cascades of their downstream signaling molecules. METHODS: A panel of mouse monoclonal antibodies was developed to recognize the ectopic phosphorylation sites of PDGFRA. RESULTS AND CONCLUSIONS: The analyses showed that these antibodies bound to the specific phosphotyrosine epitopes in the extracellular domain of PDGFRA with high affinity and specificity. The treatment of MAN2A1-FER-positive cancer HUH7 with one of the antibodies called 2-3B-G8 led to the deactivation of cell growth signaling pathways and cell growth arrest while having minimal impact on HUH7ko cells where MAN2A1-FER expression was disrupted. The treatment of 2-3B-G8 antibody also led to a large number of cell deaths of MAN2A1-FER-positive cancer cells such as HUH7, HEPG2, SNU449, etc., while the same treatment had no impact on HUH7ko cells. When severe combined immunodeficiency mice xenografted with HEPG2 or HUH7 were treated with monomethyl auristatin E-conjugated 2-3B-G8 antibody, it slowed the progression of tumor growth, eliminated the metastasis, and reduced the mortality, in comparison with the controls. Targeting the cancer-specific ectopic phosphorylation sites of PDGFRA induced by MAN2A1-FER may hold promise as an effective treatment for liver cancer.

Utility Analyses of AVITI Sequencing Chemistry.

Background: DNA sequencing is a critical tool in modern biology. Over the last two decades, it has been revolutionized by the advent of massively parallel sequencing, leading to significant advances in the genome and transcriptome sequencing of various organisms. Nevertheless, challenges with accuracy, lack of competitive options and prohibitive costs associated with high throughput parallel short-read sequencing persist. Results: Here, we conduct a comparative analysis using matched DNA and RNA short-reads assays between Element Biosciences' AVITI and Illumina's NextSeq 550 chemistries. Similar comparisons were evaluated for synthetic long-read sequencing for RNA and targeted single-cell transcripts between the AVITI and Illumina's NovaSeq 6000. For both DNA and RNA short-read applications, the study found that the AVITI produced significantly higher per sequence quality scores. For PCR-free DNA libraries, we observed an average 89.7% lower experimentally determined error rate when using the AVITI chemistry, compared to the NextSeq 550. For short-read RNA quantification, AVITI platform had an average of 32.5% lower error rate than that for NextSeq 550. With regards to synthetic long-read mRNA and targeted synthetic long read single cell mRNA sequencing, both platforms' respective chemistries performed comparably in quantification of genes and isoforms. The AVITI displayed a marginally lower error rate for long reads, with fewer chemistry-specific errors and a higher mutation detection rate. Conclusion: These results point to the potential of the AVITI platform as a competitive candidate in high-throughput short read sequencing analyses when juxtaposed with the Illumina NextSeq 550.

Therapeutic targeting at genome mutations of liver cancer by the insertion of HSV1 thymidine kinase through Cas9-mediated editing.

BACKGROUND: Liver cancer is one of the most lethal malignancies for humans. The treatment options for advanced-stage liver cancer remain limited. A new treatment is urgently needed to reduce the mortality of the disease. METHODS: In this report, we developed a technology for mutation site insertion of a suicide gene (herpes simplex virus type 1- thymidine kinase) based on type II CRISPR RNA-guided endonuclease Cas9-mediated genome editing to treat liver cancers. RESULTS: We applied the strategy to 3 different mutations: S45P mutation of catenin beta 1, chromosome breakpoint of solute carrier family 45 member 2-alpha-methylacyl-CoA racemase gene fusion, and V235G mutation of SAFB-like transcription modulator. The results showed that the herpes simplex virus type 1-thymidine kinase insertion rate at the S45P mutation site of catenin beta 1 reached 77.8%, while the insertion rates at the breakpoint of solute carrier family 45 member 2 - alpha-methylacyl-CoA racemase gene fusion were 95.1%-98.7%, and the insertion at V235G of SAFB-like transcription modulator was 51.4%. When these targeting reagents were applied to treat mouse spontaneous liver cancer induced by catenin beta 1S45P or solute carrier family 45 member 2-alpha-methylacyl-CoA racemase, the mice experienced reduced tumor burden and increased survival rate. Similar results were also obtained for the xenografted liver cancer model: Significant reduction of tumor volume, reduction of metastasis rate, and improved survival were found in mice treated with the targeting reagent, in comparison with the control-treated groups. CONCLUSIONS: Our studies suggested that mutation targeting may hold promise as a versatile and effective approach to treating liver cancers.

Serum Fusion Transcripts to Assess the Risk of Hepatocellular Carcinoma and the Impact of Cancer Treatment through Machine Learning.

Hepatocellular carcinoma (HCC) is one of the most fatal malignancies. Early diagnosis of HCC is crucial in reducing the risk for mortality. This study analyzed a panel of nine fusion transcripts in serum samples from 61 patients with HCC and 75 patients with non-HCC conditions, using TaqMan real-time quantitative RT-PCR. Seven of the nine fusions frequently detected in patients with HCC included: MAN2A1-FER (100%), SLC45A2-AMACR (62.3%), ZMPSTE24-ZMYM4 (62.3%), PTEN-NOLC1 (57.4%), CCNH-C5orf30 (55.7%), STAMBPL1-FAS (26.2%), and PCMTD1-SNTG1 (16.4%). Machine-learning models were constructed based on serum fusion-gene levels to predict HCC in the training cohort, using the leave-one-out cross-validation approach. One machine-learning model, called the four fusion genes logistic regression model (MAN2A1-FER≤40, CCNH-C5orf30≤38, SLC45A2-AMACR≤41, and PTEN-NOLC1≤40), produced accuracies of 91.5% and 83.3% in the training and testing cohorts, respectively. When serum α-fetal protein level was incorporated into the machine-learning model, a two fusion gene (MAN2A1-FER≤40, CCNH-C5orf30≤38) + α-fetal protein logistic regression model was found to generate an accuracy of 94.8% in the training cohort. The same model resulted in 95% accuracy in both the testing and combined cohorts. Cancer treatment was associated with reduced levels of most of the serum fusion transcripts. Serum fusion-gene machine-learning models may serve as important tools in screening for HCC and in monitoring the impact of HCC treatment.

Restoring glucose balance: Conditional HMGB1 knockdown mitigates hyperglycemia in a Streptozotocin induced mouse model.

Diabetes mellitus (DM) poses a significant global health burden, with hyperglycemia being a primary contributor to complications and high morbidity associated with this disorder. Existing glucose management strategies have shown suboptimal effectiveness, necessitating alternative approaches. In this study, we explored the role of high mobility group box 1 (HMGB1) in hyperglycemia, a protein implicated in initiating inflammation and strongly correlated with DM onset and progression. We hypothesized that HMGB1 knockdown will mitigate hyperglycemia severity and enhance glucose tolerance. To test this hypothesis, we utilized a novel inducible HMGB1 knockout (iHMGB1 KO) mouse model exhibiting systemic HMGB1 knockdown. Hyperglycemic phenotype was induced using low dose streptozotocin (STZ) injections, followed by longitudinal glucose measurements and oral glucose tolerance tests to evaluate the effect of HMGB1 knockdown on glucose metabolism. Our findings showed a substantial reduction in glucose levels and enhanced glucose tolerance in HMGB1 knockdown mice. Additionally, we performed RNA sequencing analyses, which identified potential alternations in genes and molecular pathways within the liver and skeletal muscle tissue that may account for the in vivo phenotypic changes observed in hyperglycemic mice following HMGB1 knockdown. In conclusion, our present study delivers the first direct evidence of a causal relationship between systemic HMGB1 knockdown and hyperglycemia in vivo, an association that had remained unexamined prior to this research. This discovery positions HMGB1 knockdown as a potentially efficacious therapeutic target for addressing hyperglycemia and, by extension, the DM epidemic. Furthermore, we have revealed potential underlying mechanisms, establishing the essential groundwork for subsequent in-depth mechanistic investigations focused on further elucidating and harnessing the promising therapeutic potential of HMGB1 in DM management.

Long-read single-cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells.

The protein diversity of mammalian cells is determined by arrays of isoforms from genes. Genetic mutation is essential in species evolution and cancer development. Accurate long-read transcriptome sequencing at single-cell level is required to decipher the spectrum of protein expressions in mammalian organisms. In this report, we developed a synthetic long-read single-cell sequencing technology based on LOOPSeq technique. We applied this technology to analyze 447 transcriptomes of hepatocellular carcinoma (HCC) and benign liver from an individual. Through Uniform Manifold Approximation and Projection analysis, we identified a panel of mutation mRNA isoforms highly specific to HCC cells. The evolution pathways that led to the hyper-mutation clusters in single human leukocyte antigen molecules were identified. Novel fusion transcripts were detected. The combination of gene expressions, fusion gene transcripts, and mutation gene expressions significantly improved the classification of liver cancer cells versus benign hepatocytes. In conclusion, LOOPSeq single-cell technology may hold promise to provide a new level of precision analysis on the mammalian transcriptome.

Bimetallic Ions Functionalized Metal-Organic-Framework Nanozyme for Tumor Microenvironment Regulating and Enhanced Photodynamic Therapy for Hypoxic Tumor.

Photodynamic therapy (PDT), as a light irradiation inducing reactive oxygen species (ROS) generation for cancer treatment, offers facile and promising solutions with respect to spatiotemporal control of ROS generation, and minimizes the systemic toxicity and side effects for highly precise tumor therapy. However, the PDT efficiency is often severely compromised by the complex tumor microenvironment (TME), such as the hypoxic condition and overexpressed antioxidants. Here, for the first time, a bimetallic ion-modified metal-organic framework nanozyme (Zr4+ -MOF-Ru3+ /Pt4+ -Ce6@HA, ZMRPC@HA) is designed. ZMRPC@HA with catalase (CAT) and glutathione oxidase (GSHOx) mimetic activities, can efficiently regulate TME by generation of O2 and deplete the GSH synergistically for enhancing the long-term PDT efficacy toward the hypoxic tumor. The in vitro cell inhibition and in vivo on tumor xenograft evaluations demonstrate the PDT strategy by using ZMRPC@HA can successfully inhibit the differentiation and proliferation of tumor cells under a 660 nm laser irradiation in deep tissues. These findings open a new avenue for the design of multimetallic ions functionalized MOF-based nanozymes with multienzyme mimetic activities toward the antitumor and various other biological applications.

Long-read single-cell sequencing reveals expressions of hypermutation clusters of isoforms in human liver cancer cells.

The protein diversity of mammalian cells is determined by arrays of isoforms from genes. Genetic mutation is essential in species evolution and cancer development. Accurate Long-read transcriptome sequencing at single-cell level is required to decipher the spectrum of protein expressions in mammalian organisms. In this report, we developed a synthetic long-read single-cell sequencing technology based on LOOPseq technique. We applied this technology to analyze 447 transcriptomes of hepatocellular carcinoma (HCC) and benign liver from an individual. Through Uniform Manifold Approximation and Projection (UMAP) analysis, we identified a panel of mutation mRNA isoforms highly specific to HCC cells. The evolution pathways that led to the hyper-mutation clusters in single human leukocyte antigen (HLA) molecules were identified. Novel fusion transcripts were detected. The combination of gene expressions, fusion gene transcripts, and mutation gene expressions significantly improved the classification of liver cancer cells versus benign hepatocytes. In conclusion, LOOPseq single-cell technology may hold promise to provide a new level of precision analysis on the mammalian transcriptome.

Effects of the circulating environment of COVID-19 on platelet and neutrophil behavior.

Introduction: Thromboinflammatory complications are well described sequalae of Coronavirus Disease 2019 (COVID-19), and there is evidence of both hyperreactive platelet and inflammatory neutrophil biology that contributes to the thromoinflammatory milieu. It has been demonstrated in other thromboinflammatory diseases that the circulating environment may affect cellular behavior, but what role this environment exerts on platelets and neutrophils in COVID-19 remains unknown. We tested the hypotheses that 1) plasma from COVID-19 patients can induce a prothrombotic platelet functional phenotype, and 2) contents released from platelets (platelet releasate) from COVID-19 patients can induce a proinflammatory neutrophil phenotype. Methods: We treated platelets with COVID-19 patient and disease control plasma, and measured their aggregation response to collagen and adhesion in a microfluidic parallel plate flow chamber coated with collagen and thromboplastin. We exposed healthy neutrophils to platelet releasate from COVID-19 patients and disease controls and measured neutrophil extracellular trap formation and performed RNA sequencing. Results: We found that COVID-19 patient plasma promoted auto-aggregation, thereby reducing response to further stimulation ex-vivo. Neither disease condition increased the number of platelets adhered to a collagen and thromboplastin coated parallel plate flow chamber, but both markedly reduced platelet size. COVID-19 patient platelet releasate increased myeloperoxidasedeoxyribonucleic acid complexes and induced changes to neutrophil gene expression. Discussion: Together these results suggest aspects of the soluble environment circulating platelets, and that the contents released from those neutrophil behavior independent of direct cellular contact.

Fusion Gene Detection in Prostate Cancer Samples Enhances the Prediction of Prostate Cancer Clinical Outcomes from Radical Prostatectomy through Machine Learning in a Multi-Institutional Analysis.

Prostate cancer remains one of the most fatal malignancies in men in the United States. Predicting the course of prostate cancer is challenging given that only a fraction of prostate cancer patients experience cancer recurrence after radical prostatectomy or radiation therapy. This study examined the expressions of 14 fusion genes in 607 prostate cancer samples from the University of Pittsburgh, Stanford University, and the University of Wisconsin-Madison. The profiling of 14 fusion genes was integrated with Gleason score of the primary prostate cancer and serum prostate-specific antigen level to develop machine-learning models to predict the recurrence of prostate cancer after radical prostatectomy. Machine-learning algorithms were developed by analysis of the data from the University of Pittsburgh cohort as a training set using the leave-one-out cross-validation method. These algorithms were then applied to the data set from the combined Stanford/Wisconsin cohort (testing set). The results showed that the addition of fusion gene profiling consistently improved the prediction accuracy rate of prostate cancer recurrence by Gleason score, serum prostate-specific antigen level, or a combination of both. These improvements occurred in both the training and testing cohorts and were corroborated by multiple models.

Oncogenic Activity of Solute Carrier Family 45 Member 2 and Alpha-Methylacyl-Coenzyme A Racemase Gene Fusion Is Mediated by Mitogen-Activated Protein Kinase.

Chromosome rearrangement is one of the hallmarks of human malignancies. Gene fusion is one of the consequences of chromosome rearrangements. In this report, we show that gene fusion between solute carrier family 45 member 2 (SLC45A2) and alpha-methylacyl-coenzyme A racemase (AMACR) occurs in eight different types of human malignancies, with frequencies ranging from 45% to 97%. The chimeric protein is translocated to the lysosomal membrane and activates the extracellular signal-regulated kinase signaling cascade. The fusion protein promotes cell growth, accelerates migration, resists serum starvation-induced cell death, and is essential for cancer growth in mouse xenograft cancer models. Introduction of SLC45A2-AMACR into the mouse liver using a sleeping beauty transposon system and somatic knockout of phosphatase and TENsin homolog (Pten) generated spontaneous liver cancers within a short period. Conclusion: The gene fusion between SLC45A2 and AMACR may be a driving event for human liver cancer development.

Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients.

Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole-exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave-one-out cross-validation analysis. When the transcriptome analysis was combined with Milan criteria using the k-top scoring pairs (k-TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant.

Detection of fusion gene transcripts in the blood samples of prostate cancer patients.

Prostate cancer remains one of the most lethal cancers for men in the United States. The study aims to detect fusion transcripts in the blood samples of prostate cancer patients. We analyzed nine fusion transcripts including MAN2A1-FER, SLC45A2-AMACR, TRMT11-GRIK2, CCNH-C5orf30, mTOR-TP53BP1, KDM4-AC011523.2, TMEM135-CCDC67, LRRC59-FLJ60017 and Pten-NOLC1147 in the blood samples from 147 prostate cancer patients and 14 healthy individuals, using Taqman RT-PCR and Sanger's sequencing. Similar analyses were also performed on 25 matched prostate cancer samples for matched-sample evaluation. Eighty-two percent blood samples from the prostate cancer patients were positive for MAN2A1-FER transcript, while 41.5% and 38.8% blood samples from the prostate cancer patients were positive for SLC45A2-AMACR and Pten-NOLC1, respectively. CCNH-c5orf30 and mTOR-TP53BP1 had low detection rates, positive in only 5.4% and 4% of the blood samples from the prostate cancer patients. Only 2 blood samples were positive for KDM4B-AC011523.2 transcript. Overall, 89.8% patients were positive for at least one fusion transcript in their blood samples. The statistical analysis showed varied sensitivity of fusion transcript detection in the blood based on the types of fusions. In contrast, the blood samples from all healthy individuals were negative for the fusion transcripts. Detection of fusion transcripts in the blood samples of the prostate cancer patients may be a fast and cost-effective way to detect prostate cancer.

Targeted transcriptome analysis using synthetic long read sequencing uncovers isoform reprograming in the progression of colon cancer.

The characterization of human gene expression is limited by short read lengths, high error rates and large input requirements. Here, we used a synthetic long read (SLR) sequencing approach, LoopSeq, to generate accurate sequencing reads that span full length transcripts using standard short read data. LoopSeq identified isoforms from control samples with 99.4% accuracy and a 0.01% per-base error rate, exceeding the accuracy reported for other long-read technologies. Applied to targeted transcriptome sequencing from colon cancers and their metastatic counterparts, LoopSeq revealed large scale isoform redistributions from benign colon mucosa to primary colon cancer and metastatic cancer and identified several previously unknown fusion isoforms. Strikingly, single nucleotide variants (SNVs) occurred dominantly in specific isoforms and some SNVs underwent isoform switching in cancer progression. The ability to use short reads to generate accurate long-read data as the raw unit of information holds promise as a widely accessible approach in transcriptome sequencing.

Pten-NOLC1 fusion promotes cancers involving MET and EGFR signalings.

Inactivation of Pten gene through deletions and mutations leading to excessive pro-growth signaling pathway activations frequently occurs in cancers. Here, we report a Pten derived pro-cancer growth gene fusion Pten-NOLC1 originated from a chr10 genome rearrangement and identified through a transcriptome sequencing analysis of human cancers. Pten-NOLC1 fusion is present in primary human cancer samples and cancer cell lines from different organs. The product of Pten-NOLC1 is a nuclear protein that interacts and activates promoters of EGFR, c-MET, and their signaling molecules. Pten-NOLC1 promotes cancer proliferation, growth, invasion, and metastasis, and reduces the survival of animals xenografted with Pten-NOLC1-expressing cancer cells. Genomic disruption of Pten-NOLC1 induces cancer cell death, while genomic integration of this fusion gene into the liver coupled with somatic Pten deletion produces spontaneous liver cancers in mice. Our studies indicate that Pten-NOLC1 gene fusion is a driver for human cancers.

Genetic Variation and Preliminary Indications of Divergent Niche Adaptation in Cryptic Clade II of Escherichia.

The evolution, habitat, and lifestyle of the cryptic clade II of Escherichia, which were first recovered at low frequency from non-human hosts and later from external environments, were poorly understood. Here, the genomes of selected strains were analyzed for preliminary indications of ecological differentiation within their population. We adopted the delta bitscore metrics to detect functional divergence of their orthologous genes and trained a random forest classifier to differentiate the genomes according to habitats (gastrointestinal vs external environment). Model was built with inclusion of other Escherichia genomes previously demonstrated to have exhibited genomic traits of adaptation to one of the habitats. Overall, gene degradation was more prominent in the gastrointestinal strains. The trained model correctly classified the genomes, identifying a set of predictor genes that were informative of habitat association. Functional divergence in many of these genes were reflective of ecological divergence. Accuracy of the trained model was confirmed by its correct prediction of the habitats of an independent set of strains with known habitat association. In summary, the cryptic clade II of Escherichia displayed genomic signatures that are consistent with divergent adaptation to gastrointestinal and external environments.

Migration sources and pathways of the pest species Sogatella furcifera in Yunnan, China, and across the border inferred from DNA and wind analyses.

The migration sources and pathways of Sogatella furcifera (Horváth) in topologically complex regions like Yunnan, China, and adjacent montane areas have long been a challenging task and a bottleneck in effective pest forecast and control. The present research reinvestigated this issue using a combination of mtDNA and long-term historical wind field data in an attempt to provide new insights. Genetic analyses showed that the 60 populations of S. furcufera collected across Myanmar, Thailand, Laos, Vietnam, Yunnan, Guizhou, and Sichuan lack genetic structure and geographic isolation, while spatial analysis of haplotype and diversity indices discovered geographic relevance between populations. Migration rate analysis combined with high-resolution 10-year wind field analysis detected the following migration sources, pathways, and impacted areas which could explain the outbreak pattern in Yunnan. (a) Dominating stepwise northward migrations originated from northern Indochina, southern Yunnan, and central-eastern Yunnan, impacting their northern areas. (b) Concurring summer-autumn southward (return) migration originated from nearly all latitude belts of Sichuan and Yunnan mainly impacting central and southern Yunnan. (c) Regular eastward and summer-autumn westward migrations across Yunnan. The northward migration reflects the temporal rhythm of gradual outbreaks from the south to the north in a year, while the return migration may explain the repeated or very severe outbreaks in the impacted areas. To form a better pest forecast and control network, attention must also be paid to the northern part of Yunnan to suppress the impact of return migration in summers and autumns.

Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study.

PURPOSE: We propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence. MATERIALS AND METHODS: Fifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected. Each pre-contrast image was voxel-wise subtracted from the co-registered post-contrast image. Dataset was randomly split into training, and testing on a 7:3 ratio, accordingly subjected to a five fold cross validation. Best feature subsets were selected by Pearson correlation coefficient and recursive feature elimination, whereupon a radiomics classifier was built with SVM. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: In all, 186 features were extracted on each subtraction map. Top nine T1WI subtraction features, top thirteen T2 FLAIR subtraction features and top thirteen combination features were selected to build optimal SVM classifiers accordingly. The accuracies/AUCs/sensitivity/specificity/PPV/NPV of SVM based on sole T1WI subtraction were 80.00%/80.00% (CI: 0.5370-1.0000)/100%/70.00%/62.50%/100%. Those results of SVM based on sole T2 FLAIR subtraction were 86.67%/84.00% (CI: 0.5962-1.0000)/100%/80%/71.43%/100%. Those results of SVM based on both T1WI subtraction and T2 FLAIR subtraction were 93.33%/94.00% (CI: 0.7778-1.0000)/100%/90%/83.33%/100%, respectively. CONCLUSION: Pre- and post-contrast T2 FLAIR subtraction provided added value for diagnosis between recurrence and TRE. SVM based on a combination of T1WI and T2 FLAIR subtraction maps was superior to the sole use of T1WI or T2 FLAIR for differentiating TRE from recurrence. The SVM classifier based on combination of pre-and post-contrast subtraction T2 FLAIR and T1WI imaging allowed for the accurate differential diagnosis of TRE from recurrence, which is of paramount importance for treatment management of postoperative glioma patients after radiation therapy.

Detection of fusion transcripts in the serum samples of patients with hepatocellular carcinoma.

Hepatocellular carcinoma is one of the most lethal cancers in the United States. Early detection of the disease is crucial for reducing the mortality of this malignancy. Recently, we identified a panel of fusion genes present in several types of human cancers, including hepatocellular carcinoma. Among 8 fusion genes, MAN2A1-FER, TRMT11-GRIK2 and CCNH-C5orf30 appear most frequently in hepatocellular carcinoma samples. In this study, we showed that the fusion transcripts of MAN2A1-FER, CCNH-C5orf30 and SLC45A2-AMACR were detected in the serum samples of liver cancer patients as circulating cell-free RNA. The distributions of these gene fusion RNA fragments largely matched those of the primary HCC samples. In contrast, the sera of all healthy individuals free of human malignancies were shown to be negative for these fusion genes. These results suggest that gene fusion RNA is frequently shed from liver cancer cells. The detection of serum cell-free fusion transcripts may provide a new approach to aid in the diagnosis, follow-up or therapy of liver cancers.