Antitumor effects of iPSC-based cancer vaccine in pancreatic cancer.
Stem cell reports
Induced pluripotent stem cells (iPSCs) and cancer cells share cellular similarities and transcriptomic profiles. Here, we show that an iPSC-based cancer vaccine, comprised of autologous iPSCs and CpG, stimulated cytotoxic antitumor CD8+ Tcell effector and memory responses, induced cancer-specific humoral immune responses, reduced immunosuppressive CD4+ T regulatory cells, and prevented tumor formation in 75% of pancreatic ductal adenocarcinoma (PDAC) mice. We demonstrate that shared gene expression profiles of "iPSC-cancer signature genes" and others are overexpressed in mouse and human iPSC lines, PDAC cells, and multiple human solid tumor types compared with normal tissues. These results support further studies of iPSC vaccination in PDAC in preclinical and clinical models and in other cancer types that have low mutational burdens.
View details for DOI 10.1016/j.stemcr.2021.04.004
View details for PubMedID 33961792
Race and Genetics in Congenital Heart Disease: Application of iPSCs, Omics, and Machine Learning Technologies.
Frontiers in cardiovascular medicine
2021; 8: 635280
Congenital heart disease (CHD) is a multifaceted cardiovascular anomaly that occurs when there are structural abnormalities in the heart before birth. Although various risk factors are known to influence the development of this disease, a full comprehension of the etiology and treatment for different patient populations remains elusive. For instance, racial minorities are disproportionally affected by this disease and typically have worse prognosis, possibly due to environmental and genetic disparities. Although research into CHD has highlighted a wide range of causal factors, the reasons for these differences seen in different patient populations are not fully known. Cardiovascular disease modeling using induced pluripotent stem cells (iPSCs) is a novel approach for investigating possible genetic variants in CHD that may be race specific, making it a valuable tool to help solve the mystery of higher incidence and mortality rates among minorities. Herein, we first review the prevalence, risk factors, and genetics of CHD and then discuss the use of iPSCs, omics, and machine learning technologies to investigate the etiology of CHD and its connection to racial disparities. We also explore the translational potential of iPSC-based disease modeling combined with genome editing and high throughput drug screening platforms.
View details for DOI 10.3389/fcvm.2021.635280
View details for PubMedID 33681306
View details for PubMedCentralID PMC7925393
- Harnessing cell pluripotency for cardiovascular regenerative medicine NATURE BIOMEDICAL ENGINEERING 2018; 2 (6): 392–98
Harnessing cell pluripotency for cardiovascular regenerative medicine.
Nature biomedical engineering
2018; 2 (6): 392–98
Human pluripotent stem cells (hPSCs), in particular embryonic stem cells and induced pluripotent stem cells, have received enormous attention in cardiovascular regenerative medicine owing to their ability to expand and differentiate into functional cardiomyocytes and other cardiovascular cell types. Despite the potential applications of hPSCs for tissue regeneration in patients suffering from cardiovascular disease, whether hPSC-based therapies can be safe and efficacious remains inconclusive, with strong evidence from clinical trials lacking. Critical factors limiting therapeutic efficacy are the degree of maturity and purity of the hPSC-derived differentiated progeny, and the tumorigenic risk associated with residual undifferentiated cells. In this Review, we discuss recent advances in cardiac-cell differentiation from hPSCs and in the direct reprogramming of non-myocyte cells for cardiovascular regenerative applications. We also discuss approaches for the delivery of cells to diseased tissue, and how such advances are contributing to progress in cardiac tissue engineering for tackling heart disease.
View details for PubMedID 31011193
A Rapid, High-Quality, Cost-Effective, Comprehensive and Expandable Targeted Next-Generation Sequencing Assay for Inherited Heart Diseases.
2015; 117 (7): 603-611
Thousands of mutations across >50 genes have been implicated in inherited cardiomyopathies. However, options for sequencing this rapidly evolving gene set are limited because many sequencing services and off-the-shelf kits suffer from slow turnaround, inefficient capture of genomic DNA, and high cost. Furthermore, customization of these assays to cover emerging targets that suit individual needs is often expensive and time consuming.We sought to develop a custom high throughput, clinical-grade next-generation sequencing assay for detecting cardiac disease gene mutations with improved accuracy, flexibility, turnaround, and cost.We used double-stranded probes (complementary long padlock probes), an inexpensive and customizable capture technology, to efficiently capture and amplify the entire coding region and flanking intronic and regulatory sequences of 88 genes and 40 microRNAs associated with inherited cardiomyopathies, congenital heart disease, and cardiac development. Multiplexing 11 samples per sequencing run resulted in a mean base pair coverage of 420, of which 97% had >20× coverage and >99% were concordant with known heterozygous single nucleotide polymorphisms. The assay correctly detected germline variants in 24 individuals and revealed several polymorphic regions in miR-499. Total run time was 3 days at an approximate cost of $100 per sample.Accurate, high-throughput detection of mutations across numerous cardiac genes is achievable with complementary long padlock probe technology. Moreover, this format allows facile insertion of additional probes as more cardiomyopathy and congenital heart disease genes are discovered, giving researchers a powerful new tool for DNA mutation detection and discovery.
View details for DOI 10.1161/CIRCRESAHA.115.306723
View details for PubMedID 26265630
DWI for Renal Mass Characterization: Systematic Review and Meta-Analysis of Diagnostic Test Performance
AMERICAN JOURNAL OF ROENTGENOLOGY
2015; 205 (2): 317-324
The objective of our study was to perform a systematic review and meta-analysis of the test performance of DWI in the characterization of renal masses.We performed searches of three electronic databases for studies on renal mass characterization using DWI. Methodologic quality was assessed for each study. We quantitatively analyzed test performance for three clinical problems: first, benign versus malignant lesions; second, clear cell renal cell carcinoma (RCC) versus other malignancies; and, third, high-versus low-grade clear cell RCCs. We summarized performance as a single pair of sensitivity and specificity values or a summary ROC curve.The studies in the literature were limited in both quantity and quality. For classification of benign versus malignant lesions, four studies with 279 lesions yielded a single summary estimate of 86% sensitivity and 78% specificity. For differentiation of clear cell RCC from other malignancies, five studies showed marked heterogeneity not conducive to meta-analysis. For differentiation of high-from low-grade clear cell RCCs, three studies with 110 lesions showed a threshold effect appropriate for summary ROC construction: The AUC was 0.83.Evidence suggests moderate accuracy of DWI for the prediction of malignancy and high-grade clear cell cancers, whereas DWI performance for ascertaining clear cell histologic grade remains unclear. To develop DWI as a noninvasive approach for the evaluation of solid renal masses, prospective studies with standardized test parameters are needed to better establish DWI performance and its impact on patient outcomes.
View details for DOI 10.2214/AJR.14.13930
View details for Web of Science ID 000358436000030
View details for PubMedID 26204281
Functional MnO nanoclusters for efficient siRNA delivery
2011; 47 (44): 12152-12154
A non-viral gene delivery nanovehicle based on Alkyl-PEI2k capped MnO nanoclusters was synthesized via a simple, facile method and used for efficient siRNA delivery and magnetic resonance imaging.
View details for DOI 10.1039/c1cc15408g
View details for Web of Science ID 000296342800035
View details for PubMedID 21991584
View details for PubMedCentralID PMC4620662