The potential of artificial intelligence-based applications in kidney pathology.

PURPOSE OF REVIEW: The field of pathology is currently undergoing a significant transformation from traditional glass slides to a digital format dependent on whole slide imaging. Transitioning from glass to digital has opened the field to development and application of image analysis technology, commonly deep learning methods (artificial intelligence [AI]) to assist pathologists with tissue examination. Nephropathology is poised to leverage this technology to improve precision, accuracy, and efficiency in clinical practice. RECENT FINDINGS: Through a multidisciplinary approach, nephropathologists, and computer scientists have made significant recent advances in developing AI technology to identify histological structures within whole slide images (segmentation), quantification of histologic structures, prediction of clinical outcomes, and classifying disease. Virtual staining of tissue and automation of electron microscopy imaging are emerging applications with particular significance for nephropathology. SUMMARY: AI applied to image analysis in nephropathology has potential to transform the field by improving diagnostic accuracy and reproducibility, efficiency, and prognostic power. Reimbursement, demonstration of clinical utility, and seamless workflow integration are essential to widespread adoption.

Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens.

Importance: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. Objective: To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. Design, Setting, and Participants: This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. Main Outcomes and Measures: Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists' estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. Results: The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. Conclusions and Relevance: The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard.

Deep learning quantification of percent steatosis in donor liver biopsy frozen sections.

BACKGROUND: Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies. METHODS: We developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequently calculates the percent steatosis. Ninety-six WSI of frozen donor liver sections from our transplant pathology service were annotated for steatosis and used to train (n = 30 WSI) and test (n = 66 WSI) the deep learning model. FINDINGS: The model had good correlation and agreement with the annotation in both the training set (r of 0.88, intraclass correlation coefficient [ICC] of 0.88) and novel input test sets (r = 0.85 and ICC=0.85). These measurements were superior to the estimates of the on-service pathologist at the time of initial evaluation (r = 0.52 and ICC=0.52 for the training set, and r = 0.74 and ICC=0.72 for the test set). INTERPRETATION: Use of this deep learning algorithm could be incorporated into routine pathology workflows for fast, accurate, and reproducible donor liver evaluation. FUNDING: Mid-America Transplant Society.

Resolution of Murine Toxic Hepatic Injury Quantified With Ultrasound Entropy Metrics.

Image-based classification of liver disease generally lacks specificity for distinguishing between acute, resolvable injury and chronic irreversible injury. We propose that ultrasound radiofrequency data acquired in vivo from livers subjected to toxic drug injury can be analyzed with information theoretic detectors to derive entropy metrics, which classify a statistical distribution of pathologic scatterers that dissipate over time as livers heal. Here we exposed 38 C57BL/6 mice to carbon tetrachloride to cause liver damage, and imaged livers in vivo 1, 4, 8, 12 and 18 d after exposure with a broadband 15-MHz probe. Selected entropy metrics manifested monotonic recovery to normal values over time as livers healed, and were correlated directly with progressive restoration of liver architecture by histologic assessment (r2 ≥ 0.95, p < 0.004). Thus, recovery of normal liver microarchitecture after toxic exposure can be delineated sensitively with entropy metrics.

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections.

Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intra-operative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criterion for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter- and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. Encouragingly, the model's performance is robust to slide preparation artifacts associated with frozen section preparation. The model substantially outperforms a model trained on image patches of isolated glomeruli, in terms of both accuracy and speed. The methodology overcomes the technical challenge of applying a pretrained CNN bottleneck model to whole-slide image classification. The traditional patch-based approach, while exhibiting deceptively good performance classifying isolated patches, does not translate successfully to whole-slide image segmentation in this setting. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.

Ultrasound modulates ion channel currents.

Transcranial focused ultrasound (US) has been demonstrated to stimulate neurons in animals and humans, but the mechanism of this effect is unknown. It has been hypothesized that US, a mechanical stimulus, may mediate cellular discharge by activating mechanosensitive ion channels embedded within cellular membranes. To test this hypothesis, we expressed potassium and sodium mechanosensitive ion channels (channels of the two-pore-domain potassium family (K2P) including TREK-1, TREK-2, TRAAK; NaV1.5) in the Xenopus oocyte system. Focused US (10 MHz, 0.3-4.9 W/cm(2)) modulated the currents flowing through the ion channels on average by up to 23%, depending on channel and stimulus intensity. The effects were reversible upon repeated stimulation and were abolished when a channel blocker (ranolazine to block NaV1.5, BaCl2 to block K2P channels) was applied to the solution. These data reveal at the single cell level that focused US modulates the activity of specific ion channels to mediate transmembrane currents. These findings open doors to investigations of the effects of US on ion channels expressed in neurons, retinal cells, or cardiac cells, which may lead to important medical applications. The findings may also pave the way to the development of sonogenetics: a non-invasive, US-based analogue of optogenetics.

Antithrombin nanoparticles inhibit stent thrombosis in ex vivo static and flow models.

OBJECTIVE: Despite significant advances in intravascular stent technology, safe prevention of stent thrombosis over prolonged periods after initial deployment persists as a medical need to decrease device failure. The objective of this project was to assess the potential of perfluorocarbon nanoparticles (NP) conjugated with the direct thrombin inhibitor D-phenylalanyl-L-prolyl-L-arginyl chloromethylketone (PPACK-NP) to inhibit stent thrombosis. METHODS: In a static model of stent thrombosis, 3 × 3-mm pieces of stainless steel coronary stents were cut and adsorbed with thrombin to create a procoagulant surface that would facilitate thrombus development. After treatment with PPACK-NP or control NP, stents were exposed to platelet-poor plasma (PPP) or platelet-rich plasma (PRP) for set time points up to 60 minutes. Measurements of final clot weight in grams were used for assessing the effect of NP treatment on limiting thrombosis. Additionally, groups of stents were exposed to flowing plasma containing various treatments (saline, free PPACK, control NP, and PPACK-NP) and generated thrombi were stained and imaged to investigate the treatment effects of PPACK-NP under flow conditions. RESULTS: The static model of stent thrombosis used in this study indicated a significant reduction in thrombus deposition with PPACK-NP treatment (0.00067 ± 0.00026 g; n = 3) compared with control NP (0.0098 ± 0.0015 g; n = 3; P = .026) in PPP. Exposure to PRP demonstrated similar effects with PPACK-NP treatment (0.00033 ± 0.00012 g; n = 3) vs control NP treatment (0.0045 ± 0.00012 g; n = 3; P = .000017). In additional studies, stents were exposed to both PRP pretreated with vorapaxar and PPACK-NP, which illustrated adjunctive benefit to oral platelet inhibitors for prevention of stent thrombosis. Additionally, an in vitro model of stent thrombosis under flow conditions established that PPACK-NP treatment inhibited thrombus deposition on stents significantly. CONCLUSIONS: This study demonstrates that antithrombin perfluorocarbon NPs exert marked focal antithrombin activity to prevent intravascular stent thrombosis and occlusion.

Delivery of a Protease-Activated Cytolytic Peptide Prodrug by Perfluorocarbon Nanoparticles.

Melittin is a cytolytic peptide derived from bee venom that inserts into lipid membranes and oligomerizes to form membrane pores. Although this peptide is an attractive candidate for treatment of cancers and infectious processes, its nonspecific cytotoxicity and hemolytic activity have limited its therapeutic applications. Several groups have reported the development of cytolytic peptide prodrugs that only exhibit cytotoxicity following activation by site-specific proteases. However, systemic administration of these constructs has proven difficult because of their poor pharmacokinetic properties. Here, we present a platform for the design of protease-activated melittin derivatives that may be used in conjunction with a perfluorocarbon nanoparticle delivery system. Although native melittin was substantially hemolytic (HD50: 1.9 µM) and cytotoxic (IC50: 2.4 µM), the prodrug exhibited 2 orders of magnitude less hemolytic activity (HD50: > 100 µM) and cytotoxicity (IC50: > 100 µM). Incubation with matrix metalloproteinase-9 (MMP-9) led to cleavage of the prodrug at the expected site and restoration of hemolytic activity (HD50: 3.4 µM) and cytotoxicity (IC50: 8.1 µM). Incubation of the prodrug with perfluorocarbon nanoparticles led to stable loading of 10,250 peptides per nanoparticle. Nanoparticle-bound prodrug was also cleaved and activated by MMP-9, albeit at a fourfold slower rate. Intravenous administration of prodrug-loaded nanoparticles in a mouse model of melanoma significantly decreased tumor growth rate (p = 0.01). Because MMPs and other proteases play a key role in cancer invasion and metastasis, this platform holds promise for the development of personalized cancer therapies directed toward a patient's individual protease expression profile.

Entropy vs. Energy Waveform Processing: A Comparison Based on the Heat Equation.

Virtually all modern imaging devices collect electromagnetic or acoustic waves and use the energy carried by these waves to determine pixel values to create what is basically an "energy" picture. However, waves also carry "information", as quantified by some form of entropy, and this may also be used to produce an "information" image. Numerous published studies have demonstrated the advantages of entropy, or "information imaging", over conventional methods. The most sensitive information measure appears to be the joint entropy of the collected wave and a reference signal. The sensitivity of repeated experimental observations of a slowly-changing quantity may be defined as the mean variation (i.e., observed change) divided by mean variance (i.e., noise). Wiener integration permits computation of the required mean values and variances as solutions to the heat equation, permitting estimation of their relative magnitudes. There always exists a reference, such that joint entropy has larger variation and smaller variance than the corresponding quantities for signal energy, matching observations of several studies. Moreover, a general prescription for finding an "optimal" reference for the joint entropy emerges, which also has been validated in several studies.

Rapamycin nanoparticles target defective autophagy in muscular dystrophy to enhance both strength and cardiac function.

Duchenne muscular dystrophy in boys progresses rapidly to severe impairment of muscle function and death in the second or third decade of life. Current supportive therapy with corticosteroids results in a modest increase in strength as a consequence of a general reduction in inflammation, albeit with potential untoward long-term side effects and ultimate failure of the agent to maintain strength. Here, we demonstrate that alternative approaches that rescue defective autophagy in mdx mice, a model of Duchenne muscular dystrophy, with the use of rapamycin-loaded nanoparticles induce a reproducible increase in both skeletal muscle strength and cardiac contractile performance that is not achievable with conventional oral rapamycin, even in pharmacological doses. This increase in physical performance occurs in both young and adult mice, and, surprisingly, even in aged wild-type mice, which sets the stage for consideration of systemic therapies to facilitate improved cell function by autophagic disposal of toxic byproducts of cell death and regeneration.

HIV-1 Nef mediates Pak phosphorylation of Mek1 serine298 and elicits an active phospho-state of Pak2.

The HIV-1 Nef protein brings about increased T cell activity and viral titers through mechanisms that are poorly understood. Nef activity has been described as an enhancer, but not an inducer, of certain signaling pathways that lead to T cell activation and viral production, particularly from resting T cells. The protein has also been found to associate with and promote autophosphorylation of a serine kinase, Pak2, but the Nef-associated kinase level is very low and difficult to study. Here we demonstrate that Nef expression mediates phosphorylation of Mek1 serine298 in T cell lines as well as primary human T cells, thus directly affecting the Erk cascade. This phosphorylation is through a Pak and Rac activity. We also find that Pak2 in Nef expressing cells is phosphorylated on serine192/197, the first biochemical description of the Nef-mediated activation state for this kinase.

Programmable nanoparticle functionalization for in vivo targeting.

The emerging demand for programmable functionalization of existing base nanocarriers necessitates development of an efficient approach for cargo loading that avoids nanoparticle redesign for each individual application. Herein, we demonstrate in vivo a postformulation strategy for lipidic nanocarrier functionalization with the use of a linker peptide, which rapidly and stably integrates cargos into lipidic membranes of nanocarriers after simple mixing through a self-assembling process. We exemplified this strategy by generating a VCAM-1-targeted perfluorocarbon nanoparticle for in vivo targeting in atherosclerosis (ApoE-deficient) and breast cancer (STAT-1-deficient) models. In the atherosclerotic model, a 4.1-fold augmentation in binding to affected aortas was observed for targeted vs. nontargeted nanoparticles (P<0.0298). Likewise, in the breast cancer model, a 4.9-fold increase in the nanoparticle signal from tumor vasculature was observed for targeted vs. nontargeted nanoparticles (P<0.0216). In each case, the nanoparticle was registered with fluorine ((19)F) magnetic resonance spectroscopy of the nanoparticle perfluorocarbon core, yielding a quantitative estimate of the number of tissue-bound nanoparticles. Because other common nanocarriers with lipid coatings (e.g., liposomes, micelles, etc.) can employ this strategy, this peptide linker postformulation approach is applicable to more than half of the available nanosystems currently in clinical trials or clinical uses.

Drosophila ryanodine receptors mediate general anesthesia by halothane.

BACKGROUND: Although in vitro studies have identified numerous possible targets, the molecules that mediate the in vivo effects of volatile anesthetics remain largely unknown. The mammalian ryanodine receptor (Ryr) is a known halothane target, and the authors hypothesized that it has a central role in anesthesia. METHODS: Gene function of the Drosophila Ryr (dRyr) was manipulated in the whole body or in specific tissues using a collection of mutants and transgenes, and responses to halothane were measured with a reactive climbing assay. Cellular responses to halothane were studied using Ca imaging and patch clamp electrophysiology. RESULTS: Halothane potency strongly correlates with dRyr gene copy number, and missense mutations in regions known to be functionally important in the mammalian Ryrs gene cause dominant hypersensitivity. Tissue-specific manipulation of dRyr shows that expression in neurons and glia, but not muscle, mediates halothane sensitivity. In cultured cells, halothane-induced Ca efflux is strictly dRyr-dependent, suggesting a close interaction between halothane and dRyr. Ca imaging and electrophysiology of Drosophila central neurons reveal halothane-induced Ca flux that is altered in dRyr mutants and correlates with strong hyperpolarization. CONCLUSIONS: In Drosophila, neurally expressed dRyr mediates a substantial proportion of the anesthetic effects of halothane in vivo, is potently activated by halothane in vitro, and activates an inhibitory conductance. The authors' results provide support for Ryr as an important mediator of immobilization by volatile anesthetics.

Postformulation peptide drug loading of nanostructures.

Cytolytic peptides have commanded attention for their anticancer potential because the membrane-disrupting function that produces cell death is less likely to be overcome by resistant mutations. Congruently, peptides that are involved in molecular recognition and biological activities become attractive therapeutic candidates because of their high specificity, better affinity, reduced immunogenicity, and reduced off target toxicity. However, problems of inadequate delivery, rapid deactivation in vivo, and poor bioavailability have limited clinical application. Therefore, peptide drug development for clinical use requires an appropriate combination of an effective therapeutic peptide and a robust delivery methodology. In this chapter, we describe methods for the postformulation insertion of peptide drugs into lipidic nanostructures, the physical characterization of peptide-nanostructure complexes, and the evaluation of their therapeutic effectiveness both in vitro and in vivo.

Use of smoothing splines for analysis of backscattered ultrasonic waveforms: application to monitoring of steroid treatment of dystrophic mice.

Duchenne muscular dystrophy (DMD) is an X-linked genetic disease characterized by progressive weakness and wasting of skeletal and cardiac muscle; boys present with weakness by the age of 5 years and, if left untreated, are unable to walk without assistance by the age of 10 years. Therapy for DMD has been primarily palliative, with oral steroids emerging as a first-line approach even though this treatment has serious side-effects. Consequently, low-cost imaging technology suitable for improved diagnosis and treatment monitoring of DMD would be of great value, especially in remote and underserved areas. Previously, we reported use of the logarithm of the signal energy, log [E(f)], and a new method for ultrasound signal characterization using entropy, H(f), to monitor prednisolone treatment of skeletal muscle in a dystrophin-deficient mouse model. Three groups were studied: mdx mice treated with prednisolone, a control group of mdx mice treated with saline, and a control group of wild-type mice treated with saline. It was found that both log [E(f)] and H(f) were required to statistically differentiate the three groups. In the current study, we show that preprocessing of the raw ultrasound using optimal smoothing splines before computation of either log [E(f)] or a rapidly computable variant of Hf, denoted I(f,∞), permits delineation of all three groups by either metric alone. This opens the way to the ultimate goal of this study, which is identification and implementation of new diagnostically sensitive algorithms on the new generation of low-cost hand-held clinical ultrasonic imaging systems.

Improved signal processing to detect cancer by ultrasonic molecular imaging of targeted nanoparticles.

In several investigations of molecular imaging of angiogenic neovasculature using a targeted contrast agent, Renyi entropy [I(f)(r)] and a limiting form of Renyi entropy (I(f,∞)) exhibited significantly more sensitivity to subtle changes in scattering architecture than energy-based methods. Many of these studies required the fitting of a cubic spline to backscattered waveforms prior to calculation of entropy [either I(f)(r) or I(f,∞)]. In this study, it is shown that the robustness of I(f,∞) may be improved by using a smoothing spline. Results are presented showing the impact of different smoothing parameters. In addition, if smoothing is preceded by low-pass filtering of the waveforms, further improvements may be obtained.

Synthesis of NanoQ, a copper-based contrast agent for high-resolution magnetic resonance imaging characterization of human thrombus.

A new site-targeted molecular imaging contrast agent based on a nanocolloidal suspension of lipid-encapsulated, organically soluble divalent copper has been developed. Concentrating a high payload of divalent copper ions per nanoparticle, this agent provides a high per-particle r1 relaxivity, allowing sensitive detection in T1-weighted magnetic resonance imaging when targeted to fibrin clots in vitro. The particle also exhibits a defined clearance and safety profile in vivo.

A fibrin-specific thrombolytic nanomedicine approach to acute ischemic stroke.

AIM: To develop a fibrin-specific urokinase nanomedicine thrombolytic agent. MATERIALS & METHODS: In vitro fibrin-clot dissolution studies were utilized to develop and characterize simultaneous coupling and loading of anti-fibrin monoclonal antibody and urokinase onto perfluorocarbon nanoparticle (NP) surface. In vivo pharmacokinetics and fibrin-specific targeting of the nanolytic agent was studied in dogs. RESULTS: Simultaneous coupling of up to 40 anti-fibrin antibodies and 400 urokinase enzymes per perfluorocarbon NP produced an effective targeted nanolytic agent with no significant surface protein-protein interference. Fibrin clot dissolution was not improved by increasing homing capacity from 10 to 40 antibodies/NP, but increasing enzymatic payload from 100 to 400/NP resulted in maximized lytic effect. Fluorescent microscopy showed that rhodamine-labeled urokinase nanoparticles densely decorated the intraluminal thrombus in canine clots in vivo analogous to the fibrin pattern, while an irrelevant-targeted agent had negligible binding. CONCLUSION: This agent offers a vascularly constrained, simple to administer, low-dose nanomedicine approach that may present an attractive alternative for treating acute stroke victims.

Using artificial fluorescent particles as tracers of livestock wastes within an agricultural catchment.

Evidence for the movement of agricultural slurry and associated pollutants into surface waters is often anecdotal, particularly with relation to its 'particulate' components which receive less attention than 'bio-available' soluble phases. To assess the extent of movement of slurry particles artificial fluorescent particles were mixed with slurry and applied to a field sub-catchment within a headwater catchment. Particles were 2-60 µm in diameter and two different densities, 2.7 and 1.2 g cm(-3) representing 'inorganic' and 'organic' material. Water samples from the field and catchment outlet were collected during two storm events following slurry application and analysed for particle and suspended sediment concentrations (SSC). SSC from the field and catchment outlet always formed clockwise hysteresis loops indicating sediment exhaustion and particles of the two densities were always found to be positively correlated. Particles from the field formed clockwise hysteresis loops during the first discharge event after slurry application, but anti-clockwise hysteresis loops during the second monitored event which indicated a depletion of readily mobilisable particles. Particles from the catchment outlet always formed anticlockwise hysteresis loops. Particle size became finer spatially, between field and catchment outlet, and temporally, between successive storm events. The results indicate that slurry particles may be readily transported within catchments but that different areas may contribute to pollutant loads long after the main peak in SSC has passed. The density of the particles did not appear to have any effect on particle transport however the size of the particles may play a more important role in the 2-60 µm range.

Transplantation of mouse HSCs genetically modified to express a CD4-restricted TCR results in long-term immunity that destroys tumors and initiates spontaneous autoimmunity.

The development of effective cancer immunotherapies has been consistently hampered by several factors, including an inability to instigate long-term effective functional antitumor immunity. This is particularly true for immunotherapies that focus on the adoptive transfer of activated or genetically modified mature CD8+ T cells. In this study, we sought to alter and enhance long-term host immunity by genetically modifying, then transplanting, mouse HSCs. We first cloned a previously identified tumor-reactive HLA-DR4-restricted CD4+ TCR specific for the melanocyte differentiation antigen tyrosinase-related protein 1 (Tyrp1), then constructed both a high-expression lentivirus vector and a TCR-transgenic mouse expressing the genes encoding this TCR. Using these tools, we demonstrated that both mouse and human HSCs established durable, high-efficiency TCR gene transfer following long-term transplantation into lethally irradiated mice transgenic for HLA-DR4. Recipients of genetically modified mouse HSCs developed spontaneous autoimmune vitiligo that was associated with the presence of a Th1-polarized memory effector CD4+ T cell population that expressed the Tyrp1-specific TCR. Most importantly, large numbers of CD4+ T cells expressing the Tyrp1-specific TCR were detected in secondary HLA-DR4-transgenic transplant recipients, and these mice were able to destroy subcutaneously administered melanoma cells without the aid of vaccination, immune modulation, or cytokine administration. These results demonstrate the creation of what we believe to be a novel translational model of durable lentiviral gene transfer that results in long-term effective immunity.