Robots have frequently been used for prostatectomy andhysterectomy, whereas their application in cardiac surgery is confined to specialized,high-volume centresRobotic cardiac surgery utilizes small port incisions avoiding the need for a fullsternotomy. This provides benefits in terms of less pain, less bleeding, earlierdischarge, quicker recovery, and improved cosmesis. Traditional, video-assisted, orlaparoscopic surgeries have many of these advantages, but they have technicallimitations due to the use of long-shafted instruments and the fulcrum effect..Robotic surgery has technical advantages as it improves dexterity by allowing theinstruments to move with six degrees of freedom. Other advantages include avoidance oftremor and ambidexterity. Additionally, the three-dimensional (3-D) high-definitionimaging greatly improves visualization. The enhanced visual feedback, throughobservation of tissue displacement and deformation, substitutes for the lack of tactilefeedback.In cardiac surgery, robotic technology has most commonly been used to perform mitralvalve surgery and coronary artery bypass surgeryThe enhanced 3-D imaging provided by robotic surgery is especially useful in mitralvalve surgery as it provides unparalleled visualization of the valve leaflets andthe sub-valvular structures.®) occurred in 1998, carried out by Carpentier, and thissystem gained the Food and Drug Administration approval for mitral valve surgery in2002.Mihaljevic et al. compared the robotic mitral valve repair (n=261)with complete sternotomy (n=114), partial sternotomy (n=170), and mini-thoracotomyapproach (n=114). There were no significant differences between the groups withregard to postoperative mortality, pulmonary complications, neurologicalcomplications, and renal failure rates. The incidence of atrial fibrillation andpleural effusion was the lowest in the robotic group, which led to a significantreduction in length of hospital stay compared to other groups. However, thecardiopulmonary bypass time was significantly longer in the robotic group than inother groups. This may be related to the steep learning curveassociated with robotic surgery.The first mitral valve repair performed with a robot , whereby the leftinternal thoracic artery (LITA) is harvested and grafted onto the left anteriordescending (LAD) artery using a robot. Alternatively, LITA can be harvested using arobot and then a minimally invasive coronary artery bypass is performed, wherebyLITA is hand-sewn to LAD via a mini-thoracotomy. It can be performed on both thebeating heart (off-pump) and the arrested heart (on-pump).®[. Since then, more than 1,000 robotic assisted bypasssurgeries have been performed. The results of 326 patients undergoing robotic TECABshowed mortality rate of 0.6%, stroke rate of 2%, perioperative myocardialinfarction of 2.5%, and long-term freedom from major adverse cardiac events reportedas 81% in the first postoperative 5 years. However, 14% of the cases had to beconverted to a larger incision, such as sternotomy ormini-thoracotomy.The first robotic TECAB was performed in 1998 by Loulmet, using the Da VinciSystemRobotic technology has the potential to provide a new standard of care for coronaryrevascularization. The new treatment strategy would combine the most beneficialaspects of coronary artery bypass grafting (CABG) (i.e. LITA to LAD anastomosis) andpercutaneous coronary intervention (PCI) (stents to other coronaries), resulting incompletely minimally invasive hybrid revascularization. There is definitive evidencethat LITA to LAD anastomosis significantly improves survival compared to PCI.PCI with drug-eluting stents is of comparable efficacy to vein grafts and is muchless invasive. Theoretically, this strategy maximizes the benefit to the patient andminimizes the risks.A randomized trial to compare this hybrid strategy with conventional CABG would stillbe warranted as the type of coronary arteries that receive vein grafts are differentfrom the coronaries that are stented due to patient selection.Robotic surgery has comparable outcomes to conventional surgery with regard tomortality and major adverse events. It has benefits in terms of shorter intensivecare and length of hospital stay, lower need for blood transfusion, and decreasedpain. These benefits are also provided by minimally invasive mitral and bypasssurgeries; therefore, robotic surgery will need to compete for a growth in themarket share.. It has been criticized for its expense with anaverage cost per patient up to US$45,914. The other major limitation in usingrobots is the steep learning curve associated with it; a competent operator requires150-250 procedures to become adept.Its widespread use is currently hindered by high initial capital investment andongoing maintenance costs. The purchase price can exceed US$2 million with aUS$100,000 annual service contract. Advancements in technology have the potential todecrease the learning time required. As many of the patents of the Da VinciSystem® have expired, companies are producing 2ndgeneration robots that may be more versatile, compact, and easier touse.However, proponents argue that the costs can be compensated by the decreased durationof intensive care and hospital stay, with Morgan et al. suggesting that totaloperational hospital costs did not significantly increase with robotictechnologyRobotic technology offers an exciting alternative to conventional surgery withpotential for a hybrid bypass operation. It also provides a minimally invasiveoption where conventional endoscopic surgery may technically be too difficult.However, it would need to prove itself as non-inferior to conventional surgery withregard to short- and long-term outcomes in randomized control trials. Then, it wouldneed to further prove itself as an economically viable option.


Direct remuscularization approaches to cell-based heart repair seek to restore ventricular contractility following myocardial infarction (MI) by introducing new cardiomyocytes (CMs) to replace lost or injured ones. However, despite promising improvements in cardiac function, high incidences of ventricular arrhythmias have been observed in animal models of MI injected with pluripotent stem cell-derived cardiomyocytes (PSC-CMs). The mechanisms of arrhythmogenesis remain unclear. Here, we present a comprehensive framework for computational modeling of direct remuscularization approaches to cell therapy. Our multiscale 3D whole-heart modeling framework integrates realistic representations of cell delivery and transdifferentiation therapy modalities as well as representation of spatial distributions of engrafted cells, enabling simulation of clinical therapy and the prediction of emergent electrophysiological behavior and arrhythmogenensis. We employ this framework to explore how varying parameters of cell delivery and transdifferentiation could result in three mechanisms of arrhythmogenesis: focal ectopy, heart block, and reentry. In its most severe presentation, acute plaque rupture can occur that restricts blood flow to the myocardium causing myocardial infarction (MI), or cardiac muscle damage. The inability to regenerate lost or injured cardiomyocytes (CMs) is an inherent biological limitation of the adult human heart that remains unaddressed by current clinical treatments. Subsequently, chronic cardiac overload and progressive myocardial remodeling can give rise to congestive heart failure (HF) and the need for heart transplantation3. Cell-based heart repair seeks to address the onset of HF by halting remodeling and restoring ventricular contractility to the post-MI heart.Ischemic heart disease remains the leading cause of death in the United States and globally9, indirect remuscularization13, to direct remuscularization25. Various types of cells have been tested in preclinical and clinical studies and include skeletal myoblasts28, bone marrow-derived stem cells30, endothelial progenitor cells33, mesenchymal stem cells34, various endogenous cardiac stem cells10, pluripotent stem cell-derived cardiac progenitor cells35, pluripotent stem cell-derived CMs (PSC-CMs)24, and transdifferentiated induced CMs (iCMs)37. Cell types that do not differentiate into myocytes  are believed to act through non-myogenic paracrine and indirect remuscularization mechanisms; specifically, they secrete proangiogenic and antiapoptotic factors, aid in reverse remodeling, or induce the proliferation of native ventricular CMs. In contrast, the introduction of de novo CMs either through PSC-CMs, iCMs, or cardiac progenitor cells that subsequently differentiate are believed to primarily act through direct remuscularization mechanisms by replacing lost CMs with new ones in the post-MI ventricles.Depending on the type of cells delivered, potential therapeutic mechanisms can range from non-myogenic paracrine effects40. In the MAGIC trial28, incidences of VT were two times greater in patients treated with skeletal myoblasts compared to placebo; VTs also tended to occur during the early postoperative period in treated patients. These postoperative VTs could be attributed to the inability of skeletal myoblasts to express connexin-43 (Cx43), electrically couple, and beat in sync with the myocardium43. To address this limitation, alternative approaches have focused primarily on cardiac progenitor cells, PSC-CMs, and iCMs as sources for de novo CMs capable of electromechanical integration. Despite no instances of arrhythmia reported in early small-animal studies23, severe acute ventricular arrhythmias were observed following PSC-CM injections (>1 × 108 PSC-CMs) into infarcted nonhuman primate hearts24. The exact mechanisms of VT remain primarily speculative. Further highlighting the need to understand the electromechanical coupling of de novo CMs, PSC-CM engineered into cell sheets have also been observed to exhibit arrhythmic activity44. Mechanistic investigations into arrhythmogenesis arising from cell-based heart repair is extremely challenging due to the difficulty of controlling many in vivo experimental variables related to the therapy . Multi-scale, biophysical-detailed cardiac electrophysiology modeling can however aid in this endeavor.Despite promising results indicating cardiac functional improvement, ventricular tachycardia (VT) has been observed in studies implementing direct remuscularization strategies, highlighting the need to better understand how cells integrate electromechanically into the myocardium. Postoperative VTs were observed in several clinical pilot studies of skeletal myoblast in post-MI heart failure patients47. Developing the ability to simulate cell therapy in a realistic manner within a biophysically-detailed, whole-heart modeling infrastructure is an important step in the quest to understand the mechanisms responsible for the unwanted emergence of arrhythmias. Identifying the primary drivers of ventricular arrhythmia and subsequently devising strategies to eliminate them are important next steps towards successful clinical translation of cell-based heart repair with de novo CMs. Predictive modeling of heart repair with de novo CMs has a great potential to complement and guide in vivo large-animal studies by providing an accurate and efficient platform for understanding the dynamics of arrhythmogenesis and assessing the feasibility of strategies for mitigating it.Cardiac electrophysiology modeling has become a vital tool for mechanistic enquiries across various scales of structural hierarchy, providing insight that cannot be acquired clinically or experimentallyde novo CMs. To achieve this, we represent and integrate key properties of therapy modality, cell engraftment, and myofibril alignment within a whole-heart modeling platform, ensuring accuracy and broad applicability of the simulation framework. We then demonstrate the utility of the framework with a set of diverse illustrative examples that identify potential mechanisms of ventricular arrhythmias – focal ectopy, heart block, and reentry –arising under different circumstances in the repaired heart. To highlight the flexibility and modularity of our framework, we conduct simulation studies with both human and animal (rabbit) heart models.The objective of this study is to develop and utilize a novel, comprehensive framework for simulating the electrophysiological consequences arising from heart repair with 49, our simulation framework enables clinical feasibility studies in stratifying patient arrhythmia risk and treatment optimization. The approach presented here makes an important leap forward in cardiac regenerative therapy by integrating predictive computational cardiac electrophysiology modeling with cell-based heart repair and tissue engineering.Our methodology enables the identification of key therapeutic parameters that mitigate arrhythmogenesis, which could guide clinical best practices and inform regulatory policy in cardiac regenerative therapies in the near future. Combined with patient-specific, clinical image-based heart modelingde novo CMs were incorporated at the appropriate scale in a modular, multiscale cardiac electrophysiology simulation framework that leverages image-based, three-dimensional (3D) heart modeling. At the cell scale  and only depolarize when stimulated, PSC-CMs and iCMs exhibit a more fetal-like phenotype arising from differences in cell morphology and ion channel expression52; specifically, they have a more depolarized diastolic membrane potential, lower action potential (AP) amplitude, slower upstroke velocity, and exhibit intrinsic automaticity53. Because of these broad similarities and a lack of detailed electrophysiological characterization of iCMs, we represent PSC-CM and iCM membrane kinetics with the Paci et al. ionic model54 for a generic ventricular-like human-induced PSC-CM; we however alter its cell scale representation, as described below, to better simulate the differences arising from PSC-CM and iCM integration among native myofibers due to therapy. The ventricular-like subtype was selected because it is the most appropriate cardiac subtype for cardiac repair. New PSC-CM or iCM ionic models can be easily incorporated as they are developed due to the modularity of our simulation framework.Advances in experimental techniques and a broader understanding of cardiac development24. Consequently, remuscularized regions with engrafted PSC-CM in the framework are assumed to only consist of cells with PSC-CM membrane kinetics  and degree of spatial clusteredness, respectively. Different parameter combinations resulted in noticeably different de novo CM spatial distributions -MRI for understanding mechanisms of arrhythmia68, arrhythmia risk stratification48, and the development of personalized treatment planning69. In the case of post-MI, regions of non-infarcted, grey zone , and scar in the heart model are determined by MRI signal intensity70. Additional structural components such as the Purkinje system, the network of specialized conducting fibers responsible for fast excitation of the ventricles, has also been included in heart models72. These advances in whole-heart model construction lay the foundation for the current platform which enables us to simulate cell therapy at the level of the whole heart.Contemporary whole-heart models incorporate 3D individualized heart geometry, tissue composition, and myofibril structure obtained from various imaging modalitiesde novo CMs, issues of optimal therapy dose and site of localized delivery are explored at the organ scale. Where and in what amounts de novo CMs should be delivered into the post-MI ventricles  apex adjacent to the infarct  and p  values (n = 10 for each combination of d and p). Tissue conductivity within regions of engrafted PSC-CMs was isotropic and reduced  because gap junction expression is minimal and distributed circumferentially in PSC-CMs during the initial stages of engraftment60. Simulations for each model configuration were then observed for the presence of spontaneous ectopic propagation originating from locations in the delivery zone; subsequently, the probability for ectopic propagation, or the fraction of simulations observed with focal ectopy, was recorded for each combination of d and p (n = 10).In the first illustrative example, we examined how parameters in cell delivery via intramyocardial injection could lead to ectopic propagations arising from the intrinsic automaticity of PSC-CMs, and under what circumstances. Specifically, we examined the role of engraftment spatial distribution , injection location , and cell dosage . Injection of PSC-CMs was simulated into a biophysically-detailed, patient-specific model of the human post-MI ventricles; the heart model was previously constructed and used in a study of VT risk stratification by our grouprct Fig. , and therct Fig. . Within p = 0.15) regardless of density (d). For moderate degrees of patchiness (p = 0.65), incidences of ectopic propagation rose monotonically with increasing cell dose ; ectopic beats were almost always observed for high degree of clustering (p = 0.95) regardless of cell dose.Results for simulated injection adjacent and distal to the infarct are summarized in Fig. d), increased cell clustering , and delivery along the boundary of the infarct. For an ectopic propagation to occur, engrafted PSC-CMs (current source) must generate sufficient depolarizing current to bring the adjacent, repolarized host myocardium (current sink) to its activation threshold. While cell dose alters the total amount of depolarizing current arising from PSC-CM automaticity, cell clustering changes the local depolarizing current density. In contrast, the local tissue composition of the delivery site determines the strength of the current sink. Compared to non-infarcted myocardium, grey zone is a weaker electrical sink due to ionic  and gap junction  remodeling. Scar is not an electrical sink at all because it is unexcitable and nonconducting. Consequently, delivery along the boundary of an infarct resulted in significantly more ectopic propagations triggered by PSC-CM automaticity because of a substantially weaker electrical sink. Overall, these simulations provide an example of how one can explore optimal intramyocardial cell delivery based on personalized heart geometry and distribution of scar.Overall, our model predicts that the propensity for ectopic propagation triggered by PSC-CM automaticity rises with higher cell dose  of the Purkinje system (0.605 cm) was simulated to model localized transdifferentiation in the superior ventricular septum Fig. . A singlResults revealed that both normal excitation of the Purkinje system as well as RBB block superior to the site of iCM engraftment, as shown in Fig. 48. Transplantation and engraftment of PSC-CM cell sheets with clinically relevant dimensions (diameter = 3.2 cm)76 was simulated onto the epicardium of the posterior LV directly over the infarct site  and anisotropic cell sheets was represented to determine the effects of conduction anisotropy in cell sheets arising from the alignment of PSC-CMs; in the latter condition, four different transplantation orientations were simulated: 0°, ± 45°, and 90° relative to the local fiber direction. VT inducibility was examined in all models, pre- and post-treatment, using our validated VARP protocol48, where the ventricles were paced rapidly from 19 uniformly distributed sites to induce VT and examine VT morphologies. The arrhythmogenic propensity of the substrate was assessed by the number of pacing sites from which VT was induced and the number of the different VT morphologies.In the last illustrative example, we used the simulation platform to compare the likelihood of induction for reentrant VT in the post-MI ventricles prior to and after simulated transplantation of PSC-CM cell sheets. PSC-CM cell sheet transplantation was simulated in another patient-specific, MRI-based human model of the post-MI ventricles; the heart model was also previously constructed and used in a study of VT risk stratification by our groupite Fig. , right aite Fig. , left. T48. Simulated cell sheet transplantation onto the lateral LV free wall in the patient heart model resulted in VT induction. In the isotropic cell sheet case, only one out of the 19 pacing sites induced VT; the resulting reentrant wave was stationary around the superior posterior LV near the RV insertion point . Electrophysiology studies of hESC-CM treated hearts at 2 weeks following cell injection revealed ectopic propagations originating from a region of the ventricles where hESC-CMs were delivered. Additionally, overdrive pacing of hESC-CM-treated hearts could not terminate the VT. In our case study, we demonstrated that the presence of ectopic propagations triggered by PSC-CM automaticity was determined by source-sink effects. Our simulation framework provides an ideal platform for further analysis of this problem and determining critical safety thresholds prior to human clinical trials. A variety of strategies could be implemented to minimize ectopic propagations arising from PSC-CM automaticity. One approach that could be explored are methods that decrease the intrinsic rate of PSC-CM automaticity; this would enable the overdrive suppression of PSC-CM pacemaker activity. Outside of enhancing PSC-CM maturation, the rate of PSC-CM automaticity can be decreased by enhancing I-K1 current in PSC-CMs via viral transfection81 or CRISPR-CAS982. Alternatively, methods that enhance source-sink mismatch could be explored. PSC-CMs could be co-delivered with fibroblasts that act as passive current sinks83. The patchiness of engrafted PSC-CMs could be increased by altering the viscosity of the cell suspension solution to maximize PSC-CM dispersion upon injection. Poor electrotonic coupling of engrafted PSC-CMs could be potentially improved by enhancing connexin expression.Focal VT arising from ectopic beats triggered by PSC-CMs have remained a constant concern because PSC-CMs spontaneously beat and have been observed to be prone to early and delayed afterdepolarizations23. Slowed conduction has been observed in engraft regions of PSC-CMs following cell delivery via intramyocardial injections23 and in PSC-CM cell sheets in vitro84. In addition to slowed conduction, PSC-CMs can also exhibit significant electrophysiological heterogeneity because they are comprised of a variety of cardiac subtypes 87; however, different enrichment90 and purification91 techniques can be used to obtain a ventricular-like population. In confluent preparations of PSC-CMs in vitro, conduction velocity and action potential variability has been observed to persist85. The combination of conduction slowing, variable conduction, and electrophysiological heterogeneity in regions of engrafted PSC-CMs can create a vulnerable substrate for reentrant VT. Despite this, recent in vivo results suggest that cell delivery either via intramyocardial injection or transplantation of cell sheets does not alter inducibility for reentrant VT; programmed electrical stimulation was used to test the vulnerability of post-MI hearts for reentrant VT. In cell delivery via intramyocardial injection, Liu et al. determined that there was no significant difference in the arrhythmia inducibility of post-MI macaque hearts between hESC-CM-injected and control groups at two weeks following treatment80. In cell delivery via PSC-CM cell sheets, Ling Gao et al.76 did not observe any significant increase in arrhythmias when PSC-CM cell sheets of a similar size were transplanted onto post-MI porcine hearts. Incidences of spontaneous ventricular arrhythmias in the PSC-CM cell sheet-treated group were not significantly different compared to the two control groups (post-MI porcine hearts without treatment or post-MI porcine hearts treated with an acellular fibrin patch). Programmed electrical stimulation at 4 weeks post treatment confirmed this result. While these experimental results differ from our simulation results, numerous factors could have contributed. In Liu et al.’s study, the smaller size of the macaque heart compared to that of a human could have limited the inducibility for reentrant VT following programmed electrical stimulation. In Ling Gao et al.’s study, cell sheets had only a ~10% rate of engraftment. Our framework enables further analysis of the role of these discrepancies on inducibility for reentrant VT.Because of poor electrotonic coupling, reentrant VT arising from conduction slowing is also a significant concern. Additional conduction slowing can also be attributed to the isotropic distribution of connexin-43While collateral engraftment of PSC-CMs or iCMs onto the Purkinje has not been experimentally reported, should it be proven to occur, it could lead to conduction block as we demonstrate in this study. Furthermore, if PSC-CMs or iCMs engrafted onto the Purkinje were to also engraft onto adjacent myocardium, abnormal ventricular excitations could also arise. Future work could analyze the severity and persistence of conduction abnormalities arising from collateral iCM or PSC-CM engraftment in a human heart model and explore how this could interact with ectopic propagations to give rise to a vulnerable substrate for reentry that includes the Purkinje system.in vivo in studies of cell delivery into post-MI macaque hearts80 making drawing conclusions about clinical safety extremely difficult. It remains unknown what factors give rise to one type of arrhythmia in one animal, another type of arrhythmia in another, and none in others. This could be due in part to many possible arrhythmia mechanisms, as we show; this could also be due in part to patient-specific considerations. In a different patient-specific post-MI model (data not shown), we observed that transplantation and engraftment of PSC-CM cell sheets could surprisingly reduce VT inducibility. The temporal dynamics of cell engraftment and electrotonic coupling is yet another contributing factor, especially in the days and weeks following therapy. In vivo, the prevalence of ventricular arrhythmias was observed to be the highest around two weeks following cell delivery before declining in subsequent weeks24. In our second case study, we demonstrated that bundle branch block was affected by the degree of electrotonic coupling of collaterally engrafting iCMs. In future work, we plan to determine and further analyze the contribution of graft coupling on the severity and persistence of ventricular arrhythmias presented here. Taken altogether, these findings suggest that treatment needs to be tailored to the specific individual–with numerous variables optimized and different temporal scales considered. Our simulation framework provides an ideal platform to further explore how this can be accomplished by first broadly understanding the mechanisms and parameters driving arrhythmogenesis and then providing a strategy for individualized treatment planning. To better understand the broader mechanism however, robust methods of in vivo cell tracking92 need to be implemented that enable the generation of accurate, geometric 3D heart models across time.Significant intersubject variability in arrhythmia type and prevalence has been observed de novo CMs, there are still numerous unknown physiological factors. However, the modularity of our simulation framework enables easy integration of new experimental findings at the appropriate scales of hierarchy. PSC-CMs and iCMs have been shown to morphologically and genetically mature in vivo60; however, a detailed electrophysiological characterization of the changes that occur during maturation has not been achieved, as the exact mechanisms remain unknown. This maturation has been observed to initially occur along the periphery of engrafted sites along the interface with viable host myocardium; it is believed that this maturation might be due in part to enhanced mechanical stimulation of the PSC-CMs located adjacent to actively contracting myocardium. Proper mechanical stimulation  has been shown to be an important driver for maturation of PSC-CMs in vitro. Other aspects of the local tissue environment, specifically the degree of ischemia and inflammation, has also been shown to affect cell viability and engraftment96. Compared to uninjured hearts, regions of engrafted PSC-CMs in the post-MI guinea pig heart exhibited more variation in graft size, spatial distribution, and electrotonic coupling23. Furthermore, chronic or acute inflammation could alter the subsequent fibrotic remodeling of the electrophysiological substrate, but how that occurs and whether the two processes differ remains unclear. In the context of remuscularization, acute inflammation can arise from post-MI healing itself or from injury arising from intramyocardial injection; transplantation of allogeneic PSC-CMs could lead to chronic inflammation. Additionally, PSC-CMs have also been shown to secrete exosomes that exert paracrine effects on native ventricular CMs as well97. However, the paracrine effects of secreted exosomes on the membrane kinetics of peri-infarcted border zone ventricular cardiomyocytes remain poorly characterized as well as their release and transport kinetics in vivo. These inherent uncertainties reaffirm the importance of our framework’s modularity—as new details become available, they can be integrated at the appropriate levels of model hierarchy and new and more accurate model results can be obtained.Although our framework integrates all presently available information regarding cell-based heart repair with 98. Another avenue of exploration is the integration of a coupled electromechanical model of the ventricles. With the development and integration of a PSC-CM or iCM myofilament model, we would be able simulate the degree of contractile improvement arising from remuscularization following cell therapy. A full electromechanically coupled 3D heart model would enable researchers to truly optimize cell delivery to maximize restoration of contractility and minimize risk of arrhythmia.As the framework continues to be refined and expanded, it can be used to explore additional exciting developments and applications of cell therapy. One avenue of exploration would be the use of PSC-CMs as biological pacemakers by leveraging their intrinsic automaticity; our simulation framework could aid in the exploration of source-sink effects to ensure robust and stable pacing from sinoatrial-like PSC-CMsde novo CMs is a predictive tool that will expedite clinical translation by driving hypotheses development and providing a platform for detailed mechanistic enquiry at the preclinical stage. Specifically, our framework can provide insight and guidance that could help anticipate pitfalls and minimize expensive, time-consuming in vivo experiments. At the clinical stage, our framework would offer patient-specific treatment planning and risk stratification. The modularity of our simulation hierarchy enables us to apply it to any heart regardless of species, size, and pathological state. Our comprehensive simulation framework represents a significant step forward in cardiac regenerative medicine by applying in silico cardiac electrophysiological modeling in the quest to understand the mechanistic basis of unwanted arrhythmias.Our computational framework for simulating cell-based heart repair with et al. formulation, which is calibrated to match experimental recordings from human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs)54. Our rationale for using the same ionic models for both types of cells is that no biophysically-detailed mathematical model of iCMs based on patch-clamp recordings exists; moreover, the exact characteristics and evolution of iCM membrane kinetics in vivo remains unknown. However, there are broad in vitro morphological and electrophysiological similarities between PSC-CMs and iCMs 99. Between the atrial-like and ventricular-like hiPSC-CM formulations, the ventricular-like subtype was chosen because it is the most appropriate type for remuscularization of the ventricles. The corresponding ionic model was downloaded from the CellML repository (https://www.cellml.org/). Model modifications were incorporated to better represent remuscularized regions arising from transdifferentiation in vivo.We modeled the membrane kinetics of both PSC-CMs and iCMs via the Paci 80 while remuscularized myocardium in transdifferentiation consists of engrafted iCMs distributed much more diffusely36. The latter arises because transdifferentiation targets fibroblasts in the interstitium between host myocytes. Because simulated cardiac tissue cannot be discretized at the spatial resolution necessary to represent individual cells and remain computationally feasible, we chose instead to model diffusely distributed iCMs using a cell tandem approach100. A similar approach has been used by our lab to simulate the delivery and engraftment of light-sensitized HEK cells into the heart46. Specifically, at each finite element node in a remuscularized region affected by transdifferentiation, modified cells were represented by resistive coupling of several iCMs to the “host” myocyte, the membrane kinetics of which depended on the tissue type . Junctional current flow  between iCMs and host myocytes was calculated by the following equation:gjunctional is the coupled host myocyte-iCM junctional conductance, Vm,host is the host myocyte transmembrane potential, and Vm,iCM is the iCM transmembrane potential. By convention, Ijunctional is negative if current flow is out of the host myocyte. Because iCMs are much smaller (10 × 15 μm) than mature myocytes (100 × 25 μm)99, numerous iCMs can engraft onto a single host myocyte. This was factored into the host myocyte membrane model using the equation:Ic,host is the net ionic current across the host myocyte lipid membrane, niCM is the number of coupled iCMs, Cm,host is the host myocyte membrane capacitance (185 pF), and +, Ca2+, K+) of the host myocyte. Both gjunctional and niCM were specified in simulations of collateral iCM engraftment onto the Purkinje system in a rabbit model. Junctional conductance, gjunctional, was varied between 0.1 to 8.0 ns to represent different levels of electrotonic coupling. A single Purkinje cell was coupled to niCM = 20 along the affected Purkinje bundle; we determined that this configuration was reasonable given the immense size mismatch between the two cell types  and linear arrangement of Purkinje cells in Purkinje fibers. In organ scale simulations, coupling between host myocytes remained unchanged in regions with engrafted iCMs. We assumed this because gap junction remodeling has not been shown to occur following transdifferentiation36.While the membrane behavior of remuscularized regions in cell delivery were modeled exclusively by that of PSC-CMs, the membrane behavior of remuscularized regions in transdifferentiation was represented as a coupled host myocyte-iCM tandem. This representation was chosen because remuscularized myocardium in cell delivery has been reported to consist of distinct, contiguous clusters of engrafted PSC-CMsde novo CMs , we implemented a stochastic algorithm previously used to model fibrosis distribution61 and the distribution of ChR2-expressing cells in models of light-sensitized hearts46. The physiological basis for these spatial patterns around sites of intramyocardial injection is a combination of cell death, cell migration, and cell proliferation. First, a 3D delivery zone denoting the localized site where de novo CMs could potentially engraft was prescribed by a sphere of radius (r) centered around a given endocardial node of the finite element mesh. Subsequently, stochastic spatial distributions of engrafted de novo CMs within this delivery zone were generated based on the parameters d and p; these parameters modulated the density or cell dosage (by volume fraction of delivery zone) and degree of spatial clusteredness, respectively. From a list of the N contained elements within the delivery zone, elements were successively tagged remuscularized until the number of tagged elements was ≥ d times N. For each new element tagged remuscularized, the probability of adding to an existing remuscularized cluster or starting a new cluster was given by p.To simulate the spatial distribution of remuscularized regions of engrafted 101. Full details of the 3D model reconstruction process and electrophysiological modeling can be found in this previous publication. Below, we briefly summarize this methodology. Finite-element ventricular meshes were derived from clinical LGE-MRI images. For each heart, boundaries of the myocardial wall in the MRI stack were first contoured; within the myocardial wall, regions were classified as non-infarcted, grey zone, and scar based on signal thresholding70. Geometries of the ventricular wall and of the 3 myocardial regions were subsequently merged using a previously validated methodology based on the variational implicit functions interpolation66 to create the final geometric reconstruction. In the reconstructed patient-specific ventricles, fiber orientations were assigned on a per-element basis using a previously validated rule-based approach102. Human ventricular cardiomyocyte membrane kinetics in non-infarcted myocardium were simulated using the ten Tusscher-Panfilov formulation103. Modifications to the ionic model were incorporated to represent electrophysiological remodeling in grey zone myocardium; specifically, peak sodium current was reduced to 38%, peak L-type calcium current was reduced to 38%, and peak potassium currents IKr and IKs were reduced to 30% and 20%, respectively. Infarcted myocardium was modeled as nonconducting and unexcitable. Tissue conductivities in non-infarcted and grey zone myocardium were assigned as previously described48 with appropriate adjustments incorporated into the latter to reflect connexin-43 remodeling observed experimentally. Parameter values for each 3D human heart model are summarized in the Supplementary Tables Patient-specific 3D human heart models were previously generated and used in a study of VT risk stratification of post-MI patients by our group48. Briefly, the ventricular model was subjected to simulated pacing from 19 uniformly distributed endocardial sites . In the left ventricle, pacing locations were automatically assigned104 based on the AHA left ventricle segments105. This distribution was selected to capture all possible VTs that could arise in the patient-specific, 3D heart model. For each pacing site, rapid pacing was delivered. The pacing train consisted of 8 initial pacing stimuli (S1) at a cycle length of 600 ms followed by a premature stimulus (S2) delivered 300 ms after the last S1 stimulus. If S2 did not result in the generation of arrhythmia, the S1–S2 interval was shortened, in 10 ms steps, until arrhythmia was induced or the S2 failed to capture in tissue. This type of pacing train mimicked ones delivered in standard clinical practice101. If arrhythmia was still not induced an additional S3, and if necessary S4, was delivered in the same fashion as S2. If arrhythmias did not self-terminate over a 2 second post-pacing period, reentrant VT was induced.The VARP  protocol for simulated programmed electrical stimulation was used to determine reentrant VT inducibility; the procedure is described in full detail in our previously published study75, collateral iCM engraftment onto the Purkinje system was simulated along a contiguous segment (0.605 cm) of the RBB, just distal to the bifurcation at the end of the His bundle. This could arise if transdifferentiation were to target the superior ventricular septum. The rabbit model was previously validated and used in numerous studies of Purkinje-mediated ventricular excitation75 and arrhythmia106. Along the affected RBB, 20 iCMs were coupled to a single Purkinje cell; this could arise due to the immense size mismatch between iCMs (10 × 15 μm) and Purkinje cells (~100 × 25 μm), enabling numerous iCMs to engraft onto a single Purkinje cell especially early on when iCMs are the smaller in size99. In the rabbit model, ventricular cardiomyocyte and Purkinje cell membrane kinetics were represented by the Mahajan-Shiferaw107 and Aslanidi-Sleiman formulations108, respectively. Additional 3D model parameters for the rabbit electrophysiological model can be found in the Supplementary Table In a biophysically-detailed rabbit ventricular model including a representation of the Purkinje systemin vitro have noticeably slower conduction velocity compared to native ventricular myocardium (~3-fold less)73.A one-element thick layer along the surface of the epicardium around a specified node was selected to simulate transplantation and engraftment of PSC-CM cell sheets. To accomplish this, the subset of points along the epicardial surface of the finite element mesh was first identified. Next, the additional subset of points around a user specified node was determined. Finally, all elements with a vertex that included at least one of those points were identified, thereby specifying the final epicardial region of PSC-CM cell sheet engraftment. Within the cell sheet, PSC-CM electrophysiological properties were assigned to the nodes. 10% tissue conductivity of normal myocardium was assigned to the elements because PSC-CM cell sheets cultured Within PSC-CM cell sheet elements, the original fiber orientation of the post-MI heart model was modified to simulate different treatment conditions. In the case of anisotropic cell sheets, different transplantation orientations were simulated by rotating fibers ±45° or ±90° about the surface normal of the nearest epicardial element face. In the case of isotropic sheets, fibers were randomly rotated about the surface normal of the nearest epicardial element face; angles of rotation were randomly sampled from a uniform distribution between ±90°. Complete engraftment was assumed .110 on a parallel computing system. Using the finite-element method, a reaction-diffusion partial differential equation, describing the spread of electrical current through the myocardium, was solved together with the ordinary and algebraic equations, describing myocyte membrane kinetics, at each node of the mesh. Details about computational resources for each set of simulations can be found in Supplementary Tables Monodomain simulations of electrical activity in the heart models were run using the CARP software package111. Briefly, patient MRIs are cropped in 3DSlicer (https://www.slicer.org/) and the myocardial walls are segmented using CardioViz3D (http://www-sop.inria.fr/asclepios/software/CardioViz3D/). Simpleware ScanIP, available from Synopsys, is used to generate 3D computational meshes from the segmented images. The set of algorithms and subroutines to assign fiber orientation in the 3D meshes using a rules-based approach are presented in the original publication102. Similarly, the set of subroutines used to stochastically assign spatial distribution of de novo CMs are presented in the original publication61. Patient MRI images and reconstructed models are available upon request and approval of Johns Hopkins Institutional Review Board. The rabbit model with realistic representations of the Purkinje system are available upon request. To set up and run monodomain simulations of electrical activity, ionic models of the human cardiomyocyte, including that of PSC-CM, can be accessed from the CellML repository (https://models.physiomeproject.org/electrophysiology). The electrophysiology simulator CARP software package and accompanying tutorials can be found online (https://carpentry.medunigraz.at/carputils/index.html).The software used to generate clinical imaging-based heart models are described in a previous publicationSupplemental Information


Salmonella species, Vibrio cholerae, Shigella species and Escherichia coli. For these organisms, I reviewed all publications describing the use of molecular subtyping methodologies to investigate outbreaks caused by multidrug-resistant (MDR) enteric bacterial infections.In sub-Saharan Africa, molecular epidemiological investigation of outbreaks caused by antimicrobial-resistant enteric bacterial pathogens have mostly been described for To describe the use of molecular subtyping methodologies to investigate outbreaks caused by MDR enteric bacterial pathogens in sub-Saharan Africa and to describe the current status of molecular subtyping capabilities in the region.A PubMed database literature search (English language only) was performed using the search strings: ‘Africa outbreak MDR’, ‘Africa outbreak multi’, ‘Africa outbreak multidrug’, ‘Africa outbreak multi drug’, ‘Africa outbreak resistance’, ‘Africa outbreak resistant’, ‘Africa outbreak drug’, ‘Africa outbreak antibiotic’, ‘Africa outbreak antimicrobial’. These search strings were used in combination with genus and species names of the organisms listed above. All results were included in the review.Salmonella Enteritidis. To date, several methodologies have been used; pulsed-field gel electrophoresis analysis and multilocus sequence typing have been the most commonly used methodologies. Investigations have particularly highlighted the emergence and spread of MDR clones; these include Salmonella Typhi H58 and Salmonella Typhimurium ST313 clones. In recent times, whole-genome sequencing (WGS) analysis approaches have increasingly been used.The year 1991 saw one of the first reports describing the use of molecular subtyping methodologies in sub-Saharan Africa; this included the use of plasmid profiling to characterise Traditional molecular subtyping methodologies are still commonly used and still have their place in investigations; however, WGS approaches have increasingly been used and are slowly gaining a stronghold. African laboratories need to start adapting their molecular surveillance methodologies to include WGS, as it is foreseen that WGS analysis will eventually replace all traditional methodologies. Salmonella, Vibrio cholerae, Shigella and Escherichia coli. By ‘molecular epidemiological investigation’, I refer to the use of molecular subtyping techniques that analyse bacterial strains at the level of their nucleic acid and so gives an indication of genetic similarity of strains.1 Molecular subtyping is vital for accurate epidemiological investigations of bacterial infections.2 Molecular subtyping allows one to segregate unlike strains of the same species or serotype of bacteria and identify clones or clusters of bacteria . Molecular subtyping allows one to track the spread of strains or clones and determine how local strains compare to those circulating worldwide.3 Molecular subtyping data is critical for successful epidemiological investigation of outbreaks of disease; in particular, outbreak-related cases of disease can be differentiated from sporadic cases of disease.4 Knowledge of the molecular epidemiology of bacterial infections provides information of major circulating (infecting) clones, so that in times of antimicrobial treatment and vaccine interventions, well-informed and educated decisions can be made to combat the disease.5For molecular epidemiological investigation of outbreaks in sub-Saharan Africa caused by antimicrobial-resistant (including multidrug-resistant [MDR]) enteric bacterial pathogens, most published data describe pathogens belonging to the genus or species of 6 These molecular subtyping techniques included plasmid profiling, ribotyping , random amplified polymorphic DNA analysis , enterobacterial repetitive intergenic consensus elements PCR, pulsed-field gel electrophoresis (PFGE) analysis, multiple-locus variable-number tandem-repeats analysis (MLVA), multilocus sequence typing (MLST), multilocus sequence analysis and whole-genome sequencing (WGS) analysis. Molecular subtyping data from sub-Saharan Africa is summarised in 11 Some molecular subtyping techniques  may only allow a local comparison of bacterial strains within a single laboratory, because the workings of the methodology are difficult to standardise between laboratories, and so the data that is produced cannot be truly and accurately compared between laboratories. In contrast, a technique such as PFGE analysis, has been standardised by PulseNet International  and is successfully used for inter-laboratory comparison of subtyping data and global comparison of bacterial strains.4The mid-1990s saw the start of noteworthy publications describing the use of molecular subtyping techniques to investigate the molecular epidemiology of antimicrobial-resistant enteric bacterial pathogens in sub-Saharan Africa.14 Analysis of WGS data for molecular epidemiological purposes can include multiple approaches; however, the more popular methods are whole-genome MLST and single nucleotide polymorphisms (SNP) analysis.15 WGS data is electronically portable and can easily be shared between laboratories, allowing for a global inter-laboratory comparison of bacterial strains. Current challenges surrounding WGS include how to standardise the quality of WGS data generated and how to standardise the analysis of WGS data, in order to allow for a successful inter-laboratory comparison of analysed WGS data. PulseNet International has taken on this challenge and published their vision for implementation of WGS for global foodborne disease surveillance and global analysis of enteric pathogens.15Newer approaches to molecular subtyping involve WGS analysis.Salmonella, Vibrio cholerae, Shigella and Escherichia coli, because most molecular epidemiological investigations have been described for these microorganisms. The aim of this review is to inform the readers about the current status of molecular subtyping capabilities and activities in sub-Saharan Africa and finally suggest that the way forward for molecular subtyping in African laboratories is the implementation of WGS analysis as soon as possible.The aim of the present manuscript is to review all publications that have described the use of molecular subtyping methodologies to investigate outbreaks due to multidrug-resistant enteric bacterial pathogens in sub-Saharan Africa. The review focuses on enteric pathogens belonging to the genus and species of A PubMed database literature search (English language only) was performed using the search strings: ‘Africa outbreak MDR’, ‘Africa outbreak multi’, ‘Africa outbreak multidrug’, ‘Africa outbreak multi drug’, ‘Africa outbreak resistance’, ‘Africa outbreak resistant’, ‘Africa outbreak drug’, ‘Africa outbreak antibiotic’, ‘Africa outbreak antimicrobial’. These search strings were used in combination with genus and species names of the organisms listed above. All results were included in the review.Salmonella (NTS), molecular epidemiological analysis of Salmonella Typhimurium and Salmonella Enteritidis are most commonly described in sub-Saharan Africa. One of the first reports using molecular subtyping techniques to investigate enteric bacterial pathogens isolated in sub-Saharan Africa was a 1991 study that used plasmid profiling (following restriction endonuclease digestion) to distinguish African isolates of Salmonella Enteritidis from United States (US) isolates.16 Molecular subtyping of NTS then progressed to PFGE analysis of MDR Salmonella Typhimurium isolated in Kenya, where 64 isolates were grouped into eight PFGE clusters and were described as multiclonal.17Salmonella Typhimurium accounts for a significant proportion of reported cases of NTS-associated invasive disease in sub-Saharan Africa. NTS-associated invasive disease has developed to become a leading public health challenge in sub-Saharan Africa.19 A predominant type of invasive Salmonella Typhimurium in sub-Saharan Africa is a MDR strain that has been designated ST313 (based on its MLST profile). Clonal spread of this MDR ST313 strain was first reported from Kenya and Malawi in 2009, with the strain showing resistance to ampicillin, chloramphenicol, sulphamethoxazole, trimethoprim, streptomycin and kanamycin.20 In Kenya, a more recent analysis of Salmonella Typhimurium outbreak isolates, using WGS-SNP analysis, identified a single clade of MDR ST313 strains showing resistance to ampicillin, ceftriaxone, chloramphenicol, sulphamethoxazole and trimethoprim; extended-spectrum beta lactamase (ESBL) genes blaCTX-M-15, blaTEM-1 and blaOXA-1 were also harboured by the isolates.21 The MDR ST313 has further been reported from Malawi, Nigeria, Democratic Republic of the Congo and South Africa.24 Leekitcharoenphon and coworkers22 used MLST, PFGE and WGS-SNP analysis to describe a close relationship between MDR ST313 strains from Nigeria and Democratic Republic of the Congo; strains were isolated from invasive (blood) and non-invasive (stool) specimens; all isolates harboured blaTEM1b, catA1, strA/B, sul1, and dfrA1 genes coding for resistance to various classes of antimicrobial agents. Keddy and coworkers23 used MLST to investigate Salmonella Typhimurium isolates from South Africa and reported that NTS meningitis in South Africa was highly associated with the MDR ST313 strain.For nontyphoidal Salmonella Typhimurium, Salmonella Enteritidis is also a major player with regard to NTS-associated invasive disease in sub-Saharan Africa. Feasey and coworkers25 used WGS-SNP analysis to investigate 675 Salmonella Enteritidis isolates from 45 countries (including 28 African countries), to describe the existence of one global epidemic clade and two ‘African’ clades, of which the African clades are geographically confined to specific regions of sub-Saharan Africa. Both African clades show MDR, with an enlarged MDR virulence plasmid. Both African clades also show patterns of genomic degradation with a similarity to those shown by other host-restricted invasive Salmonella serotypes described in Africa: patterns of genomic degradation similar to the African Salmonella Typhimurium ST313 clade.Besides Salmonella Enteritidis.28 Niehaus and coworkers26 reported on a Salmonella Enteritidis foodborne outbreak in South Africa, where human isolates and a food isolate were shown to have an indistinguishable PFGE pattern. Salmonella Enteritidis investigations have also included nosocomial outbreaks; Vaagland and coworkers28 investigated a nosocomial outbreak of neonatal Salmonella Enteritidis in Tanzania, with PFGE analysis of MDR  isolates suggesting a clonal outbreak. More recently, MLVA has proven a suitable method to investigate the molecular epidemiology of Salmonella Enteritidis in South Africa; MLVA was used to investigate multiple foodborne outbreaks over the period 2013–2015; MLVA was able to cluster outbreak isolates according to distinct MLVA profiles.29 Nosocomial outbreaks have also been investigated in South Africa, where Salmonella Isangi and Salmonella Typhimurium have been involved.31 Wadula and coworkers30 used PFGE analysis to show clonality among ESBL-producing Salmonella Isangi in paediatric hospital wards. Smith and coworkers31 used PFGE analysis, MLVA and MLST to investigate an ESBL-producing Salmonella Typhimurium outbreak in a paediatric hospital ward; outbreak isolates showed indistinguishable molecular subtyping profiles including an MLST subtype of ST34, and showed resistance to ampicillin, ceftriaxone, trimethoprim, sulphamethoxazole, chloramphenicol and tetracycline.Pulsed-field gel electrophoresis analysis is commonly used to investigate outbreaks of 32 documented one of the first reports using molecular subtyping techniques to characterise MDR Salmonella Typhi isolated in Africa. They used PFGE analysis to investigate 102 Kenyan outbreak isolates of Salmonella Typhi and identified two distinct subtypes among the infecting isolates; 82% of the isolates were MDR with resistance to ampicillin, chloramphenicol, tetracycline, streptomycin and cotrimoxazole. When PFGE analysis is standardised, as is the case with PulseNet methodology employed by participating PulseNet International laboratories, then inter-laboratory comparison of PFGE patterns can occur, to allow for a global investigation of outbreak isolates and tracking of emerging strains. Such was the case reported by Smith and coworkers,4 who used PFGE analysis to investigate an outbreak of Salmonella Typhi associated with a restaurant in South Africa; the source of the infecting strain was a restaurant worker who was tracked to Australia where a Salmonella Typhi isolate was recovered from the individual; the isolate shared an indistinguishable PFGE pattern as compared to the pattern of the outbreak strain. Using the same scenario as described above , Keddy and coworkers33 investigated a MDR (including resistance to fluoroquinolones) Salmonella Typhi isolate recovered from a South African patient who had interacted with a person who had recently travelled to Bangladesh; the PFGE pattern of the isolate had never previously been seen within the South African PFGE database, but the PFGE pattern was typical of that seen of isolates from the Indian subcontinent, evidence to support the view that the isolate originated in Bangladesh. Global tracking of Salmonella Typhi through inter-laboratory comparison of molecular subtyping data is particularly important in establishing the source (and spread) of MDR (and virulent) strains of Salmonella Typhi. An example of such a strain is Salmonella Typhi H58, a highly clonal MDR haplotype of Salmonella introduced from Asia into Africa and now currently spreading through many sub-Saharan African countries.5In 2004, Kariuki and coworkersSalmonella Typhi outbreaks using PFGE analysis have been reported from the Malawi-Mozambique border,34 in Uganda36 and in South Africa.37 In Uganda, Walters and coworkers36 investigated a prolonged waterborne outbreak in two neighbouring districts; they used PFGE analysis to document the clonal spread of MDR Salmonella Typhi from the Kasese district to the Bundibugyo district. Analysis of selected isolates showed MDR to ampicillin, chloramphenicol, cotrimoxazole, streptomycin and tetracycline. In South Africa, Keddy and coworkers37 investigated a waterborne outbreak in the Delmas area of South Africa in 2005; they used PFGE analysis and MLVA to show high relatedness among outbreak isolates. These outbreak isolates from 2005 were also shown to be highly related to isolates associated with a similar waterborne outbreak in the same area in 1993. Besides PFGE analysis, MLVA is often described as a useful molecular subtyping method for discrimination of strains belonging to the Salmonella species. Tau and coworkers38 described the development and evaluation of a MLVA assay for molecular subtyping of Salmonella Typhi in sub-Saharan Africa. They evaluated the MLVA assay on a panel of African isolates and showed that it had higher discriminatory power as compared to PFGE; the MLVA assay was able to differentiate outbreak isolates from sporadic isolates.Further investigation of Salmonella Typhi in sub-Saharan Africa; a common theme has been the description of the MDR Salmonella Typhi H58 strain.39 Wong and coworkers5 used WGS-SNP analysis to investigate 1832 Salmonella Typhi isolates from 63 global countries, to identify one major MDR lineage (named H58) that has arisen and spread across Asia and Africa over the past 30 years. Multiple H58 transfers have occurred, moving from Asia into Africa. An MDR H58 epidemic has been described in sub-Saharan Africa where H58 lineages are displacing antimicrobial-susceptible strains. MDR H58 lineages are associated with resistance to ampicillin, trimethoprim, sulphonamides, chloramphenicol, streptomycin and tetracycline, including reduced susceptibility to fluoroquinolones.39 This MDR H58 lineage has now been described in the eastern and southern regions of sub-Saharan Africa including Kenya, Tanzania, Malawi and South Africa.40 Hendriksen and coworkers41 also used WGS-SNP analysis to investigate an outbreak of Salmonella Typhi in Zambia; most isolates showed resistance to ampicillin, chloramphenicol, streptomycin, sulphamethoxazole and trimethoprim, while some isolates also had reduced susceptibility to fluoroquinolones. Isolates belonged to a new variant of the H58 haplotype, namely haplotype H58B. Interestingly, investigation of MDR Salmonella Typhi from Nigeria -western Africa, using WGS-SNP analysis, has not detected the H58 lineage; instead the majority of isolates belonged to a different lineage -H56 lineage, which carries genes coding for resistance to ampicillin, tetracycline, chloramphenicol and sulphamethoxazole . This H56 lineage is relatively common across Africa, predominantly in western and central regions of sub-Saharan Africa.42Whole-genome sequencing – single nucleotide polymor-phisms analysis has been increasingly used to investigate the molecular epidemiology of V. cholerae O1 in Africa.44 In 1996, Dalsgaard and coworkers43 documented the first molecular epidemiological investigations of MDR V. cholerae O1 in Africa. They used ribotyping to investigate outbreak isolates in Guinea-Bissau; strains isolated in 1994–1995 showed a ribotype pattern which was different fro strains isolated in 1987, suggesting that the 1994–1995 outbreak was as a result of the introduction of a novel strain into the country. Later, ribotyping analysis of V. cholerae O1 outbreak isolates from Senegal showed the same ribotype pattern as the 1994–1995 Guinea-Bissau outbreak strain, suggesting that Senegal acquired their outbreak strain from Guinea-Bissau.45 In 2001, Dalsgaard and coworkers46 reported on the clonal relationship (using ribotyping) of cholera isolates from Mozambican migrant workers associated with a 1998 outbreak that occurred in South African provinces bordering Mozambique. MDR V. cholerae O1 isolates were associated with the outbreak among the migrant workers and the isolates showed resistance to furazolidone, streptomycin, sulphamethoxazole, trimethoprim and tetracycline; isolates also showed the presence of class 1 integrons and the SXT element. Another analysis of Mozambican MDR isolates associated with the 1998 Mozambique/South Africa cholera outbreak confirmed the presence of the SXT element among isolates, with enterobacterial repetitive intergenic consensus elements PCR dividing isolates into two different molecular subtypes.47 Mobile genetic elements are mostly responsible for the molecular basis of MDR V. cholerae O1 in sub-Saharan Africa. Mobile genetic elements include transposable elements (SXT elements), integrons and conjugative plasmids. The SXT element is a self-transmissible element that integrates into the chromosome and carries genes encoding resistance to several antimicrobial agents, including chloramphenicol, sulfamethoxazole, streptomycin, trimethoprim and furazolidone. The SXT element has been reported from numerous countries in sub-Saharan Africa.51 In 2006, Scrascia and coworkers52 investigated V. cholerae O1 isolates from multiple outbreaks that occurred in Kenya during 1998 and 1999. Most isolates showed an identical ribotype profile and similar random amplified polymorphic DNA analysis profile suggesting a clonal origin for the outbreaks; isolates were resistant to chloramphenicol, spectinomycin, streptomycin, sulphamethoxazole and trimethoprim. Over the period 2008–2009, published data from Ghana, Cameroon, Ethiopia and Somalia reported similar trends (as described above) for MDR V. cholerae O1 isolates following analysis using ribotyping and random amplified polymorphic DNA analysis - that of each country reporting a clonal origin for outbreak isolates within their respective country.55Ribotyping and enterobacterial repetitive intergenic consensus elements PCR were among the first molecular subtyping techniques used to characterise V. cholerae, started shifting to an increased use of PFGE analysis. In 2007, Keddy and coworkers56 used PFGE analysis to compare the relatedness of South African V. cholerae O1 isolates from a 2001–2002 epidemic to that of a 1980–1987 epidemic; PFGE analysis showed that isolates from the 1980–1987 epidemic were distinctly different to isolates from the 2001–2002 outbreak. In 2008, Smith and coworkers57 reported on the first cholera outbreak in Namibia over the period 2006–2007; MDR V. cholerae O1 isolates showed an indistinguishable PFGE pattern and showed resistance to streptomycin, sulphamethoxazole and trimethoprim. In 2011, Ismail and coworkers48 investigated an outbreak of ESBL-producing MDR V. cholerae O1 in South Africa which occurred over the period May to July 2008; PFGE analysis showed a clonal relationship among isolates, with isolates showing resistance to ampicillin, cotrimoxazole, chloramphenicol, nalidixic acid, tetracycline, kanamycin and streptomycin. The molecular basis of antimicrobial resistance was explained by proving the presence of the SXT element and the blaTEM gene encoding TEM-63 β-lactamase. Later in 2008, a V. cholerae O1 outbreak in Zimbabwe spilled over into South Africa, which triggered a very large outbreak in South Africa over the period November 2008 to April 2009. MDR outbreak isolates showed similar PFGE patterns: isolates showed the presence of the SXT element encoding multidrug resistance; some isolates also showed ESBL activity due to the presence of the blaTEM gene encoding TEM-63 β-lactamase.58 In October 2010, the Haiti cholera outbreak started, with involvement of MDR V. cholerae O1.60 PFGE analysis of Haiti outbreak strains isolated over the period October 2010 to February 2011 showed that a single PFGE profile predominated. This predominant profile was also shown in MDR strains from Cameroon, South Africa, India, Nepal, Pakistan, Afghanistan and Oman61; this global comparison of V. cholerae O1 PFGE patterns was facilitated via PulseNet International laboratories. Further investigation with WGS-SNP analysis showed that the Haiti outbreak strain was a hybrid-type of El Tor strain encoding a classical-type of cholera toxin and that the outbreak strain was most closely related to strains originating from India and Cameroon.62From 2007 onwards, trends in molecular subtyping of V. cholerae O1 isolates; this sparked the increased use of nucleotide sequencing approaches including WGS, to generate nucleotide sequence data for comparison of isolates. In 2011, Thompson and coworkers63 characterised V. cholerae O1 outbreak isolates from Ghana using multilocus sequence analysis of housekeeping genes and identified two major clusters in Ghana. In 2011, Mutreja and coworkers64 provided evidence for multiple waves of global transmission  within the seventh cholera pandemic, which included multiple waves of transmission into sub-Saharan Africa; these data were made possible by analysis of WGS data  from a global collection of V. cholerae O1 isolates, including African outbreak isolates. In 2013, Kiiru and coworkers,65 reported a WGS-SNP analysis on Kenyan V. cholerae isolates which included environmental isolates (serogroups O1 and non-O1) and V. cholerae O1 clinical isolates associated with outbreaks; they found that a clade comprising clinical isolates and some environmental isolates mapped back onto wave three of the monophyletic seventh cholera pandemic phylogeny, while some other environmental isolates were phylogenetically very different from the monophyletic seventh pandemic lineage of V. cholerae O1. The continued presence of MDR associated STX elements were documented on the genomes of MDR clinical and MDR environmental isolates. Recent isolates of V. cholerae O1 from Tanzania have also been found to map back onto wave three of the monophyletic seventh cholera pandemic phylogeny, as determined by WGS-SNP analysis.66 Interestingly, WGS-SNP analysis of MDR V. cholerae O1 outbreak isolates recently sourced from Cameroon has found that these Cameroonian isolates do not map onto the phylogeny of isolates from other African countries such as Kenya, Tanzania, Zambia, Zimbabwe and Mozambique; instead, Cameroonian isolates are of their own distinct clonal cluster (clade), forming part of an isolated reservoir of MDR V. cholerae O1 in the Lake Chad basin.67The Haiti cholera outbreak identified the need for higher resolution molecular subtyping methodologies for comparison of V. cholerae isolates have gained a stronghold in sub-Saharan Africa, the traditional molecular subtyping methodologies of PFGE, MLVA and MLST are still commonly used to investigate the molecular epidemiology of V. cholerae in sub-Saharan Africa, as these technologies still remain more accessible and more affordable for most laboratories73. For these recent reports, the central themes remain the same as previous studies. The major conclusions for these studies are summarised as follows. The most commonly reported cholera-causing organism is characterised as V. cholerae O1 biotype El Tor, the so-called ‘atypical’ El Tor variant housing genetic determinants coding for a ‘classical-type’ of cholera toxin. The majority of V. cholerae O1 isolates show the presence of SXT elements encoding a large part of the MDR phenotype. The majority of isolates show MDR. A common profile includes resistance to ampicillin, chloramphenicol, sulphamethoxazole, trimethoprim, spectinomycin and streptomycin, including reduced susceptibility to ciprofloxacin. Molecular subtyping of V. cholerae O1 isolates shows examples of geographical clustering, as already described above in the mention of Cameroonian isolates characteristic of the Lake Chad basin region; Moore and coworkers72 found that isolates from western Africa (Togo and Guinea) form a closely related group which is separated from a distinctive cluster associated with the African Great Lakes region (Democratic Republic of the Congo and Zambia). Smith and coworkers71 found that a Mozambican cluster of isolates was distinctly different  as compared to isolates from western Africa  and central Africa (Democratic Republic of the Congo).Although WGS approaches for comparison of Shigella dysenteriae was the first Shigella species in sub-Saharan Africa that was characterised by molecular subtyping techniques; these details were first documented in the years 1996 and 1997.74 In 1996, Kariuki and coworkers6 documented an outbreak of dysentery due to MDR S. dysenteriae serotype 1 in Kenya: all 22 outbreak isolates were resistant to ampicillin, chloramphenicol, sulphamethoxazole, trimethoprim, streptomycin and tetracycline; PFGE and plasmid profiling showed clonality among the isolates. In 1997, Pillay and coworkers74 documented a nosocomial outbreak of dysentery caused by MDR S. dysenteriae serotype 1 which transpired in a psychiatric institution in South Africa. Isolates were resistant to ampicillin, chloramphenicol, tetracycline and cotrimoxazole; ribotyping showed clonality among the isolates. Later in 1996, a report documented how PFGE analysis was used to investigate MDR S. dysenteriae serotype 1 isolates associated with two separate dysentery outbreaks in the Central African Republic; PFGE analysis provided evidence to show that each outbreak was associated with a different clone.75 In 2009, Smith and coworkers76 reported a cluster of 29 watery diarrhoea cases associated with MDR Shigella boydii serotype 2 isolates in South Africa. Isolates were resistant to ampicillin, trimethoprim, sulphamethoxazole and streptomycin; PFGE analysis showed clonality among the isolates.Shigella species in sub-Saharan Africa has involved WGS-SNP analysis of isolates. Connor and coworkers77 used WGS-SNP analysis to investigate 351 global (including Africa) isolates of Shigella flexneri, to show that S. flexneri is composed of seven distinct phylogenetic groups. Each phylogenetic group contains geographically restricted sub-lineages that seem to have constantly inhabited areas for several decades; there is limited evidence of intercontinental transmission. Antimicrobial resistance determinants have been acquired and maintained locally on several episodes – to summarise, S. flexneri from sub-Saharan Africa cluster together and form a distinct lineage as compared to lineages from other parts of the world. Njamkepo and coworkers78 used WGS-SNP analysis to investigate 331 global (including Africa) isolates of S. dysenteriae serotype 1, to show that S. dysenteriae serotype 1 is composed of four distinct lineages. Lineage IV contains most of the recent (last few decades) isolates from Africa and the Indian subcontinent; lineage IV has been transmitted in several waves to Africa; most recent (since 1990) outbreaks in Africa have been caused by this lineage IV; compared to other lineages, lineage IV show antimicrobial resistance including MDR.More recent molecular epidemiological investigations of E. coli enteric pathogens (diarrhoeagenic E. coli). Diarrhoeagenic E. coli includes the following six main categories: shiga-toxin producing E. coli (which includes the enterohaemorrhagic E. coli), enteropathogenic E. coli, enteroinvasive E. coli, enterotoxigenic E. coli, enteroaggregative E. coli and diffusely adherent E. coli. In 2003, Okeke and coworkers79 reported on the etiology of acute diarrhoea in adults in Nigeria: shiga-toxin producing E. coli and enteroaggregative E. coli were significantly associated with the diarrhoea. PFGE analysis of shiga-toxin producing E. coli isolates provided evidence to suggest that an outbreak had occurred. In 2012, Tau and coworkers80 reported on the characterisation of E. coli O104 isolates from South Africa for the years 2004–2011. This study was undertaken in response to the 2011 outbreak of bloody diarrhoea and haemolytic uraemic syndrome which occurred in Germany, caused by an enteroaggregative shiga-toxin producing E. coli O104:H481. The South African investigation found two antimicrobial-susceptible enteropathogenic E. coli O104:non-H4 isolates and five enteroaggregative E. coli O104:H4 isolates . Pulsed-field gel electrophoresis analysis showed that these South African isolates were unrelated to the Germany outbreak strain.Surprisingly very little published data exist concerning molecular epidemiological investigation of outbreaks in sub-Saharan Africa caused by 84 Conclusions from these studies are unanimous and convincing, in that WGS trumps all other methodologies. Old-fashioned methodologies offer limited genetic resolution and limited discriminatory ability and there can sometimes be a lack of accuracy; all these hinder epidemiologic investigations. For example, data from old-fashioned methodologies can incorrectly conclude that unrelated isolates are indistinguishable, resulting in false alerts and unnecessary investigations. Whole-genome sequencing provides a supreme unparalleled discriminatory ability, can reflect phylogeny and can provide an evolutionary context. In addition, WGS can provide valuable information related to genetic determinants conferring antimicrobial resistance. Numerous publications have reported how analysis of WGS data can be used to infer antimicrobial resistance in bacterial pathogens;89 these studies have reported high concordance between WGS resistance predictions and conventional phenotypic antimicrobial susceptibility testing methodologies. Even so, conventional phenotypic susceptibility testing methodologies still have their place in the laboratory and must continue to be used for the foreseeable future. For now, WGS antimicrobial resistance prediction is best used as an adjunct to conventional phenotypic susceptibility testing.Whole-genome sequencing is undoubtedly the way forward for investigation of enteric pathogens in diagnostic and public health laboratories. Numerous publications have analysed data sets to compare results obtained using old-fashioned molecular subtyping methodologies versus results obtained using newer WGS analysis methodologies.93 Within the next year, the PulseNet USA programme is planning to have WGS  fully implemented as its primary method for molecular epidemiological investigation of enteric pathogens.94 The next challenge is how to effectively implement WGS analysis globally in public health laboratories, how to standardise the quality of WGS data generated and how to standardise the analysis of WGS data, all in order to allow for a successful inter-laboratory comparison of analysed WGS data. PulseNet International has taken this challenge and published their vision for implementation of WGS for global foodborne disease surveillance and global analysis of enteric pathogens.15 PulseNet Africa  believes and supports this vision for implementation of WGS for global analysis of enteric pathogens.In summary, public health laboratories in Africa need to urgently reconsider and adapt their molecular surveillance methodologies to include WGS. It is foreseen that WGS analysis will eventually replace all traditional methods currently used in the microbiology laboratory, both traditional phenotypic methods and traditional genotypic methods including molecular subtyping methods such as PFGE, MLVA and MLST.


Sc) that do not possess coding nucleic acid. Prions replicate by recruiting and converting normal cellular PrPC into infectious isoforms. In the same host species, prion strains target distinct brain regions and cause different disease phenotypes. Prion strains are associated with biophysically distinct PrPSc conformers, suggesting that strain properties are enciphered within alternative PrPSc quaternary structures. So far it is unknown how prion strains target specific cells and initiate productive infections. Deeper mechanistic insight into the prion life cycle came from cell lines permissive to a range of different prion strains. Still, it is unknown why certain cell lines are refractory to infection by one strain but permissive to another. While pharmacologic and genetic manipulations revealed subcellular compartments involved in prion replication, little is known about strain-specific requirements for endocytic trafficking pathways. This review summarizes our knowledge on how prions replicate within their target cells and on strain-specific differences in prion cell biology.Prions are infectious β-sheet-rich protein aggregates composed of misfolded prion protein (PrP Sc is mainly found associated with neurons and astrocytes . Al. Al31]. orphisms . When su strains . Further strains . The ECMdler/RML . How exaAs prions usually replicate without causing cytopathic effects in permanent cell lines, these in vitro models cannot be used to study toxic events associated with prion replication. Instead, neuropathologic changes can be observed in prion-infected primary neurons cultured for prolonged periods of time . DifficuSc formation involves conversion of PrPC to its abnormal isoform. Consequently, cellular compartments that harbor PrPC likely represent regions of initial isoform encounters and subsequent conversion processes. Research over the last few decades has demonstrated that the organization of the endocytic pathways is more complex than anticipated . EEs are highly dynamic, pleiomorph compartments that consist of a central vacuole  with membrane invaginations, intraluminal vesicles (ILVs), and emanating thin tubular extensions ,77. EEs C, a membrane protein tethered to the cell surface via a glycosyl-phosphatidyl-inositol moiety. Following synthesis of the polypeptide chain in the rough ER and attachment of complex glycans at two asparagnine-linked glycosylation sites in ER and Golgi, PrPC reaches the outer surface of the cell membrane where it resides in lipid rafts ,14085,14ormation . While srain 22L . Furtherral days .Sc accumulation in Chandler/RML-infected GT1 cells, had no effect on total PrPSc levels in 22L-infected N2a cells. Authors further demonstrated that expression of GFP-Rab22 was ineffective at inhibiting transfer of Tfn from the EE to the ERC. Thus, in N2a cells, manipulation of the Rab22a-regulated vesicle trafficking appeared to affect the recycling pathway downstream of the EE to ERC transport, implicating that the ERC can still be involved in PrPSc formation. It is unknown if silencing of Rab22a would have had similar effects. Further interesting experiments could include silencing of Rab11 to assess the role of Rab11-dependent recycling on PrPSc accumulation. In summary, while there appear to be differences in prion biogenesis that could relate to cell type or prion strain, there is general consent that efficient PrPSc formation requires functional endosomal trafficking along the recycling and/or degradative pathway.A similar reduction was observed when ESCRT-I complex protein Tsg101 was silenced in 22L-infected N2a , arguingSc accumulation and in characterizing endocytic trafficking pathways involved in prion biogenesis. Dynamic subcellular cycling of PrPSc was shown to play an essential role in efficient PrPSc formation. The cell surface and ECM, the LE/MVBs, and/or the ERC likely represent subcellular compartments of PrPSc biogenesis. Extensive research over the last few decades has reshaped our view on endocytic cargo trafficking. Endosomal compartments are highly dynamic, and different macromolecular machineries control segregation of cargo into distinct subdomains for cargo transport to the retrieving/recycling or degradative pathway. To which extent distinct vesicular trafficking pathways are involved in strain-dependent PrPSc formation remains to be determined.Understanding prion strain-specific host cell interactions is critical for the development of effective therapeutic strategies. Substantial progress has been made in identifying subcellular compartments of PrPSc, the establishment of productive infections and PrPSc formation and/or degradation. The use of tagged PrPSc as inoculum or the expression of tagged PrPC will further allow discrimination of inoculum from endogenous PrP isoforms. Ideally, those endocytic trafficking pathways should be manipulated genetically for the strongest effects on PrPSc accumulations that have been reported, such as traffic regulated by Rab7a, Rab11, or Rab22a. Concomitant functional assays should control for effects of manipulations on specific endosomal trafficking pathways, using well-characterized cargo such as dextran, cholera toxin, or Tfn. If possible, triple-detection for specific markers and cargo should be performed to more carefully characterize the subcompartments after genetic/pharmacologic manipulation. Finally, quantitative comparative studies by EM or super resolution microscopy are urgently needed to more accurately determine the subcellular distribution of PrPSc associated with different prion strains.A major complication is that the performed studies relied on different cellular models or compared prion formation in different cell lines infected with distinct prion strains. As considerable differences exist in endocytic trafficking pathways among different cell types, such comparisons are not suited to reveal strain-specific differences in prion biogenesis. Future studies will ideally focus on well-characterized prion strains in cellular models that are easy to manipulate chemically and pharmacologically. Primary neuronal and astrocytic cultures reflect more accurately the in vivo situation but do not constitute homogeneous cell populations and are more difficult to genetically or chemically manipulate. When choosing a cell model, one should also take into account the genetic instability of permanent cell lines that strongly affects permissiveness of cell clones. Consequently, different expression profiles can also be expected in prion-infected cell populations that originate from the same cell line but have separate passage histories. As the cellular requirements differ during the establishment of infection and persistent infection, highly susceptible cell clones will help to identify how manipulations of endocytic trafficking pathways affect uptake of PrP


Circulating tumor (ct) DNA is a powerful tool that can be used to track cancer beyond a single snapshot in space and time. It has potential applications in detecting minimal residual disease and predicting relapse, in selecting patients for tailored treatments, and in revealing mechanisms of response or resistance. Here, we discuss the incorporation of ctDNA into clinical trials. Advances in liquid biopsy technologies, such as the use of circulating tumor DNA (ctDNA), have empowered researchers to track cancer longitudinally through real-time monitoring. Increasingly, ctDNA is being integrated into clinical trials in order to evaluate its utility in detecting cancers before they are radiologically visible, in monitoring minimal residual disease (MRD) to predict relapse, in selecting patients for specific therapies, and in revealing mechanisms of treatment response or resistance. The validity of ctDNA as a predictive biomarker depends not only on the technical characteristics of the assay, but also on the coupling of ctDNA dynamics to clinical outcome so that they serve as a relevant biological surrogate. ctDNA exists as short fragments (150–200 base pairs) that are amenable to PCR- and next generation sequencing (NGS)-based analyses, with NGS offering greater multiplexing capabilities for mutation profiling. Beyond mutations, tools are now available to measure epigenetic features within ctDNA, including methylation; these tools may prove useful for cancer types that are associated with few recurrent mutations and for early detection and classification [Many factors influence the abundance and detectability of ctDNA in cancer patients. At diagnosis, anywhere from > 90 to < 0.1% of plasma DNA is tumor-derived . Tumor tBRCA1/2, NF1 or TP53, and mismatch repair genes. Owing to their heightened cancer risk, these carriers may undergo risk-reduction surgeries such as mastectomy and/or intensive surveillance of some, but not all, organs. Given that the ‘first-hit’ (the germline mutation) is known and their tumorigenesis follows a predictable pattern, ctDNA analysis is an attractive complementary modality to current screening protocols in this population.The use of ctDNA as a cancer screening tool in the general population is limited largely by its low sensitivity and cost effectiveness; the number of participants needed to screen to detect a true positive case is large. Thus, strategies to enrich for patient populations that are at sufficiently high risk of malignancy are important in ctDNA-based screening initiatives. Financial implications should also be considered in order to justify the implementation of a ctDNA screening strategy if clinical utility is demonstrated. An example of a risk-based ctDNA screening study is the prospective testing of circulating plasma Epstein-Barr virus (EBV) DNA in over 20,000 Chinese men from Hong Kong (aged between 40 to 62 years) to detect asymptomatic nasopharyngeal carcinoma (NPC) . In thisOne of the most appealing clinical applications of ctDNA is to detect cancer recurrence in the MRD setting after definitive local or locoregional therapy, as it offers the opportunity to eradicate micrometastatic disease and maximize cure. Observational studies correlating the presence of ctDNA or specific genomic aberrations with disease outcome have shown a prognostic role across multiple tumor types, with positive ctDNA status typically preceding the occurrence of clinical relapse by a few months . ClinicaIn the marker-by-treatment interaction design framework, all patients are tested for the marker: marker-positive patients are randomized to investigational therapy versus control; marker-negative patients can undergo the same randomization as part of the study, or more often, are offered off-trial standard treatment. An example is a recently reported study in locoregionally advanced NPC that used testing of plasma EBV DNA after radiotherapy or chemoradiotherapy to select patients who have positive EBV DNA levels for randomization to adjuvant chemotherapy or observation . In the The optimal duration of adjuvant therapy, especially when using molecularly targeted therapy or immuno-oncology therapy (IO) after definitive surgery and/or radiotherapy, is often determined empirically rather than on the basis of biological rationale. In the case of IO, activation of memory T cells can promote sustained responses irrespective of treatment duration. Randomized studies that compare different adjuvant therapy durations generally have large sample sizes in order to demonstrate non-inferiority, and are often conducted by cooperative groups rather than by pharmaceutical sponsors. Clinical trials using ctDNA clearance to inform adjuvant treatment duration, in comparison to existent standard duration, would be of interest as they may help to reduce patient exposure to unnecessary toxicity and treatment-related cost.The measurement of ctDNA in advanced cancer enables non-invasive access to genomic changes in the tumor that may guide therapeutic decisions. In some cases, predictive information can be obtained to select the optimal therapy, whereas in other cases, prognostic and pharmacodynamic information can provide a ‘headstart’ that allows intervention before clinical or radiological changes become manifest.T790 M mutation in EGFR mutant non-small cell lung cancer (NSCLC) patients who are progressing on first-generation tyrosine kinase inhibitors. If the panel size is sufficiently large, NGS data can also be used to calculate blood-based tumor mutational burden (bTMB) as a potential predictor of IO response, as demonstrated by retrospective analyses in NSCLC [ctDNA data generated using high-throughput NGS panels can provide value by directly identifying known or new actionable mutations for genotype–drug matching. For example, ctDNA has been incorporated into standard of care as a less invasive alternative to tissue biopsy for detecting the in NSCLC . ClinicaPIK3CA ctDNA levels compared to baseline after 15 days of treatment with palbociclib and fulvestrant was predictive of progression-free survival [Early changes in ctDNA dynamics upon treatment can provide information about therapeutic efficacy, as demonstrated in a retrospective analysis of samples from the phase III PALOMA-3 trial in advanced estrogen-receptor-positive breast cancer. A decline in survival . SeveralBRAF/NRAS mutations by Droplet Digital PCR (dd-PCR) successfully identified all 9 patients who had pseudo-progression [For patients on IO therapy, validated methods are lacking to distinguish patients with disease progression from those experiencing pseudo-progression resulting from immune cell infiltration in the tumor microenvironment. In a cohort of 125 melanoma patients treated with PD-1 blockade, retrospective ctDNA analysis of gression . ProspecAkin to the adjuvant setting, the optimal duration of therapy, especially IO, in those who have objective tumor response or prolonged disease stabilization is unclear and may be disease specific. Fewer than 10% of metastatic melanoma patients completing two years of PD-1 blockade relapse afterwards . ConversIn the era of precision medicine, ctDNA-based interventional studies represent a new frontier, targeting molecular changes in cancer beyond a single snapshot in space and time. Innovative strategies are being sought to incorporate ctDNA or other liquid biopsies into clinical trials in order to validate their role as a predictive biomarker across different tumor types and various disease settings. These studies can contribute important information to multi-omic and longitudinal evaluations that can best inform dynamic changes in cancer.