Despite some significant advancements, breast cancer has become the most prevalent cancer in the world. One of the main reasons for failure in treatment and metastasis has been attributed to the presence of cancer initiating cells—cancer stem cells. Consequently, research is now being focussed on targeting cancer cells along with their stem cell population. Non-oncology drugs are gaining increasing attention for their potent anticancer activities. Metformin, a drug commonly used to treat type 2 diabetes, is the best example in this regard. It exerts its therapeutic action by activating 5′ adenosine monophosphate-activated protein kinase (AMPK). Activated AMPK subsequently phosphorylates and targets several cellular pathways involved in cell growth and proliferation and the maintenance of stem-like properties of cancer stem cells. Therefore, AMPK is emerging as a target of choice for developing effective anticancer drugs. Vanadium compounds are well-known PTP inhibitors and AMPK activators. They find extensive applications in treatment of diabetes and obesity via PTP1B inhibition and AMPK-mediated inhibition of adipogenesis. However, their role in targeting cancer stem cells has not been explored yet. This review is an attempt to establish the applications of insulin mimetic vanadium compounds for the treatment of breast cancer by AMPK activation and PTP1B inhibition pathways. Breast cancer is the most common cancer in women of all races across the globe. According to the World Health Organization, 2.3 million women were diagnosed with breast cancer in 2020 alone, with approximately 685,000 deaths reported globally. As per the 2021 WHO statistics, breast cancer has become the most prevalent cancer in the world, and it is estimated that it would constitute over 30% of all cancers diagnosed in women . Recent Cancer stem cells are capable of asymmetric cell division: self-renewal and the generation of heterogeneous lineages . Breast +CD24−/Lineage− subpopulation within breast cancer cells and demonstrated that their tumorigenic ability resonated with normal stem cells. These cells can undergo self-renewal as well as differentiation. They can multiply extensively, as few as 200 of these unpassaged cells were able to form solid tumours that could be planted in mice. Their multiplication resulted in a heterogeneous population of cells composed of the CD44+CD24−/Lineage− population as well as non-tumorigenic cells that could compose the bulk of the tumour mass [The term cancer stem cells essentially refer to the relatively smaller cell population within cancer cells that can repopulate the tumour cells over a long period of time as well as retain their ability to regenerate themselves continuously. Thus, cancer stem cells, or tumour-initiating cells, function like normal stem cells but in a diseased manner . Pioneerour mass .+CD24− cells and ALDH+ cells, i.e., BCSCs within the tumour cells. Therefore, it is pertinent to treat breast cancer stem cells along with breast cancer cells to ensure a complete and effective treatment. Consequently, many treatment strategies are being designed to directly target the cancer stem cell population by modulating specific pathways involved in the cancer stem cell phenotype [+CD24− cells [Breast cancer stem cells display increased cell motility and invasion and they overexpress genes that promote metastasis . Most cahenotype ,11,12. S4− cells . This reThe most noteworthy progress in targeted breast cancer treatment involves the development of HER2 targeted therapies using trastuzumab and lapatinib. This treatment approach is also proposed to act against the ALDH1 cancer stem cell population ,15. AparThe discovery and understanding of the crucial role played by breast cancer stem cells in cancer propagation and metastasis has now led researchers to develop therapeutic strategies to target and treat cancerous stem cells. Several therapies have been tested in vivo and in vitro to target BCSCs through membrane markers and transporters as well as alteration of the cancer stem cell microenvironment. However, although some of these treatments have been promising in vitro, in vivo toxicity and pharmacokinetics remains a big challenge. Intriguingly, some success has been reported recently, with drugs used clinically to treat other ailments. There are researchers and clinicians all over the globe working towards finding a safe and effective cure for cancer. The average cost for the development of an anticancer drug is over 600 million dollars and takes approximately 10–20 years. Moreover, less than 1% of the drug candidates enter clinical trials, and the proportion of drugs successfully making it to market is even smaller. Therefore, drug repurposing is increasingly being employed particularly in cancer research ,17,18. DMany independent systematic studies reported lower risk and occurrences of cancer in diabetic patients on metformin treatment ,22. Cons+CD24− and ALDH+ stem-cell-rich population in breast cancer cells [Metformin acts on mitochondria in the cell, inhibiting the mitochondrial electron transport system by enhancing the AMP-activated protein kinase (AMPK) activity. 5′ adenosine monophosphate-activated protein kinase is an enzyme involved in cellular energy homeostasis, largely to activate glucose and fatty acid uptake and oxidation when cellular energy is low. AMPK is identified as the crucial candidate causing the interaction between metabolism and cancer ,29. Actier cells ,24. Cons2+/calmodulin-dependent protein kinase β) [5′ adenosine monophosphate-activated protein kinase (AMPK) is an enzyme complex that undergoes autoactivation under conditions of cellular stress such as exercise, hypoxia, and hypoglycaemia. Some anti-diabetic drugs, such as metformin, and thiazolidinediones, such as pioglitazone, also exert their therapeutic effect by activating AMPK . Structuinase β) ,36. The inase β) ,38,39,40AMPK is fast emerging as a target of choice in the treatment of cancer cells and cancer stem cells. It is an energy-sensitive molecule and is activated under conditions of cellular stress, a common phenomenon in cancer cells. However, a few reports argue this activation may actually protect cancer cells ; neverthAn increased AMPK activity could assist in anticancer activity through the interplay of various pathways, some of which are discussed below.About 30–60% of breast cancers are characterised by an overexpression of the cyclin D1 encoding gene, CCND1, and over 50% of breast cancers overexpress cyclin D1 protein. Cyclin D1 plays a crucial role in tumour progression and metastasis by causing abnormal phosphorylation and inhibiting the tumour suppressor protein, retinoblastoma (pRB) . It alsoAn important consequence of AMPK-mediated cyclin D1 inhibition is the arrest of cell cycle progression in the G0/G1 phase. It is also a commonly encountered pathway of cell death relating to the activation of AMPK. Many studies using metformin on different cancer cell lines as well as cancer stem cells suggest the arresting of the cell cycle at the G1:S boundary, following AMPK-mediated upregulation of CDK-inhibiting p21 and p27 ,70,71,72AMPK is the master regulator of energy molecules in the body and is involved in glucose and fat metabolism. Phosphorylation of AMPK is a crucial step involved in the inhibition of adipogenesis and lipogenesis ,74,75. FAMPK activation also leads to the downregulation of the mechanistic target of rapamycin complex 1 (mTORC1) to restrict the ATP-expensive anabolic processes. mTORC1 is a nutrition-sensitive protein complex composed of mTOR kinase and the scaffolding protein raptor, catalysing various biosynthetic pathways in the human body . mTOR isCancer cells are fast growing and proliferate at an uncontrolled rate. Therefore, they have higher energy requirements than normal cells. The greater energy demands are met by the Warburg effect—cancer cells that operate at higher rates of glucose uptake and lactate production irrespective of the available oxygen concentration and normal mitochondrial function . The exaApart from the various tumour suppression mechanisms described above, activated AMPK also inhibits several self-renewal and metastasis-related pathways that are often associated with the cancer stem cell population. There are ample reports suggesting the involvement of Wnt/β-catenin, NF-κB, TNFα, interleukin proteins, Notch, and Hedgehog signalling pathways in the differentiation and proliferation of breast cancer stem cells by inducing epithelial-to-mesenchymal transition (EMT) and tumorigenesis . AMPK acThus, AMPK is a suitable target for developing effective cancer treatments . Some re−1 body weight [The anti-diabetic effects of vanadium compounds are well documented in the literature ,108,109.y weight . DespiteVanadium compounds are known inhibitors of several ATPases and phosphatases with varying potency . The mosPTP1B is localised in the endoplasmic reticulum in the cells and downregulates leptin receptors and insulin signalling by dephosphorylating the IRS . This deThere is increasing evidence suggesting the link between PTP inhibition and AMPK activation. Several biochemical consequences of PTP inhibition are believed to be executed via AMPK activation . PTP1B iThis leads to an interesting hypothesis—can insulin mimetic vanadium compounds with PTP1B inhibitory activity also activate AMPK? This can have important implications in developing effective anticancer treatments, since vanadium compounds are also known for their anticancer action; however, their efficacy on cancer stem cells has not yet been explored in detail.Obesity is commonly associated with diabetes, and many anti-diabetic drugs are known to inhibit adipocyte differentiation. The role of vanadium in AMPK activation has mainly been focussed on vanadium-mediated inhibition of adipogenesis. This arises as a direct consequence of the fact that adipocyte differentiation depends on several growth factors including IGF and insulin. Adipogenesis is the process by which mesenchymal stem cells differentiate and mature into adipocytes and is often considered a hallmark for cancer stem cells. It is a complex process involving the interplay of several proteins, such as PPARγ, and transforming growth factors, such as KLF and IGF. Vanadium compounds are known to inhibit adipogenesis and, thus, many insulin mimetic agents can also be used to treat obesity. The most generally accepted mechanism of vanadium-induced inhibition of adipocyte differentiation involves AMPK activation along with a few other pathways. Adipose tissues form the bulk of the breast cancer microenvironment. It is involved in the synthesis of various adipokines and signalling molecules  that play crucial roles in breast tumour formation and progression. Leptin signalling plays an important role in tumour progression and maintenance of the cancer stem cell population by upregulating the cancer stem cell signalling pathways, Notch and Wnt . Similar172 by adipokines such as leptin and adiponectin. AMPK activation directly affects lipogenesis and fat accumulation. Activated AMPK phosphorylates several substrates resulting in the inhibition of the synthesis of fatty acids, triglycerides and cholesterol, and it promotes fatty acid oxidation.AMPK can undergo activation in the adipose tissue in response to stress such as malnutrition, exercising, and extreme cold. It can also be activated by endogenous molecules, such as high-density lipoproteins and IL6, in the adipose tissue including other activation pathways discussed above. Along with LKB1, AMPK in the adipose tissue can be phosphorylated at Thr4 and BMOV, can activate the GLUT translocator in adipose tissues [Brassica napus cultured in vanadium-rich Jeju water. The vanadium-containing extract was reported to exhibit anti-diabetic effects as well as reduced triglyceride accumulation and adipogenesis in in vivo models. The suggested mechanism of action involved AMPK-mediated upregulation of glycogen synthesis by enhanced triacylglycerol lipase activity resulting in increased glucose uptake in adipocytes [The role and application of vanadium compounds in AMPK-mediated inhibition of adipogenesis are currently not very clearly understood. However, there are a few recent reports suggesting AMPK activation in insulin mimetic action as well as decreased adipogenesis by vanadium compounds ,159,160. tissues . Recentl tissues . Such re tissues . The mos tissues ,164. Van tissues . Similaripocytes .Vanadium catalysed AMPK activation and reduced adipocyte differentiation might also involve simultaneous activation of the peroxisome proliferator-activated receptor (PPARγ) ,168,169.Although the exact role of PPARγ in tumorigenesis is ambiguous, it can be modulated in a cell line specific manner to achieve anticancer properties . PPARγ aThus, insulin mimetic vanadium compounds inhibit PTP1B activity and activate AMPK and PPARγ to suppress adipogenesis. Additionally, vanadium compounds are generally redox active under physiological conditions and can readily interconvert between VO(IV) and VO(V) using Fenton-like reactions within the cell. This redox interconversion results in the generation of ROS. These ROS can, in turn, further cause PTP inhibition as well as induce cytotoxicity ,176. ConNon-oncology drugs have gained increasing attention recently for their possible anticancer action. These drugs with well-defined pharmacokinetic and pharmacodynamic profiles fast track into clinical trials. The most successful non-oncology drug for cancer so far is metformin. Metformin has shown potent activity against many cancers, particularly breast cancer and breast cancer stem cells. The drug exerts its anti-diabetic and anticancer action through AMPK activation. AMPK is an energy-sensing molecule playing pivotal roles in metabolic processes. The phosphorylation and activation of AMPK have several implications that result in reduced cell proliferation and apoptosis in cancer cells. Activated AMPK phosphorylates several downstream substrates resulting in downregulation of cyclins, cell cycle arrest, suppression of fatty acid, cholesterol, triglyceride syntheses, inhibition of the mTOR pathway and induction of autophagy and Warburg effect antagonism. All these factors are involved in breast cancer as well as breast cancer stem cells and, thus, there are ample reports suggesting the inhibition of one or more of these pathways causing anticancer activity. AMPK activation also has implications in impeding adipogenesis, and many vanadium-based insulin mimetic agents can inhibit adipocyte differentiation following AMPK activation. Vanadium-based insulin mimetic compounds are known PTP1B inhibitors, and few were/are in clinical trials for the treatment of type 2 diabetes and obesity. There is clear evidence of the link between the interplay of PTP1B inhibition and AMPK activation in the prognosis and treatment of breast cancer. Although the anticancer applications of vanadium compounds are well documented, not much has been reported regarding the action of vanadium on cancer stem cells. Vanadium compounds possess all the required abilities to target and inhibit cancer and cancer stem cells. The recent development of vanadium-catalysed suppression of adipogenesis and the vast history of vanadium-mediated PTP1B inhibition and ROS generation can be carefully manipulated to target cancer stem cells, particularly breast cancer stem cells. This review presents the possibility of employing vanadium-based PTP1B inhibitors for activation of AMPK and treatment of breast cancer by targeting their stem cell population.


IntroductionThe pandemic of coronavirus disease 2019 (COVID-19) has caused over four million deaths, depleting resources and healthcare workers; therefore, in an attempt to stratify patients, the relationship between liver enzymes and clinical outcome was studied. This study aimed to assess the pattern and impact of liver enzymes on the clinical outcome of hospitalized patients with COVID-19 in Lebanon and look for possible confounding factors.MethodologyThis was a single-centered retrospective cohort study conducted between December 2020 and March 2021 on 230 patients diagnosed with COVID-19. Liver function tests (LFTs) and other laboratory values on admission and peak hospitalization were analyzed using SPSS.ResultsThe prevalence of abnormal liver tests among the sample population with severe COVID-19 infection were as follows: aspartate aminotransferase (AST), 77%; alanine aminotransferase (ALT), 49%; alkaline phosphatase (ALP), 12%; and gamma-glutamyl transferase (GGT), 37%. A severe COVID-19 infection was more likely present in patients with abnormal levels of AST (p = 0.015), ALP (p = 0.03), and GGT (p = 0.022). ANOVA test revealed no significant relationship between AST levels at peak hospitalization and the treatments received by the patient.ConclusionAbnormal liver function tests of patients at admission may be an indicator of more severe disease. In the context of scarce resources created by the pandemic, it becomes essential to establish a reliable predictor for a severe outcome of COVID-19 infection and manage accordingly. Coronavirus disease 2019 (COVID-19) was declared a global pandemic by the World Health Organization (WHO) on March 11, 2020. Since then, it has caused over four million deaths while posing major challenges to the healthcare systems worldwide. In Lebanon, the virus remains a threat as it has caused over 500,000 cases (as of August 2021) with two major waves of the pandemic pushing healthcare systems and their resources into a state of disarray  and p = 0.014, difference of 12.80 ± 55.51 U/L, CI = , respectively), while none among the other liver tests. This was followed by a one-way ANOVA test that revealed no significant relationship between AST levels at peak hospitalization and the treatments  received by the patients. Deceased patients had significantly higher levels of neutrophils, urea, and LDH on admission and at peak hospitalization than those who recovered  had a severe infection. Not a single treatment strategy impacted LFTs, even days after the initiation of therapy. Higher levels of neutrophils, urea, and LDH prevailed in deceased rather than discharged patients. An increase in the levels of AST was attributed to severe COVID-19 infection and a higher mortality among patients. As for the predictors of abnormal AST at peak hospitalization, high neutrophils and LDH were most significantly correlated. The outcome of patients with COVID-19 infection was improved only in the sample of patients receiving standard of care regimen exclusively.Our study aligns with previously published results confirming that elevated liver enzymes correlate with a more severe outcome -8. The oWithin that hepatocellular pattern, AST levels were found to be higher than those of ALT. This disparity may be accountable to the presence of AST in tissues other than the liver, such as the heart, kidneys, skeletal muscles, brain, and RBCs, unlike ALT . TherefoRemdesivir also plays a role in the pathogenesis of liver injury in the context of COVID-19 infection ,14. HoweThe dynamic changes in LFTs observed between admission and peak hospitalization showed that AST and ALP were the highest on admission rather than later during the hospital stay. Multiple studies, however, discovered that the pattern of LFT evolution took a bell-shaped form ,16, pertBesides disrupted LFTs, elevated LDH also served as a marker of severe outcome in COVID-19 disease, particularly resulting in a higher mortality rate. A study conducted by Li et al. confirmed this finding and stated that elevated serum LDH can be used to evaluate disease severity and predict in-hospital mortality in patients with COVID-19 infection . In factOur results identified three major predictors positively correlated with a severe outcome from COVID-19 infection: elevated AST levels , elevated neutrophil count (at admission), and the presence of CAD among other comorbidities (at admission). Despite the association between elevated cholestatic enzymes and severe infection, there does not seem to be a positive correlation between the two, meaning that GGT and ALP could be elevated without affecting the outcome of the patient. Interestingly, elevated ALT levels appeared to have a protective effect on patient outcomes. One speculative explanation behind this may be related to the more specific nature of ALT to liver inflammation, unlike AST, an enzyme commonly elevated as a response to the damage of other organs. This possibly postulates a greater impact of concomitant multiple organ damage on illness severity and patient outcome rather than liver involvement solely. Further research should target this hypothesis.Regarding the optimal treatment regimen of COVID-19 infection, conflicting results are present regarding the superiority of remdesivir with or without baricitinib over dexamethasone alone . NeverthAmong the several comorbidities, patients with CAD ended up developing a severe infection in the majority of cases. Multiple mechanisms may explain this association: (1) a strong correlation between coronary artery disease and viral infections , (2) direct viral invasion of the cardiac myocytes through the ACE-2 receptors, and (3) indirect immune response with increased levels of pro-inflammatory cytokines  . HoweverOlder age and male sex also contributed to a poor outcome. Marik et al. attributed this gender disparity to a heightened inflammatory response that occurs in men . In factLimitationsOur study has several limitations worth noting. This study was conducted in a single-center hospital in Lebanon with relatively small sample size, thus limiting its representativeness. As a retrospective study, not all liver enzymes were routinely ordered for all patients presenting with COVID-19 infection, accounting for missing data. Moreover, the time frame chosen for this study allowed for limited treatment strategies available for use to evaluate their impact on patient outcomes.In conclusion, patients with abnormal liver function tests at admission developed more severe disease. The pattern of disturbed LFTs was mixed (cholestatic and hepatocellular); however, only elevated AST levels correlated with a poorer outcome. This finding can further guide physicians to predict that outcome and provide more aggressive treatment when needed. Other predictors of severe disease were elevated neutrophil count and a history of CAD. Treatment strategies had no impact whatsoever on LFTs despite the recommendations to monitor them during the course of therapy. This calls for the conduction of larger studies involving a more representative sample of the population to better elucidate all the mechanisms behind liver injury in patients with COVID-19.


The increasing complexity of cardiovascular interventions requires advanced peri-procedural imaging and tailored treatment. Three-dimensional printing technology represents one of the most significant advances in the field of cardiac imaging, interventional cardiology or cardiovascular surgery. Patient-specific models may provide substantial information on intervention planning in complex cardiovascular diseases, and volumetric medical imaging from CT or MRI can be translated into patient-specific 3D models using advanced post-processing applications. 3D printing and additive manufacturing have a great variety of clinical applications targeting anatomy, implants and devices, assisting optimal interventional treatment and post-interventional evaluation. Although the 3D printing technology still lacks scientific evidence, its benefits have been shown in structural heart diseases as well as for treatment of complex arrhythmias and corrective surgery interventions. Recent development has enabled transformation of conventional 3D printing into complex 3D functional living tissues contributing to regenerative medicine through engineered bionic materials such hydrogels, cell suspensions or matrix components. This review aims to present the most recent clinical applications of 3D printing in cardiovascular medicine, highlighting also the potential for future development of this revolutionary technology in the medical field. Although three-dimensional printing has been commonly used in commercial fields in the last decade, the development of this new technology in medical field is still limited to several disciplines such as orthopedics, neurosurgery, maxillofacial surgery and cardiology ,2. In thThe recent emergence of complex renderings of cardiac magnetic resonance (cMRI), cardiac computed tomography (CCT) and echo imagery have improved the visualization of pathologies, but lack in tactile experience and in three-dimensional complex visualization ,11. ConvThe development of a patient-specific prototype of 3D anatomical models starts with the acquisition of the clinical images from volumetric imaging data, converted then to a 3D digital format and finally to a physically printed model ,15. Basi3D printing workflow includes several steps, starting with acquisition of image data using of the previous mentioned imaging technologies . The nexUsually, the unstable parts are printed with dedicated support platform to give desired position in space. In terms of time span of a fabricated 3D model depends mainly on the phantom size, structural complexity of the anatomical model and resolution/speed of the selected printer. A normal adult cardiac model may take 24 h to achieve the complete process. At the final phase, the 3D stereo-lithographic model often includes a subsidiary material for support purposes, which requires removal either by operator or by automated machine spreading special solutions to achieve a clean model. Once this stage is completed, and the extra-support material has been removed, the ultimate 3D anatomic model is exposed and ready to be used [Printing by using the polymerization method which includes stereolithography and digital light processing; it consists of a laser beam directed to a selected resin material to cure layer by layer ; phantomPowder-based printing includes selective laser sintering or melting and consist of focal heating beam to fuse the selected powdered materials ; materiaPrinting by using the droplet method requires multi-jet modeling with wax or trough laser beam induced forward transfer, which is commonly used to deploy photo-curable resins into layers ; the tecThe extrusion printing method includes a fused deposition and direct ink writing modeling, by using a thermo-sensitive plastic or ink materials, which are delivered through a special printhead, layer by layer on a dedicated support area ; the usePrinting technologies can be classified into four main categories :PrintingMaterial selection for 3D printing depends on the processing technique and the main field of activity in which the models are used. The variety of materials ranges from rigid to flexible, and also complex mixtures for combined properties.Rigid materials are used mostly in pre-interventional planning models, which have been demonstrated to improve the spatial and structural understanding. The most commonly used printing materials are acrylonitrile butadiene styrene with fused filament modeling; this is considered as the most cost-effective material in the field of rapid prototyping. Moreover, the droplet method with thermoset materials has been used to fabricate appropriate devices for patient-specific models for simulation of flow resistances, with no model fractures, regardless of the fluid flow profiles. Metals such as titanium mostly used for printing implantable devices, due to its low weight, biocompatibility and sterilization adequacy .By using flexible materials, printed models can attain various real-life haptic characteristics. This facilitates prototyping of 3D models with a wide range of flexibility and physical strength. For instance, a flexible material frequently used is thermoplastic polyurethane (TPU) which is an elastomeric resin, with a very high elasticity, flexibility and durability. This material frequently used in powder-based techniques can reproduce a wide variety of organs, tissues and vessels, thus allowing surgical and interventional simulations . SimilarMulti-material compounds represent the future development of rapid prototyping models, that may be used to achieve realistic characteristics such as elastic and biological compatibilities. Three-dimensional printing by using fibers that appropriately regulate the mechanical performance of the physical model is currently under development .The main applications of 3D printing in cardiology being experimented in present are illustrated in Stereolithography increases the stage of comprehensibility, especially in rare congenital heart disease or in post-myocardial mechanical complication, as each patient has specific cardiovascular anatomy and encompassing structures ,34. CongSchmauss et al. studied the role of surgery planning by rapid prototyping in patients with primary cardiac tumors, and demonstrated that 3D prototyping facilitates the diagnosis and therapeutic process by detailing the precise location and dissemination of the cardiac neoplasm into cardiac layers, especially when conventional imaging  was unsatisfactory. They concluded that 3D printing enhanced both theoretic and intraoperative orientation for cardiac surgeons treating complex cardiovascular diseases ,41,42. MThe innovative potential of 3D printing includes facilitation of pre-procedural simulation for advanced intra-operative orientation and patient-specific tailored approach. Several experimental trials tried to assess whether rapid prototyping is applicable with simulation. Although the droplet printing process is expensive, it is capable to produce high precision 3D models. Additionally, it can use a multitude of polymer-based materials leading to fabricate models with variable density and flexibility to achieve real-like physical properties. Hazeveld et al. conducted a study to compare various 3D printing processes in terms of dimensional accuracy. They concluded that the highest dimensional precision method was the ink-jet technique . For insA common complication of atrial fibrillation is represented by thrombosis of the left atrial appendage. According to international guidelines, percutaneous closure of the left atrial appendage is a treatment strategy adopted in selected patients, in order to prevent systemic embolization of such thrombus. Selection of the best dimensions and closure devices may be difficult in some cases with difficult anatomy. The powder-based printing method is widely utilized in producing bio-compatible implants. Considering the availability of a wide range of compatible materials, such as titanium alloy, zinc alloy, and cobalt-chrome alloy, and their mechanical characteristics and biocompatibility, this technique is the most promising in fabricating implants in cardiovascular surgery or percutaneous cardiovascular intervention field. This method is also the most appropriate to fabricate sterilizable devices . In a reLodziński et al. described the role of 3D printing to assist complex electrophysiology procedures, in a case referred for ventricular tachycardia ablation in whom aberrant anatomic relationship between right ventricular outflow tract and right coronary artery was identified. A rapid prototyping model may be generated based on cardiac CT imaging, providing detailed information about anatomic relationship between cardiac structures, thus facilitating ablation of cardiac arrhythmia ,54,55,56The main use of computational flow dynamics (CFD) simulations is to better comprehend of the relationship between vessels, hemodynamics and development and progression of cardiovascular disease. Experiments for 3D printing and CFD simulation require the combined use of rapid prototyping models with a flow measurement system by MRI or particle image velocimetry . ZelicouA novel landmark in rapid prototyping technology concerns bioprinting or tissue engineering, which aims to provide a promising research opportunity. It consists of printing, layer by layer, a complex structure to mimic a real tissue structure. The most suitable technique is the droplet process with hydrogel materials. The laser printing technique has also been used in experimental studies, demonstrating a high spatial control of the cellular environment ,77. The Bio-fabrication of 3D models integrated with tissue specific cells that can be directly delivered on damaged tissue represents a unique potential solution for myocardial regeneration after a myocardial infarction. Molecular 3D bio-printing could represent the next generation in terms of personalized patient-specific medicine. This complex technology could facilitate interventions guided by specific biomarkers, in addition to tissue-based biomarkers. The novel paradigm of personalized medicine combines conventional anatomy imaging, functional assessing, physiology quantification, and also molecular diagnosis. Bio-printed models can determine specific molecular tagets for diagnosis and intervention, and molecular 3D printing may provide the boundary of future research, such as the development of imaging modalities with 3D printing technology, combined with genomics and molecular target labelling .The most used systems in bioprinting are laser direct writing, inkjet extrusion, and cell electrospinning. Basara et al. produced a tissue engineering patch for patients with myocardial infarction, using a 3D-printed conductive titanium carbide integrated in a poly-etilen-glycol hydrogel ,82. TheiThree-dimensional printing has been introduced in clinical practice, not only for research purposes. This method has the potential to trigger a paradigm shift towards a personalized approach in diagnostics and therapeutic management of various medical conditions. Three-dimensional models may also be used in a variety of cardiovascular applications, such as customizing tailored-patient devices, organ tissues or producing complex drugs delivery systems on-demand . The mosThree-dimensional printing has a great potential to revolutionize personalized medicine. This technology may be used for advanced diagnostic and treatment applications in various medical applications, such as peri-operative planning for complex congenital heart diseases, guidance of interventional procedures or procedural simulation. Recent experimental studies are directed to emerging applications of 3D printing, including bio-printing and molecular printing, which will allow a better approach of various cardiac pathologies. However, in the long term perspective, more experimental studies are needed to generate solid evidence regarding the benefits of this novel technology, as well as its feasibility in clinical practice.


For many years, a major question in ageing research has been whether organismal ageing processes affect all tissues similarly, or whether ageing of one organ system drives deficits in others, as originally proposed by Roy Walford in the context of autoimmunity a half-century ago . YousefzIn the second part of the paper, the authors take this notion a step further, by showing that the premature organismal ageing phenotype of Ercc1-deficient mice could be prevented in several tissues by infusion of splenocytes from normal young mice. Reciprocally, transfer of splenocytes from either younger Ercc1-deficient animals or unmanipulated old animals induced senescence in several tissues of young mice . These a


ABSTRACT IMPACT: Mobile app may help improve the depression symptoms among underserved patients OBJECTIVES/GOALS: Depression is one of most common mental health conditions and the leading cause of disability worldwide, affecting about one in 10 adults in the US. The aim of this study was to explore the factors that affect feasibility of incorporating mobile app self-management tools for depression in integrated primary care settings. METHODS/STUDY POPULATION: This was a cross-sectional questionnaire study of depressed patients at two primary care clinics in a Midwest academic medical center. Adult patients (≥19 years) who had an active or previous diagnosis of depression were included in the study. A self-administered survey collected information pertaining to demographics, smartphone ownership, data plan type, smartphone application usage, mobile app self-management interest, health literacy, and patient activation. Chi-square analysis was conducted to compare the patient demographic characteristics, the smartphone ownership, phone plan, smartphone use for health information between two clinics. Multinominal logistic regression analysis was conducted to examine the association between the patient activation and patient characteristics. RESULTS/ANTICIPATED RESULTS: Over 80% of patients owned a smartphone, 80.5% were willing to use data for depression management, and 68.9% believe an app can help in depression management. A higher literacy level was significantly associated with higher level of patient activation . These results suggest that planning interventions that use mobile apps within this patient population is likely feasible and the intended underserved patients at these clinics have an interest in using depression related apps which is similar to findings found by other studies exploring app interest. DISCUSSION/SIGNIFICANCE OF FINDINGS: Understanding patient activation levels within a given population can help to shape corresponding needs. The use of depression related self-management mobile apps will likely require the development of educational materials to facilitate patient use and engagement which means understanding the literacy needs of this population as well.


An increasing number of parents use both e-cigarettes and cigarettes . Previous studies have shown that dual users may have higher rates of contemplating smoking cessation than parents who only smoke cigarettes. This study was aimed to assess the delivery of tobacco cessation treatment  among parents who report being dual users vs. cigarette-only smokers.  A secondary analysis of parent survey data collected between April and October 2017 at 10 pediatric primary care practices participating in a cluster-randomized controlled trial of the Clinical Effort Against Secondhand Smoke Exposure (CEASE) intervention was conducted. Parents were considered to be dual users of cigarettes and e-cigarettes if they reported smoking a cigarette, even a puff, in the past seven days and using an e-cigarette within the past 30 days. Parents were asked if they received a prescription for nicotine replacement therapy and referral to the quitline to help them quit from their child's clinician. Multivariable logistic regression examined factors  associated with delivery of smoking cessation treatment (receiving prescriptions and/or enrollment in quitline) to smoking parents. Further, we compared the rates of tobacco cessation treatment delivery to dual users in the usual-care control practices vs. intervention practices. Discussion. Dual users who visited CEASE intervention practices were more likely to receive treatment than cigarette-only smokers when treatments were discussed. An increased uptake of tobacco cessation treatments among dual users reinforces the importance of discussing treatment options with this group, while also recognizing that cigarette-only smokers may require additional intervention to increase the acceptance rate of cessation assistance. This trial is registered with ClinicalTrials.gov, Identifier: NCT01882348. Of 1007 smokers or recent quitters surveyed in the five intervention practices, 722 parents reported current use of cigarettes-only and 111 used e-cigarettes. Of these 111 parents, 82 (73.9%) reported smoking cigarettes. Parents were more likely to report receiving any treatment if they were dual users vs. cigarette-only smokers . Child's insurance status, parents' sex, education, and race were not associated with parental receipt of tobacco cessation treatment in the model. No dual users in the usual-care control practices reported receiving treatment.  Parental smoking exposes children to thousands of harmful chemicals and toxins in tobacco smoke, increasing the risk of ear infections and respiratory infections such as bronchiolitis and pneumonia in infants and young children . In addiThere is no safe level of tobacco smoke exposure, and the only way to prevent children from this exposure is for parents to quit smoking . AccordiElectronic cigarettes (e-cigarettes) are increasing in popularity, and their use is rapidly growing. They are perceived by some as a less toxic alternative to traditional cigarettes or used as a smoking cessation aid . The U.SParents who use e-cigarettes and continue to smoke cigarettes may be exposing their children to the harmful chemicals present in both tobacco smoke and e-cigarette vapor , 21. E-cResearch has shown that higher proportions of dual users have the strong intention to quit compared to cigarette smokers . A 2019 Child healthcare practices are ideal settings to identify parents who smoke and or use e-cigarettes and offer them evidence-based cessation treatments . No prevA secondary analysis of data collected from ten pediatric practices in five U.S. states  randomized to the usual-care control and intervention arms of the CEASE trial was conducted between September and December 2019 . The CEAResearch assistants conducted interviews with parents after their child's visit to the pediatric office between April and October 2017. Parents were eligible to enroll in the study and answer a detailed survey about their tobacco use and smoking characteristics if they reported smoking at least 100 cigarettes in their lifetime and if they had smoked a cigarette, even a puff in the last 7 days or had quit smoking within the past 2 years. Exit interviews continued until approximately 200 eligible parents completed the detailed survey at each practice, resulting in a study sample of 1,007 parents in the intervention practices and 943 in the control practices.Parents were considered to be dual users of cigarettes and e-cigarettes if they reported smoking a cigarette, even a puff, in the past seven days and using an e-cigarette within the past 30 days. The primary outcome, as assessed by parental self-report at the exit interview, was the rate of receipt of tobacco cessation treatments among dual users vs. cigarette-only smokers. Parents were asked the following questions about receipt of tobacco cessation treatments as part of the detailed survey:Ask if you smoke cigarettesAdvise you to quit smoking or stay quitDiscuss medicine to help you quit smoking or stay quitGive you a prescription for medicine to help you quit smoking or stay quitDiscuss using a telephone “quitline” or other programEnroll you in a telephone “quitline” or other programDuring your visit today, did a doctor, nurse, or other healthcare provider:Rates of parents asked about smoking, advised to quit smoking, advised about how the free tobacco control quitline could help them quit smoking, assisted with smoking cessation by discussing medications to help quit smoking and offering a prescription for NRT, and referral to the quitline were compared between dual users vs. cigarette-only smokers, as well as between intervention practices and usual-care control practices. Parents were considered to have received tobacco cessation treatment if they answered “yes” to receiving a prescription for medicine to help them quit and/or being enrolled in the telephone quitline. Rates of delivering tobacco cessation assistance were compared between dual users vs. cigarette-only smokers and, also, between intervention practices and usual-care control practices. Parents were considered to have received tobacco cessation treatment if they answered “yes” to receiving a prescription for medicine to help them quit and/or being enrolled in the telephone quitline. Rates of delivering tobacco cessation assistance were compared between dual users vs. cigarette-only smokers and, also, between intervention practices and usual-care control practices.p values are two-sided and were considered significant at p < 0.05. Analyses were conducted using the Stata statistical software .Bivariate analyses were conducted using chi-square tests to explore the association between parent and child characteristics for parents who visited an intervention arm practice who received tobacco cessation treatment vs. those who did not receive any treatment. This analysis was limited to practices randomized to the intervention arm of the trial since assistance delivery was almost negligible in the control arm. Variables that had theoretical plausibility were added step-wise to a logistic regression model. Odds ratios (OR) and 95% confidence intervals (CI) were reported for each variable from the final model. All Of the 1007 parents who completed the detailed survey after their child's visit in the intervention practices, 804 (79.8%) were current cigarette smokers (including 722 parents who reported current use of cigarettes only) and 203 (20.2%) were recent quitters. Overall, 111 parents (28.3%) reported current use of e-cigarettes, and of these, 82 parents (73.9%) also reported using cigarettes. These 82 parents met our definition of dual use.Of the 943 parents who completed the detailed survey after their child's visit in the control practices, 727 (77.1%) were current smokers (including 646 parents who reported current use of cigarettes only) and 216 (22.9%) were recent quitters. Overall, 115 parents (12.2%) reported current use of e-cigarettes, and of these, 81 parents (70.4%) also reported using cigarettes. These 81 parents met our definition of dual use.In the intervention practices, of the total 804 parents who were current smokers and answered questions about discussing and receiving treatment at that day's visit, 113 (14.1%) reported receiving tobacco cessation treatment at that day's visit. The overall rates of screening parents for tobacco use, discussing using NRT, and discussing referral to the quitline and receipt of tobacco cessation treatments  were higher among parents who were dual users of e-cigarettes and cigarettes compared to cigarette-only smokers in the intervention arm . Of the The multivariable logistic regression model  shows thData from this study shows that dual users who visited CEASE intervention practices were more likely to receive a cessation treatment than parents who smoke only cigarettes two years after intervention implementation after controlling for child's insurance, parents' sex, education, and race. The data also shows that dual users in the usual-care control practices reported not receiving any treatment.As displayed in CEASE practices were trained to ask all parents about tobacco use and to provide advice and assist every parent who smoked by discussing and providing evidence-based cessation treatment, but in this study, we found that clinicians in the intervention practices were more likely to screen dual users for tobacco use. As a result, clinicians in the intervention practices were more likely to discuss evidence-based treatment options with dual users than cigarette-only smokers. It is not clear why the providers screened or discussed treatment more often with dual users than cigarette-only smokers. It could be that the dual users may be more receptive to screening for tobacco use and discussing various treatments to help them quit smoking. Another reason for this may be that clinicians may perceive that e-cigarette use among cigarette smokers is motivated by a desire to quit smoking  and may The rates of screening and discussing treatment options were higher in dual users compared to cigarette-only smokers, but the rates of acceptability of NRT prescriptions or quitline referral were also higher in dual users compared to cigarette-only smokers when any treatment was discussed with them. Of the parents with whom the medicine was discussed in the intervention practices, dual users were significantly more likely to accept prescriptions for NRT compared to cigarette-only smokers. Similarly, of the parents with whom quitline enrollment was discussed, dual user parents were significantly more likely to be enrolled in the quitline compared to cigarette-only smokers . This daWhen parental smokers quit smoking, they improve their own health , eliminaThe sample size of dual users is relatively small so the results should be interpreted cautiously. In addition, results are based on cross-sectional exit-survey data and no causal inferences should be made for the observed associations. Finally, self-reported data are subject to recall and response bias. However, the administration of the survey, immediately following the clinical visit, decreased the likelihood of recall bias of the tobacco control services received at the office visit.Dual users may have higher rates of contemplating smoking cessation than cigarette-only smokers and are more likely than cigarette-only smokers to receive tobacco cessation assistance after having discussions about NRT and/or the tobacco quitline with clinicians. An increased uptake of tobacco cessation treatments among dual users reinforces the importance of discussing treatment options with this group, while also recognizing that cigarette-only smokers may require additional intervention to increase the acceptance rate of cessation assistance.


Tumour neoantigens arising from cancer-specific mutations generate a molecular fingerprint that has a definite specificity for cancer. Although this fingerprint perfectly discriminates cancer from healthy somatic and germline cells, and is therefore therapeutically exploitable using immune checkpoint blockade, gut and extra-gut microbial species can independently produce epitopes that resemble tumour neoantigens as part of their natural gene expression programmes. Such tumour molecular mimicry is likely not only to influence the quality and strength of the body’s anti-cancer immune response, but could also explain why certain patients show favourable long-term responses to immune checkpoint blockade while others do not benefit at all from this treatment. This article outlines the requirement for tumour neoantigens in successful cancer immunotherapy and draws attention to the emerging role of microbiome-mediated tumour neoantigen mimicry in determining checkpoint immunotherapy outcome, with far-reaching implications for the future of cancer immunotherapy. However, most targeted drugs are not exclusively specific for cancer, because they usually inhibit wild-type versions of proteins, which can often be expressed in a wide range of tissues , and some small-molecule inhibitors, particularly first-generation agents, are multi-specific, thus showing additional on-target activity . Chimeric antigen receptor T cells also lack molecular specificity for cancer as their engineered affinities generally recognise wild-type antigens expressed on non-transformed cells as well as transformed cells, potentially leading to on-target, off-tumour toxicity .12One of the hallmarks of cancer is the overly high proliferative capacity of tumour cells, which can be therapeutically exploited using classical cytotoxic chemotherapy. The merit of anti-proliferative drugs in the debulking of large tumours is beyond dispute, but these agents are molecularly unselective for cancer, and invariably also damage healthy tissue such as bone marrow and the colonic mucosa.13 and well-documented evidence for infectious contributions to tumour development (e.g. human papilloma virus14 and Helicobacter pylori15), the dogma that ‘cancer is a genetic disease’ still holds true.16 The stepwise accumulation of mutations not only facilitates and exacerbates malignancy, but also generates a molecular fingerprint—comprised of tumour neoantigens—with definite specificity for cancer. While some tumour neoantigens are recurrent among cancer entities (e.g. KRAS position 12 mutations), the overall molecular fingerprint of a cancer is unique in every patient irrespective of tumour type. This collective of cancer-specific molecular aberrations, commonly termed the ‘tumour neoantigenome’, is an ideal target for cell-mediated immunity and cancer immunotherapy, with arguably the highest possible specificity for cancer considering the natural selectivity of cytotoxic, adaptive immune cells.19 Exogenously derived antigens can also be regarded as ‘tumour neoantigens’20 but, compared with endogenous neoantigens, the presence of viral or bacterial antigens is limited to relatively few cancer entities ).21 In addition, infection-related cancers still carry endogenous neoantigens,24 and some of these malignancies can be prevented with vaccines.25 This article will therefore prioritise endogenous tumour neoantigens arisen from mutations in host cells.Despite the growing appreciation of epigenetic regulationAlthough the presence of tumour neoantigens can discriminate cancer cells from ‘normal’ cells, epitopes produced by gut and extra-gut microbial species as part of their natural gene expression programmes can resemble tumour neoantigens—a phenomenon known as molecular mimicry. Molecular mimicry of tumour neoantigens by microbial species is likely to influence the host anti-cancer immune response through neoantigen-reactive T cells, and could explain why certain patients show favourable long-term responses to immune checkpoint blockade while others do not benefit at all from this treatment—tumour neoantigens are a mechanistic requirement for successful cancer immunotherapy.In this article, we will introduce the concept of microbial tumour neoantigen mimicry emanating from the huge overall amount and diversity of microbial genetic sequences in the human body, and elaborate on the potential significance of such mimicry for checkpoint immunotherapy responsiveness and outcome.20 and are excluded from central tolerance20—that is, they are not included in the mechanism used by the host to avoid attack by the immune system. Tumour neoantigens can also, in some cases, arise from the cancer-cell-specific usage of the epigenetic machinery, which can lead to the re-expression of silenced genes, the occurrence of unique splice or structural variants, and/or read-through transcription generating chimeric or intergenic products.27Tumour neoantigens mostly constitute de novo mutations that arise in transformed cells when tumour DNA is altered through non-synonymous point mutations, gene translocations, or insertions and deletions (indels)+ naive T cells (cross-priming).30 In turn, interactions between T cells with a cognate T cell receptor and the neoantigen-positive APCs induce clonal expansion and effector/memory differentiation;31 cytotoxic tumour killing by neoantigen-specific T cells results in cancer cell lysis, which might generate an ‘in situ vaccination effect’ such that beneficial epitope spreading might occur.32 In an ideal scenario, sustained anti-cancer immunity will ensue protection from cancer re-growth.Generally, immunological visibility of tumour neoantigens requires protein synthesis and a certain stability of the neoantigen product. Following the release of cytoplasmic content during cell death, for example, tumour neoantigens expressed as proteins will be taken up by tissue-patrolling antigen-presenting cells (APCs), which will then migrate to the tumour-draining lymph node and present the molecular tumour fingerprint to CD835 and microenvironmental mechanisms.36 In terms of cancer-cell-intrinsic mechanisms, downregulation of the major histocompatibility complex class I (MHC I) presentation pathway, which is essential for immune recognition and CD8+-cytotoxic-cell-mediated killing, is often observed in tumour cells, as is the increased expression of immune checkpoint molecules, which function as a ‘brake’ on the immune system, controlling the duration and extent of immune responses, maintaining self-tolerance and preventing autoimmunity.However, clinical evidence indicates that such protective anti-cancer immunity is rare among cancer patients, owing to immune evasion caused by various cancer-cell-intrinsic40 Mechanistically, ICIs neutralise negative feedback from immunological checkpoints or increase immune-stimulatory signalling, thus reinvigorating the endogenous tumour-specific T cell response. For CTLA-4-targeted immunotherapy, it has been shown that the clinical mode of action is likely to involve therapy-enhanced T cell priming, which broadens the tumour-specific CD8+ T cell response.41 Data from anti-CTLA-4-treated melanoma patients demonstrated a significant increase in the number of newly detected tumour-specific CD8+ T cell responses after treatment, whereas pre-existing virus- and tumour-specific T cell responses remained unchanged.41 Similar findings were reported for ICIs that target PD-1, thus altogether suggesting that pre-existing tumour-reactive T cells have a limited reinvigoration potential, which is outcompeted by de novo arising T cell specificities .42 The poor reinvigoration capacity of pre-existing tumour-specific T cells might indicate a partially exhausted cell state maintained independently from checkpoint signalling, which potentially limits the use of ICIs in certain patients.Immune checkpoint inhibitors (ICIs) that target checkpoint components, such as programmed death receptor (PD-1) and its ligand programmed cell death ligand 1 (PD-L1), or cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), or other inhibitory molecules can partly release this immune system brake and have shown impressive long-term outcomes in many cancer patients.43 and have established themselves as frontline therapeutics in a broad variety of solid tumour types.45 However, the speed of further development of ICI-based immunotherapy has started to decelerate, and significant challenges lie ahead, including the need for adapted clinical trial design and customisation of clinical safety and efficacy endpoints to more specifically account for the characteristic features of ICIs compared with classical anti-cancer drugs.46The clinical significance of ICIs is tremendous, as these agents have shown unprecedented responses even in advanced-stage tumours,53 Accordingly, tumour entities with a typically high TMB  respond significantly better to ICI-based immunotherapy than do cancers with a comparatively low TMB .54 Moreover, immunological treatments that target the tumour neoantigenome through reinvigoration (in the case of ICIs) or induction (for vaccines) of antigen-specific CD8+ T cells do not rely solely on the ‘driver’ nature of mutations, as passenger mutations can be equally immunogenic and thereby also serve as targets for immune destruction;56 by contrast, targeted treatment options require that the respective target molecule is causal for tumour growth and/or progression (‘oncogene addiction’).58The relevance of tumour neoantigens in medical oncology is highlighted by the positive association between tumour mutational burden (TMB) and the response to ICI treatment.Thus, tumour neoantigens arising from cancer-specific mutations are attractive targets for cancer immunotherapy, which has shown proven clinical relevance and important conceptual advantages over other types of cancer treatment that do not exploit the natural adaptability and versatility of the immune system.60 shown by tumours. Primary resistance mechanisms include the absence of a relevant number of immunogenic tumour antigens, defects in the antigen processing and presenting machinery, and insufficient T cell infiltration.59 Adaptive resistance to cancer immunotherapy refers to mechanisms by which cancer cells adapt to attacking immune cells, for instance through downregulation of MHC class I or upregulation of PD-L1.59 Acquired resistance refers to a clinical scenario where a patient initially responds to immunotherapy but later on progresses or relapses; oftentimes, this will involve Darwinian selection of resistant clones present already before treatment start.59Although ICIs have revolutionised the treatment of various solid tumour types and enabled unprecedented long-term remissions, a significant number of patients do not benefit from these therapies, owing to primary, adaptive, or acquired resistance mechanisms63 The time lost in treating poorly responding or refractory patients, along with the potential toxicity65 and high associated costs of ICI treatment,67 demands that better tools and predictive markers are established to discriminate patients who are likely to respond to treatment from those who are not likely to respond. More importantly, identifying the underlying mechanisms of non-responsiveness will be instrumental for the development of therapies that work in combination with ICIs to resensitise tumours to checkpoint inhibition.Only a small proportion of patients with non-small cell lung cancer (NSCLC), for example, respond favourably to ICI treatment.69 However, none of these markers is perfect and they are currently unable to predict treatment response with clinically sufficient precision.71 As an example, the predictive power of baseline tumour T cell infiltration might be reduced or lost if these T cells are exhausted or otherwise non-functional. Similarly, a high TMB indicating increased statistical odds for the presence of tumour neoantigens does not predict whether these cancer-specific antigens are immunogenic to an extent that is therapeutically relevant. Finally, the proven presence of the ICI target protein only indicates that the substrate is present to inhibit, but not whether, or to what extent, this substrate is causal for tumour growth. Therefore, novel and more reliable predictive markers are desirable to refine patient stratification and further optimise ICI treatment.Aside from TMB as a surrogate indicator for tumour neoantigens, the extent of tumour T cell infiltration at baseline and the expression of the respective ICI target (e.g. PD-1/PD-L1) at the protein level represent the most established markers for predicting the responsiveness to checkpoint blockade.Bifidobacterium72 and distinct Bacteroides species73 can promote anti-cancer immunity during ICI treatment. Accordingly, co-treatment with antibiotics reduces the clinical activity of checkpoint immunotherapy,78 whereas faecal microbiome transplantation (FMT) from ICI-responding patients to germ-free or antibiotic-treated mice partly rescues the anti-cancer effects of ICI treatment, dependent on the presence of A. muciniphila Gram-negative bacteria.79 Although these data can offer possibilities for straightforward therapeutic development , the mechanisms responsible for these observations remain elusive and a deeper understanding of the underlying biology is desirable.80Within the past decade, evidence has suggested an important role for the gut microbiome in determining the responsiveness to checkpoint immunotherapy. Two articles published in 2015 demonstrated that commensal 85 , anti-CTLA-4 treatment efficacy can be restored by oral gavage of B. fragilis, immunisation with B. fragilis-derived products or adoptive transfer of T cells specific for B. fragilis.73 It is worth mentioning in this context that a 2020 publication on cancer immunotherapy targeting CD47 (another checkpoint molecule) found that the accumulation of gastrointestinal bacteria in subcutaneously grown tumours was critical for the treatment response.86 In addition, another study showed that intratumoural accumulation of bacteria is common among cancer entities, with bacterial compositions being mostly biased towards intracellular bacteria that are capable of triggering CD8+ T cell responses.87 These results highlight the far-reaching effects of gastrointestinal microbes across organ systems/compartments and clearly emphasise the significance of the local tumour microbiome for responsiveness to cancer immunotherapy. Aside from the well-known gastrointestinal microbiome, a rich flora of microbial communities can also be found in the respiratory tract89 as well as on other various mucosal surfaces and non-mucosal surfaces of the human body.92 Thus, certain carcinomas  are likely to be influenced by a distinct tumour-associated habitat that might also shape the response to ICI therapy.A likely explanation for the functional significance of the gut microbiome in checkpoint immunotherapy responsiveness is immunological mimicry of tumour neoantigens by microbial peptide products85 Fig. . SupportConvincing evidence therefore suggests that the outcome of ICI-based cancer immunotherapy is influenced, at least partially, by the composition and functional quality of the (distant) gut- and the  tumour microbiome.+ T cells. Molecularly, these cells recognise tumour neoantigens typically as 8–11-mer peptides94 bound to MHC class I on the tumour cell surface. Random 11-mer permutation of the 20 proteinogenic amino acids in humans  can generate more than 1014 theoretical combinations, although the number of possible tumour neoantigens is correspondingly smaller considering that many of these sequence combinations will already be part of the host genome.Cancer immune surveillance is, to a large extent, carried out by CD896 and viruses can harness the host cell translational machinery to exclusively use the endogenous amino acid repertoire. It is believed that the human gastrointestinal tract harbours approximately 1014 microbes, which, together, equate to roughly 1000 times the number of cells and 10000 times the DNA content of the human body.97 Exact mathematical modelling is beyond the scope of this article, but it is with definite statistical likelihood that numerous individual tumour neoantigens are immunologically mimicked by peptide products of gut and extra-gut microbial species: identical peptide mimics comprise the same amino acid sequence as the corresponding tumour neoantigens and result in identical T cell epitopes, whereas ‘notably similar’ peptide mimics contain the same epitope core but differ in their flanking region or length, thus altering antigen presentation efficiency and/or generating T cell epitopes with nuance-like specificity alterations.99 Importantly, the actual likelihood of mimicry might be smaller than theoretically possible considering that microbial genomes encode functional proteins whose sequences might not be randomly structured which might lower the number of actually possible sequence permutations. In addition, not every neoantigen-mimicking microbial peptide will reach a concentration that is high enough for immunological recognition and modulation of the anti-cancer immune response.Many microbes that colonise the human body can synthesise all 20 proteinogenic amino acids of humans,101 Such microbial-antigen-containing APCs can also cross-present their phagocytosed cargo via MHC class I to induce cellular immune responses.103 Furthermore, intracellular microbes present in both cancer and immune cells87 can trigger direct CD8+ T cell responses in the tumour microenvironment (TME). Tumour neoantigen mimicry by microbial species is therefore likely to produce T cell clones of both the CD8 and CD4 lineage that are tumour-reactive despite never having previously seen actual tumour antigens. Such independent emergence of T cell populations with overlapping specificity can lead to functional immunological crosstalk that shapes overall anti-cancer immunity and influences the outcome of checkpoint-based immunotherapy.84The abundance of class-switched IgA antibodies with high affinities for microbial species in the gut microenvironment provides clear evidence for APC-dependent, humoral responses elicited and matured in gut-associated lymphoid tissue and/or regional lymph nodes.105 as well as the default unresponsiveness to food proteins in healthy individuals .107 However, it is unknown how compartment-specific microbial tolerance influences neoantigen-directed tumour immune surveillance (even more so during perturbations such as dysbiosis or infection). Although immune tolerance to commensals and their tumour neoantigen mimics might intuitively be more associated with reduced anti-cancer immunity, evidence has shown that T cells that are specific for certain gut commensals serve immune-stimulatory functions during checkpoint-based immunotherapy.73 These data suggest that the ‘tolerogenic’ environment of the gut, including tolerance to neoantigen-mimicking commensals, can nevertheless support anti-cancer immunity based on yet-to-be-determined mechanisms; conceivable means include compartment-specific effects as well as temporary disruptions of epithelial barrier function leading to translocation into the systemic circulation and subsequent failure of tolerance.Currently, the net effect of such immune interaction is unclear. Generally, the gastrointestinal tract and other body surfaces are considered a rather tolerogenic environment, an idea that is consistent with the notion of productive interplay with commensals under homoeostatic conditions81 In these patients, immunogenic neoantigens can be identified using a quality fitness model that takes into account differential presentation and homology to microbial-derived peptides.81 Of note, ‘high-quality’ neoantigens are selectively lost upon metastatic disease progression, suggesting immunoediting and, ultimately, immunological escape of clinically relevant tumour neoantigens.81In further support of clinically relevant tumour neoantigen mimicry, long-term survivors of pancreatic cancer show tumour-neoantigen-specific T cell clones with predicted cross-reactivity to microbial epitopes.109 and inosine,110 are known to have immune-modulatory activity and to influence cancer immunotherapy. Short-chain fatty acids are differentially expressed among immunotherapy responders and non-responders, and a high faecal abundance of acetic, propionic, butyric and valeric acid correlates with prolonged progression-free survival.109 The underlying mechanisms are yet to be fully elucidated, but are likely to involve altered T cell homoeostasis and differentiation as well as modulation of APC functions109 brought about by these short-chain fatty acids. Inosine as produced by Bifidobacterium spp. in the gut facilitates enhanced ICI treatment efficacy through a mechanism that involves the increased systemic translocation (owing to ICI-induced decreased gut barrier function) of inosine and subsequent activation of anti-cancer T cells.110 Considering the basic principles of immune activation and differentiation, it is very much conceivable that T cells, as well as their correspondingly induced cytokines, that recognise mimicked tumour neoantigens are subject to regulation by microbial metabolites.The microbiome-derived metabolome refers to the aggregate of individual microbial metabolites in a given compartment, several of which, including short-chain fatty acidsAt this stage, no conclusions on the role of microbial tumour neoantigen mimicry can be drawn; mechanistic studies in mice as well as consolidation in the clinical setting are warranted. Below we provide insight into how to experimentally address microbial tumour neoantigen mimicry to better understand responsiveness to checkpoint immunotherapy and optimise patient stratification. Molecular and immunological tools are available to define the contribution of gut microbes to the responsiveness of checkpoint immunotherapy and to systematically assess whether microbial tumour neoantigen mimicry acts to foster or impede protective anti-cancer immunity.Bifidobacterium, Bacteroides and Escherichia spp.) before the genetically modified bacteria are introduced into the gastrointestinal tract of immunocompetent mice using oral gavage; sustained bacterial engraftment might involve antibiotic conditioning.111 Tumour neoantigen mimicry can be enforced by transplanting tumour cells into bacteria-reconstituted mice, and the impact of such mimicry on anti-cancer immunity and immunotherapy responsiveness can be investigated , provided that limitations with adaptive immune responses can be overcome in humanised mice.116Modern genetic engineering facilitates the expression of almost any gene of interest in prokaryotic and eukaryotic cells and is key to mechanistically dissecting the function of genes. Using this reverse genetics approach to introduce tumour neoantigens into gut commensals, mouse models of immunological tumour mimicry can be experimentally generated. To achieve this, the genetic sequences encoding tumour neoantigens need to be cloned into suitable commensals ,124 they are largely artificial and can have limited predictive and translational value.126 Conversely, tumour neoantigens from mutagen-induced  or genetically-engineered tumours  might be more relevant because they have evolved ‘naturally’ and gone through immune selection and immune-editing processes.135 Such engineered/autochthonous models will produce genetically heterogeneous tumours with a comparatively high frequency of protein-altering mutations and copy number variations.136 From such models, clonal tumour cell lines can be established, and the tumour neoantigens can be identified using next-generation sequencing at the DNA level and compared with autologous reference tissue/blood.18 RNA sequencing can determine the fraction of neoantigens putatively expressed, and peptide–MHC binding algorithms such as NetMHCpan137 can predict neoantigen immunogenicity.The mimicked (neo-)antigens can include surrogate tumour antigens such as minigene-encoded epitopes of viral glycoproteins140 The enzyme-linked immunospot assay can probe the neoantigen-specific reactivity of ex vivo stimulated T cell populations142 and T cell receptor sequencing can characterise the clonality and overall repertoire of intratumoural T cell subsets.143To investigate the functional significance of immunological tumour neoantigen mimicry for ICI responsiveness and outcome, readouts addressing the quality and strength of the anti-cancer immune response as well as changes in tumour growth are required. Flow cytometry/fluorescence-activated cell sorting can help to delineate general immune (activation) patterns in tumour tissue, blood and lymph nodes, and can further facilitate neoantigen-specific T cell tracking and purification, making use of peptide–MHC multimer technology.144 and, third, ‘metastatic’ growth in a relevant compartment.119 These scenarios will allow various clinical settings, including those of high tumour burden, minimal residual disease and oligometastatic disease, respectively, to be experimentally explored. System perturbations such as co-treatment with antibiotics to reduce or abrogate microbial tumour neoantigen mimicry could be envisaged for control purposes; here, the particular timing of antibiotic treatment should be carefully selected considering treatment duration as well as lagging phase and washout effects.75Complementing these important immune-targeted readouts, the effects on functional tumour growth need to be assessed, and a number of scenarios should be explored: first, ‘continuous’ tumour growth resulting from a high dose of transplanted tumour cells; second, tumour engraftment emanating from a critically low dose of transplanted tumour cells;18 and the gastrointestinal microbiome composition needs to be characterised using metagenomic shotgun sequencing of stool samples.145 Dedicated bioinformatics interrogation can uncover the overlapping tumour neoantigen and microbial sequences, and provide an estimate for the overall ‘tumour antigenic similarity’. Correlating the degree of this similarity with clinical endpoints such as survival, treatment response and immune-related adverse events, a potential proxy for treatment efficacy,65 will demonstrate how microbial tumour neoantigen mimicry affects patient outcomes under ICI therapy  potential. By contrast, the composition of the gut microbiome is more dynamically regulated149 might conceivably be responsible for modulating both natural81 and ICI-induced84 anti-cancer immunity; however, evidence suggests that molecular mimicry of tumour neoantigens by microbial species is likely to be an important factor. The net effect of tumour neoantigen mimicry on ICI treatment efficacy currently remains unknown and future mechanistic and clinical studies are clearly warranted. Nonetheless, the results of various studies that have investigated microbial composition in the context of checkpoint immunotherapy responsiveness support a model in which mimicry has favourable effects on anti-cancer immunity and ICI-related treatment outcome  is associated with immune activation and long-term survival of pancreatic cancer patients151 as well as a memory T cell signature and favourable response to PD-1-targeted immunotherapy in patients with NSCLC.152 Along these lines, the beneficial effects of therapeutic FMT from ICI-responding patients79 might be at least partially attributable to an increase in the overall microbial diversity, similar to the results seen for FMT in patients with inflammatory bowel disease.154Several mechanisms, including discrepancies in the levels of some metabolites,ome Fig. . First, 85 Importantly, the gut microbiome engages in crosstalk with the tumour microbiome such that therapeutic FMT will automatically also modulate the tumour microbial composition.151 Clinical research will show whether such interventions are sufficient to reinstate relevant tumour neoantigen mimicry or whether targeted engineering of commensals based on the tumour neoantigen landscape would be required. Such personalised, molecularly informed approaches are technically feasible, and could be achieved within a clinically reasonable time frame (approximately 6–8 weeks), provided that trial data can overcome safety concerns regarding the use of genetically-modified live bacteria.Assuming a protective role for microbial tumour neoantigen mimicry, interventions aimed at supplementing probiotics and/or increasing microbial diversity represent rational therapeutic approaches that warrant clinical investigation.141 Given the clarification of the definite role of tumour neoantigen mimicry in ICI responsiveness, this knowledge can help to complement MHC class I binding prediction137 and assist in the consideration of which neoantigens to choose for vaccination. In this way, the anti-cancer immune response could be (re-)directed specifically to molecularly mimicked neoantigens, provided that mimicry indeed shows beneficial effects on ICI treatment efficacy.Another promising anti-cancer strategy is the use of ICIs together with tumour-neoantigen-specific vaccines.155—by the process of immunoediting. Immunoediting of tumour neoantigens has been reported, and is associated with metastatic progression in pancreatic cancer.81 Other prominent mechanisms of cancer immune escape include somatic loss of HLA class I molecules (loss of heterozygosity (LOH))156 and defects in the MHC class I antigen processing and presenting machinery.157 Importantly, HLA class I LOH predicts poor overall survival in a subgroup of ICI-treated NSCLC patients and can be used as a complementary marker for checkpoint immunotherapy responsiveness to refine TMB-based stratification.158 In the context of tumour neoantigen mimicry and associated therapeutic interventions, HLA class I LOH needs to be considered as a potential confounder for tumour-microbiome cross-reactive T cell responses. While HLA class I LOH occurs spontaneously and is not preventable per se, any neoantigen-directed therapeutic intervention should ensure coverage of several HLA class I alleles to avoid dependence on a single genetic locus.Immunogenic cancer cells adapt to the selective pressure of the immune system, conceptually traversing through three successive phases—elimination, equilibrium and escapeThis review article has highlighted the crucial importance of tumour neoantigens for cancer immunotherapy and provided impetus for investigating the significance of microbial tumour neoantigen mimicry for checkpoint immunotherapy responsiveness and outcome. Immunologically informed considerations on ICI therapeutic use will optimise their clinical performance and set the stage for microbiome-targeted interventions to boost response rates and prolong survival.


Evaluation of gene interaction models in cancer genomics is challenging, as the true distribution is uncertain. Previous analyses have benchmarked models using synthetic data or databases of experimentally verified interactions – approaches which are susceptible to misrepresentation and incompleteness, respectively. The objectives of this analysis are to (1) provide a real-world data-driven approach for comparing performance of genomic model inference algorithms, (2) compare the performance of LASSO, elastic net, best-subset selection, Five large This analysis explores a novel approach for comparisons of model selection approaches in real genomic data from 5 cancers. Our benchmarking datasets have been made publicly available for use in future research. Our findings support the use of  Regression models are frequently used in cancer genomics, where they provide insight into the interactions between genes. Sparse regression models were developed to allow modelling of a large set of variables with a small number of samples – a scenario encountered frequently in genomics. However, evaluation of genomic model structures remains challenging, due to uncertainty regarding the true system of interactions. Previous studies have compared methods with synthetic data, which may not reflect the challenges of real-world data. In this study, genomic datasets were identified which contained enough samples to provide reasonable estimates of the true structures – which were used as ‘gold-standards’. Sparse regression methods were tasked with estimating the true structure given a small proportion of the available samples, allowing for comparison against the gold standards.Our results show that the interaction strengths estimated by the 17 The tractability of penalised regression approaches in this setting has led to a large volume of research into their applications.79 Penalised regression offers robust predictions in high dimensional data and mechanistic insights through the estimated coefficient vector.7911 However, it is difficult to test the assumptions which penalised approaches require for valid model selection in real world datasets.13 Furthermore, standard model selection approaches such as cross-validation and the Bayesian information criterion may be unreliable for model selection in the high-dimensional setting.15High-dimensional regression problems are ubiquitous in modern oncological research, as datasets often contain fewer observations than variables.16 Penalised regression methods facilitate modelling in the high-dimensional setting through the addition of bias terms. The inverse covariance matrix, Here, notation is conventionally abused such that the  penalises the model by the Ridge regression The Least Absolute Selection and Shrinkage Operator (LASSO) penalty penalises the model by the Ridge regression partially alleviates instability under collinearity by constraining coefficient magnitude.19 An additional benefit of LASSO shrinkage is a tendency to shrink small coefficients to zero, leading to a ‘sparse’  This behaviour is highly useful in bioinformatics, where analytic tasks often require the selection of a small number of predictive variables given a large candidate set. However, the lasso model structure is subject to inconsistency under subsampling. The Elastic Net is a combines the sparsity of 21 Penalties of ridge regression, LASSO and elastic net affect large coefficients more than small coefficients, biassing coefficient estimates. ‘Best subset selection’ provides a theoretical solution to this issue through the selection of the optimal model attainable with :The LASSO approach has ‘oracle’ properties under some conditions, meaning that predictions are nearly as good as if the true set of predictor variables were known.Thus, for some  This combination of simplicity and unbiasedness has been described as a ‘holy grail’ of sparse modelling. However, models suffer from inconsistency. Furthermore, issues such as non-convexity and NP-hardness complicate best-subset model selection.23 Recent developments such as mixed integer optimisation have facilitated best subset model learning. Combinations of Best subset selection be may be approximated through 2427 or semi-synthetic2830 datasets to assess variable selection performance. Real data analyses have focussed primarily on the models’ predictive capacity.3133 Accurate predictions may not guarantee correct model structure, especially in the highly collinear conditions commonly encountered in genomics. The representativeness of synthe-tic datasets is both uncertain and untestable. Further-more, results of these studies have been discordant, suggesting dependence on the benchmark datasets and validation techniques.10The true generating distribution for observational biological data is typically uncertain, complicating validation of estimated coefficient vectors. Consequently, many model assessments have employed synthetic and KEGG contain experimentally verified interactions, which may be used to externally validate genomic model structure. This approach has been used in previous analyses37 and is limited by the uncertain completeness of such databases. Furthermore, the activity profile of interactions between a given set of genes may change with experimental conditions and unobserved confounders.39 Consequently, the set of active predictors for a specific dataset may not align exactly with a static database. Finally, effect sizes may not be comparable between documented interactions, precluding the assessment of model coefficients by this method. Data-partitioning facilitates model validation without ground truth data, by assessing model generalisability to unseen observations. As training and validation observations are sampled from the same data, their distribution is asymptotically identical. However, the distribution may be difficult to estimate when 15Genomic databases such as REACTOMEGiven the limitations of currently available methods for assessment of variable selection performance in genomic data, an urgent need exists for a novel approach.The primary objectives of this study were to:Provide a real-world data-driven approach for comparing performance of high dimensional model inference algorithms in cancer genomics for both prediction and variable selection. We evaluate models by simulating Compare the performance of penalised linear regression methods for prediction and variable selection.Compare algorithmic selection by internal cross-validation to preselection according to performance in external test datasets under our validation approach.These objectives are realised by subsampling real  with the GEOquery library. Local institutional review board approval and informed participant consent were documented in each data publication.4246Five cancer genomics datasets were extracted from Gene Expression Omnibus contains serum miRNA expression profiles for 4113 individuals; 1280 with breast cancer, 54 with benign breast disease, 63 with non-benign breast disease, 451 with various other cancers and 2836 non-cancer controls. Participants with breast cancer were recruited through admissions and referrals to the National Cancer Centre Hospital Japan between 2008 and 2014. Exclusion criteria were (1) administration of medication prior to serum sampling and (2) advanced cancer in other organs. Controls were recruited from (1) National Cancer Centre Biobank, Yokohama Minoru clinic and the Toray Industries staff. Samples from individuals with non-benign breast diseases and other cancers were extracted from the National Cancer Centre Biobank. miRNA expression was measured with was collected on the Toray Industries 3D-Gene Human miRNA Oligo Chip microarray.GSE73002 contains serum miRNA expression profiles for lung cancer patients. About 1566 pre-operative and 180 post-operative samples are available, in addition to 2178 samples from patients without cancer, collected from the National Cancer Centre Japan and the Yokohama Minoru Clinic. Exclusion criteria were (1) miRNA expression quality check failure, (2) history of other malignancy, (3) missing clinical information, (4) pre-collection therapy and (5) over 180 days had passed between collection and surgery. miRNA expression was measured with was collected on the Toray Industries 3D-Gene Human miRNA Oligo Chip microarray.GSE137140 contains full length single-cell RNAseq data from 5902 cells extracted from 18 patients with stage I to IV squamous cell carcinoma (SCC) of the oral cavity at the Massachusetts Eye and Ear Infirmary. Tissue samples were extracted from surgical biopsies of the primary tumour or lymph node. Sequencing was performed on the Illumina Nextseq 500 platform and transcript-per-million values reported.GSE103322 contains single-cell RNAseq data from 22 ascites samples in 11 patients with high-grade serous ovarian cancer at Brigham and Women’s Hospital and the Dana-Farber Cancer Institute. About 9609 CD45+ depleted samples, profiled with 10× were included in this analysis. Sequencing was performed on the Illumina NextSeq 500 platform and transcript-per-million values reported.GSE146026 contains 6341 single-cell RNAseq profiles from patients with isocitrate dehydrogenase mutant astrocytoma at Massachusetts General Hospital. Tumour tissue was collected from surgical resections and malignancy confirmed under frozen section. Following disaggregation, profiling was performed by Smart-seq2. Sequencing was performed on the Illumina NextSeq 500 and transcript-per-million values reported.GSE8956748:Where datasets had Here Here 50 The same cross-validation folds were employed for each penalisation method in a given experiment. Predictive performance and variable selection performance were assessed using the remaining test observations. Experiments were repeated for 100 different training samples, for each of 5 datasets and for both cross-validation routines, yielding 1000 experiments with which to compare penalisation methods for each sample size.In each experiment, 500 design variables and a response were randomly selected from the available gene expression variables in 1 of the 5 datasets. A small proportion of the observations rameters . Regular10 As the true coefficient vector, Model assessment metrics and notation followed previous comparative analyses. measured 3 metrics of predictive performance – proportion of variance explained (PVE), relative risk (RR) and relative test error (RTE).Thus, :Higher PVE indicates superior fit, and PVE is limited by the signal to noise ratio (SNR) such that Optimal relative risk is 0 and nullity is 1.Relative risk (RR) was employed as an performance metric in Bertsimas’ analysis.Relative test error (RTE) compares error to the noise variance: we measured the cosine similarity of Following calls for model coefficient similarity assessment,t-tests:Active (non-zero) variable selection performance was also estimated under  and predictors were classified according to a cutoff Significance was adjusted for multiple comparisons using false-discovery-rate (FDR) controlt-test over all 3000 experiments.To evaluate our model validation approach, we deployed it as a penalty preselection method, comparing it to traditional selection by minimisation of the internal cross-validation error. For each experiment, for each of 3 comparison metrics , a penalisation method was ‘preselected’ according to performance in experiments of equivalent sample size in the other 4 datasets. In each relevant experiment, penalisation methods’ performances were ranked and the method with the lowest rank aggregate performance was selected. The test performance of this method was compared to that of the penalisation method which yielded the lowest mean squared error on internal cross-validation. Overall performance of preselected penalties was compared to internal cross-validation selected penalties using a 2-sided paired Experiments represented a broad range of signal:noise ratios , with high SNR in experiments sampled from GSE73002 , intermediate SNR in GSE137140  and low SNRs in GSE103322 , GSE146026  and GSE89567 . The number of significant coefficients in each experiment was typically small  and followed a right-skewed distribution . This is consistent with the scale-free property of genomic networks, in which a small number of genes have many interactions.P < 2e-16. PVE:SNR curves .Predictive performance metrics are provided in R curves  demonstrVariable selection performance metrics are provided in −16), F1 score (P = 3.3 × 10−6) and coefficient similarity (−16) when compared to selection by internal cross-validation. In many cases the same penalisation method was selected under preselection and internal validation, leading to equivalent performance. Although aggregated improvements under preselection were statistically significant, internal validation outperformed in some experiments  lacked the graduation required to compare penalisation methods at the n = 25 level. This limitation was particularly important in the setting of active variables estimated according to a sharp significance cutoff. The coefficient similarity metric proved useful in this regard, as it was continuous and independent of any significance cutoff. However, coefficient similarity provides little insight into on model complexity, a central aspect of genomic network inference. Indeed, although Real-world genomic datasets were employed in this analysis. Accordingly, our results are expected to be more representative of actual experimental modelling conditions. Data-driven model assessment was facilitated by the large number of observations available in these datasets. However, our results may not generalise to datasets with incomparably distributed signal or noise. Logistic and Cox regression tasks present addition challenges such as class imbalance and censoring, which are beyond the scope of this analysis.Click here for additional data file.Supplemental material, sj-txt-1-cix-10.1177_11769351211056298 for Sparse Regression in Cancer Genomics: Comparing Variable Selection and Predictions in Real World Data by Robert J O’Shea, Sophia Tsoka, Gary JR Cook and Vicky Goh in Cancer Informatics