Enkephalins of the opioid system exert several cardiorenal effects. Proenkephalin (PENK), a stable surrogate, is associated with heart failure (HF) development after myocardial infarction and worse cardiorenal function and prognosis in patients with HF. The association between plasma PENK concentrations and new‐onset HF in the general population remains to be established.We hypothesized that plasma PENK concentrations are associated with new‐onset HF in the general population.We included 6677 participants from the prevention of renal and vascular end‐stage disease study and investigated determinants of PENK concentrations and their association with new‐onset HF (both reduced [HFrEF] and preserved ejection fraction [HFpEF]).p < .001). After a median 8.3 (7.8–8.8) years follow‐up, 221 participants developed HF; 127 HFrEF and 94 HFpEF. PENK concentrations were higher in subjects who developed HF compared with those who did not, 56.2 (45.2–67.6) versus 52.7 (45.1–61.6) pmol/L, respectively (p = .003). In competing‐risk analyses, higher PENK concentrations were associated with higher risk of new‐onset HF , including both HFrEF  and HFpEF . These associations were, however, lost after adjustment for eGFR.Median PENK concentrations were 52.7 (45.1–61.9) pmol/L. Higher PENK concentrations were associated with poorer renal function and higher NT‐proBNP concentrations. The main determinants of higher PENK concentrations were lower estimated glomerular filtration rate (eGFR), lower urinary creatinine excretion, and lower body mass index (all In the general population, higher PENK concentrations were associated with lower eGFR and higher NT‐proBNP concentrations. Higher PENK concentrations were not independently associated with new‐onset HFrEF and HFpEF and mainly confounded by eGFR. Among these subjects, those who had already developed HF before the second screening assessment or were classified as having HF with midrange ejection fraction (left ventricular ejection fraction [LVEF] 41%–49%) were excluded , as well as subjects with missing PENK values (n = 156), resulting in a study population of 6677 subjects  study was designed to prospectively investigate the natural course of urinary albumin excretion (UAE) and its association with the development of cardiorenal disease in the general population.The PREVEND study was approved by the medical ethics committee of the University Medical Center Groningen and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants.2.2All participants completed a self‐administered questionnaire regarding demographics, cardiovascular and renal disease history, smoking habits, alcohol consumption, and medication use prior to the baseline screening assessment. Medication use was verified with community pharmacies. Blood pressure was measured on the right arm, every minute for 10 and 8 minutes, respectively during two examination visits of the second screening visit. The mean of the last two recordings from each of the two examinations was used. Fasting blood samples were obtained in the morning and stored at −80°C. All participants collected two consecutive 24‐hour urinary specimens, subsequently stored at −20°C.PENK was measured in plasma using a sandwich immunoassay targeting PENK amino acids 119–159  as described previously.2.33 (with 6 mm added in women) multiplied by QRS duration.Estimated glomerular filtration rate (eGFR) was calculated using the CKD‐EPI creatinine formula.2.4Details on the methodology for identifying new‐onset HF in PREVEND have been published previously.2.5Cardiovascular endpoints were obtained through the Dutch national registry of hospital discharge diagnoses (PRISMANT), and adjusted according to detection in hospital records. Cardiac events  included acute myocardial infarction (ICD‐10 code 410), acute and subacute ischaemic heart disease (411), coronary artery bypass grafting, and percutaneous coronary angioplasty. Cardiovascular events  included cardiac events with the addition of stroke , and vascular interventions. Data on mortality was obtained from Statistics Netherlands to allow for competing risks analysis.2.6Time to events was defined from the date of the subject's second screening visit until the date of first new‐onset HF, cardiovascular events, death, or January 1, 2011. If a person had moved to an unknown destination, the date of last contact served as the censor date.2.7t‐test or analysis of variance (ANOVA), skewed variables with the Mann–Whitney U or Kruskal–Wallis test, and categorical variables with χ2 tests.Based on the population size and range of PENK, PENK was divided into quintiles. Data are presented as mean ± SD when normally distributed, as median (Q1–Q3) for skewed variables, and as frequency (percentage) for categorical variables. Trends over PENK quintiles were statistically tested with the Cochran–Armitage trend test, Jonckheere–Terpstra test, or a linear regression model for categorical, skewed, or normally distributed variables, respectively. Otherwise, continuous normally distributed variables were tested with the student independent p < .1 in univariable analysis were included in the multivariable analysis and subjected to the backward elimination method. For all linear regression analyses, the assumption of linearity and normal distribution of residuals was checked, as well as checks for outliers. If necessary, variables were transformed using natural logarithm, including PENK. Variables in multivariable regression models were checked for multicollinearity, which led to exclusion of age from the model due to multicollinearity with eGFR, with weak contributory value from age. Variables with p < .05 were retained in the final multivariable regression model. Competing‐risk regression analysis was used to assess whether PENK concentrations were associated with new‐onset HF, HFrEF, and HFpEF, where death was considered a competing risk in all analyses. In analyses pertaining HFrEF and HFpEF specifically, the other HF entity was additionally considered a competing risk. Competing‐risk regression analysis was executed using the cmprsk package, which uses Fine–Gray regression. Competing‐risk regression models were adjusted for sex, eGFR, and body mass index (BMI), and results are expressed as hazard ratios (HRs) per doubling of PENK with their corresponding 95% confidence intervals (CIs). The assumption of proportionality of hazards and linearity were checked in all analyses. In addition, interactions were evaluated in cox proportional hazard models between PENK concentrations and sex, KDIGO risk categories, and presence of eGFR <60 ml/min/1.73 m2. Cox proportional hazard models were also constructed for cardiovascular events to evaluate the prognostic predictability of log doubling of PENK concentrations, adjusted for sex and eGFR. Additional packages that were used in the analysis included the packages clinfun, DescTools, foreign, Hmisc, ggplot2, ggpmisc, lm.beta, nephro, psych, survival, and survminer. A two‐tailed p‐value <.05 was considered statistically significant. All statistical analyses were executed using R .Determinants of PENK concentrations were analyzed using univariable and multivariable regression analyses, in which all variables with 33.1p < .001). Subjects with higher PENK concentrations were, among others, older, more often female, had a lower BMI, were more often on antihypertensive treatment, had a lower eGFR, and had higher concentrations of NT‐proBNP, serum creatinine, and urea (Table p for trend <.001). UAE approximated a U‐shape over quintiles of PENK (p < .001). In Table p < .001), and also an increase of PENK concentrations over albuminuria categories in all GFR categories except G3b .In the current study cohort, the mean age was 54 ± 12 years, and 3360 (50.3%) of subjects were female. Median plasma PENK concentrations were 52.7 (45.1–61.9) pmol/L in the overall study cohort, and 54.9 (47.2–64.0) pmol/L and 50.7 (43.3–59.3) pmol/L in women and men, respectively . Among new‐onset HF cases, 127 subjects developed HFrEF, and 94 subjects HFpEF. In univariable competing‐risk regression analysis (Table p < .001), new‐onset HFrEF , and new‐onset HFpEF . After adjustment for sex and its main determinant eGFR, PENK concentrations were no longer associated with new‐onset HF and HFrEF. After additional adjustment for BMI, PENK concentrations were also no longer associated with new‐onset HFpEF. There was no interaction between plasma PENK concentrations and sex, KDIGO risk category, nor with presence of eGFR <60 ml/min/1.73 m2 at baseline with respect to all three outcomes.In the current study cohort, a total of 221 subjects developed HF after a median follow‐up time (from the second screening visit) of 8.3 (7.8–8.8 years). The median time to HF diagnosis was 5.1 (2.9–6.7) years. In subjects who developed HF, median PENK concentrations were 56.2 (45.2–67.6) pmol/L and 52.7 (45.1–61.6) pmol/L in subjects who did not develop HF .Competing risks cumulative incidence curves for new‐onset HF, HFrEF, and HFpEF respectively over quintiles of PENK concentrations illustrate an increasing risk with higher quintiles of PENK concentrations . The fifth and highest PENK quintile was univariably associated with new‐onset HF  and new‐onset HFrEF , but not after adjustment for sex and eGFR.In 3.4p = .004 for non‐fatal cardiac events; HR = 1.55 [95% CI 1.20–2.00] per doubling of PENK, p < .001 for non‐fatal cardiovascular events; and HR = 4.07 [95% CI 2.22–7.49] per doubling of PENK, p < .001 for fatal cardiovascular evens), but not after adjustment for sex and eGFR. There was no interaction present between plasma PENK concentrations and sex, KDIGO risk category, or presence of eGFR <60 ml/min/1.73 m2 at baseline.Non‐fatal cardiac events, non‐fatal cardiovascular events, and fatal cardiovascular events occurred in 359, 434, and 38 subjects respectively. Plasma PENK concentrations were univariably associated with all three events  showed that higher PENK concentrations were associated with a combined endpoint of death and HF.2, with respect to new‐onset HF, but the numbers of subjects with a high‐risk category and/or eGFR <60 ml/min/1.73 m2 may have been too low to confidently show this interaction in these subpopulations of interest. PENK concentrations also showed an increase over albuminuria categories, even with normal eGFR or slightly/moderately decreased eGFR, implying that PENK concentrations might also be (more modestly) associated with glomerular damage. In multivariable regression analysis for PENK concentrations, UAE was retained in the final model, although strongly surpassed by eGFR. In a previous study conducted in PREVEND, no association was found between PENK concentrations and future CKD defined according to presence of albuminuria.In the present study, the strong association between higher concentrations of PENK and renal dysfunction confirmed previous findings. PENK concentrations markedly increased over KDIGO GFR categories irrespective of albuminuria category and the main independent correlate of higher PENK concentrations was lower eGFR. The association between PENK concentrations and renal dysfunction might be explained by compensatory increased PENK production to exert kidney protective effects,We also found that higher PENK concentrations were associated with higher NT‐proBNP concentrations. This suggests that activation of the opioid system might be more pronounced in subjects with higher cardiac filling pressures. Alternatively, PENK might be a representation of poorer renal function meaning that higher NT‐proBNP concentrations are associated with poorer renal function. Although still well below lower reference limits, it has been shown that higher NT‐proBNP concentrations in the general population are associated with increased risk of all‐cause mortality and cardiovascular events.Importantly, the association of PENK concentrations with new‐onset HF and HFrEF was attenuated after adjustment for eGFR. The association between PENK concentrations and new‐onset HFpEF was attenuated after additional adjustment for BMI on top of eGFR. This suggests that PENK concentrations increase with declining eGFR, but that eGFR likely is the independent predictor of new‐onset HF, and not PENK by itself. In addition, subjects in our study population rarely reached the 99th percentile cutoff of >80 pmol/L (with a previously reported median (range) for PENK concentrations in the general population being 45 (9–518) pmol/L).Our findings are in contrast with a previous study showing that after myocardial infarction, higher concentrations of PENK were independently associated with a higher risk of developing HF.4.3The findings of our study are based on a large, well‐characterized population of subjects from the general population and included a long follow‐up. HF diagnosis of both HFrEF and HFpEF was thoroughly validated and loss of follow‐up was minimal. Some rate of underdetection could however play a role especially regarding HFpEF, when diagnosis is not pursued from the general practitioner to the hospital. In addition, positioning of PENK in the general population could be performed quite extensively due to the large number of covariates that was available. Finally, optimal comparison of PENK values between different studies was ensured by the use of the same assay.The fact that PENK concentrations were only measured at the second screening visit is a disadvantage of our study, as we could not study dynamic changes of PENK concentrations, notably in closer proximity to HF diagnosis. Furthermore, 61 patients already developed HF before the second screening visit and therefore had to be excluded. The results of our study are also predominantly based on subjects of Caucasian ethnicity, limiting the applicability of our results to other ethnicities. Lastly, the PREVEND cohort was enriched with subjects with increased albumin excretion, and although adjustments were applied for the presence or absence of albuminuria, we cannot exclude that it might have affected study results, however in pooled analyses with other cohort studies the results of the PREVEND studies always matched the overall results.4.4In subjects from the general population, higher plasma PENK concentrations were associated with lower eGFR and higher NT‐proBNP. Higher PENK concentrations were however not independently associated with new‐onset HFrEF and HFpEF and mainly confounded by eGFR. In the general population, PENK can be considered as a novel renal marker primarily related to renal glomerular function.The University Medical Center Groningen, which employs several authors, has received research grants and/or fees from AstraZeneca, Abbott, Bristol‐Myers Squibb, Novartis, Roche, Trevena, and ThermoFisher GmbH. Adriaan A. Voors received consultancy fees and/or research grants from: Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Cytokinetics, Merck, Myokardia, Novartis, Novonordisk, and Roche Diagnostics. Kevin Damman received consultancy fees from Boehringer Ingelheim and AstraZeneca. Oliver Hartmann and Janin Schulte are employed by SphingoTec GmbH, the manufacturer of the PENK immunoassay. Rudolf A. de Boer received speaker fees from Abbott, AstraZeneca, Novartis, and Roche. Johanna E. Emmens, Jozine M. ter Maaten, Frank P. Brouwers, Lyanne M. Kieneker, and Stephan J. L. Bakker have nothing to disclose.Johanna E. Emmens, Jozine M. ter Maaten, and Adriaan A. Voors contributed to the conception/design of the work. Frank P. Brouwers, Lyanne M. Kieneker, Oliver Hartmann, Janin Schulte, Rudolf A. de Boer, and Stephan J. L. Bakker contributed to the acquisition of the data for the work. Johanna E. Emmens executed data analysis of the work. Jozine M. ter Maaten, Kevin Damman, and Stephan J. L. Bakker assisted in interpretation of data. Johanna E. Emmens drafted the manuscript. Johanna E. Emmens, Jozine M. ter Maaten, Frank P. Brouwers, Lyanne M. Kieneker, Oliver Hartmann, Janin Schulte, Rudolf A. de Boer, Adriaan A. Voors, Stephan J. L. Bakker, and Kevin Damman critically revised the manuscript for important intellectual content. All provided their approval for the final version of the manuscript.Data S1. Supporting information.Click here for additional data file.


To assess if in adults with COVID‐19, whether those with diabetes and complications (DM+C) present with a more severe clinical profile and if that relates to increased mortality, compared to those with diabetes with no complications (DM‐NC) and those without diabetes.Service‐level data was used from 996 adults with laboratory confirmed COVID‐19 who presented to the Queen Elizabeth Hospital Birmingham, UK, from March to June 2020. All individuals were categorized into DM+C, DM‐NC, and non‐diabetes groups. Physiological and laboratory measurements in the first 5 days after admission were collated and compared among groups. Cox proportional hazards regression models were used to evaluate associations between diabetes status and the risk of mortality.p = 0.093) and 1.18  in DM+C and DM‐C, respectively.Among the 996 individuals, 104 (10.4%) were DM+C, 295 (29.6%) DM‐NC and 597 (59.9%) non‐diabetes. There were 309 (31.0%) in‐hospital deaths documented, 40  were DM+C, 99 (9.9%) DM‐NC and 170 (17.0%) non‐diabetes. Individuals with DM+C were more likely to present with high anion gap/metabolic acidosis, features of renal impairment, and low albumin/lymphocyte count than those with DM‐NC or those without diabetes. There was no significant difference in mortality rates among the groups: compared to individuals without diabetes, the adjusted HRs were 1.39 (95% CI 0.95–2.03, Those with COVID‐19 and DM+C presented with a more severe clinical and biochemical profile, but this did not associate with increased mortality in this study. As people with diabetes are disproportionately affected by COVID‐19, there is a need to identify diabetes sub‐groups who could be at high risk of adverse outcomes when presenting to hospitals.Our real world study of people with diabetes and laboratory confirmed diagnoses of COVID‐19 uses service level data including biochemical markers for initial assessment. We compared these values and mortality levels in people with and without diabetes and hyperglycaemic complications.We found people with COVID‐19 and diabetes with existing complications presented with a more severe clinical and biochemical profile.However, the association between diabetic/hyperglycemic state in COVID‐19 and all cause‐mortality was not statistically significant in this study. People with diabetes mellitus (DM) represents one group particularly adversely affected.Recent focus has been on in‐hospital morbidity and mortality. Within UK hospital settings, DM represents a significant proportion of all COVID‐19 cases and deaths.eg vaccinations).Secondly, whilst recognized COVID‐19 risk factors, including the presence of co‐morbidities, provide useful information on those at higher risk of acquiring COVID‐19, these risk factors do not account for initial clinical status or severity on initial admission to hospitals.The aim of this study is to examine whether DM+C patients with COVID‐19 present with more adverse clinical and biochemical profiles and increased mortality compared to people with COVID‐19 and DM‐NC or without DM in an extensively phenotype adult cohort presenting to a large urban UK hospital.22.1This retrospective cohort study, using prospectively collected data, was conducted in Queen Elizabeth Hospital Birmingham (QEHB), a large teaching hospital within University Hospitals of Birmingham (UHB). It is situated in the city of Birmingham, West Midlands, UK and has over 1200 beds. West Midlands is a multi‐ethnic region with 30% of residents classified as Black, Asian and Minority Ethnic (BAME), of whom South Asians (18.9%) and Blacks (6.0%) are the most prevalent minorities.2.2We constructed the data using the Patient Administration Database (PAS) and the Electronic Medical Record system, known as the Patient Information and Communication System (PICS). The PAS database record information on age, gender, ethnicity, address (post code), primary reason for admission, discharge diagnostic codes, inpatient death, and discharge destination. Admission is defined as the time spent by an individual from recorded time of entry to recorded time of exit from the hospital. The PAS database was linked using unique patient identifiers  to the PICS. It is a purpose‐designed system which records all in‐hospital prescriptions, laboratory results and electronic observations and generates alerts to reduce prescription errors and notify abnormal blood results.2.3All adult patients (>16 years old) who presented to QEHB from 20th of March to 9th of June 2020 with a confirmed positive swab specimen result for COVID‐19 were included in the analysis.2.4Patient demographics and clinical data were collected from PAS and PICS. Clinician confirmed co‐morbidities were available from PAS and PICS, complemented by in‐hospital prescription data and diagnostic codes derived from previous hospital admissions. The PICS encodes diagnoses using NHS Digital SNOMED CT browser alongside and mapped on to ICD‐10 codes allowing for the presentation and inclusion of historically entered ICD‐10 codes. The composite CVD, hypertension, severe renal diseases, dementia, chronic obstructive pulmonary disease, cancer, asthma, atrial fibrillation, were defined by the combination of ICD‐10 codes and PAS‐PICS encoded diagnoses, whichever was available at study entry. The composite CVD was defined as one of the following presentations: myocardial infarction, peripheral vascular disease, heart failure, cerebrovascular infarction, stroke, transient ischaemic attack and ischaemic heart disease.2.5All patients underwent nasopharyngeal and oropharyngeal swab specimen (miniature absorbent pads) testing for COVID‐19. These were processed in accordance with NHS guidance within UHB NHS laboratories.Venepuncture was conducted to ascertain venous blood for routine metabolic blood tests; the first blood tests were taken before administration of any intravenous fluids. An arterial blood gas was performed to assess for acid‐base status and estimated partial pressure of oxygen and carbon dioxide levels. Physiological assessments included measurement of respiratory rate and pulse rate via a pulse auxometer, systolic and diastolic blood pressures and temperature. All swab specimens, blood tests and physiological assessments were performed by trained healthcare professionals following standard operating procedures.All physiological and laboratory measurements were categorized based on clinically meaningful thresholds and the earliest available measurement was used in the analysis. Missing data were presented as a missing category for all measurements.2.6DM was defined as those who with an ICD‐10 record of DM or its complications, or who were recorded to have been prescribed any of the DM drugs using a previously published algorithm.2.72), overweight (BMI of 25 kg/m2 to < 30 kg/m2), obesity (BMI of 30 kg/m2 to < 35 kg/m2), obesity II & III (BMI of ≥35 kg/m2). Charlson Comorbidity Index (CCI) was calculated (DM and DM complication score were removed from the equation) using ICD‐10 code and was categorized into four groups .Ethnicity was self‐reported by the patient or their family members on admission to hospital. Body mass index (BMI) was categorized based on the World Health Organisation Criteria: normal weight (BMI of < 25 kg/m2), heart rate, temperature and serum albumin). All these measurement across three groups are presented visually as mean  or median (IQR) for symmetrical and skewed continuous variables, respectively.In the non‐diabetes, DM‐NC and DM+C groups, we looked at the trends of available physiological and laboratory measurements in the first 5 days after admission. These included measures of metabolic acidosis and compensation , bicarbonate (HCO3‐) and hydrogen ions), indicators of underlying presence of inflammation , measures of immune response (lymphocyte count), serum electrolytes and renal function  and other clinically useful physiological and laboratory measurements  in hours. A small proportion (3.2%) of patients were not discharged at study end‐date.2.9Baseline characteristics for the total population and DM sub‐groups are presented as mean ± standard deviation  or median  for symmetrical/ skewed continuous variables and as frequency (percentage) for categorical variables. All physiological and laboratory measurements were compared across non‐diabetes, DM‐NC, and DM+C groups using ANOVA or Kruskal‐Wallis test depending on data distribution. For categorical variable comparisons, Chi‐square test was applied.2.10Cox proportional hazard regression models were used to calculate crude and adjusted hazard ratios (aHRs), together with their corresponding 95% Confidence intervals (CI). Covariates in the Cox model for mortality included age, sex, ethnicity, BMI categories, and CCI categories (model comprised the interaction effect between age and CCI categories). Missing data for BMI and ethnicity were included in the Cox model as a missing category. All statistical tests were two‐tailed and a p‐value < 0.05 was considered statistically significant. All analyses were conducted in R 4.0.0 .33.1There were 996 people with a laboratory confirmed positive swab specimen for COVID‐19 admitted to QEHB from 20th March to 9th June 2020. In the total cohort, the mean age was 68 years. Among them 399 (40%) had DM, of whom 104 and 295 patients had codes indicative of DM+C and DM‐NC, respectively, Table 3.2p = .001 and p < .001 respectively (Table 2 was more common in people with DM+C (54.8%) than those with DM‐NC (18.6%) and those without DM (12.9%), p < .001. People with DM+C also had lower serum albumin and lymphocyte count.Individuals with DM+C were more likely to present with a pH level < 7.3 and a higher anion gap than in those with DM‐NC or those without DM, ly Table . FeaturePeople with DM+C had lower levels of serum CRP, heart rate or temperature compared to people with DM‐NC and those without DM.Where measurements were available, these observations largely persisted in the first 5 days after admission Figures &2.3.3There were 309 in‐hospital deaths during follow‐up: 40 (38%) in patients with DM+C, 99 (34%) in people with DM‐NC and 170 (28%) in people without DM  and in those with DM‐NC  compared to patients without diabetes. However, these findings did not reach statistical significance, p‐values of 0.093 and 0.226 respectively.4Our study shows that people admitted with symptomatic COVID‐19 and DM were more likely to be men, from a BAME background, and had higher BMI and more CVD, and more ESRD compared to those without DM. In addition, patients with DM+C had higher BMI, CVD and more ESRD compared to DM‐NC, as would have been expected. Patients with DM+C had higher anion gap, urea, potassium, and lower pH, lymphocytes, albumin, compared to DM‐NC. In addition, the DM+C group had lower heart rate, higher BP, less tachypnoea, lower Hb compared to patients with DM‐NC. Patients in the DM+C group had a 39% higher mortality rates than people without DM or with DM‐NC, but this did not reach significance. Other predictors of higher mortality included age, higher CCI, men and BAME groups.The relatively higher mortality observed in people with DM compared to those without DM in this study is consistent with that reported previously in other COVID‐19 studies and other studies showing higher mortality in patients with DM in relation to influenza, SARS and MERS.The mortality risk was non‐significantly greater in people with DM+C than DM‐NC or those without DM. This in part could be due to differences in BMI, ethnicity, CCI, CVD and ESRD, which have been reported previously to be associated with increased risk of adverse COVID‐19 outcomes.DM+C is usually associated with longer duration of DM and more adverse glycaemic control, which in turn may impact on pathological mechanisms affecting the response to COVID‐19. Furthermore, the hyperglycaemic complication itself could leave the body more prone to a more adverse outcome where it impacts on ACE‐2 cell expression. A recent study found worse glycaemic control was a risk factor for increased mortality.The results of this study need to be considered in the context of its limitations. This study was conducted from a single centre which might affect the external validity of the findings. However, this single centre is a large, tertiary and receives patients form a large population beyond its localities, especially during the COVID‐19 crisis. The sample size of our study is relatively small, as was the follow‐up period of three months, which was reflected in the some of the 95% CIs reported in the study, and is reflected by the 39% increased risk of mortality in COVID‐19 DM+C patients compared to non‐diabetes not reaching significance. For the same reason, we were unable to analyse results and outcomes between those with type 1 and 2 diabetes. Secondly, the results reflect the findings in people who attended the hospital but does not account for COVID‐19 cases treated in the community setting only. Furthermore, some variables had missing data, but to minimize the impact of this on the results, missing data categories were used in the multivariable analysis. We were unable to account for some variables including use of medications for diabetes or hypertension. Also it was not possible to account for people with pre‐diabetes in this study. Finally, data regarding HbA1c and diabetes duration were not available in our analysis, although those with complications are likely to have higher HbA1c and longer diabetes duration.The strengths of this study include the in‐depth phenotyping which was made possible with the presence of the appropriate data management systems in the hospital trust. Also, cases of COVID‐19 were laboratory confirmed. In addition, our study population included multiple ethnicities and in proportions mirroring those of the West Midlands county.Finally, a recent mortality risk score has been developed for the general population, which utilizes total number of co‐morbidities as one parameter for scoring, without considering presence/ absence of diabetes separately.5In this multi‐ethnic cohort of adults with COVID‐19 presenting to hospitals, we found clinical and biochemical profiles were adverse in people with DM+C.SAM has received research support from Novo Nordisk Research Foundation UK and Academy of medical Sciences. SB has received grants, personal fees, and support to attend educational meetings from Novo Nordisk; grants from The Binding Site; personal fees from AstraZeneca, Merck, Sharpe & Dohme, and Janssen; personal fees and support to attend educational meetings from Boehringer Ingelheim; personal fees and support to attend educational meetings from Eli Lilly; and personal fees and support to attend educational meetings from Sanofi‐Aventis and NAPP. WH has received Received Research Grants, Travel Grant and Consultancy for following AZ, BI, Eli Lilly, Jansen, Novo Nordisk, Sanofi, NAPP and MSD. Other authors have no declarations.Krishna Gokhale: Software ; Writing‐original draft . Jingya Wang: Investigation ; Validation ; Writing‐review & editing . Abd A. Tahrani: Writing‐review & editing . G. Neil Thomas: Writing‐review & editing .


The majority of mutations were likely passenger missense mutations, and only few affected previously described cancer‐related genes. Using RNA‐sequencing, we identified putative viral integration sites on 18q12.3 and on 8p22 in two SCCs of the uterine cervix. The overall nonsilent mutation rate of uterine cervix SCCs was significantly lower than that of SCLCs, HPV‐driven cervical adeno‐ and squamous cell carcinomas, or HPV‐positive head and neck squamous cell carcinomas. Unlike SCLCs, which are reported to harbor almost universal TP53 and RB1 mutations and a dominant tobacco smoke‐related signature 4, uterine cervix SCCs rarely harbored mutations affecting these genes  and displayed a dominant aging (67%) or APOBEC mutational signature (17%), akin to HPV‐driven cancers, including cervical adeno‐ and squamous cell carcinomas and head and neck squamous cell carcinomas. Taken together, in contrast to SCLCs, which are characterized by highly recurrent TP53 and RB1 alterations, uterine cervix SCCs were positive for HPV leading to inactivation of the suppressors p53 and RB, suggesting that these SCCs are convergent phenotypes.Small cell carcinoma (SCC) of the uterine cervix is a rare and aggressive form of neuroendocrine carcinoma, which resembles small cell lung cancer (SCLC) in its histology and poor survival rate. Here, we sought to define the genetic underpinning of SCCs of the uterine cervix and compare their mutational profiles with those of human papillomavirus (HPV)‐positive head and neck squamous cell carcinomas, HPV‐positive cervical carcinomas, and SCLCs using publicly available data. Using a combination of whole‐exome and targeted massively parallel sequencing, we found that the nine uterine cervix SCCs, which were HPV18‐positive  of the uterine cervix is a rare and aggressive neuroendocrine carcinoma. Here, we show that uterine cervix SCCs are primarily HPV18‐positive. Using whole‐exome and/or targeted massively parallel sequencing, we found that uterine cervix SCCs have a low mutation burden, rarely harbor  HPVhuman papillomavirusSCCsmall cell carcinomaSCLCsmall cell lung cancerUCSCCuterine cervix small cell carcinomaWESwhole‐exome sequencing1Cervical cancer is the second leading cause of cancer‐related deaths among women worldwide resulting in > 4000 deaths annually in the United States alone . The mosThe term small cell carcinoma (SCC) encompasses a set of highly aggressive neuroendocrine carcinomas, which preferentially not only affect the lung   but may TP53‐ and RB1‐inactivating mutations [TP53 locus, but loss of heterozygosity of the RB1 gene locus (13q14) was found to be rare [TP53 but no KRAS mutations [TP53, PIK3CA, and KRAS [Pulmonary SCLCs have a highly characteristic molecular fingerprint, with almost universal utations , which autations , 12, 13,locus 13q was founlocus 13q was founand KRAS , 16.To date, it is unclear whether UCSCCs would have genetic features that recapitulate those of other common tumors of the uterine cervix (adenocarcinoma/ squamous cell carcinomas) or HPV‐driven cancers , or whether these tumors are more similar to SCCs of other anatomic sites such as SCLC. To address this question, we sought to define the constellation of somatic mutations, copy number alterations, and mutational signatures of UCSCCs using a combination of whole‐exome, targeted, and RNA‐sequencing. The mutational profiles of UCSCCs were compared with those from HPV‐positive head and neck squamous cell carcinomas and cervical carcinomas, and SCLCs.22.1The pathology files of University Hospital Cologne, Germany; University Hospital Muenster, Germany; Institute of Pathology Duisburg, Bethesda Hospital, Germany; and Memorial Sloan Kettering Cancer Center (MSK), New York, USA, were searched over a 20‐year period using the term ‘small cell’ and ‘neuroendocrine carcinoma’. Cases with origin in the lung were excluded after extensive chart review. For selected cases, data including tumor location, patient demographics, and information about prior treatment regimens were collected. In each case, the original hematoxylin‐and‐eosin (H&E)‐stained slide and immunohistochemistry (IHC) slides, if available, were reviewed. Cases lacking detailed clinical information or any doubt about being metastatic deposits from the lung were excluded. Cases were reviewed by four pathologists with an interest and expertise in neuroendocrine neoplasms . Diagnosis of SCC required the identification of tumors with cohesive round‐to‐ovoid cells, sparse cytoplasm, finely granulated nuclei, nuclear molding, and inconspicuous nucleoli. Cases were considered to be UCSCC if immunohistochemical expression of at least one neuroendocrine marker was detected and a high proliferation index (≥ 50%), assessed using Ki67, was observed. Sections from representative blocks of cases confirmed as UCSCCs were cut and H&E‐stained, and the block with the greatest tumor cell percentage was chosen for downstream analyses. A total number of nine UCSCCs were selected. All samples were anonymized prior to the analysis, and the approval by the institutional review boards (IRBs) of the respective contributing authors’ institutions was obtained. Informed consent was obtained from the patients following the requirements of the IRB‐approved protocols. This study is in compliance with the Declaration of Helsinki.2.2Representative 4‐μm‐thick formalin‐fixed paraffin‐embedded (FFPE) sections of each case were cut and subjected to ancillary immunohistochemical assessment using antibodies against cytokeratin (CK) AE1/E3, Ki67, chromogranin A and/or synaptophysin, and p16  were stained using nuclear fast red and subjected to microdissection by a pathologist (A.M.S.) using a sterile needle under a stereomicroscope  to ensure a percentage of tumor cells greater than 80% and that the normal tissue was devoid of any tumor cells as previously described [For each of the nine cases included in this study, ten 8‐μm‐thick tumor and matched normal tissue sections  were designed using Primer3. Standard PCR was performed using the AmpliTaq Gold 360 DNA Polymerase  following the manufacturers’ protocol. The forward and reverse primers  were used for amplification of 312‐ and 251‐bp products, respectively, in the stable domain of the HPV. DNA samples from the HPV16‐positive CaSKi and HPV18‐positive HeLa cell lines were employed as positive controls .2.5n = 6) or MSK‐IMPACT sequencing targeting 505 cancer‐related genes (n = 3) at MSK’s Integrated Genomics Operations (IGO), as previously described [in silico using a combination of mutation prediction algorithms, as previously described [et al. [Microdissected tumor and matched normal DNA samples were subjected to WES  identified by WES was subjected to orthogonal validation using Sanger sequencing, as previously described [A subset of the somatic nonsynonymous mutations (escribed  (Table S2.7n = 29) [n = 281) [n = 42 [n = 110 [https://gdc.cancer.gov/about‐data/publications/pancanatlas) [P values < 0.05 were considered statistically significant. Statistical analyses were performed using r v3.1.2 and prism 7 .The frequencies of somatic mutations identified in UCSCCs studied here were compared with those of HPV‐positive head and neck squamous cell carcinomas  , HPV‐posn = 281) , and SCL [n = 42 , n = 110anatlas) , 40 and/2.8RNA of sufficient quality and quantity for RNA‐sequencing was obtained from the two frozen cases (SSC1 and SCC2); paired‐end massively parallel RNA‐sequencing was performed at MSK’s IGO using validated protocols, as previously described , 20. The33.1The median age at diagnosis of the nine UCSCC patients was 40 years ; however, no other recurrent nonsynonymous somatic mutations were identified across the nine UCSCCs , TP53 R248Q (SCC9T), PIK3CA E545K (SCC2T), and GNAS R844H (SCC4T), as well as some truncating mutations, including PMS2 Q727* (SCC3T), KDM6A Y890* (SCC4T), and NF1 Q357*  for tumor and 146× (range 88×–181×) for normal samples, and of MSK‐IMPACT sequencing coverage of 405× range 278×–408×) for tumor and 122× (range 115×–202×) for normal samples Table . The UCS78×–408× CCs Fig. . SCC8T d×–336× fond FGFR3 , 48, 49.nd FGFR3 , includiMYC amplification in SCC9T, and SSC5T harbored a FOXO3 homozygous deletion , which did not reveal predicted in‐frame fusion transcripts/ read‐throughs with high driver probability  and with adequate encompassing and spanning reads (> 5) Table . We did,3.3P < 0.001, Benjamini–Hochberg test) than the mutation rates described for SCLCs , HPV‐driven cervical adeno‐ and squamous cell carcinomas (nonsilent mutation rate 3.7/Mb), or HPV‐positive head and neck cancers  [TP53 mutations are present in the vast majority of SCLCs (up to 98%) [TP53 mutation frequencies  [PIK3CA mutations occurred in previously reported HPV‐positive cervical adeno‐ and squamous cell carcinomas, head and neck cancers [KMT2C and KMT2D were reported in cervical adeno‐ and squamous cell carcinomas , but were absent in previously reported SCLCs and in the UCSCCs analyzed here  , 52, 53. to 98%) , 38, 40, to 98%) , and the to 98%) , 38, 40  to 98%) , 37 , while SCC7, the only case that tested positive for HPV16, displayed an APOBEC signature  and displayed mutational signatures associated with aging or the activity of the APOBEC family of deaminases, whereas SCLCs generally displayed tobacco smoke‐related mutational signatures.Here, we characterized nine UCSCCs at the histologic and the molecular level to assess whether these tumors are genetically related to their morphologic and biologic counterparts in the lung, other common types of cervical cancer, or whether these tumors are genetically more similar to other HPV‐driven carcinomas, such as carcinomas of the head and neck. Our analyses demonstrated that UCSCCs harbor a low overall mutation burden, few copy number alterations, and no highly recurrently mutated genes. Only 2 of 9 UCSCCs studied here harbored utations , 16. Anoutations . FurtherTP53 and RB1 being found in the vast majority of cases. Given that the UCSCCs analyzed here harbored HPV18 or HPV16 and that the viral oncogenes E6 and E7 inactivate p53 and pRB, we anticipated that these tumors would display wild‐type TP53 and RB1 but would harbor functional loss of these tumor suppressor genes. Consistent with this notion, only SCC2T and SCC9T harbored TP53 hotspot mutations. Hence, despite the lack of TP53 and RB1 somatic mutations in the UCSCCs analyzed here, they likely display inactivation of the protein products of these genes akin to SCLCs.All but one UCSCC studied here were HPV18‐positive, in contrast to common‐type cervical cancers, which most commonly are HPV16‐positive , 66. HigThis study has several limitations. UCSCCs are aggressive tumors, which are only rarely resected, and the availability of clinical samples for research is therefore limited; hence, the sample size of the current study is small. The genetic analysis of nine cases performed here, however, provided us with novel insights on their landscape of mutations, gene copy number alterations, and mutational signatures. In addition, we only performed whole‐exome and targeted sequencing analyses of the UCSCCs and RNA‐sequencing for two cases; we therefore cannot rule out that noncoding alterations and/or changes at the epigenetic level may play a role in UCSCCs.5TP53 and RB1 alterations, UCSCCs were found to be positive for the presence of HPV, which targets and inactivates the suppressors p53 and RB.In summary, we demonstrate here that UCSCCs are characterized by few nonrecurrent mutations and few copy number alterations, and display aging and APOBEC‐related mutational signatures, akin to other forms of HPV‐related malignancies. In contrast to SCLCs, which are characterized by DSK is a founder, consultant, and equity holder of Paige. AI and receives royalties from UpToDate and the American Registry of Pathology, outside of this work. JSR‐F reports receiving personal/consultancy fees from Goldman Sachs, Repare Therapeutics, and Paige.AI, membership of the scientific advisory boards of VolitionRx, Repare Therapeutics, and Paige.AI, membership of the Board of Directors of Grupo Oncoclinicas, and ad hoc membership of the scientific advisory boards of Roche Tissue Diagnostics, Ventana Medical Systems, Novartis, Genentech, and InVicro, outside the scope of this study. BW reports ad hoc membership of the scientific advisory board of Repare Therapeutics, outside the scope of the submitted work. The remaining authors have no conflicts of interest to declare.AMS, BW, and JSR‐F conceived and supervised the study. EW, WH, CDG, BW, and RB provided samples. AMS, MvP, DSK, and KJP performed histologic review. AMS performed sample microdissection. AMS, GSM, EMdS, SP, and BW carried out experiments. AAJ performed immunohistochemical analyses. IdB and PS performed bioinformatics analyses. AMS, IdB, PS, BW, and JSR‐F interpreted results and drafted the manuscript. All authors reviewed and approved the final version of the manuscript.Fig. S1. Comparison of whole‐exome and high‐depth targeted sequencing of uterine cervix small cell carcinomas SCC1 and SCC4.Fig. S2. Detection of HPV16 and HPV18 DNA in uterine cervix small cell carcinomas.Table S1. Antibody clones, dilutions, antigen retrievals and scoring used for the immunohistochemical analyses performed.Table S2. Primers for Sanger sequencing validation of mutations identified by whole‐exome sequencing.Table S3. Sequencing statistics, number of somatic mutations identified and validation rates.Table S4. Non‐synonymous somatic mutations identified in small cell carcinomas of the uterine cervix using whole‐exome and MSK‐IMPACT targeted sequencing.Table S5. Putative in‐frame fusion transcripts with driver probability > 0.1 identified in SCC1 and SCC2 using RNA‐sequencing.Table S6. Putative HPV integration sites based on RNA‐sequencing.Click here for additional data file.


Moreover, the CHAMP image can be transformed into a virtually stained histological image (Deep‐CHAMP) through unsupervised learning within 15 s, where significant cellular features are quantitatively extracted with high accuracy. The versatility of CHAMP is experimentally demonstrated using mouse brain/kidney and human lung tissues prepared with various clinical protocols, which enables a rapid and accurate intraoperative/postoperative pathological examination without tissue processing or staining, demonstrating its great potential as an assistive imaging platform for surgeons and pathologists to provide optimal adjuvant treatment.Rapid and high‐resolution histological imaging with minimal tissue preparation has long been a challenging and yet captivating medical pursuit. Here, the authors propose a promising and transformative histological imaging method, termed computational high‐throughput autofluorescence microscopy by pattern illumination (CHAMP). With the assistance of computational microscopy, CHAMP enables high‐throughput and label‐free imaging of thick and unprocessed tissues with large surface irregularity at an acquisition speed of 10 mm A rapid, label‐free, and slide‐free histological imaging method, termed computational high‐throughput autofluorescence microscopy by pattern illumination (CHAMP) is proposed. With the assistance of computational imaging and unsupervised learning, CHAMP enables rapid and accurate pathological examination without tissue processing or staining, demonstrating great potential as an assistive imaging platform for surgeons and pathologists to provide optimal adjuvant treatment intraoperatively. However, the imaging throughput is therefore restricted to tens of megapixels , are fundamentally suitable for time‐sensitive applications as their imaging throughput can reach hundreds of megapixels via parallel pixel acquisition. However, fluorescence labelling is generally required by these methods to visualize features that are analogous to FFPE histology, which is challenging to be integrated into the current clinical practice. Given this, label‐free imaging contrast is highly desirable in modern clinical settings.The great demand in histopathology has inspired lots of efforts in achieving a rapid and non‐invasive diagnosis for unprocessed tissues. Some cutting‐edge microscopy techniques  and reflectance confocal microscopy (RCM), pave a way for rapid label‐free inspection of human breast tissue. However, they are typically not designed at subcellular resolution.The aforementioned PAM and nonlinear microscopy play an indispensable role in non‐invasive label‐free characterization of various biological structures, through either absorption‐induced thermoelastic expansion (PAM), intrinsic autofluorescence (MPM), molecular vibration (SRS), or non‐centrosymmetric orientation (SHG). The simultaneous multiplexing22 of  and ∼20 µm in human skin)). In addition, the shallow DOF is unable to accommodate various surface irregularities and tissue debris presented in surgical resection specimens, leading to severe out‐of‐focus blurs which ultimately prevent high‐quality imaging of fine structures in thick specimens. Although extended DOF can be applied to extract in‐focus information at the tissue surface through a sequence of axially‐refocused images, the achievable throughput of the system is largely sacrificed.Standard histological images are acquired at a subcellular resolution which is essential for pathological analysis. However, the inherent trade‐offs between resolution, field‐of‐view (FOV), and depth‐of‐field (DOF) fundamentally pose an impediment for rapid and high‐resolution imaging of thick tissues through a high numerical aperture (NA) objective lens. First, the image quality will be significantly degraded once the resulting shallow DOF  is shorter than the optical‐sectioning thickness of the employed imaging modality, which is tunable with light‐sheet microscopy and SIM, but tissue‐dependent with MUSE (determined by the ultraviolet (UV) penetration depth , structural proteins , aromatic amino acids , and heterocyclic compounds , naturally form a fundamental contrast mechanism with deep‐UV excitation in CHAMP. High imaging throughput and long DOF can be achieved with the assistance of computational microscopy, making CHAMP highly suitable for intraoperative tissue assessment  where immediate feedback should be provided to surgeons for optimal adjuvant treatment. Furthermore, an unsupervised neural network is implemented to transform a CHAMP image of an unlabeled tissue into a virtually stained histological image (Deep‐CHAMP), ensuring an easy interpretation by pathologists. As thick resection tissues are inevitably deformed during the FFPE workflow , it is impractical to obtain a pixel‐to‐pixel registered label‐free CHAMP image with its corresponding histological image to form a paired training data as required by some recently reported virtual staining networks. In contrast, we employ unsupervised learning based on the architecture of a cycle‐consistent generative adversarial network (CycleGAN), which enables image translation without paired training data, fundamentally favoring the virtual staining of CHAMP images of thick and unprocessed tissues. Diagnostic features are quantitatively extracted from Deep‐CHAMP with high accuracy. The versatility of CHAMP is experimentally demonstrated using mouse brain/kidney and human lung tissues prepared with various clinical protocols, which enables rapid and accurate intraoperative/postoperative pathological examinations without tissue processing or staining. The high‐throughput, high‐versatility, cost‐effective, and ease‐of‐use features of our CHAMP microscope hold great promise in clinical translations to revolutionize the current gold standard histopathology.Here, we propose a promising and transformative histological imaging technology, termed computational high‐throughput autofluorescence microscopy by pattern illumination (CHAMP), which enables high‐throughput and label‐free imaging of thick and unprocessed tissues with large surface irregularity at an acquisition speed of 10 mmrophores,28 inc22.1Figure and other endogenous fluorophores, is used for illumination in our CHAMP system to maximize the negative contrast of cell nuclei  and two discriminators . The generator A2BG learns to transform grayscale images to color images, while the generator B2AG learns to transform color images to grayscale images. A sequence of unpaired CHAMP images and hematoxylin and eosin (H&E) stained images are fed to the neural network to undergo a forward training cycle  the assistance of autofluorescence spectroscopy, as metabolic enzymes such as NADH and FAD can effectively serve as a biomarker to differentiate normal tissues from malignant lesions, and by (ii) the development of deep‐learning algorithms.Although an exogenous contrast agent is not required for CHAMP imaging, significant variations in autofluorescence intensity can still be observed in CHAMP as the excitation light is scattered/absorbed differently among various tissue types and functional areas .37 The2 due to the use of an iterative reconstruction framework that involves a massive number of Fourier transformations. This issue can be potentially addressed by the implementation of powerful computational resources or the introduction of a deep‐learning approach, which allows super‐resolution reconstruction with a reduced number of acquisitions under low light conditions. This is expected to dramatically speed up structured illumination approaches and release the computational burden of CHAMP imaging.CHAMP can potentially reach higher imaging speed to further shorten the diagnostic timeframe for more time‐sensitive applications. Currently, the imaging speed is limited by the exposure time of each speckle‐illuminated image, which is nearly 280 ms with an illumination power of 2 mW to maintain a good signal‐to‐noise ratio (SNR). The acquisition speed can be further accelerated by increasing excitation power. Another limiting factor is the number of acquisitions that are required for super‐resolution reconstruction. The minimum number of acquisitions is related to the sparsity of the imaged features. According to our experiments, a sufficiently large scanning range (greater than twice the length of the low‐NA diffraction‐limited spot size) and fine scanning steps  can reduce distortions in the reconstruction. 2.6× resolution improvement can be obtained through at least 36 speckle‐illuminated (average grain size ∼ 1.3 µm) autofluorescence images without obvious degradation in the reconstructed CHAMP image. The system can be further optimized to balance the achievable resolution and acquisition speed for a specific application. Note that the computational efficiency may be a dominant impediment for CHAMP imaging, which takes ∼50 s/10 mmapproach,40 whi would help to address this problem, further improving the accuracy of Deep‐CHAMP images. Virtual staining through unsupervised learning should be systematically investigated in the future to enable a faithful conversion, which, however, is beyond the scope of this study.It should be admitted that the CycleGAN‐based network faces difficulty to differentiate features that demonstrate similar brightness , which can lead to some staining artifacts , where λ was the fluorescence emission wavelength, and NAobj and NAillu were the numerical apertures of the detection objective lens and illumination pattern, respectively. The resolution improvement through conventional SIM with epi‐fluorescence configuration was restricted to 2× as the illumination NA was also restricted by the detection objective . To address this issue, illuminating with a constant speckle pattern that is translated between measurements, as opposed to randomly changing speckle patterns, was utilized in this report.For simplicity and flexibility, oblique illumination with a constant speckle pattern, which featured a grain size smaller than the PSF of the detection optics, was implemented in CHAMP to obtain beyond 2× resolution enhancement. In addition, with the assistance of UV surface excitation, this method is applicable to unprocessed and unlabeled tissues with any thickness, which is not achievable with the existing super‐resolution SIM systems. Unlike SIM with sinusoidal illumination, where super‐resolution demodulation can be achieved through a one‐step analytic inversion with a few raw images, SIM with speckle illumination . The flowchart of the reconstruction algorithm is shown in Table jx,jy) of the specimen was pre‐estimated by cross‐correlation of the captured raw images. Note that the sampling rate is a prerequisite for digital image reconstruction. Undersampling issue, which occured in the CHAMP system as the sampling pixel size is larger than half of the PSF size of the low‐NA detection optics, will lead to pixel aliasing and consequently generate artifacts in the reconstruction. To tackle this issue, a sub‐sampled method was introduced. The algorithm was run on a workstation with a Core i9‐10980XE CPU @ 4.8GHz and 8×32GB RAM, and 4 NVIDIA GEFORCE RTX 3090 GPUs, which takes ∼50 s/10 mm2 for computation.The CHAMP reconstruction framework is based on a momentum‐assisted regularized ptychographic iterative engine,59 whi2/10 s with 1.1‐µm lateral resolution 2 ≈ 200 megapixels. The throughput across different imaging modalities is compared in Figure Note that the resolution improvement is theoretically infinite, which, however, will be experimentally restricted by the speckle contrast on the specimen. In principle, a condenser lens with higher illumination NA enables higher achievable resolution at the expense of more acquisitions. However, the resulting highly compressed speckle pattern not only causes a vignetting effect which darkens the corners of the captured autofluorescence images, but also degrades the speckle contrast due to the natural decay governed by the incoherent optical transfer function. Therefore, the system can be optimized to balance the tradeoffs between target resolution, acquisition speed, and computational efficiency for various applications. In this work, 2.6× resolution gain was achieved via 36 speckle‐illuminated (average grain size  ∼1.3 µm) diffraction‐limited images that were raster‐scanned with 1‐µm scanning interval. CHAMP enables rapid and label‐free imaging of thick and unprocessed tissues with large surface irregularity at an acquisition speed of 10 mmThe spatial resolution of CHAMP was measured by imaging 500‐nm‐diameter fluorescent beads   is calculated as:Figure 62 andXD) is:Similarly, the objective of the discriminator X ,63 whiλ is set to 10 and γ is set to 2. The network is implemented with Python version 3.7.3 and Pytorch version 1.0.1. The software is implemented on a desktop computer with a Core i7‐9700K CPU@ 3.6GHz and 64GB RAM, running on an Ubuntu 18.04.2 LTS operation system. The training and testing are performed by an NVIDIA Titan RTX GPU with 24 GB RAM, which allows operating on ∼25 megapixels/s for testing (including GPU computing and time to write to hard disk).The overall objective for our virtual staining network is the weighted sum of the four loss functions, which is given by:For virtual staining network of fixed mouse brains, the training data consists of 1600 unpaired CHAMP and H&E‐stained images, where CHAMP images were collectively obtained from fixed, thick/thin mouse brains, and histological images were collected from H&E‐stained thin mouse brain slices which contain similar features as the CHAMP images. For the virtual staining network of fresh mouse brains, the training data consists of 800 unpaired CHAMP and H&E‐stained images, where CHAMP images were collectively obtained from freshly excised mouse brains. For the style transformation network, the training data consists of 800 unpaired “brain‐style” Deep‐CHAMP images and H&E‐stained images of thin mouse kidney slices, where Deep‐CHAMP images were the output from the fixed mouse brain network with the CHAMP images of mouse kidney used as the input. The training details and convergence plots can be found in Figure To show the wide applicability of the unsupervised network, the fixed mouse brain/kidney tissues with various thicknesses were utilized for cross validation. The resulting virtually stained Deep‐CHAMP images are enumerated in Figure  which enables to produce pixel‐based segmentations. Based on the resulting probability maps, images were subsequently converted to a binary image where cell nuclei can be identified. The binarized Deep‐CHAMP and H&E‐stained images were analyzed in Fiji, where the cross‐sectional area and centroid of each nucleus were provided. With the localized center positions of the cell nuclei, the intercellular distance was calculated to be the shortest adjacent distance to a neighboring cell nucleus. A two‐sided Wilcoxon rank sum test was carried out across groups with n = 50 for each distribution of cross‐sectional area and intercellular distance. No assumptions were made on data distributions. The significance was defined as p* ≤ 0.05 in all cases. Statistical analysis was carried out using MATLAB .Deep‐CHAMP and H&E‐stained histological images were segmented by a free Fiji plugin, trainable Weka segmentation,64 whiT.T.W.W. has a financial interest in PhoMedics Limited, which, however, did not support this work. Y.Z., L.K., I.H.M.W., X. L., and T.T.W.W. have applied for a patent  related to the work reported in this manuscript.Y.Z. and L.K. contributed equally to the work. Y.Z., L.K., and T.T.W.W. conceived of the study. Y.Z. and L.K. built the imaging system. Y.Z., L.K., X.L., and M.K.Y.H. prepared the specimens involved in this study. Y.Z. and I.H.M.W. performed imaging experiments. L.K. and W.D. performed histological staining. Y.Z. processed the data. Y.Z. and R.C.K.C. analyzed the data. Y.Z. and T.T.W.W. wrote the manuscript. T.T.W.W. supervised the whole study.Supporting InformationClick here for additional data file.Supplemental Video 1Click here for additional data file.Supplemental Video 2Click here for additional data file.Supplemental Video 3Click here for additional data file.Supplemental Video 4Click here for additional data file.


In two 24‐week migraine prevention studies in Japan, erenumab was associated with significantly greater reductions in migraine frequency versus placebo over Weeks 13–24 (primary endpoint). This post hoc analysis evaluated the onset of efficacy within the first 4 weeks after the initiation of erenumab from the 24‐week double‐blind periods of these studies.Placebo‐adjusted differences in least squares mean (LSM) change from baseline in weekly migraine days (WMD) were assessed weekly in each study and by migraine type (episodic (EM]/chronic [CM]) (Study 20170609).p = .022) and −0.49  in favor of erenumab 70 and 140 mg, respectively. For Study 20170609, significant placebo‐adjusted differences were observed with erenumab 70 mg at Week 1 in patients with EM , and at Week 2 in patients with CM  and for the overall population .A total of 407 patients from Study 20120309  and 261 patients from Study 20170609  were included. For Study 20120309, onset of efficacy was observed as early as Week 1 in favor of erenumab versus placebo. Placebo‐adjusted differences in LSM  change from baseline in WMD at Week 1 were −0.38 (−0.71 to −0.05; Erenumab treatment significantly reduced WMD compared with placebo. Onset of erenumab efficacy occurred as early as Week 1 in patients with migraine. Despite this, preventive treatment use remains low in Japan, with acute‐phase therapies commonly used for migraine treatment  and its receptor have more recently been developed  was a randomized, double‐blind, placebo‐controlled, phase 3 pivotal study of erenumab in adult patients (N = 261) with EM and CM in Japan. Patients were eligible for inclusion if they had a history of migraine for ≥12 months, an average of <15 headache days with ≥4 migraine days (EM) or ≥15 headache days with ≥8 migraine days (CM) during the baseline period, and demonstrated ≥80% compliance with their eDiary. Patients were excluded if they had received ≥2 concomitant migraine preventive treatments or had no therapeutic response  to ≥4 migraine medication categories. Patients were randomized 1:1 to placebo or erenumab 70 mg administered monthly by SC injection, with randomization stratified by migraine preventive treatment status (prior vs. current vs. none) and subtype (EM/CM).Both studies consisted of a 24‐week double‐blind treatment period and an open‐label treatment period Figure , in whicPatients completed an eDiary daily with information about their migraine and nonmigraine headaches , patient reported outcomes , and use of acute migraine therapies during the 4‐week baseline phase and subsequent double‐blind treatment phase. The primary endpoint in both studies was the change from baseline in MMD averaged over months 4, 5, and 6, with additional prespecified analyses at the end of each monthly treatment period. The primary objective of this post hoc analysis was the change from baseline in weekly migraine days (WMD) at Weeks 1─4 in the double‐blind treatment period of both studies. A migraine day was defined as per the International Classification of Headache Disorders 3rd edition (ICHD‐3) definition for migraine in both studies  Harmonised Tripartite Guideline for Good Clinical Practice (GCP), and the Institutional Review Board (IRB) regulations. Clinical study protocols, informed consent forms, and other study‐related documents were reviewed and approved by the local or central IRBs of all study sites. Studies were conducted according to applicable local or regional regulatory requirements and in accordance with the responsibilities listed in the protocol. All patients or their legally accepted representative provided written informed consent to participate in each study. Both studies were registered at clinicaltrials.gov (NCT02630459 and NCT03812224).2.2p values were reported without multiplicity adjustment. All statistical significances were considered nominal without further specification.Full details regarding statistical analysis methodology for each study have been published previously .33.1A total of 408 patients received placebo (N = 136), erenumab 70 mg (N = 135), or erenumab 140 mg (N = 137) in Study 20120309 and 261 patients received placebo (N = 131) or erenumab 70 mg (N = 130) in Study 20170609, and comprised the full analysis set. Of these, one patient from the 140 mg group in Study 20120309 did not have a post‐baseline efficacy evaluation available and was excluded from the efficacy analysis set.3.2Baseline demographic and clinical characteristics of patients in Studies 20120309 and 20170609 have been published previously  for erenumab 70 mg and –0.49  for erenumab 140 mg. Placebo‐adjusted differences in LSM (95% CI) changes from baseline in WMD at Week 4 were −0.53  for erenumab 70 mg and −0.35  for erenumab 140 mg.In Study 20120309, reductions in WMD in favor of erenumab were observed as early as Week 1 and were sustained through Weeks 1−4  at Week 1, −0.71  at Week 2, −0.60  at Week 3, and −0.52  at Week 4.In Study 20170609, numerical reductions in WMD were similarly observed as early as Week 1 with erenumab versus placebo Figure , althougp = .012) at Week 1 and −0.78  at Week 4. Placebo‐adjusted differences in WMD in favor of erenumab were also observed after Week 2 in patients with CM. Placebo‐adjusted differences in LSM (95% CI) change from baseline in WMD for erenumab 70 mg were 0.12  at Week 1, −0.81  at Week 2, −0.86  at Week 3, and −0.13  at Week 4. However, effects in this subgroup were confounded by the large placebo effect and small sample size.When examining onset of effect by migraine subtype in Study 20170609, a similar pattern of effect was observed, with reductions in WMD observed by Week 1 in both EM and CM subtypes, which were sustained to Week 4   require a prolonged dose titration period of up to several weeks in order to minimize adverse events  and use of erenumab across both EM and CM migraine subtypes. Limitations are inherent to subgroup post hoc analyses, and include the relatively small sample size of some groups, the lack of prespecified hypothesis, and lack of control for Nevertheless, erenumab treatment was associated with significant reductions in WMD compared with placebo, with onset of erenumab efficacy occurring as early as Week 1 in patients with both EM and CM. These improvements were sustained to Week 4 in the current analysis, and continued for the duration of the 24‐week double‐blind phase, as reported previously  criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.Koichi Hirata: No funding was received for the current manuscript. Health, Labour and Welfare Sciences Research Grants received by the Ministry of Health, Labour and Welfare, Japan for the current work. Consulting fees received from Lundbeck, Otsuka, Eli Lilly, and Amgen. Payment or honoraria  received from Eli Lilly, Lundbeck, Otsuka, Eisai, Amgen, Pfizer, and Daiichi Sankyo. Takao Takeshima: Payment or honoraria (lecture fees) received from Eisai and Eli Lilly. Fumihiko Sakai: Consulting fees received from Amgen, Eli Lilly, Otsuka, and Lundbeck. Noboru Imai: Payment or honoraria (speaker's bureaus) received from Amgen, Daiichi Sankyo, Eli Lilly, and Otsuka. Yasuhiko Matsumori: Payment or honoraria (lecturer fees) received from Eli Lilly, Otsuka, and Daiichi Sankyo. Yoshihisa Tatsuoka: Advisory fees received from Eli Lilly, Otsuka, and Daiichi Sankyo. Payment or honoraria (lecturer fees) received from Eisai, Otsuka, Kyowa Kirin, Takeda, and Daiichi Sankyo. Grants received for commissioned/joint research received from Amgen, Allergan, Eli Lilly, Otsuka, Kyowa Kirin, and Ono. Yotaro Numachi: Employee of Amgen K.K. and shareholder of Amgen Inc. Ryuji Yoshida: Employee of Amgen K.K. Cheng Peng: Employee and shareholder of Amgen Inc. Daniel Mikol: Former employee of Amgen and held Amgen stock. Current employee of NervGen and hold NervGen stock. Gabriel Paiva da Silva Lima and Sunfa Cheng: Employee and shareholder of Amgen Inc.Study design: Yotaro Numachi, Ryuji Yoshida, Cheng Peng, Daniel D. Mikol, Gabriel Paiva da Silva Lima, and Sunfa Cheng. Data acquisition: Koichi Hirata, Takao Takeshima, Fumihiko Sakai, Noboru Imai, Yasuhiko Matsumori and Yoshihisa Tatsuoka. Analyses : Cheng Peng. Interpretation of data, manuscript writing, feedback, and revisions: all.https://publons.com/publon/10.1002/brb3.2526The peer review history for this article is available at


Nineteen patients were screened and 18 patients were  enrolled in the trial and received treatment. All patients in Parts A and B were from Japan and were within an age range of 43–73 years, with an ECOG PS of 0.1. No dose‐limiting toxicity or deaths were experienced in the study. Dose‐limiting toxicity equivalent toxicity of Grade 4 platelet count decreased (n = 1) and was observed in Part B. In Part A, treatment‐related Grade ≥3 TEAEs were reported in one patient (PT: ALT increased and AST increased), while in Part B, five patients reported treatment‐related Grade ≥3 TEAEs with four of the five patients reporting an event of neutrophil count decreased. No complete response was reported in either Part. One patient in Part B reported partial response while four patients in each part reported stable disease. Merestinib monotherapy was concluded to be tolerable in Japanese patients, and its combination with cisplatin and gemcitabine is a tolerable regimen for Japanese patients with BTC.This phase 1, multi‐center, nonrandomized, open‐label, dose‐escalation study consisted of Part A wherein merestinib 80 or 120 mg (40‐mg tablets) was administered orally QD during a 28‐day cycle to patients diagnosed with solid tumors and Part B wherein merestinib 80 mg (40‐mg tablets) was administered orally QD, and cisplatin 25 mg/mClinicalTrials.gov) registered on 23 January 2017.Trial registration: NCT03027284 ( The article describes the findings of Study I3O‐JE‐JSBG. The results of this phase 1, multicenter, nonrandomized, open‐label, dose‐escalation study showed that merestinib monotherapy is tolerable in Japanese patients, and its combination with cisplatin and gemcitabine is a tolerable regimen for Japanese patients with biliary tract cancer. It has also been shown to be an inhibitor of several other oncokinases, including 13 MET mutations/variants.A previously conducted phase 1 study22.1Study JSBG was a multi‐center, open‐label, non‐randomized, Phase 1 study of merestinib. It consisted of a 3 + 3 dose‐escalation part (Part A) in Japanese patients with advanced and/or metastatic cancer (solid tumors or non‐Hodgkin’s lymphoma) and a combination part (Part B) in Japanese patients with unresectable, recurrent, or metastatic BTC. Figure The primary objective of this study was to evaluate the tolerability of merestinib monotherapy or in combination with other gemcitabine and cisplatin in Japanese patients with advanced and/or metastatic cancer. The secondary objectives included evaluation of safety and toxicity profile, PK profile, and antitumor activity of merestinib in monotherapy or in combination with gemcitabine and cisplatin in the aforementioned patient pool.The study protocol was approved by Institutional Review Boards prior to patient recruitment, and each patient provided written informed consent before enrollment. The study was conducted in accordance with consensus ethics principles derived from international ethics guidelines, including the Declaration of Helsinki and the Council for International Organizations of Medical Sciences International Ethical Guideline and the International Conference on Harmonization E6 Guidelines for Good Clinical Practice.2.2Part A included Japanese patients, ≥20 years of age, diagnosed with advanced and/or metastatic cancer (solid tumors or non‐Hodgkin’s lymphoma); measurable or non‐measurable disease, as defined by RECIST Version 1.1. Part B included Japanese patients, ≥20 years of age, diagnosed with BTC that was unresectable, recurrent, or metastatic and for the treatment of which prior systemic front‐line therapy was not received; measurable disease, as defined by RECIST Version 1.1. Patients with serious preexisting medical conditions such as liver cirrhosis with a Child‐Pugh state of B or higher, symptomatic central nervous system malignancies or metastasis, history of cardiac conditions, active infections, active interstitial lung disease, or a secondary primary malignancy were excluded.2.3Part A consisted of two dose levels of merestinib: 80 mg (dose level 1) or 120 mg (dose level 2). Merestinib was supplied as 40‐mg tablets for oral administration and the assigned dose was administered QD, in a 28‐day cycle. The second dose level of 120 mg was administered if <33% of patients on dose level 1 (merestinib 80 mg), had dose‐limiting toxicity (DLT) in Cycle 1. If in Cycle 1, the frequency of DLT was the initial dose and reduced dose for dose level 1 were 80 and 40 mg/day, respectively, and the initial dose and reduced dose for dose level 2 were 120 mg/day (three tablets of 40 mg merestinib) and 80 mg/day (two tablets of 40 mg merestinib), respectively.2 intravenous (IV) and the gemcitabine dose was 1000 mg/m2 IV, with each drug administered on Day 1 and Day 8 of repeating a 21‐day cycle (weekly with 2 weeks on and 1 week off). Treatment with cisplatin and/or gemcitabine could continue for a planned maximum of eight cycles, or until no further clinical benefit, excessive toxicity or evidence of disease progression if merestinib continued.In Part B, treatment with merestinib  in combination with cisplatin and gemcitabine was initiated to run concurrent with the second dose level (120 mg) of Part A. The assigned dose of merestinib (80 mg) was administered orally QD in a 21‐day cycle. Cisplatin dose was 25 mg/mFor both parts, the planned duration of treatment with merestinib was not fixed and patients remained on the study until progressive disease (PD), development of unacceptable toxicity, or fulfilment of one of the criteria for study discontinuation.2.4Safety and tolerability were assessed based on DLT analysis, and clinical and laboratory evaluations. The Common Terminology Criteria for Adverse Events version 4.0 were used for grade adverse events (AEs). Graded AEs were subsequently coded to Medical Dictionary for Regulatory Activities preferred terms. All patient deaths were also recorded.In Part A, DLT was defined as an AE related to merestinib during Cycle 1 (28 days) while in Part B DLT was defined as an AE related to merestinib, and not a reasonably anticipated AE related to cisplatin and gemcitabine, during Cycle 1 (first 21 days). In addition, one of the following criteria had to be fulfilled: Grade ≥3 non‐hematological toxicity ; Grade 3 thrombocytopenia with grade ≥2 bleeding or thrombocytopenia which requires platelet transfusion; Grade 4 neutropenia of >7 days duration; any febrile neutropenia; anemia which requires a transfusion of packed red blood cells; Grade 4 thrombocytopenia (only for Part A).The DLT evaluation period referred to the 28 days period of Cycle 1 for Part A, and the first 21 days of Cycle 1 for Part B while DLT evaluable population included all enrolled patients who took at least 80% of assigned dose in the DLT evaluation period for all study treatments or a patient who experienced a DLT in the DLT evaluation period.Dose adjustments were carried out for merestinib, gemcitabine, and cisplatin based on DLT or DLT‐equivalent toxicity as defined in the study protocol. Adjustments for merestinib were carried out as dose omission and dose reduction steps, while they were carried out for gemcitabine as dose delay, dose reduction, and dose interrupted steps. Adjustments for cisplatin, on the other hand, were carried out as dose delay, and dose reduction steps.2.5Pharmacokinetic analyses were conducted on patients who had received at least one dose of study treatment and had sufficient samples collected throughout the first two cycles to allow the estimation of PK parameters.Cmax), area under the plasma concentration–time curve from time zero to last measurable plasma concentration (AUC0–tlast), and area under the plasma concentration–time curve from time zero to infinity (AUC0–∞) of merestinib. Other non‐compartmental parameters, such as half‐life (t1/2), apparent systemic clearance (CL/F), and apparent volume of distribution (Vz/F) were also assessed.Standard non‐compartmental methods of analysis using Phoenix WinNonlin® 8.1  were employed to determine the PK parameter estimates for merestinib and its pharmacologically active metabolites (LSN2800870 and LSN2887652) were calculated by standard non‐compartmental methods of analysis using Phoenix WinNonlin® 8.1 . The primary parameters for analysis were maximum observed plasma concentration , disease control rate defined as a percentage of patients who are either (i) responders (exhibit CR or PR), or (ii) have stable disease (SD) for at least 6 weeks divided by the total number of patients to the corresponding part and/or cohort, best overall response, change in tumor size (solid tumors only), duration of response, and duration of SD.2.7p values were not calculated all analyses were descriptive; no p values were calculated. For continuous variables, summary statistics included number of patients, mean, median, standard deviation, minimum, and maximum. Categorical endpoints were summarized using the number of patients, frequency, percentages, and standard errors. No imputation was made for missing data were not imputed.Descriptive statistics  were used to summarize the data, and 33.1n = 10; Part B, n = 8) enrolled in the trial and received treatment. Of the three patients who were assigned to Part A, dose level 1, all patients received at least one dose of study treatment. Of the eight patients who were assigned to Part A, dose level 2, seven patients received at least one dose of study treatment. Of the eight patients who were assigned to Part B, all patients received at least one dose of study treatment. All patients in Parts A and B were Asian and from Japan and were within an age range of 43–73 years, with an ECOG PS of 0 and 1. More than half of the patients in Part A  and Part B (87.5%) were male. Most patients (58.3%) had stage IV disease. Patient demographics and disease characteristics at baseline are presented in Table n = 100%; Part B, n = 100%), systemic and locoregional therapy , and surgical procedure .A total of 19 patients were screened and 18 patients were . Similarly, the cycles received per patient  also varied across Parts A and B .All patients in the study received merestinib; however, the relative dose intensity of merestinib across both parts of the study was different  in Part B. This patient experienced Grade 4 platelet count decreased on Study Day 210. The event was assessed as serious and related to the study treatment by investigator. The patient discontinued study treatment due to platelet count decreased on Study Day 225. No patients died during the study. A list of all AEs related to study treatment was classified according to maximum common terminology criteria for adverse events (CTCAE) Version 4 categories has been presented in Table .5% in PaIn Part A dose level 1 (merestinib 80 mg), there was one serious adverse event (SAE) . This SAE was reported by the investigator to be not related to study treatment. The most frequently occurring TEAEs were alanine aminotransferase (ALT) increased, aspartate aminotransferase (AST) increased, and pyrexia (two patients each). One patient in Part A dose level 1 experienced Grade ≥3 TEAE  which was deemed not related to study treatment by the investigator. None of the patients in Part A dose level 1 discontinued study treatment due to AE.In Part A dose level 2 (merestinib 120 mg), there was one SAE . This SAE was reported by the investigator to be not related to study treatment. The most frequently occurring TEAEs were ALT increased and AST increased (57.1% each). Three patients experienced Grade ≥3 TEAEs: one patient had ALT increased and AST increased (related to study treatment), one patient had white blood cell count decreased, neutropenia, and lung abscess (none related to study treatment), and one patient had hypoalbuminemia (not related to study treatment). One patient discontinued the study treatment due to AE (PT: lung abscess). This event was not considered to be related to study treatment.n = 2) determined by investigator to be possibly related to study treatment. The most frequently occurring TEAEs were platelet count decreased (87.5%), pyrexia (87.5%), constipation (75.0%), anemia (75.0%), and neutrophil count decreased (75.0%). All patients in Part B had Grade ≥3 TEAE. Grade ≥3 TEAEs that were experienced by ≥50% of patients in Part B was neutrophil count decreased (4 [50.0%]). Three patients experienced Grade 4 TEAEs with all three patients reporting neutrophil count decreased and two of the three patients reporting platelet count decreased as well. In addition, all patients in Part B discontinued study treatment on account of three reasons: PD , AEs , and withdrawal by subject . AEs including platelet count decreased and ascites caused study treatment discontinuation, among which platelet count decreased was considered related to study treatment. Dose adjustments of merestinib for AEs had to be performed for patients in both Part A and Part B and of gemcitabine and cisplatin for AEs in Part B. In Part A, six patients underwent dose adjustment for merestinib while in Part B, seven patients underwent dose adjustment for merestinib. In addition, in Part B, all eight patients underwent dose adjustment for gemcitabine and cisplatin.In Part B, there were six SAEs in five patients. Serious adverse events that were reported ≥2 patients included platelet count decreased (two patients). In two of the five patients with SAEs, there were two SAEs  occurring approximately 4–5 h post‐dose in each dose level. The geometric mean t1/2 was approximately 8–10 h. The variability in exposures across all doses, as assessed by percent coefficient of variation (%CV), ranged between 8% and 40%. When comparing exposures following oral doses of merestinib 80 mg in Part A and Part B, exposure levels in Part B were somewhat lower than those in Part A.On Day 1 in Cycles 1 and 2 Figures , absorpt1/2, however, was slightly longer than merestinib, and ranged from approximately 9 to 14 h while the variability in exposures across all doses was slightly higher than merestinib, ranging between 19% and 69%  and LSN2887652  were similar with merestinib. The geometric mean t3.4n = 10) had at least one post‐baseline tumor scan for response assessment and were considered evaluable according to RECIST v1.1; however, of the eight patients in Part B, two patients were considered non‐evaluable. No complete response (CR) was reported in Parts A and B. Although no partial response (PR) was reported in Part A, one patient reported PR in part B. A best overall response of SD was reported in four patients (two patients from each of the two dose levels) of Part A, and four patients of Part B. The ORR observed in patients in Part A was negligible while in Part B, it was reported to be 12.5%.All patients in Part A  or Part B .All patient experienced ≥1 TEAEs. In Part A at any dose level, most common TEAEs were ALT increased  and AST increased  while in Part B most common TEAEs were platelet count decreased and pyrexia.n = 8) experienced Grade ≥3 TEAEs. ALT increased and AST increased were observed in the patients treated with merestinib alone in the global study, as treatment‐related Grade ≥3 TEAE. Similarly, in this study as well, one patient in Part A reported treatment‐related Grade ≥3 TEAE of ALT increased and AST increased. In addition, one patient in Part B reported treatment‐related Grade ≥3 TEAE of neutrophil count decreased, platelet count decreased, white blood cell count decreased, anemia, ALT increased, and AST increased, all of which except AST increased were observed in the patients treated with merestinib, gemcitabine, and cisplatin in the global study as treatment‐related Grade ≥3 TEAE. These findings indicate that the safety profiles of merestinib determined in both studies might be comparable. Furthermore, the increased occurrence of events of AST increased and ALT increased is also consistent with the results of other Phase 1 MET inhibition studies  in Japanese patients with tumors wherein more than 90% of patients had experienced both events of any grade.Only four patients experienced Grade ≥3 TEAEs in Part A while all patients in Part B (0–24) after single doses of 80 and 120 mg reached or exceeded corresponding thresholds (that met the EC50 threshold for tumor growth inhibition) associated with efficacy in a preclinical (mouse) U87MG xenograft PK‐PD model.The PK profile of merestinib was characterized by slow absorption and was generally consistent with PK data reported previously. In addition, similar to the global study, exposures  had a best response of SD which may be attributed to the versatility of merestinib in that in addition to primarily inhibiting MET, it also inhibits serine/threonine kinases MKNK1 and MKNK2 and receptor tyrosine kinases MST1R, TYR03, TEK, TRKA, AXL, FLT3, DDR1, TRKB, ROS1, PDGFRA, DDR2, MERTK, and TRKC.The study was limited by the non‐randomized study design and the small sample size, although these are typical characteristics of dose‐escalation studies. Since a key concern related to MET inhibition clinical trials is patient selection, it may be worthwhile to explore potential predictive biomarkers that may help identify patients most likely to benefit from MET inhibition therapy.5Overall, based on the results of this Phase 1 study in Japanese patients with advanced/metastatic malignancies, merestinib monotherapy, and the combination of merestinib with cisplatin and gemcitabine appears to be tolerable regimens for Japanese patients. The safety profile was consistent with monotherapy for each drug, with no additive toxicities and no unexpected safety signals were seen to date.TD received consulting fees or honorarium from Amgen, MSD, Taiho, Boehringer Ingelheim, Abbvie, Takeda, Rakuten Medical, Daiichi Sankyo; payment for lectures from Abbvie, Bristol Myers Squibb, Astellas, Ono Pharma, Oncolys BioPharma, Taiho, Rakuten Medical; and grants from Lilly, Taiho, Novartis, Merck Serono, MSD, Janssen, Boehringer Ingelheim, Eisai, IQVIA, Sumitomo Dainippon, Daiichi Sankyo, Bristol Myers Squibb, Abbvie. NY received honoraria from BMS, Pfizer, AstraZeneca, Eli Lilly, ONO, Chugai, Sysmex and research funds from Astellas, Chugai, Eisai, Taiho, BMS, Pfizer, Novartis, Eli Lilly, AbbVie, Daiichi‐Sankyo, Bayer, Boehringer Ingelheim, Kyowa‐Hakko Kirin, Takeda, ONO, Janssen Pharma, MSD, MERCK, GSK, Sumitomo Dainippon, Chiome Bioscience. YN received lecture fees from Chugai, Pfizer, and Novartis and research funds from Roche. YK received grants from Amgen, Takeda, AstraZeneca, Ono Pharmaceutical Company Limited, Taiho Pharmaceutical Company Limited, Boehringer Ingelheim GmbH. AbbVie, GSK, Chugai Company Limited, Daiichi‐Sankyo and Genmab K.K; served as a consultant at Takeda; received personal fees from Taiho, Sanofi, Bayer Yakuhin, and Ono Pharmaceutical Company Limited. TK received honoraria from Cysmex and Chugai. YP, KI, NT, and HA are employees of Eli Lilly Japan K.K. SK received research funding from ASLAN Pharmaceuticals, AstraZeneca, Bayer, Eli Lilly, MSD, Boehringer Ingelheim, and Pfizer. The study was designed under the responsibility of Eli Lilly and Company, in conjunction with the steering committee; The study was funded by Eli Lilly and Company. Merestinib was provided by Eli Lilly and Company. Eli Lilly and Company collected and analyzed the data and contributed to the interpretation of the study. All authors had full access to all of the data in the study and had final responsibility for the decision to submit for publication.The study and all procedures performed in the study involving human participants were conducted in accordance with ethics principles derived from international ethics guidelines, including the Declaration of Helsinki and the Council for International Organizations of Medical Sciences International Ethical Guideline and the International Conference on Harmonization E6 Guidelines for Good Clinical Practice.Informed consent was obtained from all individual participants included in the study.Fig S1AClick here for additional data file.Fig S1BClick here for additional data file.Fig S2AClick here for additional data file.Fig S2BClick here for additional data file.