Starting with a handful of SARS-CoV-2 infections in dormitory residents in late March 2020, rapid transmission in their dense living environments ensued and by October 2020, more than 50,000 acute infections were identified across various dormitories in Singapore. The aim of the study is to identify combination of factors facilitating SARS-CoV-2 transmission and the impact of control measures in a dormitory through extensive epidemiological, serological and phylogenetic investigations, supported by simulation models. Our findings showed that asymptomatic cases and symptomatic cases who did not seek medical attention were major drivers of the outbreak. Furthermore, each resident had about 30 close contacts and each infected resident spread to 4.4 (IQR 3.5–5.3) others at the start of the outbreak. The final attack rate of the current outbreak was 76.2% (IQR 70.6–98.0%) and could be reduced by further 10% under a modified dormitory housing condition. These findings are important when designing living environments in a post COVID-19 future to reduce disease spread and facilitate rapid implementation of outbreak control measures. Urban settings such as cities are vulnerable to COVID-19 spread as close-living environments  generally lead to higher degrees of social interactions which then contribute to communicable disease spread5.Over the course of the COVID-19 pandemic, control measures in various countries have shifted from broad interventions such as widespread lockdowns and closures, to risk-based and targeted strategies in particular sub-populations or environments to minimise disruption to social and economic activities6. Some clusters occurred at construction worksites and commercial areas frequented by foreign workers. In the same period, a surge of COVID-19 cases was identified across multiple foreign worker dormitories—the place of residence for about a quarter of the 1.4 million foreign workers in Singapore8. These dormitories were built with features such as common recreational and shared facilities, such as gyms, outdoor game courts, minimarts, and cooking areas9. These facilities provide foreign workers with a good living environment and promote social interactions. However, with thousands of residents in each dormitory, the living density in a dormitory is about 4.5 square metre per resident 11. Due to the dense contact networks in these closed living environments, infectious diseases introduced to a dormitory could result in high levels of transmission. Understanding the combination of factors that could result in the surge of COVID-19 cases in dormitories would shed light on transmission patterns, and guide future housing solutions and outbreak interventions in dormitories and other similar densely populated settings to curb communicable disease transmission.In Singapore, local community clusters involving imported cases and the largely naive local population were identified in late March, 2020In this study, we developed an individual-based model of a dormitory and incorporated epidemiological and serological investigations findings to identify combinations of parameters that reproduced similar outbreak trajectory in the dormitory. We studied the temporal extent of missed infections and evaluated the effectiveness of the outbreak control measures under the current dormitory outbreak and in alternative scenarios: (i) baseline scenario: only case isolation and quarantine of roommates were implemented; (ii) enhanced response and physical distancing scenario: ground teams were deployed to expedite case isolation and enforce physical distancing measures but no strengthening of measures was applied to reduce the probability of infections occurring outside a room in the lockdown phase of the outbreak; (iii) modified dormitory setting scenario: dormitory layout with reduced number of residents per room and, en suite bathroom, shower and cooking facilities to ensure that persons under quarantine did not leave their rooms and movement restrictions across different levels for the remaining residents when the dormitory was under lockdown. Details of the respective interventions in each scenario are described in the From March 23 to June 20, 2020, 2787 COVID-19 all-male cases with a median age of 33 (IQR 28–38) were identified in a dormitory cluster; none had recent travel history 14 days prior to the onset of symptoms or notification date.In the pre-lockdown phase , 12 of the initial cases were tested based on the doctor’s clinical evaluation while one case was tested as part of pneumonia surveillance. An additional 68 cases were identified to fulfil the suspect case definition. Five of the cases were each identified to be associated with at least one other COVID-19 cluster occurring in three worksites and a commercial area frequented by foreign workers. Over the course of the outbreak, three dormitory operations personnel developed COVID-19.Following the lockdown, cases were observed to decline in mid-April 2020 Fig. . The oveFrom the 10,000 sampled parameter combinations, we observed that about 13 cases (range 6–19) were required for a sustained outbreak to occur and 70.8% (95% CI 64.5–76.9%) of the infections were subclinical or asymptomatic cases with a relative infectiousness of 17.5% (95% CI 11.3–23.7%) that of a symptomatic case  on the same level was 9.9 (95% CI 7.9–12.0), (ii) on the same block was 5.0 (95% CI 3.9–6.1), and (iii) in other blocks was 2.6 (95% CI 1.9–3.3).Based on the sampled parameter combinations, we estimated that the current outbreak peaked at 511 infections (IQR 407–732) on day 20 (IQR 19–21) with a final attack rate of 76.0% (IQR 70.6–98.0%) and the outbreak ending on day 92 (IQR 65–109) (Table The maximum daily number of infections was 905 (IQR 877–931) in the baseline scenario Fig. b, 602 caIn the current outbreak scenario, the reproduction number declined from 4.0 (IQR 2.5–5.5) to 2.0 (IQR 1.8–2.2) by the fourth generation Fig. a. In thePhylogenetic analyses showed that the genomes from the dormitory under study (dormitory A) were highly similar and belonged to the pangolin lineage B.6 (GISAID clade O). As shown in the phylogenetic tree, viruses belonging to this lineage circulated with high frequency within several countries in the region such as India, Malaysia and Australia and were different from those circulating in Wuhan, China Fig. .Figure 4Locally, the sequences were highly similar to other genomes from a cluster associated with a popular commercial area  frequented by foreign workers and travellers Fig. . DocumenDistinguishable clusters characterized by unique genetic markers were found for 3 dormitories  Fig. . Interes12, South Korea13, India14 and Hong Kong15. In Singapore, epidemiological investigation of the initial cases in the dormitory under this study did not reveal a common source of infection. Some of the cases were associated with more than one COVID-19 clusters occurring at their worksites or social-recreational areas thereby increasing the chances of multiple introductions of COVID-19 infections into the dormitory from the foreign worker community. Furthermore, phylogenetic analysis of the genetic sequences showed close similarity with other circulating viruses in India and Malaysia, and hence, import-related transmission to the dormitory, directly or through other clusters, cannot be ruled out. Based on the epidemic trajectory, our modelling outputs suggest that there were approximately 13 cases that seeded the outbreak.The spread of COVID-19 in densely populated residential areas have been documented in major cities and countries such as AustraliaGiven the uncertainties of the contact network patterns and disease transmission dynamics in a dormitory, we explored and showed that varied parameter combinations could result in similar observations in the early and late phases of the outbreak. As such, multiple interventions are essential to successfully contain an outbreak in these settings. In the current outbreak scenario, with strengthened outbreak containment measures, the overall attack rate was about 22% lower than the baseline scenario. The strengthened measures help to flatten the epidemic curve with peak cases more than 40% lower as compared to the baseline scenario. However, the outbreak duration in the current scenario was lengthened by about 1–2 months compared to the baseline. The surge in cases into the hundreds per day further justified the need for ground healthcare and operations teams to assist in managing ill residents in situ, as most of the infections were mild in this group of mostly young and healthy individuals. Despite the large number of dormitory cases, strict infection control observed by these workers ensured that infections in dormitory operations personnel and spill over into the community were kept to a minimum .17. We also estimated that 57% of the symptomatic cases did not seek medical attention and this could be attributed to mildly symptomatic persons who did not report symptoms. The overall extent of missed infections corroborates with a WHO report where 80% of the infections were mild or asymptomatic18. More could be done to educate individuals on the symptoms of COVID-19 and to encourage testing.COVID-19 antibodies were detected in 72.0% of the 7367 persons with no prior PCR confirmation of SARS-CoV-2 infection. Our model estimated that about 71% of the infections in the current outbreak were attributed to subclinical asymptomatic infections. This estimate falls within the observed range of asymptomatic rates discussed in review studies and lies close to the upper limits10. The modelled number of infections under a modified dormitory setting would be reduced by at least 10%, and more if we incorporate a reduced probability of infection within the room given the increase in living area per individual. Furthermore, our model suggested that the majority of the random contacts outside the room were made with persons within the same block for a dormitory with shared facilities. Given that these persons could be exposed prior to the isolation of their infector, future outbreak containment could consider the movement restrictions of all persons in a block when a case of COVID-19 is identified. This would be at odds with the design of such living environments to promote social interaction but is necessary in the post-COVID-19 era to reduce the spread of infectious diseases in a naïve population. As the modification of the built environment of these residential areas comes at higher capital and operational cost, cost-effectiveness studies will need to be performed to determine the optimal build strategy.Our model estimated that each worker had approximately 18 contacts outside the household with nearly 80% of the contacts were formed within the same block and the remaining with persons in other blocks. The dense contact network facilitated disease transmission with the reproduction number in the dormitory exceeding 1 for six generations. This was several times higher than that observed in the community given the dense living conditions and because dormitories are specifically set up for social interactions among foreign workers. Hence, modified living environments for dormitories and similar living settings should be explored for the post-COVID-19 era to include increased living space, lowered occupancy per room and en suite facilities where possible19 could have occurred but were not modelled. Cases in the lockdown phase of the dormitory outbreak were not used for model fitting as mass testing was conducted among dormitory residents to identify asymptomatic and symptomatic infections but the symptom onset dates were not routinely collected for symptomatic cases. The incomplete symptom onset dates for these cases resulted in reduced data available for model fitting. This potentially led to higher uncertainty in the daily incidence during the lockdown phase of the modelled current outbreak scenario as compared to the start of the outbreak  had over time fine-tuned and adjusted the local COVID-19 suspect case definitions along with the growth and evolution of global evidence base. Under the Infectious Diseases Act in Singapore, suspect COVID-19 cases are required to undergo medical investigation23, which grants the use of outbreak investigation data for analysis and evaluation. As part of the outbreak management, the Ministry is authorised and collected personally identifiable information of the COVID-19 cases. However, for the purpose of the study, only de-identified aggregate numbers were used for analysis and presentation.On March 30, 2020, MOH identified a COVID-19 outbreak in a foreign worker dormitory in Singapore and all epidemiological investigations and outbreak containment measures were implemented under the Infectious Diseases Act Section 59AThe outbreak response in the dormitory was divided into two phases based on the extent of transmission in the dormitory. In the pre-lockdown phase in the dormitory , all laboratory-confirmed cases were interviewed to collect data on demographic characteristics, clinical symptoms, and activity patterns for the 14 days preceding symptom onset or notification date until isolation in hospital.Contact tracing was initiated to identify close contacts within the dormitory (persons who stay in the same room as a confirmed case) and in the work and social contexts (persons who spend at least 30 min within 2 m of a confirmed case). These contacts were placed under quarantine for 14 days from last exposure to the case at designated government quarantine facilities located outside the dormitory. The health status of all persons under quarantine was monitored daily and those who developed symptoms would undergo medical investigation as part of active case finding.To facilitate outbreak investigations and management, details of the dormitory layout and all dormitory residents were requested from the dormitory operator. Employers of a confirmed case and the dormitory operators were also advised to monitor the health condition of all other workers and dormitory residents respectively daily and to advise any person who was unwell to seek medical attention immediately.25. Residents who were unwell were advised to seek medical attention immediately and were investigated for SARS-CoV-2 infection.The dormitory was locked down on April 6, 2020. During the lockdown phase, healthcare and other workers were deployed to set up medical posts and perform active case finding via swabbing exercises, case isolation and quarantine of close contacts in situ, implementation of safe distancing measures while ensuring the welfare of the dormitory residents. All personnel involved in the dormitory operations were required to observe proper infection control measures at all times and to monitor their healthAll primary samples or residual extracted nucleic acid tested positive for SARS-CoV-2 by RT-PCR at diagnostic laboratories were forwarded to Singapore’s National Public Health Laboratory. Available samples from the dormitory cases and those linked to the dormitory outbreak  between April 1 and June 6, 2020 were randomly selected for next generation sequencing.26, where One-Step RT-PCR was performed using the SuperScript™ III One-Step RT-PCR System with Platinum™ Taq DNA Polymerase (ThermoFisher Scientific). Sequencing libraries were prepared using the Nextera XT and sequenced on MiSeq (Illumina) to generate 300 bp paired-end reads. The reads were subjected to a hard-trim of 50 bp on each side to remove primer artifacts using BBMap27 prior to consensus sequence generation by Burrows–Wheeler Aligner-MEM v0.7.17, with default settings. Only sequences with ≥ 98% genome coverage and supported by an average depth of 100 × were included for phylogenetic analysis. The generated consensus sequences were shared via GISAID28. To place these sequences into global context, we searched for closely related strains using BLASTN29 against all genomes in the GISAID database and retained representative hits with 99.99% identity and matching the time window of our sequences  and 1,000 steps of ultrafast bootstrapping34 with zero length branches collapsed in the final tree, visualized with Figtree35.Selected residual diagnostic RNA were subjected to tiled amplicon PCR using ARTIC nCoV-2019 version 3 panel36.To determine the extent of undetected infections within the dormitory, we undertook a prospective cross-sectional seroprevalence survey in a convenience sample of 7367 dormitory residents who had no travel history 14 days prior to the onset of the first dormitory case and were not previously identified as a confirmed case of acute SARS-CoV-2 infection. Blood samples were collected between May 13 to June 1, 2020  were tested for SARS-CoV-2 Immunoglobin G (IgG) using either Abbott Architect SARS-CoV-2 IgG assay or Roche Anti-SARS-CoV-2 assay. For the overall seroprevalence, we computed the 95% confidence intervals (CI) for binomial proportions using Wilson’s methodDespite the strengthening of public health measures during the lockdown phase, cases continued to rise. This could be attributed to the dense contact networks and living conditions resulting in rapid transmission, the presence of pre- or asymptomatic transmissions, and variable health seeking behaviour of symptomatic persons that could have resulted in a delay or failure to isolate cases who went on to transmit the virus to others. Hence, to estimate the outbreak trajectory within the dormitory and to evaluate the effectiveness of the outbreak control measures, we used an individual-based model of COVID-19 transmission in a simulated population of 12,091 individuals  residing in a dormitory with a similar number of blocks, levels and rooms as the dormitory under study  the number of cases and number of affected locations since the earliest observed onset date (day 1) till day 12 of the outbreak (prior to the lockdown of the dormitory) and (ii) the overall serology outcomes in persons with no recent travel history and no laboratory confirmation of SARS-CoV-2 infection tested by day 79 of the outbreak. For each parameter combination, the full outbreak was simulated ten times to generate 500,000 outputs. The fit of the model against the observed data in the early and late phases of the outbreak was determined by computing the likelihood . Observe10. Details of the respective interventions in each scenario are described in the Parameter combinations were assigned a weight based on the corresponding likelihood and weighted sampling of the parameter combinations with replacement was performed 10,000 times. We simulated the current outbreak scenario with interventions including the deployment of ground teams to expedite case isolation, quarantine of roommates, enforcement of physical distancing and reduction in probability of infection outside the room in the lockdown phase of the outbreak . Using t37. A full description of the model is available in the For each parameter combination, disease progression was tracked over time and generations (A full record of the data is maintained at the Ministry of Health, Singapore and samples that were selected for next generation sequencing are uploaded to GSAID. These details can be made available through formal request.Supplementary Information.


Our recent studies have shown that glucose-dependent insulinotropic polypeptide (GIP), but not glucagon-like peptide 1 (GLP-1), augments Na-glucose transporter 1- (SGLT1-) mediated glucose absorption in mouse jejunum. Na-dependent glucose absorption sharply rose and peaked in 3 months of high-fat  compared to normal  diet fed animals. Previous studies have shown that GIP-augmented SGLT1 and PEPT1 (peptide transporter 1) are regulated by protein kinase A (PKA) signaling in mouse jejunum. Additional studies have indicated that cAMP and PI3 kinase signaling augment PEPT1 through EPAC and AKT activation pathways, respectively, through increased apical PEPT1 trafficking in intestinal epithelial cells. However, little is known about how the signaling glucose transport paradigm is altered over a long period. Early on, increased glucose absorption occurs through SGLT1, but as the obesity and diabetes progress, there is a dramatic shift towards a Na-independent mechanism. Surprisingly, at the peak of glucose absorption during the fifth month of the progression of obesity, the SGLT1 activity was severely depressed, while a Na-independent glucose absorptive process begins to appear. Since glucose transporter 2 (GLUT2) is expressed on the apical membrane of the small intestine in obese patients and animal models of obesity, it was hypothesized to be the new more efficient route. Western blot analyses and biotinylation of the apical membrane revealed that the GIP expression increases in the obese animals and its trafficking to the apical membrane increases with the GIP treatment. However, it may not be the only one. Other GLUT proteins or other transporters may also be involved and should not be discounted. GIP effects on SGLT1 and GLUT2 are quite profound by an increase in the apical expression of these transporters more likely by an increase in trafficking of these proteins from intracellular stores. Because the response is less than an hour, trafficking of the SGLT1 and GLUT2 transporters is the most likely mechanism. In normal jejunum where only SGLT1 is present, it is trafficked to the membrane upon treatment with GIP (at five months or nine months). However, in the obese animals, SGLT1 is unaffected by GIP but GLUT2 is trafficked to the apical membrane instead. What is unexpected is that even in the ninth month, the expression of GLUT2 and its GIP response does not appear to change despite decreases in both the Isc and the flux of glucose. Recent studies have shown that during the diabetic state, less glucose is absorbed and perhaps even excreted as a response to increasing blood glucose levels. However, serosal to mucosal transport of glucose in our study did not rise to significant levels to support such a conclusion (data not shown). Therefore, it would suggest that GIP as well as glucose insensitivity becomes an issue in the diabetic state.


HBA1, HBA2 and HBB) strengthen the binding of oxygen to hemoglobin (Hb), bringing about tissue hypoxia and a secondary erythrocytosis. The diagnosis of HOAH is based upon the identification of a mutation in HBA1, HBA2 or HBB in specialized laboratories. Phenotypic studies of Hb are also useful, but electrophoretic analysis can be normal in 1/3 of cases. The establishment of the dissociation curve of Hb can be used as another screening test, a shift to the left indicating an increased affinity for Hb. The direct measurement of venous P50 using a Hemox Analyzer is of great importance, but due to specific analytic conditions, it is only available in a few specialized laboratories. Alternatively, an estimated measurement of the P50 can be obtained in most of the blood gas analyzers on venous blood. The aim of our study was therefore to determine whether a normal venous P50 value could rule out HOAH. We sequenced the HBB, HBA1 and HBA2 genes of 75 patients with idiopathic erythrocytosis. Patients had previously undergone an exhaustive medical check-up after which the venous P50 value was defined as normal. Surprisingly, sequencing detected HOAH in three patients . A careful retrospective examination of their medical files revealed that (i) one of the P50 samples was arterial; (ii) there was some air in another sample; and (iii) the P50 measurement was not actually done in one of the patients. Our study shows that in real life conditions, due to pre-analytical contingencies, a venous P50 value that is classified as being normal may not be sufficient to rule out a diagnosis of HOAH. Therefore, we recommend the systematic sequencing of the HBB, HBA1 and HBA2 genes in the exploration of idiopathic erythrocytosis.High oxygen affinity hemoglobin (HOAH) is the main cause of constitutional erythrocytosis. Mutations in the genes coding the alpha and beta globin chains ( JAK2 mutation in exons 14 or 12, which is found in nearly 98% of patients [Erythrocytosis and polycythemia are generic terms referring to red cell diseases characterized by an increase in hematocrit (Ht) and/or hemoglobin (Hb) concentration. Among this group of diseases, polycythemia vera arises from a primary acquired clonal abnormality of the red blood cell lineage, leading to a myeloproliferative neoplasm. Its diagnosis has been standardized by the WHO criteria, relying particularly on the presence of a patients . Howeverpatients .HBA1, HBA2 and HBB) strengthen the binding of oxygen to the Hb. The oxygen cannot therefore be released to the tissues from the Hb, which brings about hypoxia, increased EPO secretion and thus secondary erythrocytosis. The diagnosis of HOAH is based on the discovery of a specific sequence variation in the alpha or beta globin genes in specialized laboratories, associated with a written and signed consent form.High oxygen affinity hemoglobin (HOAH) is the main constitutional cause of erythrocytosis . HOAH shNowadays, more than 200 different variants have been identified as responsible for HOAH . ClinicaIt is important that clinicians recognize HOAH in order to spare the patient multiple unnecessary and expensive complementary examinations and to institute appropriate patient management. Screening tests are of two types based upon the biochemical characteristics of the globin chains or upon the oxygen affinity itself. Therefore, Hb phenotypic studies including electrophoresis or chromatography can be very useful in the characterization of abnormal Hb; nevertheless, a routine electrophoretic approach may fail to detect abnormal Hb, so molecular analysis is essential for establishing a diagnosis .The establishment of the dissociation curve of Hb, which represents the percentage of oxygen-saturated Hb according to the partial pressure of oxygen, makes it possible to perform a direct functional study of Hb. A shift of the curve to the left indicates an increased affinity for Hb. The reference technique is the Hemox Analyzer, which studies Hb oxygen saturation of washed erythrocytes in a buffer. However, due to the very short delivery times for samples and the time-consuming nature of the technique, it is done in very few specialized laboratories. Alternatively, the Hb dissociation can be estimated through venous P50, the oxygen tension at which 50% of Hb is saturated. Indeed, the venous P50, which is normally measured at 27 ± 1 mmHg under physiological conditions, is decreased in HOAH. An estimated measure of the P50 can be obtained inexpensively by most blood gas analyzers.Previous studies have reported that lowered P50 leads the diagnosis towards either HOAH or a bisphosphoglycerate mutase deficiency . NeverthBPGM genes, it seems difficult to make a statement about its predictive value when within the normal range.While it seems now well established that decreased venous P50 is strongly suggestive of HOAH (or rarely of bisphosphoglycerate mutase deficiency), which is then confirmed by sequencing of globin or HBB, HBA1, HBA2 genes on a large series of 75 consecutive patients from various centers in France who had already completed an exhaustive medical check-up in order to compare their results with the venous P50 measures [To evaluate the relevance of the venous P50 measured using blood gas automats, we performed genotypic analysis of the measures ,11.JAK2 mutations on exons 14 and 12 and red cell mass measurement.Seventy-five patients from various centers in France with well-characterized idiopathic erythrocytosis were tested. Inclusion criteria implied an exhaustive standard medical check-up. Hence, diagnosis of idiopathic erythrocytosis was confirmed by clinical and biological analyses including measurements of full blood count, blood electrolytes, serum EPO and blood gases, functional respiratory tests, an abdominal ultrasound scan, screening for the JAK2 mutations or acquired conditions associated with erythrocytosis. They also had no mutations in genes involved (i) in the regulation of the hypoxia pathway (PHD1 (EGLN2), PHD2 (EGLN1), PHD3 (EGLN3), HIF-1A, HIF-2A (EPAS1), HIF-3A, VHL, VHLL), (ii) in proliferation and differentiation of erythroid progenitors , CBL), or (iii) in mature cell function (bisphosphoglycerate mutase (BPGM)), using a dedicated NGS panel. In 4% of the patients, a PIEZO1 mutation was noted, leading ultimately to the diagnosis of hereditary xerocytosis with well-compensated hemolysis [HBB, HBA1 and HBA2 genes encoding the globin chains, DNA samples were tested in a second step for all patients, regardless the P50 value, using another NGS erythrocyte gene panel that included HBB, HBA1 and HBA2 genes. Each nucleotide variation expected to be probably pathogeneous (class 4) or pathogeneous (class 5 of the ACMG classification ref 2015) was checked using conventional Sanger sequencing. Written consent was obtained from all patients.None of the patients had emolysis . As the A total of 68 males and seven females  were included in our study. Biological data are reported in HBB, HBA1 and HBA2 genes, HOAH was detected in three patients (Hb Olympia in two patients (Patients #1 and #2), Hb St Nazaire in a third patient (Patient #3)). The two patients with Hb Olympia were from the same family (father and son) and had previously never been detected. As expected, the electrophoresis and chromatography profiles showed no separation between HbA for Hb Olympia. They carry the same nucleotide variation on the HBB gene ). Due to the discrepancy between the presence of Hb Olympia and the venous P50 value, which was recorded as normal, a careful retrospective examination of the medical files was performed. We finally discovered that the P50 value came not from venous but from arterial blood gas for the two patients. We thus recommended that venous blood gas be analyzed. A decreased venous P50 (21.8 mmHg) was obtained for the father, but the result was unfortunately uninterpretable in the son because there was air in the sample.Surprisingly, after the sequencing of the HBB gene (NM_000518.5:c.310T>A or NP_000509.1:p.(Phe104Ile)). The variant hemoglobin is not separated from HbA using standard electrophoresis or chromatography methods, in agreement with the variant description given in the Globin Gene Server . Only isoelectric focusing can reveal a slight band broadening of the Hb A band and the highly specific reversed phase HPLC methodology (which is not performed in our lab) is needed to separate the variant from the normal beta chain. He initially was recorded as having normal venous blood P50. After a careful inspection of his medical record, it appeared that venous blood gases had not actually been collected. A venous blood sample was therefore collected, and showed low venous P50, evaluated at 19 mmHg. Biological and clinical data of these three patients are summarized in For the patient with Hb St Nazaire, the sequencing noted a nucleotide variation on the JAK2 mutation (exons 14 and 12), serum EPO, arterial blood gases (to rule out hypoxia or carbon monoxide poisoning) and venous blood gases . Abdominopelvic ultrasound is also useful to evaluate the presence of splenomegaly, renal abnormalities, uterine leiomyoma or hepatocarcinoma.Idiopathic erythrocytosis is a common condition in clinical hematology and is investigated in the first line with standard biological tests, including HBB, HBA1 and HBA2 genes were not part of our initial NGS panel since in our initial diagnostic strategy, blood gases were performed as part of the screening workup for erythrocytosis. Therefore, in the case of lowered venous P50, simple Sanger sequencing of the HBB, HBA1 and HBA2 genes was performed. This strategy allowed us to avoid performing complete NGS analysis in all cases since it is a costly and time-consuming test [Our cohort included 75 patients with idiopathic erythrocytosis who had previously been tested using NGS with a dedicated panel for erythrocytosis. The In the current study, we wanted to check whether our diagnostic approach was relevant and did not overlook cases of HOAH. However, sequencing detected HOAH in three patients: Hb St Nazaire in one patient, and Hb Olympia in two members in the same family (father and son).Our results underline the fact that the P50 findings were not trustworthy in a small proportion of cases. These discrepancies were not the result of a poor correlation between venous P50 and the presence of high affinity hemoglobin ; they were associated with the conditions under which the blood gases were taken or with an error on the part of the clinician. The three clinical situations reported here demonstrate that when P50 is tested in arterial blood or when there are technical issues , it can artificially modify this parameter and therefore falsely eliminate the hypothesis of HOAH. Indeed, due to very short delivery times for samples for the testing of blood gases, it is usually recommended that the tests be performed in the local laboratory. Thus, if there is no guarantee that the pre-analytical and analytical conditions are respected, one could reasonably question whether the P50 is a reliable screening test. On the other hand, except for the decrease in venous P50, it was not possible to distinguish patients with HOAH from other causes of idiopathic erythrocytosis, only on the parameters of routine biology.n = 762), a P50 < 24 mmHg was found in almost 99% of patients. In other words, the probability of having HOAH with a P50 ≥ 24 is about 1%, which makes this parameter a good screening test [In a very interesting study published by Oliveira et al. from a large series of patients with HOAH Hb . At a time when molecular genetic tests are being generalized, our study strongly suggests that the


There are many types of cancers. Although they share some hallmarks, such as proliferation and metastasis, they are still very different from many perspectives. They grow on different organ or tissues. Does each cancer have a unique gene expression pattern that makes it different from other cancer types? After the Cancer Genome Atlas (TCGA) project, there are more and more pan-cancer studies. Researchers want to get robust gene expression signature from pan-cancer patients. But there is large variance in cancer patients due to heterogeneity. To get robust results, the sample size will be too large to recruit. In this study, we tried another approach to get robust pan-cancer biomarkers by using the cell line data to reduce the variance. We applied several advanced computational methods to analyze the Cancer Cell Line Encyclopedia (CCLE) gene expression profiles which included 988 cell lines from 20 cancer types. Two feature selection methods, including Boruta, and max-relevance and min-redundancy methods, were applied to the cell line gene expression data one by one, generating a feature list. Such list was fed into incremental feature selection method, incorporating one classification algorithm, to extract biomarkers, construct optimal classifiers and decision rules. The optimal classifiers provided good performance, which can be useful tools to identify cell lines from different cancer types, whereas the biomarkers  and rules identified in this work may provide a meaningful and precise reference for differentiating multiple types of cancer and contribute to the personalized treatment of tumors. It is becoming a serious public health problem worldwide. In 2020 alone, over 19.29 million new cases of cancer were diagnosed, and more than 9.58 million people died from cancer . The halAlthough many cancers share the hallmarks of cancer, they are still very different. They grow on different organs and tissue. Pan-cancer studies provide an opportunity to understand the commonalities, heterogeneity, and emergent themes of multiple tumors . IncreasThe sample size of TCGA based pan-cancer studies is already very large as a multi-omics data source. But it is still not enough to get robust pan-cancer biomarkers if we consider the large variances among cancer patients across cancer types and even within the same cancer. Tumor heterogeneity can be broadly categorized into intratumor heterogeneity and inter tumor heterogeneity . Inter tTo get robust pan-biomarkers, there are two approaches: increase the sample size or reduce the variance. TCGA and the following works tried the first approach of increasing sample size. In this study, we would like to try the second approach of reducing the variance by analyzing the cancer cell line data from Cancer Cell Line Encyclopedia (CCLE) . The impXiao et al.  downloadThe CCLE data involved a large number of genes (features). Obviously, not all genes are associated with the investigated tumor types. Therefore, filtering the important genes is necessary. Here, we applied the Boruta  method tThe Boruta method is a wrapping algorithm that is based on random forest (RF) and involves the following steps: 1) the new shuffled data are generated by copying the original dataset and shuffling original features; 2) a RF classifier that can output the importance score of each feature is trained by using the new feature matrix as the input; and 3) the features in the original features that are sincerely relevant to the labels are retained, and the shuffled data are removed. Boruta finally selects the relevant features after several iterations of the above three steps.https://github.com/scikit-learn-contrib/boruta_py and was set to default parameters for execution.The Boruta program that we used in this research was downloaded from After feature filtering by using the Boruta method, the mRMR  feature The mRMR method evaluates the importance of target features by using max-relevance and min-redundancy. Features with great relevance to the category labels and low redundancy with other features are considered to be influential. It uses mutual information (MI) to measure relevance and redundancy. The score of the MI between two variables http://penglab.janelia.org/proj/mRMR/and executed with default parameters.The mRMR program used in this research was obtained from Even though the mRMR method produces a ranked list of features on the basis of the importance of features, we still cannot determine the influential features. The IFS  method cDT  is a modSVM  is a supIn this study, 20 tumor types needed to be classified. This task was a multiclass classification problem. Therefore, we applied the one-versus-rest strategy to train a multiclass SVM, which was split into numerous binary SVMs. For each binary SVM, samples of one class were regarded as positive examples, and samples of all other classes were used as negative examples. We directly used the tool “SMO” in Weka software  in this As can be seen from X be a matrix representing predicted labels yielded by one classifier and Y be another matrix indicating the actual labels of samples. The calculation formula of MCC is as follows:In this study, several multiclass classifiers were used to distinguish samples from 20 tumor types. We adopted 10-fold cross-validation  to evaluith cancer type was computed byNi represents the number of samples in the ith cancer type and ni denotes correctly predicted samples in the ith cancer type. The overall accuracy was calculated byIn addition to MCC, we also calculated accuracy on each cancer type and overall accuracy. The accuracy on the MCC was set as the key measurement and others were also provided for reference in this study.In this study, several computational methods were used to analyze the CCLE dataset of 20 tumor types. The analysis process is shown in All features were first analyzed by using the Boruta method. A total of 54,634 features were removed, and 3,186 features were retained. These retained features are provided in The feature list produced by the mRMR method was fed into the IFS method. A series of feature subsets were generated by setting the step size to 10. The DT and SVM were used to build classifiers on each feature subset. Then, all classifiers were evaluated by 10-fold cross-validation to obtain evaluation metrics, such as accuracy on each cancer type, overall accuracy and MCC. The above measurements that were acquired by the two classification algorithms for each subset of features are shown in In addition to the above SVM algorithm, we used the DT, which is a white-box classification algorithm. In this process, the step size of IFS with the DT was also set to 10, and only the top 400 features in the mRMR list were considered. The IFS results are also available in Analysis of Decision Rules.As mentioned above, the DT classifier with the top 100 features exhibited high performance. Thus, we constructed a DT with these features and all samples. Consequently, we obtained 275 rules, which are presented in Results of the Incremental Feature Selection Method, the SVM classifiers with top 400 features gave a little lower performance than the best SVM classifier. However, it had much higher efficiency because much less features were used in this classifier. Thus, these 400 features may be highly related to distinguish different cancer types. Thus, we conducted GO and KEGG enrichment analysis on these features (genes). The results can be found in Analysis of Essential Genes, the discussion on the enrichment analysis results would be given.As mentioned in In this study, we used the Boruta and mRMR methods to analyze features and applied the IFS method combined with SVM and DT to construct classifiers and decision rules. Some essential features (genes) see  were extGO and KEGG Enrichment Analysis, top GO terms and KEGG pathways are illustrated in Firstly, we performed GO/KEGG enrichment analysis to find whether our 400 selected features were significantly enriched in specific terms. Results were described in We found that the significantly enriched GO terms mainly involve actin organization, cell matrix components and cell polarization. Actin assembly is very important for cell migration, and abnormal regulation of cell migration can drive cancer invasion and metastasis . DiffereSecondly, among the 400 selected features (genes), the top-ranked genes were usually highly decisive for distinguishing different cell lines. Therefore, some of them were selected for analysis, which are listed in The highest-ranking feature is NCKAP1 (ENSG00000061676). It encodes the NCK-associated protein 1 as a part of the WAVE (WASF) complex that regulates lamellipodia formation. Past studies have revealed that NCKAP1 is associated with multiple types of human cancer. A previous study showed that the WASF3 gene is a promoter of cell invasion in breast cancer and the Nckap1 can keep WASF3 in an inactive conformation through binding to the WASF homology domain at the N-terminus. The activation of WASF3 depends on the combination with RAC1 which can be prevented by the absence of NCKAP1. Thus, the downregulation of NCKAP1 inhibits the activity of WASF3 and may suppresses metastasis in breast cancer cells. In addition, univariate survival analysis have found that high expression level of NCKAP1 is correlated with short overall survival . The funin vivo experiments have revealed that TNFRS12A knockdown can inhibit cancer cell proliferation and migration (in vitro (TNFRSF12A (ENSG00000006327) encodes the receptor of TNFSF12/TWEAK, which is also known as fibroblast growth factor-inducible molecule 14. It can promote endothelial cell proliferation and angiogenesis. Studies have demonstrated that TNFRSF12A is highly expressed in breast cancer, and a high TNFRSF12A level associated with matrix metalloproteinase (MMP)-9 overexpression is related to cancer progression; thus, TNFRSF12A-targeting therapy could improve survival rates in cancer . Furtherigration . TNFRSF1in vitro . Other sin vitro . Interesin vitro . Therefoin vitro .LAMB2 (ENSG00000172037) encodes a subunit of laminins, which are one of the major glycoproteins present in the basement membrane of the extracellular matrix and are related to tumor angiogenesis, invasion, and metastasis. A previous study revealed that the downregulation of LAMB2 caused by HE4 gene interference results in the invasion and metastasis of ovarian cancer cells . Studiesp = 0.041); and that FKBP9 may be a cancer promoter that enhances prostate cancer progression (FKBP9 (ENSG00000122642), which encodes FKBP prolyl isomerase 9, is known to be associated with chaperonin-mediated protein folding and protein metabolism. A recent study has found that FKBP9 could be an independent prognostic marker for predicting the poor prognosis of patients with prostate cancer; that high FKBP9 levels and short biochemical-recurrence-free survival are significantly correlated  encodes an actin monomer-binding protein. It participates in regulating actin aggregation in response to extracellular signals and cell motility. Recently, PFN2 has emerged as a key regulator of cancer development and progression. PFN2 has been reported to be highly expressed in triple-negative breast cancer (TNBC); it could promote the proliferation, migration, and invasion of TNBC cells and may be partially responsible for the worsened survival associated with high PFN2 levels . In esopTOM1L1 (ENSG00000141198) encodes the target of myb1-like 1 membrane trafficking protein. ERBB2-induced breast cancer cell invasion has been documented to be caused by the TOM1L1-derived membrane delivery of MT1-MMP, and ERBB2 and TOM1L1 are frequently coamplified in the breast . Other sAs analyzed above, the selected genes from our results showed strong expression differences in multiple cancer cells. These genes could be good therapeutic targets. By the same token, distinct gene expression patterns could also be remarkably decisive features for different cancer cell lines.Previously, we constructed 275 decision rules on the basis of the top 100 selected features and all cell lines. Each rule contained several criteria. The numbers of rules and criteria for each cancer type are listed in in vivo  encodes the B subunit of lactate dehydrogenase enzyme, which participates in glycolysis. A study found that LDHB is specifically upregulated in basal-like TNBC, and the loss of LDBH arrests tumor growth in vivo . One stuin vivo . The samin vivo .Among the 275 rules, 51 could identify lung cancer cell lines with 740 criteria involving 80 genes. Here, we provide clear experimental evidence for some genes that are well established in the literature. SOX10 (ENSG00000100146) is a transcription factor that encodes genes involved in the regulation of embryonic development and cell-fate decisions. Studies have demonstrated that SOX10 is usually overexpressed in multiple cancers. It can activate stem/progenitor cells through the Wnt/β-catenin signaling pathway and induces mesenchymal transformation expression . Howeverin vitro. Clinical association analysis revealed that A1BG-AS1 is downregulated in HCC, and low A1BG-AS1 level is also associated with advanced tumor stage, microvascular invasion, and high tumor grade  is a RNA gene, and its transcriptional product is a lncRNA. A study found that A1BG-AS1 inhibits HCC cell proliferation, migration, and invasion or grade . CMTM4  only been shown in the rules of lung cancer cell lines. CD276 (B7-H3) encode a member of the immunoglobulin superfamily and is an important immune checkpoint member of the B7/CD28 families. It is induced by antigen presenting cells and participates in the regulation of T cell-mediated immune response . StudiesWe provided pieces of evidence for some decisive genes in the decision rules for four classes of cancer cell lines in the preceding discussion. Although these genes also have different expression patterns in other cancer cell lines and a large number of remaining genes have not yet been explained in detail, we can confirm the reliability of our results from the substantial evidence that we presented. Notably, some distinctive and decisive genes in our rules have not previously been investigated by other researchers. These genes may give new insight into tumor growth and progression, as well as novel potential therapeutic targets.NCKAP1, TNFRSF12A, LAMB2, FKBP9, PFN2, TOM1L1) and expression rules for the identification of different cancers at the transcriptome level were discovered. These biomarkers and rules can be useful materials to uncover mechanism underlying different cancer types, thereby improving our understanding on cancer.This study gave a computational investigation on the cell line gene expression data of cancer cell lines. Several machine learning algorithms were applied on such data. On one hand, we constructed efficient classifiers, which can be latent tools to identify different cancer types. On the other hand, a new set of potential biomarkers (