Blood basophil concentration at diagnosis, not percentage, correlates with therapy-related outcomes in newly-diagnosed chronic phase chronic myeloid leukaemia

Blood basophil concentration at diagnosis, not percentage, correlates with therapy-related outcomes in newly-diagnosed chronic phase chronic myeloid leukaemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Blood basophil concentration at diagnosis, not percentage, correlates with therapy-related outcomes in newly-diagnosed chronic phase chronic myeloid leukaemia Edgar Faber, Lucia Vrablova, Xiao-shuai Zhang, Eva Kriegova, Milos Kudelka, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5403143/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Blood basophils ≥ 20 percent is reportedly associated with a poor prognosis and used to define accelerated phase of chronic myeloid leukaemia (CML) in some classifications. However, quantification of blood basophils is by percentage is inaccurate. Using a Patient Similarity Network (PSM) approach we identified basophil concentration rather than percentage as the more accurate predictive co-variate. To test this observation we interrogated data for a possible correlation between blood basophils quantified by concentration in a training cohort of 131 subjects with newly-diagnosed chronic phase CML receiving tyrosine kinase-inhibitor (TKI)-therapy. Subjects with a basophil concentration ≥ 12.2 x 10E + 9/L had poorer event-free survival (EFS, Odds Ratio [OR] = 12.3 [95% Confidence Interval [CI]. 4.2, 36.1]; p < 0.0001) and failure-free survival (FFS; OR = 10.4 [3.89, 27.72]; p < 0.0001). TKI switch-free survival and progression-free survival were also correlated with basophil concentration. The negative impact of a high basophil concentration was validated in an independent cohort of 1,870 subjects. We explain why basophil concentration is a more accurate co-variate. Our data indicate blood basophil concentration at diagnosis rather than percentage is a more accurate predictor of outcomes in persons with newly-diagnosed chronic phase CML receiving TKI-therapy. Health sciences/Risk factors Biological sciences/Cancer/Haematological cancer/Leukaemia/Chronic myeloid leukaemia Basophils chronic myeloid leukaemia prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Blood basophils ≥ 20 percent in chronic myeloid leukaemia (CML) is reported to be associated with a poor prognosis and was used to define accelerated phase in the 2016 WHO classification of accelerated phase. 1 Accelerated phase is now dropped from the 2022 WHO classification but remains in the 2023 International Consensus Classification. 2 , 3 However, regardless of which classification of CML disease phases one uses accurate quantification of blood basophils is important. Why percentage is used rather than basophil concentration is unclear. 4 When we analyzed basophil dynamics by patient similarity networks (PSNs) basophil concentration at CML diagnosis was a more accurate predictor of outcomes of tyrosine kinase-inhibitor (TKI) therapy. Also, in a recent study we found persons with chronic phase CML and an increased basophil percentage ≥ 20 percent at diagnosis had outcomes like those of persons with intermediate-risk CML in the European LeukemiaNet EUTOS Long Term Survival (ELTS) risk classification. 5 Based on these considerations we studied whether blood basophil concentration at diagnosis rather than percentage might be a more accurate outcomes predictor. We found this was so. Methods The training cohort concluded consecutive subjects with chronic phase CML receiving tyrosine kinase inhibitor (TKI)-therapy with imatinib or nilotinib at one centre. Potential subjects with ≥ 20 percent blood basophils were excluded. Diagnosis of CML was based on 2020 European LeukemiaNet (ELN) criteria. 6 Initial laboratory data included a CBC with manual differential count. WBC was measured using Sysmex XE5000 and Sysmex XN20 analysers (Sysmex, Kobe, Japan). In all subjects blood basophil concentrations were calculated from multiplying WBC and percentage basophils determined by light microscopy. The concurrent version of the ELN recommendations was used to assess response. 6 Haematologic response was monitored every 1–2 weeks until a complete haematologic response (CHR) and every 3 months with molecular monitoring until a major molecular response. Bone marrow cytogenetics were done at diagnosis and every 3–6 months until a complete cytogenetic response (CCyR). A validation cohort consisted of 1,870 consecutive subjects with newly-diagnosed BCR::ABL1 -positive chronic phase CML was treated at Peking University People’s Hospital 2006–2023 diagnosed using the same criteria. Basophil concentration was calculated from WBC and basophil percentage. RT-PCR for BCR::ABL1 transcripts followed concurrent ELN recommendations. 6 Molecular monitoring was done at 3 to 4 months intervals. Laboratories performing RT-PCR participated in the system of external control and have validated International Standards. 7 Statistics, patient similarity networks and definitions Statistical analyses including the Mann–Whitney–Wilcoxon test, receiver-operator characteristic (ROC) curves, Kaplan–Meier survival plots, Cox proportional hazards regression analyses, Hazard Ratios (HRs) and 95% Confidence Intervals (Cis) used the R statistical software package ( https://www.r-project.org/ ; v4.2.3). Before analyses a dataset validation was done to ensure basic assumptions of these analyses were appropriate. A 2-sided p -value < 0.05 was considered significant. We used a selected set of co-variates to construct patient similarity networks (PSNs) to visualise relationships between subject co-variates (WBC, basophil concentration and basophil percentage at diagnosis and during initial cytoreduction and TKI-switch). Network construction was based on nearest-neighbour analyses which displays similarities between subjects and automatically analysed to detect clusters. 8 – 10 Weighted modularity and silhouette methods were used to assess consistency within the clusters with the aim to identify subject cohorts with a higher incidence of negative events (EFS, FFS, PFS, TKI-switch and survival) and similar combinations of haematological co-variate dynamics during the initial cytoreduction period. Thresholds were calculated for each co-variate for every cluster using ROC analyses followed by defining a combined multi-ROC threshold. Survival was defined as the interval from TKI start until death from any reason. Progression-free survival (PFS) was defined as interval from TKI-start to haematologic progression, progression to blast phase (BP) or CML-related death. The ELN definition of BP was used. 11 FFS was defined as the interval from TKI-start until therapy-failure defined as > 10% BCR::ABL 1 transcripts at 6 months and > 1% BCR::ABL1 transcripts after 1 year, a TKI-resistant mutation or high-risk additional chromosome abnormalities (ACAs). 6 Switch-free survival (SFS) was defined as the interval from TKI-start until a switch to another TKI for any reason. Event-free survival (EFS) was defined as the interval from TKI-start to any event included in survival, PFS, FFS or SFS. Survival probabilities were estimated using the Kaplan–Meier method. Estimates at specific time points have 95% confidence intervals (CIs). Results Patient similarity network Percentage and concentration of basophils were analysed using PSNs and visualised in the training cohort ( Figure 1) . There was a log-normal distribution and co-variates values were log-transformed. After receiver-operator curve (ROC) analyses and threshold determinations co-variate values were returned to their original scale. The constructed network indicated 5 clusters (C1–5) differing in basophil concentration and percentage at diagnosis ( Figure 1 A) . Cluster quality was assessed by silhouette analyses ( Figure 1 ). Low negative values indicate only a few subjects studied were ambiguously assigned to a cluster whereas high values indicate subjects indicate distance from the decision boundary between adjacent clusters. Clusters C1 (n = 7), C5 (n = 34) and C4, n = 23) were characterised by increased basophil concentrations at diagnosis whereas cluster C2 (n = 16) and C3; (n = 51) with the low basophil concentrations at diagnosis ( Figure 1 D ). A multi-variable PSN analysis indicates 3 cohorts at-risk for TKI-switch. Frequencies of TKI-switch were highest in C1 (71% of subjects), C5 (41%) and C4 (30%) and lowest in C2 (13%) and C3 (14%; Figure 1 C ). Clusters with the highest WBCs and basophil concentrations and percentages were associated with EFS and FFS ( Figure 2 B ). CML risk scores correspond to the risk stratification of subjects with this sequence at highest risk: C1 (median Sokal 1.6, Hasford 1,390, ELTS 2.3, EUTOS 182.6), C5 (Sokal 1.4, Hasford 1,30, ELTS 2.4, EUTOS 78.2) and C4 (Sokal 4.4, Hasford 1,25, ELTS 2.1, EUTOS 50.3; Figure 2 A ). Subjects The training cohort included 131 subjects 74 of whom were male. Median age was 64 years (Interquartile Range [IQR] 54–74 years). 121 subjects with a WBC at diagnosis > 30 x 10E+9/L received a brief course of hydroxyurea followed immediately by TKI-therapy. Diagnostic haematological co-variates were determined before starting hydroxyurea or TKI. 120 subjects received imatinib and 11, nilotinib as initial therapy. TKI dose was adjusted according to response and/or adverse events based on concurrent ELN recommendations. 6 Data were analysed with a median follow-up of 6.4 years (IQR 3.8–10.38 years). Incidence and reasons for TKI-switch are displayed in Table 1 . Table 1. Subject co-variates and data in the training cohort (N = 131) Male 74 Age at diagnosis (years); median (IQR) 59 (50–69) WBC at diagnosis (×10E+9/L); median (IQR) 126 (60–241) Basophils at diagnosis (%); median (IQR) 4 (6) Basophils at diagnosis (×10E+9/L); median (IQR) 4 (1.5–10.4) Haemoglobin at diagnosis (g/L); median (IQR) 122 (100–136) Blasts at diagnosis (%); median (IQR) 1 (0–3) Splenomegaly at diagnosis (cm); median (IQR) 0 (0–10) Sokal score; median (IQR) 1.00 (0.80–1.38) Hasford score; median (IQR) 970 (708–1,423) EUTOS score; median (IQR) 42 (21–79) Follow-up (years); median (IQR) 6.4 (3.8–10.3) CML-related deaths 13 Blast phase 12 TKI-switch 42 TKI change because of treatment failure 34 Cytogenetic response (MCyR) 112 Major molecular response (MMR) 101 Abbreviations: IQR, Inter-Quartile Range; EUTOS, European Treatment and Outcome Study for CML; CML, chronic myeloid leukaemia; TKI, tyrosine kinase inhibitor. The validation cohort was 1,870 subjects; 1,139 of whom were male (61%). Median age was 41 years (IQR, 30-52 years). Initial TKI-therapy was with imatinib (N = 1,473), nilotinib (N = 272), dasatinib (N = 67) or flumatinib (N= 58). Data were analysed with a median follow-up of 5.5 years (IQR 2.3–7.5 years). T hresholds associated with prognosis Thresholds were obtained from multi-ROC and PSN analyses. In the training cohort subjects with a blood basophil concentration ≥ 12.2 x 10E+9/L had worst event-free survival (EFS; Odds Ratio [OR] = 12.31 [95% Confidence Interval [CI]. 4.20, 36.11]; p < 0.0001), failure-free survival (FFS; OR = 10.39 [3.89, 27.72]; p < 0.0001), switch-free survival (SFS; OR = 4.39 [1.75, 10.99]; p = 0.001) and progression-free survival (PFS; OR = 4.19 [1.38, 12.70] p = 0.008) but comparable survival compared with subjects with a lower basophil concentration. Subjects with a WBC concentration >181.5 x 10E+9/L had worse EFS (OR = 5.61 [2.53, 12.44]; p < 0.0001), FFS (OR = 5.36 [2.33, 12.31]; p < 0.0001), SFS (OR = 2.88 [1.28, 6.49]; p = 0.009) and PFS (OR = 5.96 [1.92, 18.54]; p = 0.001). The threshold for basophil percentage on EFS was 5.5% (OR = 2.15 [0.10, 4.62]; p = 0.05). Independent prognostic significance of WBC (< or ≥ 181.5 x 10E+9/L), basophil concentration (< or ≥ 12.2 x 10E+9/L) and basophil percentage (< or ≥ 5.5%) was not confirmed by the Cox multi-variable regression analyses (data not shown). Survival analysis of training and validation cohorts Co-variates correlated with survival were interrogated in all subjects. Basophil concentration ≥12.2 x 10E+9/L and WBC concentration ≥181.5 x 10E+9/L were significantly associated with EFS ( p < 0.001, p < 0.001), FFS ( p < 0.001, p < 0.001), SFS ( p = 0.004, p = 0.001), PFS ( p = 0.005, p = 0.001) and survival ( p = 0.05, p = 0.183), respectively ( Figure 3 ). Percentage blood basophils was associated with EFS ( p = 0.02), FFS ( p = 0.02), survival ( p = 0.04) using ≥ 5.5%, but not other outcomes ( Figure S1 ). In the validation cohort basophil concentration ≥ 12.2×10E+9/L and WBC concentration ≥181.5 x 10E+9/L was associated with worse EFS ( p < 0.001, p < 0.001), FFS ( p < 0.001, p < 0.001), SFS ( p < 0.001, p < 0.001) and PFS ( p = 0.18, p = 0.003) but not survival ( Figure 4 ). Basophils ≥ 5.5% was not significantly associated with outcomes in the validation cohort (data not shown). Discussion Our data indicate blood basophil concentration at diagnosis rather than percentage is a more accurate outcomes predictor in persons with newly-diagnosed chronic phase CML receiving TKI-therapy and should replace percentage as a predictive co-variate. This conclusion is independent of whether a measure of basophils is used to predict outcomes or define accelerated phase. As we opined we think CML is a bi-phasic leukaemia and accelerated phase should be dropped. 12 There are two methods to enumerate percentage basophils. The first one is visual where cell types are assigned by an observer based on blood cytology using a microscope review of stained cells on a slide, typically of 100 nucleated cells. This method is 2dimensional and has several potential sources of error including intra- and inter-observer variability and small sample size. For example, the 95 percent Confidence Interval of observing 5 basophils in 100 nucleated cells (5%) is ​0.7–9%. The likelihood of accurately distinguishing someone with 19 percent basophils rather than 20 percent basophils is obviously very low. The 2nd method is automated enumeration using a particle counter based on the Coulter principle available from 1989. This method uses particle volume rather than cytology and is 3dimensional. Percentage is determined by comparing absolute numbers of basophils with absolute numbers of all uncleared cell types after processing thousands of nucleated cells. The 95 percent confidence of a 5 percent basophil estimate is narrow. These methods produce different results. For example, Supplement Figure S2 displays the poor correlation between these methods in 62 subjects in the training cohort where values for both methods were available. Previously percentage basophils were calculated visually. 5 , 13 – 15 Some predictive scores during the TKI-era such as EURO and EUTOS score do not specify how to estimate basophil percentage. 14 , 15 However, presently most physicians use the particle volume-based calculation of basophil percentage rather than the visual method. To avoid potential discordances with prior studies we used the visual method to develop our model. Whether a model based on basophil percentage or concentration based on a particle volume-based method would be a more accurate predictor is unknown and under study. Regardless of which method to estimate basophils is used the question is whether the percentage of basophils independent of the WBC concentration or absolute numbers of basophils (dependent on the WBC concentration) is a more accurate outcomes predictor. Our data indicate blood basophil concentration rather than percentage should be used as an outcomes predictor. Perhaps the underlying but unanswered question is why any estimate of basophils should correlate with outcomes of TKI-therapy. Similar conclusions for quantifying cells by concentration rather than percentage is suggested for other haematological cancers. 16 In conclusion we show basophil concentration at diagnosis rather than percentage is a more accurate outcomes predictor in persons with chronic phase CML receiving TKI-therapy. Declarations Funding Supported, in part, by the Internal Grant Agency of Palacky University (IGA_LF_2024_01, IGA_LF_2024_013) and the Ministry of Health of Czech Republic (MH CZ – DRO (FNOL, 00098892). Qian Jiang was supported by the National Nature Science Foundation of China (No. 81970140 and No. 82370161). Acknowledgement RPG acknowledges support from the UK National Institute of Health Research (NIHR). Authour Contributions LV – designed the study, prepared the typescript, data interpretation, subject enrolments and data collection; XSZ – statistical analyses; EK – design of the study, data interpretation, correction of the manuscript; MK – multivariate analysis and its interpretation, conception of statistical analysis, editing the typescript; MR – statistical analysis; SY – subject data collection; JJ – laboratory data evaluation and collection; TP – collection of subject data, editing the typescript; RPG – interpretation of the study results, editing the typescript; QJ – subject data interpretation, editing the typescript; EF – study-design, data interpretation, editing the typescript. All authors approved the final typescript, accept responsibility for the content and agreed to submit for publication. Conflict of inter est : EF is a speaker for Novartis; RPG is a consultant to Antengene Biotech LLC; Medical Director, FFF Enterprises Inc.; A speaker for Janssen Pharma and Hengrui Pharma; Board of Directors: Russian Foundation for Cancer Research Support and Scientific Advisory Board, StemRad Ltd; TP is a speaker for Novartis. QJ is a speaker for Novartis and Ascentage Pharma Group, Inc. Ethics Approved by the Ethic Committees of University Hospital Olomouc and Palacký University Olomouc and Peking University People Hospital. Subjects gave written informed consent consistent with the precepts of the revised Helsinki Declaration. References Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, Thiele J: WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. Revised 4th Edition. IARC, Lyon; 2017. Khoury JD, Solary E, Abla O, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. Leukemia . 2022;36(7):1703-1719. Thiele J, Kvasnicka HM, Orazi A, et al. The international consensus classification of myeloid neoplasms and acute leukemias: myeloproliferative neoplasms. Am J Hematol . 2023;98(1):166-179. Theologides A. Unfavorable signs in patients with chronic myelocytic leukemia. Ann Intern Med . 1972;76(1):95-99. Pfirrmann M, Baccarani M, Saussele S, et al. Prognosis of long-term survival considering disease-specific death in patients with chronic myeloid leukemia. Leukemia . 2016;30(1):48-56. Hochhaus A, Baccarani M, Silver RT, et al. European LeukemiaNet 2020 recommendations for treating chronic myeloid leukemia. Leukemia . 2020; 34(4):966-984. Cross NCP, White HE, Müller MC, Saglio G, Hochhaus A. Standardized definitions of molecular response in chronic myeloid leukemia. Leukemia . 2012;26(10):2172-2175. Kudělka M, Zehnalová Š, Horák Z, et al. Local dependency in networks. Int J Applied Math Comp Sci . 2015; 25(2):281-293. Ochodkova E, Zehnalova S, Kudelka M, Cao Y, Chen J. Graph Construction Based on Local Representativeness, Comput. Comb., Springer International Publishing. 2017; pp. 654–665. van den Elzen S, van Wijk JJ. Multivariate Network Exploration and Presentation: From Detail to Overview via Selections and Aggregations. IEEE Trans Vis Comput Graph . 2014;20(12):2310-2319. Baccarani M, Deininger MW, Rosti G, et al. European LeukemiaNet recommendations for the management of chronic myeloid leukemia: 2013. Blood . 2013;122(6):872-884. Gale RP, Jiang Q, Apperley JF, Hochhaus A. Is there really an accelerated phase of chronic myeloid leukaemia? Leukemia . 2024;38(10):2085-2086. Sokal JE, Cox EB, Baccarani M, et al. Prognostic discrimination in "good-risk" chronic granulocytic leukemia. Blood . 1984;63(4):789-799. Hasford J, Pfirrmann M, Hehlmann R, et al. A new prognostic score for survival of patients with chronic myeloid leukemia treated with interferon alfa. Writing Committee for the Collaborative CML Prognostic Factors Project Group. J Natl Cancer Inst . 1998; 90(11):850-858. Hasford J, Baccarani M, Hoffmann V, et al. Predicting complete cytogenetic response and subsequent progression-free survival in 2060 patients with CML on imatinib treatment: the EUTOS score. Blood . 2011; 118(3):686-692. Calvo, X. Should we give oligomonocytic chronic myelomonocytic leukemia a higher prominence in the next WHO Classification of Haematolymphoid Tumors? Leukemia. 2023;37:250-251. Additional Declarations Yes there is potential conflict of interest. Supplementary Files FigureS1.jpg Figure S1. Percentage blood basophil and survival with a ≥ 5.5% cut-off in the training cohort. FigureS2.jpg Figure S2. Correlation between basophil concentration from visual and particle counting. Data of 62 subjects from the training cohort in with both values. The correlation plot shows that when there are no basophils visually (only 100 cells examined) the particle counter detects them (10,000s of cells). Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5403143","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":376698723,"identity":"f87576e5-c436-40b9-b7bd-310830c97b21","order_by":0,"name":"Edgar 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University","correspondingAuthor":false,"prefix":"","firstName":"Milos","middleName":"","lastName":"Kudelka","suffix":""},{"id":376698728,"identity":"9ea15b72-ae73-4875-819c-039dd6e0f62b","order_by":5,"name":"Martin Radvansky","email":"","orcid":"","institution":"Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Radvansky","suffix":""},{"id":376698729,"identity":"cd03978f-da50-4030-9d4f-ee8eb877f616","order_by":6,"name":"Sen Yang","email":"","orcid":"","institution":"Peking University Peoples Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sen","middleName":"","lastName":"Yang","suffix":""},{"id":376698730,"identity":"e78bdf09-c8c1-4050-88ba-cbf7f70b7cad","order_by":7,"name":"Jarmila Juranova","email":"","orcid":"","institution":"Faculty Hospital and Faculty of Medicine and Dentistry, Palacký University","correspondingAuthor":false,"prefix":"","firstName":"Jarmila","middleName":"","lastName":"Juranova","suffix":""},{"id":376698731,"identity":"dc52aa62-36b7-489c-8c4e-4e7ab7f5caee","order_by":8,"name":"Tomas Papajik","email":"","orcid":"","institution":"Palacky University and University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tomas","middleName":"","lastName":"Papajik","suffix":""},{"id":376698732,"identity":"3d74e6e5-3c07-48e6-8a9e-8e393ecbf913","order_by":9,"name":"Robert Gale","email":"","orcid":"https://orcid.org/0000-0002-9156-1676","institution":"Hammersmith Hospital","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Gale","suffix":""},{"id":376698733,"identity":"d4c44dbf-a913-451d-9459-101401ac6c24","order_by":10,"name":"Qian Jiang","email":"","orcid":"https://orcid.org/0000-0001-7131-0522","institution":"Peking University People's Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2024-11-06 13:24:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5403143/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5403143/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71554803,"identity":"8e43f1f0-0f5a-4494-be70-e7f1e158c6f0","added_by":"auto","created_at":"2024-12-16 16:17:57","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":257172,"visible":true,"origin":"","legend":"\u003cp\u003ePatient similarity networks (PSNs) using leukocyte and basophil concentrations and percentages at the diagnosis and during cytoreduction in the training cohort of patients with chronic myeloid leukaemia. Five patient groups (clusters C1–5) were formed based on the similarity and diversity of studied parameters (B), achieving a good silhouette. The incidence of TKI-switch (red dot) was the highest in C1 (71.4%), C5 (41.2%) C4 (30.4%) compared with C2 (12.5%) and C3 (14%), reflecting a decreasing concentration of basophils at diagnosis (C). In terms of concentration and percentage of leukocytes and basophils at diagnosis and during cytoreduction (D), the darker the dot, the higher the value.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403143/v1/70d126ece7def877bb614133.jpg"},{"id":71554804,"identity":"de85af94-6e6e-4ed7-bc49-fdde37231631","added_by":"auto","created_at":"2024-12-16 16:17:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177403,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of patients with chronic myeloid leukaemia in clusters revealed by PSNs visualised by the value of (A) basophil concentration and EUTOS and ELTS scores and (B) prognostic measures (event-free survival EFS, failure-free survival FFS, switch-free survival SFS). The darker the dot, the higher the value of basophil concentration or prognostic scores. For survival measures, the dark dots are patients with short survival.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403143/v1/ba4af7a5e9647b00ae35da7a.jpg"},{"id":71556690,"identity":"828145f6-4fa0-48a2-aa64-075dcc6a5f65","added_by":"auto","created_at":"2024-12-16 16:25:57","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128292,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of thresholds (concentration of leukocytes ≥181.5 x 0E+9/L and concentration of basophils ≥12.2 x 10E+9/L) on prognostic measures (event-free survival, failure-free survival, switch-free survival, progression-free survival and overall survival) in the training cohort of patients with chronic myeloid leukaemia.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403143/v1/20eb39ab292de011d452aeb4.jpg"},{"id":71554806,"identity":"e7622378-44d7-4de0-808a-ae85fcee8926","added_by":"auto","created_at":"2024-12-16 16:17:57","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":192271,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of thresholds (concentration of leukocytes ≥181.5 x 0E+9/L and concentration of basophils ≥12.2 x 10E+9/L) on prognostic measures (event-free survival, failure-free survival, switch-free survival, progression-free survival and overall survival) in the independent validation cohort of 1,870 patients with chronic myeloid leukaemia.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403143/v1/16bb57a0ce9386e140421199.jpg"},{"id":72644254,"identity":"c11d6a07-0f59-435c-bb25-a10a66598ede","added_by":"auto","created_at":"2024-12-30 16:39:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1181346,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5403143/v1/6820389c-f5d8-4164-9ff4-832f83430047.pdf"},{"id":71554802,"identity":"a7868f2f-13a5-481a-b36c-1cca97c97540","added_by":"auto","created_at":"2024-12-16 16:17:57","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":72929,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. \u003c/strong\u003ePercentage blood basophil and survival with a \u0026nbsp;≥ 5.5% cut-off in the training cohort.\u003c/p\u003e","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403143/v1/a8ea2adb579b1965fb80a6f7.jpg"},{"id":71556689,"identity":"599788cd-aa4f-436d-a4cb-6d49b3409886","added_by":"auto","created_at":"2024-12-16 16:25:57","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":76141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. \u003c/strong\u003eCorrelation between basophil concentration from visual and particle counting. Data of 62 subjects from the training cohort in with both values. The correlation plot shows that when there are no basophils visually (only 100 cells examined) the particle counter detects them (10,000s of cells).\u003c/p\u003e","description":"","filename":"FigureS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5403143/v1/1afa3f8a74c6e8c86e52252a.jpg"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential conflict of interest.","formattedTitle":"Blood basophil concentration at diagnosis, not percentage, correlates with therapy-related outcomes in newly-diagnosed chronic phase chronic myeloid leukaemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBlood basophils\u0026thinsp;\u0026ge;\u0026thinsp;20 percent in chronic myeloid leukaemia (CML) is reported to be associated with a poor prognosis and was used to define accelerated phase in the 2016 WHO classification of accelerated phase.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Accelerated phase is now dropped from the 2022 WHO classification but remains in the 2023 International Consensus Classification.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e However, regardless of which classification of CML disease phases one uses accurate quantification of blood basophils is important.\u003c/p\u003e \u003cp\u003eWhy percentage is used rather than basophil concentration is unclear.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e When we analyzed basophil dynamics by patient similarity networks (PSNs) basophil concentration at CML diagnosis was a more accurate predictor of outcomes of tyrosine kinase-inhibitor (TKI) therapy. Also, in a recent study we found persons with chronic phase CML and an increased basophil percentage\u0026thinsp;\u0026ge;\u0026thinsp;20 percent at diagnosis had outcomes like those of persons with intermediate-risk CML in the European LeukemiaNet EUTOS Long Term Survival (ELTS) risk classification.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Based on these considerations we studied whether blood basophil concentration at diagnosis rather than percentage might be a more accurate outcomes predictor. We found this was so.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe training cohort concluded consecutive subjects with chronic phase CML receiving tyrosine kinase inhibitor (TKI)-therapy with imatinib or nilotinib at one centre. Potential subjects with \u0026ge;\u0026thinsp;20 percent blood basophils were excluded. Diagnosis of CML was based on 2020 European LeukemiaNet (ELN) criteria.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Initial laboratory data included a CBC with manual differential count. WBC was measured using Sysmex XE5000 and Sysmex XN20 analysers (Sysmex, Kobe, Japan). In all subjects blood basophil concentrations were calculated from multiplying WBC and percentage basophils determined by light microscopy.\u003c/p\u003e \u003cp\u003eThe concurrent version of the ELN recommendations was used to assess response.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Haematologic response was monitored every 1\u0026ndash;2 weeks until a complete haematologic response (CHR) and every 3 months with molecular monitoring until a major molecular response. Bone marrow cytogenetics were done at diagnosis and every 3\u0026ndash;6 months until a complete cytogenetic response (CCyR).\u003c/p\u003e \u003cp\u003eA validation cohort consisted of 1,870 consecutive subjects with newly-diagnosed \u003cem\u003eBCR::ABL1\u003c/em\u003e-positive chronic phase CML was treated at Peking University People\u0026rsquo;s Hospital 2006\u0026ndash;2023 diagnosed using the same criteria. Basophil concentration was calculated from WBC and basophil percentage.\u003c/p\u003e \u003cp\u003eRT-PCR for \u003cem\u003eBCR::ABL1\u003c/em\u003e transcripts followed concurrent ELN recommendations.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Molecular monitoring was done at 3 to 4 months intervals. Laboratories performing RT-PCR participated in the system of external control and have validated International Standards.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistics, patient similarity networks and definitions\u003c/h2\u003e \u003cp\u003eStatistical analyses including the Mann\u0026ndash;Whitney\u0026ndash;Wilcoxon test, receiver-operator characteristic (ROC) curves, Kaplan\u0026ndash;Meier survival plots, Cox proportional hazards regression analyses, Hazard Ratios (HRs) and 95% Confidence Intervals (Cis) used the R statistical software package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; v4.2.3). Before analyses a dataset validation was done to ensure basic assumptions of these analyses were appropriate. A 2-sided \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003cp\u003eWe used a selected set of co-variates to construct patient similarity networks (PSNs) to visualise relationships between subject co-variates (WBC, basophil concentration and basophil percentage at diagnosis and during initial cytoreduction and TKI-switch). Network construction was based on nearest-neighbour analyses which displays similarities between subjects and automatically analysed to detect clusters.\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Weighted modularity and silhouette methods were used to assess consistency within the clusters with the aim to identify subject cohorts with a higher incidence of negative events (EFS, FFS, PFS, TKI-switch and survival) and similar combinations of haematological co-variate dynamics during the initial cytoreduction period. Thresholds were calculated for each co-variate for every cluster using ROC analyses followed by defining a combined multi-ROC threshold.\u003c/p\u003e \u003cp\u003eSurvival was defined as the interval from TKI start until death from any reason. Progression-free survival (PFS) was defined as interval from TKI-start to haematologic progression, progression to blast phase (BP) or CML-related death. The ELN definition of BP was used.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e FFS was defined as the interval from TKI-start until therapy-failure defined as \u0026gt;\u0026thinsp;10% \u003cem\u003eBCR::ABL\u003c/em\u003e1 transcripts at 6 months and \u0026gt;\u0026thinsp;1% \u003cem\u003eBCR::ABL1\u003c/em\u003e transcripts after 1 year, a TKI-resistant mutation or high-risk additional chromosome abnormalities (ACAs).\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Switch-free survival (SFS) was defined as the interval from TKI-start until a switch to another TKI for any reason. Event-free survival (EFS) was defined as the interval from TKI-start to any event included in survival, PFS, FFS or SFS. Survival probabilities were estimated using the Kaplan\u0026ndash;Meier method. Estimates at specific time points have 95% confidence intervals (CIs).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient similarity network\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePercentage and concentration of basophils were analysed using PSNs and visualised in the training cohort (\u003cstrong\u003eFigure 1)\u003c/strong\u003e. There was a log-normal distribution and co-variates values were log-transformed. After receiver-operator curve (ROC) analyses and threshold determinations co-variate values were returned to their original scale. The constructed network indicated 5 clusters (C1\u0026ndash;5) differing in basophil concentration and percentage at diagnosis (\u003cstrong\u003eFigure 1 A)\u003c/strong\u003e. Cluster quality was assessed by silhouette analyses (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Low negative values indicate only a few subjects studied were ambiguously assigned to a cluster whereas high values indicate subjects indicate distance from the decision boundary between adjacent clusters. Clusters C1 (n = 7), C5 (n = 34) and C4, n = 23) were characterised by increased basophil concentrations at diagnosis whereas cluster C2 (n = 16) and C3; (n = 51) with the low basophil concentrations at diagnosis (\u003cstrong\u003eFigure 1 D\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA multi-variable PSN analysis indicates 3 cohorts at-risk for TKI-switch. Frequencies of TKI-switch were highest in C1 (71% of subjects), C5 (41%) and C4 (30%) and lowest in C2 (13%) and C3 (14%; \u003cstrong\u003eFigure 1 C\u003c/strong\u003e). Clusters with the highest WBCs and basophil concentrations and percentages were associated with\u0026nbsp;EFS and FFS (\u003cstrong\u003eFigure 2 B\u003c/strong\u003e). \u0026nbsp;CML risk scores correspond to the risk stratification of subjects with this sequence at highest risk: C1 (median Sokal 1.6, Hasford 1,390, ELTS 2.3, EUTOS 182.6), C5 (Sokal 1.4, Hasford 1,30, ELTS 2.4, EUTOS 78.2) and C4 (Sokal 4.4, Hasford 1,25, ELTS 2.1, EUTOS 50.3; \u003cstrong\u003eFigure 2 A\u003c/strong\u003e).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjects\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe training cohort included 131 subjects 74 of whom were male. Median age was 64 years (Interquartile Range [IQR] 54\u0026ndash;74 years). 121 subjects with a WBC at diagnosis \u0026gt; 30 x 10E+9/L received a brief course of hydroxyurea followed immediately by TKI-therapy. Diagnostic haematological co-variates were determined before starting hydroxyurea or TKI. 120 subjects received imatinib and 11, nilotinib as initial therapy. \u0026nbsp;TKI dose was adjusted according to response and/or adverse events based on concurrent ELN recommendations.\u003csup\u003e6\u003c/sup\u003e Data were analysed with a median follow-up of 6.4 years (IQR 3.8\u0026ndash;10.38 years). Incidence and reasons for TKI-switch are displayed in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eSubject co-variates and data in the\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003etraining cohort (N = 131)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eAge at diagnosis (years); median (IQR)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e59 (50\u0026ndash;69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eWBC at diagnosis (\u0026times;10E+9/L); median (IQR)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e126 (60\u0026ndash;241)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eBasophils at diagnosis (%); median (IQR)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e4 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eBasophils at diagnosis (\u0026times;10E+9/L); median (IQR)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e4 (1.5\u0026ndash;10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eHaemoglobin at diagnosis (g/L); median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e122 (100\u0026ndash;136)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eBlasts at diagnosis (%); median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e1 (0\u0026ndash;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eSplenomegaly at diagnosis (cm); median (IQR)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e0 (0\u0026ndash;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eSokal score; median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e1.00 (0.80\u0026ndash;1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eHasford score; median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e970 (708\u0026ndash;1,423)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eEUTOS score; median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e42 (21\u0026ndash;79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eFollow-up (years); median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e6.4 (3.8\u0026ndash;10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eCML-related deaths\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eBlast phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eTKI-switch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eTKI change because of treatment failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eCytogenetic response (MCyR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 415px;\"\u003e\n \u003cp\u003eMajor molecular response (MMR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: IQR, Inter-Quartile Range; EUTOS, European Treatment and Outcome Study for CML; CML, chronic myeloid leukaemia; TKI, tyrosine kinase inhibitor.\u003c/p\u003e\n\u003cp\u003eThe validation cohort was 1,870 subjects; 1,139 of whom were male (61%). Median age was 41 years (IQR, 30-52 years). Initial TKI-therapy was with imatinib (N = 1,473), nilotinib (N = 272), dasatinib (N = 67) or flumatinib (N= 58). Data were analysed with a median follow-up of 5.5 years (IQR 2.3\u0026ndash;7.5 years).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003cstrong\u003ehresholds associated with prognosis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThresholds were obtained from multi-ROC and PSN analyses. In the training cohort subjects with a blood basophil concentration \u0026ge; 12.2 x 10E+9/L had worst event-free survival (EFS; Odds Ratio [OR] = 12.31 [95% Confidence Interval [CI]. 4.20, 36.11]; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), failure-free survival (FFS; OR = 10.39 [3.89, 27.72]; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), switch-free survival (SFS; OR = 4.39 [1.75, 10.99]; \u003cem\u003ep\u003c/em\u003e = 0.001) and progression-free survival (PFS; OR = 4.19 [1.38, 12.70] \u003cem\u003ep\u003c/em\u003e = 0.008) but comparable survival compared with subjects with a lower basophil concentration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubjects with a WBC concentration \u0026gt;181.5 x 10E+9/L had worse EFS (OR = 5.61 [2.53, 12.44]; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), FFS (OR = 5.36 [2.33, 12.31]; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001), SFS (OR = 2.88 [1.28, 6.49]; \u003cem\u003ep\u003c/em\u003e = 0.009) and PFS (OR = 5.96 [1.92, 18.54]; \u003cem\u003ep\u003c/em\u003e = 0.001). The threshold for basophil percentage on EFS was 5.5% (OR = 2.15 [0.10, 4.62]; \u003cem\u003ep\u003c/em\u003e = 0.05).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependent prognostic significance of WBC (\u0026lt; or \u0026ge; 181.5 x 10E+9/L), basophil concentration (\u0026lt; or \u0026ge; 12.2 x 10E+9/L) and basophil percentage (\u0026lt; or \u0026ge; 5.5%) was not confirmed by the Cox multi-variable regression analyses (data not shown).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvival analysis of training and validation cohorts\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCo-variates correlated with survival were interrogated in all subjects.\u0026nbsp;Basophil concentration \u0026ge;12.2\u0026nbsp;x 10E+9/L and WBC concentration \u0026ge;181.5 x 10E+9/L were significantly associated with EFS (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), FFS (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), SFS (\u003cem\u003ep\u003c/em\u003e = 0.004, \u003cem\u003ep\u003c/em\u003e = 0.001), PFS (\u003cem\u003ep\u003c/em\u003e = 0.005, \u003cem\u003ep\u003c/em\u003e = 0.001) and survival (\u003cem\u003ep\u003c/em\u003e = 0.05, \u003cem\u003ep\u003c/em\u003e = 0.183), respectively (\u003cstrong\u003eFigure 3\u003c/strong\u003e). Percentage blood basophils was associated with EFS (\u003cem\u003ep\u003c/em\u003e = 0.02), FFS (\u003cem\u003ep\u003c/em\u003e = 0.02), survival (\u003cem\u003ep\u003c/em\u003e = 0.04) using \u0026ge; 5.5%, but not other outcomes (\u003cstrong\u003eFigure S1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the validation cohort basophil concentration \u0026ge; 12.2\u0026times;10E+9/L and WBC concentration \u0026ge;181.5 x\u0026nbsp;10E+9/L was associated with worse EFS (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), FFS (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), SFS (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and PFS (\u003cem\u003ep\u003c/em\u003e = 0.18, \u003cem\u003ep\u003c/em\u003e = 0.003) but not survival (\u003cstrong\u003eFigure 4\u003c/strong\u003e). Basophils \u0026ge; 5.5% was not significantly associated with outcomes in the validation cohort (data not shown).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur data indicate blood basophil concentration at diagnosis rather than percentage is a more accurate outcomes predictor in persons with newly-diagnosed chronic phase CML receiving TKI-therapy and should replace percentage as a predictive co-variate. This conclusion is independent of whether a measure of basophils is used to predict outcomes or define accelerated phase. As we opined we think CML is a bi-phasic leukaemia and accelerated phase should be dropped. \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThere are two methods to enumerate percentage basophils. The first one is visual where cell types are assigned by an observer based on blood cytology using a microscope review of stained cells on a slide, typically of 100 nucleated cells. This method is 2dimensional and has several potential sources of error including intra- and inter-observer variability and small sample size. For example, the 95 percent Confidence Interval of observing 5 basophils in 100 nucleated cells (5%) is ​0.7\u0026ndash;9%. The likelihood of accurately distinguishing someone with 19 percent basophils rather than 20 percent basophils is obviously very low.\u003c/p\u003e \u003cp\u003eThe 2nd method is automated enumeration using a particle counter based on the Coulter principle available from 1989. This method uses particle volume rather than cytology and is 3dimensional. Percentage is determined by comparing absolute numbers of basophils with absolute numbers of all uncleared cell types after processing thousands of nucleated cells. The 95 percent confidence of a 5 percent basophil estimate is narrow.\u003c/p\u003e \u003cp\u003eThese methods produce different results. For example, \u003cb\u003eSupplement Figure S2\u003c/b\u003e displays the poor correlation between these methods in 62 subjects in the training cohort where values for both methods were available.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePreviously percentage basophils were calculated visually.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Some predictive scores during the TKI-era such as EURO and EUTOS score do not specify how to estimate basophil percentage.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e However, presently most physicians use the particle volume-based calculation of basophil percentage rather than the visual method. To avoid potential discordances with prior studies we used the visual method to develop our model. Whether a model based on basophil percentage or concentration based on a particle volume-based method would be a more accurate predictor is unknown and under study.\u003c/p\u003e \u003cp\u003eRegardless of which method to estimate basophils is used the question is whether the percentage of basophils independent of the WBC concentration or absolute numbers of basophils (dependent on the WBC concentration) is a more accurate outcomes predictor. Our data indicate blood basophil concentration rather than percentage should be used as an outcomes predictor. Perhaps the underlying but unanswered question is why any estimate of basophils should correlate with outcomes of TKI-therapy. Similar conclusions for quantifying cells by concentration rather than percentage is suggested for other haematological cancers. \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn conclusion we show basophil concentration at diagnosis rather than percentage is a more accurate outcomes predictor in persons with chronic phase CML receiving TKI-therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eSupported, in part,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eby the Internal Grant Agency of Palacky University (IGA_LF_2024_01, IGA_LF_2024_013) and the Ministry of Health of Czech Republic (MH CZ \u0026ndash; DRO (FNOL, 00098892). Qian Jiang was supported by the National Nature Science Foundation of China (No. 81970140 and No. 82370161).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u0026nbsp; RPG acknowledges support from the UK National Institute of Health Research (NIHR).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthour Contributions\u0026nbsp;\u003c/strong\u003eLV \u0026ndash; designed the study, prepared the typescript, data interpretation, subject enrolments and data collection; XSZ \u0026ndash; statistical analyses; EK \u0026ndash; design of the study, data interpretation, correction of the manuscript; MK \u0026ndash; multivariate analysis and its interpretation, conception of statistical analysis, editing the typescript; MR \u0026ndash; statistical analysis; SY \u0026ndash; subject data collection; JJ \u0026ndash; laboratory data evaluation and collection; TP \u0026ndash; collection of subject data, editing the typescript; \u0026nbsp;RPG \u0026ndash; interpretation of the study results, editing the typescript; QJ \u0026ndash; subject data interpretation, editing the typescript; EF \u0026ndash; study-design, data interpretation, editing the typescript. \u0026nbsp;All authors approved the final typescript, accept responsibility for the content and agreed to submit for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of inter\u003c/strong\u003e\u003cstrong\u003eest\u003c/strong\u003e: \u0026nbsp;EF is a speaker for Novartis; RPG is a consultant to Antengene Biotech LLC; Medical Director, FFF Enterprises Inc.; A speaker for Janssen Pharma and Hengrui Pharma; Board of Directors: Russian Foundation for Cancer Research Support and Scientific Advisory Board, StemRad Ltd; TP is a speaker for Novartis. QJ is a speaker for Novartis and Ascentage Pharma Group, Inc.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e Approved by the Ethic Committees of University Hospital Olomouc and Palack\u0026yacute; University Olomouc and Peking University People Hospital. \u0026nbsp;Subjects gave written informed consent consistent with the precepts of the revised Helsinki Declaration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSwerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H, Thiele J: WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. Revised 4th Edition. IARC, Lyon; 2017.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eKhoury JD, Solary E, Abla O, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic\u0026nbsp;Neoplasms.\u0026nbsp;\u003cem\u003eLeukemia\u003c/em\u003e. 2022;36(7):1703-1719.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThiele J, Kvasnicka HM, Orazi A, et al. The international consensus classification of myeloid neoplasms and acute leukemias: myeloproliferative neoplasms. \u003cem\u003eAm J Hematol\u003c/em\u003e. 2023;98(1):166-179.\u003c/li\u003e\n \u003cli\u003eTheologides A. Unfavorable signs in patients with chronic myelocytic leukemia. \u003cem\u003eAnn Intern Med\u003c/em\u003e. 1972;76(1):95-99.\u003c/li\u003e\n \u003cli\u003ePfirrmann M, Baccarani M, Saussele S, et al. Prognosis of long-term survival considering disease-specific death in patients with chronic myeloid leukemia.\u0026nbsp;\u003cem\u003eLeukemia\u003c/em\u003e. 2016;30(1):48-56.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHochhaus A, Baccarani M, Silver RT, et al. European LeukemiaNet 2020 recommendations for treating chronic myeloid leukemia. \u003cem\u003eLeukemia\u003c/em\u003e. 2020; 34(4):966-984.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"7\"\u003e\n \u003cli\u003eCross NCP, White HE, M\u0026uuml;ller MC, Saglio G, Hochhaus A. Standardized definitions of molecular response in chronic myeloid leukemia. \u003cem\u003eLeukemia\u003c/em\u003e. 2012;26(10):2172-2175.\u003c/li\u003e\n \u003cli\u003eKudělka M, Zehnalov\u0026aacute; \u0026Scaron;, Hor\u0026aacute;k Z, et al. Local dependency in networks. \u003cem\u003eInt J Applied Math Comp Sci\u003c/em\u003e. 2015; 25(2):281-293.\u003c/li\u003e\n \u003cli\u003eOchodkova E, Zehnalova S, Kudelka M, Cao Y, Chen J. Graph Construction Based on Local Representativeness, Comput. Comb., Springer International Publishing. 2017; pp. 654\u0026ndash;665.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003evan den Elzen S, van Wijk JJ. Multivariate Network Exploration and Presentation: From Detail to Overview via Selections and Aggregations.\u0026nbsp;\u003cem\u003eIEEE Trans Vis Comput Graph\u003c/em\u003e. 2014;20(12):2310-2319.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBaccarani M, Deininger MW, Rosti G, et al. European LeukemiaNet recommendations for the management of chronic myeloid leukemia: 2013.\u0026nbsp;\u003cem\u003eBlood\u003c/em\u003e. 2013;122(6):872-884.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"12\"\u003e\n \u003cli\u003eGale RP, Jiang Q, Apperley JF, Hochhaus A. Is there really an accelerated phase of chronic myeloid leukaemia? \u003cem\u003eLeukemia\u003c/em\u003e. 2024;38(10):2085-2086.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"13\"\u003e\n \u003cli\u003eSokal JE, Cox EB, Baccarani M, et al. Prognostic discrimination in \u0026quot;good-risk\u0026quot; chronic granulocytic leukemia.\u0026nbsp;\u003cem\u003eBlood\u003c/em\u003e. 1984;63(4):789-799.\u003c/li\u003e\n \u003cli\u003eHasford J, Pfirrmann M, Hehlmann R, et al. A new prognostic score for survival of patients with chronic myeloid leukemia treated with interferon alfa. Writing Committee for the Collaborative CML Prognostic Factors Project Group. \u003cem\u003eJ Natl Cancer Inst\u003c/em\u003e. 1998; 90(11):850-858.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHasford J, Baccarani M, Hoffmann V, et al. Predicting complete cytogenetic response and subsequent progression-free survival in 2060 patients with CML on imatinib treatment: the EUTOS score. \u003cem\u003eBlood\u003c/em\u003e. 2011; 118(3):686-692.\u003c/li\u003e\n \u003cli\u003eCalvo, X. Should we give oligomonocytic chronic myelomonocytic leukemia a higher prominence in the next WHO Classification of Haematolymphoid Tumors? \u003cem\u003eLeukemia.\u003c/em\u003e 2023;37:250-251.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Basophils, chronic myeloid leukaemia, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-5403143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5403143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBlood basophils\u0026thinsp;\u0026ge;\u0026thinsp;20 percent is reportedly associated with a poor prognosis and used to define accelerated phase of chronic myeloid leukaemia (CML) in some classifications. However, quantification of blood basophils is by percentage is inaccurate. Using a Patient Similarity Network (PSM) approach we identified basophil concentration rather than percentage as the more accurate predictive co-variate. To test this observation we interrogated data for a possible correlation between blood basophils quantified by concentration in a training cohort of 131 subjects with newly-diagnosed chronic phase CML receiving tyrosine kinase-inhibitor (TKI)-therapy. Subjects with a basophil concentration\u0026thinsp;\u0026ge;\u0026thinsp;12.2 x 10E\u0026thinsp;+\u0026thinsp;9/L had poorer event-free survival (EFS, Odds Ratio [OR]\u0026thinsp;=\u0026thinsp;12.3 [95% Confidence Interval [CI]. 4.2, 36.1]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and failure-free survival (FFS; OR\u0026thinsp;=\u0026thinsp;10.4 [3.89, 27.72]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). TKI switch-free survival and progression-free survival were also correlated with basophil concentration. The negative impact of a high basophil concentration was validated in an independent cohort of 1,870 subjects. We explain why basophil concentration is a more accurate co-variate. Our data indicate blood basophil concentration at diagnosis rather than percentage is a more accurate predictor of outcomes in persons with newly-diagnosed chronic phase CML receiving TKI-therapy.\u003c/p\u003e","manuscriptTitle":"Blood basophil concentration at diagnosis, not percentage, correlates with therapy-related outcomes in newly-diagnosed chronic phase chronic myeloid leukaemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-16 16:17:53","doi":"10.21203/rs.3.rs-5403143/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3fe50084-c018-4889-b2bb-c5e21cb7c49e","owner":[],"postedDate":"December 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40098341,"name":"Health sciences/Risk factors"},{"id":40098342,"name":"Biological sciences/Cancer/Haematological cancer/Leukaemia/Chronic myeloid leukaemia"}],"tags":[],"updatedAt":"2024-12-30T16:30:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-16 16:17:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5403143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5403143","identity":"rs-5403143","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}