The Anti-CD19 Antibody-Drug Conjugate Loncastuximab Tesirine Achieved Responses in Patients with Diffuse Large B-Cell Lymphoma Who Relapsed after Anti-CD19 CAR T-Cell Therapy

Introduction: Patients with diffuse large B-cell lymphoma (DLBCL) that is resistant to chimeric antigen receptor T-cell (CAR-T) therapy have poor outcomes (Chow VA, et al. Am J Hematol. 2019;94:E209-E13). The majority of patients with DLBCL who relapse after CAR-T therapy do so with disease that continues to express CD19 surface antigen (Shah NN, Fry TJ. Nat Rev Clin Oncol. 2019;16:372-85); however, it is unknown whether treatment with CD19-targeted agents is an effective strategy for patients with prior failure of anti-CD19 CAR-T therapy. Loncastuximab tesirine (loncastuximab tesirine-lpyl; Lonca) is an FDA-approved CD19-directed antibody-drug conjugate (ADC) which had encouraging phase 1 antitumor activity and acceptable safety in non-Hodgkin lymphoma (Hamadani M, et al. Blood. 2021;137:2634-2645). In the Phase 2 LOTIS-2 trial (NCT03589469) the efficacy and safety of Lonca was evaluated in patients with relapsed or refractory (R/R) DLBCL after ≥2 lines of systemic treatments (Caimi PF, et al. Lancet Oncol. 2021;22:790-800). The overall response rate (ORR) was 48.3%. The aim of this post-hoc analysis of the LOTIS-2 trial was to investigate the antitumor activity of Lonca in patients with DLBCL relapsed or refractory after CAR-T therapy. Methods: The methodology of the LOTIS-2 trial has been published. Briefly, patients were treated with Lonca (0.15 mg/kg for the first 2 cycles then 0.075 mg/kg for subsequent cycles) administered as a single 30-minute infusion, once every 3 weeks for up to 1 year, or until progressive disease or unacceptable toxicity. Patients with previous anti-CD19 CAR-T therapy were required to have persistent CD19 expression, evaluated by local review of immunohistochemistry of a post-CAR-T biopsy. The primary endpoint was ORR, defined as the proportion of patients with best overall response of complete response (CR) or partial response (PR), determined by independent review. Secondary endpoints included overall survival (OS), progression free survival (PFS), and duration of response (DOR). PET/CT imaging was performed 6 and 12 weeks after the first Lonca dose and every 9 weeks thereafter. Response was assessed using the Lugano 2014 criteria. Kaplan Meier survival analysis was performed from initiation of Lonca treatment. Results: The characteristics of 13 patients with DLBCL with disease relapse or progression after anti-CD19 CAR-T therapy are shown in table 1. The median time interval between CAR-T infusion and Lonca treatment was 7 months (range, 45-400 days). Ten (77%) patients received Lonca as the first therapy after CAR-T failure, 3 patients received other treatments prior to Lonca (chemoimmunotherapy [R-GemOx], n = 1; allogenic stem cell transplant, n = 1; chemoimmunotherapy [R-GemOx] followed by venetoclax + bromodomain inhibitor, n =1). The ORR to Lonca was 46.2% (n=6; CR, 15.4% [n = 2]; PR, 30.8% [n = 4]) after a median of 2 cycles (range, 1-9). Of the 6 patients who achieved a response to Lonca, 5 had a previous response to CAR-T and 1 had prolonged, stable disease for >1 year after CAR-T. With a median follow-up of 8 months, the median OS and PFS were 8.2 and 1.4 months, respectively (Figure 1); the 1-year OS estimate was 33.3%. The median DOR to Lonca was 8 months. Conclusions: Lonca achieved a response in 6 out of 13 patients who had failed prior CAR-T therapy. Five out of 6 responding patients had previously presented at least a partial response after CAR-T therapy. These data suggest that in patients without CD19 antigen loss, repeat therapy with another agent targeting this antigen can result in disease control. Prior response to anti-CD19 therapy may be associated with subsequent response to a second anti-CD19 treatment. Further studies are needed to confirm the feasibility and value of repeated anti-CD19 treatments in patients with B-cell non-Hodgkin lymphoma. Funding: This study was funded by ADC Therapeutics; medical writing support was provided by CiTRUS Health Group. Figure 1 Figure 1. Disclosures Caimi: Amgen Therapeutics.: Consultancy; XaTek: Patents & Royalties: Royalties from patents (wife); ADC Theraputics: Consultancy, Research Funding; Genentech: Research Funding; Kite Pharmaceuticals: Consultancy; Verastem: Consultancy; Seattle Genetics: Consultancy; TG Therapeutics: Honoraria. Ardeshna: Gilead, Beigene, Celegene, Novartis and Roche: Honoraria; Norvartis, BMS, Autolus, ADCT, Pharmocyclics and Jansen: Research Funding; Gilead, Beigene, Celegene, Novartis and Roche: Membership on an entity's Board of Directors or advisory committees. Reid: Aptose Biosciences: Other: Serves as Principle Investigator, Research Funding; ADC Therapeutics: Other: Serves as Principle Investigator, Research Funding; Millennium Pharmaceuticals: Other: Serves as Principle Investigator, Research Funding; Xencor: Other: Serves as Principle Investigator, Research Funding. Ai: Kymria, Kite, ADC Therapeutics, BeiGene: Consultancy. Lunning: Myeloid Therapeutics: Consultancy; Spectrum: Consultancy; Daiichi-Sankyo: Consultancy; Verastem: Consultancy; Janssen: Consultancy; AstraZeneca: Consultancy; Morphosys: Consultancy; Beigene: Consultancy; Legend: Consultancy; ADC Therapeutics: Consultancy; Acrotech: Consultancy; Celgene, a Bristol Myers Squibb Co.: Consultancy; AbbVie: Consultancy; Kite, a Gilead Company: Consultancy; TG Therapeutics: Consultancy; Novartis: Consultancy; Kyowa Kirin: Consultancy; Karyopharm: Consultancy. Zain: Secura Bio, DaichiSankyo, Abbvie: Research Funding; Kiyoaw Kirin, Secura Bio, Seattle Genetics: Honoraria; Secura Bio, Ono , Legend, Kiyowa Kirin, Myeloid Therapeutics Verastem Daichi Sankyo: Consultancy. Solh: ADCT Therapeutics: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy; BMS: Consultancy; Partner Therapeutics: Research Funding. Kahl: AbbVie, Acerta, ADCT, AstraZeneca, BeiGene, Genentech: Research Funding; AbbVie, Adaptive, ADCT, AstraZeneca, Bayer, BeiGene, Bristol-Myers Squibb, Celgene, Genentech, Incyte, Janssen, Karyopharm, Kite, MEI, Pharmacyclics, Roche, TG Therapeutics, and Teva: Consultancy. Hamadani: Takeda, Spectrum Pharmaceuticals and Astellas Pharma: Research Funding; Janssen, Incyte, ADC Therapeutics, Omeros, Morphosys, Kite: Consultancy; Sanofi, Genzyme, AstraZeneca, BeiGene: Speakers Bureau.

First-Line Treatment with R-CHOP Vs Dose-Adjusted R-EPOCH for Double-Expressor Lymphoma: A University of California Hematologic Malignancies Consortium Retrospective Analysis

Background Dual overexpression of BCL2 and MYC in diffuse large B-cell lymphomas, termed double-expressor lymphoma (DEL), is associated with poor outcomes after treatment with chemoimmunotherapy consisting of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). The use of R-CHOP versus more intensified regimens remains controversial and is due to the lack of clear data to guide clinical decision-making. Retrospective studies have reported mixed results with the use of dose-adjusted rituximab, etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin (R-EPOCH) for the treatment of DEL, but the previously largest published retrospective study suggests a clinical benefit in patients <65 years with DEL when compared to R-CHOP. To better define the role of dose-intensifying regimens in managing DEL, we conducted the largest retrospective analysis to date comparing the outcomes of R-CHOP and R-EPOCH as frontline treatment for DEL. Methods We retrospectively analyzed 130 adults with DEL at the University of California campuses in Davis, Irvine, and Fresno who were diagnosed from January 2005-February 2021 and received first-line treatment with R-CHOP or R-EPOCH for DEL. DEL was defined as expression of MYC ≥40% and BCL2 ≥50% by immunohistochemistry (IHC). Triple expression was defined as DEL + BCL6 ≥50% by IHC. MYC, BCL2, and BCL6 rearrangements (-R) were determined by fluorescence in situ hybridization (FISH). The primary endpoint was 2-year progression-free survival (PFS), and secondary endpoints were 2-year overall survival (OS), overall response rate (ORR), complete response (CR) rate, and duration of response (DoR). Time-to-event analyses were conducted using the Kaplan-Meier method and were compared by a log-rank test. Univariate and multivariate analyses were conducted via the Cox proportional hazards model. Patients alive at the end of the study were censored at their last follow-up date. Responses were evaluated via the Lugano criteria. Results Baseline characteristics of the 130 DEL patients are listed in Table 1. Forty-six received R-CHOP, and 84 patients received R-EPOCH. The median follow-up for the R-CHOP and R-EPOCH groups were 18 months (range, 2-143) and 22 months (range, 1-136), respectively. The median number of cycles competed for each group was 6 (range, 1-8). The 2-year PFS and OS with R-CHOP vs R-EPOCH for the overall population were 60% (95% confidence interval [CI], 46-77) vs 66% (95% CI, 56-78) (Figure 1A), and 79% (95% CI, 66-95) vs 78% (95% CI, 69-89) (Figure 1B), respectively. For patients <65 years old, the 2-year PFS for those receiving R-CHOP vs R-EPOCH were 60% vs 71% (p=0.34), and the 2-year OS were 72% vs 88% (p=0.15), respectively. The ORR and CR rate for R-CHOP were 89% and 78%, and for R-EPOCH were 85% and 75%, respectively. For patients achieving a partial response (PR) or CR, the median DoR was 2.75 years for R-CHOP, and was not reached for R-EPOCH (p = 0.0067). A univariate analysis among all 130 DEL patients of age ≥65, stage ≥3, cell of origin, transformation, triple expression, MYC-R, BCL2-R and/or BCL6-R, and double-hit/triple-hit (DH/TH) status revealed that MYC-R and DH/TH were significant for worse PFS, hazard ratios (HR) 2.10 (95% CI, 1.11-3.95) and 3.64 (95% CI, 1.79-7.42), respectively. Only DH/TH was significant for worse OS, HR 4.20 (95% CI, 1.65-10.66). None of these factors were significant in a multivariate analysis for PFS or OS. A separate subgroup univariate analysis of R-CHOP vs R-EPOCH is provided in Figure 2. In total, 14 received an autologous stem cell transplant during their treatment course for relapsed/refractory disease, 7 in the R-CHOP group, 7 in the R-EPOCH group. Conclusion In this multi-center study, R-CHOP and R-EPOCH led to similar OS and PFS rates in patients with DEL. However, R-EPOCH led to more durable remissions than R-CHOP for those achieving a PR or CR. Across multiple subgroups of DEL by age, stage, cell of origin, transformation, triple expression, and MYC-R, BCL2-R and/or BCL6-R, neither regimen demonstrated superior outcomes. Larger randomized, prospective trials are needed to clarify the role of dose-intensified regimens for first-line treatment of DEL. Figure 1 Figure 1. Disclosures Brem: TG Therapeutics: Consultancy; KiTE Pharma: Membership on an entity's Board of Directors or advisory committees; ADC Therapeutics: Membership on an entity's Board of Directors or advisory committees; Morphosys/Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pharmacyclics/Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; SeaGen: Speakers Bureau; BeiGene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Bayer: Membership on an entity's Board of Directors or advisory committees. Abdulhaq: BMS, Alexion, Oncopeptides, Morphosys, Pfizer, Norvartis: Honoraria; Oncopeptides, Alexion, Amgen: Speakers Bureau; Morphosys, BMS, Amgen: Membership on an entity's Board of Directors or advisory committees. Reid: ADC Therapeutics: Other: Serves as Principle Investigator, Research Funding; Aptose Biosciences: Other: Serves as Principle Investigator, Research Funding; Millennium Pharmaceuticals: Other: Serves as Principle Investigator, Research Funding; Xencor: Other: Serves as Principle Investigator. Tuscano: BMS, Seattle Genetics, Takeda, Acrotech, Genentech, Pharmacyclics, Abbvie: Research Funding.

Frequency, Histologic, and Prognostic Significance of CD30 Expression in AIDS-associated Diffuse Large B-Cell Lymphoma

Introduction: AIDS-related Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, with a variable response to chemotherapy depending on, and not limited to, cell of origin, double/triple hit, or MYC/BCL-2 co-expression status. Similar to DLBCL, AIDS-related DLBCL (ARL) with non-germinal center histology or MYC expression reports poorer response to treatment. In the immunocompetent population CD30+ DLBCL defines a histology with improved survival, however, the characteristics and outcomes of ARL expressing CD30 are not well studied. Methods: We assessed 3 cohorts of ARL. The first cohort, consisted of of an immunohistochemistry tissue microarray (TMA) of 30 ARL patients, followed by two validation cohorts. The first validation was a TMA of 80 ARL, from the AIDS Cancer Specimen Resource. Both TMAs were stained for CD10, BCL6, MUM1, CD20, Ki67, EBER, MYC (by IHC) cut off at 40%, BCL2 (by IHC) cut off 50%, and CD30 (considered positive if any CD30 was expressed on the malignant cells). The third validation cohort was from the County Hospital AIDS Malignancy Project (CHAMP), a prospective database of patients with hematological malignancies and HIV. Of the 100 cases with ARL, only 25 cases were found to have CD30 staining performed, thus only those cases were included. In total, 135 patients diagnosed with ARL were assessed. Cell of origin and germinal (GCB) vs. non-germinal center (NGC) was determined by the Hans algorithm. Statistical differences between groups were analyzed by the fisher exact test. Survival data, when available, was estimated using the Kaplan-Meier method and compared using the log-rank test. Results: Of the 135 ARL, 30% (n=41) were CD30+. Ninety-one% of the cohort was male. EBER was 23% positive in the entire cohort (n=29/126). EBER was positive in the CD30+ vs. CD30- population, 59% (N=17) vs 26% (p<0.01). Despite 59% of the CD30+ population being EBER positive, 92% of the population had a NGC phenotype, 2% was germinal, and 5% had a null phenotype (p<0.01). Of the 86 patients that were CD30-, 88% were GC vs. 12% NGC (P<0.01). The CD4+ T-cell count at presentation was higher in the CD30- cohort with a mean CD4+ T-cell count of 234 vs.164 cells/ul (p<0.05), similar to historical studies demonstrating a similar effect in germinal vs. non-germinal center ARL. Ki67 > 80% was also higher in the CD30- vs the CD30+ cohort 75 vs.60%, (p=0.052). Myc, BCL2, and double expressor lymphomas were identified 59 vs. 57%, 59 vs. 57%, and 31 vs. 28%, respectively, in the CD30+ ARL vs. the CD30- population, none were statistically significant. Survival data was only obtained for 56 of the patients. In the patients treated in the combined anti-retroviral era (ART), there was no difference in survival in the CD30+ vs. CD30- population, 74% (n=18) vs. 84% (n=12) at 5 years (p=0.8). In the 15 patients treated in the early ART era, the OS at 5 years was 48% for the CD30+ vs. 52% (p=0.4), the rest were treated in the pre-ART era. Conclusion: CD30+ ARL in this cohort represents 30% of all ARL evaluated, and presents almost exclusively as a non-germinal center phenotype and has a strong correlation with EBV. While no differences in survival were identified in this study, possible due to the small numbers of patients assessed with survival data, historically, NGC ARL have been shown to have poorer outcomes, by 20-30% in studies with da-EPOCH. As such, the need for better therapies, potentially to overcome these poor prognostic factors, should be studied further. Figure 1 Figure 1. Disclosures Reid: ADC Therapeutics: Other: Serves as Principle Investigator, Research Funding; Aptose Biosciences: Other: Serves as Principle Investigator, Research Funding; Millennium Pharmaceuticals: Other: Serves as Principle Investigator, Research Funding; Xencor: Other: Serves as Principle Investigator.

Building a laboratory at a Primarily Undergraduate Institution (PUI)

Scientists who are interested in building research programs at primarily-undergraduate institutions (PUIs) have unique considerations compared to colleagues at research-intensive (R1) institutions. Maintaining a research program at a PUI holds unique challenges that should be considered before prospective faculty go on the job market, as they negotiate a job offer, and after they begin a new position. In this article we describe some of the considerations that aspiring and newly hired faculty should keep in mind as they plan out how they will set up a laboratory as a new Principle Investigator (PI) at a PUI.Anyone hoping to start a research program at a PUI should understand both the timeframe of interviews, job offers, and negotiations and the challenges and rewards of working with undergraduate researchers. Once a job is offered, candidates should be aware of the range of negotiable terms that can be part of a start-up package. Space and equipment considerations are also important, and making the most of shared spaces, existing infrastructure, and deals can extend the purchasing power of start-up funds as a new PIs builds their lab. PUIs’ focus on undergraduate education and mentorship leads to important opportunities for collaboration, funding, and bringing research projects directly into undergraduate teaching laboratories.A major focus of any new laboratory leader must be on building a productive, equitable, and supportive laboratory community. Equitable onboarding, mentorship plans, and formalized expectations, can all help build a productive and sustainable laboratory research program. However, important considerations about safety, inclusion, student schedules, and a PI’s own professional commitments are also extremely important concerns when working with undergraduates in research. A successful research program at a PUI will bring students into meaningful scientific inquiry and requires insights and skills that are often not the focus of scientific training. This article aims to describe the scope of setting up a new laboratory as a way to alleviate some of the burden that new and prospective faculty often feel.

Interdisciplinary Projects as an Expert-Network

We report an analysis of how an interdisciplinary project bringing together biologists, physicists and engineers worked in practice. The authorship team are the Principle Investigator who led the project, and a social scientist who studied the project as it was conducted by interviewing participants and observing practice. We argue it is accurate and productive to think of the interdisciplinary team as an Expert-Network, which means it was a managed set of relationships between disciplinary groups punctuated by specific junctions at which interdisciplinary exchange of materials, knowledge, and in limited cases, practices, occurred. We stress the role of trust in knowledge exchange, and document how hard sharing knowledge – and especially tacit knowledge - between disciplines can be. Key is the flexible management of the network, as the membership and required skill set change. Our analysis is embedded within, and contributes to, the Sociology of Experience and Expertise (SEE) framework. We close by suggesting advice for others seeking to manage a similar interdisciplinary Expert-Network.

So you want to be a Super Researcher

Publishing original scientific research is inherent to the work of a researcher. However, the pressure to maintain productivity and scientific impact can lead to research group publishing excessively, negatively affecting the mental health of a researcher. Ph.D. students and early career researchers are particularly susceptible to this pressure due to the inherent vulnerability of their positions. At present, there are no resources that concisely summarise the publication culture of a research group to help the researcher make an informed decision before joining. In this article, we present the ‘Super Researcher’ app, an R Shiny application(app) with a user-friendly interface. Using text-mining methodology to extract publicly available author data from Scopus, this pilot app has four fundamental functions to provide snapshot information that will help researchers grasp the publication culture of a research group within minutes. The ‘Super Researcher’ app provides information on: 1) institution data, 2) author’s publication, 3) co-author network plots and 4) publication journals.The ‘Super Researcher’ app is built on R shiny which provides an interactive interface to users. This app utilizes the Big Data framework Apache Spark to mine relevant information from a huge author information database. The author’s information is stored and manipulated using both SQL(SQLite) and NoSQL(HBase) databases. Hbase is used for local data storage and manipulation while SQLite feeds data to the R Shiny interface.In this paper, we introduce these functionalities and illustrate how this information can help guide a researcher to select a new Principle Investigator (PI) with better compatibility in terms of publication attitude using a case study. Available: https://researchmind.co.uk/super-researcher/

Twelve months into a feasibility trial: reflections on three experiences of public and patient involvement in research

In this Open Letter we present reflections from three different perspectives on the integration of public and patient involvement (PPI) in a research trial. We reflect on the experience of having a patient employed as a contract researcher, with no prior research experience, on a feasibility trial of cognitive rehabilitation in multiple sclerosis. This Open Letter is written by the PPI research team member with reflections from a researcher on the trial and the principle investigator. We will discuss some of the changes made and the impacts that have been resulted from of PPI input into the trial. We focus on PPI involvement in participant recruitment, the development of trial material, integration of PPI along the research cycle, and collaboration. We hope that this Open Letter will encourage principle investigators and groups to include PPI members as part of the research team and help patients and members of the public understand what the experience of PPI members is like.

Building a Laboratory at a Primarily Undergraduate Institution (PUI)

Scientists who are interested in building research programs at primarily-undergraduate institutions (PUIs) have unique considerations compared to colleagues at research-intensive (R1) institutions. Maintaining a research program at a PUI holds unique challenges that should be considered before prospective faculty go on the job market, as they negotiate a job offer, and after they begin a new position. In this article we describe some of the considerations that aspiring and newly hired faculty should keep in mind as they plan out how they will set up a laboratory as a new Principle Investigator (PI) at a PUI. Anyone hoping to start a research program at a PUI should understand both the timeframe of interviews, job offers, and negotiations and the challenges and rewards of working with undergraduate researchers. Once a job is offered, candidates should be aware of the range of negotiable terms that can be part of a start-up package. Space and equipment considerations are also important, and making the most of shared spaces, existing infrastructure, and deals can extend the purchasing power of start-up funds as a new PIs builds their lab. PUIs’ focus on undergraduate education and mentorship leads to important opportunities for collaboration, funding, and bringing research projects directly into undergraduate teaching laboratories. A major focus of any new laboratory leader must be on building a productive, equitable, and supportive laboratory community. Equitable onboarding, mentorship plans, and formalized expectations, can all help build a productive and sustainable laboratory research program. However, important considerations about safety, inclusion, student schedules, and a PI’s own professional commitments are also extremely important concerns when working with undergraduates in research. A successful research program at a PUI will bring students into meaningful scientific inquiry and requires insights and skills that are often not the focus of scientific training. This article aims to describe the scope of setting up a new laboratory as a way to alleviate some of the burden that new and prospective faculty often feel.

Understanding why healthcare workers refuse the flu vaccine

Background: Vaccination is one of the most effective interventions used to reduce infections. Nonetheless, concerns and misconceptions about vaccines have resulted in an increased rate of refusal of vaccination among healthcare workers (HCWs) and within the general population. Aim/ Objective: To understand the factors that underlie vaccination-refusal. Methods: A questionnaire was given to the HCWs in the inpatient area of an acute London hospital during May 2019. The sample was one of convenience, i.e. the wards where the principle investigator covered. All staff were approached: 306 in total completed the questionnaire. Findings/results: In total, 212 (69.28%) healthcare professionals had the flu vaccine during the 2018–2019 campaign, 94 (30.62%) did not have the flu vaccine and 37 (39.36%) of the HCWs that did not receive the flu vaccine reported they had previously a bad reaction following administration of the vaccine. Discussion: This report will provide the Trust and other healthcare settings with information integrating the disconnection between misconceptions and vaccination knowledge, which may inform practical interventions to address the challenges of vaccination in future campaigns. Increasing HCWs’ knowledge of flu vaccine side effects could address this and may increase rates of vaccination compliance.

Road to Hierarchical Diabetes Management at Primary Care (ROADMAP) Study in China: Protocol for the Statistical Analysis of a Cluster Randomized Controlled Trial

Background As the management of type 2 diabetes remains suboptimal in primary care, the Road to Hierarchical Diabetes Management at Primary Care (ROADMAP) study was designed and conducted in diverse primary care settings to test the effectiveness of a three-tiered diabetes management model of care in China. Objective This paper aims to predetermine the detailed analytical methods for the ROADMAP study before the database lock to reduce potential bias and facilitate transparent analyses. Methods The ROADMAP study adopts a community-based, cluster randomized controlled trial design that compares the effectiveness of a tiered diabetes management model on diabetes control with usual care among patients with diabetes over a 1-year study period. The primary outcome is the control rate of glycated hemoglobin (HbA1c) <7% at 1 year. Secondary outcomes include the control rates of ABC (HbA1c, blood pressure, and low-density lipoprotein cholesterol [LDL-C], individual and combined) and fasting blood glucose, and the change in each outcome. The primary analysis will be the log-binomial regression with generalized estimating equation (GEE), which accounts for the clustering within communities, for binary outcomes and linear regression with GEE for continuous outcomes. For both, the baseline value of the analyzed outcome will be the covariate. The other covariate further adjusted models and the repetitive models after multiple imputation (when more than 10% of observations in HbA1c after 1 year are missing) will be used for sensitivity analysis. Five prespecified subgroup analyses have also been planned to explore the heterogeneity of the intervention effects by adding the subgroup variable and its interaction with the intervention to the primary model. Results This plan has been finalized, approved, and signed off by the principle investigator, co-principle investigator, and lead statisticians as of November 22, 2019, and made public on the institutional website without any knowledge of intervention allocation. Templates for the main figure and tables are presented. Conclusions This statistical analysis protocol was developed for the main results of the ROADMAP study by authors blinded to group allocation and with no access to study data, which will guarantee the transparency and reduce potential bias during statistical analysis. Trial Registration Chinese Clinical Trial Registry ChiCTR-IOC-17011325; https://tinyurl.com/ybpr9xrq International Registered Report Identifier (IRRID) DERR1-10.2196/18333