Introduction: The United States Medical Licensing Examination (USMLE) Step 1 recently shifted from a three-digit scoring format to a pass/fail scoring rubric. Lake Erie College of Osteopathic Medicine (LECOM) is among a number of osteopathic medical schools that traditionally included passing Step 1 as a graduation requirement. However, LECOM removed this requirement following the change in scoring format. National Board of Medical Examiners (NBME) subject examinations have a significant impact on the clerkship grades of third-year medical students. Therefore, our pilot study compared NBME subject examination scores among third-year LECOM medical students that did and did not take and pass Step 1. We anticipate that both high pre-clinical grade point average (GPA) and having passed Step 1 will be associated with higher subject exam scores, but passing Step 1 will have a relationship with higher subject exam scores that is independent of pre-clinical GPA.Methods: Using voluntary response sampling, 201 osteopathic medical students from LECOM completed an online survey through Google Forms regarding their pre-clinical GPA, subject exam scores, whether they took and passed USMLE Step 1, and their study resources used throughout clerkships. p < 0.05) found between pre-clinical GPA and exam scores across all subjects among students that took Step 1. There was no relationship between pre-clinical GPA and exam scores across all subjects among students that did not take Step 1 (p > 0.05). Students that took Step 1 had a higher pre-clinical GPA than those that did not. Students that took and passed Step 1 scored higher on subject exams. Fifty-nine percent of respondents indicated they would have studied more for Step 1 if these exams were scored on the three-digit format, while zero respondents indicated they would have studied less.Results: There was a positive correlation (Conclusion: Although higher pre-clinical GPA and taking Step 1 were associated with higher scores on subject exams, taking Step 1 appears to have an independent influence on subject exams because there was no relationship found between pre-clinical GPA and subject exam scores among students that did not take Step 1. Therefore, there may be features related to preparing for this exam that better equip osteopathic medical students to perform well on subject exams. As of January 26, 2022, the National Board of Medical Examiners (NBME) transitioned the scoring of the United States Medical Licensing Examination (USMLE) Step 1 from a three-digit format to a pass/fail rubric . In the Historically, preparation by students for Step 1 and Level 1 involved the use of a variety of resources, including question banks, NBME Comprehensive Basic Science Exams (CBSE), and third-party video resources . It is rPrevious research has demonstrated the predictive value of Level 1 scores for Step 1 scores . HoweverOn October 4, 2022, the authors distributed a survey to 649 third-year medical students across all Lake Erie College of Osteopathic Medicine (LECOM) campuses. Two hundred and one (31%) of students voluntarily responded to the survey. The survey was distributed via a secured Outlook e-mail from the Office of Clinical Education at LECOM, and participation was voluntary. Students were offered a second opportunity to complete the survey via e-mail reminder on October 12, 2022. Students were reminded once via email. The survey and data collection continued until November 14, 2022, after which the survey closed. The survey was administered using Google Forms. To maintain anonymity, the ‘collect email addresses’ and ‘limit to one response’ features were unchecked, confirming that respondents did not need to log into their google account to fill out the survey. This study was exempt from Institutional Review Board (IRB) review and approval. For access to specific questions asked in the survey, refer to the following link: https://forms.gle/7wb97C5a3hdGbomU6. A PDF version of the survey is also made available upon request to the corresponding author.Survey data were downloaded to and organized in Microsoft Excel . Upon download, categorical information was tabulated into “dummy” variables (ones and zeros). Incomplete responses were omitted prior to importing the data into RStudio for analyses. A Shapiro-Wilk test confirmed the data were not normally distributed. Mann-Whitney U tests were performed to compare exam scores for each subject across categorical variables. Correlations were computed as Pearson’s coefficients. For all analyses an ⍺ of 0.05 was used to determine significance. The distribution for pre-clinical GPA was negatively (left) skewed, a graphical demonstration of which is presented in Figure With non-parametric testing, median pre-clinical GPA was found to be higher for students that chose to take Step 1 across all subject exams . Pre-clinical GPA was not positively or negatively correlated with exam scores across all subjects among students that passed Level 1 alone. Among students that passed both Level 1 and Step 1, pre-clinical GPA was positively correlated with exam scores in all subjects except for Internal Medicine and Surgery. Additionally, pre-clinical GPA was positively correlated with exam scores in the Step 1 agnostic group among all subjects except for Internal Medicine Table .NBME subject examinations are an important assessment tool for third-year medical students. Although residency programs may not have access to a student’s subject exam scores, they are factored into the student’s final grades and averaged with other coursework, as well as their preceptor evaluations . AdditioThe results of the correlation analyses performed in this pilot study suggest value in replicating similar analyses on a larger data set in order to better understand the role of Step 1 in osteopathic medical education. Passing both Level 1 and Step 1  was associated with higher subject exam scores; and higher pre-clinical GPA was found to be associated with passing both Level 1 and Step 1. Therefore, an appropriate next question is: what really drives higher scores on subject exams? Pre-clinical GPA, passing Level 1 and Step 1, or both? Because there was no relationship identified between pre-clinical GPA and subject exam scores among students that did not take Step 1, we cannot definitively suggest that pre-clinical GPA is independently correlated with subject exam performance. Additionally, despite 59% of respondents indicating they would have studied more for Level 1 and/or Step 1 if scored with a three-digit format, taking Step 1 was nonetheless associated with higher subject exam scores. Therefore, we propose that higher pre-clinical GPA and passing Step 1 might jointly assist students in their preparation for subject exams.Research cited earlier in this article supports that scores on Step 1 could reasonably predict subject exam scores. As we found that students with high pre-clincial GPA were more likely to take Step 1, and that students who took Step 1 scored higher on subject exams, we were unable to isolate the influence of taking Step 1 on subject exam performance. However, because there was no relationship identified between pre-clinical GPA and subject exam scores among students that did not take Step 1, we were able to somewhat isolate the influence of (or lack thereof) pre-clinical GPA on subject exam scores. Although Step 1 is no longer scored with a three-digit rubric, we argue against discounting the preparation for Step 1 as a valuable tool to strengthen rising third-year osteopathic medical students for their clerkships. We caution readers to avoid deferring to aptitude as an underlying explanation of our findings, particularly because students that did not take or pass Step 1 had a similar range of pre-clinical GPAs compared to those that did. There are limitations to our pilot study. As the survey was distributed partway through participants’ clerkships, no single student had completed each NBME subject exam. Therefore, although we had 201 respondents (each with one or more subject exam scores reported), sample sizes for comparing two independent variables for each subject were relatively small. We also cannot rule out the influence of response bias, as an abundance of students with higher reported pre-clinical GPA responded to our survey. Finally, although this study investigated exam preparation-related features , it would be beneficial to acquire more detailed information regarding study strategies.Optimizing medical education is complex and traditionally relies heavily on quantitative measures of student performance. However, the pass/fail era of USMLE Step 1 and COMLEX Level 1 eliminates valuable measures of such. There are many hypotheses regarding the impact of the pass/fail era on osteopathic medical students’ candidacies for residency, but since there is insufficient data on the matter, further exploration is warranted. The pass/fail era for Step 1 began in January 2022, so NBME subject exams among the classes of 2024 provide the most immediate quantitative assessment of its impact.While our analyses demonstrated an overall positive relationship between pre-clinical GPA and scores on NBME subject exams; a relationship between higher pre-clinical GPA and choosing to take Step 1; and a relationship between taking Step 1 and higher subject exam scores; there was no relationship between pre-clinical GPA and subject exam scores among students that did not take Step 1. This helps justify the value of further investigation into the possible benefits for osteopathic medical students that undertake the preparation for Step 1. Osteopathic medical students with higher baseline academic performance may be more likely to take Step 1. However, our analyses suggest the possibility of additional, independent value in taking Step 1 with regard to preparation for subject exams during students' third year. Subject exam scores impact clerkship grades, and clerkship grades impact students’ competitiveness for residency. Therefore, more research on the role of Step 1 in the education of osteopathic medical students is warranted so that osteopathic medical schools are adequately informed on the role of Step 1 in the education of their students.


Ancient DNA studies reveal the genetic structure of Africa before the expansion of Bantu-speaking agriculturalists; however, the impact of now extinct hunter-gatherer and herder societies on the genetic makeup of present-day African groups remains elusive. Here, we uncover the genetic legacy of pre-Bantu populations from the Angolan Namib Desert, where we located small-scale groups associated with enigmatic forager traditions, as well as the last speakers of the Khoe-Kwadi family’s Kwadi branch. By applying an ancestry decomposition approach to genome-wide data from these and other African populations, we reconstructed the fine-scale histories of contact emerging from the migration of Khoe-Kwadi–speaking pastoralists and identified a deeply divergent ancestry, which is exclusively shared between groups from the Angolan Namib and adjacent areas of Namibia. The unique genetic heritage of the Namib peoples shows how modern DNA research targeting understudied regions of high ethnolinguistic diversity can complement ancient DNA studies in probing the deep genetic structure of the African continent. DNA research from present-day populations deemed uncontactable or extinct helps probe the deep genetic structure of Africa. It is generally accepted that the precolonial genetic diversity of southern Africa resulted from the sequential layering of at least three different sets of peoples with contrasting genetic, linguistic, and livelihood profiles: (i) an early occupation by the ancestors of foragers now speaking languages of the Kx’a and Tuu families   , peripatetic , pastoral , and agropastoral (Ovimbundu and Nyaneka) lifeways (text S1 and table S1). To contextualize our findings within the wider area of southern Africa and beyond, we further combined the data from Angola with other Africans previously genotyped on the same array  with increasing amounts of an ancestry component revealed at k = 6 by the unsupervised population clustering approach implemented in ADMIXTURE    (Ne) across time leveraging information from shared IBD segments  segments shared between individuals ( 0.0016) . Estimatet al.  (fig. S16). Some Nama and ǂKhomani individuals are additionally related to East African groups with high amounts of Eurasian ancestry, likely due to admixture with Europeans during colonial times.Ancestry-specific PCA (figs. S17 and S18) and clustering analysis based on average pairwise differences (fig. S19) further show that southern African– and Bantu-related ancestries of Khoe-Kwadi groups are highly heterogeneous and mirror the genetic composition of their neighbors. This pattern becomes especially clear when the local ancestry information is combined with IBD inferences to obtain southern African– and Bantu-specific IBD sharing (Ne) of 20,000 individuals (text S4 and table S9) , all sampled peripatetic groups display elevated levels of an eastern African ancestry and a previously undocumented southern African–related component (KS-Namib)  to 6. ThWhile at present no hunter-gatherers resembling Kx’a- and Tuu-speaking groups exist in the Angolan Namib, an early account by 16th-century traveler Duarte Pacheco-Pereira states that the areas around the Kuroka River mouth were then inhabited by nomadic groups who lived on fishing and built houses from whale ribs that they covered with seaweed . Hence, An ancient occupation of the Namib coast is also supported by oral stories describing an encounter between the Kwadi-speaking Kwepe and resident peoples who had no fire and ate raw fish on the beach , the three groups are virtually indistinguishable once the effects of genetic drift are attenuated , and SNPs from nonautosomal markers. These filters yielded a final set of 607,761 SNPs. No individuals had missing rates above 10%. We excluded from the analyses 37 individuals from Angola and 25 individuals from elsewhere because of cryptic relatedness. Specifically, we removed one individual from each pair that exhibited a proportion of IBD higher than 0.2, computed in PLINK v1.9 as P(IBD = 2) + 0.5 × P(IBD = 1) .All present-day samples analyzed were jointly phased with Beagle 4.1 , allowing one inconsistent genotype between the gap and block regions. The IBD blocks were then partitioned into three length categories  to investigate IBD sharing across different time periods (We used refinedIBD v.17Jan20.102 (Ne) though time based on IBD blocks of at least 2 cM shared within populations, using the software IBDNe v.23Apr20.ae9  algorithm and a subset of chromosomes . The inferred parameters were averaged by chromosome (taking into account their number of SNPs) and then by individuals. We fixed these parameters and performed an additional CHROMOPAINTER run for all chromosomes.The copy profiles generated for each recipient individual under each analysis were displayed in coancestry matrices that represent the average copy profiles of three runs. The difference between the average copy profiles of any pair of populations x and y, under each set, was quantified using the total variation distance (TVDxy) , we display the results according to different criteria (text S2 and fig. S13).We used qpAdm v.650  for all southern African individuals. Equal-size samples (n = 13) of Yorubans, Somalis, and southern African individuals  that occupy the rightmost position in PC1  approach  in each haploid genome. In addition, we masked all positions within each haploid genome for which the marginal probability returned by RFMix was smaller than 1, thus excluding any parts of the genome where the ancestry assignment was ambiguous. We first confirmed the validity of the local ancestry inference and masking procedures by computing a PCA on the training sources used in RFMix  and projecting on it all target southern African haploid genomes after masking (fig. S15). The PCA was carried out with smartpca  and filtering out SNPs with a missing rate higher than 10% (fig. S15). Southern African individuals with less than 95% missing data after masking the non–East African ancestry were then projected, together with previously published ancient genomes from East Africa (fig. S16). The PCA of the autochthonous southern African ancestry was built with a subset of southern African individuals displaying <25% of missing data after masking the non–southern African ancestries and the additional removal of SNPs with a missing rate higher than 15%. The remaining southern African individuals having between 25 and 95% missing data after masking were projected (fig. S17). Similarly, the PCA of Bantu-specific ancestry was constructed using southern African individuals, as well as Bantu-speaking groups from East Africa, having <25% of missing data after masking the non-Bantu ancestries, with the additional removal of SNPs with a missing rate higher than 15%. The remaining individuals having between 25 and 95% missing data after masking the non-Bantu ancestries were projected (fig. S18).PCA was additionally carried out with an alternative method , designed to handle high missingness in genetic datasets  (44). Thhttps://R-project.org/). Specifically, we used the function hclust, the hierarchical clustering method complete linkage, and a correlation matrix to compute the distance between both rows and columns of the pairwise distance matrix, represented in R by as.dist[1-cor(x)] (fig. S19).We calculated ancestry-specific pairwise differences and constructed heatmaps with built-in dendrograms using R .Last, we combined the information on IBD sharing between southern African individuals with their local ancestry profiles to obtain ancestry-specific IBD segments. For this analysis, we used the raw IBD blocks before the merging step, because that step would lead to loss of information about the haplotype of origin. While an IBD block might be a mosaic of more than one ancestry, the ancestry along two haplotypes that are identical by descent should, in theory, match. Yet, in practice, mismatches can be found if the RFMix inference is not perfect. Thus, we excluded from this analysis IBD blocks whose ancestry along both haplotypes is inconsistent for more than 25% of the corresponding IBD length (in centimorgans). For each of the remaining IBD blocks shared between two (haploid) individuals, we recorded the length of the block (in centimorgans) associated with each specific ancestry and then computed the sum of all lengths per ancestry. The average sum of IBD lengths (in centimorgans) from a specific ancestry and the average sum of IBD lengths excluded because of inconsistent ancestry assignments are displayed for each pair of populations using stacked barplots (fig. S20). This procedure, made available in We additionally summarized the results for length category 1 to 5 cM by computing the average IBD sharing between each group and pools of populations representing the genetic diversity of southern African and Bantu-related ancestries. These comprise West Bantu– and East Bantu–related ancestries, as well as southern African ancestries represented by Kx’a-Ju–, Tuu-Taa–, and Tuu-!Ui–speaking populations. We further included in the comparisons an “unknown” component to account for the southern African–related ancestry shared with peripatetic groups from the Angolan Namib . A UPGMAWe used genealogical concordance , we randomly sampled one nonmissing allele per population at each site and calculated the number of concordant Nconc (AABB) and discordant Ndisc (ABBA or ABAB) topologies. We then computed the excess of putatively concordant topologies over the second most frequent discordant category (disc1) as in Schlebusch et al.  and (ii) that have a minor allele frequency > 10% and more than 10 alleles per population. In addition, because the chance of finding a polymorphism depends on the sample size and the amount of nonmissing alleles in each population is quite variable, we first downsampled individuals so that, on average, there was a similar number (10 to 12) of nonmissing alleles per SNP per population. Moreover, we repeated all tests excluding SNPs discovered in a Juǀ’hoan North individual [San ascertainment;  was estimated as in Schlebusch et al. (Pconc is the proportion of concordant genealogies. Confidence intervals were computed with a maximum likelihood framework (T̂, which is measured in units of coalescent effective population size, into chronological dates by assuming Ne values ranging from 1000 to 20,000 for KS-Namib, Ne values of 20,000 for the remaining in-groups, and a generation time of 29 years (We additionally used genealogical concordance to infer divergence times between pairs of populations (


NCT04093167.Circulating tumor DNA (ctDNA) has shown promise in capturing primary resistance to immunotherapy. BR.36 is a multi-center, randomized, ctDNA-directed, phase 2 trial of molecular response-adaptive immuno-chemotherapy for patients with lung cancer. In the first of two independent stages, 50 patients with advanced non-small cell lung cancer received pembrolizumab as standard of care. The primary objectives of stage 1 were to ascertain ctDNA response and determine optimal timing and concordance with radiologic Response Evaluation Criteria in Solid Tumors (RECIST) response. Secondary endpoints included the evaluation of time to ctDNA response and correlation with progression-free and overall survival. Maximal mutant allele fraction clearance at the third cycle of pembrolizumab signified molecular response (mR). The trial met its primary endpoint, with a sensitivity of ctDNA response for RECIST response of 82% : 52–97%) and a specificity of 75% (90% CI: 56.5–88.5%). Median time to ctDNA response was 2.1 months (90% CI: 1.5–2.6), and patients with mR attained longer progression-free survival (5.03 months versus 2.6 months) and overall survival (not reached versus 7.23 months). These findings are incorporated into the ctDNA-driven interventional molecular response-adaptive second stage of the BR.36 trial in which patients at risk of progression are randomized to treatment intensification or continuation of therapy. ClinicalTrials.gov ID:  In the first stage of the BR.36 adaptive trial in patients with non-small cell lung cancer receiving anti-PD1 immunnotherapy, the primary endpoint of concordance between circulating tumor DNA (ctDNA) molecular response and RECIST response was met. The results will inform the second, ctDNA-directed stage. As the landscape of IO-based therapies and clinical trials is expanding, we face emerging challenges related to heterogeneity in clinical responses and insufficiency of imaging to rapidly and accurately capture therapeutic response3. Furthermore, currently used predictive biomarkers, such as programmed death-ligand 1 (PD-L1) expression or tumor mutation burden (TMB), fail to consistently predict therapeutic response5. These challenges highlight the urgent unmet need to implement molecular response-driven approaches to interpret outcomes and guide therapy selection in IO. Liquid biopsy analyses of circulating cell-free tumor DNA (ctDNA) have shown promise in capturing tumor burden dynamics during immune checkpoint blockade, allowing patients with primary resistance to be rapidly identified and redirected to receive alternative therapies14. Minimally invasive dynamic ctDNA next-generation sequencing (NGS) analyses that can be used as an early endpoint of immunotherapy response may, thus, help guide therapy to maximize therapeutic benefit and minimize toxicity risks for patients4.Liquid biopsies are gaining momentum in immuno-oncology (IO) as they can be used to rapidly and accurately determine clinical response, especially in the metastatic setting15 and combination pembrolizumab–carboplatin–taxane/pemetrexed16 are US Food and Drug Administration and Health Canada approved first-line treatment options, it is crucial to determine which patients would benefit from pembrolizumab monotherapy and which should receive combination immuno-chemotherapy. Such therapeutic decisions are not currently supported by either tumor PD-L1 or TMB status, introducing an unmet clinical need and an opportunity for dynamic assessments of ctDNA to guide treatment selection based on real-time tracking of circulating tumor burden. Nevertheless, several outstanding urgent questions need to be answered before implementation of liquid biopsy-guided ctDNA molecular responses in clinical decision-making. What is the best measure for tracking circulating tumor burden and which ctDNA features accurately capture survival outcomes? Should a tumor-informed or tumor-agnostic approach be implemented? What signifies a ctDNA molecular response and when does it occur? What is the concordance between ctDNA molecular response and Response Evaluation Criteria in Solid Tumors (RECIST) radiographic response? What are the patient subsets that would most benefit from a molecularly refined response assessment?Focusing on patients with non-small cell lung cancer (NSCLC) as a representative example, where both pembrolizumabTo address these challenging questions and further establish the role of ctDNA response as an early endpoint for clinical outcomes with immune checkpoint blockade, we designed a two-stage ctDNA-directed molecular response adaptive clinical trial, with the first stage focusing on answering the questions posed above and reported here.EGFR and ALK clinically actionable mutation-negative, immune checkpoint inhibitor-naive and chemotherapy-naive metastatic NSCLC with a PD-L1 expression level of ≥1% , patients with advanced/metastatic NSCLC who were eligible to receive standard-of-care single-agent pembrolizumab were enrolled in a single-arm study to evaluate, through serial liquid biopsy analyses, the optimal definition, timing and concordance of ctDNA molecular response with radiographic response . The first and last patients were enrolled on trial on 26 May 2020 and 5 April 2022, respectively. A total of 50 patients were accrued to the trial; all patients were followed for ctDNA molecular response and clinical response for a minimum of 12 weeks, and the clinical trial database was locked on 20 September 2022 Fig. . Median The primary objectives of the study were to establish the concordance of ctDNA molecular response with radiographic RECIST/immune RECIST (iRECIST) response and define the optimal timepoint of ctDNA molecular response. Secondary objectives included the association of ctDNA molecular response with progression-free survival (PFS) and overall survival (OS), the correlation of depth of ctDNA kinetics with radiographic response and analyses of time to ctDNA molecular response.P = 0.04; P = 0.13). Median duration of radiographic response was 10.1 months (90% CI: 5.6–19.2 months) and 9.7 months (90% CI: 5.6–10.7 months) for RECIST and iRECIST response, respectively.Best overall radiographic response was evaluated using RECIST and iRECIST at 12 weeks , and tum17, we employed a binomial model to calculate the estimated limit of detection based on the error-corrected coverage (KRAS G12A with a MAF of 0.19% (BR360030), NRAS Q61L with a MAF of 0.14% (BR360041) and KRAS G12C with a MAF of 0.14% (BR360048), and, collectively, these findings support the sensitive detection of ctDNA mutations by the NGS assay used.With respect to ctDNA molecular response, we employed a tumor-agnostic, white blood cell (WBC) DNA-informed NGS approach and first determined the cellular origin of sequence variants detected in plasma  and 6. Ocoverage . These a6. Among the 50 trial participants, five were not evaluable for ctDNA molecular response because of missed plasma collection or non-evaluable RECIST response; of the remaining 45 patients, 10 had undetectable ctDNA, and, as such, 35 patients (77.8%) were evaluable for ctDNA molecular response . There were no differences in PFS or OS in the evaluable patient subset (n = 35) compared to the overall BR.36 study population (n = 50). Among the 35 evaluable patients, 15 were classified in the mR category, with an evaluable mR rate of 43% (90% CI: 0.29–0.58).The maximal mutant allele fraction (maxMAF) of tumor-derived plasma mutations was tracked from the pre-treatment timepoint C1D1) to on-therapy timepoints (C2D1 and C3D1), with maxMAF clearance signifying molecular response (mR), whereas persistence of maxMAF indicated molecular disease progression (mPD) (Supplementary Table D1 to on-We identified four patterns of ctDNA kinetics: (1) ctDNA maxMAF clearance at C2D1, (2) maxMAF clearance at C3D1, (3) maxMAF reduction more than 85% in C2 or C3 and (4) ctDNA persistence Fig. . Of the n = 10); most patients with RECIST SD were classified in the mPD group (80%), with two patients showing mR. Of the two patients with mR/RECIST SD, BR360017 had adequate follow-up on the BR.36 trial to assess long-term clinical outcome, and, notably, ctDNA accurately reflected an ongoing PFS and OS of more than 13 months   had a PFS and OS of not reached versus 4.27 months and not reached versus 8.08 months, respectively, compared to patients with SD and mPD (n = 8). To put this in context and as part of the post hoc analyses, we evaluated ctDNA responses in patients with stable disease on immunotherapy from previously published studies19. These analyses further highlighted the heterogeneity of radiographic stable disease and the value of ctDNA molecular response to capture survival  Fig. . As radily) Fig. . In comply) Fig. . In postWe next explored the correlation of the degree of ctDNA reduction with radiographic RECIST response Fig. . The perP = 0.43, Extended Data Fig. n = 10) had numerically longer PFS and OS than patients with detectable ctDNA , indicating that ctDNA kinetics and molecular response was the most significant predictor of outcome .As part of post hoc analyses, we explored the association between baseline maxMAF and clinical outcomes. Although we did not identify any differences in baseline maxMAF between patients attaining a radiographic response (CR/PR) compared to patients in the SD/PD group (Wilcoxon n = 11), ctDNA molecular responses were 100% concordant between the tumor-informed and tumor-agnostic WBC DNA-informed approach. Notably, for patient BR360006, none of the plasma variants was detected in NGS of the matched tumor; however, by employing our tumor-agnostic approach, an ATM Q3000H mutation and a KIT C537W mutation were classified as tumor derived and evaluated for ctDNA kinetics. Similarly, for patient BR360026, there was no overlap between plasma variants and tumor NGS; nevertheless, the tumor-agnostic WBC-informed approach revealed a tumor-derived KRAS hotspot mutation that was tracked to determine ctDNA molecular response. A KRAS G12C was missed by tumor NGS for patient BR360036 but was detected, classified as tumor derived and tracked to determine molecular response. Although these exploratory analyses were limited by the lack of a trial-mandated tissue biopsy, our findings exemplify the challenges with the practical implementation of a tumor-informed liquid biopsy approach for assessing response to treatment as well as improved capture of tumor heterogeneity by liquid biopsies compared to archival tissue NGS analyses.As part of the study’s exploratory analyses, we evaluated whether a tumor-informed approach would more accurately capture ctDNA molecular responses. Of the 50 patients enrolled in the BR.36 study, only 19 (38%) had available matched tumor samples . FurtherThere is an unmet clinical need to implement real-time minimally invasive molecular analyses to capture therapeutic response and guide decision-making in the context of precision IO. We report here the findings of stage 1 of the Canadian Cancer Trials Group (CCTG) BR.36 trial, which aimed to define ctDNA molecular response, optimal timing of assessment and concordance with radiographic response. Employing a tumor-agnostic WBC DNA-informed panel NGS approach, ctDNA molecular response, defined as complete clearance of circulating tumor load after two cycles of pembrolizumab, was largely concordant with radiographic response assessments but, notably, was more informative in predicting OS. These findings are now incorporated into the second ctDNA molecular response-driven interventional randomized stage of the trial that will assess the value of adjusting therapy based on ctDNA response.The landscape of IO is expanding rapidly; however, optimal patient selection for immunotherapy represents a critical challenge that is intensified by the heterogeneity of clinical responses and insufficiency of imaging to fully and timely capture changes in tumor burden and therapeutic response. Snapshot biomarkers currently used to guide therapy selection, such PD-L1 expression and TMB, rely on analysis of tumor tissue obtained through invasive single-region biopsies, which may not sufficiently capture the clonal complexity of tumors, may be problematic in the event of low tumor purity sampling and, importantly, do not capture the evolving tumor under the selective pressure of immunotherapy. These challenges highlight the urgent unmet need to develop molecularly informed strategies to improve patient selection, enable early and accurate response assessment and facilitate long-term response monitoring during immunotherapy. To this end, liquid biopsies are emerging as powerful minimally invasive approaches to monitor tumor burden as cancer cells go through immunotherapy-induced evolutionary bottlenecks and to rapidly and precisely capture therapeutic response.23. Despite the substantial progress made, there remain outstanding questions that need to be answered before implementation of ctDNA molecular responses in clinical decision-making, starting with what signifies a ctDNA molecular response. Although different measures have been used to capture circulating tumor burden during immune checkpoint blockade, including the mean21 or maximum23 MAF of tumor-derived alterations, a consensus definition of circulating tumor load is lacking. Similarly, several definitions have been proposed for ctDNA molecular response that are, in part, dependent on the NGS assay used and its sensitivity and negative predictive value18. Circulating tumor load regression more than 50%25, any ctDNA reduction14 or complete elimination18 have been proposed as potential definitions of ctDNA molecular response. In the ctMoniTR pooled analyses of ctDNA dynamics in IO-treated NSCLC, changes in maxMAF were shown to be the most predictive of therapeutic response7. The heterogeneity in cohort composition, treatment administered, timepoints analyzed and ctDNA methodology used represent critical challenges in these previous retrospective proof-of-concept studies as well as their pooled analyses. BR.36 was prospectively designed to address these challenges, and serial liquid biopsy analyses revealed that ctDNA complete elimination better captured clinical outcomes in the context of the NGS tumor-agnostic assay used in the study, which also incorporated patient-matched WBCs. Furthermore, we investigated whether mean, median or maximal MAF of tumor-derived variants can differentially serve as a proxy for circulating tumor burden, but we did not identify any differences, which is, in part, related to our definition of molecular response that entails ctDNA reduction to undetectable levels.We hypothesized that ctDNA molecular response can be used as an enrichment strategy to identify patients at high risk of clinical disease progression (those with molecular disease progression), thus limiting the heterogeneity of the target population, which, in turn, opens a therapeutic widow of opportunity for early intervention and interception of therapeutic primary resistance. An increasing number of studies support the role of ctDNA molecular response as an early endpoint of therapeutic response26. In the first stage of BR.36, we found that the optimal timepoint is at C3D1, after two cycles of pembrolizumab; taking into account treatment delays, ctDNA response in BR.36 was determined at 8 weeks from treatment initiation. Importantly, understanding the true concordance between molecular and radiographic responses and how these differentially capture long-term outcomes in a clinical trial setting is imperative to support the clinical utility of ctDNA response. ctDNA molecular response may be most informative in characterizing the heterogenous group of patients with radiographically stable disease. ctDNA response has been shown to predict outcome with immune checkpoint blockade, such that patients with NSCLC with stable disease that cleared ctDNA had significantly longer PFS than patients who did not clear6. Patients with radiographic stable disease at first assessment who eventually attained a radiographic response have been reported to predominantly show ctDNA molecular responses24. Although a dedicated analysis of the heterogeneity of stable disease with respect to ctDNA response and long-term clinical outcomes was not included in the pre-specified analyses of the BR.36 study, patients within the stable disease subset had differential clinical outcomes that matched their ctDNA molecular response. Taken together, radiographic imaging may fail to timely detect the magnitude of therapeutic response for patients with stable disease, and ctDNA response may be of particular value in assessing therapeutic response in this setting.How early does molecular response occur? Several proof-of-concept studies have indicated that the optimal timepoint for ctDNA response lies between 4 weeks and 9 weeks from single-agent immune checkpoint blockade initiation29 while retaining the advantage of capturing tumor heterogeneity and, as such, serve as a compelling alternative to tumor-informed approaches. WBC DNA-informed approaches also address the emergent challenge with biological noise in liquid biopsies, driven by mutations related to clonal hematopoiesis28. Nevertheless, it is plausible that alterations deemed as ‘tumor derived’ by a tumor-agnostic WBC DNA-informed approach may be derived from another lineage or represent ultra-low abundance clonal hematopoiesis alterations that were not detected by error-correction NGS of matched WBC. Notably, in the BR.36 cohort, only 22% of patients would have been evaluable by employing a tumor-informed liquid biopsy approach. Although the tissue–plasma NGS concordance analyses were limited by the lack of a trial-mandated tissue biopsy, it is important to note that, for the small fraction of patients with matched plasma and tumor NGS, we noted 100% concordance in ctDNA molecular responses, which further supports the validity of our tumor-agnostic WBC DNA-informed approach.Furthermore, we explored whether a tumor NGS-informed approach would be feasible and more informative than the tumor-agnostic WBC DNA-informed approach that we employed in BR.36. Tumor-informed liquid biopsy approaches for patients with metastatic disease may not be feasible from a tissue sufficiency standpoint and may restrict the evaluable plasma variants to these detected by single-region heterogenous tumor sample NGS. WBC DNA-informed liquid biopsy approaches can improve the specificity for tracking circulating tumor load compared to plasma-only approachesThis study has several limitations, including the cohort size and the RECIST BOR of 32%, which likely reflected the real-world nature of the trial and reduced the statistical power to assess correlation of ctDNA with RECIST responses. Furthermore, as expected, approximately 20% of patients enrolled were not evaluable for ctDNA molecular response due to undetectable ctDNA, which represents a limitation given the shrinkage of the study population. Conceptually, a more sensitive multiplex polymerase chain reaction (PCR) tumor-informed liquid biopsy bespoke approach may reduce the number of cases with undetectable ctDNA; however, such an approach may not be feasible, especially in the context of a real-time ctDNA molecular response-adaptive interventional trial. Finally, the BR.36 study was powered to address the concordance between ctDNA and radiographic response, and, as such, assessment of additional endpoints may be limited by the study design. Per the trial design, we were limited in assessing the lead time between ctDNA response and RECIST response. ctDNA molecular response was assessed on C3D1 , whereas best radiographic response was assessed at 12 weeks after treatment initiation. Although RECIST responses were evaluated at 6 weeks, together with ctDNA responses, BOR did not occur at 6 weeks, rather at 12 weeks; as such, ctDNA response provides the earliest and most accurate readout of best overall therapeutic responses.Taken together, we show that ctDNA molecular response can identify patients with metastatic NSCLC less likely to attain favorable clinical outcomes with single-agent anti-PD-1 therapy, and this opens a therapeutic window of opportunity for treatment intensification for patients with molecular disease progression. Our findings were implemented in the design of the planned stage 2 of the BR.36 trial, which uses ctDNA detection after two cycles of standard-of-care pembrolizumab monotherapy to identify patients with metastatic NSCLC with PD-L1 ≥50% at high risk for disease progression, who are subsequently randomized to treatment intensification with pembrolizumab and chemotherapy versus continuation of pembrolizumab. Overall, our findings support the implementation of liquid biopsies in interventional IO clinical trials and further advance the evidentiary roadmap toward integration of ctDNA molecular responses in clinical decision-making for the increasing number of patients receiving immunotherapy.NCT04093167) was a phase 2, multi-center trial centrally activated on 17 October 2019. BR.36 stage 1 was designed as a single-arm, unblinded observational trial. BR.36 stage 2 is a randomized phase 2/3 trial that will evaluate whether adding chemotherapy to pembrolizumab for patients with advanced PD-L1+NSCLC who have persistent ctDNA at 6 weeks will result in better PFS and OS compared to patients who remain on pembrolizumab therapy until clinical progression. Primary endpoints are PFS and OS for the phase 2 and 3 portions, respectively  every 3 weeks. After the first three cycles, investigators had the option of switching to pembrolizumab 400 mg or 4 mg kg−1 IV every 6 weeks. Patients continued on trial until radiographically defined progression or unacceptable toxicity or until maximum duration of treatment (24 months).The CCTG BR.36 trial  NSCLC or stage III NSCLC if they are not candidates for surgical resection or definitive chemoradiation.Adult patients (≥18 years of age) with previously untreated, histologically or cytologically confirmed metastatic PD-L1ECOG PS of 0 or 1.Patients have to be eligible to receive treatment with pembrolizumab as standard of care, have an ECOG PS of 0 or 1 and have measurable disease and acceptable organ function.No prior systemic chemotherapy or immunotherapy for advanced metastatic NSCLC. Chemotherapy for non-metastatic disease  or immunotherapy for locally advanced stage III disease is allowed if at least 6 months have elapsed since the prior therapy and enrollment. Local therapy  is allowed as long as a period of 2 weeks has passed since completion. Patients must have recovered to ≤grade 1 from all reversible toxicity related to prior systemic or radiation therapy.Previous major surgery is permitted provided that surgery occurred at least 28 d before patient enrollment and that wound healing has occurred.Clinically and/or radiologically documented disease with at least one lesion measurable as defined by RECIST version 1.1.Imaging investigations, including computed tomography (CT) of the chest, abdomen and pelvis and magnetic resonance imaging (MRI) of the brain (if known brain metastases) or other scans as necessary to document all sites of disease must be done within 28 d before enrollment.9 per liter , absolute neutrophils ≥1.5 × 109 per liter  and platelets ≥100 × 109 per liter (100 × 103 per microliter). Chemistry: bilirubin ≤1.5× the upper limit of normal (ULN)*, AST and/or ALT ≤3× ULN, <5× ULN for patients with liver metastases; serum creatinine or creatinine clearance** ≤1.5× ULN or ≥40 ml min−1  × weight in kg serum creatinine in μmol L−1, males: GFR = 1.23 × (140 − age) × weight in kg serum creatinine in μmol L−1).Adequate hematology and organ function as defined below (must be done within 14 d before enrollment). Hematology: WBC ≥2.0 × 10EGFR or ALK genomic alterations, with symptomatic and uncontrolled brain metastases, who were pregnant/lactating or who were unwilling to use appropriate contraception were not eligible. Testing for EGFR and ALK is not required for patients with squamous histology.Patients with large cell neuroendocrine carcinoma (LCNEC) and patients with clinically actionable Patients have to consent to provision of a representative archival formalin-fixed, paraffin-embedded (FFPE) tumor block.Patients must consent to collection of liquid biopsy (blood) samples for ctDNA analysis by a CLIA central laboratory and for correlative analysis by a research central laboratory.Patient consent must be appropriately obtained in accordance with applicable local and regulatory requirements. Each patient must sign a consent form before enrollment to the trial to document their willingness to participate.Patients must be accessible for treatment and follow-up. Investigators must assure themselves that the patients enrolled on this trial will be available for complete documentation of the treatment, adverse events, collection of blood samples, response assessments and follow-up. Patients must agree to return to their primary care facility for response assessments as well as for any adverse events that may occur through the course of the trial.In accordance with CCTG policy, protocol treatment with pembrolizumab is to begin within two working days of patient enrollment.Women/men of childbearing potential must have agreed to use a highly effective contraceptive method. A woman is considered to be of ‘childbearing potential’ if she has had menses at any time in the preceding 12 consecutive months. In addition to routine contraceptive methods, ‘effective contraception’ also includes heterosexual celibacy and surgery intended to prevent pregnancy (or with a side effect of pregnancy prevention), defined as a hysterectomy, bilateral oophorectomy or bilateral tubal ligation or vasectomy/vasectomized partner. However, if at any point a previously celibate patient chooses to become heterosexually active during the time period for use of contraceptive measures outlined in the protocol, he/she is responsible for beginning contraceptive measures. Women of childbearing potential will have a pregnancy test to determine eligibility as part of the pre-study evaluation; this may include an ultrasound to rule out pregnancy if a false positive is suspected. For example, when beta-human chorionic gonadotropin is high and the partner is vasectomized, it may be associated with tumor production of hCG, as seen with some cancers. Patient will be considered eligible if an ultrasound is negative for pregnancy.Inclusion criteriaOf note, a PD-L1 TPS ≥50% was initially indicated as an inclusion criterion for the BR.36 trial, given the regulatory approvals in the United States and in Canada at the time of study design and activation. Throughout the duration of the BR.36 trial, the practice guidance changed in both the United Stated and Canada, allowing for pembrolizumab monotherapy for NSCLC with PD-L1 TPS ≥1%, and this was reflected in a trial amendment and revision of the eligibility criteria to include NSCLC with PD-L1 TPS ≥1%. This amendment was implemented in January 2021, after which 18 patients were enrolled. Despite the revised eligibility criteria, most patients enrolled in BR.36 had tumors with PD-L1 ≥50%, reflecting the preference for pembrolizumab monotherapy in this context given the higher magnitude of benefit.Patients with a prior malignancy whose natural history or treatment has the potential to interfere with the safety or efficacy assessment of the investigational regimen are not eligible for this trial.Patients with symptomatic central nervous system (CNS) metastases and/or CNS metastases requiring immunosuppressive doses of systemic corticosteroids . Patients with known CNS metastases who are asymptomatic and on a stable dose of corticosteroids ≤10 mg per day prednisone equivalents before enrollment are eligible.Patients who are not suitable candidates for treatment with pembrolizumab according to the current guidance/indications described in the Product Monograph (Canada) or Drug Label (US), including, but not limited to, patients with active infection, autoimmune disease, conditions that require systemic immunosuppressive therapy (such as transplant patients) and patients with a history of severe immune-mediated adverse reactions or known hypersensitivity to pembrolizumab or its components. Patients with pre-existing conditions, such as colitis, hepatic impairment, respiratory or endocrine disorders (such as hypothyroidism or hyperthyroidism or diabetes mellitus), can be considered for enrollment to this study provided that pembrolizumab is administered with caution and patients are closely monitored.History of substantial neurologic or psychiatric disorder that would impair the ability to obtain consent or limit compliance with study requirements.Concurrent treatment with other anti-cancer therapy or other investigational anti-cancer agents.Pregnant or lactating women.Exclusion criteriaThe primary objective of stage 1 of the BR.36 trial was to validate the concordance of ctDNA molecular response with radiographic RECIST version 1.1 response, ascertain its definition and identify the optimal timepoint for ctDNA molecular response. Secondary objectives included the evaluation of time to ctDNA molecular response, the correlation of ctDNA molecular response with PFS and OS and exploration of the degree of ctDNA reduction with clinical outcomes. The time to ctDNA molecular response was defined similarly based on changes in ctDNA levels, as described below. Tertiary objectives included the collection of archival tumor tissue samples and additional longitudinal plasma samples for future translational studies.30) and iRECIST31, radiographic restaging was initially performed every 6 weeks until week 54 and then every 12 weeks until disease progression. The protocol was amended on 14 October 2021 to allow imaging every 6 weeks until week 12 and then every 9 weeks until week 48 and then every 12 weeks until disease progression. RECIST BOR and iRECIST iBOR as well as first radiographic response (at 6 weeks from treatment initiation) were evaluated. The expected BOR rate was defined using the KEYNOTE-024 clinical trial as a reference15. Time to clinical response was defined from the date of starting the study treatment to the date of first documented response of CR/PR for those who achieved a CR/PR during the study, whereas it was censored at the date alternative therapy began for those who received non-protocol anti-cancer therapy before documented PD or the date of documented PD or date of death, whichever came first, or censored at the date of last disease assessment for those with SD and still alive at the end of the study.Patients who received at least one cycle of pembrolizumab and had their disease re-evaluated after baseline were considered evaluable. Patients with objective disease progression before the end of cycle 1 were also considered evaluable. Using RECIST version 1.1 (ref. Safety was assessed using Common Terminology Criteria for Adverse Events (CTCAE) version 5. As patients were receiving pembrolizumab as standard of care, reporting of adverse events was required for all higher-grade toxicity (grades 3–5) and lower-grade toxicities if they led to treatment modifications  using the Circulating Nucleic Acid Kit (Qiagen), and the concentration was assessed using the Qubit dsDNA High-Sensitivity Assay (Thermo Fisher Scientific). Genomic DNA was isolated from patient-matched baseline WBC samples  using the QIAamp DNA Blood Mini Kit (Qiagen), and the concentration was assessed using the Qubit dsDNA High-Sensitivity Assay (Thermo Fisher Scientific). Subsequently, genomic DNA was sheared to a target size of approximately 200-bp fragments using Covaris focused ultrasonication. NGS libraries were prepared from cfDNA and fragmented genomic WBC DNA using a target of 40 ng of sample input through end-repair, A-tailing and adapter ligation with custom molecular barcoded adapters. Subsequently, libraries were PCR amplified, and target enrichment was performed through in-solution hybrid capture using the PGDx elio plasma resolve 33-gene panel32. Finally, libraries were pooled and sequenced with 150-bp paired-end reads using the Illumina NextSeq 550 platform. Although the liquid biopsy analyses in the first stage of the BR.36 trial were not performed in real time, the turnaround time of the assay is 6–8 d, which allows for the implementation of this approach in the second interventional stage of the trial. An overview of the plasma and WBC DNA samples sequenced, together with their sequencing quality control metrics, is shown in Supplementary Table 35. The analytical performance of the PGDx elio plasma resolve assay was previously described in contrived samples, showing >99% specificity; >95% sensitivity for detection of alterations with a MAF of 0.25–1.0%; >95% repeatability and precision across different laboratory conditions; and >95% positive percent agreement and negative percent agreement compared to orthogonal methods across the majority of alteration types assessed17. To further expand these analyses beyond contrived samples and incorporate the ctDNA findings from the BR.36 study, we evaluated the variants detected and used for ctDNA molecular response assessment to calculate the estimated limit of detection based on the error-corrected coverage obtained through a binomial model. To determine sensitivity of the assay, we analyzed the set of 56 genomic positions harboring tumor-derived mutations at the baseline timepoint (C1D1). At each position, given the observed distinct coverage, the probability of observing a minimum of three error-corrected mutation counts at any given alteration MAF level was calculated using the binomial distribution. For each MAF level, the median of the estimated probabilities across the 56 positions was determined and used as an estimate for the assay sensitivity.Cell-free DNA (cfDNA) was isolated from serial plasma samples from 45 patients (n = 27) underwent hematoxylin and eosin staining and pathological review with a minimum of 20% tumor content required for sample testing and analysis. Two tumor samples had insufficient tumor purity upon pathology review, and tumor tissue macrodissection was performed to further enrich for tumor content for evaluable samples. Genomic DNA was extracted from FFPE tumor tissue using the Qiagen FFPE Tissue Kit and was sheared to a target size of approximately 200-bp fragments using Covaris focused ultrasonication. Five additional tumor samples were deemed insufficient due to suboptimal DNA yield. NGS libraries were prepared from fragmented genomic DNA using a target of 100 ng (minimum 50 ng) of sample input through end-repair, A-tailing and adapter ligation with custom barcoded adapters. Subsequently, these libraries were PCR amplified, and target enrichment was performed through in-solution hybrid capture using the PGDx elio tissue complete 505-gene panel36. Finally, libraries were pooled and sequenced with 150-bp paired-end reads using the Illumina NextSeq 550 platform. Average total and distinct coverage were 2,268× and 1,168×, respectively, and sequencing metrics are summarized in Supplementary Table 38. Of the 20 sequenced tumor samples, 19 also had matched plasma NGS performed.Thirty-four (68%) tumor samples were available, of which seven were macroscopically of insufficient quantity. FFPE tumor tissue sections for cases with available tumor material  were considered tumor derived independent of detection in the matched WBC DNA samples, the latter likely indicating buffy coat contamination by circulating tumor cells or suboptimal pre-analytical processing. Supplementary Table DNMT3A, which is canonically mutated in clonally expanded hematopoietic cells. Although tumor NGS was not used to determine variant cellular origin or ctDNA molecular response, as described below, a limited exploratory tumor tissue–plasma concordance analysis was performed for 19 patients with matched tumor and plasma NGS.A tumor-agnostic WBC DNA-informed approach was implemented to determine the cellular origin of variants detected by plasma NGSn = 45). To alleviate technical challenges associated with the sensitivity of error-correction NGS at lower MAFs and according to previous studies18, the maxMAF of tumor-derived mutations was used as an indicator of cell-free circulating tumor load and computed for each timepoint analyzed. Similarly, given the broader CI in MAF estimates with decreasing MAF, around the assay limit of detection, a greater relative ctDNA reduction reflected in ctDNA clearance more accurately captures circulating tumor burden contraction24. Taken together, we defined ctDNA molecular response as maxMAF clearance; ctDNA molecular responses were assigned blindly with respect to radiographic responses and clinical outcomes. Having defined ctDNA molecular response as maxMAF clearance, we then determined the timepoint that this condition was met for individuals on the BR.36 trial, which was on C2D1 for 13 patients  and on C3D1 for two individuals, leading to the selection of C3D1 as the timepoint were the maxMAF clearance condition was met. Reduction of maxMAF to undetectable levels in C2D1 or C3D1 (ctDNA clearance at C2D1 and C3D1 or at C3D1) signified mR, whereas persistence of maxMAF at C3D1 indicated mPD6. The molecular response rate was calculated as the number of patients with mR divided by the number of all patients who were evaluable for ctDNA molecular response.All patients who received at least one cycle of therapy and had ctDNA evaluated in at least one timepoint in addition to baseline were considered evaluable for ctDNA molecular response 15 and that the sensitivity and specificity of the ctDNA molecular response are both no less than 70%; 18 responders would ensure that the lower bound of the 90% CI for estimated sensitivity is higher than 50%. Similarly, with 22 non-responders, the lower bound of the 90% CI for estimated specificity is higher than 50%. Among the patients with detectable ctDNA and evaluable ctDNA molecular response, the concordance of ctDNA molecular response with radiographic response and the sensitivity and specificity of ctDNA molecular response were estimated with 90% CI. Pre-specified analysis populations included the per-protocol population ; all accrued patients in the trial; and the as-treated population .The BR.36 study was powered to address the concordance between ctDNA and radiographic responses. The sample size was determined to ensure that the lower 95% confidence bound of the estimated sensitivity and specificity was higher than 50%, assuming the observed sensitivity and specificity were no less than 70%. The required sample size was 50 patients, assuming that 20% of patients will have undetectable ctDNA before therapyDiscrete variables were summarized with the number and proportion of study participants falling into the category of interest. Continuous and ordinal categorical variables were summarized using the mean, median, standard error, minimum and maximum and interquartiles values where appropriate. For the time-to-event outcomes, the distributions were estimated with the Kaplan–Meier product limit method and summarized with median survival and 90% CI. Cox proportional hazards regression analysis was employed to assess the association of continuous maxMAF values with OS. The specificity and sensitivity and their CIs were estimated using the exact binomial distribution method.i) and for each on-therapy timepoint (Cx), the change in ctDNA level compared to baseline We determined concordance between the depth of molecular response and radiographic RECIST response as follows. In each patient . Supplementary Table 2. Treatment-related adverse events of the BR.36 study-evaluable patients (n = 50). Supplementary Table 3. Overview of anatomic location of target and non-target lesions . Supplementary Table 4. Summary of RECIST and iRECIST responses in the trial cohort. Supplementary Table 5. Sequencing metrics for plasma and matched WBC DNA NGS. Supplementary Table 6. Plasma variants detected by NGS and their cellular origin. Supplementary Table 7. ctDNA kinetics and molecular response classification. Supplementary Table 8. Post hoc analyses of ctDNA response and RECIST response concordance using different baseline maxMAF thresholds. Supplementary Table 9. Sequencing metrics for tumor tissue NGS. Supplementary Table 10. Plasma variants detected in matched tumor tissue NGS.Supplementary Table 1. Toxicity/adverse events for the BR.36 study-evaluable patients (


Microglia regulate multiple processes in the central nervous system, exhibiting a considerable level of cellular plasticity which is facilitated by an equally dynamic transcriptional environment. While many gene networks that regulate microglial functions have been characterised, the influence of epigenetic regulators such as small non-coding microRNAs (miRNAs) is less well defined. We have sequenced the miRNAome and mRNAome of mouse microglia during brain development and adult homeostasis, identifying unique profiles of known and novel miRNAs. Microglia express both a consistently enriched miRNA signature as well as temporally distinctive subsets of miRNAs. We generated robust miRNA-mRNA networks related to fundamental developmental processes, in addition to networks associated with immune function and dysregulated disease states. There was no apparent influence of sex on miRNA expression. This study reveals a unique developmental trajectory of miRNA expression in microglia during critical stages of CNS development, establishing miRNAs as important modulators of microglial phenotype. Microglial miRNA signatures are characterised in the developing mouse brain, with specific miRNA-mRNA networks being associated with CNS development, immune function and dysregulated disease states. In the adult brain, microglia continue to regulate existing neural networks, as well as supporting neurogenic niches and oligodendrocyte precursor cell pools6. Adult microglia adopt a ramified phenotype and extend processes to survey the local environment and maintain tissue homoeostasis8. Upon interaction with pathological stimuli, microglia can rapidly shift their phenotype to initiate apoptotic clearance and cytokine signalling to suppress inflammation and promote a neuroprotective environment9.Microglia are dynamic regulators of the central nervous system (CNS) where they support early development, adult homoeostasis and immune function. Following embryonic colonisation of the brain, microglia regulate neurogenesis, synaptogenesis and myelination via both the clearance (efferocytosis) of immature cells and synapses, and secretion of trophic factors10. Perturbed microglial activity is also implicated in neurodevelopmental disorders including autism spectrum disorder (ASD), schizophrenia and epilepsy14.Given the broad spectrum of phenotypes and activities of microglia in the healthy CNS, appropriate microglial function is heavily dependent upon precise regulation of gene expression. Consequently, genetic dysregulation of microglial biology is implicated in numerous neurological disorders. Chronically activated ‘neurotoxic’ microglia have been identified as a hallmark of neurodegeneration and autoimmunity, with key genes and signalling pathways explicitly linked to the pathology of multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), Alzheimer’s disease (AD) and Huntington’s disease (HD)19. In addition, comprehensive profiling has identified distinct programmes of gene expression that tightly regulate developmental phenotypes and age-specific microglial functions21. Pre-natal and early postnatal microglia are highly reactive and adopt an amoeboid morphology similar to that of adult-activated microglia during inflammation and disease. However, there is no strong gene expression overlap between these cell subsets, indicating that developmental microglia are a distinct cell population that uniquely contribute to CNS development. These studies highlight the multiplicity of transcriptional programmes which can be activated by microglia in response to normal development or pathological challenge. An important question then arises as to how these transcriptional programmes are controlled? One strong potential candidate is the class of small RNAs known as microRNAs (miRNAs).Multiple sequencing studies of microglial populations have captured genetic and epigenetic networks that regulate cell identity and define microglial phenotypes in the healthy and diseased brain22. Mature miRNAs are integrated into a catalytic complex that binds to target mRNAs and triggers degradation or stalled translation of the target mRNA transcript23. Ubiquitous in the mammalian genome, miRNAs regulate fundamental processes including cell differentiation, proliferation and immune homeostasis25. Tissue-specific sequencing of miRNAs has revealed that ubiquitously expressed miRNAs are less common than previously predicted and emphasise the importance of cell-specific studies to capture accurate miRNA profiles26. Normal specification of microglia is known to be reliant upon miRNAs. Conditional knockout of Dicer in microglia ablates the miRNA biogenesis pathway, which results in perturbation of microglia and induces hyper-responsiveness to inflammatory stimuli27. In addition, specific miRNAs have a profound influence on microglial biology. For example, miR-155 and miR-124 have been identified as ‘master regulators’ of activated and quiescent microglia, respectively30. However, much of the evidence for miRNA-mediated regulation of microglia stems from studies of adult mice, and thus the role of miRNAs in regulating developmental microglial functions are not as well understood. Further, studies of the miRNAome are often limited in their ability to predict transcriptional effects, as they rely on in silico predictions of miRNA–mRNA interactions rather than integrated network analyses.miRNAs are small (18–22 bp) non-coding RNAs that act as negative post-transcriptional regulators of gene expressionIn this study, we have characterised the miRNAome of microglia in male and female mice at three developmental timepoints: postnatal days 6, 15 and 8 weeks. These timepoints represent critical stages in CNS development in the mouse. We identified a unique microglial miRNA profile compared with other CNS cell types. Differential expression analysis identified age-specific subsets of miRNA expression, suggesting a gene regulatory programme that influences microglial biology during development. Unexpectedly, we did not observe any sex-specific differential miRNA expression, and found only a modest difference in mRNA expression, indicating that microglia are not necessarily innately different between sexes during development. Integrated miRNA–mRNA expression analyses identified over 200 mRNAs that were negatively correlated with enriched microglial miRNAs, and that were predicted to target key neurodevelopmental processes, including neurogenesis, axonal guidance and myelination. These data provide a unique profile of the microglial transcriptome and its epigenetic regulation, providing insight into the role of microglial miRNAs in regulating microglial developmental programmes.+ve microglia isolated from three age groups of C57Bl/6 mice  as well as the miRNA expression of the CD45−ve bulk CNS cells   contamination per sample  as well as age  analysis of miRNA sequencing libraries identified strong separation of sample populations by both cell type  which identified 213 miRNAs as upregulated in microglia . miRNA–mRNA pairs were further filtered for experimental evidence of interaction as reported in the existing literature and relevant databases (collated by multiMiR) and constructed into ‘high confidence’ miRNA–mRNA regulatory networks for each age group  and Hmgcr (regulated miR-29a-3p). In addition, pathways related to neurodegenerative disease were overrepresented in all networks. These age-specific regulatory networks were primarily composed of miRNAs that were enriched in all age groups, and therefore define robust regulation of conserved microglial transcriptional networks during CNS development and adult homoeostasis.A consistent observation for networks was a high degree of interconnectedness, with many genes being targeted by more than one miRNA Figs. . Ten miR21. In order to identify the miRNAs that are most likely to regulate developmental transitions in microglia, we next interrogated changes in miRNA expression between age groups. We performed direct pairwise comparisons of miRNA expression profiles between different development ages to define subsets of differentially expressed miRNAs . In the absence of experimental evidence for novel miRNA binding interactions, we calculated predicted interactions using the hybridisation tool RNAhybrid, filtering for those that were predicted to bind with minimum free fold energy <25 kcal/mol (P < 0.05)33. Fourteen miRNA–mRNA interactions passed these criteria, all of which included the top novel miRNA candidate chr3_29427 , microglia do not derive from neuroepithelial lineages, but instead arise from the embryonic yolk sac before migrating to and establishing a self-sufficient population in the developing CNS43. Our microglial mRNA data also reflected this distinct signature. Amongst the top expressed mRNA transcripts in this sequencing data were well-defined microglial identity genes including Cx3cr1, Csf1r, Sparc, Tmem119 and Hexb44. Together, these results highlight a unique genetic identity of microglia compared to the surrounding CNS environment.We identified a strong miRNA enrichment signature in microglia when compared to the surrounding CNS cells, with nearly 300 miRNAs upregulated in microglia in at least one age group. This was not unexpected, as studies of microglial transcriptomes have highlighted these cells as having a unique transcriptional signature46. These miRNAs have defined functions in the homeostatic regulation of microglial phenotype and their dysregulation is linked to inflammation and neurodegeneration50. In addition to this common microglial signature, our study identified 129 miRNAs that were specifically enriched in a single age group. Although most of these miRNAs were expressed in adult microglia there was enrichment of specific miRNAs at all ages, suggesting that miRNAs could be important for regulating age-specific microglial functions.The present study has defined a dynamic microglial miRNA expression profile, comprised of a core signature of consistently enriched miRNAs and subsets of temporally specific miRNAs. In conjunction with integrated miRNA–mRNA network analysis, we were able to characterise age-specific domains of miRNA expression and explore their influence upon microglial mRNA transcription throughout development. Firstly, 84 miRNAs were observed to be significantly enriched in microglia at all ages compared to other CNS cell types. Many of the highly expressed miRNAs in this subset have been previously identified in microglia, including miR-146, miR-223 and miR-14231. Network analysis identified numerous miRNA candidates that could provide important regulatory influence on the microglial cell cycle. For example, the miR-301/130b cluster was identified as strongly enriched in P6 and P15 microglia. This cluster is associated with hematopoietic cell proliferation and migration. Targets of this cluster in microglia in our data included Serpine1, Socs5 and Ifi27 which are implicated in cell proliferation, motility, interferon signalling and endocytosis55. Also enriched in microglia was the miR-29 cluster , which is also associated with cell proliferation dynamics and downregulation of cyclins and cyclin-dependent kinases56. Another interesting candidate for the regulation of microglial development is miR-10a-5p. Previously uncharacterised in microglia, miR-10a-5p, is uniquely enriched in adult mice and is predicted to target and therefore potentially inhibit Mcm5, an established regulator of microglial cell cycle that is strongly expressed during embryonic and early postnatal microglial development38.To fully characterise the roles of miRNAs in cellular systems it is important to define the gene networks which they regulate. miRNA–mRNA networks in this study were constructed using both stringent correlation thresholds and experimental validation of miRNA–mRNA interactions. Pathway analysis of target mRNAs in age-specific networks were strongly enriched for cell cycle-related processes. Developmental microglia proliferate as they colonise the brain. This includes a wave of proliferation that peaks during the second postnatal week of development, before numbers stabilise in adulthood57. Amongst neurogenesis related genes represented in miRNA–mRNA networks was Dypls3  a cytoskeleton associated gene that is strongly enriched in amoeboid microglia during the first week of postnatal development59. Knockdown of Dypls3 inhibits microglial phagocytosis and migration and therefore could be important for maintaining a specific microglial phenotype associated with postnatal neurogenesis58. Microglia also regulate developmental myelination and maintain white matter integrity60. We identified several interesting miRNA–mRNA interactions that may mediate these processes. This included miR-29a-3p targeting Hmgcr, a regulator of cholesterol homeostasis that is enriched in white matter-associated microglia during myelinogenesis61. Another interesting candidate is miR-3473b, represented in both the P6 and P15 networks that targets a single gene, Padi2. PADI2 regulates citrullination of myelin basic protein (MBP), which alters myelin ultrastructure. Of note, it has been hypothesised that myelin modifications of this nature could be a source of autoimmune pathogens potentially implicated in the pathogenesis of MS62. Padi2 is enriched in subsets of microglia isolated from human MS tissue and therefore miR-3473b could play an important role in refining Padi2 expression, both during development and in demyelinating contexts19.In addition to cell cycle regulation, pathway analysis of miRNA–mRNA networks revealed an overrepresentation of processes related to CNS development, including neurogenesis, axon guidance and myelination. Microglia are essential modulators of these processes and their dysregulation is a major contributor towards neurodevelopmental disorders64. We also observed enrichment of miRNAs with links to inflammation and neurodegeneration which are not well defined in microglia including miR-511-3p, miR-1895 and miR-6983-5p. miR-511-3p has been characterised in tumour and lung associated macrophage populations, encoded by and co-regulated with the CD206 gene, where it is associated with anti-inflammatory (M2-like) activation. miR-6983 is downregulated in oligodendrocytes during experimental autoimmune encephalomyelitis (EAE), an animal model of demyelination, and is associated with disrupted ferroptosis pathways that are implicated in demyelination and neurodegeneration65. With functions related to macrophage polarisation and pathways of neurodegeneration, further work is required to elucidate specific roles for these highly expressed miRNAs in microglia. Furthermore, we acknowledge that the networks we have identified are based on in silico correlations, which may not necessarily represent genuine interactions in cells and tissues. In vitro and in vivo experiments will be crucial to support our identified networks.More broadly, pathway analysis of targets in all miRNA–mRNA networks identified a significant enrichment of pathways related to inflammatory signalling pathways and immune activation. This included PI3k-Akt and JAK-STAT signalling, central regulators of microglial mediated neuroinflammationCdca5 and Birc5. BIRC5 has been previously characterised as highly expressed by proliferating microglia during development and in response to injury66. These predicted novel candidates may be critical regulators of unique microglial biology and warrant future functional validation in vitro and in vivo.In addition to previously annotated miRNAs, we identified 24 novel miRNA candidates enriched in microglia. Many of these, including the highly expressed chr3_29427, may regulate important microglial functions in the CNS. Amongst putative mRNA targets of chr3_29427 were key regulators of cell cycle and neurogenesis, including 67. Importantly, these innate differences are exacerbated during immune challenge and stress and are suggested to be a prominent influence in observed sex biases of neurological disease69. Transcriptomics studies have defined subsets of mRNAs and miRNAs that are differentially expressed between male and female microglia, in steady state and particularly following immune challenge in adult mice70. In contrast, we did not observe any differentially expressed miRNAs, a finding which was further corroborated with qPCR analysis of four candidates that were previously identified as sexually dimorphic at baseline in 6-month-old B6C3F1 mice35. Conversely, our study identified 6 differentially expressed mRNA transcripts across development, all of which are located on sex chromosomes and specifically differentiated in adult mice. The robust and predictable differentiation of sex-linked genes including Xist and Ddx3y support the veracity of the dataset.We did not identify any association of sex with microglial miRNA abundance. Specifically, 6 mRNAs but no miRNAs were identified as differentially expressed between male and female microglia across development. There is growing evidence to suggest that sex hormones influence the developmental trajectories of male and female microglia manifesting in differential regional densities, morphology, phagocytic capacity and metabolic influx67. Firstly, age seems to be an important factor. A comprehensive developmental study by Hanamsagar et al. (2017) observed that transcriptionally, male and female microglia are quite similar until early adulthood (P60). Other reported transcriptional differences studies come mainly from adult mice, (12 weeks and older) reinforcing the idea that the effects of sex are more prevalent with age70. However, our dataset and others have reported a minimal effect of sex even in young adult cells (8 weeks)20. Further studies that assess transcription at expanded timepoints (beyond 8 weeks of development) will assist in an accurate representation of the effect of sexual development on microglial gene expression. Another hypothesis for these discrepancies is the method of microglial isolation. Microglial cells are dynamic and respond quickly to changes in their local environment15. While an unavoidable caveat, it has been shown that different methods of isolation  can differentially alter the activation status of microglia and hence gene expression, potentially contributing to differing reports of baseline sexual dimorphism in studies of microglia40. In addition, aspects of sexual dimorphism in microglial gene expression are region-dependent68. Sexually dimorphic region-specific signatures would be diluted in the current study, given that the microglial populations were derived from the whole brain. Nevertheless, this result adds another perspective to the growing discussion of sexual dimorphism of microglia and suggests that sex may have a lesser influence on microglial transcription than previously reported. Specifically, dimorphism may be more prevalent post-activation, whether induced by isolation, immune challenge or in ageing-related neurodegeneration.What might be causing this discrepancy? Interestingly, while functional and structural differences between male and female microglia have been consistently observed, transcriptomic results are less congruentIn summary, we characterised a unique and highly controlled dataset of miRNA expression in developing microglia, capturing a dynamic profile that may contribute to the complex roles that microglia adopt in postnatal development and the establishment of homeostasis. This miRNAome is composed of both a relatively unchanging subset of highly enriched miRNAs and subsets of miRNAs that exhibit age-dependent expression profiles. Correlation of known and novel miRNAs with mRNA expression data enabled the identification of target mRNA networks that mediate neurodevelopmental and immune processes that are central to microglial function in the CNS. In combination with environmental cues, epigenetic mechanisms shape the genetic landscape of microglia and refine phenotypes. Dysregulation of these epigenetic mechanisms can be a major driver of neurodevelopmental and neurodegenerative disorders. This developmental profile of paired miRNA and mRNA expression represents an important step in understanding the genetic regulation of microglia and their impact on the CNS in health and disease.The experimental paradigm was designed to identify miRNAs expressed specifically in microglia compared with other CNS cells across three developmental timepoints; P6, P15 and 8 weeks. In addition, all groups contained an equal number of male and female mice to allow for the identification of any sex-specific miRNAs. An overview of the experimental and analytical design is given in Fig. All chemicals were purchased from Sigma  and all cell culture media were purchased from Gibco  unless otherwise stated.−ve bulk cell populations were isolated using immunopanning (adapted from refs. 72).All animal experiments were approved by the animal ethics committee at the Florey Institute of Neuroscience and Mental Health and conducted in accordance with the National Health and Medical Research Council Guidelines. The whole brain was extracted from male and female C57Bl/6 mice at appropriate ages Fig. . Microgl3) pieces. Tissue was incubated in 10 ml papain buffer , 0.46% glucose, 26 mM NaHCO3 0.5 mM EDTA, 1× EBSS) and 200 µl DNase I  at 34 °C for 100 min with a consistent CO2 flow and periodic shaking.Following brain collection, meninges and blood clots were manually removed with forceps and brain tissue was processed into small  in 10 ml 50 mM Tris-HCL (pH 9.5). Following overnight incubation the plate was washed three times with DPBS and then incubated with 20 µl anti-CD45 antibody  in 12 ml of 0.2% BSA for a minimum of 2 hours. Plates were washed three times with DPBS and single-cell suspensions were added to pre-coated plates and incubated for 30 min with periodic shaking. Unbound cells were collected, spun down and then resuspended in 700 µl Qiazol. This population of microglial-depleted CNS cells was referred to as ‘CD45−ve bulk’ and used as a comparison population for downstream analysis. Bound cells (CD45+ve microglia) were scraped off the plate into 700 µl Qiazol for further processing.Digested tissue was firstly washed and then gently triturated in low ovomucoid protease inhibitor solution (0.015 g/ml BSA (Sigma), 0.015 g/ml trypsin inhibitor (Worthington) in DPBS at pH of 7.4) to form a single-cell suspension. High ovomucoid solution (0.030 g/ml BSA (Sigma), 0.030 g/ml trypsin inhibitor (Worthington), in DPBS at pH of 7.4) was gently pipetted to form a layer underneath the cell suspension and cells were spun down for 15 min at 250×Total RNA was extracted using the miRNeasy mini kit (Qiagen) in accordance with manufacturer’s protocol. The quality of RNA samples was assessed using a bioanalyzer. Samples with an RNA integrity score (RIN) greater than 7 were used for library construction. One sample did not pass RNA quality control and so was excluded from library preparation. mRNA libraries were prepared using MGIEasy RNA Directional RNA library prep chemistry (MGI), and small RNA libraries were prepared using the MGIEasy Small RNA library prep chemistry (MGI). Libraries were pooled and sequenced across four lanes of MGISEQ2000-RS sequencing technology, yielding approximately 400 M reads per lane (2 lanes for miRNAseq (50 bp single end), 4 lanes for mRNAseq (paired-end 100 bp)). Library construction and sequencing were performed by personnel at Micromon Genomics .https://www.mirbase.org), and the remaining reads were mapped to the mouse reference genome (mm10/GRCm38) using mirDeep2 with default settings (ver 0.1.3)32. Candidate novel miRNAs detected by miRDeep2 with a miRDeep2 score >4 and randfold P value < 0.05 were included in downstream analysis. mRNA sequencing data were mapped to the mouse reference genome (mm10/GRCm38) using STAR with default settings (ver 2.7.3). Gene read counts were generated using the featureCounts pipeline in Subread with the following flags (-T 8 -s 2 -p -a) (ver 2.0.0).Adapter sequences were removed from small RNA sequencing data, and the remaining reads were filtered by size ( > 18 bp) using fastp (ver 0.20.0). Trimmed reads were mapped to mature miRNAs from miRbase (−ve bulk) as fixed effects and mouse ID as a random effect73. The general enrichment analysis was performed by direct comparison of miRNA expression between all microglia and all CD45−ve bulk samples. Specific age enrichment analyses compared only the microglia and CD45−ve bulk samples within a relevant age group. Pairwise analyses of microglial miRNA expression were performed by direct comparison of miRNA expression between microglial age groups. Sex-specific analyses were performed by comparing male and female microglia samples within each age group. For all differential expression analyses, differentially expressed genes were identified using the TREAT method (t tests relative to a threshold), set at a minimum threshold of a ± 2-fold (minimum absolute log2FC of 1) change in expression with a false discovery rate (FDR) < 0.0574. Volcano plots were generated using EnhancedVolcano . Supplementary Tables https://cran.r-project.org/web/packages/gt/index.html).Normalisation and differential expression analysis of miRNA and mRNA was performed using limma  and edgeR . Based on multidimensional scaling (MDS) analysis one mouse was identified as an outlier, and its miRNA/mRNA data was removed from downstream analysis. miRNAs were filtered for a minimum expression of 1 count per million (CPM) (for miRNA), and 0.5 CPM (for mRNA) in at least five samples. Gene counts were normalised using the TMM normalisation method (edgeR) and linear modelling was performed with limma-voom. All regression analyses were set up as mixed effects models including age, sex, and cell type (microglia or CD45https://CRAN.R-project.org/package=psych). Only those correlations that were strongly negative and significant  were retained for downstream analysis.To infer miRNA–mRNA interactions from gene expression data, Pearson correlations were calculated from normalised counts for all pairs of miRNA–mRNA using psych , whereby novel miRNA sequences were aligned with 3’-UTR mouse sequences  using the following parameters (-e -25 -p 0.05).To further validate significant negative correlations, miRNA–mRNA interactions were screened for experimental evidence of interaction in any cellular system using multimiR  with default settings. For novel candidate miRNAs, mRNA target binding prediction was performed using RNAhybrid . KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (gene ontology) enrichment analysis were performed on the target mRNAs in generated networks using the kegga and goana modules in limma, respectively .miRNA–mRNA interactions which were identified as negatively correlated from expression datasets and validated by multimiR or RNAHybrid were included in downstream pathway analyses and network construction. Visualisations of miRNA–mRNA networks were generated using Cytoscape  of CD45 and Cx3cr1 FACS sorted cells throughout the mouse lifespan 20. Crucially the authors identified both microglia subpopulations and a small population of intermixed monocytes and macrophages. CIBERSORTx currently recommends user input of no more than 5000 different single-cell profiles when creating the signature matrix. To meet this recommendation, we randomly downsampled the scRNA-seq dataset to 5000 cells using the ‘sample’ command in R with the seed set to 4076. After creation of the reference matrix, CIBERSORTx was run with ‘S-mode’ batch correction, filtration to immune relevant genes, faction set to 0.25, and sampling set to 1.Cellular proportions were inferred from bulk immunopanned RNA-seq gene expression matrices using CIBERSORTx High-Resolution Docker container−ve bulk samples was reverse transcribed using Taqman reverse transcription reagents (Invitrogen) in accordance with the manufacturer’s instructions. In total, 25 µl qPCR reactions were prepared as follows; 12.5 µl 2× SYBR green master mix , 1 µM of forward and reverse primers and DNAse free water. Primer sequences are outlined in Table 77.In all, 100 ng of total RNA isolated from microglia/ CD45−ve bulk samples was reverse transcribed using the miRCURY LNA RT kit (Qiagen) in accordance with the manufacturer’s instructions. The miRCURY LNA miRNA PCR assay system (Qiagen) was used to detect specific miRNA expression. Expression of ribosomal 5 S was used to normalise for RNA input for all samples. Relative fold change of expression was calculated using the 2(-delta-delta C(T)) method and log transformed77. Paired t tests were performed to identify consistent expression differences between paired microglia/ CD45−ve bulk samples for each miRNA. Unpaired t tests were performed to identify differences in candidate miRNA expression between male and female microglia subgroups. Statistical analyses were performed using GraphPad Prism .Overall, 50 ng of total RNA isolated from microglia/ CD4535, such that comparisons between age groups were 12 vs 12 (matched for sex), and between tissue types were 36 vs 36 . For differential expression analysis, we used the empirical Bayes procedure implemented in R limma software to maximise sensitivity to detect differentially expressed genes. To prioritise differences most likely to be biologically relevant, we subsequently applied the limma treat procedure to discard results with an absolute fold change <2. Statistical analyses are described within each relevant section.RNA-seq sample size was determined to match or exceed those of similar studiesFurther information on research design is available in the Peer Review FileSupplementary figuresDescription of Additional Supplementary FilesSupplementary data 1Supplementary data 2Supplementary data 3Supplementary data 4Supplementary data 5Supplementary data 6Supplementary data 7Reporting Summary