Dihydropyrimidine dehydrogenase gene variants for predicting grade 4-5 fluoropyrimidine-induced toxicity: FUSAFE individual patient data meta-analysis.

BACKGROUND: Dihydropyrimidine dehydrogenase (DPD) deficiency is the main known cause of life-threatening fluoropyrimidine (FP)-induced toxicities. We conducted a meta-analysis on individual patient data to assess the contribution of deleterious DPYD variants *2A/D949V/*13/HapB3 (recommended by EMA) and clinical factors, for predicting G4-5 toxicity. METHODS: Study eligibility criteria included recruitment of Caucasian patients without DPD-based FP-dose adjustment. Main endpoint was 12-week haematological or digestive G4-5 toxicity. The value of DPYD variants *2A/p.D949V/*13 merged, HapB3, and MIR27A rs895819 was evaluated using multivariable logistic models (AUC). RESULTS: Among 25 eligible studies, complete clinical variables and primary endpoint were available in 15 studies (8733 patients). Twelve-week G4-5 toxicity prevalence was 7.3% (641 events). The clinical model included age, sex, body mass index, schedule of FP-administration, concomitant anticancer drugs. Adding *2A/p.D949V/*13 variants (at least one allele, prevalence 2.2%, OR 9.5 [95%CI 6.7-13.5]) significantly improved the model (p < 0.0001). The addition of HapB3 (prevalence 4.0%, 98.6% heterozygous), in spite of significant association with toxicity (OR 1.8 [95%CI 1.2-2.7]), did not improve the model. MIR27A rs895819 was not associated with toxicity, irrespective of DPYD variants. CONCLUSIONS: FUSAFE meta-analysis highlights the major relevance of DPYD *2A/p.D949V/*13 combined with clinical variables to identify patients at risk of very severe FP-related toxicity.

Post-treatment sperm cryopreservation practices in childhood and young adult cancer survivors.

RESEARCH QUESTION: What are current practices of post-treatment fertility preservation in male childhood cancer survivors (CCS) who have not benefitted from pre-therapeutic fertility preservation in France and other European countries? DESIGN: A survey was conducted of all fertility preservation centres in France (n = 30) and European fertility specialists (n = 9) in five European countries. Eight clinical cases and 40 questions were included to assess the effect of age at diagnosis, type of treatment (alkylating-agents, orchidectomy, testicular radiotherapy) and sperm parameters on the probability of a post-treatment fertility preservation proposal. Demographic characteristics of the responding practitioner were also collected. RESULTS: Post-treatment sperm cryopreservation was proposed by 100% of fertility specialists in cases of severe oligoasthenoteratozoospermia, 77-88% in cases of moderate oligoasthenoteratozoospermia and in 65-77% in cases of sperm motility and vitality impairment. In cases of normal sperm parameters, 27-54% of fertility specialists would propose post-treatment sperm cryopreservation. These results did not differ significantly according to the type of treatment received or to responder-related factors. Practices of European specialists were also guided by sperm parameter results; 44-67% of specialists responding that they would propose sperm cryopreservation in cases of moderate to severe sperm parameter alterations. CONCLUSION: Post-treatment semen analysis could be widely proposed to CCS who have not benefitted from pre-therapeutic fertility preservation. Post-treatment fertility preservation could be proposed in cases of persistent moderate to severe sperm parameter alterations. Guidelines would be important to homogenize practices and to encourage oncologists to refer CCS for fertility assessments.

Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicine.

BACKGROUND: The research of biomarker-treatment interactions is commonly investigated in randomized clinical trials (RCT) for improving medicine precision. The hierarchical interaction constraint states that an interaction should only be in a model if its main effects are also in the model. However, this constraint is not guaranteed in the standard penalized statistical approaches. We aimed to find a compromise for high-dimensional data between the need for sparse model selection and the need for the hierarchical constraint. RESULTS: To favor the property of the hierarchical interaction constraint, we proposed to create groups composed of the biomarker main effect and its interaction with treatment and to perform the bi-level selection on these groups. We proposed two weighting approaches (Single Wald (SW) and likelihood ratio test (LRT)) for the adaptive lasso method. The selection performance of these two approaches is compared to alternative lasso extensions (adaptive lasso with ridge-based weights, composite Minimax Concave Penalty, group exponential lasso and Sparse Group Lasso) through a simulation study. A RCT (NSABP B-31) randomizing 1574 patients (431 events) with early breast cancer aiming to evaluate the effect of adjuvant trastuzumab on distant-recurrence free survival with expression data from 462 genes measured in the tumour will serve for illustration. The simulation study illustrates that the adaptive lasso LRT and SW, and the group exponential lasso favored the hierarchical interaction constraint. Overall, in the alternative scenarios, they had the best balance of false discovery and false negative rates for the main effects of the selected interactions. For NSABP B-31, 12 gene-treatment interactions were identified more than 20% by the different methods. Among them, the adaptive lasso (SW) approach offered the best trade-off between a high number of selected gene-treatment interactions and a high proportion of selection of both the gene-treatment interaction and its main effect. CONCLUSIONS: Adaptive lasso with Single Wald and likelihood ratio test weighting and the group exponential lasso approaches outperformed their competitors in favoring the hierarchical constraint of the biomarker-treatment interaction. However, the performance of the methods tends to decrease in the presence of prognostic biomarkers.

Response to first line platinum-based chemotherapy in mismatch repair deficient (MMRd)/ microsatellite instability high (MSI-high) endometrial carcinoma.

BACKGROUND: Around 15% of metastatic endometrial carcinoma (EC) are MMRd/MSI-H improving response to immune checkpoint inhibitors (ICI). So far, few data existed considering the chemotherapy (CT) sensitivity in MMRd/MSI-H EC, especially response to first-line platinum-based treatment. PATIENTS AND METHODS: We performed a multicentric retrospective analysis reporting the response to first line platinum CT in MMRd/MSI-H EC patients. The primary endpoints were objective response rate (ORR) and progression-free survival (PFS) with first line platinum-based CT. RESULTS: A total of 112 patients MMRd/MSI-H EC from 8 centers were identified. Median overall survival was 58.0 months (95% CI: 45.3-95.1). Among them, 78 patients received first line platinum CT in recurrent/metastatic setting. With a median follow up of 32.6 months (min: 0.03; max: 135.0), ORR and DCR (disease control rate) were 50% (95% CI: 38.5-61.5) and 68% (95% CI: 56.4-78.1), respectively. Median PFS and OS from first line platinum-based CT was 7.8 months (95% CI: 6.0-9.0) and 51.9 months (95% CI: 28.0-NE), respectively. Median PFS with ICI in second line (n = 48) was 10.7 months (95% CI: 3.4-NE) from ICI initiation. CONCLUSION: ORR in first line metastatic MMRd/MSI-H EC is consistent with efficacy in an all comer metastatic EC population.

Penalized Poisson model for network meta-analysis of individual patient time-to-event data.

Network meta-analysis (NMA) allows the combination of direct and indirect evidence from a set of randomized clinical trials. Performing NMA using individual patient data (IPD) is considered as a "gold standard" approach as it provides several advantages over NMA based on aggregate data. For example, it allows to perform advanced modeling of covariates or covariate-treatment interactions. An important issue in IPD NMA is the selection of influential parameters among terms that account for inconsistency, covariates, covariate-by-treatment interactions or nonproportionality of treatments effect for time to event data. This issue has not been deeply studied in the literature yet and in particular not for time-to-event data. A major difficulty is to jointly account for between-trial heterogeneity which could have a major influence on the selection process. The use of penalized generalized mixed effect model is a solution, but existing implementations have several shortcomings and an important computational cost that precludes their use for complex IPD NMA. In this article, we propose a penalized Poisson regression model to perform IPD NMA of time-to-event data. It is based only on fixed effect parameters which improve its computational cost over the use of random effects. It could be easily implemented using existing penalized regression package. Computer code is shared for implementation. The methods were applied on simulated data to illustrate the importance to take into account between trial heterogeneity during the selection procedure. Finally, it was applied to an IPD NMA of overall survival of chemotherapy and radiotherapy in nasopharyngeal carcinoma.

A two-stage drop-the-losers design for time-to-event outcome using a historical control arm.

Phase II immuno-oncology clinical trials screen for efficacy an increasing number of treatments. In rare cancers, using historical control data is a pragmatic approach for speeding up clinical trials. The drop-the-losers design allows dropping off ineffective arms at interim analyses. We extended the original drop-the-losers design for a time-to-event outcome using a historical control through the one-sample log-rank statistic. Simulated trials featured three arms at the first stage, one at the second stage, nine scenarios, eight sample sizes with 5%- and 10%- nominal family-wise error rate (FWER). A numerical algorithm is provided to solve power calculations at the design stage. Our design was compared with a group of three independent single-arm trials (fixed design) with and without correction for multiplicity. Our design allowed strict control of the FWER at nominal levels while the misspecification of survival distribution and fixed design inflated the FWER up to three times the nominal level. The empirical power of our design increased with the sample size, the treatment effect and the number of effective treatments and dropped when more patients were recruited at the second stage. The fixed design with correction showed comparable power, while our design advantageously included more patients to the most promising arm. Recommendations for future applications are given. By taking advantage of the use of historical control data and a time-to-event outcome, the drop-the-losers design is a promising tool to meet the challenge of improving phase II clinical trials in immuno-oncology.

Phase II study of temozolomide and topotecan (TOTEM) in children with relapsed or refractory extracranial and central nervous system tumors including medulloblastoma with post hoc Bayesian analysis: A European ITCC study.

AIM: To assess objective response after two cycles of temozolomide and topotecan (TOTEM) in children with refractory or relapsed miscellaneous extracranial solid and central nervous system (CNS) tumors, including medulloblastoma and primitive neuroectodermal tumors (PNET). PROCEDURE: Multicenter, nonrandomized, phase 2 basket trial including children with solid tumors, completed by a one-stage design confirmatory cohort for medulloblastoma, and an exploratory cohort for PNET. Main eligibility criteria were refractory/relapsed measurable disease and no more than two prior treatment lines. Temozolomide was administered orally at 150 mg/m2 /day followed by topotecan at 0.75 mg/m2 /day intravenously for five consecutive days every 28 days. Tumor response was assessed every two cycles according to WHO criteria and reviewed independently. RESULTS: Thirty-two patients were enrolled and treated in the miscellaneous solid tumor and 33 in the CNS strata; 20 patients with medulloblastoma and six with PNET were included in the expansion cohorts. The median age at inclusion was 10.0 years (range, 0.9-20.9). In the basket cohorts, confirmed complete and partial responses were observed in one glioma, four medulloblastoma, and one PNET, leading to the extension. The overall objective response rate (ORR) in medulloblastoma was 28% (95% CI, 12.7-47.2) with 1/29 complete and 7/29 partial responses, those for PNET 10% (95% CI, 0.3-44.5). Post hoc Bayesian analysis estimates that the true ORR in medulloblastoma is probably between 20% and 30% and below 20% in PNET. The most common treatment-related toxicities of the combination therapy were hematologic. CONCLUSIONS: Temozolomide-topotecan results in significant ORR in children with recurrent and refractory medulloblastoma with a favorable toxicity profile.

A new prognostic model for survival in second line for metastatic renal cell carcinoma: development and external validation.

BACKGROUND: In patients with metastatic renal cell carcinoma (mRCC), the oncologic benefit of second-line treatment for high volume tumors or presence of more than five risk factors remain to be defined. Our aim was to develop and externally validate a new model most likely to correctly predict overall survival (OS) categories in second line. METHOD: mRCC patients treated within clinical trials at Gustave Roussy Cancer Campus (GRCC) formed the discovery set. Patients from two phase III trials from Pfizer database (PFIZERDB), AXIS (NCT00678392), and INTORSECT (NCT00474786), formed the external validation set. New prognostic factors were analyzed using a multivariable Cox model with a backward selection procedure. Performance of the GRCC model and the prognostic classification scheme derived from it, measuring by R2, c-index, and calibration, was evaluated on the validation set and compared to MSKCC and IMDC models. RESULTS: Two hundred and twenty-one patients were included in the GRCC cohort and 855 patients in the PFIZERDB. Median OS was similar in the discovery and validation cohorts (16.8 [95% CI 12.9-21.7] and 15.3 [13.6-17.2] months, respectively). Backward selection procedure identified time from first to second-line treatment and tumor burden as new independent prognostic factors significantly associated to OS after adjusting for IMDC prognostic factors (HR 1.68 [1.23-2.31] and 1.43 [1.03-1.99], respectively). Dividing patients into four risk groups, based on the number of factors selected in GRCC model, median OS from the start of second line in the validation cohort was not reached (NE) [95% CI 24.9-NE] in the favorable risk group (n = 20), 21.8 months [18.6-28.2] in the intermediate-risk group (n = 367), 12.7 months [11.0-15.8] in the low poor-risk group (n = 347), and 5.5 months [4.7-6.4] in the high poor-risk group (n = 121). Finally, this model and its prognostic classification scheme provided the better fit, with higher R2 and higher c-index compared to other possible classification schemes. CONCLUSION: A new prognostic model was developed and validated to estimate overall survival of patients with previously treated mRCC. This model is an easy-to-use tool that allows accurate estimation of patient survival to inform decision making and follow-up after first line for mRCC.

Incorporating individual historical controls and aggregate treatment effect estimates into a Bayesian survival trial: a simulation study.

BACKGROUND: Performing well-powered randomised controlled trials (RCTs) of new treatments for rare diseases is often infeasible. However, with the increasing availability of historical data, incorporating existing information into trials with small sample sizes is appealing in order to increase the power. Bayesian approaches enable one to incorporate historical data into a trial's analysis through a prior distribution. METHODS: Motivated by a RCT intended to evaluate the impact on event-free survival of mifamurtide in patients with osteosarcoma, we performed a simulation study to evaluate the impact on trial operating characteristics of incorporating historical individual control data and aggregate treatment effect estimates. We used power priors derived from historical individual control data for baseline parameters of Weibull and piecewise exponential models, while we used a mixture prior to summarise aggregate information obtained on the relative treatment effect. The impact of prior-data conflicts, both with respect to the parameters and survival models, was evaluated for a set of pre-specified weights assigned to the historical information in the prior distributions. RESULTS: The operating characteristics varied according to the weights assigned to each source of historical information, the variance of the informative and vague component of the mixture prior and the level of commensurability between the historical and new data. When historical and new controls follow different survival distributions, we did not observe any advantage of choosing a piecewise exponential model compared to a Weibull model for the new trial analysis. However, we think that it remains appealing given the uncertainty that will often surround the shape of the survival distribution of the new data. CONCLUSION: In the setting of Sarcome-13 trial, and other similar studies in rare diseases, the gains in power and accuracy made possible by incorporating different types of historical information commensurate with the new trial data have to be balanced against the risk of biased estimates and a possible loss in power if data are not commensurate. The weights allocated to the historical data have to be carefully chosen based on this trade-off. Further simulation studies investigating methods for incorporating historical data are required to generalise the findings.

Bayesian survival analysis in clinical trials: What methods are used in practice?

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.

ERCC1 isoform expression and DNA repair in non-small-cell lung cancer.

BACKGROUND: The excision repair cross-complementation group 1 (ERCC1) protein is a potential prognostic biomarker of the efficacy of cisplatin-based chemotherapy in non-small-cell lung cancer (NSCLC). Although several ongoing trials are evaluating the level of expression of ERCC1, no consensus has been reached regarding a method for evaluation. METHODS: We used the 8F1 antibody to measure the level of expression of ERCC1 protein by means of immunohistochemical analysis in a validation set of samples obtained from 494 patients in two independent phase 3 trials (the National Cancer Institute of Canada Clinical Trials Group JBR.10 and the Cancer and Leukemia Group B 9633 trial from the Lung Adjuvant Cisplatin Evaluation Biology project). We compared the results of repeated staining of the entire original set of samples obtained from 589 patients in the International Adjuvant Lung Cancer Trial Biology study, which had led to the initial correlation between the absence of ERCC1 expression and platinum response, with our previous results in the same tumors. We mapped the epitope recognized by 16 commercially available ERCC1 antibodies and investigated the capacity of the different ERCC1 isoforms to repair platinum-induced DNA damage. RESULTS: We were unable to validate the predictive effect of immunostaining for ERCC1 protein. The discordance in the results of staining for ERCC1 suggested a change in the performance of the 8F1 antibody since 2006. We found that none of the 16 antibodies could distinguish among the four ERCC1 protein isoforms, whereas only one isoform produced a protein that had full capacities for nucleotide excision repair and cisplatin resistance. CONCLUSIONS: Immunohistochemical analysis with the use of currently available ERCC1 antibodies did not specifically detect the unique functional ERCC1 isoform. As a result, its usefulness in guiding therapeutic decision making is limited. (Funded by Eli Lilly and others.).

New insights into the evaluation of randomized controlled trials for rare diseases over a long-term research horizon: a simulation study.

Large sample sizes are required in randomized clinical trials designed to meet typical one-sided 2.5% α-level and 80% power. This may not be achievable when the disease is rare. We simulated a series of two-arm superiority trials over a 15-year period. The design parameters examined were the α-level and the number of trials conducted over the 15-year period (thus, trial sample size). Different disease severities and accrual rates were considered. The future treatment effect was characterized by its associated hazard rate; different hypotheses of how treatments improve over time were considered. We defined the total survival benefit as the relative difference of the hazard rates at year 15 versus year 0. The optimal design was defined by maximizing the expected total survival benefit, provided that the risk of selecting at year 15 a treatment inferior to the initial control treatment remains below 1%. Compared with two larger trials with typical one-sided 2.5% α-level, performing a series of small trials with relaxed α-levels leads on average to larger survival benefits over a 15-year research horizon, but also to higher risk of selecting a worse treatment at the end of the research period. Under reasonably optimistic assumptions regarding the future treatment effects, optimal designs outperform traditional ones when the disease is severe (baseline median survival ≤ 1 year) and the accrual is ≥100 patients per year, whereas no major improvement is observed in diseases with better prognosis. Trial designs aiming to maximize survival gain over a long research horizon across a series of trials are worth discussing in the context of rare diseases. Copyright © 2016 John Wiley & Sons, Ltd.

Systematic review and meta-analysis of phase I/II targeted therapy combined with radiotherapy in patients with glioblastoma multiforme: quality of report, toxicity, and survival.

To perform a systematic review and meta-analysis of severe adverse events (SAE) reported in early trials combining molecularly targeted therapies (MTT) with radiotherapy (RT), and to compare them to standard therapy. A summary data meta-analysis was performed and compared to the historical standard. Inclusion criteria were phase I and/or II trials published between 2000 and 2011, with glioblastoma multiforme patients treated with RT and MTT. Pooled incidence rates (IR) of SAE were estimated as well as the pooled median progression-free survival (PFS) and overall survival (OS). Nineteen prospective trials (9 phase I, 1 phase I/II and 9 phase II) out of 29 initially selected were included (n = 755 patients). The exact number of patients who had experienced SAE was mentioned in 37 % of the trials, concerning only 17 % of the patients. Information such as the period during which adverse events were monitored, the planned treatment duration, and late toxicity were not reported in the trials. The pooled IR of overall SAE was 131.2 (95 % CI 88.8-193.7) per 1000 person-months compared to 74.7 (63.6-87.8) for standard therapy (p < 0.01). Significant differences were observed for gastrointestinal events (p = 0.05) and treatment-related deaths (p = 0.02), in favour of standard therapy. No significant difference was observed in PFS and OS. Reporting a summary of toxicity data in early clinical trials should be stringently standardized. The use of MTT with RT compared to standard therapy increased SAE while yielded comparable survival in glioblastoma multiforme patients.

Long-term results of the combination of the N7 induction chemotherapy and the busulfan-melphalan high dose chemotherapy.

BACKGROUND: To evaluate long-term survival of the first cohort of stage-4 neuroblastoma patients treated with the N7 induction chemotherapy, surgery of the primary tumor and high-dose chemotherapy (HDC) containing Busulfan-Melphalan (Bu-Mel) followed by autologous stem cell transplantation (ASCT). PROCEDURE: From 1998 to 1999, 47 children were included in the NB97 trial and treated with induction chemotherapy according to the N7 protocol, followed by surgery of the primary tumor. HDC (Busulfan, 600 mg/m(2) Melphalan, 140 mg/m(2) ) was administered in patients with partial response of metastases with no more than 3 mIBG spots. Radiotherapy was delivered to the primary tumor site when tumors displayed MYCN amplification. RESULTS: Thirty-nine patients received Bu-Mel (83%): 23 who had achieved complete response (CR) of metastases, 20 after induction treatment and 3 after second-line chemotherapy, and 16 in partial response (PR). The toxicity of the whole treatment was manageable. The main HDC related-toxicity was hepatic veno-occlusive disease grade > 2 occurring in 15% of the patients. The 8-year EFS of the whole cohort was 34% (95% CI, 22-48%). The 8-year EFS of the 39 patients who received Bu-Mel and ASCT was 41% (95% CI, 27-57%). Patients who achieved a CR of metastases at the end of induction chemotherapy had a significantly better outcome than the others (8-year EFS, 52% vs. 20%; P = 0.02). CONCLUSIONS: The long-term results of this first prospective cohort of patients with metastatic disease treated with the N7 induction chemotherapy and HDC (Bu-Mel) confirm published data with stable survival curves but with a longer follow-up.

A framework to characterise the reproducibility of meta-analysis results with its application to direct oral anticoagulants in the acute treatment of venous thromboembolism.

The number of meta-analyses of aggregate data has dramatically increased due to the facility of obtaining data from publications and the development of free, easy-to-use, and specialised statistical software. Even when meta-analyses include the same studies, their results may vary owing to different methodological choices. Assessment of the replication of meta-analysis provides an example of the variation of effect 'naturally' observed between multiple research projects. Reproducibility of results has mostly been reported using graphical descriptive representations. A quantitative analysis of such results would enable (i) breakdown of the total observed variability with quantification of the variability generated by the replication process and (ii) identification of which variables account for this variability, such as methodological quality or the statistical analysis procedures used. These variables might explain systematic mean differences between results and dispersion of the results. To quantitatively characterise the reproducibility of meta-analysis results, a bivariate linear mixed-effects model was developed to simulate both mean results and their corresponding uncertainty. Results were assigned to several replication groups, those assessing the same studies, outcomes, treatment indication and comparisons classified in the same replication group. A nested random effect structure was used to break down the total variability within each replication group and between these groups to enable calculation of an intragroup correlation coefficient and quantification of reproducibility. Determinants of variability were investigated by modelling both mean and variance parameters using covariates. The proposed model was applied to the example of meta-analyses evaluating direct oral anticoagulants in the acute treatment of venous thromboembolism.

Personalized Chemotherapy on the Basis of Tumor Marker Decline in Poor-Prognosis Germ-Cell Tumors: Updated Analysis of the GETUG-13 Phase III Trial.

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical Trial Updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported.GETUG-13 established that switching patients with poor-prognosis nonseminomatous germ-cell tumors with an unfavorable marker decline to intensified chemotherapy resulted in improved outcomes. Here, we report the GETUG-13 long-term efficacy and toxicity. Two hundred and sixty-three patients with International Germ Cell Cancer Consensus Group poor prognosis received one cycle of bleomycin, etoposide, and cisplatin (BEP): 51 with a favorable tumor marker decline continued with three cycles of BEP (Fav-BEP) and 203 with an unfavorable decline were randomly treated with three BEP (Unfav-BEP) cycles or a dose-dense regimen (Unfav-dose-dense; two cycles of paclitaxel-BEP-oxaliplatin + two cycles of cisplatin, ifosfamide, and bleomycin). The median follow-up was 7.1 years (range, 0.3-13.3). Five-year progression-free survival (PFS) rates were 58.9% in the Unfav-dose-dense arm and 46.7% in the Unfav-BEP arm (hazard ratio [HR], 0.65 [95% CI, 0.44 to 0.97]; P = .036). Five-year overall survival rates were 70.9% and 61.3% (HR, 0.74 [95% CI, 0.46 to 1.20]; P = .22). Side effects evolved favorably, with only three patients in the Unfav-dose-dense arm reporting grade 3 motor neurotoxicity at 1 year and no reported toxicity over grade 1 after year 2. Salvage high-dose chemotherapy plus a stem-cell transplant was used in 8% in the Unfav-dose-dense arm and 17% in the Unfav-BEP arm (P = .035). Long-term outcomes suggest a sustained benefit of intensified chemotherapy in terms of PFS and numerically better survival, with a minimal toxicity and reduced use of salvage high-dose chemotherapy plus stem-cell transplant.

Diffuse midline glioma invasion and metastasis rely on cell-autonomous signaling.

BACKGROUND: Diffuse midline gliomas (DMG) are pediatric tumors with negligible 2-year survival after diagnosis characterized by their ability to infiltrate the central nervous system. In the hope of controlling the local growth and slowing the disease, all patients receive radiotherapy. However, distant progression occurs frequently in DMG patients. Current clues as to what causes tumor infiltration circle mainly around the tumor microenvironment, but there are currently no known determinants to predict the degree of invasiveness. METHODS: In this study, we use patient-derived glioma stem cells (GSCs) to create patient-specific 3D avatars to model interindividual invasion and elucidate the cellular supporting mechanisms. RESULTS: We show that GSC models in 3D mirror the invasive behavior of the parental tumors, thus proving the ability of DMG to infiltrate as an autonomous characteristic of tumor cells. Furthermore, we distinguished 2 modes of migration, mesenchymal and ameboid-like, and associated the ameboid-like modality with GSCs derived from the most invasive tumors. Using transcriptomics of both organoids and primary tumors, we further characterized the invasive ameboid-like tumors as oligodendrocyte progenitor-like, with highly contractile cytoskeleton and reduced adhesion ability driven by crucial over-expression of bone morphogenetic pathway 7 (BMP7). Finally, we deciphered MEK, ERK, and Rho/ROCK kinases activated downstream of the BMP7 stimulation as actionable targets controlling tumor cell motility. CONCLUSIONS: Our findings identify 2 new therapeutic avenues. First, patient-derived GSCs represent a predictive tool for patient stratification in order to adapt irradiation strategies. Second, autocrine and short-range BMP7-related signaling becomes a druggable target to prevent DMG spread and metastasis.

Weight and skeletal muscle loss with cabozantinib in metastatic renal cell carcinoma.

BACKGROUND: Cabozantinib, a standard of care metastatic renal cell carcinoma (mRCC), may be associated with weight and muscle loss. These effects of new generation VEGFR tyrosine kinase inhibitor on muscle mass loss are poorly described. METHODS: All cabozantinib-treated mRCC patients from January 2014 to February 2019 in our institution were included. Clinical data including weight were collected during therapy. Computed tomography images were centrally reviewed for response assessment, and axial sections at the third lumbar vertebrae were used to measure the total muscle area. Toxicities and cabozantinib outcomes were evaluated. Co-primary endpoints included skeletal muscle loss and weight loss (WL), longitudinally evaluated during treatment. WL has been classified according to CTCAEv5.0: Grade 1 (loss of 5 to <10% of baseline body weight), Grade 2 (loss of 10% to <20% of baseline body weight), and Grades 3-4 (loss >20% of baseline body weight). RESULTS: Patients were mostly men (70.3%), median age was 59.2 (range: 22.0-78.0) years, and median baseline body mass index was 25.0 (range: 16.4-49.3) kg/cm2 . Prognosis according to International Metastatic RCC Database Consortium score was good, intermediate, and poor for 13 (13.0%), 63 (63.0%), and 24 (24.0%) patients, respectively. Out of a total of 120 patients, 101 patients with a median follow-up of 22.3 months (range: 4.5-62.2) were eligible for analysis; 85 experienced muscle loss and muscle loss >10% increased during cabozantinib exposition, especially after 6 months of treatment. At cabozantinib baseline, 71 patients (70.3%) had sarcopenia, and 16/30 (53.3%) non-sarcopenic patients developed sarcopenia during treatment. Baseline sarcopenia was associated with lower response rates (P = 0.031) and higher grades 3-4 toxicities (P = 0.001). Out of 92 patients included in the WL analysis, 44 (47.8%) and 12 (13.0%) experienced grades 2 and 3 WL, respectively. CONCLUSIONS: We report a high incidence of grades 3-4 WL, fourth times higher than reported in prior pivotal trials, and half of the patients developed sarcopenia while on cabozantinib treatment. Weight and muscle mass loss with cabozantinib are underreported and may require further investigations and early management.

Escherichia coli-Specific CXCL13-Producing TFH Are Associated with Clinical Efficacy of Neoadjuvant PD-1 Blockade against Muscle-Invasive Bladder Cancer.

Biomarkers guiding the neoadjuvant use of immune-checkpoint blockers (ICB) are needed for patients with localized muscle-invasive bladder cancers (MIBC). Profiling tumor and blood samples, we found that follicular helper CD4+ T cells (TFH) are among the best therapeutic targets of pembrolizumab correlating with progression-free survival. TFH were associated with tumoral CD8 and PD-L1 expression at baseline and the induction of tertiary lymphoid structures after pembrolizumab. Blood central memory TFH accumulated in tumors where they produce CXCL13, a chemokine found in the plasma of responders only. IgG4+CD38+ TFH residing in bladder tissues correlated with clinical benefit. Finally, TFH and IgG directed against urothelium-invasive Escherichia coli dictated clinical responses to pembrolizumab in three independent cohorts. The links between tumor infection and success of ICB immunomodulation should be prospectively assessed at a larger scale. SIGNIFICANCE: In patients with bladder cancer treated with neoadjuvant pembrolizumab, E. coli-specific CXCL13 producing TFH and IgG constitute biomarkers that predict clinical benefit. Beyond its role as a biomarker, such immune responses against E. coli might be harnessed for future therapeutic strategies. This article is highlighted in the In This Issue feature, p. 2221.

A frequentist one-step model for a simple network meta-analysis of time-to-event data in presence of an effect modifier.

INTRODUCTION: Individual patient data (IPD) present particular advantages in network meta-analysis (NMA) because interactions may lead an aggregated data (AD)-based model to wrong a treatment effect (TE) estimation. However, fewer works have been conducted for IPD with time-to-event contrary to binary outcomes. We aimed to develop a general frequentist one-step model for evaluating TE in the presence of interaction in a three-node NMA for time-to-event data. METHODS: One-step, frequentist, IPD-based Cox and Poisson generalized linear mixed models were proposed. We simulated a three-node network with or without a closed loop with (1) no interaction, (2) covariate-treatment interaction, and (3) covariate distribution heterogeneity and covariate-treatment interaction. These models were applied to the NMA (Meta-analyses of Chemotherapy in Head and Neck Cancer [MACH-NC] and Radiotherapy in Carcinomas of Head and Neck [MARCH]), which compared the addition of chemotherapy or modified radiotherapy (mRT) to loco-regional treatment with two direct comparisons. AD-based (contrast and meta-regression) models were used as reference. RESULTS: In the simulated study, no IPD models failed to converge. IPD-based models performed well in all scenarios and configurations with small bias. There were few variations across different scenarios. In contrast, AD-based models performed well when there were no interactions, but demonstrated some bias when interaction existed and a larger one when the modifier was not distributed evenly. While meta-regression performed better than contrast-based only, it demonstrated a large variability in estimated TE. In the real data example, Cox and Poisson IPD-based models gave similar estimations of the model parameters. Interaction decomposition permitted by IPD explained the ecological bias observed in the meta-regression. CONCLUSION: The proposed general one-step frequentist Cox and Poisson models had small bias in the evaluation of a three-node network with interactions. They performed as well or better than AD-based models and should also be undertaken whenever possible.