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Understanding the evolutionary pathways to metastasis and resistance to immune-checkpoint inhibitors (ICI) in melanoma is critical for improving outcomes. Here, we present the most comprehensive intrapatient metastatic melanoma dataset assembled to date as part of the Posthumous Evaluation of Advanced Cancer Environment (PEACE) research autopsy program, including 222 exome sequencing, 493 panel-sequenced, 161 RNA sequencing, and 22 single-cell whole-genome sequencing samples from 14 ICI-treated patients. We observed frequent whole-genome doubling and widespread loss of heterozygosity, often involving antigen-presentation machinery. We found KIT extrachromosomal DNA may have contributed to the lack of response to KIT inhibitors of a KIT-driven melanoma. At the lesion-level, MYC amplifications were enriched in ICI nonresponders. Single-cell sequencing revealed polyclonal seeding of metastases originating from clones with different ploidy in one patient. Finally, we observed that brain metastases that diverged early in molecular evolution emerge late in disease. Overall, our study illustrates the diverse evolutionary landscape of advanced melanoma.Significance:Despite treatment advances, melanoma remains a deadly disease at stage IV. Through research autopsy and dense sampling of metastases combined with extensive multiomic profiling, our study elucidates the many mechanisms that melanomas use to evade treatment and the immune system, whether through mutations, widespread copy-number alterations, or extrachromosomal DNA.

Supplementary figure 1: Cohort overview. Number of samples sequenced with whole exome, panel or whole RNA sequencing. Supplementary figure 2: Phylogeny and WGD events in CRUKP1047. Supplementary figure 3: Ploidy and SCNA burden. Supplementary figure 4: Overview of each case. Supplementary figure 5: MEDICC2 copy number sample trees. Supplementary figure 6: MEDICC tree of all exome samples demonstrating that samples cluster together by patient, and not by melanoma subtype. Supplementary figure 7: SCNA frequency of cutaneous (a), acral (b) and melanoma of unknown primary (MUP, c). Supplementary figure 8: Correlation between liver copy number distance to other sites and time of emergence after stage IV diagnosis. Supplementary figure 9: Examination of tumour heterogeneity of alterations to antigen-presentation machinery genes, with site and patient annotation. Supplementary figure 10: Boxplots indicating the proportion of losses in the cohort for each segment. Supplementary figure 11: Balance of expression between nonsynonymous mutations that were not predicted to be neoantigens and clonal predicted neoantigens. Supplementary figure 12: Barplot of TIL infiltration score frequencies, determined by pathologist assessment of histology, across all samples. Supplementary figure 13: Number of samples per patient that are classified as either none-low in terms of TILs or moderate-heavy. Supplementary figure 14: Histogram of purity for samples with RNA-seq data. Supplementary figure 15: TME deconvolution. Supplementary figure 16: The effect of metastatic site on transcriptional profiles. Supplementary figure 17: Association of PHF3 copy number with expression. Supplementary figure 18: Overview of gene fusions identified in RNA-seq data. Supplementary figure 19: Comparison of ploidy estimates from panel sequencing data, exome data and FISH. Supplementary figure 20: Comparison of ploidy estimates in panel, exome, FISH and single cell data. Supplementary figure 21: FACs sort plot for CRUKP2567 diaphragmatic metastasis. Supplementary figure 22: Ploidy and wGII values from single cell sequencing of FACS-sorted tumour cells. Supplementary figure 23: Copy number profiles on chromosome 5 for bulk samples from primary and DI_2_R2, a diaphragmatic metastasis. Supplementary figure 24: Histogram of purity of samples for which RNA-seq was performed faceted by patient. Supplementary figure 25: Histogram of purity of samples for which RNA-seq was performed faceted by tissue site.

S. Table 1: List of PEACE Consortium members. S. Table 2: Sample database used for the study for Panel, Exome and RNASeq samples. S. Table 3: Overview of lines of treatment given to each patient. S. Table 4: Lesions and patients included in the lesion-level response to ICI analysis. S. Table 5: Hallmark genesets significantly upregulated in normal tissue vs tumor tissue. Table shows p-values for tissue type, purity; T value for tissue type, purity; q-value for tissue type, purity (FDR corrected). S. Table 6: Hallmark genesets significantly upregulated in tumor tissue vs normal tissue. Table shows p-values for tissue type, purity; T value for tissue type, purity; q-value for tissue type, purity (FDR corrected). S. Table 7: Genes included in the target panel. S. Table 8: Tree topology comparisons between manually constructed trees and pairtree-constructed trees.

The treatment of metastatic melanoma has been revolutionized by the introduction of immune checkpoint inhibitors and BRAF/MEK inhibition. Nevertheless, almost half of patients will progress or show primary resistance to treatment. The combination of BRAF/MEK and immune checkpoint inhibition might achieve higher response rates and improve long-term disease control. The IMspire150 trial investigated the combination of atezolizumab, cobimetinib and vemurafenib versus cobimetinib and vemurafenib alone.This review covers the efficacy and safety of atezolizumab, cobimetinib and vemurafenib for patients with advanced or metastatic BRAF mutant melanoma. The combination is compared with the current standard of care including BRAF/MEK inhibition and treatment with immune checkpoint inhibitors.Atezolizumab plus cobimetinib and vemurafenib showed superior progression-free survival in metastatic melanoma compared to cobimetinib and vemurafenib alone. Triplet therapy might be an option in situations of urgent need for disease control, when oncologists choose BRAF/MEK inhibition over immune checkpoint inhibition as first line treatment. At this time results are not mature yet, and longer follow-up including overall survival data is needed. The future role of this combination will also be determined by a comparison with the combination of ipilimumab and nivolumab.

International audience; Concerns have been raised that regulatory programs to accelerate approval of cancer drugs in cancer may increase uncertainty about benefits and harms for survival and quality of life (QoL). We analyzed all pivotal clinical trials and all non-pivotal randomized controlled trials (RCTs) for all cancer drugs approved for the first time by the FDA between 2000 and 2020. We report regulatory and trial characteristics. Effects on overall survival (OS), progression-free survival and tumor response were summarized in meta-analyses. Effects on QoL were qualitatively summarized. Between 2000 and 2020, the FDA approved 145 novel cancer drugs for 156 indications based on 190 clinical trials. Half of indications (49%) were approved without RCT evidence; 82% had a single clinical trial only. OS was primary endpoint in 14% of trials and QoL data were available from 25%. The median OS benefit was 2.55 months (IQR, 1.33-4.28) with a mean hazard ratio for OS of 0.75 (95%CI, 0.72-0.79, I(2) = 42). Improvement for QoL was reported for 7 (4%) of 156 indications. Over time, priority review was used increasingly and the mean number of trials per indication decreased from 1.45 to 1.12. More trials reported results on QoL (19% in 2000-2005; 41% in 2016-2020). For 21 years, novel cancer drugs have typically been approved based on one single, often uncontrolled, clinical trial, measuring surrogate endpoints. This leaves cancer patients without solid evidence that novel drugs improve their survival or QoL and there is no indication towards improvement.

Purpose: These are the final results of a national registry on cancer patients with COVID-19 in Switzerland. Methods: We collected data on symptomatic COVID-19-infected cancer patients from 23 Swiss sites over a one-year period starting on 1 March 2020. The main objective was to assess the outcome (i.e., mortality, rate of hospitalization, ICU admission) of COVID-19 infection in cancer patients; the main secondary objective was to define prognostic factors. Results: From 455 patients included, 205 patients (45%) had non-curative disease, 241 patients (53%) were hospitalized for COVID-19, 213 (47%) required oxygen, 43 (9%) invasive ventilation and 62 (14%) were admitted to the ICU. Death from COVID-19 infection occurred in 98 patients, resulting in a mortality rate of 21.5%. Age ≥65 years versus

Patients with blood cancer continue to have a greater risk of inadequate immune responses following three COVID-19 vaccine doses and risk of severe COVID-19 disease. In the context of the CAPTURE study (NCT03226886), we report immune responses in 80 patients with blood cancer who received a fourth dose of BNT162b2. We measured neutralizing antibody titers (NAbTs) using a live virus microneutralization assay against wild-type (WT), Delta, and Omicron BA.1 and BA.2 and T cell responses against WT and Omicron BA.1 using an activation-induced marker (AIM) assay. The proportion of patients with detectable NAb titers and T cell responses after the fourth vaccine dose increased compared with that after the third vaccine dose. Patients who received B cell-depleting therapies within the 12 months before vaccination have the greatest risk of not having detectable NAbT. In addition, we report immune responses in 57 patients with breakthrough infections after vaccination.

Patients with cancer have higher COVID-19 morbidity and mortality. Here we present the prospective CAPTURE study, integrating longitudinal immune profiling with clinical annotation. Of 357 patients with cancer, 118 were SARS-CoV-2 positive, 94 were symptomatic and 2 died of COVID-19. In this cohort, 83% patients had S1-reactive antibodies and 82% had neutralizing antibodies against wild type SARS-CoV-2, whereas neutralizing antibody titers against the Alpha, Beta and Delta variants were substantially reduced. S1-reactive antibody levels decreased in 13% of patients, whereas neutralizing antibody titers remained stable for up to 329 days. Patients also had detectable SARS-CoV-2-specific T cells and CD4+ responses correlating with S1-reactive antibody levels, although patients with hematological malignancies had impaired immune responses that were disease and treatment specific, but presented compensatory cellular responses, further supported by clinical recovery in all but one patient. Overall, these findings advance the understanding of the nature and duration of the immune response to SARS-CoV-2 in patients with cancer.

Not all patients with cancer, in particular those with hematogic malignancies, develop functional immunity against SARS-CoV-2 variants of concern (VOC) following COVID-19 vaccines. Durability of vaccine-induced immunity after two doses and the impact of a third dose were evaluated in CAPTURE (NCT03226886), a longitudinal prospective cohort study of vaccine responses in patients with cancer. In evaluating 316 patients, at a median of 111 days following two doses of either BNT16b2 or ChadOX, we observed a time-dependant decline in neutralising antibody titres (NAbT) in a proportion of patients, where NAbTs became undetectable against Delta and Beta in 17% and 15% of patients, respectively. Vaccine-induced T cell responses declined in 44% of patients. Patients with breakthrough infections following two vaccines doses were characterised by absent/low NAbT to Delta prior to infection. Administration of the third vaccine dose boosted NAb responses against VOC in the majority of patients with cancer, especially those with solid cancer. In patients with hematologic malignancies who had undetectable NAbT against Delta after two vaccine doses, 54% did not develop NAb against both Beta and Delta following the third dose. Third vaccine dose boosted T cell responses were boosted in patients with both solid and hematologic malignancies. These results provide critical information on vaccine responses in patients with cancer, especially against VOCs and support widespread access to a third COVID-19 vaccination in this patient group.

This collaborative meta-analysis was supported by the Swiss National Science Foundation and the Laura and John Arnold Foundation (grant supporting the post-doctoral fellowship at the Meta-Research Innovation Center at Stanford (METRICS), Stanford University). The funders had no role in the design of this collaborative meta-analysis; in the collection, analysis, and interpretation of data; or in the report writing., We would like to express our warm gratitude to all participating patients and convalescent plasma donors. We thank Katja Suter and Sina Ullrich, University of Basel, for their administrative assistance. For their helpful contribution to individual trials, we thank Erica Wood, Iain Gosbell, Richard Charlewood, Thomas Hills, Veronica Hoad, Kristina Kairaitis, Aikaj Jindal, John Gerrard, Hong Foo, Adam Stewart, and Nanette Trask (ASCOT trial); Amalia Bravo-Lindoro, Ral Carrillo-Esper, Karla Maldonado-Silva, Catalina Casillas-Suárez, Orlando Carrillo-Torres, Sandra Murrieta, Elizabeth Diaz-Padilla, Eli Omar Zavaleta, Yadira Bejar-Ramirez, and Evelyn Cortina-de la Rosa (CPC-SARS trial); Sheri Renaud, Roel Rolando-Almario, and Jacqueline Day (NCT04385199 trial); Sandra Tingsgrd, MD, Karen Brorup Heje Pedersen, MD, Michaela Tinggaard, MD, Louise Thorlacius-Ussing, MD, Clara Lundetoft Clausen, MD, Nichlas Hovmand, MD, Simone Bastrup Israelsen, MD, Cecilie Leding, MD, Katrine Iversen, MD, Maria Engel Miller, MD, Hkon Sandholdt, MSc biostatistician (CCAP-2 trial). On behalf of the IRCT20200310046736N1 trial, we thank the Khuzestan Blood Transfusion Organization for specialized assistance in the preparation and maintenance of plasma samples. On behalf of the NCT04332835 trial, we thank all the members of the "PC-COVID-19 Group" at the Clinica del Occidente and Hospital Universitario Mayor Mederi in Bogota, and Clinica CES in Medellin. On behalf of the NCT04403477 trial, we thank Miles Carroll for his support regarding the antibody testing in Bangladesh. The Co-CLARITY team would like to extend their gratitude to the Department of Science and Technology Philippine Council for Health Research and Development and the UP-Philippine General Hospital for all their support in the setting up and conduct of the trial. For helpfully communicating details of their trial, we thank members of the PlasmAr trial, NCT04359810 trial (Max O´Donnell), NCT04468009 trial, and CTRI/2020/05/025299 trial. Support for title page creation and format was provided by AuthorArranger, a tool developed at the National Cancer Institute (https://www.cancer.gov/)., During the conduct of the study, the following is reported: Dr. Berry reports being employee with ownership role at Berry Consultants (receives payments for statistical modeling and design of REMAP-CAP). Dr. Castillo reports grants from DOST PCHRD. Dr. Daly reports grants from Medical Research Future Fund (Australian Govt) and RBWH. Dr. Denkinger reports grants from German Ministry for Education and Research. Dr. Dumagay reports grants from Philippine Council for Health Research and Development. Dr. Dunachie reports grants from UK Department of Health and Social Care, grants from UK National Institute of Health Research. Dr. Gauiran reports grants from Department of Science and Technology—Philippine Council for Health Research and Development. Dr. Gordon reports grants from NIHR, grants from NIHR Research Professorship (RP-2015-06-18), and non-financial support from NIHR Clinical Research Network. Dr. Higgins reports grants from NHMRC and from the Minderoo Foundation. Dr. Hills reports grants from Health Research Council of New Zealand. Dr. Holm reports grants from Swedish Government Funds for Clinical Research (ALF). Dr. Janssen and Dr. Müller-Tidow report grants from the Federal Ministry of Education and Research in Germany (BMBF) to the RECOVER clinical trial. Dr. Krapp reports grants from Department of Foreign Affairs, Trade, and Development of Canada, grants from Fundación Telefónica del Perú. Dr. J. Lim reports grants from the Department of Science and Technology, Philippine Council for Health Research and Development. Dr. Lucero reports grants from Philippine Council for Health Research and Development. Dr Manrique reports economic support from Grupo ISA Intercolombia for the project development of trial NCT04332835. Drs. McQuilten and Wood report grants from Medical Research Future Fund. Dr. McVerry reports grants from The Pittsburgh Foundation, Translational Breast Cancer Research Consortium, and from UPMC Learning While Doing Program. Mr. Mouncey reports grants from National Institute for Health Research and from the European Union FP7: PREPARE. Dr. Najdovski reports payment from KUL Leuven to Belgian Red Cross for supply of convalescent plasma. Dr. Nichol reports grants from Health Research Board of Ireland. Dr. D. Roberts reports grants from the National Institute for Health (UKRIDHSC COVID-19 Rapid Response Rolling Call—Grant Reference Number COV19-RECPLAS) and the European Commission (SUPPORT-E #101015756). Dr. Rowan reports grants from the European Commission and from the UK National Institute for Health Research. Dr. Shankar-Hari reports grants from National Institute for Health Research UK, grants from UKRI-National Institute for Health Research UK. Dr. Turgeon reports grants from Canadian Institutes of Health Research. Dr. Venkatesh reports grants from Baxter. Dr. Webb reports grants from National Health and Medical Research Council, grants from Minderoo Foundation. Dr. Zacharowski reports grants from EU Horizon 2020. The ASCOT trial team (Drs Bowen, Daly, Davis, Denholm, Hammond, Jha, L. Lim, McQuilten, Molton, Morpeth, O’Sullivan, Paterson, Perry, Price, Rees, Roberts, Rogers, Sasadeusz, Snelling, Tong, Venkatesh, Wood) is funded by grants from from Royal Brisbane and Women’s Hospital Foundation, Pratt Foundation, Minderoo Foundation, BHP Foundation, Hospital Research Foundation, Macquarie Group Foundation, Health Research Council of New Zealand, Australian Partnership for Preparedness Research on Infectious Disease Emergencies (APPRISE), and the collection and supply of convalescent plasma was conducted within Lifeblood’s funding arrangements. The CONFIDENT trial is funded by the Belgian KCE (blood establishments received payment for the convalescent plasma supplied in the clinical trial). REMAP-CAP was supported in part by funding from UKRIDHSC COVID-19 Rapid Response Rolling Call (Grant Reference Number COV19-RECPLAS). Collection of convalescent plasma for REMAP-CAP was funded by the Department of Health and Social Care, UK. The IRCT20200310046736N1 trial was supported by the Ahvaz Jundishapur University of Medical Sciences (Grant No. R.AJUMS.REC.1399.003, Dr. Pouladzadeh). The PC-COVID-19 Group is supported by the Universidad del Rosario, IDCBIS, ISA Group and Suramericana (Colombia). Outside the submitted work, the following is reported: Dr. Axfors reports postdoctoral grants from the Knut and Alice Wallenberg Foundation, Uppsala University, the Swedish Society of Medicine, the Blanceflor Foundation, and the Sweden-America Foundation. Dr. Aomar reports personal fees from SOBI, GEDEON RICHTER, and GSK. Dr. Benfield reports grants from Novo Nordisk Foundation, Simonsen Foundation, Lundbeck Foundation, Kai Hansen Foundation, Erik and Susanna Olesen’s Charitable Fund; grants and personal fees from GSK, Pfizer, Gilead; and personal fees from Boehringer Ingelheim, MSD, and Pentabase ApS. Dr. Estcourt reports being an investigator on the RECOVERY trial. Dr. Gordon reports personal fees from GlaxoSmithKline, Bristol Myers Squibb, and 30 Respiratory. Dr. Jha reports grants and personal fees from Baxter Healthcare, personal fees from Astra Zeneca, grants from NephroPlus. Dr. Laterre reports personal fees from Adrenomed. Dr. McVerry reports grants from NIH/NHLBI and Bayer Pharmaceuticals, Inc. Dr. Mondragon reports financial activities outside the submitted work (employment at Johnson & Johnson). Dr. Perry reports partner being employed at CSL and owning shares in CSL. Dr. Paterson reports involvement with ALLIANCE trial of COVID-19 treatments. Dr. J. Roberts reports other COVID-19 related trials (in different patient groups): tocilizumab in ICU patients; hydroxychloroquine dosing in ICU patients; planned study of remdesivir pharmacokinetics in patients during expanded access program; and in silico evaluation of ivermectin dosing. Dr Sasadeusz reports grants from various Pharma companies including Gilead Sciences, Abvvie, Merck, and Takeda. Dr. Zacharowski reports personal fees from Biotest AG, CSL Behring, GE Heathcare, and is President of the ESAIC., Background: Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.17605/OSF.IO/GEHFX). Methods: In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung–Knapp–Sidik–Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence. Results: A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92; 1.02) with between-study heterogeneity not beyond chance (I2 = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3% of the weight in the meta-analysis. Conclusions: Convalescent plasma treatment of patients with COVID-19 did not reduce all-cause mortality. These results provide strong evidence that convalescent plasma treatment for patients with COVID-19 should not be used outside of randomized trials. Evidence synthesis from collaborations among trial investigators can inform both evidence generation and evidence application in patient care., Amalia Bravo-Lindoro, BHP Foundation, Blanceflor Foundation, Department of Foreign Affairs, Trade, and Development of Canada, Eli Omar Zavaleta, Erik and Susanna Olesen’s Charitable Fund, Federal Ministry of Education and Research in Germany, Fundación Telefónica del Perú, IDCBIS, ISA Group and Suramericana, Kai Hansen Foundation, Katja Suter and Sina Ullrich, Khuzestan Blood Transfusion Organization, Macquarie Group Foundation, Medical Research Future Fund, NephroPlus, RBWH, Roel Rolando-Almario NCT04385199, Royal Brisbane, Sheri Renaud, Simonsen Foundation, UKRI-National Institute for Health Research UK, UKRIDHSC COV19-RECPLAS, UP-Philippine General Hospital CTRI/2020/05/025299, NCT04359810, NCT04468009, Women’s Hospital Foundation, National Institutes of Health, National Heart, Lung, and Blood Institute, Pittsburgh Foundation, Pfizer, Baxter International, Stanford University, Gilead Sciences, National Institute on Handicapped Research RP-2015-06-18, Meso Scale Diagnostics, Universität Basel, Laura and John Arnold Foundation, Health Research Board, Pratt Foundation, Canadian Institutes of Health Research, National Institute for Health Research, Department of Health and Social Care, European Commission 101015756, National Health and Medical Research Council, Department of Science and Technology, Ministry of Science and Technology, India, Health Research Council of New Zealand, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Sweden-America Foundation, Bundesministerium für Bildung und Forschung, Lundbeckfonden, Knut och Alice Wallenbergs Stiftelse, Seventh Framework Programme, Ahvaz Jundishapur University of Medical Sciences, Université Pierre et Marie Curie, Uppsala Universitet, Horizon 2020, Svenska Läkaresällskapet, Universidad del Rosario, Pharmaceuticals Bayer, Novo Nordisk Fonden, Department of Science and Technology, Philippines, Philippine Council for Health Research and Development, Minderoo Foundation

Coronavirus disease 2019 (COVID-19) antiviral response in a pan-tumor immune monitoring (CAPTURE) (NCT03226886) is a prospective cohort study of COVID-19 immunity in patients with cancer. Here we evaluated 585 patients following administration of two doses of BNT162b2 or AZD1222 vaccines, administered 12 weeks apart. Seroconversion rates after two doses were 85% and 59% in patients with solid and hematological malignancies, respectively. A lower proportion of patients had detectable titers of neutralizing antibodies (NAbT) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOC) versus wild-type (WT) SARS-CoV-2. Patients with hematological malignancies were more likely to have undetectable NAbT and had lower median NAbT than those with solid cancers against both SARS-CoV-2 WT and VOC. By comparison with individuals without cancer, patients with hematological, but not solid, malignancies had reduced neutralizing antibody (NAb) responses. Seroconversion showed poor concordance with NAbT against VOC. Previous SARS-CoV-2 infection boosted the NAb response including against VOC, and anti-CD20 treatment was associated with undetectable NAbT. Vaccine-induced T cell responses were detected in 80% of patients and were comparable between vaccines or cancer types. Our results have implications for the management of patients with cancer during the ongoing COVID-19 pandemic.

The CD-20 antibody rituximab is a standard component of treatment of non-Hodgkin B-cell lymphomas, including diffuse large B-cell lymphoma (DLBCL). Primary DLBCL of the central nervous system, also called primary central nervous system lymphoma (PCNSL), is a DLBCL confined to the central nervous system. There has been debate whether intravenous rituximab accumulates sufficiently in the central nervous system to exert an effect. In this systematic review, we assess the benefits and harms of rituximab in the treatment of immunocompetent patients with PCNSL. By searching MEDLINE, CENTRAL, and ClincialTrials.gov up to March 2019, we identified randomized controlled trials (RCTs) investigating the effect of rituximab in patients with PCNSL. We extracted study characteristics and results, assessed risk of bias, performed trial-level random-effects meta-analyses, and graded the certainty of evidence. The protocol was registered with PROSPERO (CRD42019121965). Main outcomes were overall survival (time to death), progression-free survival (time to progression or death), quality of life, grades 3 and 4 toxicity, and treatment-related mortality. We included two RCTs with a total of 343 participants. Overall survival was not statistically significantly improved (HR 0.76; 95% CI, 0.52-1.12; low certainty), with 187 fewer to 39 more deaths after 2 years in 1000 treated patients. Low certainty of evidence indicated that rituximab improved progression-free survival (HR 0.65; 95% CI, 0.45-0.95), which translated into 137 fewer progressions or deaths after 2 years in 1000 treated patients (231 to 18 fewer). None of the RCTs provided data on quality of life. We found no evidence that rituximab increased grades 3 and 4 toxicity or treatment-related mortality (RR 0.53; 95% CI, 0.20-1.37; low certainty). Overall, the available evidence suggests with low certainty that rituximab in combination with methotrexate-based chemotherapy may improve progression-free survival in immunocompetent patients with newly diagnosed PCNSL, the pooled effect estimates did not show evidence for improvement of overall survival.

Substantial COVID-19 research investment has been allocated to randomized clinical trials (RCTs) on hydroxychloroquine/chloroquine, which currently face recruitment challenges or early discontinuation. We aim to estimate the effects of hydroxychloroquine and chloroquine on survival in COVID-19 from all currently available RCT evidence, published and unpublished. We present a rapid meta-analysis of ongoing, completed, or discontinued RCTs on hydroxychloroquine or chloroquine treatment for any COVID-19 patients (protocol: https://osf.io/QESV4/ ). We systematically identified unpublished RCTs (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, Cochrane COVID-registry up to June 11, 2020), and published RCTs (PubMed, medRxiv and bioRxiv up to October 16, 2020). All-cause mortality has been extracted (publications/preprints) or requested from investigators and combined in random-effects meta-analyses, calculating odds ratios (ORs) with 95% confidence intervals (CIs), separately for hydroxychloroquine and chloroquine. Prespecified subgroup analyses include patient setting, diagnostic confirmation, control type, and publication status. Sixty-three trials were potentially eligible. We included 14 unpublished trials (1308 patients) and 14 publications/preprints (9011 patients). Results for hydroxychloroquine are dominated by RECOVERY and WHO SOLIDARITY, two highly pragmatic trials, which employed relatively high doses and included 4716 and 1853 patients, respectively (67% of the total sample size). The combined OR on all-cause mortality for hydroxychloroquine is 1.11 (95% CI: 1.02, 1.20; I² = 0%; 26 trials; 10,012 patients) and for chloroquine 1.77 (95%CI: 0.15, 21.13, I² = 0%; 4 trials; 307 patients). We identified no subgroup effects. We found that treatment with hydroxychloroquine is associated with increased mortality in COVID-19 patients, and there is no benefit of chloroquine. Findings have unclear generalizability to outpatients, children, pregnant women, and people with comorbidities.

Key Points Question Is evidence from randomized clinical trials associated with the reimbursement decisions that determine access to off-label use of cancer drugs in Switzerland? Findings In this cross-sectional study including 3046 patients with cancer, there were a total of 695 off-label use requests for 598 patients (20%). A total of 67% of requests were reimbursed when supporting trials showed overall survival benefit, but 65% of requests were reimbursed without evidence for overall survival or progression-free survival benefit. Meaning These findings suggest that reimbursement for off-label cancer treatment in Switzerland was not evidence-based and that a transparent process considering clinical evidence is urgently needed for reimbursement decisions to guarantee fair access to off-label cancer drugs., This cross-sectional study examines the association of evidence for benefit from randomized clinical trials with off-label cancer drug reimbursement decisions in Switzerland., Importance In many health systems, access to off-label drug use is controlled through reimbursement restrictions by health insurers, especially for expensive cancer drugs. Objective To determine whether evidence from randomized clinical trials is associated with reimbursement decisions for requested off-label use of anticancer drugs in the Swiss health system. Design, Setting, and Participants This cross-sectional study used reimbursement requests from routinely collected health records of 5809 patients with drug treatment for cancer between January 2015 and July 2018 in 3 major cancer centers, covering cancer care of approximately 5% of the Swiss population, to identify off-label drug use. For each off-label use indication with 3 or more requests, randomized clinical trial evidence on treatment benefits was systematically identified for overall survival (OS) or progression-free survival (PFS). Data were analyzed from August 2018 to December 2020. Exposures Available randomized clinical trial evidence on benefits for OS or PFS for requested off-label use indications. Main Outcomes and Measures The main outcome was the association between evidence for treatment benefit (expressed as improved OS or PFS) and reimbursement in multivariable regression models. Results Among 3046 patients with cancer, 695 off-label use reimbursement requests in 303 different indications were made for 598 patients (median [interquartile range] age, 64 [53-73] years; 420 [60%] men). Off-label use was intended as first-line treatment in 311 requests (45%). Reimbursement was accepted in 446 requests (64%). For 71 indications, including 431 requests for 376 patients, there were 3 or more requests. Of these, 246 requests (57%) had no supporting evidence for OS or PFS benefit. Reimbursement was granted in 162 of 246 requests without supporting evidence (66%). Of 117 requests supported by OS benefit, 79 (67%) were reimbursed, and of 68 requests supported by PFS benefit alone, 54 (79%) were reimbursed. Evidence of OS benefit from randomized clinical trials was not associated with a higher chance of reimbursement (odds ratio, 0.76, 95% CI, 0.45-1.27). Conclusions and Relevance These findings suggest that in a health care system enabling access to off-label use, it was frequently intended as a first-line treatment in cancer care. Availability of randomized clinical trial evidence showing survival benefit was not associated with reimbursement decisions for off-label anticancer drug treatment in Switzerland. A transparent process with criteria considering clinical evidence is needed for evidence-based reimbursement decisions to ensure fair access to cancer treatments.

BackgroundSubstantial COVID-19 research investment has been allocated to randomized clinical trials (RCTs) on hydroxychloroquine/chloroquine, which currently face recruitment challenges or early discontinuation. We aimed to estimate the effects of hydroxychloroquine and chloroquine on survival in COVID-19 from all currently available RCT evidence, published and unpublished. Methods: Rapid meta-analysis of ongoing, completed, or discontinued RCTs on hydroxychloroquine or chloroquine treatment for any COVID-19 patients (protocol: https://osf.io/QESV4/). We systematically identified published and unpublished RCTs by September 14, 2020 (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, PubMed, Cochrane COVID-19 registry). All-cause mortality was extracted (publications/preprints) or requested from investigators and combined in random-effects meta-analyses, calculating odds ratios (ORs) with 95% confidence intervals (CIs), separately for hydroxychloroquine/chloroquine. Prespecified subgroup analyses included patient setting, diagnostic confirmation, control type, and publication status.ResultsSixty-two trials were potentially eligible. We included 16 unpublished trials (1596 patients) and 10 publications/preprints (6317 patients). The combined summary OR on all-cause mortality for hydroxychloroquine was 1.08 (95%CI: 0.99, 1.18; I2=0%; 24 trials; 7659 patients) and for chloroquine 1.77 (95%CI: 0.15, 21.13, I2=0%; 4 trials; 307 patients). We identified no subgroup effects.ConclusionsWe found no benefit of hydroxychloroquine or chloroquine on the survival of COVID-19 patients. For hydroxychloroquine, the confidence interval is compatible with increased mortality (OR 1.18) or negligibly reduced mortality (OR 0.99). Findings have unclear generalizability to outpatients, children, pregnant women, and people with comorbidities.

Background Target-specific anticancer drugs are under rapid development. Little is known, however, about the risk of administering target-specific drugs to patients who have tumours with molecular alterations or other characteristics that can make the drug ineffective or even harmful. An increasing number of randomised clinical trials (RCTs) investigating target-specific anticancer drugs include subgroup analyses based on tumour characteristics. Such subgroup analyses have the potential to be more credible and influential than subgroup analyses based on traditional factors such as sex or tumour stage. In addition, they may more frequently lead to qualitative subgroup effects, that is, show benefit in one but harm in another subgroup of patients (eg, if the tumour characteristic makes the drug ineffective or even enhance tumour growth). If so, subgroup analyses based on tumour characteristics would be highly relevant for patient safety. The aim of this study is to systematically assess the frequency and characteristics of subgroup analyses based on tumour characteristics, the frequency of qualitative subgroup effects, their credibility, and the interpretations that investigators and guidelines developers report. Methods and analysis We will perform a systematic survey of 433 RCTs testing the effect of target-specific anticancer drugs. Teams of methodologically trained investigators and oncologists will identify eligible studies, extract relevant data and assess the credibility of putative subgroup effects using a recently developed formal instrument. We will systematically assess how trial investigators interpret apparent subgroup effects based on tumour characteristics and the extent to which they influence subsequent practice guidelines. Our results will provide empirical data characterising an increasingly used type of subgroup analysis in cancer trials and its potential impact on precision medicine to predict benefit or harm. Ethics and dissemination Formal ethical approval is not required for this study. We will disseminate the findings in a peer-reviewed and open-access journal publication.

Background: Patients with cancer are at increased risk of severe outcomes from COVID-19. Understanding the impact of SARS-CoV-2 infection and vaccination induced-immunity is an area of unmet need. Methods: CAPTURE (NCT03226886) is a prospective longitudinal cohort study of COVID-19 vaccine or SARS-CoV-2 infection-induced immunity. SARS-CoV-2 infections were confirmed by RT-PCR and ELISA. Neutralising antibody titres (NAbT) against wild-type (WT) SARS-CoV-2 and variants of concern (VOC;Alpha, Beta, Delta) and SARS-CoV-2 specific T-cells (SsT-cells) were quantified. Results: 118 patients (89% solid malignancy, [SM]) were SARS-CoV-2-positive (median follow-up: 154 days). 85% patients were symptomatic;2 died of COVID-19. 82% had S1-reactive antibodies, of whom 89% had neutralising antibodies (NAbs);NAbT were lower against all VOCs. While S1-reactive antibody levels declined over time, NAbT remained stable up to 329 days. Most patients had detectable SsT-cells (76% CD4+, 52% CD8+). Haematological malignancy (HM) patients had impaired immune responses that were disease and treatment-specific (anti-CD20), but with evidence suggestive of compensation from T-cells. 585 patients were evaluated following 2 doses of BNT162b2 or AZD1222 vaccines, administered 12 weeks apart. Seroconversion rates after 2 doses were 85% and 54% in patients with SM and HM, respectively. A lower proportion of patients had detectable NAbs against SARS-CoV-2 VOC (Alpha 62%, Beta 54%, Delta 49%) vs WT (84%), with corresponding significantly lower NAbT. Patients with HM were more likely to have an undetectable NAb and had lower NAbT vs solid malignancies to both WT and VOCs. Seroconversion showed poor concordance with NAbTs against VOCs. Prior SARS-CoV-2 infection boosted NAbT including against VOCs. Anti-CD20 treatment was associated with severely diminished NAbTs. Vaccine-induced T-cell responses were detected in 80% of patients, with no differences between vaccines or cancer types. Conclusions: Patients with HM had blunted humoural responses to infection and vaccination, particularly against VOCs, but preserved cellular responses might contribute to protection. Our results lend support to prioritisation of all cancer patients for further booster vaccination. Clinical trial identification: NCT03226886. Legal entity responsible for the study: The Royal Marsden NHS Foundation Trust. Funding: The Royal Marsden Charity;The National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at the Royal Marsden Hospital and Institute for Cancer Research (ICR). Disclosure: All authors have declared no conflicts of interest.

BackgroundOff-label drug use (OLU) reflects a perceived unmet medical need, which is common in oncology. Cancer drugs are often highly expensive and their reimbursement is a challenge for many health care systems. OLU is frequently regulated by reimbursement restrictions. For evidence-based health care, treatment ought to be reimbursed if there is sufficient clinical evidence for treatment benefit independently of patient factors not related to the treatment indication. However, little is known about the reality of OLU reimbursement and its association with the underlying clinical evidence. Here we aim to investigate the relationship of reimbursement decisions with the underlying clinical evidence.Methods/DesignWe extract patient characteristics and details on treatment and reimbursement of cancer drugs from over 3000 patients treated in three Swiss hospitals. We systematically search for clinical trial evidence on benefits associated with OLU in the most common indications. We will describe the prevalence of OLU in Switzerland and its reimbursement in cancer care, and use multivariable logistic regression techniques to investigate the association of approval/rejection of a reimbursement requests to the evidence on treatment effects and to further factors, including type of drug, molecular predictive markers and the health insurer.DiscussionOur study will provide a systematic overview and assessment of OLU and its reimbursement reality in Switzerland. We may provide a better understanding of the access to cancer care that is regulated by health insurers and we hope to identify factors that determine the level of evidence-based cancer care in a highly diverse Western health care system.

Importance: Convalescent plasma is a proposed treatment for COVID-19. Objective: To assess clinical outcomes with convalescent plasma treatment vs placebo or standard of care in peer-reviewed and preprint publications or press releases of randomized clinical trials (RCTs). Data Sources: PubMed, the Cochrane COVID-19 trial registry, and the Living Overview of Evidence platform were searched until January 29, 2021. Study Selection: The RCTs selected compared any type of convalescent plasma vs placebo or standard of care for patients with confirmed or suspected COVID-19 in any treatment setting. Data Extraction and Synthesis: Two reviewers independently extracted data on relevant clinical outcomes, trial characteristics, and patient characteristics and used the Cochrane Risk of Bias Assessment Tool. The primary analysis included peer-reviewed publications of RCTs only, whereas the secondary analysis included all publicly available RCT data (peer-reviewed publications, preprints, and press releases). Inverse variance-weighted meta-analyses were conducted to summarize the treatment effects. The certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation. Main Outcomes and Measures: All-cause mortality, length of hospital stay, clinical improvement, clinical deterioration, mechanical ventilation use, and serious adverse events. Results: A total of 1060 patients from 4 peer-reviewed RCTs and 10 722 patients from 6 other publicly available RCTs were included. The summary risk ratio (RR) for all-cause mortality with convalescent plasma in the 4 peer-reviewed RCTs was 0.93 (95% CI, 0.63 to 1.38), the absolute risk difference was -1.21% (95% CI, -5.29% to 2.88%), and there was low certainty of the evidence due to imprecision. Across all 10 RCTs, the summary RR was 1.02 (95% CI, 0.92 to 1.12) and there was moderate certainty of the evidence due to inclusion of unpublished data. Among the peer-reviewed RCTs, the summary hazard ratio was 1.17 (95% CI, 0.07 to 20.34) for length of hospital stay, the summary RR was 0.76 (95% CI, 0.20 to 2.87) for mechanical ventilation use (the absolute risk difference for mechanical ventilation use was -2.56% [95% CI, -13.16% to 8.05%]), and there was low certainty of the evidence due to imprecision for both outcomes. Limited data on clinical improvement, clinical deterioration, and serious adverse events showed no significant differences. Conclusions and Relevance: Treatment with convalescent plasma compared with placebo or standard of care was not significantly associated with a decrease in all-cause mortality or with any benefit for other clinical outcomes. The certainty of the evidence was low to moderate for all-cause mortality and low for other outcomes.

Key Points Question What are the available data on cancer treatment outcomes for new cancer therapies approved by the US Food and Drug Administration? Findings In this comparative effectiveness study of 92 novel cancer therapies approved for 100 indications over 17 years, 44% of drug approvals were based on data from nonrandomized clinical trials. Randomized clinical trials typically reported that these drugs were associated with substantial tumor responses and delays in the time to progression or death, but the median absolute increase in overall survival was only 2 months. Meaning This study’s findings indicate that, at the time of drug approval, limited supporting data are available to decision-makers, and the increase in overall survival associated with new cancer drugs is typically small., Importance Clinical trial evidence used to support drug approval is typically the only information on benefits and harms that patients and clinicians can use for decision-making when novel cancer therapies become available. Various evaluations have raised concern about the uncertainty surrounding these data, and a systematic investigation of the available information on treatment outcomes for cancer drugs approved by the US Food and Drug Administration (FDA) is warranted. Objective To describe the clinical trial data available on treatment outcomes at the time of FDA approval of all novel cancer drugs approved for the first time between 2000 and 2016. Design, Setting, and Participants This comparative effectiveness study analyzed randomized clinical trials and single-arm clinical trials of novel drugs approved for the first time to treat any type of cancer. Approval packages were obtained from drugs@FDA, a publicly available database containing information on drug and biologic products approved for human use in the US. Data from January 2000 to December 2016 were included in this study. Main Outcomes and Measures Regulatory and clinical trial characteristics were described. For randomized clinical trials, summary treatment outcomes for overall survival, progression-free survival, and tumor response across all therapies were calculated, and median absolute survival increases were estimated. Tumor types and regulatory characteristics were assessed separately. Results Between 2000 and 2016, 92 novel cancer drugs were approved by the FDA for 100 indications based on data from 127 clinical trials. The 127 clinical trials included a median of 191 participants (interquartile range [IQR], 106-448 participants). Overall, 65 clinical trials (51.2%) were randomized, and 95 clinical trials (74.8%) were open label. Of 100 indications, 44 indications underwent accelerated approval, 42 indications were for hematological cancers, and 58 indications were for solid tumors. Novel drugs had mean hazard ratios of 0.77 (95% CI, 0.73-0.81; I2 = 46%) for overall survival and 0.52 (95% CI, 0.47-0.57; I2 = 88%) for progression-free survival. The median tumor response, expressed as relative risk, was 2.37 (95% CI, 2.00-2.80; I2 = 91%). The median absolute survival benefit was 2.40 months (IQR, 1.25-3.89 months). Conclusions and Relevance In this study, data available at the time of FDA drug approval indicated that novel cancer therapies were associated with substantial tumor responses but with prolonging median overall survival by only 2.40 months. Approval data from 17 years of clinical trials suggested that patients and clinicians typically had limited information available regarding the benefits of novel cancer treatments at market entry., This comparative effectiveness research examines clinical trial data on treatment outcomes used to support US Food and Drug Administration approval of novel cancer therapies that were approved for the first time between 2000 and 2016.

Background: There is ongoing controversy regarding the outcome of COVID-19 in cancer patients This is one of few registries on the impact of COVID-19 in cancer patients in a country severly affected by the pandemic Methods: This cohort study is collecting data on symptomatic Sars-CoV-2 infected patients with a cancer diagnosis from 23 Swiss sites, starting March 1, 2020 The main objective of the study is to assess the outcome of COVID-19 infection in patients with solid and hematological malignancies, while the main secondary objective is to define prognostic factors of COVID-19 outcome Results: With a cutoff date of July 16, 2020, 357 patients with a diagnosis of cancer and symptomatic COVID-19 were included into this first analysis The most frequent malignancies were breast in 63 cases (18%), lung in 40 cases (11%), prostate cancer in 24 cases (7%) and myeloma in 16 cases (5%), with 104 (38%) patients having non-curative disease Anticancer treatment within 3 months prior to the diagnosis of COVID-19 included chemotherapy in 65 patients (18%), targeted therapy in 54 patients (15%), steroids in 39 (11%), checkpoint inhibitors in 22 (6%) or no anticancer treatment in 155 patients (43%) 230 patients (65%) were hospitalized for COVID-19 or were already in hospital;167 of the hospitalized patients (73%) required oxygen treatment, 43 patients (19%) intensive care, 31 (14%) invasive ventilation 63 patients died from COVID-19 infection, resulting in a mortality rate of 18% Significant risk factors for death included age ≥65 versus