Cancer drug approvals that displaced existing standard-of-care therapies, 2016-2021

Importance Although several cancer drugs receive US Food and Drug Administration (FDA) approval each month, it is unclear how many of these cancer drugs transform the treatment landscape significantly by tumor group. Specifically, it remains unclear how many of these newly approved cancer drugs displace the existing standard-of-care therapies for their indication vs being added to existing therapies. Objective To examine how many cancer drugs displace the standard-of-care therapies vs being added to existing therapy or filling breaks in systemic treatments in the metastatic setting, adjuvant setting, or maintenance setting. Design, Setting, and Participants Retrospective cross-sectional study using landmark trials leading to FDA approval of cancer drugs between May 1, 2016, and May 31, 2021. The study evaluated all FDA approvals for cancer drugs between May 1, 2016, and May 31, 2021, using the FDA Oncology (Cancer)/Hematologic Malignancies Approval Notifications website. All clinical trials leading to FDA approval of cancer drugs during this period were examined. Main Outcomes and Measures A drug was determined to have displaced the prior standard-of-care therapy by evaluating the comparator arm (or lack thereof) in the clinical trial leading to the drug’s approval and also by reviewing National Comprehensive Cancer Network Guidelines. Cancer drug approvals were categorized as first-line displacing if a drug was approved for use in the first-line setting and displaced the prior standard-of-care drug for an indication, first-line drug alternatives/new if a drug was approved for use in the first-line setting but did not displace the standard of care at the time of approval or was a new drug that was first of its class for an approved indication, add on if a drug was approved in combination with a previously approved therapy for a disease or if a drug was approved for use in the adjuvant or maintenance settings, and later line if a drug was approved for use in the second-, third-, or later-line settings. Results Between May 1, 2016, and May 31, 2021, there were 207 FDA cancer drug approvals in oncology and malignant hematology. Of these 207 approvals, 28 drugs (14%) were first-line displacing therapies. A total of 32 drugs (15%) were first-line drug alternatives/new drugs. A total of 61 drugs (29%) were add-on therapies. Finally, 86 drugs (42%) were approved as later-line therapies. Conclusions and Relevance In this study, most cancer drug approvals between 2016 and 2021 were in the later-line settings as opposed to displacing the current standard-of-care therapy for the approved indication. These later-line drugs may benefit patients with few alternatives but add to the cost of care because competition in the drug markets is a key factor in leading to lower drug prices

Additional consensus recommendations for conducting complex innovative trials of oncology agents: a post-pandemic perspective

In our 2020 consensus paper, we devised ten recommendations for conducting Complex Innovative Design (CID) trials to evaluate cancer drugs. Within weeks of its publication, the UK was hit by the first wave of the SARS-CoV-2 pandemic. Large CID trials were prioritised to compare the efficacy of new and repurposed COVID-19 treatments and inform regulatory decisions. The unusual circumstances of the pandemic meant studies such as RECOVERY were opened almost immediately and recruited record numbers of participants. However, trial teams were required to make concessions and adaptations to these studies to ensure recruitment was rapid and broad. As these are relevant to cancer trials that enrol patients with similar risk factors, we have added three new recommendations to our original ten: employing pragmatism such as using focused information sheets and collection of only the most relevant data; minimising negative environmental impacts with paperless systems; and using direct-to-patient communication methods to improve uptake. These recommendations can be applied to all oncology CID trials to improve their inclusivity, uptake and efficiency. Above all, the success of CID studies during the COVID-19 pandemic underscores their efficacy as tools for rapid treatment evaluation

No evidence for UV-based nest-site selection in sticklebacks

BACKGROUND: Nests are built in various animal taxa including fish. In systems with exclusive male parental care, the choice of a nest site may be an important component of male fitness. The nest site may influence male attractiveness as a mate, and male, embryo, and juvenile survival probabilities. Reproductively active three-spined stickleback males establish and defend a territory in which they build a nest. Territories can differ remarkably in qualities that influence male and female reproductive success like predation risk or abiotic factors such as dissolved oxygen concentration or lighting conditions. The latter may be important because in sticklebacks the extended visual capability into the ultraviolet (UV) wave range plays a role in female mate choice. Males are thus expected to be choosy about the habitat in which they will build their nest. RESULTS: We tested nest-site choice in male three-spined sticklebacks with respect to different UV lighting conditions. Reproductively active males were given the simultaneous choice to build their nest either in an UV-rich (UV+) or an UV-lacking (UV-) environment. Males exhibited no significant nest-site preferences with respect to UV+ or UV-. However, larger males and also heavier ones completed their nests earlier. CONCLUSION: We found that UV radiation as well as differences in luminance had no influence on nest-site choice in three-spined sticklebacks. Males that built in the UV-rich environment were not different in any trait (body traits and UV reflection traits) from males that built in the UV-poor environment. There was a significant effect of standard length and body mass on the time elapsed until nest completion in the UV experiment. The larger and heavier a male, the faster he completed his nest. In the brightness control experiment there was a significant effect only of body mass on the duration of nest completion. Whether nest building preferences with respect to UV lighting conditions are context dependent needs to be tested for instance by nest-site choice experiment under increased predation risk

20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years

The administration of endocrine therapy for 5 years substantially reduces recurrence rates during and after treatment in women with early-stage, estrogen-receptor (ER)-positive breast cancer. Extending such therapy beyond 5 years offers further protection but has additional side effects. Obtaining data on the absolute risk of subsequent distant recurrence if therapy stops at 5 years could help determine whether to extend treatment

The impact of viral mutations on recognition by SARS-CoV-2 specific T cells.

We identify amino acid variants within dominant SARS-CoV-2 T cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T cell responsiveness was seen due to Q213K in the A∗01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215; due to P13L, P13S, and P13T in the B∗27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17; and due to T362I and P365S in the A∗03:01/A∗11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T cell lines unable to recognize variant epitopes have diverse T cell receptor repertoires. These data demonstrate the potential for T cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T cell as well as humoral immunity.This work is supported by the UK Medical Research Council (MRC); Chinese Academy of Medical Sciences(CAMS) Innovation Fund for Medical Sciences (CIFMS), China; National Institute for Health Research (NIHR)Oxford Biomedical Research Centre, and UK Researchand Innovation (UKRI)/NIHR through the UK Coro-navirus Immunology Consortium (UK-CIC). Sequencing of SARS-CoV-2 samples and collation of data wasundertaken by the COG-UK CONSORTIUM. COG-UK is supported by funding from the Medical ResearchCouncil (MRC) part of UK Research & Innovation (UKRI),the National Institute of Health Research (NIHR),and Genome Research Limited, operating as the Wellcome Sanger Institute. T.I.d.S. is supported by a Well-come Trust Intermediate Clinical Fellowship (110058/Z/15/Z). L.T. is supported by the Wellcome Trust(grant number 205228/Z/16/Z) and by theUniversity of Liverpool Centre for Excellence in Infectious DiseaseResearch (CEIDR). S.D. is funded by an NIHR GlobalResearch Professorship (NIHR300791). L.T. and S.C.M.are also supported by the U.S. Food and Drug Administration Medical Countermeasures Initiative contract75F40120C00085 and the National Institute for Health Research Health Protection Research Unit (HPRU) inEmerging and Zoonotic Infections (NIHR200907) at University of Liverpool inpartnership with Public HealthEngland (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford.L.T. is based at the University of Liverpool. M.D.P. is funded by the NIHR Sheffield Biomedical ResearchCentre (BRC – IS-BRC-1215-20017). ISARIC4C is supported by the MRC (grant no MC_PC_19059). J.C.K.is a Wellcome Investigator (WT204969/Z/16/Z) and supported by NIHR Oxford Biomedical Research Centreand CIFMS. The views expressed are those of the authors and not necessarily those of the NIHR or MRC

Spatial growth rate of emerging SARS-CoV-2 lineages in England, September 2020-December 2021

This paper uses a robust method of spatial epidemiological analysis to assess the spatial growth rate of multiple lineages of SARS-CoV-2 in the local authority areas of England, September 2020–December 2021. Using the genomic surveillance records of the COVID-19 Genomics UK (COG-UK) Consortium, the analysis identifies a substantial (7.6-fold) difference in the average rate of spatial growth of 37 sample lineages, from the slowest (Delta AY.4.3) to the fastest (Omicron BA.1). Spatial growth of the Omicron (B.1.1.529 and BA) variant was found to be 2.81× faster than the Delta (B.1.617.2 and AY) variant and 3.76× faster than the Alpha (B.1.1.7 and Q) variant. In addition to AY.4.2 (a designated variant under investigation, VUI-21OCT-01), three Delta sublineages (AY.43, AY.98 and AY.120) were found to display a statistically faster rate of spatial growth than the parent lineage and would seem to merit further investigation. We suggest that the monitoring of spatial growth rates is a potentially valuable adjunct to outbreak response procedures for emerging SARS-CoV-2 variants in a defined population

SARS-CoV-2 lineage dynamics in England from September to November 2021: high diversity of Delta sub-lineages and increased transmissibility of AY.4.2

Background: Since the emergence of SARS-CoV-2, evolutionary pressure has driven large increases in the transmissibility of the virus. However, with increasing levels of immunity through vaccination and natural infection the evolutionary pressure will switch towards immune escape. Genomic surveillance in regions of high immunity is crucial in detecting emerging variants that can more successfully navigate the immune landscape. Methods: We present phylogenetic relationships and lineage dynamics within England (a country with high levels of immunity), as inferred from a random community sample of individuals who provided a self-administered throat and nose swab for rt-PCR testing as part of the REal-time Assessment of Community Transmission-1 (REACT-1) study. During round 14 (9 September–27 September 2021) and 15 (19 October–5 November 2021) lineages were determined for 1322 positive individuals, with 27.1% of those which reported their symptom status reporting no symptoms in the previous month. Results: We identified 44 unique lineages, all of which were Delta or Delta sub-lineages, and found a reduction in their mutation rate over the study period. The proportion of the Delta sub-lineage AY.4.2 was increasing, with a reproduction number 15% (95% CI 8–23%) greater than the most prevalent lineage, AY.4. Further, AY.4.2 was less associated with the most predictive COVID-19 symptoms (p = 0.029) and had a reduced mutation rate (p = 0.050). Both AY.4.2 and AY.4 were found to be geographically clustered in September but this was no longer the case by late October/early November, with only the lineage AY.6 exhibiting clustering towards the South of England. Conclusions: As SARS-CoV-2 moves towards endemicity and new variants emerge, genomic data obtained from random community samples can augment routine surveillance data without the potential biases introduced due to higher sampling rates of symptomatic individuals. © 2022, The Author(s)

Tracking SARS-CoV-2 mutations and variants through the COG-UK-Mutation Explorer

COG-UK Mutation Explorer (COG-UK-ME, https://sars2.cvr.gla.ac.uk/cog-uk/—last accessed date 16 March 2022) is a web resource that displays knowledge and analyses on SARS-CoV-2 virus genome mutations and variants circulating in the UK, with a focus on the observed amino acid replacements that have an antigenic role in the context of the human humoral and cellular immune response. This analysis is based on more than 2 million genome sequences (as of March 2022) for UK SARS-CoV-2 data held in the CLIMB-COVID centralised data environment. COG-UK-ME curates these data and displays analyses that are cross-referenced to experimental data collated from the primary literature. The aim is to track mutations of immunological importance that are accumulating in current variants of concern and variants of interest that could alter the neutralising activity of monoclonal antibodies (mAbs), convalescent sera, and vaccines. Changes in epitopes recognised by T cells, including those where reduced T cell binding has been demonstrated, are reported. Mutations that have been shown to confer SARS-CoV-2 resistance to antiviral drugs are also included. Using visualisation tools, COG-UK-ME also allows users to identify the emergence of variants carrying mutations that could decrease the neutralising activity of both mAbs present in therapeutic cocktails, e.g. Ronapreve. COG-UK-ME tracks changes in the frequency of combinations of mutations and brings together the curated literature on the impact of those mutations on various functional aspects of the virus and therapeutics. Given the unpredictable nature of SARS-CoV-2 as exemplified by yet another variant of concern, Omicron, continued surveillance of SARS-CoV-2 remains imperative to monitor virus evolution linked to the efficacy of therapeutics