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The majority of targeted therapies for non-small-cell lung cancer (NSCLC) are directed against oncogenic drivers that are more prevalent in patients with light exposure to tobacco smoke 1 – 3 . As this group represents around 20% of all patients with lung cancer, the discovery of stratified medicine options for tobacco-associated NSCLC is a high priority. Umbrella trials seek to streamline the investigation of genotype-based treatments by screening tumours for multiple genomic alterations and triaging patients to one of several genotype-matched therapeutic agents. Here we report the current outcomes of 19 drug–biomarker cohorts from the ongoing National Lung Matrix Trial, the largest umbrella trial in NSCLC. We use next-generation sequencing to match patients to appropriate targeted therapies on the basis of their tumour genotype. The Bayesian trial design enables outcome data from open cohorts that are still recruiting to be reported alongside data from closed cohorts. Of the 5,467 patients that were screened, 2,007 were molecularly eligible for entry into the trial, and 302 entered the trial to receive genotype-matched therapy—including 14 that re-registered to the trial for a sequential trial drug. Despite pre-clinical data supporting the drug–biomarker combinations, current evidence shows that a limited number of combinations demonstrate clinically relevant benefits, which remain concentrated in patients with lung cancers that are associated with minimal exposure to tobacco smoke. Current outcomes are reported from the ongoing National Lung Matrix Trial, an umbrella trial for the treatment of non-small-cell lung cancer in which patients are triaged according to their tumour genotype and matched with targeted therapeutic agents.

BackgroundReceiving a cancer diagnosis can be overwhelming. Patients report difficulty understanding and remembering initial discussions around their diagnosis and treatment.The first interaction between an oncologist and patient is usually in the out-patient setting. Here, the oncology team discuss diagnosis, treatment options, side effects and prognosis. The team faces several challenges to ensure that patients understand their diagnosis and treatment, including determining how much their patients wish to know.This project aims to determine how well a sample of patients from The Beatson West of Scotland Cancer Centre (BWoSCC) understand their diagnosis and treatment.Methods31 patients participated in our two-part survey between May and July 2021 at BWoSCC. The two-part survey comprised: part 1 – determined patients’ understanding of their diagnosis; part 2 – determined patients’ understanding of their treatment plan.This was a convenience sample. Exclusions: treated under AWI, unable to engage due to acute illness, or approaching end of life. Clinical information was correlated with medical notes. Microsoft Excel was used for statistical analysis.ResultsDiagnosis: 29 patients had their diagnosis explained to them at their first oncology appointment. 17 patients remembered their diagnosis fully; 12 partially. 90% of patients’ descriptions of their diagnosis were factually accurate.Treatment: 23 patients felt they were adequately informed about treatment side effects, 5 felt partially informed and 3 felt uninformed. Shock at the news was the greatest barrier to remembering diagnosis and treatment.ConclusionOver 90% of patients reported the oncology team explaining their diagnosis and treatment and were able to remember their diagnosis and treatment correctly. Shock at hearing the news was reported as the greatest barrier to patients remembering their diagnosis and treatment. Patients were very happy with their interaction with the oncology team and how they were helped to understand their diagnosis and treatment.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The difference is that I now tell patients there is a risk that their potentially curable lung cancer may progress and become incurable while they are on our waiting list.

Apr 1, 2001 EDITOR-The et al in their paper describe the generation of false optimism about recovery and its ultimate cost to patients with small cell lung cancer and their relatives in terms of regrets and unfinished business. 1 The stories told in this study will be familiar to all those concerned with caring for patients with advanced cancer, whether in hospital or in the community. 2 Breaking the cycle of collusion is difficult, because, as The et al acknowledge, awareness cannot be forced on the patient: it can only be supported. The participation of specialist palliative care doctors or nurses in joint consultations with oncologists would give patients an opportunity to pause and assimilate the seriousness of the bad news, and a chance to come to terms with the reality of their situation.

Extended laboratory culture and antimicrobial susceptibility testing timelines hinder rapid species identification and susceptibility profiling of bacterial pathogens associated with bovine respiratory disease, the most prevalent cause of cattle mortality in the United States. Whole-genome sequencing offers a culture-independent alternative to current bacterial identification methods, but requires a library of bacterial reference genomes for comparison. To contribute new bacterial genome assemblies and evaluate genetic diversity and variation in antimicrobial resistance genotypes, whole-genome sequencing was performed on bovine respiratory disease–associated bacterial isolates (Histophilus somni, Mycoplasma bovis, Mannheimia haemolytica, and Pasteurella multocida) from dairy and beef cattle. One hundred genomically distinct assemblies were added to the NCBI database, doubling the available genomic sequences for these four species. Computer-based methods identified 11 predicted antimicrobial resistance genes in three species, with none being detected in M. bovis. While computer-based analysis can identify antibiotic resistance genes within whole-genome sequences (genotype), it may not predict the actual antimicrobial resistance observed in a living organism (phenotype). Antimicrobial susceptibility testing on 64 H. somni, M. haemolytica, and P. multocida isolates had an overall concordance rate between genotype and phenotypic resistance to the associated class of antimicrobials of 72.7% (P < 0.001), showing substantial discordance. Concordance rates varied greatly among different antimicrobial, antibiotic resistance gene, and bacterial species combinations. This suggests that antimicrobial susceptibility phenotypes are needed to complement genomically predicted antibiotic resistance gene genotypes to better understand how the presence of antibiotic resistance genes within a given bacterial species could potentially impact optimal bovine respiratory disease treatment and morbidity/mortality outcomes.