Low T-cell Receptor Diversity, High Somatic Mutation Burden, and High Neoantigen Load as Predictors of Clinical Outcome in Muscle-invasive Bladder Cancer

AbstractBackgroundThe success of cancer immunotherapies has highlighted the potent ability of local adaptive immune responses to eradicate cancer cells by targeting neoantigens generated by somatic alterations. However, how these factors interact to drive the natural history of muscle-invasive bladder cancer (MIBC) is not well understood.ObjectiveTo investigate the role of immune regulation in MIBC disease progression, we performed massively parallel T-cell receptor (TCR) sequencing of tumor-infiltrating T cells (TILs), in silico neoantigen prediction from exome sequences, and expression analysis of immune-related genes.Design, setting, and participantsWe analyzed 38 MIBC tissues from patients who underwent definitive surgery with a minimum clinical follow-up of 2 yr.Outcome measurements and statistical analysisRecurrence-free survival (RFS) was determined. TCR diversity was quantified using Simpson's diversity index. The main analyses involved the Mann-Whitney U test, Kaplan-Meier survival analysis, and Cox proportional hazards models.Results and limitationsLow TCRβ chain diversity, correlating with oligoclonal TIL expansion, was significantly correlated with longer RFS, even after adjustment for pathologic tumor stage, node status, and receipt of adjuvant chemotherapy (hazard ratio 2.67, 95% confidence interval 1.08–6.60; p=0.03). Patients with both a high number of neoantigens and low TCRβ diversity had longer RFS compared to those with fewer neoantigens and high TCR diversity (median RFS 275 vs 30 wk; p=0.03). Higher expression of immune cytolytic genes was associated with nonrecurrence among patients with low TCR diversity or fewer neoantigens. Limitations include the sample size and the inability to distinguish CD8+ and CD4+ T cells using TCR sequencing.ConclusionsThese findings are the first to show that detailed tumor immune-genome analysis at definitive surgery can identify molecular patterns of antitumor immune response contributing to better clinical outcomes in MIBC.Patient summaryWe discovered that clonal expansion of certain T cells in tumor tissue, possibly targeting cancer-specific antigens, contributes to prevention of bladder cancer recurrence

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

Mechanisms of acquired resistance to TRK inhibitors

3104 Background: First-generation TRK tyrosine kinase inhibitors (TKIs) are approved in a tumor-agnostic fashion in more than 40 countries for patients with NTRK fusion-positive adult and pediatric cancers. While resistance to these agents has previously been described, the exact frequency with which major mechanisms of resistance emerges is not clearly understood. Methods: Patients with an NTRK-fusion-positive tumor who received a first-generation TRK TKI were eligible. We retrospectively identified those patients that had post-progression tumor tissue analyzed by next-generation sequencing (NGS). The pattern of serial resistance to a second-generation TKI was analyzed when available. Results: Eighteen patients were identified. The median age was 46 years (range 2-67). Nine unique fusions were detected in ten different tumor types. NTRK1, NTRK2, and NTRK3 fusions were found in eight (44%), one (6%), and nine (50%) patients, respectively. Thirteen patients (72%) were treated with larotrectinib and five patients (28%) received entrectinib. NGS (MSK-IMPACT n = 17, Foundation One n = 1) carried out on post-progression tissue revealed the following profile of acquired resistance: on-target resistance (83%, n = 15/18), off-target resistance (11%, n = 2/18), and no identifiable mechanism (6%, n = 1/18). Among patients with on-target resistance, the most common mutation involved the solvent front (87%, n = 13/15: n = 7 NTRK3 G623R, n = 4 NTRK1 G595R, n = 1 NTRK2 G639L, n = 1 NTRK3 G623E) followed by the gatekeeper region (13%, n = 2/15: n = 1 NTRK1 F589L, n = 1 NTRK3 F617I). Two patients developed off-target alterations. One acquired BRAF V600E mutation and the other MET amplification. Interestingly, solvent front mutation loss was observed in two patients who transitioned to and progressed on a second-generation TRK TKI. One patient with a baseline NTRK1 G595R mutation developed polyclonal resistance with acquisition of KRAS G12A and NTRK1 G667A alterations as well as NTRK1 G595R loss. The other patient with NTRK3 G623R developed an NTRK3 F617I gatekeeper mutation with NTRK3 G623R loss. Conclusions: In NTRK fusion-positive cancers, on-target resistance preferentially involving the solvent front is more frequent than off-target resistance to first-generation TKI therapy. Furthermore, the sequential use of second-generation therapy appears to alter the evolutionary kinetics of mutation retention and acquisition

Lorlatinib Tolerability and Association With Clinical Outcomes in Patients With Advanced ALK- or ROS1-Rearranged NSCLC: A Brief Report

Introduction: Treatment with lorlatinib for patients with advanced ALK- and ROS1-rearranged NSCLC (ALK+ and ROS1+ NSCLC) is associated with a unique set of adverse events (AEs) often requiring dose reduction. However, the impact of dose reductions on outcomes remains unclear and is mainly limited to analyses from prospective studies of lorlatinib in the first-line setting. Methods: We reviewed the course of 144 patients with advanced ALK- or ROS1-rearranged NSCLC treated with lorlatinib in the second-line or later setting to assess the frequency of dose reductions resulting from treatment-related AEs (TRAEs) and the association between dose reductions and progression-free survival (PFS) and overall survival (OS). Results: A total of 58 patients (40%) had TRAE-related dose reductions, most (59%) owing to neurocognitive AEs or neuropathy. Among all patients, the median PFS was 8.1 months (95% confidence interval [CI]: 6.4–11.8); the median OS was 20.7 months (95% CI: 16.3–30.5). Among patients who were started on lorlatinib 100 mg/d (n = 122), a Cox regression model with the occurrence of a dose reduction as a time-dependent covariate indicated no association between dose reduction and PFS (hazard ratio = 0.86, 95% CI: 0.54–1.39) or OS (hazard ratio = 0.78, 95% CI: 0.47–1.30). Conclusions: Lorlatinib dose reductions were not associated with inferior clinical outcomes in this multicenter analysis. Prompt identification of lorlatinib TRAEs and implementation of dose reductions may help maximize tolerability without compromising outcomes

Response to Immune Checkpoint Inhibition as Monotherapy or in Combination With Chemotherapy in Metastatic ROS1-Rearranged Lung Cancers

Introduction: ROS1 fusions are oncogenic drivers in 1% to 3% of NSCLCs. The activity of immune checkpoint inhibitor (ICI) monotherapy or in combination with chemotherapy (chemotherapy with ICI [chemo-ICI]) in these tumors and their immunophenotype have not been systematically described. Methods: In this multi-institutional retrospective study, tumor programmed death-ligand 1 (PD-L1) expression and tumor mutational burden (TMB) were evaluated in patients with ROS1-rearranged NSCLC. Time-to-treatment discontinuation (TTD) and objective response rate (ORR) (Response Evaluation Criteria in Solid Tumors [RECIST] version 1.1) were calculated for patients treated with ICI or chemo-ICI in the metastatic setting. Results: A total of 184 patients were identified. Among 146 assessable cases, PD-L1 expression was less than 1% in 60 (41%), 1% to 49% in 35 (24%), and greater than or equal to 50% in 51 tumors (35%). Of 100 (92%) TMB-assessable tumors, 92 had less than 10 mutations per megabase. TMB was significantly lower for ROS1-rearranged tumors (n = 97) compared with tumors with EGFR (n = 1250) or KRAS alterations (n = 1653) and all other NSCLC tumors (n = 2753) evaluated with Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (median TMB = 2.6 versus 3.5, 7.0, and 6.1 mutations per megabase, p < 0.001). Among patients treated with ICI, median TTD was 2.1 months (95% confidence interval [CI]: 1.0–4.2 mo; n = 28) and ORR 13% (2 of 16 RECIST-assessable; 95% CI: 2%–38%). Among patients treated with chemo-ICI, median TTD was 10 months (95% CI: 4.7–14.1 mo; n = 11) and ORR 83% (5 of 6 RECIST-assessable; 95% CI: 36%–100%). There was no difference in PD-L1 expression (p = 0.91) or TMB (p = 0.83) between responders and nonresponders. Conclusions: Most ROS1-rearranged NSCLCs have low PD-L1 expression and TMB. The activity of ICI in these tumors is modest. In contrast, chemo-ICI can achieve meaningful activity

Whole-genome sequencing reveals host factors underlying critical COVID-19

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Cognitive and psychiatric symptom trajectories 2–3 years after hospital admission for COVID-19: a longitudinal, prospective cohort study in the UK

Background: COVID-19 is known to be associated with increased risks of cognitive and psychiatric outcomes after the acute phase of disease. We aimed to assess whether these symptoms can emerge or persist more than 1 year after hospitalisation for COVID-19, to identify which early aspects of COVID-19 illness predict longer-term symptoms, and to establish how these symptoms relate to occupational functioning. Methods: The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a prospective, longitudinal cohort study of adults (aged ≥18 years) who were hospitalised with a clinical diagnosis of COVID-19 at participating National Health Service hospitals across the UK. In the C-Fog study, a subset of PHOSP-COVID participants who consented to be recontacted for other research were invited to complete a computerised cognitive assessment and clinical scales between 2 years and 3 years after hospital admission. Participants completed eight cognitive tasks, covering eight cognitive domains, from the Cognitron battery, in addition to the 9-item Patient Health Questionnaire for depression, the Generalised Anxiety Disorder 7-item scale, the Functional Assessment of Chronic Illness Therapy Fatigue Scale, and the 20-item Cognitive Change Index (CCI-20) questionnaire to assess subjective cognitive decline. We evaluated how the absolute risks of symptoms evolved between follow-ups at 6 months, 12 months, and 2–3 years, and whether symptoms at 2–3 years were predicted by earlier aspects of COVID-19 illness. Participants completed an occupation change questionnaire to establish whether their occupation or working status had changed and, if so, why. We assessed which symptoms at 2–3 years were associated with occupation change. People with lived experience were involved in the study. Findings: 2469 PHOSP-COVID participants were invited to participate in the C-Fog study, and 475 participants (191 [40·2%] females and 284 [59·8%] males; mean age 58·26 [SD 11·13] years) who were discharged from one of 83 hospitals provided data at the 2–3-year follow-up. Participants had worse cognitive scores than would be expected on the basis of their sociodemographic characteristics across all cognitive domains tested (average score 0·71 SD below the mean [IQR 0·16–1·04]; p<0·0001). Most participants reported at least mild depression (263 [74·5%] of 353), anxiety (189 [53·5%] of 353), fatigue (220 [62·3%] of 353), or subjective cognitive decline (184 [52·1%] of 353), and more than a fifth reported severe depression (79 [22·4%] of 353), fatigue (87 [24·6%] of 353), or subjective cognitive decline (88 [24·9%] of 353). Depression, anxiety, and fatigue were worse at 2–3 years than at 6 months or 12 months, with evidence of both worsening of existing symptoms and emergence of new symptoms. Symptoms at 2–3 years were not predicted by the severity of acute COVID-19 illness, but were strongly predicted by the degree of recovery at 6 months (explaining 35·0–48·8% of the variance in anxiety, depression, fatigue, and subjective cognitive decline); by a biocognitive profile linking acutely raised D-dimer relative to C-reactive protein with subjective cognitive deficits at 6 months (explaining 7·0–17·2% of the variance in anxiety, depression, fatigue, and subjective cognitive decline); and by anxiety, depression, fatigue, and subjective cognitive deficit at 6 months. Objective cognitive deficits at 2–3 years were not predicted by any of the factors tested, except for cognitive deficits at 6 months, explaining 10·6% of their variance. 95 of 353 participants (26·9% [95% CI 22·6–31·8]) reported occupational change, with poor health being the most common reason for this change. Occupation change was strongly and specifically associated with objective cognitive deficits (odds ratio [OR] 1·51 [95% CI 1·04–2·22] for every SD decrease in overall cognitive score) and subjective cognitive decline (OR 1·54 [1·21–1·98] for every point increase in CCI-20). Interpretation: Psychiatric and cognitive symptoms appear to increase over the first 2–3 years post-hospitalisation due to both worsening of symptoms already present at 6 months and emergence of new symptoms. New symptoms occur mostly in people with other symptoms already present at 6 months. Early identification and management of symptoms might therefore be an effective strategy to prevent later onset of a complex syndrome. Occupation change is common and associated mainly with objective and subjective cognitive deficits. Interventions to promote cognitive recovery or to prevent cognitive decline are therefore needed to limit the functional and economic impacts of COVID-19. Funding: National Institute for Health and Care Research Oxford Health Biomedical Research Centre, Wolfson Foundation, MQ Mental Health Research, MRC-UK Research and Innovation, and National Institute for Health and Care Research.</p