IL-36 expression is increased in NSCLC with IL-36 stimulation of lung cancer cells promoting a pro-tumorigenic phenotype

The IL-36 cytokines are a recently described subset of the IL-1 family of cytokines, and have been shown to play a role in the pathogenesis of respiratory diseases such as asthma and COPD. Given the common aetiological links between COPD and lung cancer development, as well as the involvement of other IL-1 family members in lung tumorigenesis, the aim of this work was to investigate the role of IL-36 cytokines in the pathogenesis of lung cancer. In this study we demonstrate that expression of IL-36 cytokines and receptor mRNA and protein are significantly increased in lung cancer tissue compared to adjacent non-tumour tissue. In vitro assays showed that stimulation of two lung cancer cell lines, SKMES-1 human squamous cell and LLC murine lung cancer, with IL-36R agonists resulted in increased cellular migration and proliferation. All IL-36 cytokines induced the expression of pro-inflammatory chemokines in both lung cancer cell lines with synergistic effects identified upon co-stimulation of cells with IL-17, IL-22 and TNFα. Furthermore, we report that IL-36 cytokines induce protein expression of the immune checkpoint inhibitor protein PD-L1 on lung cancer cells. Taken together, this data indicates that targeting IL-36R signalling may be a useful targeted therapy for lung cancer patients with IL-36R⁺ cancer cells.

OS08.6.A Glioblastoma treatment response machine learning monitoring biomarkers: a systematic review and meta-analysis

BACKGROUND

The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed through machine learning (ML).

MATERIAL AND METHODS

PRISMA methodology was followed. Articles published 09/2018-01/2021 (since previous reviews) were searched for using MEDLINE, EMBASE, and the Cochrane Register by two reviewers independently. Included study participants were adult patients with high grade glioma who had undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide) and subsequently underwent follow-up imaging to determine treatment response status (specifically, distinguishing progression/recurrence from progression/recurrence mimics - the target condition). Risk of bias and applicability was assessed with QUADAS 2. A third reviewer arbitrated any discrepancy. Contingency tables were created for hold-out test sets and recall, specificity, precision, F1-score, balanced accuracy calculated. A meta-analysis was performed using a bivariate model for recall, false positive rate and area-under the receiver operator characteristic curve (AUC).

RESULTS

Eighteen studies were included with 1335 patients in training sets and 384 in test sets. To determine whether there was progression or a mimic, the reference standard combination of follow-up imaging and histopathology at re-operation was applied in 67% (13/18) of studies. The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data. Ten studies (10/18, 56%) had internal or external hold-out test set data that could be included in a meta-analysis of monitoring biomarker studies. The pooled sensitivity was 0.77 (0.65–0.86). The pooled false positive rate (1-specificity) was 0.35 (0.25–0.47). The summary point estimate for the AUC was 0.77.

CONCLUSION

There is likely good diagnostic performance of machine learning models that use MRI features to distinguish between progression and mimics. The diagnostic performance of ML using implicit features did not appear to be superior to ML using explicit features. There are a range of ML-based solutions poised to become treatment response monitoring biomarkers for glioblastoma. To achieve this, the development and validation of ML models require large, well-annotated datasets where the potential for confounding in the study design has been carefully considered. Therefore, multidisciplinary efforts and multicentre collaborations are necessary.

Change in infection control practices and awareness of hospital medical staff in the aftermath of SARS

Severe Acute Respiratory Syndrome (SARS) epidemic illustrated the crucial role of infection surveillance and control measures in the combat of any highly transmissible disease. We conducted an interview survey of 121 medical staff 145 doctors, 46 staff nurses and 30 medical assistants) in a state hospital in Malaysia three months after the end of SARS epidemic (from October to December 2003). Staff was grouped according to those directly involved in the care of suspected SARS patients [S+ group n=41] and those who were not [S- group; n=80]. On hand washing following sneezing, coughing and touching patients, the proportions of medical staff that reported an increase after the SARS crisis were 22.3%, 16.5% and 45.5% respectively. On wearing masks, gloves, and aprons when meeting potentially infectious patients, the proportions that reported an increase were 39.7%, 47.1% and 32.2% respectively. Significantly more staff in S+ than S- group reported these increases. Sixty percent of staff was aware of changes in hospital infection control policies after SARS; 93.4% was aware of notifying procedures, and 81.8% was aware of whom to notify in the hospital. Regarding infection isolation ward, Infectious Control Nurse and Infection Control Committee Chairman in the hospital, the proportions of staff that could correctly name them were 39.7%, 38.3% and 15.7% respectively. Significantly more in S+ than S- group could do so. However, more than half the staff claimed ignorance on the knowledge of infection isolation ward (56.2%), Infection Control Nurse (57.9%) and Chairman (65.3%). Our findings demonstrated that SARS crisis had some positive impact on the infection control practices and awareness of medical staff especially on those with direct SARS involvement. Implications for future control of infectious diseases are obvious.