Predicting necrotising soft tissue infections in people who inject drugs: poor performance of the Laboratory Risk Indicator for Necrotising Fasciitis score and development of a novel clinical predictive nomogram in a retrospective cohort with internal validation.

INTRODUCTION: Necrotising soft tissue infections (NSTI) can threaten life and limb. Early identification and urgent surgical debridement are key for improved outcomes. NSTI can be insidious. Scoring systems, like the Laboratory Risk Indicator for Necrotising Fasciitis (LRINEC), exist to aid diagnosis. People who inject drugs (PWID) are high risk for NSTI. This study aimed to assess the utility of the LRINEC in PWID with lower limb infections and develop a predictive nomogram. METHODS: A retrospective database of all hospital admissions due to limb-related complications secondary to injecting drug use between December 2011 and December 2020 was compiled through discharge codes and a prospectively maintained Vascular Surgery database. All lower limb infections were extracted from this database, dichotomised by NSTI and non-NSTI with the LRINEC applied. Specialty management times were evaluated. Statistical analyses involved: chi-square; Analysis of "variance"; Kaplan-Meier, and receiver operating characteristic curves. Nomograms were developed to facilitate diagnosis and predict survival. RESULTS: There were 557 admissions for 378 patients, with 124 (22.3%; 111 patients) NSTI. Time from admission to: theatre and computed tomography imaging respectively varied significantly between specialties ( P =0.001). Surgical specialties were faster than medical ( P =0.001). Vascular surgery received the most admissions and had the quickest time to theatre. During follow-up there were 79 (20.9%) deaths: 27 (24.3%) NSTI and 52 (19.5%) non-NSTI. LRINEC ≥6 had a positive predictive value of 33.3% and sensitivity of 74% for NSTI. LRINEC <6 had a negative predictive value of 90.7% and specificity of 63.2% for non-NSTI. Area under the curve was 0.697 (95% CI: 0.615-0.778). Nomogram models found age, C-reactive protein, and non-linear albumin to be significant predictors of NSTI, with age, white cell count, sodium, creatinine, C-reactive protein, and albumin being significant in predicting survival on discharge. CONCLUSION: There was reduced performance of the LRINEC in this PWID cohort. Diagnosis may be enhanced through use of this predictive nomogram.

Profiling lung adenocarcinoma by liquid biopsy: can one size fit all?

BACKGROUND: Cancer is first and foremost a disease of the genome. Specific genetic signatures within a tumour are prognostic of disease outcome, reflect subclonal architecture and intratumour heterogeneity, inform treatment choices and predict the emergence of resistance to targeted therapies. Minimally invasive liquid biopsies can give temporal resolution to a tumour's genetic profile and allow the monitoring of treatment response through levels of circulating tumour DNA (ctDNA). However, the detection of ctDNA in repeated liquid biopsies is currently limited by economic and time constraints associated with targeted sequencing. METHODS: Here we bioinformatically profile the mutational and copy number spectrum of The Cancer Genome Network's lung adenocarcinoma dataset to uncover recurrently mutated genomic loci. RESULTS: We build a panel of 400 hotspot mutations and show that the coverage extends to more than 80% of the dataset at a median depth of 8 mutations per patient. Additionally, we uncover several novel single-nucleotide variants present in more than 5% of patients, often in genes not commonly associated with lung adenocarcinoma. CONCLUSION: With further optimisation, this hotspot panel could allow molecular diagnostics laboratories to build curated primer banks for 'off-the-shelf' monitoring of ctDNA by droplet-based digital PCR or similar techniques, in a time- and cost-effective manner.

BMP2/BMPR1A is linked to tumour progression in dedifferentiated liposarcomas.

Bone Morphogenic Protein 2 (BMP2) is a multipurpose cytokine, important in the development of bone and cartilage, and with a role in tumour initiation and progression. BMP2 signal transduction is dependent on two distinct classes of serine/threonine kinase known as the type I and type II receptors. Although the type I receptors (BMPR1A and BMPR1B) are largely thought to have overlapping functions, we find tissue and cellular compartment specific patterns of expression, suggesting potential for distinct BMP2 signalling outcomes dependent on tissue type. Herein, we utilise large publicly available datasets from The Cancer Genome Atlas (TCGA) and Protein Atlas to define a novel role for BMP2 in the progression of dedifferentiated liposarcomas. Using disease free survival as our primary endpoint, we find that BMP2 confers poor prognosis only within the context of high BMPR1A expression. Through further annotation of the TCGA sarcoma dataset, we localise this effect to dedifferentiated liposarcomas but find overall BMP2/BMP receptor expression is equal across subsets. Finally, through gene set enrichment analysis we link the BMP2/BMPR1A axis to increased transcriptional activity of the matrisome and general extracellular matrix remodelling. Our study highlights the importance of continued research into the tumorigenic properties of BMP2 and the potential disadvantages of recombinant human BMP2 (rhBMP2) use in orthopaedic surgery. For the first time, we identify high BMP2 expression within the context of high BMPR1A expression as a biomarker of disease relapse in dedifferentiated liposarcomas.