We are in the midst of the novel coronavirus disease 2019 (COVID-19) outbreak worldwide, but limited data are available regarding patients who undergo surgical treatment and either have positive results for COVID-19 or, soon after surgery, develop it.¹ Brescia is located in Lombardy, the region in Italy most affected by the current pandemic; it experienced its first official case on February 23, 2020.^2,3 Spedali Civili Hospital became 1 of 3 regional referral centers for major trauma, neurosurgery, and orthopedics, with a catchment area of more than 3 million people.⁴

The majority of prostate cancers harbor gene fusions of the 5′-untranslated region of the androgen-regulated transmembrane protease serine 2 (TMPRSS2) promoter with erythroblast transformation–specific transcription factor family members. The common fusion between TMPRESS2 and v-ets erythroblastosis virus E26 oncogene homolog (avian) (ERG) is associated with a more aggressive clinical phenotype, implying the existence of a distinct subclass of prostate cancer defined by this fusion. We used complementary DNA–mediated annealing, selection, ligation, and extension to determine the expression profiles of 6144 transcriptionally informative genes in archived biopsy samples from 455 prostate cancer patients in the Swedish Watchful Waiting cohort (1987–1999) and the United States–based Physicians^’ Health Study cohort (1983–2003). A gene expression signature for prostate cancers with the TMPRSS2–ERG fusion was determined using partitioning and classification models and used in computational functional analysis. Cell proliferation and TMPRSS2–ERG expression in androgen receptor–negative (NCI-H660) prostate cancer cells after treatment with vehicle or estrogenic compounds were assessed by viability assays and quantitative polymerase chain reaction, respectively. All statistical tests were two-sided. We identified an 87-gene expression signature that distinguishes TMPRSS2–ERG fusion prostate cancer as a discrete molecular entity (area under the curve = 0.80, 95% confidence interval [CI] = 0.792 to 0.81; P < .001). Computational analysis suggested that this fusion signature was associated with estrogen receptor (ER) signaling. Viability of NCI-H660 cells decreased after treatment with estrogen (viability normalized to day 0, estrogen vs vehicle at day 8, mean = 2.04 vs 3.40, difference = 1.36, 95% CI = 1.12 to 1.62) or ERβ agonist (ERβ agonist vs vehicle at day 8, mean = 1.86 vs 3.40, difference = 1.54, 95% CI = 1.39 to 1.69) but increased after ERα agonist treatment (ERα agonist vs vehicle at day 8, mean = 4.36 vs 3.40, difference = 0.96, 95% CI = 0.68 to 1.23). Similarly, expression of TMPRSS2–ERG decreased after ERβ agonist treatment (fold change over internal control, ERβ agonist vs vehicle at 24 hours, NCI-H660, mean = 0.57- vs 1.0-fold, difference = 0.43-fold, 95% CI = 0.29- to 0.57-fold) and increased after ERα agonist treatment (ERα agonist vs vehicle at 24 hours, mean = 5.63- vs 1.0-fold, difference = 4.63-fold, 95% CI = 4.34- to 4.92-fold). TMPRSS2–ERG fusion prostate cancer is a distinct molecular subclass. TMPRSS2–ERG expression is regulated by a novel ER-dependent mechanism.

Background: Current prostate cancer prognostic models are based on pre-treatment prostate specific antigen (PSA) levels, biopsy Gleason score, and clinical staging but in practice are inadequate to accurately predict disease progression. Hence, we sought to develop a molecular panel for prostate cancer progression by reasoning that molecular profiles might further improve current clinical models. Methods: We analyzed a Swedish Watchful Waiting cohort with up to 30 years of clinical follow up using a novel method for gene expression profiling. This cDNA-mediated annealing, selection, ligation, and extension (DASL) method enabled the use of formalin-fixed paraffin-embedded transurethral resection of prostate (TURP) samples taken at the time of the initial diagnosis. We determined the expression profiles of 6100 genes for 281 men divided in two extreme groups: men who died of prostate cancer and men who survived more than 10 years without metastases (lethals and indolents, respectively). Several statistical and machine learning models using clinical and molecular features were evaluated for their ability to distinguish lethal from indolent cases. Results: Surprisingly, none of the predictive models using molecular profiles significantly improved over models using clinical variables only. Additional computational analysis confirmed that molecular heterogeneity within both the lethal and indolent classes is widespread in prostate cancer as compared to other types of tumors. Conclusions: The determination of the molecularly dominant tumor nodule may be limited by sampling at time of initial diagnosis, may not be present at time of initial diagnosis, or may occur as the disease progresses making the development of molecular biomarkers for prostate cancer progression challenging.

Background: Molecular markers and the rich biological information they contain have great potential for cancer diagnosis, prognostication and therapy prediction. So far, however, they have not superseded routine histopathology and staging criteria, partly because the few studies performed on molecular subtyping have had little validation and limited clinical characterization. Methods: We obtained gene expression and clinical data for 412 breast cancers obtained from population-based cohorts of patients from Stockholm and Uppsala, Sweden. Using the intrinsic set of approximately 500 genes derived in the Norway/Stanford breast cancer data, we validated the existence of five molecular subtypes – basal-like, ERBB2, luminal A/B and normal-like – and characterized these subtypes extensively with the use of conventional clinical variables. Results: We found an overall 77.5% concordance between the centroid prediction of the Swedish cohort by using the Norway/Stanford signature and the k-means clustering performed internally within the Swedish cohort. The highest rate of discordant assignments occurred between the luminal A and luminal B subtypes and between the luminal B and ERBB2 subtypes. The subtypes varied significantly in terms of grade (p < 0.001), p53 mutation (p < 0.001) and genomic instability (p = 0.01), but surprisingly there was little difference in lymph-node metastasis (p = 0.31). Furthermore, current users of hormone-replacement therapy were strikingly over-represented in the normal-like subgroup (p < 0.001). Separate analyses of the patients who received endocrine therapy and those who did not receive any adjuvant therapy supported the previous hypothesis that the basal-like subtype responded to adjuvant treatment, whereas the ERBB2 and luminal B subtypes were poor responders. Conclusion: We found that the intrinsic molecular subtypes of breast cancer are broadly present in a diverse collection of patients from a population-based cohort in Sweden. The intrinsic gene set, originally selected to reveal stable tumor characteristics, was shown to have a strong correlation with progression-related properties such as grade, p53 mutation and genomic instability.

Untimely diagnosis of intracranial hypertension may lead to delays in therapy and worsening of outcome. Transcranial Doppler (TCD) detects variations in cerebral blood flow velocity which may correlate with intracranial pressure (ICP). We investigated if intracranial hypertension can be accurately excluded through use of TCD. This was a multicenter prospective pilot study in patients with acute brain injury requiring invasive ICP (ICPi) monitoring. ICP estimated with TCD (ICPtcd) was compared with ICPi in three separate time frames: immediately before ICPi placement, immediately after ICPi placement, and 3 hours following ICPi positioning. Sensitivity and specificity, and concordance correlation coefficient between ICPi and ICPtcd were calculated. Receiver operating curve (ROC) and the area under the curve (AUC) analyses were estimated after measurement averaging over time. A total of 38 patients were enrolled, and of these 12 (31.6%) had at least one episode of intracranial hypertension. One hundred fourteen paired measurements of ICPi and ICPtcd were gathered for analysis. With dichotomized ICPi (≤20 mmHg vs >20 mmHg), the sensitivity of ICPtcd was 100%; all measurements with high ICPi (>20 mmHg) also had a high ICPtcd values. Bland-Altman plot showed an overestimation of 6.2 mmHg (95% CI 5.08–7.30 mmHg) for ICPtcd compared to ICPi. AUC was 96.0% (95% CI 89.8–100%) and the estimated best threshold was at ICPi of 24.8 mmHg corresponding to a sensitivity 100% and a specificity of 91.2%. This study provides preliminary evidence that ICPtcd may accurately exclude intracranial hypertension in patients with acute brain injury. Future studies with adequate power are needed to confirm this result.