Real-world prevalence of homologous recombination repair gene (BRCA1/2 and ATM) mutations (HRRm) in patients (pts) with advanced prostate cancer (aPC) as detected by comprehensive genomic profiling (CGP) of circulating cell-free DNA (cfDNA).
256 Background: PARP inhibitors, olaparib and rucaparib recently improved survival outcomes (in the Profound and Triton-2 trials) in pts with aPC harboring HRRm. Notably, ~35% of pts screened for the PROfound trial did not meet eligibility solely due to the lack of adequate quality/quantity of tumor tissue required for CGP (De Bono, NEJM, 2020). cfDNA analysis non-invasively assesses tumor-derived genomic alterations. We evaluated the prevalence of HRRm in a real-world aPC population, who had commercially available cfDNA assay results available. Methods: cfDNA based CGP (using a clinically-validated 73- to 74-gene panel i.e. Guardant360 or G360) from consecutive aPC pts between 11/2016–8/2020 were used for detection of HRRm. Frameshift and nonsense mutations were included as pathogenic. Variants of unknown significance were excluded. In this unmatched study, the prevalence of each HRRm was compared with those reported in literature using the chi square test. Results: cfDNA CGP from 7701 aPC pts were available for interrogation of BRCA1/BRCA2 mutations, & from 4671 aPC pts for ATM mutations. Prevalence of BRCA1 and 2 as detected by cfDNA was similar to historical CGP of primary tissue. Prevalence of BRCA1 was higher than historical CGP of metastatic tissue. Prevalence of ATM in cfDNA was higher than historic tumor tissue CGP but similar to prior reports from cfDNA testing (Table). Conclusions: In this large real-world population of pts with aPC undergoing routine cfDNA CGP by a CLIA certified lab, prevalence of HRRm was similar (or higher for BRCA1 and ATM) to what has been previously reported from the tumor tissue. These data provide the rationale for utilizing cfDNA CGP as a routine test for detection of HRRm in men with aPC to identify men who are candidates for PARPi. [Table: see text]
Abstract PO-023: Differentiation of tumefactive multiple sclerosis and glioblastoma using radiomics features extracted from magnetic resonance imaging and machine learning
Abstract Introduction Tumefactive multiple sclerosis (MS) and glioblastoma (GBM) can be difficult to differentiate. Clinical presentations are nonspecific and may occur in patients with both diseases. Magnetic resonance imaging (MRI) scans are routinely used to generate an initial differential diagnosis. Although imaging features can help distinguish these entities, there are no pathognomonic signs, neither on traditional nor advanced MRI. Moreover, the ability to differentiate these entities depends on the radiologists’ experience. Ensuring the correct diagnosis has important therapeutic and prognostic implications, as the management of these diseases is very different. To date, a brain biopsy often represents the only option to obtain a diagnosis, but it may be inconclusive and could add unnecessary morbidity. We hypothesize that a machine-learning tool based on routinely performed MRI scans could guarantee a more reliable, fast, and reproducible diagnosis. Methods Only patients with a histological diagnosis were included in the study. Patients that underwent any form of radiation therapy before the surgery were excluded to avoid confounding factors due to treatment-related effects. A total of 99 MS and 257 GBMs were included, and preoperative T2-weighted images were collected. For each patient, the lesions' segmentation was performed, including both the contrast-enhancing and non-enhancing areas of the lesions. The segmentation masks were used to extract radiomic features from the T2-weighted sequences. All MRI images were standardized to the same mean and standard deviation before feature extraction and resampled to a voxel size of 1.0 × 1.0 × 1.0 mm. Features extraction was performed using Pyradiomics (v. 3.0.1) and included first order, shape, and texture features, totaling 100 features. The classifier consisted of a support vector machine (SVM) model. Patients were randomly assigned to the training and test sets with an 80-20% proportion for each class. To select the best hyperparameters for the SVM classifier, a grid-search analysis was performed on the training set using a nested cross-validation scheme (k=5). All experiments were performed using Python (v. 3.5) and the Scikit-learn library (v. 0.23.1). Results The mean accuracy and area under the ROC curve for the best set of hyperparameters on the validation set were 0.83 and 0.86, respectively. The weighted-average precision, recall, and F1 scores on the test set were 0.78, 0.76, and 0.77, respectively. Conclusions Radiomic features extracted from a routinely acquired MRI sequence showed promising results in differentiating tumefactive multiple sclerosis from glioblastoma, opening the possibility to obtain a reliable diagnosis without the need for a brain biopsy. Future experiments Future experiments will focus on using post-contrast T1-weighted images for the radiomic features extractions, testing multiple machine learning models and features selection algorithms, and including more diagnostic classes (i.e., metastasis, lymphomas). Citation Format: Gian Marco Conte, Jeanette E. Eckel-Passow, W. Oliver Tobin, Paul Decker, Daniel H. Lachance, Robert B. Jenkins, Bradley J. Erickson. Differentiation of tumefactive multiple sclerosis and glioblastoma using radiomics features extracted from magnetic resonance imaging and machine learning [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-023.
All You Need to Know and More about the Diagnosis and Management of Rare Mold Infections
Invasive mold infections caused by molds other than Aspergillus spp. or Mucorales are emerging. The reported prevalences of infection due to these rare fungal pathogens vary among geographic regions, driven by differences in climatic conditions, susceptible hosts, and diagnostic capabilities.
The state of neuro-oncology during the COVID-19 pandemic: a worldwide assessment
AbstractBackgroundIt remains unknown how the COVID-19 pandemic has changed neuro-oncology clinical practice, training, and research efforts.MethodsWe performed an international survey of practitioners, scientists, and trainees from 21 neuro-oncology organizations across 6 continents, April 24–May 17, 2020. We assessed clinical practice and research environments, institutional preparedness and support, and perceived impact on patients.ResultsOf 582 respondents, 258 (45%) were US-based and 314 (55%) international. Ninety-four percent of participants reported changes in their clinical practice. Ninety-five percent of respondents converted at least some practice to telemedicine. Ten percent of practitioners felt the need to see patients in person, specifically because of billing concerns and pressure from their institutions. Sixty-seven percent of practitioners suspended enrollment for at least one clinical trial, including 62% suspending phase III trial enrollments. More than 50% believed neuro-oncology patients were at increased risk for COVID-19. Seventy-one percent of clinicians feared for their own personal safety or that of their families, specifically because of their clinical duties; 20% had inadequate personal protective equipment. While 69% reported increased stress, 44% received no psychosocial support from their institutions. Thirty-seven percent had salary reductions and 63% of researchers temporarily closed their laboratories. However, the pandemic created positive changes in perceived patient satisfaction, communication quality, and technology use to deliver care and mediate interactions with other practitioners.ConclusionsThe pandemic has changed treatment schedules and limited investigational treatment options. Institutional lack of support created clinician and researcher anxiety. Communication with patients was satisfactory. We make recommendations to guide clinical and scientific infrastructure moving forward and address the personal challenges of providers and researchers.
Systematic review on the use of patient-reported outcome measures in brain tumor studies: part of the Response Assessment in Neuro-Oncology Patient-Reported Outcome (RANO-PRO) initiative
Abstract Background The Response Assessment in Neuro-Oncology Patient-Reported Outcome (RANO-PRO) working group aims to provide guidance on the use of PROs in brain tumor patients. PRO measures should be of high quality, both in terms of relevance and other measurement properties. This systematic review aimed to identify PRO measures that have been used in brain tumor studies to date. Methods A systematic literature search for articles published up to June 25, 2020 was conducted in several electronic databases. Pre-specified inclusion criteria were used to identify studies using PRO measures assessing symptoms, (instrumental) activities of daily living [(I)ADL] or health-related quality of life (HRQoL) in adult patients with glioma, meningioma, primary central nervous system lymphoma, or brain metastasis. Results A total of 215 different PRO measures were identified in 571 published and 194 unpublished studies. The identified PRO measures include brain tumor-specific, cancer-specific, and generic instruments, as well as instruments designed for other indications or multi- or single-item study-specific questionnaires. The most frequently used instruments were the EORTC QLQ-C30 and QLQ-BN20 (n = 286 and n = 247), and the FACT-Br (n = 167), however, the majority of the instruments were used only once or twice (150/215). Conclusion Many different PRO measures assessing symptoms, (I)ADL or HRQoL have been used in brain tumor studies to date. Future research should clarify whether these instruments or their scales/items exhibit good content validity and other measurement properties for use in brain tumor patients.
Comparison of Clinical and Radiographic Outcomes Following Arthroscopic Debridement With Extracellular Matrix Augmentation and Osteochondral Autograft Transplantation for Medium-Size Osteochondral Lesions of the Talus
Background: Historically, microfracture has been used to treat small talar osteochondral lesions with good results, whereas osteochondral autologous transplantation (OAT) has proven effective for the treatment of larger lesions. It is not clear which method is more effective for medium-sized lesions around the critical size of 150 mm 2 , above which microfracture outcomes tend to be poor. The purpose of this study was to determine the potential advantages of OAT augmented with a combination of extracellular matrix and bone marrow aspirate concentrate (ECM-BMAC) compared to debridement with ECM-BMAC (DEB) in the treatment of medium-sized osteochondral lesions of the talus (OLTs). Methods: Clinical and radiographic data were collected retrospectively for patients treated by a single fellowship-trained foot and ankle surgeon. Magnetic resonance images (MRIs) were scored using the Magnetic Resonance Observation of Cartilage Tissue (MOCART) system and were evaluated for the presence of cysts and edema. Fifty-two patients met inclusion criteria, with 25 who received an OAT procedure. Age, body mass index, lesion size, lesion location, and follow-up time were similar between groups. Average MRI follow-up times were 16.7 months for the OAT group and 20.3 months for the DEB group ( P = .38). Results: Patients treated with OAT had significantly higher average total MOCART scores (69 vs 55, P = .04) and significantly lower rates of cyst (14% vs 55%, P < .01), edema (59% vs 90%, P = .04), revision surgery (0% vs 19%, P = .05), and therapeutic injection for pain (4% vs 30%, P = .02) compared to patients treated with DEB. No significant differences were detected in patient-reported outcome scores between groups. Conclusion: The native hyaline cartilage introduced by OAT appears to result in higher-quality repair tissue when compared to DEB, as evidenced by OAT patients’ higher MOCART scores and lower rates of cyst and edema. There was no difference in clinical outcome scores, though OAT patients did not require revision surgery or therapeutic injection for pain as frequently as DEB patients. Level of Evidence: Level III, retrospective comparative study.
Smoking Modifies Pancreatic Cancer Risk Loci on 2q21.3
Abstract Germline variation and smoking are independently associated with pancreatic ductal adenocarcinoma (PDAC). We conducted genome-wide smoking interaction analysis of PDAC using genotype data from four previous genome-wide association studies in individuals of European ancestry (7,937 cases and 11,774 controls). Examination of expression quantitative trait loci data from the Genotype-Tissue Expression Project followed by colocalization analysis was conducted to determine whether there was support for common SNP(s) underlying the observed associations. Statistical tests were two sided and P < 5 × 10–8 was considered statistically significant. Genome-wide significant evidence of qualitative interaction was identified on chr2q21.3 in intron 5 of the transmembrane protein 163 (TMEM163) and upstream of the cyclin T2 (CCNT2). The most significant SNP using the Empirical Bayes method, in this region that included 45 significantly associated SNPs, was rs1818613 [per allele OR in never smokers 0.87, 95% confidence interval (CI), 0.82–0.93; former smokers 1.00, 95% CI, 0.91–1.07; current smokers 1.25, 95% CI 1.12–1.40, Pinteraction = 3.08 × 10–9). Examination of the Genotype-Tissue Expression Project data demonstrated an expression quantitative trait locus in this region for TMEM163 and CCNT2 in several tissue types. Colocalization analysis supported a shared SNP, rs842357, in high linkage disequilibrium with rs1818613 (r2 = 0. 94) driving both the observed interaction and the expression quantitative trait loci signals. Future studies are needed to confirm and understand the differential biologic mechanisms by smoking status that contribute to our PDAC findings. Significance: This large genome-wide interaction study identifies a susceptibility locus on 2q21.3 that significantly modified PDAC risk by smoking status, providing insight into smoking-associated PDAC, with implications for prevention.