Reproducible Radiomics Features from Multi-MRI-Scanner Test-Retest-Study: Influence on Performance and Generalizability of Models.

BACKGROUND: Radiomics models trained on data from one center typically show a decline of performance when applied to data from external centers, hindering their introduction into large-scale clinical practice. Current expert recommendations suggest to use only reproducible radiomics features isolated by multiscanner test-retest experiments, which might help to overcome the problem of limited generalizability to external data. PURPOSE: To evaluate the influence of using only a subset of robust radiomics features, defined in a prior in vivo multi-MRI-scanner test-retest-study, on the performance and generalizability of radiomics models. STUDY TYPE: Retrospective. POPULATION: Patients with monoclonal plasma cell disorders. Training set (117 MRIs from center 1); internal test set (42 MRIs from center 1); external test set (143 MRIs from center 2-8). FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T; T1-weighted turbo spin echo. ASSESSMENT: The task for the radiomics models was to predict plasma cell infiltration, determined by bone marrow biopsy, noninvasively from MRI. Radiomics machine learning models, including linear regressor, support vector regressor (SVR), and random forest regressor (RFR), were trained on data from center 1, using either all radiomics features, or using only reproducible radiomics features. Models were tested on an internal (center 1) and a multicentric external data set (center 2-8). STATISTICAL TESTS: Pearson correlation coefficient r and mean absolute error (MAE) between predicted and actual plasma cell infiltration. Fisher's z-transformation, Wilcoxon signed-rank test, Wilcoxon rank-sum test; significance level P < 0.05. RESULTS: When using only reproducible features compared with all features, the performance of the SVR on the external test set significantly improved (r = 0.43 vs. r = 0.18 and MAE = 22.6 vs. MAE = 28.2). For the RFR, the performance on the external test set deteriorated when using only reproducible instead of all radiomics features (r = 0.33 vs. r = 0.44, P = 0.29 and MAE = 21.9 vs. MAE = 20.5, P = 0.10). CONCLUSION: Using only reproducible radiomics features improves the external performance of some, but not all machine learning models, and did not automatically lead to an improvement of the external performance of the overall best radiomics model. TECHNICAL EFFICACY: Stage 2.

Frequency and Prognostic Value of Circulating Tumor Cells in Cancer of Unknown Primary.

BACKGROUND: Cancer of unknown primary (CUP) is defined as a primary metastatic malignancy, in which the primary tumor remains elusive in spite of a comprehensive diagnostic workup. The frequency and prognostic value of circulating tumor cells (CTCs), which are considered to be the source of metastasis, has not yet been systematically evaluated in CUP. METHODS: A total of 110 patients with a confirmed diagnosis of CUP according to the European Society for Medical Oncology (ESMO) guidelines, who presented to our clinic between July 2021 and May 2023, provided blood samples for CTC quantification using CellSearch methodology. CTC counts were correlated with demographic, clinical, and molecular data generated by comprehensive genomic profiling of tumor tissue. RESULTS: CTCs were detected in 26% of all patients at initial presentation to our department. The highest CTC frequency was observed among patients with unfavorable CUP (35.5%), while patients with single-site/oligometastatic CUP harbored the lowest CTC frequency (11.4%). No statistically significant association between CTC positivity and the number of affected organs (P = 0.478) or disease burden (P = 0.120) was found. High CTC levels (≥5 CTCs/7.5 mL; 12/95 analyzed patients) predicted for adverse overall survival compared to negative or low CTC counts (6-months overall survival rate 90% vs 32%, log-rank P < 0.001; HR 5.43; 95% CI 2.23-13.2). CTC dynamics were also prognostic for overall survival by landmark analysis (log-rank P < 0.001, HR 10.2, 95% CI 1.95-52.9). CONCLUSIONS: CTC frequency is a strong, independent predictor of survival in patients with CUP. CTC quantification provides a useful prognostic tool in the management of these patients.

Predictors of early morbidity and mortality in newly diagnosed multiple myeloma: data from five randomized, controlled, phase III trials in 3700 patients.

Early morbidity and mortality affect patient outcomes in multiple myeloma. Thus, we dissected the incidence and causes of morbidity/mortality during induction therapy (IT) for newly diagnosed multiple myeloma (NDMM), and developed/validated a predictive risk score. We evaluated 3700 transplant-eligible NDMM patients treated in 2005-2020 with novel agent-based triplet/quadruplet IT. Primary endpoints were severe infections, death, or a combination of both. Patients were divided in a training (n = 1333) and three validation cohorts (n = 2367). During IT, 11.8%, 1.8%, and 12.5% of patients in the training cohort experienced severe infections, death, or both, respectively. Four major, baseline risk factors for severe infection/death were identified: low platelet count (<150/nL), ISS III, higher WHO performance status (>1), and age (>60 years). A risk score (1 risk factor=1 point) stratified patients in low (39.5%; 0 points), intermediate (41.9%; 1 point), and high (18.6%; ≥2 points) risk. The risk for severe infection/death increased from 7.7% vs. 11.5% vs. 23.3% in the low- vs. intermediate- vs. high-risk groups (p < 0.001). The risk score was independently validated in three trials incorporating quadruplet IT with an anti-CD38 antibody. Our analyses established a robust and easy-to-use score to identify NDMM patients at risk of severe infection/death, covering the latest quadruplet induction therapies. Trial registrations: HOVON-65/GMMG-HD4: EudraCT No. 2004-000944-26. GMMG-MM5: EudraCT No. 2010-019173-16. GMMG-HD6: NCT02495922. EMN02/HOVON-95: NCT01208766. GMMG-HD7: NCT03617731.

Weakly Supervised MRI Slice-Level Deep Learning Classification of Prostate Cancer Approximates Full Voxel- and Slice-Level Annotation: Effect of Increasing Training Set Size.

BACKGROUND: Weakly supervised learning promises reduced annotation effort while maintaining performance. PURPOSE: To compare weakly supervised training with full slice-wise annotated training of a deep convolutional classification network (CNN) for prostate cancer (PC). STUDY TYPE: Retrospective. SUBJECTS: One thousand four hundred eighty-nine consecutive institutional prostate MRI examinations from men with suspicion for PC (65 ± 8 years) between January 2015 and November 2020 were split into training (N = 794, enriched with 204 PROSTATEx examinations) and test set (N = 695). FIELD STRENGTH/SEQUENCE: 1.5 and 3T, T2-weighted turbo-spin-echo and diffusion-weighted echo-planar imaging. ASSESSMENT: Histopathological ground truth was provided by targeted and extended systematic biopsy. Reference training was performed using slice-level annotation (SLA) and compared to iterative training utilizing patient-level annotations (PLAs) with supervised feedback of CNN estimates into the next training iteration at three incremental training set sizes (N = 200, 500, 998). Model performance was assessed by comparing specificity at fixed sensitivity of 0.97 [254/262] emulating PI-RADS ≥ 3, and 0.88-0.90 [231-236/262] emulating PI-RADS ≥ 4 decisions. STATISTICAL TESTS: Receiver operating characteristic (ROC) and area under the curve (AUC) was compared using DeLong and Obuchowski test. Sensitivity and specificity were compared using McNemar test. Statistical significance threshold was P = 0.05. RESULTS: Test set (N = 695) ROC-AUC performance of SLA (trained with 200/500/998 exams) was 0.75/0.80/0.83, respectively. PLA achieved lower ROC-AUC of 0.64/0.72/0.78. Both increased performance significantly with increasing training set size. ROC-AUC for SLA at 500 exams was comparable to PLA at 998 exams (P = 0.28). ROC-AUC was significantly different between SLA and PLA at same training set sizes, however the ROC-AUC difference decreased significantly from 200 to 998 training exams. Emulating PI-RADS ≥ 3 decisions, difference between PLA specificity of 0.12 [51/433] and SLA specificity of 0.13 [55/433] became undetectable (P = 1.0) at 998 exams. Emulating PI-RADS ≥ 4 decisions, at 998 exams, SLA specificity of 0.51 [221/433] remained higher than PLA specificity at 0.39 [170/433]. However, PLA specificity at 998 exams became comparable to SLA specificity of 0.37 [159/433] at 200 exams (P = 0.70). DATA CONCLUSION: Weakly supervised training of a classification CNN using patient-level-only annotation had lower performance compared to training with slice-wise annotations, but improved significantly faster with additional training data. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Onset Time and Characteristics of Postprocedural Bleeding after Endoscopic Resection of Colorectal Lesions: A Multicenter Retrospective Study.

INTRODUCTION: Postprocedural bleeding is a major adverse event after endoscopic resection of colorectal lesions, but the optimal surveillance time after endoscopy is unclear. In this study, we determined onset time and characteristics of postprocedural bleeding events. METHODS: We retrospectively screened patients who underwent endoscopic resection of colorectal lesions at three German hospitals between 2010 and 2019 for postprocedural bleeding events using billing codes. Only patients who required re-endoscopy were included for analysis. For identified patients, we collected demographic data, clinical courses, characteristics of colorectal lesions, and procedure-related variables. Factors associated with late-onset bleeding were determined by univariate and multivariate logistic regression analysis. RESULTS: From a total of 6,820 patients with eligible billing codes, we identified 113 cases with postprocedural bleeding after endoscopic mucosal (61.9%) or snare resection (38.1%) that required re-endoscopy. The median size of the culprit lesion was 20 mm (interquartile range 14-30 mm). The median onset time of postprocedural bleeding was day 3 (interquartile range: 1-6.5 days), with 48.7% of events occurring within 48 h. Multivariate logistic regression analysis demonstrates that a continued intake of antiplatelet drugs (OR: 3.98, 95% CI: 0.89-10.12, p = 0.025) and a flat morphology of the colorectal lesion (OR: 2.98, 95% CI: 1.08-8.01, p = 0.031) were associated with an increased risk for late postprocedural bleeding (>48 h), whereas intraprocedural bleeding was associated with a decreased risk (OR: 0.12, 95% CI: 0.04-0.50, p = 0.001). CONCLUSION: Significant postprocedural bleeding can occur up to 18 days after endoscopic resection of colorectal lesions, but was predominantly observed within 48 h. Continued intake of antiplatelet drugs and a flat polyp morphology are associated with risk for late postprocedural bleeding.

RNA-sequencing based first choice of treatment and determination of risk in multiple myeloma.

Background: Immunotherapeutic targets in multiple myeloma (MM) have variable expression height and are partly expressed in subfractions of patients only. With increasing numbers of available compounds, strategies for appropriate choice of targets (combinations) are warranted. Simultaneously, risk assessment is advisable as patient's life expectancy varies between months and decades. Methods: We first assess feasibility of RNA-sequencing in a multicenter trial (GMMG-MM5, n=604 patients). Next, we use a clinical routine cohort of untreated symptomatic myeloma patients undergoing autologous stem cell transplantation (n=535, median follow-up (FU) 64 months) to perform RNA-sequencing, gene expression profiling (GEP), and iFISH by ten-probe panel on CD138-purified malignant plasma cells. We subsequently compare target expression to plasma cell precursors, MGUS (n=59), asymptomatic (n=142) and relapsed (n=69) myeloma patients, myeloma cell lines (n=26), and between longitudinal samples (MM vs. relapsed MM). Data are validated using the independent MMRF CoMMpass-cohort (n=767, FU 31 months). Results: RNA-sequencing is feasible in 90.8% of patients (GMMG-MM5). Actionable immune-oncological targets (n=19) can be divided in those expressed in all normal and >99% of MM-patients (CD38, SLAMF7, BCMA, GPRC5D, FCRH5, TACI, CD74, CD44, CD37, CD79B), those with expression loss in subfractions of MM-patients (BAFF-R [81.3%], CD19 [57.9%], CD20 [82.8%], CD22 [28.4%]), aberrantly expressed in MM (NY-ESO1/2 [12%], MUC1 [12.7%], CD30 [4.9%], mutated BRAF V600E/K [2.1%]), and resistance-conveying target-mutations e.g., against part but not all BCMA-directed treatments. Risk is assessable regarding proliferation, translated GEP- (UAMS70-, SKY92-, RS-score) and de novo (LfM-HRS) defined risk scores. LfM-HRS delineates three groups of 40%, 38%, and 22% of patients with 5-year and 12-year survival rates of 84% (49%), 67% (18%), and 32% (0%). R-ISS and RNA-sequencing identify partially overlapping patient populations, with R-ISS missing, e.g., 30% (22/72) of highly proliferative myeloma. Conclusion: RNA-sequencing based assessment of risk and targets for first choice treatment is possible in clinical routine.

Comparison of early and late 68Ga-FAPI-46-PET in 33 patients with possible recurrence of pancreatic ductal adenocarcinomas.

Positron emission tomography with 68Gallium (68Ga) labeled inhibitors of fibroblast activation protein (68Ga-FAPI-PET) is a promising imaging technique for patients with recurrent pancreatic ductal adenocarcinomas (PDAC). To date, it is not clear if different acquisition timepoints for 68Ga-FAPI-PET may result in comparable imaging information and if repetitive 68Ga-FAPI-PET imaging may add diagnostic value to single timepoint acquisition for recurrent PDAC. Here we analyzed retrospectively early (20 min p.i.) and late (60 min p.i.) 68Ga-FAPI-PET imaging using FAPI-46 of 33 patients with possible recurrence of PDAC concerning detection rates and uptake over time of local recurrences, metastases, inflammatory lesions of the pancreas, cholestatic lesions of the liver and reactive tissue. 33 patients with histologically confirmed PDAC after complete or partial resection of the pancreas and possible recurrence were examined by 68Ga-FAPI-46-PET acquired 20- and 60-min post injection (p.i.) of the radiotracer. FAPI-positive lesions were classified as local recurrences, metastases, inflammatory lesions of the pancreas (ILP), cholestatic lesions of the liver and reactive tissue based on histology, PET- and CT-morphology and clinical information. Lesions were contoured, and standardized uptake values (SUVmax and SUVmean) and target-to-background ratios (TBR) were analyzed for both acquisition timepoints. In total, 152 FAPI-positive lesions (22 local relapses, 47 metastases, 26 inflammatory lesions of the pancreas, 28 reactive tissues, and 29 cholestatic lesions) were detected. Detection rates for the early and late acquisition of 68Ga-FAPI-46-PET were almost identical except cholestatic lesions, which showed a higher detection rate at early imaging. SUV parameters and TBRs of ILP significantly decreased over time. Cholestatic lesions showed a tendency towards decreasing uptake. All other types of lesions showed relatively stable uptake over time. Early and late acquisition of 68Ga-FAPI-PET results in comparable imaging information in patients with possible recurrence of PDAC. Two timepoint imaging offers additional diagnostic potential concerning differential diagnoses.

Class I HDAC inhibition reduces DNA damage repair capacity of MYC-amplified medulloblastoma cells.

PURPOSE: MYC-driven Group 3 medulloblastoma (MB) (subtype II) is a highly aggressive childhood brain tumor. Sensitivity of MYC-driven MB to class I histone deacetylase inhibitors (HDACi) has been previously demonstrated in vitro and in vivo. In this study we characterize the transcriptional effects of class I HDACi in MYC-driven MB and explore beneficial drug combinations. METHODS: MYC-amplified Group 3 MB cells (HD-MB03) were treated with class I HDACi entinostat. Changes in the gene expression profile were quantified on a microarray. Bioinformatic assessment led to the identification of pathways affected by entinostat treatment. Five drugs interfering with these pathways (olaparib, idasanutlin, ribociclib, selinexor, vinblastine) were tested for synergy with entinostat in WST-8 metabolic activity assays in a 5 × 5 combination matrix design. Synergy was validated in cell count and flow cytometry experiments. The effect of entinostat and olaparib on DNA damage was evaluated by γH2A.X quantification in immunoblotting, fluorescence microscopy and flow cytometry. RESULTS: Entinostat treatment changed the expression of genes involved in 22 pathways, including downregulation of DNA damage response. The PARP1 inhibitors olaparib and pamiparib showed synergy with entinostat selectively in MYC-amplified MB cells, leading to increased cell death, decreased viability and increased formation of double strand breaks, as well as increased sensitivity to additional induction of DNA damage by doxorubicin. Non-MYC-amplified MB cells and normal human fibroblasts were not susceptible to this triple treatment. CONCLUSION: Our study identifies the combination of entinostat with olaparib as a new potential therapeutic approach for MYC-driven Group 3 MB.

Nivolumab and ipilimumab in recurrent or refractory cancer of unknown primary: a phase II trial.

Cancer of unknown primary has a dismal prognosis, especially following failure of platinum-based chemotherapy. 10-20% of patients have a high tumor mutational burden (TMB), which predicts response to immunotherapy in many cancer types. In this prospective, non-randomized, open-label, multicenter Phase II trial (EudraCT 2018-004562-33; NCT04131621), patients relapsed or refractory after platinum-based chemotherapy received nivolumab and ipilimumab following TMBhigh vs. TMBlow stratification. Progression-free survival (PFS) represented the primary endpoint; overall survival (OS), response rates, duration of clinical benefit and safety were the secondary endpoints. The trial was prematurely terminated in March 2021 before reaching the preplanned sample size (n = 194). Among 31 evaluable patients, 16% had a high TMB ( > 12 mutations/Mb). Overall response rate was 16% (95% CI 6-34%), with 7.7% (95% CI 1-25%) vs. 60% (95% CI 15-95%) in TMBlow and TMBhigh, respectively. Although the primary endpoint was not met, high TMB was associated with better median PFS (18.3 vs. 2.4 months) and OS (18.3 vs. 3.6 months). Severe immune-related adverse events were reported in 29% of cases. Assessing on-treatment dynamics of circulating tumor DNA using combined targeted hotspot mutation and shallow whole genome sequencing as part of a predefined exploratory analysis identified patients benefiting from immunotherapy irrespective of initial radiologic response.

Are T2WI PI-RADS sub-scores of transition zone prostate lesions biased by DWI information? A multi-reader, single-center study.

PURPOSE: According to PI-RADS v2.1, T2-weighted imaging (T2WI) is the dominant sequence for transition zone (TZ) lesions. This study aimed to assess, whether diffusion-weighted imaging (DWI) information influences the assignment of T2WI scores. METHOD: Out of 283 prostate MRI examinations with correlated biopsy results, fourty-four patients were selected retrospectively: first, 22 patients with a TZ lesion with T2WI and DWI scores ≥ 4, to represent lesions with unequivocal suspicion on T2WI and DWI. Second, 22 additional patients with TZ lesions of similar T2WI appearance but with corresponding DWI score ≤ 3 were added as control. Four residents and one board-certified radiologist each performed two assessments of the included patients: First, only T2WI was available (T2-only read); second, both T2WI and DWI sequences were available (biparametric read). Lesion scores were assessed using Wilcoxon signed-rank test, inter-reader agreement using weighted kappa and Kendall's W statistics, and sensitivity/specificity using McNemar test. RESULTS: The T2WI scores were significantly different between the T2-only and biparametric read for 3 out of 4 residents (p ≤ 0.049) but not for the radiologist. The overall PI-RADS scores derived from the two reading sessions differed considerably for 35/220 cases (all readers pooled). Inter-reader agreement was fair for the T2WI and overall PI-RADS scores (mean kappa 0.27-0.30) and moderate for the DWI scores (mean kappa 0.43). CONCLUSIONS: For inexperienced readers, assessment of T2WI is variable and potentially biased by availability of DWI information, which can lead to changes of overall PI-RADS score and consequently clinical management. Assessment of T2WI should be performed before reviewing DWI to ensure non-biased interpretation of TZ lesions in the dominant sequence.

Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability.

OBJECTIVES: To evaluate a fully automatic deep learning system to detect and segment clinically significant prostate cancer (csPCa) on same-vendor prostate MRI from two different institutions not contributing to training of the system. MATERIALS AND METHODS: In this retrospective study, a previously bi-institutionally validated deep learning system (UNETM) was applied to bi-parametric prostate MRI data from one external institution (A), a PI-RADS distribution-matched internal cohort (B), and a csPCa stratified subset of single-institution external public challenge data (C). csPCa was defined as ISUP Grade Group ≥ 2 determined from combined targeted and extended systematic MRI/transrectal US-fusion biopsy. Performance of UNETM was evaluated by comparing ROC AUC and specificity at typical PI-RADS sensitivity levels. Lesion-level analysis between UNETM segmentations and radiologist-delineated segmentations was performed using Dice coefficient, free-response operating characteristic (FROC), and weighted alternative (waFROC). The influence of using different diffusion sequences was analyzed in cohort A. RESULTS: In 250/250/140 exams in cohorts A/B/C, differences in ROC AUC were insignificant with 0.80 (95% CI: 0.74-0.85)/0.87 (95% CI: 0.83-0.92)/0.82 (95% CI: 0.75-0.89). At sensitivities of 95% and 90%, UNETM achieved specificity of 30%/50% in A, 44%/71% in B, and 43%/49% in C, respectively. Dice coefficient of UNETM and radiologist-delineated lesions was 0.36 in A and 0.49 in B. The waFROC AUC was 0.67 (95% CI: 0.60-0.83) in A and 0.7 (95% CI: 0.64-0.78) in B. UNETM performed marginally better on readout-segmented than on single-shot echo-planar-imaging. CONCLUSION: For same-vendor examinations, deep learning provided comparable discrimination of csPCa and non-csPCa lesions and examinations between local and two independent external data sets, demonstrating the applicability of the system to institutions not participating in model training. CLINICAL RELEVANCE STATEMENT: A previously bi-institutionally validated fully automatic deep learning system maintained acceptable exam-level diagnostic performance in two independent external data sets, indicating the potential of deploying AI models without retraining or fine-tuning, and corroborating evidence that AI models extract a substantial amount of transferable domain knowledge about MRI-based prostate cancer assessment. KEY POINTS: • A previously bi-institutionally validated fully automatic deep learning system maintained acceptable exam-level diagnostic performance in two independent external data sets. • Lesion detection performance and segmentation congruence was similar on the institutional and an external data set, as measured by the weighted alternative FROC AUC and Dice coefficient. • Although the system generalized to two external institutions without re-training, achieving expected sensitivity and specificity levels using the deep learning system requires probability thresholds to be adjusted, underlining the importance of institution-specific calibration and quality control.

MAPK inhibitor sensitivity scores predict sensitivity driven by the immune infiltration in pediatric low-grade gliomas.

Pediatric low-grade gliomas (pLGG) show heterogeneous responses to MAPK inhibitors (MAPKi) in clinical trials. Thus, more complex stratification biomarkers are needed to identify patients likely to benefit from MAPKi therapy. Here, we identify MAPK-related genes enriched in MAPKi-sensitive cell lines using the GDSC dataset and apply them to calculate class-specific MAPKi sensitivity scores (MSSs) via single-sample gene set enrichment analysis. The MSSs discriminate MAPKi-sensitive and non-sensitive cells in the GDSC dataset and significantly correlate with response to MAPKi in an independent PDX dataset. The MSSs discern gliomas with varying MAPK alterations and are higher in pLGG compared to other pediatric CNS tumors. Heterogenous MSSs within pLGGs with the same MAPK alteration identify proportions of potentially sensitive patients. The MEKi MSS predicts treatment response in a small set of pLGG patients treated with trametinib. High MSSs correlate with a higher immune cell infiltration, with high expression in the microglia compartment in single-cell RNA sequencing data, while low MSSs correlate with low immune infiltration and increased neuronal score. The MSSs represent predictive tools for the stratification of pLGG patients and should be prospectively validated in clinical trials. Our data supports a role for microglia in the response to MAPKi.

Same-day repeatability and Between-Sequence reproducibility of Mean ADC in PI-RADS lesions.

PURPOSE: This study aimed to assess repeatability after repositioning (inter-scan), intra-rater, inter-rater and inter-sequence variability of mean apparent diffusion coefficient (ADC) measurements in MRI-detected prostate lesions. METHOD: Forty-three patients with suspicion for prostate cancer were included and received a clinical prostate bi-/multiparametric MRI examination with repeat scans of the T2-weighted and two DWI-weighted sequences (ssEPI and rsEPI). Two raters (R1 and R2) performed single-slice, 2D regions of interest (2D-ROIs) and 3D-segmentation-ROIs (3D-ROIs). Mean bias, corresponding limits of agreement (LoA), mean absolute difference, within-subject coefficient of variation (CoV) and repeatability/reproducibility coefficient (RC/RDC) were calculated. Bradley & Blackwood test was used for variance comparison. Linear mixed models (LMM) were used to account for multiple lesions per patient. RESULTS: Inter-scan repeatability, intra-rater and inter-sequence reproducibility analysis of ADC showed no significant bias. 3D-ROIs demonstrated significantly less variability than 2D-ROIs (p < 0.01). Inter-rater comparison demonstrated small significant systematic bias of 57 × 10-6 mm2/s for 3D-ROIs (p < 0.001). Intra-rater RC, with the lowest variation, was 145 and 189 × 10-6 mm2/s for 3D- and 2D-ROIs, respectively. For 3D-ROIs of ssEPI, RCs and RDCs were 190-198 × 10-6 mm2/s for inter-scan, inter-rater and inter-sequence variation. No significant differences were found for inter-scan, inter-rater and inter-sequence variability. CONCLUSIONS: In a single-scanner setting, single-slice ADC measurements showed considerable variation, which may be lowered using 3D-ROIs. For 3D-ROIs, we propose a cut-off of âˆ¼ 200 × 10-6 mm2/s for differences introduced by repositioning, rater or sequence effects. The results suggest that follow-up measurements should be possible by different raters or sequences.

Class I HDAC inhibitor entinostat synergizes with PLK1 inhibitors in MYC-amplified medulloblastoma cells.

PURPOSE: We and others have demonstrated that MYC-amplified medulloblastoma (MB) cells are susceptible to class I histone deacetylase inhibitor (HDACi) treatment. However, single drug treatment with HDACi has shown limited clinical efficacy. We hypothesized that addition of a second compound acting synergistically with HDACi may enhance efficacy. METHODS: We used a gene expression dataset to identify PLK1 as a second target in MB cells and validated the relevance of PLK1 in MB. We measured cell metabolic activity, viability, and cycle progression in MB cells after treatment with PLK1-specific inhibitors (PLK1i). Chou-Talalay synergy calculations were used to determine the nature of class I HDACi entinostat and PLK1i interaction which was validated. Finally, the clinical potential of the combination was assessed in the in vivo experiment. RESULTS: MYC-amplified tumor cells are highly sensitive towards treatment with ATP-competitive PLK1i as a monotherapy. Entinostat and PLK1i in combination act synergistically in MYC-driven MB cells, exerting cytotoxic effects at clinically relevant concentrations. The downstream effect is exerted via MYC-related pathways, pointing out the potential of MYC amplification as a clinically feasible predictive biomarker for patient selection. While entinostat significantly extended survival of mice implanted with orthotopic MYC-amplified MB PDX, there was no evidence of the improvement of survival when treating the animals with the combination. CONCLUSION: The combination of entinostat and PLK1i showed synergistic interaction in vitro, but not in vivo. Therefore, further screening of blood-brain barrier penetrating PLK1i is warranted to determine the true potential of the combination as no on-target activity was observed after PLK1i volasertib treatment in vivo.

Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.

OBJECTIVES: In multiple myeloma and its precursor stages, plasma cell infiltration (PCI) and cytogenetic aberrations are important for staging, risk stratification, and response assessment. However, invasive bone marrow (BM) biopsies cannot be performed frequently and multifocally to assess the spatially heterogenous tumor tissue. Therefore, the goal of this study was to establish an automated framework to predict local BM biopsy results from magnetic resonance imaging (MRI). MATERIALS AND METHODS: This retrospective multicentric study used data from center 1 for algorithm training and internal testing, and data from center 2 to 8 for external testing. An nnU-Net was trained for automated segmentation of pelvic BM from T1-weighted whole-body MRI. Radiomics features were extracted from these segmentations, and random forest models were trained to predict PCI and the presence or absence of cytogenetic aberrations. Pearson correlation coefficient and the area under the receiver operating characteristic were used to evaluate the prediction performance for PCI and cytogenetic aberrations, respectively. RESULTS: A total of 672 MRIs from 512 patients (median age, 61 years; interquartile range, 53-67 years; 307 men) from 8 centers and 370 corresponding BM biopsies were included. The predicted PCI from the best model was significantly correlated ( P ≤ 0.01) to the actual PCI from biopsy in all internal and external test sets (internal test set: r = 0.71 [0.51, 0.83]; center 2, high-quality test set: r = 0.45 [0.12, 0.69]; center 2, other test set: r = 0.30 [0.07, 0.49]; multicenter test set: r = 0.57 [0.30, 0.76]). The areas under the receiver operating characteristic of the prediction models for the different cytogenetic aberrations ranged from 0.57 to 0.76 for the internal test set, but no model generalized well to all 3 external test sets. CONCLUSIONS: The automated image analysis framework established in this study allows for noninvasive prediction of a surrogate parameter for PCI, which is significantly correlated to the actual PCI from BM biopsy.

Test-retest, inter- and intra-rater reproducibility of size measurements of focal bone marrow lesions in MRI in patients with multiple myeloma.

OBJECTIVE: To investigate the reproducibility of size measurements of focal bone marrow lesions (FL) in MRI in patients with monoclonal plasma cell disorders under variation of patient positioning and observer. METHODS: A data set from a prospective test-retest study was used, in which 37 patients with a total of 140 FL had undergone 2 MRI scans with identical parameters after patient repositioning. Two readers measured long and short axis diameter on the initial scan in T1 weighted, T2 weighted short tau inversion recovery and diffusion-weighted imaging sequences. The first reader additionally measured FL on the retest-scan. The Bland-Altman method was used to assess limits of agreement (LoA), and the frequencies of absolute size changes were calculated. RESULTS: In the simple test-retest experiment with one identical reader, a deviation of ≥1 mm / ≥2 mm / ≥3 mm for the long axis diameter in T1 weighted images was observed in 66% / 25% / 8% of cases. When comparing measurements of one reader on the first scan to the measurement of the other reader on the retest scan, a change of ≥1 mm / ≥3 mm / ≥5 mm for the long axis diameter in T1 weighted images was observed in 78% / 21% / 5% of cases. CONCLUSION: Small deviations in FL size are common and probably due to variation in patient positioning or inter-rater variability alone, without any actual biological change of the FL. Knowledge of the uncertainty associated with size measurements of FLs is critical for radiologists and oncologists when interpreting changes in FL size in clinical practice and in clinical trials. ADVANCES IN KNOWLEDGE: According to the MY-RADs criteria, size measurements of focal lesions in MRI are now of relevance for response assessment in patients with monoclonal plasma cell disorders.Size changes of 1 or 2 mm are frequently observed due to uncertainty of the measurement only, while the actual focal lesion has not undergone any biological change.Size changes of at least 6 mm or more in T1 weighted or T2 weighted short tau inversion recovery sequences occur in only 5% or less of cases when the focal lesion has not undergone any biological change.

Secretogranin II influences the assembly and function of MHC class I in melanoma.

Melanoma is the deadliest form of skin cancer showing rising incidence over the past years. New insights into the mechanisms of melanoma progression contributed to the development of novel treatment options, such as immunotherapies. However, acquiring resistance to treatment poses a big problem to therapy success. Therefore, understanding the mechanisms underlying resistance could improve therapy efficacy. Correlating expression levels in tissue samples of primary melanoma and metastases revealed that secretogranin 2 (SCG2) is highly expressed in advanced melanoma patients with poor overall survival (OS) rates. By conducting transcriptional analysis between SCG2-overexpressing (OE) and control melanoma cells, we detected a downregulation of components of the antigen presenting machinery (APM), which is important for the assembly of the MHC class I complex. Flow cytometry analysis revealed a downregulation of surface MHC class I expression on melanoma cells that showed resistance towards the cytotoxic activity of melanoma-specific T cells. IFNγ treatment partially reversed these effects. Based on our findings, we suggest that SCG2 might stimulate mechanisms of immune evasion and therefore be associated with resistance to checkpoint blockade and adoptive immunotherapy.

Sphingosine-1-Phosphate Recruits Macrophages and Microglia and Induces a Pro-Tumorigenic Phenotype That Favors Glioma Progression.

Glioblastoma is the most aggressive brain tumor in adults. Treatment failure is predominantly caused by its high invasiveness and its ability to induce a supportive microenvironment. As part of this, a major role for tumor-associated macrophages/microglia (TAMs) in glioblastoma development was recognized. Phospholipids are important players in various fundamental biological processes, including tumor-stroma crosstalk, and the bioactive lipid sphingosine-1-phosphate (S1P) has been linked to glioblastoma cell proliferation, invasion, and survival. Despite the urgent need for better therapeutic approaches, novel strategies targeting sphingolipids in glioblastoma are still poorly explored. Here, we showed that higher amounts of S1P secreted by glioma cells are responsible for an active recruitment of TAMs, mediated by S1P receptor (S1PR) signaling through the modulation of Rac1/RhoA. This resulted in increased infiltration of TAMs in the tumor, which, in turn, triggered their pro-tumorigenic phenotype through the inhibition of NFkB-mediated inflammation. Gene set enrichment analyses showed that such an anti-inflammatory microenvironment correlated with shorter survival of glioblastoma patients. Inhibition of S1P restored a pro-inflammatory phenotype in TAMs and resulted in increased survival of tumor-bearing mice. Taken together, our results establish a crucial role for S1P in fine-tuning the crosstalk between glioma and infiltrating TAMs, thus pointing to the S1P-S1PR axis as an attractive target for glioma treatment.

DNA methylation biomarkers for noninvasive detection of triple-negative breast cancer using liquid biopsy.

Noninvasive detection of aberrant DNA methylation could provide invaluable biomarkers for earlier detection of triple-negative breast cancer (TNBC) which could help clinicians with easier and more efficient treatment options. We evaluated genome-wide DNA methylation data derived from TNBC and normal breast tissues, peripheral blood of TNBC cases and controls and reference samples of sorted blood and mammary cells. Differentially methylated regions (DMRs) between TNBC and normal breast tissues were stringently selected, verified and externally validated. A machine-learning algorithm was applied to select the top DMRs, which then were evaluated on plasma-derived circulating cell-free DNA (cfDNA) samples of TNBC patients and healthy controls. We identified 23 DMRs accounting for the methylation profile of blood cells and reference mammary cells and then selected six top DMRs for cfDNA analysis. We quantified un-/methylated copies of these DMRs by droplet digital PCR analysis in a plasma test set from TNBC patients and healthy controls and confirmed our findings obtained on tissues. Differential cfDNA methylation was confirmed in an independent validation set of plasma samples. A methylation score combining signatures of the top three DMRs overlapping with the SPAG6, LINC10606 and TBCD/ZNF750 genes had the best capability to discriminate TNBC patients from controls (AUC = 0.78 in the test set and AUC = 0.74 in validation set). Our findings demonstrate the usefulness of cfDNA-based methylation signatures as noninvasive liquid biopsy markers for the diagnosis of TNBC.