Citations

Abstract BACKGROUND Interpretation of serial magnetic resonance imaging (MRI) for glioblastoma following radiation therapy (RT) is complicated by difficulty differentiating tumor from treatment-related changes, even using updated RANO criteria. The incorporation of novel imaging such as 3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine (18F-DOPA) PET/CT to post-treatment serial imaging may improve prognostication and better facilitate future treatment decisions. METHODS The secondary analysis of a recent phase II prospective clinical trial of 18F-DOPA PET/CT-directed dose-escalated RT included patients with IDH-wildtype, MGMT-unmethylated glioblastoma who underwent post-treatment serial 18F-DOPA PET/CT surveillance. Quantitative features were extracted from pre-RT and post-RT serial PET/CT images, and robust prognostic features were selected using an in-house workflow. Both an automated machine learning (ML) algorithm and an interpretable ML algorithm were utilized to correlate surveillance PET image features with subsequent survival of greater than 12 months versus less than 12 months from the surveillance timepoint. Changes from pre-RT to post-RT PET/CT (delta model) were also assessed for association with post-RT survival and validated with a separate cohort. RESULTS Thirty-five patients with IDH-wildtype, MGMT-unmethylated glioblastoma who had at least one available (range: 1-14) post-treatment 18F-DOPA PET/CT were included. Twenty-four were used for model training, while 11 were used for validation. Ultimately, a five-feature post-RT model utilizing two shape, two texture, and one first-order radiomic feature was selected. For the delta model, five texture, two shape, and one first order radiomic feature were selected. The models show 90% accuracy in predicting survival < 12 months post-surveillance on the training set, and 68%-73% accuracy (AUC 0.64-0.73) for the validation cohort. Delta features were significantly associated with overall survival (p < 0.05). CONCLUSIONS Post-RT serial 18F-DOPA PET/CT imaging can help distinguish true tumor progression in patients with glioblastoma using a radiomics model. Tumor response evaluated for changes from pre-RT to post-RT 18F-DOPA PET/CT also predicted subsequent overall survival.

Abstract Diffuse gliomas are aggressive brain tumors that lack effective therapies. Despite advances in the molecular characterization of gliomas, targeted treatments have failed to improve patient outcomes to date. Here, we employed a functional genomics approach to rapidly identify therapeutic vulnerabilities and provide a strategy to individualize patient treatment. Our process combined comprehensive genomic and transcriptomic tumor profiling, custom algorithms and visualization software for data integration, and preclinical 3-dimensional ex vivo models (mcancers) for drug screening to assess response to therapeutic agents targeting specific molecular alterations. A total of 32 cases, including 5 oligodendrogliomas (1 primary and 4 recurrent), 5 astrocytomas (1 primary and 4 recurrent), and 22 glioblastomas (5 primary and 17 recurrent), were included in the study. Immunohistochemistry and integrated genomics confirmed that mcancers maintain key molecular alterations, including mutations, chromosomal rearrangements and focal amplifications, cellularity, and heterogeneity, faithfully recapitulating the original tumor. A total of 119 single drugs and 183 combinations were tested, including a panel of 26-brain penetrant targeted agents (18 single drugs and 8 combinations) that was curated to target common glioma oncogenic pathways and was tested across all cases. A strong correlation was observed between therapeutic vulnerabilities of glioma mcancers to targeted agents (EGFR, MET, PDGFR, HDAC inhibitors, etc.) and clinical experience. Single agent activity of panel compounds in the ex vivo setting was generally poor, with only 35% mCancers exhibiting > 70% growth inhibition at Cmax. In contrast, combination strategies, especially ones including epigenetic drugs, were significantly more effective (affecting 22 out of 32 mCancers), underscoring the molecular complexity of gliomas and the need for clinical trials testing combination treatments. Our data identified a number of novel combination treatments with significant efficacy across glioma subtypes and molecular profiles, suggesting that our functional genomics pipeline can guide treatment selection and inform clinical trial enrollment for gliomas.

Abstract Glioblastoma (GBM) is the most aggressive adult brain tumor with no effective therapy. Despite advances on its molecular characterization, targeted therapeutics remain out of reach. This lack-of-progress is largely attributed to molecular and spatial heterogeneity and absence of clinically applicable models to guide patient care. Moreover, as standard surgical resection is aimed at contrast-enhancing (CE) tumor, the molecular landscape and treatment response of the remaining non-contrast enhancing (nCE) component is largely unknown despite its active role in tumor progression and local recurrence. Here, we aimed to identify the functional impact of tumor heterogeneity and to explore therapeutic vulnerabilities of both CE and nCE components. Four tumor regions (two CE and two nCE) from each of five GBM patients were collected under neuronavigation. The resulting tissue was subjected to generation of 3D tumor models (a brain tumor initiating-enriched cell line and a spheroid-based mcancer model) with comparable success in both CE and nCE samples. A panel of 25 brain penetrant drugs (alone and in combination) targeting GBM drivers (i.e. RTK/Ras/PI3K, RB/cell cycle, p53/apoptosis, DNA damage repair, transcription addiction) was developed and used to test for therapeutic vulnerabilities. From each tumor region, 4.5 ± 3.7 (average ± SD) different targeting strategies were identified exhibiting at least 70% growth inhibition at Cmax. Drug response heterogeneity was relatively rare, varied among cases, and was independent of tumor enhancement status. To our knowledge, this is the first study to systemically assess treatment response from both CE and nCE patient-derived GBM models. Ongoing studies include comprehensive genome and transcriptome profiling and pathway analysis from these different tumor regions. We postulate that interrogation of molecular landscapes and vulnerability profiles of both CE and nCE tumor regions, will enable us to personalize and prioritize effective targeted interventions for GBM treatment.

Objective: Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability. Methods: Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability. Results: Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73–0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80). Conclusion: Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability. Advances in knowledge: Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.

SummaryBackgroundCrohn's disease has a substantial negative impact on health‐related quality of life (HRQoL).AimTo examine the effects of risankizumab on HRQoL in Crohn's diseaseMethodsWe analysed data from patients with Crohn's disease from 12‐week induction trials ADVANCE (N = 850) and MOTIVATE (N = 569) with risankizumab 600 mg or 1200 mg intravenous (IV) versus placebo IV and a 52‐week maintenance trial FORTIFY (N = 462) with risankizumab 180 or 360 mg subcutaneous (SC) versus placebo SC. Outcomes included Inflammatory Bowel Disease Questionnaire (IBDQ), Functional Assessment of Chronic Illness Therapy‐Fatigue (FACIT‐F), 36‐item Short Form Health Survey (SF‐36), EuroQol 5‐Dimension‐5‐Level (EQ‐5D‐5L) and work productivity. The mean change and percentages of patients achieving clinically meaningful improvement in all outcomes were determined at weeks 12 and 52.ResultsAt week 12, more patients in the risankizumab 600 or 1200 mg groups achieved IBDQ response than with placebo (ADVANCE: 70.2%, 75.5% vs. 47.8%, p ≤ 0.001; MOTIVATE: 61.7%, 68.5% vs. 48.2%, p ≤ 0.01) and FACIT‐F response (ADVANCE: 51.3%, 48.0% vs. 35.7%, p ≤ 0.01; MOTIVATE: 44.2%, 49.1% vs. 33.7%, p < 0.05). These improvements persisted at week 52 with risankizumab maintenance treatment. Similar trends were observed for SF‐36 physical and mental component summary scores, EQ‐5D‐5L and activity impairment within work productivity measures.ConclusionsRisankizumab induction therapy (600 or 1200 mg IV) led to clinically meaningful improvements in disease‐specific and general patient‐reported outcomes, including fatigue, in patients with moderate to severe Crohn's disease. These improvements were sustained after 52 weeks of risankizumab (180 or 360 mg SC) maintenance therapy.

Abstract CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC’s functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing &gt;3200 variants in &gt;470 genes from &gt;3100 publications.

AbstractPatients with soft tissue tumors of the lower extremities are at greater risk to develop postoperative disruption of lymphatic vessels. Currently, there is no widely effective cure for lymphatic dysfunction. Therefore, the best strategy is to prevent it and reconstruct efficient drainage as soon as the original pathway is damaged. We present a report of three prophylactic LVA cases after sarcoma resection in the lower limb, and a literature review to show the feasibility of prophylactic LVAs. The patients were 35, 73, and 77 years old, respectively, at the time of the procedure. All three patients had sarcoma in the medial thigh and underwent radiation therapy before the surgery. The locations of the LVAs include the medial thigh and medial and lateral calf. During the surgery, methylene blue and/or indocyanine green were injected to identify lymphatic vessels. Postoperative recovery was uneventful immediately after the surgery. At follow‐up visits, all three patients reported improved functions with no significant swelling in the lower limb. One patient experienced a surgical wound infection that resolved after antibiotic admission. Two patients had a history of cardiac diseases, a major risk factor for developing postoperative lymphedema, but these two patients did not develop lymphedema with the treatment of prophylactic LVAs. These results suggest that prophylactic LVA may be an effective strategy to prevent secondary lymphedema after sarcoma resection. Further investigation is warranted.

AbstractBackgroundAlthough intensity‐modulated radiation therapy and volumetric arc therapy have revolutionized photon external beam therapies, the technological advances associated with electron beam therapy have fallen behind. Modern linear accelerators contain technologies that would allow for more advanced forms of electron treatments, such as beam collimation, using the conventional photon multi‐leaf collimator (MLC); however, no commercial solutions exist that calculate dose from such beam delivery modes. Additionally, for clinical adoption to occur, dose calculation times would need to be on par with that of modern dose calculation algorithms.PurposeThis work developed a graphics processing unit (GPU)‐accelerated Monte Carlo (MC) engine incorporating the Varian TrueBeam linac head geometry for a rapid calculation of electron beams collimated using the conventional photon MLC.MethodsA compute unified device architecture framework was created for the following: (1) transport of electrons and photons through the linac head geometry, considering multiple scattering, Bremsstrahlung, Møller, Compton, and pair production interactions; (2) electron and photon propagation through the CT geometry, considering all interactions plus the photoelectric effect; and (3) secondary particle cascades through the linac head and within the CT geometry. The linac head collimating geometry was modeled according to the specifications provided by the vendor, who also provided phase‐space files. The MC was benchmarked against EGSnrc/DOSXYZnrc/GEANT by simulating individual interactions with simple geometries, pencil, and square beam dose calculations in various phantoms. MC‐calculated dose distributions for MLC and jaw‐collimated electron fields were compared to measurements in a water phantom and with radiochromic film.ResultsPencil and square beam dose distributions are in good agreement with DOSXYZnrc. Angular and spatial distributions for multiple scattering and secondary particle production in thin slab geometries are in good agreement with EGSnrc and GEANT. Dose profiles for MLC and jaw‐collimated 6–20‐MeV electron beams showed an average absolute difference of 1.1 and 1.9 mm for the FWHM and 80%–20% penumbra from measured profiles. Percent depth doses showed differences of <5% for as compared to measurement. The computation time on an NVIDIA Tesla V100 card was 2.5 min to achieve a dose uncertainty of <1%, which is ∼300 times faster than published results in a similar geometry using a single‐CPU core.ConclusionsThe GPU‐based MC can quickly calculate dose for electron fields collimated using the conventional photon MLC. The fast calculation times will allow for a rapid calculation of electron fields for mixed photon and electron particle therapy.

Abstract Women who have had breast cancer in the past are at increased risk of developing a second primary cancer (SPC), including second primary breast cancer (SPBC) or a second primary non-breast cancer (SPNBC). In the Multiethnic Cohort (MEC) Study, we conducted a prospective cohort analysis in 3,223 female breast cancer survivors from five racial/ethnic populations (White, African American, Japanese American, Latino, and Native Hawaiian) to assess the association of rare pathogenic variants (PV) in 37 known cancer predisposition genes with risk of SPC. A total of 719 (22.3%) women developed SPC, of which, 323 (10.0%) were SPBC. Germline PVs in BRCA1 (HR, 2.28; 95% CI, 1.11–4.65) and ERCC2 (HR, 3.51; 95% CI, 1.29–9.54) were significantly enriched in women with SPC. In the subtype analysis for SPBC, a significant association of ERCC2 PVs (HR, 5.09; 95% CI, 1.58–16.4) and a suggestive association of BRCA2 PVs (HR, 2.24; 95% CI, 0.91–5.55) were observed. There was also a higher risk of SPNBC in carriers of BRCA1 PVs (HR, 2.98; 95% CI, 1.21–7.36). These results provide evidence that germline PVs in BRCA1, BRCA2, and ERCC2 contribute to the development of SPC in breast cancer survivors. These findings also suggest that compromised DNA repair mechanisms could be a predisposition factor for SPC in patients with breast cancer, supporting the need for closer monitoring of SPC in women carrying PVs in these genes. Significance: This multiethnic study links germline pathogenic variants in BRCA1, BRCA2, and ERCC2 to the development of second primary cancer in breast cancer survivors, providing biological insights and biomarkers to guide patient monitoring.