Citations

Liver transplantation (LT) for alcohol associated hepatitis (AH) remains controversial. We convened a consensus conference to examine various aspects of LT for AH. The goal was not to unequivocally endorse LT for AH; instead, it was to propose recommendations for programs that perform or plan to perform LT for AH. Criteria were established to determine candidacy for LT in the setting of AH and included the following: (1) AH patients presenting for the first time with decompensated liver disease that are nonresponders to medical therapy without severe medical or psychiatric comorbidities; (2) a fixed period of abstinence prior to transplantation is not required; and (3) assessment with a multidisciplinary psychosocial team, including a social worker and an addiction specialist/mental health professional with addiction and transplantation expertise. Supporting factors included lack of repeated unsuccessful attempts at addiction rehabilitation, lack of other substance use/dependency, acceptance of diagnosis/insight with a commitment of the patient/family to sobriety, and formalized agreement to adhere to total alcohol abstinence and counseling. LT should be avoided in AH patients who are likely to spontaneously recover. Short‐term and longterm survival comparable to other indications for LT must be achieved. There should not be further disparity in LT either by indication, geography, or other sociodemographic factors. Treatment of alcohol‐use disorders should be incorporated into pre‐ and post‐LT care. The restrictive and focused evaluation process described in the initial LT experience for AH worldwide may not endure as this indication gains wider acceptance at more LT programs. Transparency in the selection process is crucial and requires the collection of objective data to assess outcomes and minimize center variation in listing. Oversight of program adherence is important to harmonize listing practices and outcomes.

Introduction: As previously reported, the combination of brentuximab vedotin with doxorubicin, vinblastine and dacarbazine (A+AVD) demonstrated a statistically significant improvement in modified progression free survival (modified PFS) compared with doxorubicin, bleomycin, vinblastine and dacarbazine (ABVD) in patients with newly diagnosed Stage III or IV classical HL in the phase 3 ECHELON-1 trial (NCT01712490). The benefit of A+AVD in the ITT population observed in the primary analysis was maintained at 3-years median follow-up [3-year PFS: A+AVD: 83.1% (79.9-85.9), ABVD: 76% (72.4-79.2)] and appears independent of interim PET status, disease stage, and prognostic risk factors. Here we present the efficacy and safety results of longer follow-up at a median 43.3 months. Methods: Newly diagnosed patients with Stage III or IV cHL were randomized 1:1 to receive A+AVD (n=664) or ABVD (n=670) intravenously on days 1 and 15 of each 28-day cycle for up to 6 cycles. The primary endpoint of the study was modified PFS per independent central review. The present follow-up PFS analysis is exploratory and per investigator assessment, with a cutoff date of June 17th, 2019. Patients with ongoing peripheral neuropathy (PN) at end of treatment were followed for resolution or improvement (defined as improved by ≥1 grade from worst grade as of the latest assessment) during the post-treatment follow-up period. Results: With a median follow-up of 43.3 months, the 42-month PFS per investigator for all patients was 82.4% (95% CI, 79.1-85.2) on the A+AVD arm and 76.2% (95% CI, 72.6-79.4) on the ABVD arm [overall HR 0.697 (95% CI, 0.547-0.890)]. Exploratory subgroup analyses of PET2(+) and PET2(-) patients showed a treatment effect in favor of A+AVD. The 42-month PFS in PET2(-) patients was 85.0% (95% CI, 81.6-87.7) for A+AVD and 79.6% (95% CI, 75.9-82.8) for ABVD [overall HR 0.695 (95% CI, 0.526-0.919)]; in PET2(+) patients 42-month PFS was 68.3% (95% CI, 54.5-78.7) for A+AVD and 51.5% (95% CI, 38.2-63.4) for ABVD [overall HR 0.552 (95% CI, 0.308-0.989)]. Upon continued follow-up, 81% (356/442) of patients with PN in the A+AVD arm had either complete resolution (64%, 283/442) or improvement (17%, 73/442) of their PN events compared with 83% (236/286) with either complete resolution (74%, 212/286) or improvement (8%, 24/286) in the ABVD arm. Among patients with ongoing PN after continued follow-up, the majority were Grade 1/2 events, with 89% (141/159; 59% Grade 1) and 95% (70/74; 65% Grade 1) on the A+AVD and ABVD arms, respectively. Overall survival data are not yet mature; per protocol, the final analysis will be performed after 112 deaths have occurred. Additional follow-up at an estimated median of ~4 years, including data from prespecified subgroups, will be presented. Conclusions: With a median follow-up of 43.3 months, A+AVD continues to provide a robust, durable benefit for patients with previously untreated Stage III or IV cHL compared with ABVD; the benefit is evident regardless of patient status at interim PET [PET2(+) or PET2(-)] and without the need for treatment intensification. PN continued to completely resolve or improve in patients on the A+AVD and ABVD arms. Together, these data further support the clinical advantages of A+AVD versus ABVD as treatment for patients with previously untreated Stage III or IV cHL. Disclosures Bartlett: Affimed Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Celgene: Research Funding; Dynavax: Research Funding; Forty-Seven: Research Funding; Genentech: Research Funding; Gilead: Research Funding; Immune Design: Research Funding; Janssen: Research Funding; Kite Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Medimmune: Research Funding; Merck: Research Funding; Millennium: Research Funding; Novartis: Research Funding; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pharmacyclics: Research Funding; Seattle Genetics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Straus:Elsevier (PracticeUpdate): Consultancy, Honoraria; Hope Funds for Cancer Research: Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Honoraria. Dlugosz-Danecka:Servier: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Macrogenomics: Research Funding; Roche: Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau. Illes:Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Seattle Genetics: Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees. Feldman:Takeda: Honoraria, Speakers Bureau; Celgene: Honoraria, Research Funding, Speakers Bureau; Cell Medica: Research Funding; Amgen: Research Funding; Viracta: Research Funding; Bayer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Honoraria, Other: Travel expenses, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Pharmacyclics: Honoraria, Other: Travel expenses, Speakers Bureau; Pfizer: Research Funding; Portola Pharma: Research Funding; Roche: Research Funding; Eisai: Research Funding; Corvus: Research Funding; Roche: Research Funding; AbbVie: Honoraria, Other: Travel expenses, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Other: Travel expenses, Speakers Bureau; Kyowa Hakko Kirin: Research Funding; Trillium: Research Funding. Smolewski:Roche: Other: Travel Expenses. Savage:Seattle Genetics, Inc.: Consultancy, Honoraria, Research Funding; BMS, Merck, Novartis, Verastem, Abbvie, Servier, and Seattle Genetics: Consultancy, Honoraria. Walewski:Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses, Research Funding; Takeda: Honoraria, Research Funding; Incyte: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Servier: Honoraria; Gilead: Other: Travel Expenses; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Consultancy, Membership on an entity's Board of Directors or advisory committees; GlaxoSmithKline: Research Funding; Janssen: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Zinzani:Portola: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Immune Design: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sandoz: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celltrion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Consultancy; Eusapharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; MSD: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Verastem: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kyowa Kirin: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; TG Therapeutics: Honoraria, Speakers Bureau. Hutchings:Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses, Research Funding; Genmab: Membership on an entity's Board of Directors or advisory committees, Research Funding; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses, Research Funding; Novartis: Research Funding; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel Expenses, Research Funding. Radford:AstraZeneca: Equity Ownership, Research Funding; Novartis: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; ADC Therapeutics: Consultancy, Research Funding; Takeda: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria; GSK: Equity Ownership. Munoz:AstraZeneca: Speakers Bureau; Kite Pharma: Consultancy, Research Funding, Speakers Bureau; Pharmacyclics LLC an AbbVie Company: Consultancy, Research Funding, Speakers Bureau; Gilead: Consultancy, Speakers Bureau; Fosunkite: Speakers Bureau; Kyowa: Consultancy, Honoraria, Speakers Bureau; Bayer: Consultancy, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Research Funding; Celgene: Research Funding; Portola: Research Funding; Incyte: Research Funding. Kim:Roche: Research Funding; Kyowa-Kirin: Research Funding; Novartis: Research Funding; J&J: Research Funding; Mundipharma: Research Funding; Celltrion: Research Funding; Donga: Research Funding. Advani:Cell Medica, Ltd: Consultancy; Pharmacyclics: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding; Kura: Research Funding; Infinity Pharma: Research Funding; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees, Research Funding; Bayer: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; Celmed: Consultancy, Membership on an entity's Board of Directors or advisory committees; Autolus: Consultancy, Membership on an entity's Board of Directors or advisory committees; Agensys: Research Funding; Stanford University: Employment, Equity Ownership; Takeda: Consultancy, Membership on an entity's Board of Directors or advisory committees; Seattle Genetics: Consultancy, Research Funding; Kyowa Kirin Pharmaceutical Developments, Inc.: Consultancy; Regeneron: Research Funding; Millennium: Research Funding; Janssen: Research Funding; Roche/Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead Sciences, Inc./Kite Pharma, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Forty-Seven: Research Funding; AstraZeneca: Consultancy, Membership on an entity's Board of Directors or advisory committees. Ansell:Mayo Clinic Rochester: Employment; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Trillium: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment. Younes:Roche: Consultancy, Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Curis: Honoraria, Research Funding; Merck: Honoraria, Research Funding; Abbvie: Honoraria; Takeda: Honoraria; Pharmacyclics: Research Funding; AstraZeneca: Research Funding; Genentech: Research Funding; Biopath: Consultancy; Xynomics: Consultancy; Epizyme: Consultancy, Honoraria; Celgene: Consultancy, Honoraria; HCM: Consultancy; BMS: Research Funding; Syndax: Research Funding. Gallamini:Takeda: Consultancy; Roche: Consultancy. Miao:Millennium Pharmaceuticals, Inc., Cambridge, MA, a wholly owned subsidiary of Takeda Pharmaceutical Company Limited: Employment, Equity Ownership. Liu:Takeda: Employment. Fenton:Seattle Genetics, Inc.: Employment, Equity Ownership. Forero-Torres:Seattle Genetics: Employment, Equity Ownership, Honoraria, Research Funding.

ROR1 is a transmembrane receptor with tightly controlled expression during development. It is present on multiple tumor types but not on normal adult tissues. Hematological malignancies are often ROR1-positive, including chronic lymphocytic leukemia (CLL), mantle cell lymphoma (MCL), and diffuse large B cell lymphoma (DLBCL). Given its unique pattern of expression, ROR1 represents a tumor-specific therapeutic target. The anti-ROR1 antibody, UC-961, is ahumanized IgG1 monoclonal antibody (mAb) that binds with high affinity to a specific extracellular epitope of human ROR1 receptor and can block Wnt5a-induced ROR1 signaling. Nonclinical studies document that UC-961 does not react with normal adult human tissues and selectively binds to tumor cells. Because of the antibody high specificity, rapid internalization, and trafficking to lysosomes, UC-961 appears ideally suited to serve as the targeting moiety for an anti-ROR1 ADC. Accordingly, we have developed VLS-101, a UC-961-linker-monomethyl auristatin E (MMAE) ADC that preserves the high-affinity binding and specificity of UC-961 and allows for ROR1-targeted intracellular release of MMAE. RS is an aggressive lymphoma, typically of DLBCL type, arising as transformation of CLL. Despite, progressive improvements in the therapy of CLL, very few effective treatment options exist for patients with RS. Using our recently established RS patient-derived xenografts (RS-PDXs), we explored the expression and signaling properties of ROR1 in RS and investigated the ex-vivo and in vivo effects of VLS-101. When assessed by flow cytometry (FACS), immunohistochemistry (IHC), and reverse-transcriptase-polymerase chain reaction (RT-PCR), 3 of 4 RS-PDXs showed ROR1 positivity (2 highly positive: 99% and 80% of cells; 1 medium/low positive: 25% of cells by FACS). The extent of ROR1 expression correlated among the 3 assays methods and was consistent with ROR1 expression data reported for non-RS DLBCL samples. When engaged by its ligand Wnt-5a, ROR1 activated downstream targets, Rac1 and RhoA, and induced phosphorylation of the p65 subunit of NF-kB and Jnk in RS cells. When cells purified from RS-PDX tumor masses were exposed to VLS-101 ex-vivo, the drug induced time- and dose-dependent apoptosis, as shown by increases in annexin V/propidium iodide and by Caspase-3 and PARP cleavage. VLS-101 efficacy was then assessed in vivo in both subcutaneous and systemic RS-PDX models. When palpable masses had formed in subcutaneous models, mice were randomly assigned to vehicle or VLS-101, bi-weekly treated for 3 consecutive weeks, and then compared for tumor growth and survival. In the 3 ROR1-expressing RS-PDX models, VLS-101 caused highly significant disease regressions. Complete regressions were observed even in RS-PDXs without universal ROR1 expression, suggesting a MMAE bystander effect. After treatment discontinuation, VLS-101-treated animals showed no tumor regrowth for several months and had significantly extended survival. Data were confirmed in systemic ROR1-expressing RS models in which tumor cells were intravenously injected and allowed to engraft for ~14 days before starting treatment. VLS-101 was administered with the same schedule adopted for the subcutaneous model. VLS-101 eliminated RS cells in bone marrow, peripheral blood, and spleen, increasing survival in VLS-101-treated animals relative to controls (approximately 20-50 days, depending on the RS-PDX model). Of note, no in vivo VLS-101 efficacy was observed in the ROR1-negative RS-PDX, confirming the specificity of VLS-101 targeting. VLS-101 showed no adverse effects on animal well-being or body weight. Collectively, these results indicate that ROR1 is expressed on RS cells where it can transduce pro-survival signals and can be diagnostically evaluated for selective targeting. Nonclinical data document that the MMAE-containing ADC, VLS-101, can cause RS-PDX apoptosis in vitro and can safely and selectively induce complete tumor regressions in in vivo models of RS derived from patient tumors with heavy prior clinical treatment and variable levels of ROR1 expression. Building on these types of results, a Phase 1 clinical trial of VLS-101 (NCT03833180) is ongoing in patients with lymphoid cancers. Disclosures Vaisitti: VelosBio Inc.: Research Funding; Verastem Inc: Research Funding. Jessen:VelosBio Inc.: Employment. Vo:VelosBio Inc: Employment. Ko:VelosBio Inc: Employment. Allan:Sunesis Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Pharmacyclics LLC, an AbbVie company: Consultancy; Acerta Pharma: Consultancy; Genentech: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie, Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees; Verastem Oncology, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Honoraria; Bayer: Consultancy. Furman:Acerta Pharma: Consultancy; Pharmacyclics: Consultancy; Beigene: Consultancy; AstraZeneca: Consultancy; Genentech: Consultancy; Incyte: Consultancy; Oncotracker: Consultancy; Verastem: Consultancy; Abbvie: Consultancy; Sunesis: Consultancy; TG Therapeutics: Consultancy; Janssen: Consultancy. Miller:VelosBio Inc.: Employment. Lanutti:VelosBio Inc.: Employment. Deaglio:iTeos Therapeutics: Research Funding; Verastem Inc: Research Funding; VelosBio Inc.: Research Funding. OffLabel Disclosure: The drug used in this project is an anti-ROR1-toxin-conjugated antibody

Abstract Background Extensive work in basic and clinical science suggests that biological mechanisms of aging are causally related to the development of disease and disability in late life. Modulation of the biological mechanisms of aging can extend both life span and health span in animal models, but translation to humans has been slow. Methods Summary of workshop proceedings from the 2018–2019 Epidemiology of Aging Workshop hosted by the Intramural Research Program at the National Institute on Aging. Results Epidemiologic studies play a vital role to progress in this field, particularly in evaluating new risk factors and measures of biologic aging that may influence health span, as well as developing relevant outcome measures that are robust and relevant for older individuals. Conclusions Appropriately designed epidemiological studies are needed to identify targets for intervention and to inform study design and sample size estimates for future clinical trials designed to promote health span.

Abstract Pathogenic sequence variants (PSV) in BRCA1 or BRCA2 (BRCA1/2) are associated with increased risk and severity of prostate cancer. We evaluated whether PSVs in BRCA1/2 were associated with risk of overall prostate cancer or high grade (Gleason 8+) prostate cancer using an international sample of 65 BRCA1 and 171 BRCA2 male PSV carriers with prostate cancer, and 3,388 BRCA1 and 2,880 BRCA2 male PSV carriers without prostate cancer. PSVs in the 3′ region of BRCA2 (c.7914+) were significantly associated with elevated risk of prostate cancer compared with reference bin c.1001-c.7913 [HR = 1.78; 95% confidence interval (CI), 1.25–2.52; P = 0.001], as well as elevated risk of Gleason 8+ prostate cancer (HR = 3.11; 95% CI, 1.63–5.95; P = 0.001). c.756-c.1000 was also associated with elevated prostate cancer risk (HR = 2.83; 95% CI, 1.71–4.68; P = 0.00004) and elevated risk of Gleason 8+ prostate cancer (HR = 4.95; 95% CI, 2.12–11.54; P = 0.0002). No genotype–phenotype associations were detected for PSVs in BRCA1. These results demonstrate that specific BRCA2 PSVs may be associated with elevated risk of developing aggressive prostate cancer. Significance: Aggressive prostate cancer risk in BRCA2 mutation carriers may vary according to the specific BRCA2 mutation inherited by the at-risk individual.

Abstract INTRODUCTION Neuroimaging dogma for glioblastoma asserts that hyperintensity on T1Gd MRI reveals the bulk of the tumor, while T2/FLAIR signal indicates edema. However, it is unclear whether this edema results from immune response or increased tumor cells. Further, one significant driver of the known sex differences in glioblastoma may be differences in immune response, due to the X-linkage of many immune genes. Based on this, we hypothesized that assumptions regarding tumor cellularity in T2/FLAIR images should be tailored to the biological sex of the patient. METHODS Using a retrospective cohort of 18 primary glioblastoma patients receiving multiple image-localized biopsies (82 total) and standard MRI, we assessed: distance of biopsy from T1Gd and T2 areas; a pathologist’s score of percent tumor cell density; and an imaging-based invasion metric, D/ρ. This metric is derived from the biomathematical Proliferation-Invasion model of glioma growth, which features two parameters, net growth rate (ρ) and net invasion rate (D). Their ratio D/ρ is related to degree of invasion, and is estimated from volumetric measurements of MRI abnormalities. Additionally, 25 patient-derived xenograft models implanted in females were grown until moribund, at which point brains were excised and stained for DAPI (to show all cells) and Lamin (to highlight tumor cells). Image processing of lamin-stained sections defines contours of intensity correlating with cell density. RESULTS Outside both the T1Gd and T2 region, male patient biopsies had higher tumor cell densities than females. Males also tended to have higher invasion metrics. Although each set derived from different patients, preclinical metrics of invasion were positively correlated with clinical invasion in females but negatively correlated in males. CONCLUSION Our preliminary finding that cell distribution patterns correlate with imaging metrics differently between the sexes supports the hypothesis that the degree of tumor cell density represented on certain MRI sequences may be sex-specific.

Abstract With increasing epidemiological and biological studies on meningiomas, standardized clinical data capture will be necessary to interpret results across studies and to translate them to clinical practice. To meet this need for consistent clinical data capture, the International Consortium on Meningioma, has developed a set of common data elements (CDEs), to be used across clinical and biological research. Building from terminology used in clinical practice and ongoing studies, we used a consensus-of-experts approach to arrive at a set of standardized and vetted data elements. Areas of focus include patient characteristics, patient history, radiological and intra-operative description, histological diagnosis and molecular testing, non-surgical interventions, and surveillance. These data elements are implemented in Research Electronic Data Capture (REDCap) forms. With REDCap implementation, components of the CDE set can be selected and instituted in a local REDCap instance for efficient study start-up. The scope of the current CDE set focuses on surgically treated meningioma. However, modular forms provide flexibility and to allow the scope to be expanded to include non-surgically treated meningioma. Dissemination plans are underway to introduce the CDEs to neuro-oncology researchers and clinicians through publication and presentations, where feedback will be solicited. Adoption of CDEs for research will improve consistency and interpretability across studies, expediting research in this frequently occurring tumor.

Abstract BACKGROUND Conventional magnetic resonance imaging (MR) guides patient care in GBM. However, there is mounting awareness that MR enhancement is non-specific reflecting either tumor progression or non-tumoral inflammatory changes. Histological evaluation of GBM is held as the gold standard for disease assessment. However, the invasiveness of this methodology and the sample sparsity limit its usefulness. Methods to infer histological underpinnings of MRI are needed to improve clinical care. METHODS A transfer learning mixed effects model based on T1Gd and FLAIR MR voxel based image features trained and cross-validated to predict FPKM values of CD68, CASP3, CD8A and IL13RA2. Training data included RNAseq from 38 image-localized biopsies from 15 newly-diagnosed GBM patients. These models were then applied to two independent patients, chosen based on therapy and the quality of image registration, at three different time points, just prior to receiving IL13RA2 targeted CAR-T therapy for rGBM, after 2 cycles and after 4 cycles, resulting in a voxel-based prediction of the relative expression levels of these genes. RESULTS Cross-validation of the proposed machine learning models demonstrated a Pearson Correlation coefficient between predicted and observed FPKM values of 0.89 (CD68), 0.92 (CASP3), 0.93 (CD8A), and 0.88 (IL13RA2). When the models were applied to the two CAR-T patients, CD68-modeled expression levels were seen to increase throughout therapy for one patient, while remaining stable for the other. Survival from first CAR-T infusion was, respectively, 412 and 83 days suggesting that the increased CD68 was indicative of therapeutic effectivity. Further, the spatial activity predictions were consistent with expected therapeutic action as the correlation coefficient between CASP3 and the product of IL13R2 and CD8A expression was 0.81, suggesting the T-cells targeting IL13RA2 were inducing cell death. CONCLUSIONS Preliminary results with our model highlights the potential of spatially resolved radiomic maps to provide insight into regional therapeutic effectivity.

Abstract PURPOSE Brain tumor related epilepsy (BTE) is a major co-morbidity in patients with glioma. It is difficult to determine whether the use of anti-epileptic drugs is necessary. We attempted to build a machine-learning model to predict the probability of seizure presentation (SP) with glioma. METHODS We trained a random forest classifier using the following variables: volumetric data of pre-treatment MR images (T1Gd and T2-FLAIR sequences), patient demographics (age; sex), and measurements of tumor proliferation (log(ρ)), invasiveness (log(D)) and their relative ratio (log(ρ/D)). Our cohort consisted of 221 patients total. Using an 80-20 ratio, we used 176 patients (76 SP, 100 nSP) for training and the remaining 45 patients (19 SP, 26 nSP) were used for testing. We also trained on male-only and female-only cohorts to evaluate any sex differences in prediction. For training, 108 males (53 SP, 55 nSP) were used and 28 for testing (14 SP, 14 nSP). We used 72 females (21 SP, 49 nSP) for training and 15 (7 SP, 8 nSP) for testing. We corrected for class imbalance in the female cohort before training. Using 10-fold cross-validation and a separate testing set, we measured performance by ROC curve (AUC), accuracy, sensitivity, and specificity of predictions (average of folds in cross validation). RESULTS The female model achieved the highest AUC (0.853) followed by the mixed model (0.726) and the male model (0.651). In the validation set, the accuracy/sensitivity/specificity of the three cohorts were as follows: mixed (0.726/0.696/0.750), female (0.853/0.830/0.875), and male (0.651/0.577/0.722). The performance of the testing set, in terms of accuracy/sensitivity/specificity were: mixed (0.733/0.74/0.73), female (0.8/0.57/1), and male (0.714/0.64/0.79). CONCLUSION We found a negative correlation between seizure probability and size and invasiveness of tumors. Our model shows promising performance on testing set data. Further cohort studies and training is warranted.

Abstract Recurrent glioblastoma is challenging to distinguish from so called “treatment effect” on routine clinical imaging. Further, within tumor heterogeneity reveals that some regions can be histologically dominated by tumor progression whilst others can be dominated by secondary effects of treatment response. Apparent tumor progression on MRI can be very difficult to manage clinically as it is unclear the degree to which the imaging changes are actually tumor progression vs response to treatment (including inflammatory response and necrosis). In this analysis, we study a unique cohort of patients for whom image localized-biopsies reveal heterogeneity in response vs progression. Our dataset included 70 biopsy samples from 32 patients with GBM each histolopatholgically characterized for tumor abundance vs immune infiltrate. Six multiparametric MRI contrasts were available, including T1, T1gd, T2, FLAIR, SWI, and ADC. Images were co-registered. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. Machine learning models were built to predict each biomarker using radiomic features. Leave-one-out cross validation was used to evaluate the prediction accuracy. Radiomic features were found to be informative to the prediction of biomarkers. ANOVA tests show significant improvement of using radiomic features compared with the null model. The prediction accuracy was higher when considering the biomarkers on a binary scale using the median as the cutoff than on a numerical scale. Spatially-informed radiomics models for tumor progression vs treatment effect are possible and can play an instrumental role in navigating confounding imaging changed common during treatment progression.