Citations

AbstractPurposeIdentifying predictive factors for all‐cause reoperation after anterior cruciate ligament reconstruction could inform clinical decision making and improve risk mitigation. The primary purposes of this study are to (1) determine the incidence of all‐cause reoperation after anterior cruciate ligament reconstruction, (2) identify predictors of reoperation after anterior cruciate ligament reconstruction using machine learning methodology, and (3) compare the predictive capacity of the machine learning methods to that of traditional logistic regression.MethodsA longitudinal geographical database was utilized to identify patients with a diagnosis of new anterior cruciate ligament injury. Eight machine learning models were appraised on their ability to predict all‐cause reoperation after anterior cruciate ligament reconstruction. Model performance was evaluated via area under the receiver operating characteristics curve. To explore modeling interpretability and radiomic feature influence on the predictions, we utilized a game‐theory‐based method through SHapley Additive exPlanations.ResultsA total of 1400 patients underwent anterior cruciate ligament reconstruction with a mean postoperative follow‐up of 9 years. Two‐hundred and eighteen (16%) patients experienced a reoperation after anterior cruciate ligament reconstruction, of which 6% of these were revision ACL reconstruction. SHapley Additive exPlanations plots identified the following risk factors as predictive for all‐cause reoperation: diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. XGBoost was the best‐performing model (area under the receiver operating characteristics curve of 0.77) and outperformed logistic regression in this regard.ConclusionsAll‐cause reoperation after anterior cruciate ligament reconstruction occurred at a rate of 16%. Machine learning models outperformed traditional statistics and identified diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair as predictive risk factors for reoperation. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. These models will allow surgeons to tabulate individualized risk for future reoperation for patients undergoing anterior cruciate ligament reconstruction.Level of evidenceIII.

Abstract The receptor tyrosine kinase AXL is a member of the TYRO3, AXL, and proto-oncogene tyrosine-protein kinase MER family and plays pleiotropic roles in cancer progression. AXL is expressed in immunosuppressive cells, which contributes to decreased efficacy of immunotherapy. Therefore, we hypothesized that AXL inhibition could serve as a strategy to overcome resistance to chimeric antigen receptor T (CAR T)–cell therapy. To test this, we determined the impact of AXL inhibition on CD19-targeted CAR T (CART19)–cell functions. Our results demonstrate that T cells and CAR T cells express high levels of AXL. Specifically, higher levels of AXL on activated Th2 CAR T cells and M2-polarized macrophages were observed. AXL inhibition with small molecules or via genetic disruption in T cells demonstrated selective inhibition of Th2 CAR T cells, reduction of Th2 cytokines, reversal of CAR T-cell inhibition, and promotion of CAR T-cell effector functions. AXL inhibition is a novel strategy to enhance CAR T-cell functions through two independent, but complementary, mechanisms: targeting Th2 cells and reversing myeloid-induced CAR T-cell inhibition through selective targeting of M2-polarized macrophages.

Abstract Glioblastoma multiforme (GBM) is the most aggressive and common form of brain tumor, and it is characterized by an immunosuppressive tumor microenvironment (TME) that supports tumor growth and pathology. An integral part of the GBM stroma is the mesenchymal stem cells (MSC), which promote GBM growth. Receptor tyrosine kinase EphA3, a member of the Eph family, is overexpressed in GBM tumors and in MSCs within TME, making it an attractive target for Chimeric antigen receptor T-cell (CART) therapy. CART has proven incredible promise in certain hematological malignancies therapy but remained short in solid tumors, particularly GBM. We hypothesize that concurrent targeting of cancers and TME by EphA3-CART can be a potential therapeutic option in GBM. We first showed the expression of the EphA3 receptor by Immunohistochemistry and western blot on GBM patient-derived xenograft (PDX), as well as on the SNB-19 and U87 human GBM cell lines and adipose-derived MSCs using flow cytometry. We then generated EphA3-CAR construct derived from a clinically validated anti-EphA3 scFv cloned in a lentiviral vector containing CD28 and CD3ζ, and generated EphA3 CART cells with over 90% efficiency. EphA3 CART cells demonstrated potent antigen specific effector functions. To demonstrate the antitumor activity in vivo, we subcutaneously grafted 1 × 106 GBM cells per NOD-SCID-γ-/- mice and at ~150-300 mm3 tumor volume, mice were then treated with 5 × 106 EphA3-CART or un-transduced (UTD) T cells control. Tumors were monitored by bioluminescence imaging (BLI) and caliper measurements, and mice were followed for survival. Mice treated with EphA3 CART cells exhibited a significant reduction in tumor volume (p<0.005), and prolonged overall survival (p=0.0015). Finally, we tested the ability of EphA3-CART cells to overcome MSC induced CART cell suppression by co-culturing EphA3-CART cells with MSC and EphA3+ GBM target cells while using the CD19+ mantle cell lymphoma cell line JeKo-1 and CART19 as control. MSC conditioned coculture of GBM and CART showed significant suppression of CAR19 in the CART19 - JeKo-1 system (p=000.3), but no significant suppression of EphA3-CART cells. In summary, our results indicate that EphA3 CART cells exhibited potent and specific antitumor activity in vitro and in vivo and ameliorated MSC induced inhibition of CART cell functions, representing a potentially promising therapeutic option in GBM. Citation Format: Ekene J. Ogbodo, Michael W. Ruff, Reona L. Sakemura, Claudia ManriquezRoman, Truc Huynh, James H. Girsch, Olivia L. Sirpilla, Kun Yun, Carli M. Stewart, Ismail Can, Lionel K. Fonkoua, Mehrdad Hefazi, Elizabeth L. Siegler, Saad S. Kenderian. Simultaneous targeting of EphA3 on glioblastoma and tumor microenvironment to overcome resistance to CART cell therapy in brain cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5017.

TPS9162 Background: Immune checkpoint inhibitors (ICI) are a standard of care (SOC) treatment that have improved outcomes for patients (pts) with non-small cell lung cancer (NSCLC). However, most pts progress, signaling a need for combination approaches to further improve outcomes. CAN-2409 is a replication-defective adenoviral construct encoding the herpes simplex virus thymidine kinase (HSV-tk) gene that is injected intratumorally followed by 2 weeks of prodrug (valacyclovir or acyclovir). HSV-tk converts the prodrug in the tumor microenvironment into a toxic metabolite, resulting in immunogenic cell death, while the adenoviral proteins provide a strong pro-inflammatory signal. Together, this leads to in situ vaccination against the patient’s own tumor. As a result, CD8+ cytotoxic T cells are educated on how to recognize and kill tumor cells in both the injected tumor and the uninjected metastases. A completed neoadjuvant phase I trial in resectable NSCLC demonstrated safety and immune stimulation (Predina et al 2021), warranting further investigation of CAN-2409 + prodrug as a therapy that can lead to improved long-term outcomes for pts with NSCLC who have a suboptimal response to ICI. Methods: LuTK02 (NCT04495153) is an open-label, multi-arm, phase II clinical trial to evaluate CAN-2409 + prodrug added to SOC ICI (anti-PD-(L)1) therapy in pts with stage III/IV NSCLC presenting with inadequate response (as defined below) to ICI. Cohort 1 includes pts with nonresponding but stable disease ≥18 weeks after starting ICI; cohort 2 includes pts with progressive disease ≥18 weeks after starting ICI. Key eligibility criteria include RECIST-evaluable disease; an injectable lesion (via bronchoscopy or US/IR guidance); ability to continue ICI for the treatment period; no change of ICI or prior interruptions of > 4 weeks within 6 months of enrollment; and no focal therapy at > 3 sites of disease within 12 months prior to enrollment. Key exclusion criteria include a history of ICI immune-related adverse events and known alterations in EGFR, ALK, or ROS1. Eligible patients receive 2 injections of CAN-2409 approximately 6 weeks apart followed by prodrug for 14 days. After treatment of ~40 evaluable pts in cohort 2 with two injections of CAN-2409, the protocol has been amended to include a new cohort (n~20) of pts treated with three injections of CAN-2409 approximately 4 weeks apart, each followed by prodrug for 14 days, to further define the optimal therapeutic regimen. Primary endpoints are overall response and/or disease control rate per RECIST 1.1 criteria and safety of CAN-2409 + prodrug. Secondary endpoints include duration of response, PFS, OS, and biomarker studies. Exploratory endpoints include evaluation of effects in non-injected lesions, changes in non-target (per RECIST) lesions, tumor growth trajectory, and viral shedding. Clinical trial information: NCT04495153 .

OBJECTIVE The purpose of this study was to evaluate the performance of different supervised machine learning algorithms to predict achievement of minimum clinically important difference (MCID) in neck pain after surgery in patients with cervical spondylotic myelopathy (CSM). METHODS This was a retrospective analysis of the prospective Quality Outcomes Database CSM cohort. The data set was divided into an 80% training and a 20% test set. Various supervised learning algorithms (including logistic regression, support vector machine, decision tree, random forest, extra trees, gaussian naïve Bayes, k–nearest neighbors, multilayer perceptron, and extreme gradient boosted trees) were evaluated on their performance to predict achievement of MCID in neck pain at 3 and 24 months after surgery, given a set of predicting baseline features. Model performance was assessed with accuracy, F1 score, area under the receiver operating characteristic curve, precision, recall/sensitivity, and specificity. RESULTS In total, 535 patients (46.9%) achieved MCID for neck pain at 3 months and 569 patients (49.9%) achieved it at 24 months. In each follow-up cohort, 501 patients (93.6%) were satisfied at 3 months after surgery and 569 patients (100%) were satisfied at 24 months after surgery. Of the supervised machine learning algorithms tested, logistic regression demonstrated the best accuracy (3 months: 0.76 ± 0.031, 24 months: 0.773 ± 0.044), followed by F1 score (3 months: 0.759 ± 0.019, 24 months: 0.777 ± 0.039) and area under the receiver operating characteristic curve (3 months: 0.762 ± 0.027, 24 months: 0.773 ± 0.043) at predicting achievement of MCID for neck pain at both follow-up time points, with fair performance. The best precision was also demonstrated by logistic regression at 3 (0.724 ± 0.058) and 24 (0.780 ± 0.097) months. The best recall/sensitivity was demonstrated by multilayer perceptron at 3 months (0.841 ± 0.094) and by extra trees at 24 months (0.817 ± 0.115). Highest specificity was shown by support vector machine at 3 months (0.952 ± 0.013) and by logistic regression at 24 months (0.747 ± 0.18). CONCLUSIONS Appropriate selection of models for studies should be based on the strengths of each model and the aims of the studies. For maximally predicting true achievement of MCID in neck pain, of all the predictions in this balanced data set the appropriate metric for the authors’ study was precision. For both short- and long-term follow-ups, logistic regression demonstrated the highest precision of all models tested. Logistic regression performed consistently the best of all models tested and remains a powerful model for clinical classification tasks.

Background Risks and benefits of intravenous thrombolysis (IVT) in patients undergoing mechanical thrombectomy (MT) have been a topic of interest. However, IVT’s specific effects on stent retriever (SR) and aspiration thrombectomy (ASP) outcomes remain largely unexplored. In this meta-analysis, we aimed to investigate the effects of IVT on SR and ASP thrombectomy outcomes. Methods In accordance with PRISMA guidelines, a systematic literature review was conducted using Medline, Embase, Scopus, Web of Science, and Cochrane Center of Clinical Trials databases. Outcomes of interest included successful recanalization (modified Thrombolysis In Cerebral Infarction (mTICI) ≥2b), modified first pass efficacy (mFPE), functional independence (modified Rankin Scale (mRS) ≤2), symptomatic intracranial hemorrhage (sICH), and embolization to new territories (ENT). Results Four randomized controlled trials with 1176 patients were included. SR and ASP resulted in similar mTICI ≥2b, mFPE, and mRS 0–2 rates in patients with and without IVT administration. SR without IVT was associated with a significantly lower rate of mFPE compared with the SR+IVT (RR 0.85, 95% CI 0.74 to 0.97). Furthermore, ASP without IVT resulted in a lower rate of mRS 0–2 than the ASP+IVT with a strong trend towards significance (RR 0.78, 95% CI 0.60 to 1.01). Finally, bridging therapy did not increase sICH and ENT rates after ASP or SR thrombectomy. Conclusions Our findings suggest that SR and ASP thrombectomy have comparable safety and efficacy profiles, regardless of prior IVT administration. Additionally, our results indicate that the addition of IVT may improve certain efficacy outcomes based on the employed first-line MT technique.

OBJECTIVE Clear diagnostic delineation is necessary for the development of a strong evidence base in lumbar spinal surgery. Experience with existing national databases suggests that International Classification of Diseases, Tenth Edition (ICD-10) coding is insufficient to support that need. The purpose of this study was to assess agreement between surgeon-specified diagnostic indication and hospital-reported ICD-10 codes for lumbar spine surgery. METHODS Data collection for the American Spine Registry (ASR) includes an option to denote the surgeon’s specific diagnostic indication for each procedure. For cases treated between January 2020 and March 2022, surgeon-delineated diagnosis was compared with the ICD-10 diagnosis generated by standard ASR electronic medical record data extraction. For decompression-only cases, the primary analysis focused on the etiology of neural compression as determined by the surgeon versus that determined on the basis of the related ICD-10 codes extracted from the ASR database. For lumbar fusion cases, the primary analysis compared structural pathology, which may have required fusion, as determined by the surgeon versus that determined on the basis of the extracted ICD-10 codes. This allowed for identification of agreement between surgeon delineation and extracted ICD-10 codes. RESULTS In 5926 decompression-only cases, agreement between the surgeon and ASR ICD-10 codes was 89% for spinal stenosis and 78% for lumbar disc herniation and/or radiculopathy. Both the surgeon and database indicated no structural pathology (i.e., none) suggesting the need for fusion in 88% of cases. In 5663 lumbar fusion cases, agreement was 76% for spondylolisthesis but poor for other diagnostic indications. CONCLUSIONS Agreement between surgeon-specified diagnostic indication and hospital-reported ICD-10 codes was best for patients who underwent decompression only. In the fusion cases, agreement with ICD-10 codes was best in the spondylolisthesis group (76%). In cases other than spondylolisthesis, agreement was poor due to multiple diagnoses or lack of an ICD-10 code that reflected the pathology. This study suggested that standard ICD-10 codes may be inadequate to clearly define the indications for decompression or fusion in patients with lumbar degenerative disease.