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OBJECTIVE While single-position surgery (SPS) eliminates the need for patient repositioning, the placement of screws in the unconventional lateral position poses unique challenges related to asymmetry relative to the surgical table. Use of robotic guidance or intraoperative navigation can help to overcome this. The aim of this study was to compare the relative accuracies offered by these various navigation modalities for pedicle screws placed in lateral SPS. METHODS According to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the PubMed/Medline, Embase, and Cochrane Library databases were queried for studies reporting pedicle screw placement accuracy using fluoroscopic, CT-navigated, O-arm, or robotic guidance in lateral SPS, and a systematic review and meta-analysis was performed. Included studies all compared evaluated screw placement accuracy in lateral SPS using a single navigation method. Quality assessment was performed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system; risk of bias was assessed using the Newcastle-Ottawa Scale and the Joanna Briggs Institute checklist. The primary outcome, rate of pedicle screw breach, was analyzed using random-effects meta-analysis. RESULTS Eleven studies were included comprising 548 patients who underwent the placement of instrumentation with 2488 screws. For the fluoroscopic, CT-navigated, O-arm, and robotic guidance cohorts, there were 3, 2, 3, and 3 studies, respectively. Breach rates by modality were as follows: fluoroscopic guidance (6.6%), CT navigation (4.7%), O-arm (3.9%), and robotic guidance (3.9%). Random-effects meta-analysis showed a significant difference between studies, with an overall breach rate of 4.9% (95% CI 3.1%–7.5%; p < 0.001); however, testing for subgroup differences failed to show a significant difference between guidance modalities (QM = 0.69, df = 3; p = 0.88). Heterogeneity between studies was significant (I2 = 79.0%, τ2 = 0.41, χ2 = 47.65, df = 10; p < 0.001). CONCLUSIONS Robotic guidance of screws is noninferior to alternative guidance modalities in lateral SPS; however, additional prospective studies directly comparing different guidance types are merited.

Abstract Background: There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer. Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for pancreatic cancer. Methods: Data from genome-wide association studies (GWAS) within the Pancreatic Cancer Cohort Consortium (PanScan; cases n = 5,090, controls n = 8,733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n = 4,163, controls n = 3,792) were analyzed. We used data on 68 genetic variants with four different MR methods [inverse variance weighting (IVW), MR-Egger, simple median, and penalized weighted median] separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and pancreatic cancer risk, using logistic regression to calculate ORs and 95% confidence intervals (CI), adjusting for PC risk factors, including obesity and diabetes. Results: No association was found between genetically predicted NAFLD and pancreatic cancer risk in the PanScan or PanC4 samples [e.g., PanScan, IVW OR, 1.04; 95% confidence interval (CI), 0.88–1.22; MR-Egger OR, 0.89; 95% CI, 0.65–1.21; PanC4, IVW OR, 1.07; 95% CI, 0.90–1.27; MR-Egger OR, 0.93; 95% CI, 0.67–1.28]. None of the four MR methods indicated an association between genetically predicted NAFLD and pancreatic cancer risk in either sample. Conclusions: Genetic predisposition to NAFLD is not associated with pancreatic cancer risk. Impact: Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and pancreatic cancer might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and pancreatic cancer.

Introduction: Deep-learning artificial intelligence (AI) algorithms can identify hypertrophic cardiomyopathy (HCM) from the electrocardiogram (ECG). Preliminary data indicate that AI-ECG may also track treatment response in HCM patients (pts) receiving mavacamten (Mava). In this placebo-controlled analysis, we investigated the performance of AI-ECG in HCM detection and its changes during Mava treatment compared to placebo in the phase 3, EXPLORER-HCM trial. Methods: Pts with non-paced ECGs enrolled in EXPLORER-HCM, evaluating Mava over 30 weeks of treatment, were included. All pts had a 12-lead ECG pre-treatment, at each study visit, and 8 weeks after treatment end. Two independently developed AI-ECG convolutional neural network algorithms from the Mayo Clinic and the University of California, San Francisco (UCSF) were applied to all ECGs suitable for analysis. Serial AI-ECG-predicted scores, which correlate with HCM likelihood (range 0-1), were examined between Mava and placebo groups. Results: A total of 101 Mava and 110 placebo pts were included (mean age 58.6, female 40%). At baseline, the mean Mayo and UCSF AI-ECG scores for HCM were 0.82 and 0.65, with 94% and 88% of pts exceeding the respective diagnostic thresholds of 0.11 (Mayo) and 0.4 (UCSF). During 30 weeks of follow-up in the Mava group, the algorithms showed a mean AI-ECG score reduction of 0.32 (Mayo) and 0.17 (UCSF) in all pts between start and end of treatment. In comparison, AI-ECG scores decreased by 0.05 (Mayo) and increased by 0.02 (UCSF) in the placebo group (Mava vs placebo, t-test p-values <0.001 for both algorithms; Figure ) Conclusion: In the EXPLORER-HCM trial population, two independent deep-learning AI-ECG algorithms correctly identified most HCM pts and demonstrated significant reductions in predicted HCM scores after treatment with Mava when compared to placebo. This relative normalization of the ECG suggests disease-modifying properties of Mava, deserving further investigation.

This discussion summarizes the NCCN Clinical Practice Guidelines for managing squamous cell anal carcinoma, which represents the most common histologic form of the disease. A multidisciplinary approach including physicians from gastroenterology, medical oncology, surgical oncology, radiation oncology, and radiology is necessary. Primary treatment of perianal cancer and anal canal cancer are similar and include chemoradiation in most cases. Follow-up clinical evaluations are recommended for all patients with anal carcinoma because additional curative-intent treatment is possible. Biopsy-proven evidence of locally recurrent or persistent disease after primary treatment may require surgical treatment. Systemic therapy is generally recommended for extrapelvic metastatic disease. Recent updates to the NCCN Guidelines for Anal Carcinoma include staging classification updates based on the 9th edition of the AJCC Staging System and updates to the systemic therapy recommendations based on new data that better define optimal treatment of patients with metastatic anal carcinoma.

Background Immune checkpoint inhibitors (ICIs) are now a first-line treatment option for patients with pleural mesothelioma with the recent approval of ipilimumab and nivolumab. Mesothelioma has a low tumor mutation burden and no robust predictors of survival with ICI. Since ICIs enable adaptive antitumor immune responses, we investigated T-cell receptor (TCR) associations with survival in participants from two clinical trials treated with ICI. Methods We included patients with pleural mesothelioma who were treated with nivolumab (NivoMes, NCT02497508) or nivolumab and ipilimumab (INITIATE, NCT03048474) after first-line therapy. TCR sequencing was performed with the ImmunoSEQ assay in 49 and 39 pretreatment and post-treatment patient peripheral blood mononuclear cell (PBMC) samples. These data were integrated with TCR sequences found in bulk RNAseq data by TRUST4 program in 45 and 35 pretreatment and post-treatment tumor biopsy samples and TCR sequences from over 600 healthy controls. The TCR sequences were clustered into groups of shared antigen specificity using GIANA. Associations of TCR clusters with overall survival were determined by cox proportional hazard analysis. Results We identified 4.2 million and 12 thousand complementarity-determining region 3 (CDR3) sequences from PBMCs and tumors, respectively, in patients treated with ICI. These CDR3 sequences were integrated with 2.1 million publically available CDR3 sequences from healthy controls and clustered. ICI-enhanced T-cell infiltration and expanded T cell diversity in tumors. Cases with TCR clones in the top tertile in the pretreatment tissue or in circulation had significantly better survival than the bottom two tertiles (p<0.04). Furthermore, a high number of shared TCR clones between pretreatment tissue and in circulation was associated with improved survival (p=0.01). To potentially select antitumor clusters, we filtered for clusters that were (1) not found in healthy controls, (2) recurrent in multiple patients with mesothelioma, and (3) more prevalent in post-treatment than pretreatment samples. The detection of two-specific TCR clusters provided significant survival benefit compared with detection of 1 cluster (HR<0.001, p=0.026) or the detection of no TCR clusters (HR=0.10, p=0.002). These two clusters were not found in bulk tissue RNA-seq data and have not been reported in public CDR3 databases. Conclusions We identified two unique TCR clusters that were associated with survival on treatment with ICI in patients with pleural mesothelioma. These clusters may enable approaches for antigen discovery and inform future targets for design of adoptive T cell therapies.

9069 Background: With the emergence of effective targeted-therapies for KRAS G12C, and the development of promising agents for KRAS G12V and G12D among others, the identity of unique KRAS mutations in non-small cell lung cancer (NSCLC) has become increasingly relevant. Acquired KRAS mutations are a known mechanism of resistance observed across driver mutation-positive (DM+) NSCLC. The incidence and diversity of these acquired alterations, and whether they differ from those observed in de novo KRAS-mutated NSCLC, is unknown. Methods: NSCLC molecular profiles were obtained using next-generation sequencing (Caris Life Sciences) with paired whole-transcriptomic sequencing (Illumina NovaSeq) and immunohistochemistry (Caris Life Sciences). Demographic data were abstracted from medical records. KRAS mutation diversity was defined for de novo KRAS-mutated ( KRASmt) NSCLC ( KRAS as only identified driver – KRASmt_dn) and DM+ NSCLC with acquired KRAS mutations (concurrent KRASmt with other known driver – KRASmt_acq). Fisher’s exact test was used to compare the distribution of unique KRAS mutations between the groups. Due to the unique biology of NSCLC with class II/III BRAF mutations, this subset was removed from the KRASmt_acq subgroup for the final analysis. Results: A total of 5932 KRASmt NSCLC samples were identified, among which 5879 were KRASmt_dn and 53 were KRASmt_acq (see table). The distribution of unique KRAS mutations was not significantly different between groups (p = 0.14). Within the KRASmt_dn group, KRAS G12C was most common (40%), followed by G12V (19%), G12D (14%), G12A (7%) and Q61H/G13C (4% each). In the KRASmt_acq group, KRAS G12C was most common (30%), followed by G12D (19%), G12V (17%), Q61H (11%), G12A (7%), and G13C (6%). The most common observed driver mutations in the KRASmt_acq group were EGFR (38%) and MET (30%). In the EGFR group, KRAS G12D (25%), Q61H/G12C (20% each), G12V/G13C (10% each), and G12F/A/R (5% each) were observed. In the MET group, KRAS G12C (25%), G12D/V/A (19% each), Q61H (12%) and G12S (6%) were seen. Among 7 patients with a documented history of smoking in the KRASmt_acq group, KRAS G12D and Q61H were most common (29% each), followed by G12V/F and G13C (14% each). Conclusions: While the distribution of unique KRAS mutations did not differ significantly between KRASmt_dn and KRASmt_acq groups, acquired KRAS mutations were seen across DM+ NSCLC subsets, among which the frequency of observed mutations appeared to vary. The functional and immunological significance of these mutations, and their impact on clinical outcomes, warrants further investigation. [Table: see text]

9037 Background: The clinical outcomes for patients with NSCLC remain suboptimal despite recent advancements in anti-PD-1/PD-L1 immunotherapies. Thus, identification of rational combinations of novel immune checkpoint (IC) strategies is imperative. This study aimed to comprehensively characterize the expression of IC targets to inform drug development and design effective combinatorial strategies, to advance the search for optimized therapies for NSCLC. Methods: We analyzed the differential gene expression of IC targets in 537 primary lung adenocarcinoma (LUAD) samples and 59 normal lung tissue samples pre-immunotherapy receipt using RNA-sequencing data from The Cancer Genome Atlas. Gene expression of IC-targets including PDCD1 (PD-1), CD274 (PD-L1), CTLA4, TIGIT, LAG3, HAVCR2 (TIM-3), VSIR (VISTA), ICOS, CD276 (B7-H3), BTLA, IDO1, KLRD1 (CD94/NKG2A), NT5E (CD73), TNFRSF4 (OX-40), TNFRSF18 (GITR), and TNFRSF9 (4-1BB) were obtained. Differential gene expression was determined intra-tumorally by comparison of transcripts per million and inter-tumorally using the DESeq2 algorithm. Log2 fold change >0.5 and false discovery rate adjusted P<0.05 were used as criteria for significant differential expression. Hierarchical clustering was used to identify subsets of tumors with similar profiles of IC-targetable gene expression. Results: Tumors showed significantly increased expression of TNFRSF18 (GITR), NT5E (CD73), TNFRSF9 (4-1BB), LAG3, TIGIT, CTLA4, PD-1, BTLA, B7-H3, and OX-40 (Table 1). On the other hand, tumors exhibited significant downregulation of CD274 (PD-L1), HAVCR2 (TIM-3), ICOS, KLRD1 (CD94/NKG2A), and VSIR (VISTA). Hierarchical clustering revealed recurrent patterns of IC gene overexpression which were characterized by: 1) IDO1-hi, 2) CD276-mid + TNFRSF18-mid, 3) VSIR-hi, 4) CD276-hi, 5) NT5E-hi, and 6) CD276-mid + NT5E-mid. Interestingly, patterns of IC gene expression distinguish tumors from controls. Of note, high VSIR expression was specific to normal tissue rather than tumors. Conclusions: Our study identified many IC targetable genes that were significantly overexpressed in tumors. However, subsets of tumors showed distinct and segregable combinations of overexpressed IC genes. These results suggest heterogeneous mechanisms of immune evasion in NSCLC, which may explain suboptimal outcomes with anti-PD-1/PD-L1 based strategies in this patient population. Our findings emphasize the importance of biomarker-guided approaches for the optimal selection of ICs and the development of rational combinatorial immunotherapies in NSCLC.

8567 Background: Adjuvant osimertinib (osi) improves disease-free survival (DFS) in patients (pts) with resected, early-stage, EGFR-mutant (EGFRmut) NSCLC, yet the benefit of osi after CRT in pts with unresectable locally advanced NSCLC is unknown. Post-hoc analysis of the PACIFIC trial showed a lack of survival benefit with consolidation durva versus placebo in pts with EGFRmut NSCLC. Comparisons between consolidation durva and EGFR TKIs in unresectable EGFRmut NSCLC following CRT are lacking. Methods: We conducted a multi-institutional retrospective analysis of pts with stage III unresectable EGFRmut NSCLC (exon19 deletion, exon21 L858R), who received EGFR TKI or durva after ≥ 2 cycles of platinum-based chemotherapy plus definitive radiation therapy between 2015-2022. Baseline characteristics including age, sex, smoking history, PD-L1 status, and outcomes on DFS, overall survival (OS), and safety were collected. Multivariable (MVA) cox regression analysis was used for statistical analysis. Treatment-related adverse events (trAE) were defined using CTCAE 5.0. Results: Seventeen pts from 12 institutions received an EGFR TKI (osi, n=15; erlotinib, n=2), and 13 pts received consolidation durva. Median follow-up was 23 months. Median age of all pts was 61 years (IQR:52-72) and 76.7% were female. Most pts in both groups had never-smoked and had adenocarcinoma. All pts received ≥ 60 Gy of radiation with concurrent chemotherapy. PD-L1 expression was ≥ 50% in 1/10 (10%) pts treated with durva vs 6/13 (46.2%) treated with TKI, p=0.09. Median duration on treatment for EGFR TKI and durva was 12.2 months and 4.8 months, respectively. Pts treated with EGFR TKI had significantly longer 24-month DFS versus pts treated with durva after adjusting for stage (3A vs 3B vs 3C) (Table). Any grade tRAE occurred in 58.8% (10/17) of pts treated with EGFR TKI vs 38.5% (5/13) with durva. Grade > 3 tRAE occurred in 15.4% (1 pneumonitis and 1 AST/ALT elevation) of pts treated with durva but in none of pts treated with EGFR TKI. Three pts within each arm came off treatment due to toxicity (EGFR TKI: 1 pneumonitis and 2 dermatitis; durva: 1 of each pneumonitis, Type 1 diabetes, AST/ALT elevation). Conclusions: EGFR TKI therapy after definitive CRT was associated with significantly longer DFS compared to durva in this retrospective study of pts with stage III unresectable EGFRmut NSCLC, without unanticipated safety signals. Follow-up is ongoing and OS outcomes will be evaluated subsequently. Further investigation is warranted to define the optimal therapy for locally advanced EGFRmut NSCLC. [Table: see text]

e16259 Background: Patients with KRAS mutant (mt) pancreatic ductal adenocarcinoma (PADC) have worse survival outcomes compared to KRAS wild-type (wt) PDAC patients. However, the prognostic implication of the KRAS mutation subtype (e.g. G12D, G12R, G12V and Q61) remains unclear. Furthermore, the clinical and molecular characteristics associated with these mutation subtypes remain not well defined. Methods: A retrospective review was conducted on patients with KRAS PDAC who underwent Next Generation Sequencing at Mayo Clinic between December 1, 2018 and December 1, 2021. Their somatic mutations, RNA expression, demographics, disease characteristics, therapies offered, and clinical outcomes were collected via chart review. Results on KRAS wt were reported previously; KRAS mt are reported here. Results: A total of 158 KRAS mt patients (at diagnosis: average age 63.8 years, 57% male, 88 (56%) Stage IV) were included. 10% of patients had KRAS Q61 mutation, 14% G12R, 30% G12V and 46% G12D. G12R had a longer overall survival (OS) compared to the others (median 24.8 vs 17.5 months, unadjusted hazard ratio (HR) 0.68, p=0.05), particularly in patients with localized disease at diagnosis (median 35.4 vs 28.3 months, p=0.08). Among patients with metastatic disease at diagnosis, G12V had the lowest OS (median 9.8 vs 12.4 months, unadjusted HR 1.8, p=0.02). 137 (87%) patients had at least one pathogenic somatic variant, with statistically significant differences in SMAD4, TP53, MYC, RB1, KMT2D, RBM10, RNF43, KDM6A, KMT2C, RBM10, LRP1B, PIK3CA, and SMARC among the KRAS mt subtypes. 82 (52%) patients had at least one RNA expression variant, with statistically significant differences in CCND1, CCNE1, HRAS, MET, EGFR, and ERB3 among KRAS mt subtypes. Between these subtypes, there were no statistically significant differences in TMB value, location of PDAC or subsequent metastasis, lines of treatment (average 2.3), age at diagnosis, or gender. Conclusions: KRAS mt subtypes may confer different PDAC phenotypes. In our cohort, G12R is associated with improved OS while G12V correlates with worse prognosis in de novo metastatic disease. Additionally, there are differences in genomic variations and RNA expression between the KRAS mt subtypes. To further examine this, transcriptomic analysis is underway. [Table: see text]