Citations

11504 Background: This phase 2, open-label trial (SPEARHEAD-1; NCT04044768) is designed to evaluate the efficacy, safety, and tolerability of afamitresgene autoleucel in 45 patients (pts) with advanced/metastatic synovial sarcoma or Myxoid/Round Cell Liposarcoma (MRCLS). Methods: Eligible pts are HLA-A*02 positive with MAGE-A4-expressing tumors. Pts undergo leukapheresis for collection of autologous T-cells for processing and manufacture into afamitresgene autoleucel cells. Pts were treated with afamitresgene autoleucel doses between 1–10 × 109 transduced T-cells after receiving lymphodepleting chemotherapy. The primary endpoint is overall response rate per RECIST v1.1 by independent review. An independent Data Safety Monitoring Board reviews ongoing safety and benefit: risk during the interventional phase. Results: As of Feb 4, 2021, 32 pts received afamitresgene autoleucel. Of these pts, 59% were male, 87.5% had synovial sarcoma, the median age was 43 yrs (range: 24–73), and they had a median of 3 prior systemic lines of therapy. The MAGE-A4 antigen expression level (histoscore) ranged from 112–300, and the transduced cell dose ranged from 2.7–9.9 x 109. At the data cutoff, 25 pts were evaluable for preliminary efficacy (23 with synovial sarcoma and 2 with MRCLS) and 7 pts (5 with synovial sarcoma and 2 with MRCLS) had insufficient follow-up (<8 weeks follow-up and/or awaiting first scan). Of the 25 evaluable pts, the investigator-assessed responses were: complete response (2 pts), partial response (8 pts), stable disease (11 pts), and progressive disease (4 pts). All responses were confirmed. Nine of the 10 responders had ongoing response at the data cutoff and 3 responders had MAGE-A4 antigen histoscores <200. The most common AEs of any grade (>30% pts) were neutropenia, lymphopenia, nausea, cytokine release syndrome, leukopenia, fatigue, pyrexia, and anemia. Cytokine release syndrome of any grade occurred in 19/32 pts; 95% of those events were ≤Grade 2. No immune effector cell-associated neurotoxicity syndrome (ICANS) has been reported to date. Cytopenia (≥G3) at 4 wks post-infusion was observed in 6 pts (anemia 3 pts, neutropenia 2 pts, and thrombocytopenia 1 pt). Conclusion: These preliminary data demonstrate afamitresgene autoleucel is efficacious and well tolerated in heavily pre-treated pts. Objective responses are reported across a wide range of MAGE-A4 antigen levels and deep responses have been observed. Initial durability data is encouraging. Preliminary response data in SPEARHEAD-1 is comparable to the findings of the prior Phase 1 trial [1]. To date, the safety profile of afamitresgene autoleucel has been favorable, with mainly low-grade cytokine release syndrome and tolerable/reversible hematologic toxicities. [1]. Van Tine BA, et al. CTOS; November 18-21, 2020; Virtual. Clinical trial information: NCT04044768.

11503 Background: EHE is a rare vascular cancer arising in liver, lung, soft tissue and bone. The natural history of metastatic disease varies considerably from indolent growth over years to rapid growth with fatal outcome in months. Treatment of patients (pts) with metastatic EHE with antiangiogenic therapy induces tumor response in a minority of pts, and median PFS is 6-12 months. TAZ-CAMTA1 translocation results in activation of MAPK pathway and is an oncogenic driver in EHE. We sought to evaluate the effect of MEK inhibition using trametinib in pts with unresectable EHE. Methods: A phase 2 trial of trametinib 2 mg daily was conducted in pts with EHE though the Experimental Therapeutics Clinical Trials Network supported by NCI in collaboration with SARC. Additional support was provided by the EHE Rare Cancer Charity and the EHE Foundation. Pts had to have evidence of objective tumor progression or EHE-related pain requiring narcotics for relief prior to enrollment. Presence of TAZ-CAMTA1 translocation was analyzed by fusion-FISH after enrollment. Primary trial endpoint was objective response rate (ORR) per RECIST1.1 with at least 1 objective response required in the 1st 13 pts to expand enrollment to 27. The trial was amended after stage 1 to continue enrollment to 27 pts with TAZ-CAMTA1 detected by FISH with goal of >4 objective responses in this group. Secondary objectives were PFS and OS rates, safety and change in pt-reported global health and pain scores per PROMIS questionnaires. Results: 43 pts were enrolled between 6/2017 – 9/2020 across 10 sites and 41 started therapy. TAZ-CAMTA1 fusion was detected in 26, not detected in 7, test failed in 5 and was not performed due to insufficient tumor in 5. Median pt age was 54 (range 22-81 yrs) and 11 were >65 yrs; 25 were female; ECOG was 0 in 23, 1 in 16 and 2 in 3 pts. Most pts experienced reduction in tumor size. ORR per RECIST was 7% (3/41); in pts with TAZ-CAMTA1 detected, the ORR was 0% (0/26). Mean pain intensity and interference scores had a statistically significant improvement and global quality of life scores did not statistically change after 4 weeks of therapy. 17 pts remained on treatment > 6 months and 7 > 12 months. 25 pts stopped trametinib due to EHE progression, 6 died during treatment, 6 withdrew from treatment, 3 stopped drug due to adverse event and 1 is on treatment. The most common AEs related to trametinib were rash, fatigue, nausea/vomiting, diarrhea, alopecia and edema; Grade >3 AEs included anemia, dyspnea, hypoxia, hypotension, syncope and dermatitis. Conclusions: To our knowledge, this is the largest prospective clinical study focused on pts with EHE. Although the trial did not meet the ORR goal, stable disease > 6 months was seen in 40% of pts, and EHE-related pain improved on treatment. Trametinib was associated with expected cutaneous and GI adverse effects. Additional pt-reported outcomes and biomarkers of inflammation are undergoing analysis. Clinical trial information: NCT03148275.

e18739 Background: Hair loss is a well-known side effect of chemotherapy. The Paxman Hair Loss Prevention System, a scalp cooling device, has been shown to be effective in reducing chemotherapy induced alopecia in patients receiving chemotherapy (Nangia, JAMA, 2017). The National Comprehensive Cancer Network and the European Society for Medical Oncology guidelines have recommended scalp cooling as category 2A and 2B options, respectively. Methods: The real world use of scalp cooling using the Paxman device, as documented by orders through the Paxman Hub during the years of 2017-2020 was summarized. Descriptive statistics were used to summarize demographics and utilization. Results: Data from 6649 patients who used scalp cooling were reviewed. Patients with breast cancer were the most common users of scalp cooling (78%, n=5197) followed by gynecology (12%, n=775), gastrointestinal (3%, n=201), lung (1%, n=81) and genitourinary (1%, n=52). The majority of patients were between the ages of 45-65 (55%), followed by 65-74 (18%), older than 75 (5%), and 25-44 (2%). Average number (#) of cycles of cooling completed was 6.53 (range of average # of cycles 4.50-12). Scalp cooling with this device was commonly used in 39 out of 50 states. Conclusions: This is the largest report of scalp cooling usage in the real world setting in the USA, including scalp cooling usage in older adults. Uptake of scalp cooling across various cancers has not been uniform and this deserves further study.

TPS2661 Background: Letetresgene autoleucel (lete-cel; GSK3377794) is an autologous T-cell therapy using a genetically modified T-cell receptor (TCR) to improve recognition of cancer cells expressing NY-ESO-1/LAGE-1a. Next generation NY-ESO-1 TCR T-cell therapies, such as GSK3901961 and GSK3845097, integrate added genetic modifications to enhance anticancer activity. GSK3901961 co-expresses the CD8α chain to stabilize TCR-human leukocyte A (HLA) class I interactions on CD4+ T cells, improving T-cell persistence and helper functions such as Type 1 T-helper antitumor responses. GSK3845097 co-expresses a dominant negative transforming growth factor-β (TGF-β) type II receptor to reduce TGF-β pathway activation and maintain T-cell proliferation, cytokine production, and cytotoxicity in the tumor microenvironment. A first-time-in-human master protocol (NCT04526509) will evaluate safety, tolerability, and recommended phase 2 dose (RP2D) of these and possible subsequent therapies. Substudy 1 will assess GSK3901961 in patients (pts) with advanced non-small cell lung cancer (NSCLC) or synovial sarcoma (SS). Substudy 2 will assess GSK3845097 in pts with advanced SS. Methods: Each substudy includes a dose confirmation stage to assess RP2D and a dose expansion stage. Key inclusion criteria are age ≥18 y; measurable disease per RECIST v1.1; HLA-A*02:01, A*02:05, or A*02:06 positivity; NY-ESO-1/LAGE-1a tumor expression; advanced (metastatic/unresectable) SS with t(X;18) translocation and anthracycline-based therapy receipt/completion/intolerance (SS only); and Stage IV NSCLC, receipt of ≥1 prior line(s) of standard of care (SOC) therapy including programmed death receptor- or ligand-1 inhibitors, and SOC chemotherapy receipt/intolerance (Substudy 1 only). Key exclusion criteria are prior malignancy that is not in complete remission or clinically significant systemic illness; prior receipt of gene/NY-ESO-1–specific therapy or allogenic stem cell/solid organ transplant; central nervous system metastases (SS only); and actionable genetic aberration and receipt/failure of ≥3 systemic therapy lines (Substudy 1 only). Primary endpoints are safety (adverse events) and tolerability (dose-limiting toxicities). Secondary endpoints include investigator-assessed overall response rate, duration of response, and maximum expansion/persistence and phenotype of infiltrating transduced T cells. Exploratory endpoints include laboratory parameters, overall survival, and anti-GSK3901961 or -GSK3845097 titers as applicable. Analyses will be descriptive. The substudies are enrolling. Funding: GSK (209012; NCT04526509). Editorial support was provided by Eithne Maguire, PhD, of Fishawack Indicia, part of Fishawack Health; funded by GSK. Previously presented at AACR 2021 (CT219). Clinical trial information: NCT04526509.

Abstract Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here, we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy. Specifically, we investigated (i) whether there are indicators of abnormal brain electrophysiology in normal EEGs of epilepsy patients, and (ii) whether such abnormalities are modulated by the side of the brain generating seizures in focal epilepsy. We analysed awake scalp EEG recordings of age-matched groups of 144 healthy individuals and 48 individuals with drug-resistant focal epilepsy who had normal scalp EEGs. After preprocessing, using a bipolar montage of eight channels, we extracted the fraction of spectral power in the alpha band (8–13 Hz) relative to a wide band of 0.5–40 Hz within 10-s windows. We analysed the extracted features for (i) the extent to which people with drug-resistant focal epilepsy differed from healthy subjects, and (ii) whether differences within the drug-resistant focal epilepsy patients were related to the hemisphere generating seizures. We then used those differences to classify whether an EEG is likely to have been recorded from a person with drug-resistant focal epilepsy, and if so, the epileptogenic hemisphere. Furthermore, we tested the significance of these differences while controlling for confounders, such as acquisition system, age and medications. We found that the fraction of alpha power is generally reduced (i) in drug-resistant focal epilepsy compared to healthy controls, and (ii) in right-handed drug-resistant focal epilepsy subjects with left hemispheric seizures compared to those with right hemispheric seizures, and that the differences are most prominent in the frontal and temporal regions. The fraction of alpha power yielded area under curve values of 0.83 in distinguishing drug-resistant focal epilepsy from healthy and 0.77 in identifying the epileptic hemisphere in drug-resistant focal epilepsy patients. Furthermore, our results suggest that the differences in alpha power are greater when compared with differences attributable to acquisition system differences, age and medications. Our findings support that EEG-based measures of normal brain function, such as the normalized spectral power of alpha activity, may help identify patients with epilepsy even when an EEG does not contain any epileptiform activity, recorded seizures or other abnormalities. Although alpha abnormalities are unlikely to be disease-specific, we propose that such abnormalities may provide a higher pre-test probability for epilepsy when an individual being screened for epilepsy has a normal EEG on visual assessment.

Introduction: An artificial intelligence (AI) algorithm applied to electrocardiography (ECG) during normal sinus rhythm (NSR) has been shown to predict concomitant atrial fibrillation (AF). We sought to characterize the value of AI-ECG as a predictor of future AF and assess its performance compared to other clinical prediction scores in a population-based sample. Methods: We calculated the AI-ECG probability during NSR in patients who enrolled in the population-based Mayo Clinic Study of Aging with at least one ECG in NSR within two years prior and no history of AF at the time of the baseline study visit. The cumulative incidence of AF was estimated for strata defined by AI-ECG probability and CHARGE-AF score. Cox proportional hazards were fit to assess the independent prognostic value and interaction of AI-ECG probability and CHARGE-AF score. Concordance (c) statistics were calculated for AI-ECG probability, CHARGE-AF score and combined AI-ECG and CHARGE-AF score. Results: A total of 1,936 patients with a median age 75.8 (quartile range [QR] 70.4, 81.8) years, median CHARGE-AF score 14.0 (QR 13.2, 14.7) and median CHADS2VASC score 3 (QR 2, 4) were included in the analysis. The cumulative incidence of AF increased in a stepwise fashion across quartiles of AI-ECG probability and CHARGE-AF score (Figure 1). When compared in the same model, both AI-ECG probability (hazard ratio [HR] 1.76, 95% confidence interval [CI] 1.51-2.04) and CHARGE-AF score (HR 1.90, 95% CI 1.58-2.28) independently predicted AF without significant interaction (p=0.54). C statistics were 0.69 (95% CI 0.66-0.72) for AI-ECG probability, 0.69 (95% CI 0.66-0.71) for CHARGE-AF and 0.72 (95% CI 0.69-0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the Mayo Clinic Study of Aging, both the AI-ECG probability and CHARGE-AF score independently predicted time to AF. The AI-ECG may offer a means to assess risk with a single test without requiring manual or automated clinical data abstraction.

Abstract Background We have demonstrated that a neural network is able to predict a person’s age from the electrocardiogram (ECG) [artificial intelligence (AI) ECG age]. However, some discrepancies were observed between ECG-derived and chronological ages. We assessed whether the difference between AI ECG and chronological age (Age-Gap) represents biological ageing and predicts long-term outcomes. Methods and results We previously developed a convolutional neural network to predict chronological age from ECGs. In this study, we used the network to analyse standard digital 12-lead ECGs in a cohort of 25 144 subjects ≥30 years who had primary care outpatient visits from 1997 to 2003. Subjects with coronary artery disease, stroke, and atrial fibrillation were excluded. We tested whether Age-Gap was correlated with total and cardiovascular mortality. Of 25 144 subjects tested (54% females, 95% Caucasian) followed for 12.4 ± 5.3 years, the mean chronological age was 53.7 ± 11.6 years and ECG-derived age was 54.6 ± 11 years (R2 = 0.79, P &lt; 0.0001). The mean Age-Gap was small at 0.88 ± 7.4 years. Compared to those whose ECG-derived age was within 1 standard deviation (SD) of their chronological age, patients with Age-Gap ≥1 SD had higher all-cause and cardiovascular disease (CVD) mortality. Conversely, subjects whose Age-Gap was ≤1 SD had lower all-cause and CVD mortality. Results were unchanged after adjusting for CVD risk factors and other survival influencing factors. Conclusion The difference between AI ECG and chronological age is an independent predictor of all-cause and cardiovascular mortality. Discrepancies between these possibly reflect disease independent biological ageing.

Echocardiography (Echo), a widely available, noninvasive, and portable bedside imaging tool, is the most frequently used imaging modality in assessing cardiac anatomy and function in clinical practice. On the other hand, its operator dependability introduces variability in image acquisition, measurements, and interpretation. To reduce these variabilities, there is an increasing demand for an operator- and interpreter-independent Echo system empowered with artificial intelligence (AI), which has been incorporated into diverse areas of clinical medicine. Recent advances in AI applications in computer vision have enabled us to identify conceptual and complex imaging features with the self-learning ability of AI models and efficient parallel computing power. This has resulted in vast opportunities such as providing AI models that are robust to variations with generalizability for instantaneous image quality control, aiding in the acquisition of optimal images and diagnosis of complex diseases, and improving the clinical workflow of cardiac ultrasound. In this review, we provide a state-of-the art overview of AI-empowered Echo applications in cardiology and future trends for AI-powered Echo technology that standardize measurements, aid physicians in diagnosing cardiac diseases, optimize Echo workflow in clinics, and ultimately, reduce healthcare costs.