Citations

Abstract Previous studies implicated protein arginine methyltransferase 5 (PRMT5) as a synthetic lethal target for MTAP-deleted (MTAP del) cancers; however, the pharmacologic characterization of small-molecule inhibitors that recapitulate the synthetic lethal phenotype has not been described. MRTX1719 selectively inhibited PRMT5 in the presence of MTA, which is elevated in MTAP del cancers, and inhibited PRMT5-dependent activity and cell viability with >70-fold selecti­vity in HCT116 MTAP del compared with HCT116 MTAP wild-type (WT) cells. MRTX1719 demonstrated dose-dependent antitumor activity and inhibition of PRMT5-dependent SDMA modification in MTAP del tumors. In contrast, MRTX1719 demonstrated minimal effects on SDMA and viability in MTAP WT tumor xenografts or hematopoietic cells. MRTX1719 demonstrated marked antitumor activity across a panel of xenograft models at well-tolerated doses. Early signs of clinical activity were observed including objective responses in patients with MTAP del melanoma, gallbladder adenocarcinoma, mesothelioma, non–small cell lung cancer, and malignant peripheral nerve sheath tumors from the phase I/II study. Significance: PRMT5 was identified as a synthetic lethal target for MTAP del cancers; however, previous PRMT5 inhibitors do not selectively target this genotype. The differentiated binding mode of MRTX1719 leverages the elevated MTA in MTAP del cancers and represents a promising therapy for the ∼10% of patients with cancer with this biomarker. See related commentary by Mulvaney, p. 2310. This article is featured in Selected Articles from This Issue, p. 2293

Abstract Background Immune checkpoint inhibitor combined with platinum‐etoposide is the standard first‐line therapy for patients with extensive‐stage small cell lung cancer (ES‐SCLC). The phase 3 clinical trials that led to the approval of chemoimmunotherapy in ES‐SCLC excluded patients who had an Eastern Cooperative Group (ECOG) performance status (PS) of 2–3. Therefore, data on the efficacy of chemoimmunotherapy in patients with an ECOG PS of 2–3 are limited. Methods A retrospective analysis was performed on patients diagnosed with ES‐SCLC who received chemoimmunotherapy (atezolizumab or durvalumab) within the Mayo Clinic Health System between January 2016 and January 2021. The objective of this study was to compare the overall survival (OS), progression‐free survival (PFS), and best clinical response to therapy in patients with an ECOG PS of 0–1 vs. patients with an ECOG PS of 2–3 who received chemoimmunotherapy for newly diagnosed ES‐SCLC. Results In total, 82 patients were included in the study. The mean ± standard deviation age was 68.1 ± 8.3 years. Of these, 56 patients were identified with an ECOG PS of 0–1, and 26 patients were identified with an ECOG PS of 2–3. The median PFS was similar regardless of ECOG PS (5.8 months [95% CI, 4.3–6.0 months] in the ECOG PS 0–1 group vs. 4.1 months [95% CI, 3.8–6.9 months] in the ECOG PS 2–3; p = .2994). The median OS was also similar regardless of ECOG PS (10.6 months [95% CI, 8.4–13.4 months] in the ECOG PS 0–1 group vs. 9.3 months [95% CI, 4.9–12.8 months]; p = .2718) in the ECOG PS 2‐3 group. Conclusions The study results demonstrated no significant difference in PFS or OS among the ECOG PS 2–3 and ECOG PS 0–1 groups. Therefore, chemoimmunotherapy should be considered for patients who have ES‐SCLC with an ECOG PS of 2–3.

Abstract Introduction: Most rare disease patients (75 − 50%) undergoing genomic sequencing remain unsolved, often due to lack of information about variants identified. Data review over time can leverage novel information regarding disease-causing variants and genes, increasing this diagnostic yield. However, time and resource constraints have limited reanalysis of genetic data in clinical laboratories setting. We developed RENEW, (REannotation of NEgative WES/WGS) an automated reannotation procedure that uses relevant new information in on-line genetic databases to enable rapid review of genetic findings. Methods We tested RENEW in an unselected cohort of 1,066 undiagnosed cases from the Mayo Clinic Center for Individualized Medicine using new information in ClinVar, HGMD and OMIM between the date of previous analysis/testing and April of 2022. Results 5,741 variants prioritized by RENEW were rapidly reviewed by variant interpretation specialists. Mean analysis time was approximately 20 seconds per variant (32 hours total time). Reviewed cases were classified as: 879 (93.0%) undiagnosed, 63 (6.6%) putatively diagnosed, and 4 (0.4%) definitively diagnosed. Discussion New strategies are needed to enable efficient review of genomic findings in unsolved cases. We report on a fast and practical approach to address this need and improve overall diagnostic success in patient testing through a recurrent reannotation process.

Shared decision making (SDM) is a method of care that is suitable for the care of patients with cancer. It involves a collaborative conversation seeking to respond sensibly to the problematic situation of the patient, cocreating a plan of care that makes sense intellectually, practically, and emotionally. Genetic testing to identify whether a patient has a hereditary cancer syndrome represents a prime example of the importance for SDM in oncology. SDM is important for genetic testing because not only results affect current cancer treatment, cancer surveillance, and care of relatives but also these tests generate both complex results and psychological concerns. SDM conversations should take place without interruptions, disruptions, or hurry and be supported, where available, by tools that assist in conveying the relevant evidence and in supporting plan development. Examples of these tools include treatment SDM encounter aids and the Genetics Adviser. Patients are expected to play a key role in making decisions and implementing plans of care, but several evolving challenges related to the unfettered access to information and expertise of varying trustworthiness and complexity in between interactions with clinicians can both support and complicate this role. SDM should result in a plan of care that is maximally responsive to the biology and biography of each patient, maximally supportive of each patient's goals and priorities, and minimally disruptive of their lives and loves.

Background: An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM AI-score). However, its clinical application may be challenging due to the low disease prevalence and potentially high false-positive rate when screening unenriched cohorts. Aims: To identify clinical characteristics associated with false-positive HCM AI-score results to improve screening tool application. Methods: From a cohort of 20,677 patients with a digital 12-lead ECG in routine clinical practice in 2021, we performed a detailed chart review of the 200 patients with highest HCM AI-score to determine their HCM status and collect clinical data. Stepwise forward logistic regression was used to create a clinical variable-based “candidacy for HCM screening” score (CHS score), differentiating true- and likely true-positive from likely false-positive HCM AI-score results. We then validated the CHS score in 200 patients with the highest HCM AI-scores from an independent cohort of 15,147 patients with an ECG in 2022. Results: Among the 200 patients in 2021 (median age 71 years, 48% female), 96 (48%) were likely false-positive HCM AI-score results, 71 (36%) definite or likely true-positive results, and 33 (16%) indeterminate. The CHS score included a history of surgical septal myectomy, coronary artery bypass graft surgery, cardiac valve replacement, and pacemaker, and had an AUC of 0.88 (95% CI 0.83-0.92) for the differentiation of true- versus false-positive AI results. In the 2022 cohort, the CHS score had an AUC of 0.82 (95% CI 0.76 - 0.88). After limiting the cohort to HCM candidates identified by the CHS score, the false-positive results rate was reduced to 27/114 (24%). Conclusions: By applying the CHS score to patients with positive HCM AI-screening results, the false-positive rate decreased from 49% to 24%. Utilizing a clinical model used in tandem with an AI model improved diagnostic yield.

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.

Introduction: Cardiac resynchronization therapy (CRT) has known benefit for heart failure (HF) with reduced left ventricular ejection fraction (LVEF) and dyssynchrony. However, very little is known about its benefit in infiltrative cardiomyopathies such as cardiac amyloidosis (CA) due to relative rarity of these conditions. Methods: This retrospective case series (2003-2021) comprised patients with HF due to transthyretin wild-type CA with (case) or without (control) CRT implant. Only controls with an indication for CRT or LVEF <40% were included. To further match disease severity, cases were 1:1 matched (propensity score tolerance 0.04) to controls for age, NYHA class, and CA stage (UK Staging). Primary outcome was MACE (major adverse cardiac events) which included stroke, myocardial infarction, HF hospitalization and cardiac mortality during a 3-year follow-up. Loss to follow-up was censored as per last visit date. LVEF and NYHA were noted closest to 1-year follow-up. Survival analysis for MACE was performed with Kaplan-Meier (KM) and multivariable cox regression models. Results: Study Cohort (n=160) had a mean age of 75.5 (SD 7.8) years and 97.5% were males. A majority had NYHA class 2 or 3 (92.3%) and CA stage 2 (42.2.%). Notably, median LVEF at baseline was lower in controls (33.5 vs 36.5; P = .001) and 77.5% of controls had an indication for CRT device. A total of 79 MACE events occurred, most frequent being HF hospitalization (n=45). KM curves were comparable for MACE events in the two groups (log-rank test; P = .059). Even after adjusting, as in table, CRT device was not associated with MACE events (adjusted HR 0.66; 95% CI 0.40-1.09; P = .106). Lastly, change in LVEF ( P = .84) and NYHA ( P = .86) at follow-up was similar in cases vs controls. Conclusions: The findings of this largest yet observational study to examine this question suggest that CRT in patients with transthyretin wild-type CA does not favorably impact outcomes for MACE events, improve LVEF or NYHA functional class.