Citations

Abstract Endocrine therapies (ET) with cyclin-dependent kinase 4/6 (CDK4/6) inhibition are the standard treatment for estrogen receptor-α-positive (ER+) breast cancer, however drug resistance is common. In this study, proteogenomic analyses of patient-derived xenografts (PDXs) from patients with 22 ER+ breast cancer demonstrated that protein kinase, membrane-associated tyrosine/threonine one (PKMYT1), a WEE1 homolog, is estradiol (E2) regulated in E2-dependent PDXs and constitutively expressed when growth is E2-independent. In clinical samples, high PKMYT1 mRNA levels associated with resistance to both ET and CDK4/6 inhibition. The PKMYT1 inhibitor lunresertib (RP-6306) with gemcitabine selectively and synergistically reduced the viability of ET and palbociclib-resistant ER+ breast cancer cells without functional p53. In vitro the combination increased DNA damage and apoptosis. In palbociclib-resistant, TP53 mutant PDX-derived organoids and PDXs, RP-6306 with low-dose gemcitabine induced greater tumor volume reduction compared to treatment with either single agent. Our study demonstrates the clinical potential of RP-6306 in combination with gemcitabine for ET and CDK4/6 inhibitor resistant TP53 mutant ER+ breast cancer.

Abstract Background and Aims Presentation, outcome, and management of females with degenerative mitral regurgitation (DMR) are undefined. We analysed sex-specific baseline clinical and echocardiographic characteristics at referral for DMR due to flail leaflets and subsequent management and outcomes. Methods In the Mitral Regurgitation International Database (MIDA) international registry, females were compared with males regarding presentation at referral, management, and outcome (survival/heart failure), under medical treatment, post-operatively, and encompassing all follow-up. Results At referral, females (n = 650) vs. males (n = 1660) were older with more severe symptoms and higher MIDA score. Smaller cavity diameters belied higher cardiac dimension indexed to body surface area. Under conservative management, excess mortality vs. expected was observed in males [standardized mortality ratio (SMR) 1.45 (1.27–1.65), P < .001] but was higher in females [SMR 2.00 (1.67–2.38), P < .001]. Female sex was independently associated with mortality [adjusted hazard ratio (HR) 1.29 (1.04–1.61), P = .02], cardiovascular mortality [adjusted HR 1.58 (1.14–2.18), P = .007], and heart failure [adjusted HR 1.36 (1.02–1.81), P = .04] under medical management. Females vs. males were less offered surgical correction (72% vs. 80%, P < .001); however, surgical outcome, adjusted for more severe presentation in females, was similar (P ≥ .09). Ultimately, overall outcome throughout follow-up was worse in females who displayed persistent excess mortality vs. expected [SMR 1.31 (1.16–1.47), P < .001], whereas males enjoyed normal life expectancy restoration [SMR 0.92 (0.85–0.99), P = .036]. Conclusions Females with severe DMR were referred to tertiary centers at a more advanced stage, incurred higher mortality and morbidity under conservative management, and were offered surgery less and later after referral. Ultimately, these sex-related differences yielded persistent excess mortality despite surgery in females with DMR, while males enjoyed restoration of life expectancy, warranting imperative re-evaluation of sex-specific DMR management.

Abstract Atrial fibrillation (AF) is a globally prevalent cardiac arrhythmia with significant genetic underpinnings, as highlighted by recent large-scale genetic studies. A prominent clinical and genetic overlap exists between AF, heritable ventricular cardiomyopathies, and arrhythmia syndromes, underlining the potential of AF as an early indicator of severe ventricular disease in younger individuals. Indeed, several recent studies have demonstrated meaningful yields of rare pathogenic variants among early-onset AF patients (∼4%–11%), most notably for cardiomyopathy genes in which rare variants are considered clinically actionable. Genetic testing thus presents a promising opportunity to identify monogenetic defects linked to AF and inherited cardiac conditions, such as cardiomyopathy, and may contribute to prognosis and management in early-onset AF patients. A first step towards recognizing this monogenic contribution was taken with the Class IIb recommendation for genetic testing in AF patients aged 45 years or younger by the 2023 American College of Cardiology/American Heart Association guidelines for AF. By identifying pathogenic genetic variants known to underlie inherited cardiomyopathies and arrhythmia syndromes, a personalized care pathway can be developed, encompassing more tailored screening, cascade testing, and potentially genotype-informed prognosis and preventive measures. However, this can only be ensured by frameworks that are developed and supported by all stakeholders. Ambiguity in test results such as variants of uncertain significance remain a major challenge and as many as ∼60% of people with early-onset AF might carry such variants. Patient education (including pretest counselling), training of genetic teams, selection of high-confidence genes, and careful reporting are strategies to mitigate this. Further challenges to implementation include financial barriers, insurability issues, workforce limitations, and the need for standardized definitions in a fast-moving field. Moreover, the prevailing genetic evidence largely rests on European descent populations, underscoring the need for diverse research cohorts and international collaboration. Embracing these challenges and the potential of genetic testing may improve AF care. However, further research—mechanistic, translational, and clinical—is urgently needed.

Behavioral neurology & neuropsychiatry (BNNP) is a field that seeks to understand brain–behavior relationships, including fundamental brain organization principles and the many ways that brain structures and connectivity can be disrupted, leading to abnormalities of behavior, cognition, emotion, perception, and social cognition. In North America, BNNP has existed as an integrated subspecialty through the United Council for Neurologic Subspecialties since 2006. Nonetheless, the number of behavioral neurologists across academic medical centers and community settings is not keeping pace with increasing clinical and research demand. In this commentary, we provide a brief history of BNNP followed by an outline of the current challenges and opportunities for BNNP from the behavioral neurologist’s perspective across clinical, research, and educational spheres. We provide a practical guide for promoting BNNP and addressing the shortage of behavioral neurologists to facilitate the continued growth and development of the subspecialty. We also urge a greater commitment to recruit trainees from diverse backgrounds so as to dismantle persistent obstacles that hinder inclusivity in BNNP—efforts that will further enhance the growth and impact of the subspecialty. With rapidly expanding diagnostic and therapeutic approaches across a range of conditions at the intersection of neurology and psychiatry, BNNP is well positioned to attract new trainees and expand its reach across clinical, research, and educational activities.

Abstract Artificial intelligence (AI) models based on deep learning of mammogram images have been developed for lesion detection and diagnosis as well as breast cancer (BC) risk. We and others have shown that these AI models, coupled with breast density, predict long-term breast cancer risk. Common germline genetic variation, in particular a polygenic risk score (PRS) that captures the cumulative impact of hundreds of common risk variants, has been shown associated with 3-fold increased BC risk. The BC-PRS may complement imaging risk factors to improve identification of women at high BC risk and better target prevention strategies. We evaluated the contribution of a BC-PRS to two imaging-based AI models (Transpara AI cancer detection system and Mirai 5-year risk) and breast density measures to long-term BC risk. We hypothesized that the BC-PRS will improve long-term risk prediction above AI and density measures. We examined this hypothesis within a cohort of 10,271 women enrolled to the Mayo Clinic Biobank between 2009 and 2015. Eligible women had no prior cancer, were ages 35 to 85, had a full-field digital screening mammogram within two years of enrollment and genotyping by Regeneron. Incident invasive and DCIS BC was identified through the Mayo tumor registry and follow-up questionnaires, and person years were calculated from date of mammogram through date of BC, last mammogram at Mayo or last questionnaire. We estimated Transpara AI malignancy score (1-10), 5-year Mirai risk and Volpara volumetric density measures (volumetric percent density, dense volume) on screening mammograms at enrollment and obtained clinical BI-RADS density. We calculated the BC-PRS using 268 SNPs (of the 313-SNP score) and the weighted sum of the SNPs using odds ratios from the largest BC GWAS of European ancestry to date. We performed cox proportional hazards regression adjusting for age and BMI to estimate hazard ratios (HRs), 95% confidence intervals (CI), and C-statistics (AUC) to describe the contribution of BC-PRS (per standard deviation (SD)) to the association of AI scores and breast density with BC risk. Likelihood ratio tests (LRT) and bootstrapping were used to compare model performance with vs. without inclusion of BC-PRS, at 10-years follow-up. Comparisons to the Tyrer-Cuzick model are ongoing. Over a median of 9.1 years (Interquartile range, 6.8 to 10.6), 446 incident BC were identified of which 285 invasive and 103 DCIS were confirmed by pathology report (60 were self-reported); 399 (256 invasive) occurred within 10 years and were used in primary analyses. Transpara score [HR per 1 unit=1.18 (1.14, 1.23), AUC=0.655 (0.628, 0.682)] and Mirai risk [HR per SD log risk=1.54 (1.43, 1.65), AUC=0.681 (0.655, 0.706)] were both associated with BC. All three breast density measures were also significantly associated with BC, including in models with AI scores (Table). Addition of the BC-PRS to models with AI scores and breast density improved both model fit (All PLRT < 0.001; Table) and discrimination, with increases in AUC ranging from 0.033 to 0.034 for models with Transpara and density and 0.025 to 0.026 for models with Mirai and density (all P-values < 0.001; Table). Analyses restricted to invasive BC showed identical results. But, the BC-PRS contribution at 5-year follow-up was attenuated. In this cohort study, we found BC-PRS contributed to long-term risk prediction of BC above imaging AI models and breast density. Imaging and genetic risk factors are complementary and may outperform existing risk models and provide more accurate risk assessment to aid in precision prevention strategies. Citation Format: Celine Vachon, Christopher Scott, Imon Banerjee, Ramon Correa Moderno, Dan Hursh, Matthew Jensen, Yvonne Wang, Kathleen Brandt, Karla Kerlikowske, Sandhya Pruthi, Fergus Couch, Stacey Winham. Contribution of a breast cancer polygenic risk score to mammography artificial intelligence models and breast density for long term breast cancer risk prediction [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS10-06.

Abstract Artificial intelligence (AI) models applied to mammography have been shown to improve breast cancer (BC) detection and risk estimation. The Transpara AI detection system has been associated with short and longer-term risk of invasive BC. Whether Transpara can identify changes in the breast associated with tamoxifen (TAM) and aromatase inhibitor (AIs) treatment is unknown. We compare changes in the Transpara malignancy score in the unaffected breast of BC cases using TAM or AIs to those of untreated women without cancer. Cases and controls were sampled from the Mayo Clinic Rochester Breast Screening Practice. Eligible cases were women with breast cancer treated with TAM and/or AIs for at least 8 months who had full field digital (FFDM) mammograms within two years prior to start of treatment (pretreatment mammogram). Controls did not have BC, were not using TAM, AIs, or postmenopausal hormones, and had at least two FFDM screening mammograms. Transpara scores (1-10) were calculated for the contralateral breast in cases and a randomly selected side for controls. Two controls were matched to each case on menopausal status at both mammograms, time between mammograms (< 6 months) and baseline Transpara score (±2 units). Conditional logistic regression was used to assess the association of treatment (case (TAM/AIs) vs. control) with change in Transpara score between the pretreatment mammogram and the mammogram taken closest to 13 months later. Change was classified as having a decrease in Transpara score between the two mammograms vs. no change or an increase. Models were adjusted for age and Transpara score at pretreatment mammogram, and time between the two mammograms. We performed analyses on all women combined and stratified by time on treatment, menopausal status, and treatment type (TAM vs. AIs). A total of 134 cases were identified, with 53 on TAM (21 premenopausal and 32 postmenopausal) and 81 on AIs (postmenopausal); 268 controls were matched to the cases. Characteristics of the study population are summarized (Table). Most case mammograms were obtained within six months prior to treatment start [median 2.63 months (interquartile range (IQR), 1.62, 5.72)] and median time on TAM/AIs was 10.9 months (IQR, 8.03, 16.6) at time of second mammogram. The median absolute change in Transpara score between the two mammograms was similar in cases [0 (IQR, -2, 1)] vs. controls [0 (IQR, -1.25, 1)]; 40.3% of cases and 39.2% of controls had any decrease. Conditional logistic models found no evidence for an association between TAM/AIs and decrease in Transpara score [odds ratio (OR) = 1.07; 95% confidence interval (CI), 0.65, 1.78]. When stratified by median time on treatment (10.9 months), there was suggestion of a stronger association for those on treatment for a longer duration (OR = 1.21; 95% CI, 0.61, 2.40 for >10.9 months vs. OR = 0.96; 95% CI, 0.43, 2.13 for < 10.9 months) but these results were not statistically significant. Similarly, odds ratios were stronger in postmenopausal (OR = 1.18; 95% CI, 0.67, 2.07) compared to premenopausal (OR = 0.91; 95% CI, 0.24, 3.45) women, and for AIs (OR = 1.21, 95% CI, 0.64, 2.29) vs. TAM (OR = 0.93; 95% CI, 0.39, 2.22) but none of these results reached statistical significance. In summary, we found no association between treatment with TAM/AIs and change in Transpara score. However, our study had limited power. Also, longer time on treatment as well as investigation of AI-based scores trained directly to assess BC risk may be necessary to identify relevant changes. Citation Format: Yvonne Wang, Christopher Scott, Matthew Jensen, Dan Hursh, Kathleen Brandt, Aaron Norman, Fang Fang Wu, Stacey Winham, Karla Kerlikowske, Celine Vachon. Changes in an artificial intelligence model score for breast cancer detection (Transpara) with tamoxifen and aromatase inhibitor treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-09-04.

Introduction: Loss-of-function (LOF) and gain-of-function (GOF) pathogenic variants in the KCNH2 -encoded Kv11.1 potassium channel cause type 2 long QT syndrome(LQT2) and type 1 short QT syndrome (SQT1), respectively. Given the prevalence of KCNH2- mediated heart disease (1 in 8000 for LQT2,1 in 100,000 for SQT1) and the reduced penetrance/variable expressivity seen in LQTS and SQTS, large databases such as the Genome Aggregation Database (gnomAD, n=141,352 individuals) might harbor subjects with unidentified LQT2/SQT1-causative mutations. Objective: To determine which “ultra-rare” KCNH2 missense variants (MVs) in gnomAD are most likely to be LQT2- or SQT1-causative. Methods: A list of gnomAD-derived MVs in KCNH2 was compiled. The frequency of each MV in gnomAD was assessed. Each MV was analyzed by 8 in silico pathogenicity prediction tools. MVs that were seen once in gnomAD and predicted damaging by all 8 tools were considered possible LQT2/SQT1-causative MVs worthy of functional validation via whole-cell patch clamp. Results: Overall, 233/474 (49%) of gnomAD KCNH2 MVs were seen in just 1 of >140,000 subjects. Of these, 2 (0.4%) MVs (S320W, S379Y) were predicted damaging by 8 in silico tools. Both S320W and S379Y localize to the N-terminus of the Kv11.1 potassium channel. There was no significant change in peak or tail current density between heterozygously expressed KCNH2-S320W (n=13, p=NS) and KCNH2-WT (n=13). However, heterozygous KCNH2-S379Y (n=12) produced a marked increase in peak current density across the range -30 mV to +20 mV and shifted the V 1/2 of activation by -17 mV (p < 0.001). Also, tail current density of S379Y channels significantly increased across the range -20 mV to +20 mV and the V 1/2 of deactivation shifted by -26 mV (p < 0.001). Conclusion: With this stringent requirement of ultra-rarity, universal prediction of damaging impact, and functionally abnormal electrophysiological properties, one SQT1-causative pathogenic variant in gnomAD was identified consistent with the predicted frequency of SQTS. The search continues among the remaining 32 ultra-rare MVs in KCNH2 where at least 75% of the in silico tools predicted deleterious impact of the MV to find the 15-20 subjects in gnomAD’s population who ought to have an LQT2-causative MV.

Introduction: Loss-of-function (LOF) variants in the KCNH2 -encoded Kv11.1 potassium channel cause type 2 long QT syndrome (LQT2). To date, hundreds of KCNH2 missense variants (MVs) have been published as “disease-causative”. However, it has been suggested that 10% of rare LQTS variants in the literature may be "false positives”. Objective: To determine previously published KCNH2 variants that are likely false positives and warrant demotion to at least likely benign status. Methods: A list of all case-derived LQT2-associated MVs from six large published compendia was compiled. The frequency of each MV within the Genome Aggregation Database (gnomAD, n = 141,352 individuals) was recorded. Eight in silico variant assessment tools were used to grade each MV. Variants (n=8) absent in gnomAD but predicted “benign” by all 8 tools were considered potential false positives, despite being ultra-rare, and were characterized functionally using whole-cell patch clamp. Results: Overall, 339 KCNH2 MVs were identified among LQTS cases. Of these, 13 (4%) were seen at a minor allele frequency (MAF) > 0.0004 in gnomAD. This MAF cut-off is derived from the frequency of R176W-KCNH2, a well-established albeit weakly penetrant (i.e. < 20%) LQT2-causative MV. Variants seen at MAF > R176W-KCNH2 are unlikely to be LQT2-contributing. However, 253 MVs (75%) were absent in gnomAD. Of these, 8 (4%) MVs (I96V, G187S, A203T, P241L, H254Q, G314S, P935S, and P963T) were predicted by all 8 in silico tools to be benign and had never been characterized functionally. Patch clamp studies did not demonstrate any LOF perturbation for these 8 MVs. Conclusion: This study offers compelling evidence for the demotion from LQT2-causative status to likely benign status for 21 (6%) of the 339 previously published LQT2 MVs based either on i) gnomAD MAF unacceptably high for a 1:8000 disorder (LQT2) for 13 MVs or ii) ultra-rarity (never seen in gnomAD) but in silico variant assessment tools predicting benign impact of the amino acid substitution and in vitro functional validation studies devoid of LOF for 8 additional MVs. This meticulous “pruning” exercise must be conducted for all published variants previously implicated as the monogenic cause for LQTS in particular and all genetic heart diseases in general.