Citations

Abstract Background: In an adaptive randomized trial, when new treatment combinations are being tested, it is important to be able to identify patients who are progressing on treatment so that they can be changed to a different therapeutic regimen. We know that even within the molecularly high risk patients in I-SPY 2, there is considerable variation in biology. In this study, we will present results of using MRI-calculated functional tumor volume (FTV) to identify tumor progression for each breast cancer subtype. Methods: Patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Four MRI exams were performed for each patient: pre-NAC (T0), after 3 weeks of NAC (T1), between regimens (T2), and post-NAC (T3). Functional tumor volume (FTV) was calculated at each exam by summing voxels meeting enhancement thresholds. Tumor progression at T1, T2 or T3 was identified by a positive FTV change relative to T0. Visual inspection was used to exclude false progression due to strong background parenchymal enhancement post-contrast, prominent vessels, motion, or insufficient image quality. pCR was defined as no invasive disease in the breast and lymph nodes. Negative predictive value for pCR was defined as:NPV=number of true non-pCRs / number of patients with MRI assessed tumor progressions, where “true non-pCRs” referred to patients who were non-pCRs at surgery and were assessed as progressors by MRI. The analysis was performed in the full cohort and in sub-cohorts defined by HR and HER2 statuses. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Total and non-pCR numbers for each subtype, number of patients with tumor progression assessed by MRI at T1, T2, and T3, and NPVs, are shown in Table 1. In the full cohort, the NPV increased consistently over treatment, from T1 (NPV=83%) to T2 (93%), and to T3 (100%). The HER2+ cancer subtypes showed fewer MRI-assessed tumor progressions than HER2- subtypes: e.g. 10/209 (5%) vs. 108/669 (16%) at T1. NPV was 100% for HER2+ subtypes at T1 and T2 except for a single misclassification of a HR- tumor at T1. Only 6 tumor progressors, all HER2- were identified at T3, and all were confirmed at surgery as non-pCRs (NPV=100%). For HR+/HER2-, the NPV increased slightly from 89% at T1 to 91% at T2, while triple negative subtype had a more substantial increase, from 78% to 92%. Conclusions: Our study showed strong association between tumor progressors assessed by MRI with true non-pCRs after NAC. For HER2+ tumors, although MRI progressors are rare, they strongly indicate non-pCR at all treatment time points, while HER2- subtypes show more accurate results later in treatment. We are evaluating MRI change at 6 weeks to determine if that time point is sufficient to predict progressors. Table 1 MRI assessed tumor progression at different treatment time pointN/non-pCRs/%non-pCRMRI assessed tumor progressionT1 (after 3 weeks)T2 (inter-regimen)T3 (post-NAC)NNPV (%)NNPV (%)NNPV (%)Full cohort878/572/65%11883.14192.76100%HR+/HER2-344/280/81%4588.91190.93100%HR+/HER2+134/85/63%610021000N/AHR-/HER2+75/23/31%47521000N/Atriple negative325/184/57%6377.82692.33100% Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Jessica Gibbs, Lisa J Wilmes, Ella F Jones, Bonnie N Joe, Laura S Sit, Christina Yau, A. Jo Chien, Elissa Price, Kathy S Albain, Theresa Kuritza, Kevin Morley, Judy C Boughey, Kathy Brandt, Sadia Choudhery, Amy S Clark, Mark Rosen, Elizabeth S McDonald, Anthony D Elias, Dulcy Wolverton, Kelly Fountain, David M Euhus, Heather S Han, Bethany Niell, Jennifer Drukteinis, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Smita M Asare, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van't Veer, Donald A Berry, Angela DeMichele, Hiroyuki Abe, Deepa Sheth, Kirsten K Edmiston, Erin D Ellis, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Michael Nelson, An Church, Claudine Isaacs, Qamar J Khan, Karen Y Oh, Neda Jafarian, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Pulin Sheth, Linda Hovanessian-Larsen, Mohammad Eghtedari, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Sally Goudreau, Thelma Brown, Laura J Esserman, Nola M Hylton. Breast cancer subtype specific association of pCR with MRI assessed tumor volume progression during NAC in the I-SPY 2 trial [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD9-04.

Abstract Background: Strong background parenchymal enhancement (BPE) may cause overestimation in tumor volume measured from dynamic contrast-enhanced (DCE) MRI, which may adversely affect the ability of MR tumor volume to predict treatment outcome for patients undergoing neoadjuvant chemotherapy (NAC). Specifically, an overestimation of tumor volume can result in misclassification of patients with complete pathologic response (pCR) as non-responders, leading to less confidence in MRI prediction. As well, overestimation of extent of disease might lead to more aggressive surgical therapy than necessary. This study investigated whether high BPE in the contralateral breast influences the predictive performance of MRI-measured functional tumor volume (FTV) for patients with locally advanced breast cancer undergoing NAC. Methods: patients (n=990) enrolled in the I-SPY 2 TRIAL who were randomized to the graduated experimental drug arms or controls from 2010 to 2016 were analyzed. Each patient had 4 MRI exams: pre-NAC (T0), after 3 weeks of NAC (T1), between NAC regimens (T2), and post-NAC (T3). FTV was calculated at each MRI exam by summing voxels meeting enhancement thresholds. Background parenchymal enhancement (BPE) in the contralateral breast was calculated automatically as mean percentage enhancement on the early (nominal 150sec post-contrast) image in the fibroglandular tissue segmented from 5 continuous axial slices centered in the inferior-to-superior stack. For each treatment time point, patients having both FTV and BPE measurements were included in the analysis. The area under the ROC curve (AUC) was estimated as the association between FTV and pCR at T1, T2, and T3. The analysis was conducted in the full patient cohort and in sub-cohorts defined by hormone receptor (HR) and HER2 status. In each patient cohort, a cut-off BPE value was selected to classify patients with high vs. low BPE by testing AUCs estimated with low-BPE patients reached maximum when the cut-off value varied from median to maximum in steps of 10%. Results: Out of 990 patients, 878 had pCR outcome data (pCR or non-pCR, pCR rate = 35%). Table 1 shows the number of patients, pCR rate, and AUC of FTV for predicting pCR using all patients available vs. a subset patients with low BPE (< BPE cut-off). In the full cohort, AUC increased slightly across all time points after patients with high BPE were removed. In the HR+/HER2- subtype, AUC increased at T1 after removal of cases with high BPE (0.65 vs. 0.71). For HR-/HER2+, AUC increased substantially after removal of high BPE cases (0.65 to 0.86 at T1, 0.71 to 0.87 at T2, and 0.71 to 0.89 at T3), with greater improvement at the early time point (T1) compared to later time points (T2 and T3). Only a slight improvement in the AUC was observed in the HR+/HER2+ and HR-/HER2- subtypes across all time points. Conclusions: High background parenchymal enhancement adversely affected the predictive performance of functional tumor volume measured by DCE-MRI, at early treatment time point for HR+/HER2- and across all time points for HR-/HER2+ cancer subtype. The adverse effect might be offset using subtype-optimized enhancement threshold in calculating functional tumor volume. Table 1 Effect of BPE on the prediction of pCR using FTV at various treatment time pointsT1T2T3npCR rateAUCBPE cut-offnpCR rateAUCBPE cut-offnpCR rateAUCBPE cut-offFullAll64734%0.662762334%0.701761134%0.6925Subset45334%0.6831133%0.7230534%0.72HR+/HER2-All26218%0.651924918%0.718225518%0.7519Subset13118%0.7124818%0.7120419%0.76HR+/HER2+All10636%0.642110538%0.62269634%0.7120Subset5332%0.668438%0.665740%0.73HR-/HER2+All5175%0.65204774%0.71204973%0.7116Subset3073%0.862871%0.872475%0.89HR-/HER2-All22842%0.682822243%0.751821143%0.6916Subset15940%0.7111137%0.7810540%0.75 Citation Format: Wen Li, Natsuko Onishi, David C Newitt, Roy Harnish, Ella F Jones, Lisa J Wilmes, Jessica Gibbs, Elissa Price, Bonnie N Joe, A. Jo Chien, Donald A Berry, Judy C Boughey, Kathy S Albain, Amy S Clark, Kirsten K Edmiston, Anthony D Elias, Erin D Ellis, David M Euhus, Heather S Han, Claudine Isaacs, Qamar J Khan, Julie E Lang, Janice Lu, Jane L Meisel, Zaha Mitri, Rita Nanda, Donald W Northfelt, Tara Sanft, Erica Stringer-Reasor, Rebecca K Viscusi, Anne M Wallace, Douglas Yee, Rachel Yung, Michelle E Melisko, Jane Perlmutter, Hope S Rugo, Richard Schwab, W. Fraser Symmans, Laura J van't Veer, Christina Yau, Smita M Asare, Angela DeMichele, Sally Goudreau, Hiroyuki Abe, Deepa Sheth, Dulcy Wolverton, Kelly Fountain, Richard Ha, Ralph Wynn, Erin P Crane, Charlotte Dillis, Theresa Kuritza, Kevin Morley, Michael Nelson, An Church, Bethany Niell, Jennifer Drukteinis, Karen Y Oh, Neda Jafarian, Kathy Brandt, Sadia Choudhery, Dae Hee Bang, Christiane Mullins, Stefanie Woodard, Kathryn W Zamora, Haydee Ojeda-Fornier, Mohammad Eghedari, Pulin Sheth, Linda Hovanessian-Larsen, Mark Rosen, Elizabeth S McDonald, Michael Spektor, Marina Giurescu, Mary S Newell, Michael A Cohen, Elise Berman, Constance Lehman, William Smith, Kim Fitzpatrick, Marisa H Borders, Wei Yang, Basak Dogan, Laura J Esserman, Nola M Hylton. The effect of background parenchymal enhancement on the predictive performance of functional tumor volume measured in MRI [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-02-01.

Background: Risk stratification in catecholaminergic polymorphic ventricular tachycardia remains ill defined. Heart rate recovery (HRR) immediately after exercise is regulated by autonomic reflexes, particularly vagal tone, and may be associated with symptoms and ventricular arrhythmias in patients with catecholaminergic polymorphic ventricular tachycardia. Our objective was to evaluate whether HRR after maximal exercise on the exercise stress test (EST) is associated with symptoms and ventricular arrhythmias. Methods: In this retrospective observational study, we included patients ≤65 years of age with an EST without antiarrhythmic drugs who attained at least 80% of their age- and sex-predicted maximal HR. HRR in the recovery phase was calculated as the difference in heart rate (HR) at maximal exercise and at 1 minute in the recovery phase (ΔHRR1′). Results: We included 187 patients (median age, 36 years; 68 [36%] symptomatic before diagnosis). Pre-EST HR and maximal HR were equal among symptomatic and asymptomatic patients. Patients who were symptomatic before diagnosis had a greater ΔHRR1′ after maximal exercise (43 [interquartile range, 25–58] versus 25 [interquartile range, 19–34] beats/min; P <0.001). Corrected for age, sex, and relatedness, patients in the upper tertile for ΔHRR1′ had an odds ratio of 3.4 (95% CI, 1.6–7.4) of being symptomatic before diagnosis ( P <0.001). In addition, ΔHRR1′ was higher in patients with complex ventricular arrhythmias at EST off antiarrhythmic drugs (33 [interquartile range, 22–48] versus 27 [interquartile range, 20–36] beats/min; P =0.01). After diagnosis, patients with a ΔHRR1′ in the upper tertile of its distribution had significantly more arrhythmic events as compared with patients in the other tertiles ( P =0.045). Conclusions: Catecholaminergic polymorphic ventricular tachycardia patients with a larger HRR following exercise are more likely to be symptomatic and have complex ventricular arrhythmias during the first EST off antiarrhythmic drug.

Introduction: Anti-PD-1 monoclonal antibodies (mAbs) are highly active in relapsed/refractory classical Hodgkin lymphoma (cHL), but most patients (pts) will still relapse. Given this, allogeneic stem cell transplantation (alloHSCT) remains an important option for pts after PD-1 blockade, as it offers the possibility of cure. Prior reports have suggested that alloHSCT after PD-1 mAbs may be associated with severe immune-related complications including acute graft-versus-host disease (GVHD), veno-occlusive disease (VOD) and cytokine release/febrile non-infectious syndrome (CRS). Prior studies of alloHSCT after PD-1 blockade in cHL have been limited by the small number of pts and short follow-up, preventing an accurate assessment of long-term outcomes and complications, risk factors for early toxicity, and the impact of transplant strategies such as choice of GVHD prophylaxis. We therefore assembled a large retrospective international cohort of cHL pts who underwent alloHSCT after PD-(L)1 blockade to better answer these questions, including an assessment of the impact of post-transplant cyclophosphamide (PTCy) on efficacy and toxicity. Methods: Medical records and databases were reviewed at 26 European and United States transplant centers to identify pts with cHL who underwent an alloHSCT any time after receiving a PD-1 or PD-L1 mAb. Response assessment was performed by local investigators according to Lugano 2014 criteria. Overall survival (OS), progression-free survival (PFS), cumulative incidence (CumInc) of relapse (CIR), non-relapse mortality (NRM), acute (a) and chronic (c) GVHD were estimated, as was the association between baseline variables and these outcomes. Results: Between 2014 and 2019, 150 pts were identified who underwent alloHSCT after a median of 10 (range, 1-74) doses of nivolumab (n=118), pembrolizumab (n=31), or avelumab (n=1). The median age was 31 (range 17-68) and pts had received a median of 4 (range, 2-11) lines of therapy prior to PD-(L)1 blockade. 138 pts (92%) had failed BV and 111 (74%) autologous HSCT. The best overall response to PD-(L)1 mAbs was CR for 62 pts (41%), PR for 55 (37%), SD for 17 (11%), PD for 15 (10%) and unknown for 1 (1%). Median time from last dose of PD-(L)1 mAb to alloHSCT was 80 days (range, 17-756) with 70 pts (47%) receiving intervening systemic therapy. At alloHSCT, 90 pts were in CR (60%), 45 in PR (30%), 5 in SD (3%), and 10 in PD (7%). Donors were haploidentical (n=71, 47%), matched sibling (n=29, 19%), matched unrelated (n=39, 26%), mismatched unrelated (n=7, 5%), cord blood (n=2, 1%), or unknown (n=2, 1%). Stem cell source was bone marrow (n=38, 25%), peripheral blood (n=110, 73%), or cord blood (n=2, 1%). GVHD prophylaxis included PTCy in 88 pts (59%) (69/71 (97%) with haploidentical donors; 19/79 (24%) with other donors). With a median post-alloHSCT follow-up for survivors of 23.8 months (range, 1-67), the 2y OS and PFS were 79% (95CI 71-86%) and 65% (95CI 55-73%), respectively, while the 2y CumIncs of relapse and NRM were 21% (95CI 13-29%) and 14%, (95CI, 8-22%), respectively (Fig. 1A-B). 27 pts have died, 3 due to disease and 24 to NRM, including aGVHD (n=7) and VOD (n=2). Veno-occlusive disease (VOD) occurred in 5 pts (day 100 CumInc 4%) and 29 pts (19%) developed CRS (grade 1 n=16; grade 2 n=7; grade 3 n=4; grade 4 n=2). The 6-month CumIncs of grade 2-4, grade 3-4 and grade 4 aGVHD were 39%, 16% and 8%, respectively. Hyperacute GVHD (onset ≤ 14 days after alloHSCT) occurred in 4% of pts and was fatal in 2 pts. The 2y CumInc of cGVHD was 45%. Neither receipt of &gt; 10 doses (median) of anti-PD-(L)1 mAb nor undergoing alloHSCT ≤80 days (median) after last dose of PD-(L)1 mAb were associated with PFS or OS. However, pts with a shorter time to transplant (≤80 days) appeared to have a higher risk of severe (grade 3-4) aGVHD (6m CumInc 24% vs 9%, p=0.006). Recipients of PTCy in this cohort had lower 2y CumIncs of cGVHD (34% vs 58%, p=0.01) and relapse (12% vs 31%, p=0.02), superior 2y PFS (76% vs 54%, p=0.015), and similar rates of severe aGVHD (15% vs 18%, p=0.5), 2y NRM (12% vs 16%, p=0.5), and 2y OS (82% vs 78%, p=0.6). Conclusions: With extended follow-up of a large international cohort, our results argue that alloSCT performed after PD-(L)1 mAbs is a feasible strategy associated with an excellent PFS and a very low CIR for this disease. The use of PT-Cy appears to be associated with improved outcomes and may at present represent the optimal transplant strategy in this pt population. Figure Disclosures Corradini: kite: Honoraria; Abbvie: Honoraria; Servier: Honoraria; Sanofi: Honoraria; Takeda: Honoraria; Roche: Honoraria; Novartis: Honoraria; KiowaKirin: Honoraria; Janssen: Honoraria; Gilead: Honoraria; Daiichi Sankyo: Honoraria; Celgene: Honoraria; Amgen: Honoraria. Ho:Jazz Pharmaceuticals: Research Funding; Jazz Pharmaceuticals: Consultancy; Omeros Corporation: Membership on an entity's Board of Directors or advisory committees. Jaglowski:Kite: Consultancy, Other: advisory board, Research Funding; Novartis: Consultancy, Other: advisory board, Research Funding; Juno: Consultancy, Other: advisory board; Unum Therapeutics Inc.: Research Funding. Herrera:Adaptive Biotechnologies: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Gilead Sciences: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding; AstraZeneca: Research Funding; Merck: Consultancy, Research Funding; Genentech, Inc.: Consultancy, Research Funding; Pharmacyclics: Research Funding; Immune Design: Research Funding; Kite Pharma: Consultancy, Research Funding. Blaise:Jazz Pharmaceuticals: Honoraria; Sanofi: Honoraria; Pierre Fabre medicaments: Honoraria; Molmed: Consultancy, Honoraria. Hamadani:Pharmacyclics: Consultancy; Sanofi Genzyme: Research Funding, Speakers Bureau; Merck: Research Funding; Otsuka: Research Funding; Janssen: Consultancy; Medimmune: Consultancy, Research Funding; ADC Therapeutics: Consultancy, Research Funding; Takeda: Research Funding; Celgene: Consultancy. Ansell:LAM Therapeutics: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Affimed: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Trillium: Research Funding; Bristol-Myers Squibb: Research Funding; Mayo Clinic Rochester: Employment; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Affimed: Research Funding. Nieto:Astra-Zeneca: Research Funding; Affimed: Consultancy; Affimed: Research Funding; Novartis: Research Funding. Feldman:Celgene: Honoraria, Research Funding, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Other: Travel expenses, Speakers Bureau; Pfizer: Research Funding; AbbVie: Honoraria, Other: Travel expenses, Speakers Bureau; Portola Pharma: Research Funding; Kite Pharma: Honoraria, Other: Travel expenses, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Pharmacyclics: Honoraria, Other: Travel expenses, Speakers Bureau; Kyowa Hakko Kirin: Research Funding; Eisai: Research Funding; Bayer: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Cell Medica: Research Funding; Roche: Research Funding; Corvus: Research Funding; Viracta: Research Funding; Trillium: Research Funding; Roche: Research Funding; Takeda: Honoraria, Speakers Bureau. McGuirk:ArticulateScience LLC: Other: Assistance with manuscript preparation; Bellicum Pharmaceuticals: Research Funding; Kite Pharmaceuticals: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Gamida Cell: Research Funding; Pluristem Ltd: Research Funding; Juno Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astellas: Research Funding; Novartis: Research Funding; Fresenius Biotech: Research Funding. Mohty:Jazz Pharmaceuticals: Honoraria, Research Funding. Stamatoulas Bastard:Celgene: Honoraria; Takeda: Consultancy. Houot:Bristol Myers Squibb: Honoraria; Merck Sharp Dohme: Honoraria. Manson:Bristol Myers Squibb: Honoraria. Orvain:Incyte: Honoraria; Novartis: Honoraria; Jazz Pharmaceuticals: Other: Travel & accommodations; Pfizer: Other: Travel & accommodations. Bouabdallah:Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Frigault:Novartis: Consultancy; Kite/Gilead: Honoraria; Nkarta: Consultancy; Incyte: Consultancy; Juno/Celgene: Consultancy; Foundation Medicine: Consultancy; Xenetic: Consultancy. Chen:Takeda: Consultancy; Kiadis: Consultancy; Magenta: Consultancy; Abbvie: Consultancy; Incyte: Consultancy. Lynch:T.G. Therapeutics: Research Funding; Rhizen Pharmaceuticals S.A: Research Funding; Takeda Pharmaceuticals: Research Funding; Juno Therapeutics: Research Funding; Incyte Corporation: Research Funding; Johnson Graffe Keay Moniz & Wick LLP: Consultancy. Smith:AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck Sharp & Dohme Corp: Consultancy, Research Funding; Acerta Pharma BV: Research Funding; Portola Pharmaceuticals: Research Funding; Pharmacyclics: Research Funding; Bristol-Myers Squibb (spouse): Research Funding; Denovo Biopharma: Research Funding; Genentech: Research Funding; Ignyta (spouse): Research Funding; Incyte Corporation: Research Funding; Ayala (spouse): Research Funding; Seattle Genetics: Research Funding. Byrne:Karyopharm: Research Funding. Cohen:Hutchison: Research Funding; Astra Zeneca: Research Funding; Janssen Pharmaceuticals: Consultancy; Seattle Genetics, Inc.: Consultancy, Research Funding; Bristol-Meyers Squibb Company: Research Funding; Takeda Pharmaceuticals North America, Inc.: Research Funding; Gilead/Kite: Consultancy; Genentech, Inc.: Consultancy, Research Funding; UNUM: Research Funding; ASH: Research Funding; LAM Therapeutics: Research Funding; Lymphoma Research Foundation: Research Funding. Svoboda:AstraZeneca: Consultancy; Celgene: Research Funding; Incyte: Research Funding; Pharmacyclics: Consultancy, Research Funding; Kyowa: Consultancy; Merck: Research Funding; BMS: Consultancy, Research Funding; Seattle Genetics: Consultancy, Research Funding. Santoro:Bayer: Consultancy, Speakers Bureau; MSD: Speakers Bureau; Arqule: Consultancy, Speakers Bureau; Pfizer: Consultancy, Speakers Bureau; AstraZeneca: Speakers Bureau; Gilead: Consultancy, Speakers Bureau; Servier: Consultancy, Speakers Bureau; Takeda: Speakers Bureau; BMS: Speakers Bureau; Roche: Speakers Bureau; Abb-Vie: Speakers Bureau; Amgen: Speakers Bureau; Celgene: Speakers Bureau; Novartis: Speakers Bureau; BMS: Consultancy; Lilly: Speakers Bureau; Sandoz: Speakers Bureau; Eisai: Consultancy, Speakers Bureau. Armand:Sigma Tau: Research Funding; Otsuka: Research Funding; Pfizer: Consultancy; ADC Therapeutics: Consultancy; Tensha: Research Funding; Affimed: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Research Funding; Adaptive: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Infinity: Consultancy; Genentech: Research Funding. Zinzani:Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Portola: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Immune Design: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sandoz: Membership on an entity's Board of Directors or advisory committees; Servier: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen-Cilag: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celltrion: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Sanofi: Consultancy; Eusapharma: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Verastem: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; MSD: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Kyowa Kirin: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; TG Therapeutics: Honoraria, Speakers Bureau. Carlo-Stella:Servier: Consultancy, Honoraria, Other: Travel, accommodations; Genenta Science srl: Consultancy; Boehringer Ingelheim: Consultancy; F. Hoffmann-La Roche Ltd: Honoraria, Other: Travel, accommodations, Research Funding; Novartis: Consultancy, Research Funding; ADC Therapeutics: Consultancy, Other: Travel, accommodations, Research Funding; Sanofi: Consultancy, Research Funding; Rhizen Pharmaceuticals: Research Funding; Celgene: Research Funding; Amgen: Honoraria; AstraZeneca: Honoraria; Janssen Oncology: Honoraria; MSD: Honoraria; BMS: Honoraria; Janssen: Other: Travel, accommodations; Takeda: Other: Travel, accommodations.

There is a movement in the United States to transform family medicine practices from single physician–based patient care to team-based care. These teams are usually composed of multiple disciplines, including social workers, pharmacists, registered nurses, physician assistants, nurse practitioners, and physicians. The teams support patients and their families, provide holistic care to patients of all ages, and allow their members to work to the highest level of their training in an integrated fashion. Grouping care team members together within visual and auditory distance of each other is likely to enhance communication and teamwork, resulting in more efficient care for patients. This grouping is termed colocation. The authors describe how the use of colocation can lead to clearer, faster communication between care team members. This practice style has the potential to be expanded into various clinical settings in any given health system and to almost all clinical specialties and practices.

The association of progesterone/progesterone metabolites with elevated mammographic breast density (MBD) and delayed age-related terminal duct lobular unit (TDLU) involution, strong breast cancer risk factors, has received limited attention. Using a reliable liquid chromatography-tandem mass-spectrometry assay, we quantified serum progesterone/progesterone metabolites and explored cross-sectional relationships with MBD and TDLU involution among women, ages 40–65, undergoing diagnostic breast biopsy. Quantitative MBD measures were estimated in pre-biopsy digital mammograms. TDLU involution was quantified in diagnostic biopsies. Adjusted partial correlations and trends across MBD/TDLU categories were calculated. Pregnenolone was positively associated with percent MBD-area (MBD-A, rho: 0.30; p-trend = 0.01) among premenopausal luteal phase women. Progesterone tended to be positively associated with percent MBD-A among luteal phase (rho: 0.26; p-trend = 0.07) and postmenopausal (rho: 0.17; p-trend = 0.04) women. Consistent with experimental data, implicating an elevated 5α-pregnanes/3α-dihydroprogesterone (5αP/3αHP) metabolite ratio in breast cancer, higher 5αP/3αHP was associated with elevated percent MBD-A among luteal phase (rho: 0.29; p-trend = 0.08), but not postmenopausal women. This exploratory analysis provided some evidence that endogenous progesterone and progesterone metabolites might be correlated with MBD, a strong breast cancer risk factor, in both pre- and postmenopausal women undergoing breast biopsy. Additional studies are needed to understand the role of progesterone/progesterone metabolites in breast tissue composition and breast cancer risk.

ABSTRACTCoxsackievirus B3 (CVB3) infection induces myocardial inflammation and myocyte necrosis in some, but not all, strains of mice. C57BL/6 mice, which inherently lack major histocompatibility complex (MHC) class II IE antigen, develop minimal cardiac lesions despite high levels of virus in the heart. The present experiments evaluate the relative roles of class II IA and IE expression on myocarditis susceptibility in four transgenic C57BL/6 mouse strains differing in MHC class II antigen expression. Animals lacking MHC class II IE antigen (C57BL/6 [IA+IE−] and ABo[IA−IE−]) developed minimal cardiac lesions subsequent to infection despite high concentrations of virus in the heart. In contrast, strains expressing IE (ABoEα [IA−IE+] and Bl.Tg.Eα [IA+IE+]) had substantial cardiac injury. Myocarditis susceptibility correlated to a Th1 (gamma interferon-positive) cell response in the spleen, while disease resistance correlated to a preferential Th2 (interleukin-4-positive) phenotype. Vγ/Vδ analysis indicates that distinct subpopulations of γδ+T cells are activated after CVB3 infection of C57BL/6 and Bl.Tg.Eα mice. Depletion of γδ+T cells abrogated myocarditis susceptibility in IE+animals and resulted in a Th1→Th2 phenotype shift. These studies indicate that the MHC class II antigen haplotype controls myocarditis susceptibility, that this control is most likely mediated through the type of γδ T cells activated during CVB3 infection, and finally that different subpopulations of γδ+T cells may either promote or inhibit Th1 cell responses.

OBJECTIVES Administrative records such as Medicare fee‐for‐service (FFS) claims provide accurate information on services paid for by Medicare. However, the increasing availability of electronic health records means many researchers may be inclined to rely on data coded in hospital information systems rather than claims. The current quality and accuracy of hospital reports on the use of post‐acute care (PAC) services are not known. DESIGN This study examined differences in the PAC use between hospital discharge status recorded on Medicare Provider and Analysis Review inpatient hospital records and claims for PAC services. SETTING In addition to assessments of the three types of Medicare‐reimbursed PAC (home health agency [HHA], skilled nursing facility [SNF], and inpatient rehabilitation facility [IRF]), the analysis also considered home without PAC services as a default discharge location. PARTICIPANTS The analysis was conducted using data for FFS beneficiaries who participated in the Medicare Current Beneficiary Survey and had one or more inpatient hospitalizations from 2006 to 2011. MEASUREMENTS This study measured discrepancies between hospital‐reported discharges to PAC and PAC use based on Medicare claims. RESULTS The study found that, on average, 27.9% of hospital reports of discharging to Medicare‐covered PAC services were not substantiated by Medicare PAC claims. Among all the discharge pathways, discharging to HHAs had the highest discrepancy rate (29.6%), followed by IRFs (14.7%) and SNFs (13.8%). CONCLUSION The study results call for cautions about the extent to which the reported discharge locations on hospital claims may differ from actual PAC services used. Assuming that Medicare FFS claims were complete and accurate, researchers using the discharge status reported on Medicare hospital claims should be aware of possible measurement errors when using hospital‐reported discharge locations. J Am Geriatr Soc 68:847–851, 2020

Abstract Delayed terminal duct lobular unit (TDLU) involution is associated with elevated mammographic breast density (MD). Both are independent breast cancer risk factors among women with benign breast disease (BBD). Prior digital analyses of normal breast tissues revealed that epithelial nuclear density (END) and TDLU involution are inversely correlated. Accordingly, we examined associations of END, TDLU involution, and MD in BBD clinical biopsies. This study included digitized images of 262 representative image-guided hematoxylin and eosin–stained biopsies from 224 women diagnosed with BBD, enrolled within the cross-sectional BREAST-Stamp project that were visually assessed for TDLU involution (TDLU count/100 mm2, median TDLU span and median acini count per TDLU). A digital algorithm estimated nuclei count per unit epithelial area, or END. Single X-ray absorptiometry of prebiopsy ipsilateral craniocaudal digital mammograms measured global and localized MD surrounding the biopsy region. Adjusted ordinal logistic regression models assessed relationships between tertiles of TDLU and END measures. Analysis of covariance examined mean differences in MD across END tertiles. TDLU measures were positively associated with increasing END tertiles [TDLU count/100 mm2, ORT3vsT1: 3.42, 95% confidence interval (CI), 1.87–6.28; acini count/TDLUT3vsT1, OR: 2.40, 95% CI, 1.39–4.15]. END was significantly associated with localized, but not, global MD. Relationships were most apparent among patients with nonproliferative BBD. These findings suggest that quantitative END reflects different but complementary information to the histologic information captured by visual TDLU and radiologic MD measures and merits continued evaluation in assessing cellularity of breast parenchyma to understand the etiology of BBD.

Abstract Background Schwannomas are rare tumours that pose a significant management challenge in the abdomen, retroperitoneum and pelvis. No data are available to inform management strategy. Methods A collaborative international cohort study, across specialist sarcoma units, was conducted to include adults presenting between 2000 and 2017 with histopathologically confirmed schwannomas within the abdomen, retroperitoneum or pelvis. Results Of 485 patients across 12 centres, 38 (7·8 per cent) were discharged without follow-up, 199 (41·0 per cent) underwent early resection and 248 (51·1 per cent) had radiological monitoring. Of these 248 patients, 96 (38·7 per cent) eventually had surgery, giving an overall resection rate of 60·8 per cent (295 of 485). At baseline, median tumour volume was 90·1 (i.q.r. 26·5–262·0) cm3. The estimated growth rate was 10·5 (95 per cent c.i. 9·4 to 11·6) per cent per year, and was consistent in the short term (within 2 years of diagnosis) and long term (beyond 2 years) (ρ = 0·405, P = 0·021). A decision to operate was more common in symptomatic patients (P &lt; 0·001) and for rapidly growing tumours (growth rate more than 20 per cent per year) (P = 0·025). R0/R1 resection was achieved in 91·6 per cent of patients (263 of 287). Kaplan–Meier long-term recurrence rates after R0/R1 resection were 2·3 and 6·7 per cent at 3 and 5 years respectively. Conclusion Specific recommendations include: indications for early surgery, prediction of growth from radiological monitoring, promotion of selective submacroscopic resection and cessation of postoperative imaging surveillance.