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Background: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. Methods: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. Results: 1383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32–1.67]); Black patients (aOR 1.74; 95 CI 1.24–2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70–6.79) and Other (aOR 2.97; 95 CI 1.71–5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83–12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63–3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20–2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66–3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89–22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. Conclusions: Using one of the largest registries on cancer and COVID-19, we identified patient and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to non-Hispanic White patients. Funding: This study was partly supported by National Cancer Institute grant number P30 CA068485 to Tianyi Sun, Sanjay Mishra, Benjamin French, Jeremy L Warner; P30-CA046592 to Christopher R Friese; P30 CA023100 for Rana R McKay; P30-CA054174 for Pankil K Shah and Dimpy P Shah; KL2 TR002646 for Pankil Shah and the American Cancer Society and Hope Foundation for Cancer Research (MRSG-16-152-01-CCE) and P30-CA054174 for Dimpy P Shah. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication. Clinical trial number: CCC19 registry is registered on ClinicalTrials.gov, NCT04354701 .

OBJECTIVE Spine metastases are commonly treated with radiotherapy for local tumor control; pathologic fracture is a potential complication of spinal radiotherapy. Both Hounsfield units (HUs) on CT and vertebral bone quality (VBQ) on MRI have been argued to predict stability as measured by odds of pathologic fracture, although it is unclear if there is a difference in the predictive power between the two methodologies. The objective of the present study was to examine whether one methodology is a better predictor of pathologic fracture following radiotherapy for mobile spine metastases. METHODS Patients who underwent radiotherapy (conventional external-beam radiation therapy, stereotactic body radiation therapy, or intensity-modulated radiation therapy) for mobile spine (C1–L5) metastases at a tertiary care center were retrospectively identified. Details regarding underlying pathology, patient demographics, and tumor morphology were collected. Vertebral involvement was assessed using the Weinstein-Boriani-Biagini (WBB) system. Bone quality of the non–tumor-involved bone was assessed on both pretreatment CT and MRI. Univariable analyses were conducted to identify independent predictors of fracture, and Kaplan-Meier analyses were used to identify significant predictors of time to pathologic fracture. Stepwise Cox regression analysis was used to determine independent predictors of time to fracture. RESULTS One hundred patients were included (mean age 62.7 ± 11.9 years; 61% male), of whom 35 experienced postradiotherapy pathologic fractures. The most common histologies were lung (22%), prostate (21%), breast (14%), and renal cell (13%). On univariable analysis, the mean HUs of the vertebrae adjacent to the fractured vertebra were significantly lower among those experiencing fracture; VBQ was not significantly associated with fracture odds. Survival analysis showed that average HUs ≤ 132, nonprostate pathology, involvement of ≥ 3 vertebral body segments on the WBB system, Spine Instability Neoplastic Score (SINS) ≥ 7, and the presence of axial pain all predicted increased odds of fracture (all p < 0.001). Cox regression found that HUs ≤ 132 (OR 2.533, 95% CI 1.257–5.103; p = 0.009), ≥ 3 WBB vertebral body segments involved (OR 2.376, 95% CI 1.132–4.987; p = 0.022), and axial pain (OR 2.036, 95% CI 0.916–4.526; p = 0.081) predicted increased fracture odds, while prostate pathology predicted decreased odds (OR 0.076, 95% CI 0.009–0.613; p = 0.016). Sensitivity analysis suggested that an HU threshold of ≤ 132 and a SINS of ≥ 7 identified patients at increased risk of fracture. CONCLUSIONS The present results suggest that bone density surrogates as measured on CT, but not MRI, can be used to predict the risk of pathologic fracture following radiotherapy for mobile spine metastases. More extensive vertebral body involvement and the presence of mechanical axial pain additionally predict increased fracture odds.

The NCCN Guidelines for Prostate Cancer provide a framework on which to base decisions regarding the workup of patients with prostate cancer, risk stratification and management of localized disease, post-treatment monitoring, and treatment of recurrence and advanced disease. The Guidelines sections included in this article focus on the management of metastatic castration-sensitive disease, nonmetastatic castration-resistant prostate cancer (CRPC), and metastatic CRPC (mCRPC). Androgen deprivation therapy (ADT) with treatment intensification is strongly recommended for patients with metastatic castration-sensitive prostate cancer. For patients with nonmetastatic CRPC, ADT is continued with or without the addition of certain secondary hormone therapies depending on prostate-specific antigen doubling time. In the mCRPC setting, ADT is continued with the sequential addition of certain secondary hormone therapies, chemotherapies, immunotherapies, radiopharmaceuticals, and/or targeted therapies. The NCCN Prostate Cancer Panel emphasizes a shared decision-making approach in all disease settings based on patient preferences, prior treatment exposures, the presence or absence of visceral disease, symptoms, and potential side effects.

AbstractBackgroundThe 2018 Leibovich prognostic model for nonmetastatic renal cell carcinoma (RCC) combines clinical, surgical, and pathologic factors to predict progression‐free survival (PFS) and cancer‐specific survival (CSS) for patients with clear cell (ccRCC), papillary (pRCC), and chromophobe (chRCC) histology. Despite high accuracy, <1% of the original cohort was Black. Here, the authors examined this model in a large population with greater Black patient representation.MethodsBy using a prospectively maintained RCC institutional database, patients were assigned Leibovich model risk scores. Survival outcomes included 5‐year and 10‐year PFS and CSS. Prognostic accuracy was determined using area under the curve (AUC) analysis and calibration plots. Black patient subanalyses were conducted.ResultsIn total, 657 (29%) of 2295 patients analyzed identified as Black. Declines in PFS and CSS were observed as scores increased. Discrimination for ccRCC was strong for PFS (AUC: 5‐year PFS, 0.81; 10‐year PFS, 0.78) and for CSS (AUC: 5‐year CSS, 0.82; 10‐year CSS, 0.74). The pRCC AUC for PFS was 0.74 at 5 years and 0.71 at 10 years; and the AUC for CSS was 0.74 at 5 years and 0.70 at 10 years. In chRCC, better performance was observed for CSS (AUC at 5 years, 0.75) than for PFS (AUC: 0.66 at 5 years; 0.55 at 10 years). Black patient subanalysis revealed similar‐to‐improved performance for ccRCC at 5 years (AUC: PFS, 0.79; CSS, 0.87). For pRCC, performance was lower for PFS (AUC at 5 years, 0.63) and was similar for CSS (AUC at 5 years, 0.77). Sample size limited Black patient 10‐year and chRCC analyses.ConclusionsThe authors externally validated the 2018 Leibovich RCC prognostic model and found optimal performance for ccRCC, followed by pRCC, and then chRCC. Importantly, the results were consistent in this large representation of Black patients.Plain Language Summary In 2018, a model to predict survival in patients with renal cell carcinoma (kidney cancer) was introduced by Leibovich et al. This model has performed well; however, Black patients have been under‐represented in examination of its performance. In this study, 657 Black patients (29%) were included, and the results were consistent. This work is important for making sure the model can be applied to all patient populations.

Abstract Histone H3 lysine 9 methylation (H3K9me), which is written by the Euchromatic Histone Lysine Methyltransferases EHMT1 and EHMT2 and read by the heterochromatin protein 1 (HP1) chromobox (CBX) protein family, is dysregulated in many types of cancers. Approaches to inhibit regulators of this pathway are currently being evaluated for therapeutic purposes. Thus, knowledge of the complexes supporting the function of these writers and readers during the process of cell proliferation is critical for our understanding of their role in carcinogenesis. Here, we immunopurified each of these proteins and used mass spectrometry to define their associated non-histone proteins, individually and at two different phases of the cell cycle, namely G1/S and G2/M. Our findings identify novel binding proteins for these writers and readers, as well as corroborate known interactors, to show the formation of distinct protein complex networks in a cell cycle phase-specific manner. Furthermore, there is an organizational switch between cell cycle phases for interactions among specific writer–reader pairs. Through a multi-tiered bioinformatics-based approach, we reveal that many interacting proteins exhibit histone mimicry, based on an H3K9-like linear motif. Gene ontology analyses, pathway enrichment, and network reconstruction inferred that these comprehensive EHMT and CBX-associated interacting protein networks participate in various functions, including transcription, DNA repair, splicing, and membrane disassembly. Combined, our data reveals novel complexes that provide insight into key functions of cell cycle-associated epigenomic processes that are highly relevant for better understanding these chromatin-modifying proteins during cell cycle and carcinogenesis.

Context.— In 2018 the College of American Pathologists Diagnostic Immunology and Flow Cytometry Committee designed and implemented a new plasma cell neoplasia flow cytometry proficiency testing program—PCNEO—to allow clinical flow cytometry laboratories to monitor and assess their performance compared with a peer group. Objective.— To report the results from the first 4 years of the PCNEO program. Design.— Program participants were sent 2 sets of challenges per year, each including 1 wet challenge and 2 dry challenges, with associated clinical and laboratory findings. The wet challenges were composed of myeloma cell line specimens (with or without dilution in preserved whole blood) for flow cytometric analysis. The dry (paper) challenges were composed of clinical case summaries and images of flow cytometric test results from various flow cytometry laboratories of committee members. Results.— A total of 116 to 145 laboratories from 17 countries enrolled in the proficiency testing program. For the wet challenges, almost all participants (97%–100%; cumulative, 98.2%) correctly identified the presence of neoplastic plasma cell populations based on flow cytometric analysis of undiluted myeloma cell lines. Slightly fewer participants (89.0%–97.4%; cumulative, 95.2%) correctly identified the presence of neoplastic plasma cell populations based on flow cytometric analysis of diluted myeloma cell lines (10% or 50% dilutions into peripheral blood) intended to better represent a typical clinical sample. There was generally agreement among 80% or more of participants for positive or negative staining for CD38, CD138, CD19, CD20, and surface and cytoplasmic κ and λ light chains. Similarly, 84% to 100% of participants were able to correctly identify the presence of neoplastic plasma cell populations in paper challenges, including the presence of small, neoplastic plasma cell populations (0.01%–5.0% clonal plasma cells) and the presence of nonneoplastic plasma cell populations (correctly identified by 91%–96% of participants). Conclusions.— Participant performance in the new proficiency testing program was excellent overall, with the vast majority of participants able to perform flow cytometric analysis and identify neoplastic plasma cell populations and to identify small plasma cell clones or expanded populations of reactive plasma cells in dry challenge flow cytometry results. This program will allow laboratories to verify the accuracy of their testing program and test interpretations for the assessment of patients suspected of having a plasma cell neoplasm.