Background Patients with cancer may be at high risk of adverse outcomes from SARS-CoV-2 infection. We analyzed a cohort of patients with cancer and COVID-19 reported to the COVID-19 and Cancer Consortium (CCC19) to identify prognostic clinical factors, including laboratory measurements and anti-cancer therapies. Patients and Methods Patients with active or historical cancer and a laboratory-confirmed SARS-CoV-2 diagnosis recorded between March 17-November 18, 2020 were included. The primary outcome was COVID-19 severity measured on an ordinal scale (uncomplicated, hospitalized, admitted to intensive care unit, mechanically ventilated, died within 30 days). Multivariable regression models included demographics, cancer status, anti-cancer therapy and timing, COVID-19-directed therapies, and laboratory measurements (among hospitalized patients). Results 4,966 patients were included (median age 66 years, 51% female, 50% non-Hispanic white); 2,872 (58%) were hospitalized and 695 (14%) died; 61% had cancer that was present, diagnosed, or treated within the year prior to COVID-19 diagnosis. Older age, male sex, obesity, cardiovascular and pulmonary comorbidities, renal disease, diabetes mellitus, non-Hispanic Black race, Hispanic ethnicity, worse ECOG performance status, recent cytotoxic chemotherapy, and hematologic malignancy were associated with higher COVID-19 severity. Among hospitalized patients, low or high absolute lymphocyte count, high absolute neutrophil count, low platelet count, abnormal creatinine, troponin, LDH, and CRP were associated with higher COVID-19 severity. Patients diagnosed early in the COVID-19 pandemic (January-April 2020) had worse outcomes than those diagnosed later. Specific anti-cancer therapies (e.g. R-CHOP, platinum combined with etoposide, and DNA methyltransferase inhibitors) were associated with high 30-day all-cause mortality. Conclusions Clinical factors (e.g. older age, hematological malignancy, recent chemotherapy) and laboratory measurements were associated with poor outcomes among patients with cancer and COVID-19. Although further studies are needed, caution may be required in utilizing particular anti-cancer therapies.

There is insufficient to low strength of evidence that any non-pharmacologic intervention improves sleep quality or quantity of general inpatients. Further studies are needed in this area to guide clinicians.

IMPORTANCE COVID-19 is a life-threatening illness for many patients. Prior studies have established hematologic cancers as a risk factor associated with particularly poor outcomes from COVID-19. To our knowledge, no studies have established a beneficial role for anti-COVID-19 interventions in this at-risk population. Convalescent plasma therapy may benefit immunocompromised individuals with COVID-19, including those with hematologic cancers.OBJECTIVE To evaluate the association of convalescent plasma treatment with 30-day mortality in hospitalized adults with hematologic cancers and COVID-19 from a multi-institutional cohort. DESIGN, SETTING, AND PARTICIPANTSThis retrospective cohort study using data from the COVID-19 and Cancer Consortium registry with propensity score matching evaluated patients with hematologic cancers who were hospitalized for COVID-19. Data were collected between

Genetic models for cancer have been evaluated using almost exclusively European data, which could exacerbate health disparities. A polygenic hazard score (PHS1) is associated with age at prostate cancer diagnosis and improves screening accuracy in Europeans. Here, we evaluate performance of PHS2 (PHS1, adapted for OncoArray) in a multi-ethnic dataset of 80,491 men (49,916 cases, 30,575 controls). PHS2 is associated with age at diagnosis of any and aggressive (Gleason score ≥ 7, stage T3-T4, PSA ≥ 10 ng/mL, or nodal/distant metastasis) cancer and prostate-cancer-specific death. Associations with cancer are significant within European (n = 71,856), Asian (n = 2,382), and African (n = 6,253) genetic ancestries (p < 10−180). Comparing the 80th/20th PHS2 percentiles, hazard ratios for prostate cancer, aggressive cancer, and prostate-cancer-specific death are 5.32, 5.88, and 5.68, respectively. Within European, Asian, and African ancestries, hazard ratios for prostate cancer are: 5.54, 4.49, and 2.54, respectively. PHS2 risk-stratifies men for any, aggressive, and fatal prostate cancer in a multi-ethnic dataset.

Aim-The development of chemoradiation -the concurrent administration of chemotherapy and radiotherapy -has led to significant improvements in local tumor control and survival. However, it is limited by its high toxicity. In this study, we report the development of a novel NP (nanoparticle) therapeutic, ChemoRad NP, which can deliver biologically targeted chemoradiation.Method-A biodegradable and biocompatible lipid-polymer hybrid NP that is capable of delivering both chemotherapy and radiotherapy was formulated. 111 and yttrium 90 as model drugs, we demonstrated that the ChemoRad NP can encapsulate chemotherapeutics (up to 9% of NP weight) and radiotherapeutics (100 mCi of radioisotope per gram of NP) efficiently and deliver both effectively. Using prostate cancer as a disease model, we demonstrated the targeted delivery of ChemoRad NPs and the higher therapeutic efficacy of ChemoRad NPs. Results-Using docetaxel, indium Conclusion-We Financial & competing interests disclosureThe authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. NIH Public AccessAuthor Manuscript Nanomedicine (Lond). Author manuscript; available in PMC 2011 February 1. Published in final edited form as:Nanomedicine (Lond). 2010 April ; 5(3): 361-368. doi:10.2217/nnm.10.6. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptThe advent of concurrent administration of chemotherapy and radiotherapy (chemoradiation) has significantly improved cancer care. Currently, it is the standard of treatment for many cancers, including esophageal, gastric, head and neck, and rectal cancers [1]. However, chemoradiation is limited by its higher (potentially life-threatening) toxicity, thereby precluding patients with poor general health from undergoing treatment. One potential strategy to improve chemoradiation utilizes advancements in drug delivery technology to improve efficacy and lower toxicity of the treatment. In particular, advances in nanotechnology have led to the development of nanoparticle (NP) drug-delivery vehicles, which can potentially improve the codelivery of chemoradiation. NPs are particularly well suited for cancer applications as they passively accumulate in tumors through the enhanced permeability and retention effect [2]. The proof-of-principle was observed in liposomal formulations of chemotherapeutics, such as Doxil, which demonstrated lower toxicity than their small molecular counterparts [3]. Our group and other investigators have demonstrated that the combination of biological targeting and NP delivery result in a higher concentration of chemotherapeutics within cancer cells [4][5][6][7][8][9][10][11][12]. Considering the many favorable characteristics of targeted NPs, we became interested in developing a targeted NP platform that is capable ...

Background: Clinical variables—age, family history, genetics—are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death. Methods: Genotype/phenotype data were obtained from a nested case-control Cohort of Swedish Men (n=3,279; 2,163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests. Results: Median age at last follow-up/prostate cancer death were 78.0 (IQR: 72.3–84.1) and 81.4 (75.4–86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95%CI 2.78–4.17]), family history (HR 1.72 [1.46–2.03]), alcohol (HR 1.74 [1.40–2.15]), diabetes (HR 0.53 [0.37–0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99–2.97]), family history (HR 1.73 [1.48–2.03]), alcohol (HR 1.45 [1.19–1.76]), diabetes (HR 0.62 [0.42–0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer ( p <10 −15 ). Conclusions: PHS had the most robust association with fatal prostate cancer in a multivariable model with common risk factors, including family history. Adding PHS to clinical variables may improve prostate cancer risk stratification strategies.

181 Background: Genetic risk stratification may inform the decision of whether—and at what age—a man should undergo prostate cancer (PCa) screening. We previously validated a polygenic hazard score (PHS) for accurate prediction of age of onset of aggressive PCa and for improved screening PSA performance. The PHS is a weighted sum of 54 SNP genotypes. Here, we applied the PHS to population data to assess its potential impact on individualized screening. Methods: Age-specific PCa incidence data were obtained for men aged 40-70 years from the United Kingdom (Cancer Research UK, 2013-2015) and fit to an exponential curve as a continuous model of age-specific PCa incidence. Using hazard ratios estimated from ProtecT study data, annualized incidence rate curves were calculated for the following percentiles of genetic risk: 1, 5, 20, 50, 80, 95, and 99. The proportion of incidence classified as aggressive (Gleason score ≥7) was estimated as 59.7%, the reported result from the CAP trial. PHS was combined with incidence data to give a risk-equivalent age, when a man with given PHS percentile will have the same risk of aggressive PCa as that of a typical man at age 50 years. Results: The age-specific incidence rate of PCa for the UK population was modeled as: 0.004 e0.203(age-40) ( R2 = 0.957, p = 0.001). Table shows risk-equivalent age for each genetic risk percentile. For example, a man with a PHS in the 95th percentile reached PCa risk equivalent to a typical 50-year-old man at age 44 years; conversely, a man with a PHS in the first percentile does not reach this risk level until age 60 years. Initiation of screening discussions could be adjusted accordingly. Conclusions: PHS may inform PCa screening with individualized estimates of risk-equivalent age for aggressive PCa. Risk-equivalent age for aggressive prostate cancer, by PHS percentile. [Table: see text]

Background and Purpose: The amygdalae are deep brain nuclei critical to emotional processing and the creation and storage of memory. It is not known whether the amygdalae are affected by brain radiotherapy (RT). We sought to quantify dose-dependent amygdala change one year after brain RT. Materials and Methods: 52 patients with primary brain tumors were retrospectively identified. Study patients underwent high-resolution, volumetric magnetic resonance imaging before RT and 1 year afterward. Images were processed using FDA-cleared software for automated segmentation of amygdala volume. Tumor, surgical changes, and segmentation errors were manually censored. Mean amygdala RT dose was tested for correlation with amygdala volume change 1 year after RT via the Pearson correlation coefficient. A linear mixed-effects model was constructed to evaluate potential predictors of amygdala volume change, including age, tumor hemisphere, sex, seizure history, and bevacizumab treatment during the study period. As 51 of 52 patients received chemotherapy, possible chemotherapy effects could not be studied. A two-tailed p-value <0.05 was considered statistically significant. Results: Mean amygdala RT dose (r=−0.28, p=0.01) was significantly correlated with volume loss. On multivariable analysis, the only significant predictor of amygdala atrophy was radiation