Epidemiology of early vs. Late recurrence among women with Early-Stage estrogen Receptor-Positive breast cancer in the pathways study.

BACKGROUND: Relatively little is known about the differences in prognostic factors for early vs late recurrence among women with early-stage estrogen receptor-positive (ER+) breast cancer. METHODS: We analyzed factors related to early (<5 years) vs late (≥5 years) recurrence in 2,992 women with stage I-IIB ER+ breast cancer in the Pathways Study, a prospective cohort of women with breast cancer enrolled between 2006 and 2013, with ascertainment of recurrence and death through December 2021. RESULTS: After a median follow-up of 13.3 years, 341 (13.8%) women had recurrences, including 181 (53.7%) with late recurrence. Higher stage and grade were associated with recurrence regardless of timing, whereas progesterone receptor (PR) negativity was associated with early but not late recurrence. Receipt of endocrine therapy was associated with reduced risk of overall recurrence, but the length of endocrine therapy was not significant in multivariable models. Minoritized racial and ethnic groups, including Asian, Black, and Hispanic women, had higher risk of early but not late recurrence, compared with non-Hispanic White women. The trend of higher risk of early recurrence among these groups remained after adjustment for clinical, demographic, and socioeconomic factors, but was statistically significant only in Asian women. CONCLUSIONS: Our study revealed potentially important distinctions for early vs late recurrence, including the associations with PR-negativity and self-identified race and ethnicity. Possible higher risk of early recurrence among Asian, Black, and Hispanic women provides novel evidence for the existence of disparities in cancer outcomes, even within the breast cancer subtype indicative of generally good prognosis.

A polygenic score associated with fracture risk in breast cancer patients treated with aromatase inhibitors.

Identifying women at high risk of osteoporotic fracture from aromatase inhibitor (AI) therapy for breast cancer is largely based on known risk factors for healthy postmenopausal women, which might not accurately reflect the risk in breast cancer patients post-AI therapy. To determine whether a polygenic score associated with fracture in healthy women is also significant in women treated with AIs for breast cancer, we used data from a prospective observational cohort of 2152 women diagnosed with hormonal receptor positive breast cancer treated with AIs as the initial endocrine therapy and examined a polygenic score of heel quantitative ultrasound speed of sound (gSOS) in relation to incident osteoporotic fracture after AI therapy during a median 6.1 years of follow up after AI initiation. In multivariable models, patients with the second and third highest tertiles (T) versus the lowest tertile of gSOS had significantly lower risk of fracture (T2: adjusted HR = 0.61, 95% CI: 0.46-0.80; T3: adjusted HR = 0.53, 95% CI: 0.40-0.70). The lower risk of fracture in patients with the highest tertile of gSOS remained significant after further adjustment for BMD at the hip (T3: adjusted HR = 0.62, 95% CI: 0.42-0.91). In conclusion, our analysis showed gSOS as a novel genetic predictor for fracture risk independent of BMD among breast cancer patients treated with AIs. Future studies are warranted to evaluate the performance of incorporating gSOS in prediction models for the risk of AI-related fracture in breast cancer patients.

A Mendelian Randomization Analysis of 55 Genetically Predicted Metabolic Traits with Breast Cancer Survival Outcomes in the Pathways Study.

Previous studies suggest associations of metabolic syndromes with breast cancer prognosis, yet the evidence is mixed. In recent years, the maturation of genome-wide association study findings has led to the development of polygenic scores (PGS) for many common traits, making it feasible to use Mendelian randomization to examine associations between metabolic traits and breast cancer outcomes. In the Pathways Study of 3,902 patients and a median follow-up time of 10.5 years, we adapted a Mendelian randomization approach to calculate PGS for 55 metabolic traits and tested their associations with seven survival outcomes. Multivariable Cox proportional hazards models were used to derive HRs and 95% confidence intervals (CI) with adjustment for covariates. The highest tertile (T3) of PGS for cardiovascular disease was associated with shorter overall survival (HR = 1.34, 95% CI = 1.11-1.61) and second primary cancer-free survival (HR = 1.31, 95% CI = 1.12-1.53). PGS for hypertension (T3) was associated with shorter overall survival (HR = 1.20, 95% CI = 1.00-1.43), second primary cancer-free survival (HR = 1.24, 95% CI = 1.06-1.45), invasive disease-free survival (HR = 1.18, 95% CI = 1.01-1.38), and disease-free survival (HR = 1.21, 95% CI = 1.04-1.39). PGS for serum cystatin C levels (T3) was associated with longer disease-free survival (HR = 0.82, 95% CI = 0.71-0.95), breast event-free survival (HR = 0.74, 95% CI = 0.61-0.91), and breast cancer-specific survival (HR = 0.72, 95% CI = 0.54-0.95). The above associations were significant at a nominal P < 0.05 level but not after correcting for multiple testing (Bonferroni P < 0.0009). Our analyses revealed notable associations of PGS for cardiovascular disease, hypertension, and cystatin C levels with breast cancer survival outcomes. These findings implicate metabolic traits in breast cancer prognosis. Significance: To our knowledge, this is the largest study of PGS for metabolic traits with breast cancer prognosis. The findings revealed significant associations of PGS for cardiovascular disease, hypertension, and cystatin C levels with several breast cancer survival outcomes. These findings implicate an underappreciated role of metabolic traits in breast cancer prognosis that would warrant further exploration.

Pan-Cancer Characterization of Intratumoral Autonomic Innervation in 32 Cancer Types in the Cancer Genome Atlas.

Over the past two decades, multiple studies have demonstrated the important role that the autonomic nervous system (ANS) plays in tumorigenesis and cancer progression. However, the mechanisms by which this process occurs have only recently begun to be elucidated. Further, the extent of autonomic innervation in various cancer types and its effects on tumor molecular, immunological, and histopathological features, as well as on patient outcomes, are not yet fully characterized. In this study, we analyzed intratumoral ANS gene expression signatures, including overall intratumoral neuron growth and sympathetic and parasympathetic markers, across 32 cancer types using tumor transcriptomic and clinical annotation data available from The Cancer Genome Atlas (TCGA). Our analysis revealed wide variations in intratumoral ANS expression both within and across cancer types. The association of ANS signatures with tumor histopathological characteristics and survival outcomes also varied by cancer type. We found intratumoral ANS expression to be commonly correlated with angiogenesis, TGF-ß signaling, and immunosuppression in the tumor microenvironment of many cancer types, which provide mechanistic insights into the involvement of intratumoral innervation in cancer development and progression. Our findings suggest that the potential benefits of cancer therapies targeting ß-adrenergic receptor-mediated stress signaling pathways are likely dependent on cancer type.

Multiplexed digital spatial profiling of invasive breast tumors from Black and White women.

The NanoString GeoMx digital spatial profiling is a new multiplexed platform that quantifies the abundance of tumor- and immune-related proteins in a spatially resolved manner. We performed DSP for the simultaneous assessment of 52 analytes within spatially resolved tissue compartments defined by pan-cytokeratin expression. We compared protein targets between 94 African American/Black and 65 European American/White cases, tumor and stromal tissue compartments, estrogen receptor alpha (ER)-positive and ER-negative cases, and explored potential biomarkers of survival. Of 33 analytes with robust signal for analysis, results were highly replicable. For a subset of markers, correlative analyses between DSP analytes and traditional immunohistochemistry scores revealed moderate to very strong associations between the two platforms. Similarly, DSP analytes and gene expression scores were concordant for 21 of 25 markers with overlap between the two datasets. Several analytes varied by ER status, and across the 25 immune markers surveyed, 14 had a significant inverse association with ER expression. B7 homolog 3 (B7-H3; encoded by CD276) was the only analyte to show a significant difference by race, being lower in both the tumor and stromal compartments in Black women. DSP markers that were associated with survival included CD8, CD25, CD56, CD127, EpCAM, ER, Ki-67, and STING. We conclude that DSP is an efficient tool for screening tumor- and immune-related markers in a simultaneous fashion and yields results that are concordant with established immune profiling assays. DSP immune analytes were inversely associated with ER expression, in agreement with a substantial body of previous work that documents higher immune infiltration in ER-negative breast cancers. This technology revealed that scores of the B7-H3 protein were significantly lower in breast cancers from Black women compared with White women, an intriguing finding that requires replication in independent and racially diverse female populations.

Heteroatom Polymer-Derived 3D High-Surface-Area and Mesoporous Graphene Sheet-Like Carbon for Supercapacitors.

Current supercapacitors suffer from low energy density mainly due to the high degree of microporosity and insufficient hydrophilicity of their carbon electrodes. Development of a supercapacitor capable of simultaneously storing as much energy as a battery, along with providing sufficient power and long cycle stability would be valued for energy storage applications and innovations. Differing from commonly studied reduced graphene oxides, in this work we identified an inexpensive heteroatom polymer (polyaniline-PANI) as a carbon/nitrogen precursor, and applied a controlled thermal treatment at elevated temperature to convert PANI into 3D high-surface-area graphene-sheet-like carbon materials. During the carbonization process, various transition metals including Fe, Co, and Ni were added, which play critical roles in both catalyzing the graphitization and serving as pore forming agents. Factors including post-treatments, heating temperatures, and types of metal were found crucial for achieving enhanced capacitance performance on resulting carbon materials. Using FeCl3 as precursor along with optimal heating temperature 1000 °C and mixed acid treatment (HCl+HNO3), the highest Brunauer-Emmett-Teller (BET) surface area of 1645 m2g-1 was achieved on the mesopore dominant graphene-sheet-like carbon materials. The unique morphologies featured with high-surface areas, dominant mesopores, proper nitrogen doping, and 3D graphene-like structures correspond to remarkably enhanced electrochemical specific capacitance up to 478 Fg-1 in 1.0 M KOH at a scan rate of 5 mV s-1. Furthermore, in a real two-electrode system of a symmetric supercapacitor, a specific capacitance of 235 Fg-1 using Nafion binder is obtained under a current density of 1 Ag-1 by galvanostatic charge-discharge tests in 6.0 M KOH. Long-term cycle stability up to 5000 cycles by using PVDF binder in electrode was systematically evaluated as a function of types of metals and current densities.

Size-controlled large-diameter and few-walled carbon nanotube catalysts for oxygen reduction.

We demonstrate a new strategy for tuning the size of large-diameter and few-walled nitrogen-doped carbon nanotubes (N-CNTs) from 50 to 150 nm by varying the transition metal (TM = Fe, Co, Ni or Mn) used to catalyze graphitization of dicyandiamide. Fe yielded the largest tubes, followed by Co and Ni, while Mn produced a clot-like carbon morphology. We show that morphology is correlated with electrocatalytic activity for the oxygen reduction reaction (ORR). A clear trend of Fe > Co > Ni > Mn for the ORR catalytic activity was observed, in both alkaline media and more demanding acidic media. The Fe-derived N-CNTs exhibited the highest BET (∼870 m(2) g(-1)) and electrochemically accessible (∼450 m(2) g(-1)) surface areas and, more importantly, the highest concentration of nitrogen incorporated into the carbon planes. Thus, in addition to the intrinsic high activity of Fe-derived catalysts, the high surface area and nitrogen doping contribute to high ORR activity. This work, for the first time, demonstrates size-controlled synthesis of large-diameter N-doped carbon tube electrocatalysts by varying the metal used in N-CNT generation. Electrocatalytic activity of the Fe-derived catalyst is already the best among studied metals, due to the high intrinsic activity of possible Fe-N coordination. This work further provides a promising route to advanced Fe-N-C nonprecious metal catalysts by generating favorable morphology with more active sites and improved mass transfer.