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PurposeBlack women have a 40% increased risk of breast cancer-related mortality. These outcome disparities may reflect differences in tumor pathways and a lack of targetable therapies for specific subtypes that are more common in Black women. Hepatocyte growth factor (HGF) is a targetable pathway that promotes breast cancer tumorigenesis, is associated with basal-like breast cancer, and is differentially expressed by race. This study assessed whether a 38-gene HGF expression signature is associated with recurrence and survival in Black and non-Black women.MethodsStudy participants included 1957 invasive breast cancer cases from the Carolina Breast Cancer Study. The HGF signature was evaluated in association with recurrence (n = 1251, 171 recurrences), overall, and breast cancer-specific mortality (n = 706, 190/328 breast cancer/overall deaths) using Cox proportional hazard models.ResultsWomen with HGF-positive tumors had higher recurrence rates [HR 1.88, 95% CI (1.19, 2.98)], breast cancer-specific mortality [HR 1.90, 95% CI (1.26, 2.85)], and overall mortality [HR 1.69; 95% CI (1.17, 2.43)]. Among Black women, HGF positivity was significantly associated with higher 5-year rate of recurrence [HR 1.73; 95% CI (1.01, 2.99)], but this association was not significant in non-Black women [HR 1.68; 95% CI (0.72, 3.90)]. Among Black women, HGF-positive tumors had elevated breast cancer-specific mortality [HR 1.80, 95% CI (1.05, 3.09)], which was not significant in non-Black women [HR 1.52; 95% CI (0.78, 2.99)].ConclusionThis multi-gene HGF signature is a poor-prognosis feature for breast cancer and may identify patients who could benefit from HGF-targeted treatments, an unmet need for Black and triple-negative patients.

Simple Summary: Adolescents and young adults (AYAs) experiencing cancer have support needs that differ from older adult cancer patients. The aim of our qualitative study was to determine the holistic needs of AYA cancer survivors and to develop a patient-centered conceptual model of AYA survivorship care. Analysis of our interview results reveals eight key domains critical to holistic patient-centered AYA survivorship care. We offer a conceptual model that differs from current conceptual models of AYA survivorship care by centering the patient and their support systems, emphasizing the need for continuing supportive navigation, and the importance of repeated support along the identified domains over time. Purpose: Conceptual models provide frameworks to illustrate relationships among patient-, provider-, system-, and community-level factors that inform care delivery and research. Existing models of cancer survivorship care focus largely on pediatric or adult populations whose needs differ from adolescents and young adults (AYAs). We developed a patient-centered conceptual model of AYA survivorship care. Methods: We conducted a narrative literature review of current conceptual and theoretical models of care. We engaged AYA cancer survivors (n = 25) in semi-structured one-hour telephone interviews. Most participants were in their 20s and 30s, and the majority (84%) were women. Recruitment was stratified by age and time since cancer diagnosis. We conducted a thematic analysis of interview transcripts to identify themes that exemplified patient-centered care. Results: Most participants identified as white and female. Leukemia and breast cancer were the most common cancer types. Main themes included the need for (1) care coordination, (2) ongoing mental health support, (3) connection to AYA peer support, (4) support during fertility preservation efforts, (5) support with financial burden, (6) support for quality of life, (7) information about and support with side effects and late effects, and (8) attention to the unique needs of young adults. Conclusions: We present a patient-centered conceptual model of AYA survivorship care needs that can inform future cancer care delivery and research. [ABSTRACT FROM AUTHOR]

Background Many women diagnosed with cancer as adolescents and young adults (AYAs, age 15-39 years) want biological children after cancer but lack information on the potential impact of their cancer history on future reproductive outcomes. We investigated the risk of adverse birth outcomes among AYA cancer survivors. Methods We identified insured women diagnosed with AYA breast cancer, thyroid cancer, gynecologic cancers, lymphoma, or melanoma from 2003 to 2016 in the state of North Carolina or the Kaiser Permanente health care systems in northern and southern California. Post-diagnosis births to cancer survivors were each matched with up to 5 births to women without cancer. Risk ratios for preterm birth (<37 completed weeks), very preterm birth (<34 completed weeks), low birth weight (<2500 g), and small for gestational age (SGA, <10th percentile of weight for gestational age) were estimated using modified Poisson regression. Results Analyses included 1648 births to 1268 AYA cancer survivors and 7879 births to 6066 women without cancer. Overall, risk of preterm birth, very preterm birth, low birth weight, and SGA did not significantly differ between births to women with and without cancer. However, births to women with gynecologic cancers had a significantly increased risk of low birth weight (risk ratio = 1.82; 95% confidence interval: 1.03 to 3.21) and suggested increased risk of preterm birth (risk ratio = 1.59; 95% confidence interval: 0.99 to 2.54). Chemotherapy exposure was not associated with increased risk of adverse birth outcomes. Conclusions Women with gynecologic cancers, but not other cancers, had an increased risk of adverse birth outcomes compared to women without cancer. [ABSTRACT FROM AUTHOR]

In survival data analysis, a competing risk is an event whose occurrence precludes or alters the chance of the occurrence of the primary event of interest. In large cohort studies with long‐term follow‐up, there are often competing risks. Further, if the event of interest is rare in such large studies, the case‐cohort study design is widely used to reduce the cost and achieve the same efficiency as a cohort study. The conventional additive hazards modeling for competing risks data in case‐cohort studies involves the cause‐specific hazard function, under which direct assessment of covariate effects on the cumulative incidence function, or the subdistribution, is not possible. In this paper, we consider an additive hazard model for the subdistribution of a competing risk in case‐cohort studies. We propose estimating equations based on inverse probability weighting methods for the estimation of the model parameters. Consistency and asymptotic normality of the proposed estimators are established. The performance of the proposed methods in finite samples is examined through simulation studies and the proposed approach is applied to a case‐cohort dataset from the Sister Study. [ABSTRACT FROM AUTHOR]