Exploring Spirituality, Loneliness and HRQoL In Hispanic Cancer Caregivers

Providing care to an aging society in the new normal requires increased attention to the informal caregivers who support the health and well-being of older adults with chronic conditions. Hispanic caregivers carry a high caregiver-associated burden. Health disparities experienced by Hispanics, coupled with the emotional, social and physical demands of caregiving, may set an unprecedented risk for lower health-related quality of life (HRQoL). In a quantitative analysis, we investigated the relationship between spirituality, loneliness and HRQoL in Hispanic cancer caregivers using baseline data from the Support for Latinas with Breast Cancer study (N= 234 Hispanic caregivers). Findings suggested an indirect effect of spirituality on HRQoL through reduced loneliness among more spiritual caregivers, effects that were independent of age. The second study was conducted using qualitative semi-structured interviews (N= 10) with Hispanic caregivers. Interviews evaluated spirituality and HRQoL in Hispanic cancer caregivers who reported variable levels of loneliness. Five themes emerged: caregiver experience, coping strategies, loneliness, religion to gain strength or support, and spirituality to gain strength or support. Results supported the role of spirituality in promoting higher HRQoL in Hispanic cancer caregivers and elucidated pathways to intervene on HRQoL through spirituality. With Hispanics often underutilizing formal services, having an improved understanding of caregiving experiences, particularly related to spirituality, will support the development of culturally-relevant strategies and programming to promote HRQoL for Hispanic caregivers.

Independent Clinical Validation of the Automated Ki67 Scoring Guideline from the International Ki67 in Breast Cancer Working Group

Ki67 is an important biomarker with prognostic and potential predictive value in breast cancer. However, the lack of standardization hinders its clinical applicability. In this study, we aimed to investigate the reproducibility among pathologists following the guidelines of the International Ki67 in Breast Cancer Working Group (IKWG) for Ki67 scoring and to evaluate the prognostic potential of this platform in an independent cohort. Four algorithms were independently built by four pathologists based on our study cohort using an open-source digital image analysis (DIA) platform (QuPath) following the detailed guideline of the IKWG. The algorithms were applied on an ER+ breast cancer study cohort of 157 patients with 15 years of follow-up. The reference Ki67 score was obtained by a DIA algorithm trained on a subset of the study cohort. Intraclass correlation coefficient (ICC) was used to measure reproducibility. High interobserver reliability was reached with an ICC of 0.938 (CI: 0.920–0.952) among the algorithms and the reference standard. Comparing each machine-read score against relapse-free survival, the hazard ratios were similar (2.593–4.165) and showed independent prognostic potential (p ≤ 0.018, for all comparisons). In conclusion, we demonstrate high reproducibility and independent prognostic potential using the IKWG DIA instructions to score Ki67 in breast cancer. A prospective study is needed to assess the clinical utility of the IKWG DIA Ki67 instructions.

Lift the Veil of Breast Cancers Using Four or Fewer Critical Genes

Tackling breast cancer problems is like mastering a puzzle, and the mystery is not yet solved. Reported key genes in the literature could not be confirmed whether they are vital to breast cancer formations due to lack of convincing accuracy, although they may be biologically directly related to breast cancer based on present biological knowledge. It is hoped vital genes can be identified with the highest possible accuracy, e.g., 100% accuracy and convincing causal patterns beyond what has been known in breast cancer. One hope is that finding gene-gene interaction signatures and functional effects may solve the puzzle. This research uses a recently developed competing linear factor analysis method in differentially expressed gene detection to advance the study of breast cancer formation to its deepest root level as deep as possible. Surprisingly, three genes are detected to be differentially expressed in TNBC, and non-TNBC (Her2, Luminal A, Luminal B) samples with 100% sensitivity and 100% specificity in one study of triple-negative breast cancers (TNBC, with 54675 genes and 265 samples). These three genes show a clear signature pattern of how TNBC patients can be grouped. For another TNBC study (with 54673 genes and 66 samples), four genes bring the same accuracy of 100% sensitivity and 100% specificity. Four genes are found to have the same accuracy of 100% sensitivity and 100% specificity in one breast cancer study (with 54675 genes and 121 samples), and the same four genes bring an accuracy of 100% sensitivity and 96.5% specificity in the fourth breast cancer study (with 60483 genes and 1217 samples.) These results show the four-gene-based classifiers are robust and accurate. The detected genes naturally classify patients into subtypes, e.g., seven subtypes. These findings demonstrate the clearest gene-gene interaction patterns and functional effects with the smallest numbers of genes and the highest accuracy compared with findings reported in the literature. The four genes are considered to be essential for breast cancer studies and practice. They can provide focused, targeted researches and precision medicine for each subtype of breast cancer. New breast cancer disease types may be detected using the classified subtypes, and hence new effective therapies can be developed.

Breast cancer treatment patterns by age and time since last pregnancy in the Carolina Breast Cancer Study-Phase III

PurposeTo describe breast cancer treatment patterns among premenopausal women by age and time since last pregnancy. MethodsData were analyzed from 1,179 women diagnosed with premenopausal breast cancer in the Carolina Breast Cancer Study. Of these, 160 had a recent pregnancy (within 5 years of cancer diagnosis). Relative frequency differences (RFDs) and 95% confidence intervals (CIs) were used to compare cancer stage, treatment modality received, treatment initiation delay (>30 days), and prolonged treatment duration (>2 to >8 months depending on the treatment received) by age and recency of pregnancy. ResultsRecently postpartum women were significantly more likely to have stage III disease [RFD (95% CI): 12.2% (3.6%, 20.8%)] and to receive more aggressive treatment compared to nulliparous women. After adjustment for age, race and standard clinical tumor characteristics, recently postpartum women were significantly less likely to have delayed treatment initiation [RFD (95% CI): -11.2% (-21.4%, -1.0%)] and prolonged treatment duration [RFD (95% CI): -17.5% (-28.0%, -7.1%)], and were more likely to have mastectomy [RFD (95% CI): 14.9% (4.8%, 25.0%)] compared to nulliparous. Similarly, younger women (<40 years of age) were significantly less likely to experience prolonged treatment duration [RFD (95% CI): -5.6% (11.1%, -0.0%)] and more likely to undergo mastectomy [RFD (95% CI): 10.6% (5.2%, 16.0%)] compared to older women (≥40 years of age). ConclusionThese results suggest that recently postpartum and younger women often received prompt and aggressive breast cancer treatment. Higher mortality and recurrence among recently pregnant women are unlikely to be related to under-treatment.

Multiple imputation with missing data indicators

Multiple imputation is a well-established general technique for analyzing data with missing values. A convenient way to implement multiple imputation is sequential regression multiple imputation, also called chained equations multiple imputation. In this approach, we impute missing values using regression models for each variable, conditional on the other variables in the data. This approach, however, assumes that the missingness mechanism is missing at random, and it is not well-justified under not-at-random missingness without additional modification. In this paper, we describe how we can generalize the sequential regression multiple imputation imputation procedure to handle missingness not at random in the setting where missingness may depend on other variables that are also missing but not on the missing variable itself, conditioning on fully observed variables. We provide algebraic justification for several generalizations of standard sequential regression multiple imputation using Taylor series and other approximations of the target imputation distribution under missingness not at random. Resulting regression model approximations include indicators for missingness, interactions, or other functions of the missingness not at random missingness model and observed data. In a simulation study, we demonstrate that the proposed sequential regression multiple imputation modifications result in reduced bias in the final analysis compared to standard sequential regression multiple imputation, with an approximation strategy involving inclusion of an offset in the imputation model performing the best overall. The method is illustrated in a breast cancer study, where the goal is to estimate the prevalence of a specific genetic pathogenic variant.