Gynecologic oncology patient perspectives and knowledge on advance care planning: A quality improvement intervention

OBJECTIVES: Assess and improve advance care planning (ACP) awareness and uptake among gynecologic oncology patients. METHODS: Using a quality improvement Plan-Do-Check-Act framework, we completed a single institution needs assessment and intervention. The needs assessment was a 26-question survey assessing baseline ACP knowledge and preferences of gynecologic oncology patients. We used this survey to implement an outpatient intervention in which patients were offered ACP resources (pamphlet, discussion with their gynecologic oncologist, and/or social work referral). We conducted a post-intervention survey among patients who had and had not received ACP resource(s) to assess whether our intervention increased ACP knowledge, discussions, or uptake. RESULTS: Among 106 patients surveyed in the needs assessment, 33 % had ACP documents, 26 % had discussed ACP with a physician, and 82 % thought discussing ACP was important. The majority preferred these conversations in the outpatient setting (52 %) with their gynecologic oncologist (80 %) instead of nurses or trainees. In the intervention, 526 patients were offered ACP resources. Compared to women who did not receive resources (n = 324), patients who received ACP resource(s) (n = 202) were more likely to have ACP discussions with their gynecologic oncologist (38 % vs 68 %, CONCLUSIONS: ACP uptake among gynecologic oncology patients is low, but ACP discussions with an oncologist during outpatient visits are important to patients and improve their knowledge regarding completing ACP documents

Gender, Pregnancy, and Treatment Completion by Criminal Justice Referral Status

Background. Court-mandated drug treatment has become more common in the United States over the last decades. However its effectiveness has not been investigated nationally in terms of gender and pregnancy status. Methods. We used the 2006 Treatment Episode Data Set. The primary outcome was treatment completion including transfers to further care. The primary exposure was criminal justice referrals. Demographic and treatment characteristics were compared with chi-squared and t-tests. Confounding was assessed via a backwards elimination method using change-in-estimate criteria. Logistic regression models were stratified by gender and pregnancy. Results are reported as Odds Ratios (OR) with 95% confidence intervals (CI). Results. Of the 1.5 million treatment admissions, 38% of men, 29% of women, and 35% of pregnant women entered treatment from the criminal justice system. Overall pregnant women were younger, with lower educational achievement and employment, less likely to use alcohol, and more likely to use methamphetamines. Pregnant women were less likely to complete treatment (55%) than non-pregnant women (57%) or men (61%). After controlling for confounders, the subset of pregnant women (OR 1.37 [1.29, 1.46]) and non-pregnant women (1.25 [1.23, 1.26]) who entered treatment via the criminal justice system had a higher odds of treatment completion than men (0.95 [95% CI: 0.94, 0.96]). Conclusion. Criminal justice referral appears to be an important effect measure modifier in drug treatment completion. The reasons why pregnant women especially do better in this context needs to be further explored

p5RHH nanoparticle-mediated delivery of AXL siRNA inhibits metastasis of ovarian and uterine cancer cells in mouse xenografts

Abstract Ovarian and uterine serous cancers are extremely lethal diseases that often present at an advanced stage. The late-stage diagnosis of these patients results in the metastasis of their cancers throughout the peritoneal cavity leading to death. Improving survival for these patients will require identifying therapeutic targets, strategies to target them, and means to deliver therapies to the tumors. One therapeutic target is the protein AXL, which has been shown to be involved in metastasis in both ovarian and uterine cancer. An effective way to target AXL is to silence its expression with small interfering RNA (siRNA). We investigate the ability of the novel siRNA delivery platform, p5RHH, to deliver anti-AXL siRNA (siAXL) to tumor cells both in vitro and in vivo as well as examine the phenotypic effects of this siRNA interference. First, we present in vitro assays showing p5RHH-siAXL treatment reduces invasion and migration ability of ovarian and uterine cancer cells. Second, we show p5RHH nanoparticles target to tumor cells in vivo. Finally, we demonstrate p5RHH-siAXL treatment reduces metastasis in a uterine cancer mouse xenograft model, without causing an obvious toxicity. Collectively, these findings suggest that this novel therapy shows promise in the treatment of ovarian and uterine cancer patients

Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot study

Over 150 000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually over 50 000 individuals will die from CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. Tumor metastasis is the primary factor related to the risk of recurrence and mortality. Yet, screening for nodal and distant metastasis is costly, and invasive and incomplete resection may hamper adequate assessment. Signatures of the tumor-immune microenvironment (TIME) at the primary site can provide valuable insights into the aggressiveness of the tumor and the effectiveness of various treatment options. Spatially resolved transcriptomics technologies offer an unprecedented characterization of TIME through high multiplexing, yet their scope is constrained by cost. Meanwhile, it has long been suspected that histological, cytological, and macroarchitectural tissue characteristics correlate well with molecular information (e.g., gene expression). Thus, a method for predicting transcriptomics data through inference of RNA patterns from whole slide images (WSI) is a key step in studying metastasis at scale. In this work, we collected tissue from 4 stage-III (pT3) matched colorectal cancer patients for spatial transcriptomics profiling. The Visium spatial transcriptomics (ST) assay was used to measure transcript abundance for 17 943 genes at up to 5000 55-micron (i.e., 1–10 cells) spots per patient sampled in a honeycomb pattern, co-registered with hematoxylin and eosin (H&E) stained WSI. The Visium ST assay can measure expression at these spots through tissue permeabilization of mRNAs, which are captured through spatially (i.e., x–y positional coordinates) barcoded, gene specific oligo probes. WSI subimages were extracted around each co-registered Visium spot and were used to predict the expression at these spots using machine learning models. We prototyped and compared several convolutional, transformer, and graph convolutional neural networks to predict spatial RNA patterns at the Visium spots under the hypothesis that the transformer- and graph-based approaches better capture relevant spatial tissue architecture. We further analyzed the model’s ability to recapitulate spatial autocorrelation statistics using SPARK and SpatialDE. Overall, the results indicate that the transformer- and graph-based approaches were unable to outperform the convolutional neural network architecture, though they exhibited optimal performance for relevant disease-associated genes. Initial findings suggest that different neural networks that operate on different scales are relevant for capturing distinct disease pathways (e.g., epithelial to mesenchymal transition). We add further evidence that deep learning models can accurately predict gene expression in whole slide images and comment on understudied factors which may increase its external applicability (e.g., tissue context). Our preliminary work will motivate further investigation of inference for molecular patterns from whole slide images as metastasis predictors and in other applications