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Background: Mindfulness-based interventions (MBIs) are supported by clinical practice guidelines as effective non-pharmacologic interventions for common symptoms experienced by cancer patients, including anxiety, depression, and fatigue. However, the evidence predominately derives from White breast cancer survivors. Racial and ethnic minority patients have less access to integrative oncology care and worse cancer outcomes. To address these gaps, we designed and piloted a series of mindfulness-based group medical visits (MB-GMVs), embedded into comprehensive cancer care, for racially and ethnically diverse patients in cancer treatment. Methods: As a quality improvement project, we launched a telehealth MB-GMV series for patients undergoing cancer treatment, delivered as four weekly 2-hour visits billable to insurance. Content was concordant with evidence-based guidelines and established MBIs and adapted to improve cultural relevance and fit (eg, access-centered, trauma-informed, with inclusive communication practices). Program structure was adapted to address barriers to participation, with ≥50% slots per series reserved for racial and ethnic minority patients. Intake surveys incorporated a demographic questionnaire and symptom assessments. Evaluations were sent following the visits. Results: In our first ten cohorts (n = 78), 80% of referred patients enrolled. Participants were: 22% Asian, 14% Black, 17% Latino, 45% non-Latino White; 65% female; with a median age of 54 years (range 27-79); and 80% had metastatic cancer. Common baseline symptoms included lack of energy, difficulty sleeping, and worrying. Most patients (90%) attended ≥3 visits. On final evaluations, 87% patients rated the series as "excellent"; 81% "strongly agreed" that they liked the GMV format; and 92% would "definitely" recommend the series to others. Qualitative themes included empowerment and connectedness. Conclusion: Telehealth GMVs are a feasible, acceptable, and financially sustainable model for increasing access to MBIs. Diverse patients in active cancer treatment were able to participate and reported high levels of satisfaction with this series that was tailored to center health equity and inclusion.
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Prognostication for patients with cancer is important for clinical planning and management, but remains challenging given the large number of factors that can influence outcomes. As such, there is a need to identify features that can robustly predict patient outcomes. We evaluated 8608 patient tumor samples across 16 cancer types from The Cancer Genome Atlas and generated distinct survival classifiers for each using clinical and histopathological data accessible to standard oncology workflows. For cancers that had poor model performance, we deployed a random-forest-embedded sequential forward selection approach that began with an initial subset of the 15 most predictive clinicopathological features before sequentially appending the next most informative gene as an additional feature. With classifiers derived from clinical and histopathological features alone, we observed cancer-type-dependent model performance and an area under the receiver operating curve (AUROC) range of 0.65 to 0.91 across all 16 cancer types for 1- and 3-year survival prediction, with some classifiers consistently outperforming those for others. As such, for cancers that had poor model performance, we posited that the addition of more complex biomolecular features could enhance our ability to prognose patients where clinicopathological features were insufficient. With the inclusion of gene expression data, model performance for 3 select cancers (glioblastoma, stomach/gastric adenocarcinoma, ovarian serous carcinoma) markedly increased from initial AUROC scores of 0.66, 0.69, and 0.67 to 0.76, 0.77, and 0.77, respectively. As a whole, this study provides a thorough examination of the relative contributions of clinical, pathological, and gene expression data in predicting overall survival and reveals cancer types for which clinical features are already strong predictors and those where additional biomolecular information is needed.
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PURPOSE: Perineural invasion (PNI) is associated with aggressive tumor behavior, recurrence, and metastasis, and can influence the administration of adjuvant treatment. However, standard histopathologic examination has limited sensitivity in detecting PNI and does not provide insights into its mechanistic underpinnings. EXPERIMENTAL DESIGN: A multivariate Cox regression was performed to validate associations between PNI and survival in 2,029 patients across 12 cancer types. Differential expression and gene set enrichment analysis were used to learn PNI-associated programs. Machine learning models were applied to build a PNI gene expression classifier. A blinded re-review of hematoxylin and eosin (H&E) slides by a board-certified pathologist helped determine whether the classifier could improve occult histopathologic detection of PNI. RESULTS: PNI associated with both poor overall survival [HR, 1.73; 95% confidence interval (CI), 1.27-2.36; P < 0.001] and disease-free survival (HR, 1.79; 95% CI, 1.38-2.32; P < 0.001). Neural-like, prosurvival, and invasive programs were enriched in PNI-positive tumors (P adj < 0.001). Although PNI-associated features likely reflect in part the increased presence of nerves, many differentially expressed genes mapped specifically to malignant cells from single-cell atlases. A PNI gene expression classifier was derived using random forest and evaluated as a tool for occult histopathologic detection. On a blinded H&E re-review of sections initially described as PNI negative, more specimens were reannotated as PNI positive in the high classifier score cohort compared with the low-scoring cohort (P = 0.03, Fisher exact test). CONCLUSIONS: This study provides salient biological insights regarding PNI and demonstrates a role for gene expression classifiers to augment detection of histopathologic features.
