Palliative care tumor board: a narrative review and presentation of a novel conference to enhance collaboration and coordination of pain and symptom management for patients with advanced cancer.

BACKGROUND AND OBJECTIVE: The World Health Organization endorses that palliative care has a significant impact on the outcomes of patients with cancer. Integration of palliative care into standard oncology practice has been shown to improve a variety of patient outcomes. In this article, we present our experience with the development of a palliative care tumor board. METHODS: Starting in June 2021, we implemented a multidisciplinary palliative care and oncology tumor board focused on pain and symptom management. Complex cases were presented bimonthly. We retrospectively reviewed our experience. Data were collected on the attendees, the case presented, and the resultant therapeutic decisions made. KEY CONTENT AND FINDINGS: Between June 2021 and September 2022, tumor board meetings were conducted in person and virtually. An average of twelve people attended, including physicians and nurse practitioners from the palliative care, oncology, interventional radiology, radiation oncology, psychiatry, pediatric palliative care, and physical medicine and rehab disciplines. There were 68 patients presented with the most frequently discussed cancer being breast cancer, followed by lung cancer. A total of 18 patients (26%) were referred for procedure, including 7 patients (10%) for radiation and 11 patients (16%) for interventional procedures, and 34 patients (50%) had medication changes as outcomes of the meeting. CONCLUSIONS: The development of a biweekly palliative care conference modeled after traditional oncologic tumor board meetings allows patients to be discussed in a multidisciplinary setting and commonly results in changes in the management for pain and other cancer-related symptoms.

Deceptive learning in histopathology.

AIMS: Deep learning holds immense potential for histopathology, automating tasks that are simple for expert pathologists and revealing novel biology for tasks that were previously considered difficult or impossible to solve by eye alone. However, the extent to which the visual strategies learned by deep learning models in histopathological analysis are trustworthy or not has yet to be systematically analysed. Here, we systematically evaluate deep neural networks (DNNs) trained for histopathological analysis in order to understand if their learned strategies are trustworthy or deceptive. METHODS AND RESULTS: We trained a variety of DNNs on a novel data set of 221 whole-slide images (WSIs) from lung adenocarcinoma patients, and evaluated their effectiveness at (1) molecular profiling of KRAS versus EGFR mutations, (2) determining the primary tissue of a tumour and (3) tumour detection. While DNNs achieved above-chance performance on molecular profiling, they did so by exploiting correlations between histological subtypes and mutations, and failed to generalise to a challenging test set obtained through laser capture microdissection (LCM). In contrast, DNNs learned robust and trustworthy strategies for determining the primary tissue of a tumour as well as detecting and localising tumours in tissue. CONCLUSIONS: Our work demonstrates that DNNs hold immense promise for aiding pathologists in analysing tissue. However, they are also capable of achieving seemingly strong performance by learning deceptive strategies that leverage spurious correlations, and are ultimately unsuitable for research or clinical work. The framework we propose for model evaluation and interpretation is an important step towards developing reliable automated systems for histopathological analysis.

Enrichment analyses identify shared associations for 25 quantitative traits in over 600,000 individuals from seven diverse ancestries.

Since 2005, genome-wide association (GWA) datasets have been largely biased toward sampling European ancestry individuals, and recent studies have shown that GWA results estimated from self-identified European individuals are not transferable to non-European individuals because of various confounding challenges. Here, we demonstrate that enrichment analyses that aggregate SNP-level association statistics at multiple genomic scales-from genes to genomic regions and pathways-have been underutilized in the GWA era and can generate biologically interpretable hypotheses regarding the genetic basis of complex trait architecture. We illustrate examples of the robust associations generated by enrichment analyses while studying 25 continuous traits assayed in 566,786 individuals from seven diverse self-identified human ancestries in the UK Biobank and the Biobank Japan as well as 44,348 admixed individuals from the PAGE consortium including cohorts of African American, Hispanic and Latin American, Native Hawaiian, and American Indian/Alaska Native individuals. We identify 1,000 gene-level associations that are genome-wide significant in at least two ancestry cohorts across these 25 traits as well as highly conserved pathway associations with triglyceride levels in European, East Asian, and Native Hawaiian cohorts.

Germline Features Associated with Immune Infiltration in Solid Tumors.

The immune composition of the tumor microenvironment influences response and resistance to immunotherapies. While numerous studies have identified somatic correlates of immune infiltration, germline features that associate with immune infiltrates in cancers remain incompletely characterized. We analyze seven million autosomal germline variants in the TCGA cohort and test for association with established immune-related phenotypes that describe the tumor immune microenvironment. We identify one SNP associated with the amount of infiltrating follicular helper T cells; 23 candidate genes, some of which are involved in cytokine-mediated signaling and others containing cancer-risk SNPs; and networks with genes that are part of the DNA repair and transcription elongation pathways. In addition, we find a positive association between polygenic risk for rheumatoid arthritis and amount of infiltrating CD8+ T cells. Overall, we identify multiple germline genetic features associated with tumor-immune phenotypes and develop a framework for probing inherited features that contribute to differences in immune infiltration.