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.

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.

Abstract 1737: Association of inherited variants with chromosomal breaks in cancer

Background: Cancer cells frequently harbor genomic rearrangements and somatic copy number alterations that result from chromosome breaks, which vary in frequency across the genome. The distribution of breakpoint locations depends on features of the local genomic context, such as presence of certain retrotransposons. Moreover, inherited variants have been associated with specific chromosomal alterations in myeloproliferative neoplasms and in blood from cancer-free individuals. Here, we developed and applied computational methodology to broadly identify associations between inherited variants and the location of somatic breakpoints across cancers.

Methods: We analyzed TCGA Affymetrix Genome-Wide Human SNP Array 6.0 data from matched tumor-normal pairs (n = 5,923 samples remaining after quality control and selecting for individuals of predominantly European ancestry). Germline genotypes were called from normal samples using Birdseed v2. Allele-specific copy number analysis using both normal and tumor samples for each individual was performed using ASCAT v2.5.1. Breakpoints were inferred as the intervals between ASCAT-called somatic chromosomal segments.

We assigned individuals binary phenotypes corresponding to whether a somatic breakpoint was detected in non-overlapping 5 megabase bins of the genome. We then performed an association analysis between each phenotype and variants located on the same chromosome as the corresponding bin, adjusting for sex and ancestry via logistic regression with plink2. Analyses were performed separately for each disease type followed by a meta-analysis of association summary statistics with METAL.

Results and Conclusions: We identify several candidate associations between inherited variants and the presence of somatic breakpoints in specific regions of the genome (using a p-value cutoff of 5e-8 for each phenotype). Our results suggest that heritable genetic variation may be associated with risk of development of localized chromosome breaks in somatic cells. Ongoing work is aimed at validating the putative associations (for example using data from the PanCancer Analysis of Whole Genomes study) and identifying potential mechanisms associated with putative risk alleles.

Citation Format: Meng Xiao He, Sahar Shahamatdar, Amaro Taylor-Weiner, Travis Zack, Sohini Ramachandran, Eliezer Van Allen. Association of inherited variants with chromosomal breaks in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1737.