Resolving the immune landscape of human prostate at a single-cell level in health and cancer

Tuong, Zewen Kelvin; Loudon, Kevin; Berry, Brendan; Richoz, Nathan; Jones, Julia; Tan, Xiao; Nguyen, Quan; George, Anne; Hori, Satoshi; Field, Sarah; Lynch, Andy G.; Kania, Katarzyna; Coupland, Paul; Babbage, Anne; Grenfell, Richard; Barrett, Tristan; Warren, Anne Y.; Gnanapragasam, Vincent; Massie, Charlie E.; Clatworthy, Menna R.

doi:10.1016/j.celrep.2021.110132

Cited by 48 publications

(65 citation statements)

References 97 publications

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“…Prolif-TAMs were identified in a pan-cancer study [12] and in studies of several cancer types, including gastric cancer [42], CRC [12], fibrosarcoma [37], prostate cancer [66], NSCLC [27,61], OVC [67], and CNS tumors [34,52]. This population is characterized by the expression of proliferation marker Ki-67 (MKI67), and cell cycle genes, such as cyclin-dependent kinase 1 (CDK1) and cell division cycle 45 (CDC45).…”

Section: Trends In Immunologymentioning

confidence: 99%

“…This population is characterized by the expression of proliferation marker Ki-67 (MKI67), and cell cycle genes, such as cyclin-dependent kinase 1 (CDK1) and cell division cycle 45 (CDC45). Recent studies reported high expression of high mobility group box 1 (HMGB1) by Prolif-TAMs, suggesting their proinflammatory function [66]. In mice, RTM proliferation was maintained at a steady number and density during normal homeostasis, as detected from immunostaining following BrdU incorporation [68]; by contrast, RTMs expanded rapidly in an IL4Ra-dependent manner, without monocyte input, during Litomosoides sigmodontis infection [69] or from injury in an incisional wound model [70].…”

Section: Trends In Immunologymentioning

confidence: 99%

“…Using similar approaches, recent studies also identified SMAD3 and MEF2A as key transcription factors Box 1. Tumor-infiltrating monocytes TIMs have been identified in many cancer types, including breast cancer (BRCA), colorectal cancer (CRC), esophageal cancer (ESCA), heptocellular carcinoma (HCC), gastric cancer, kidney cancer, lymphoma, melanoma, nasopharyngeal carcinoma (NPC), ovarian cancer (OVC), thyroid carcinoma (THCA), uterine corpus endometrial carcinoma (UCEC) [25], nonsmall cell lung cancer (NSCLC) [27], prostate cancer [66,89], pancreatic ductal adenocarcinoma (PDAC) [49], as well as in Renca and CT26 CRC mouse models [27,29]. TIMs resemble CD14 + CD16classical (inflammatory) monocytes in most studies.…”

Section: Epigenetic Diversity Of Tams At the Single Cell Levelmentioning

confidence: 99%

“…TIMs resemble CD14 + CD16classical (inflammatory) monocytes in most studies. Common signature genes of TIMs include CD14, S100A4/6/8/9, FCN1 [25,27,29,49,66,89], VCAN, CD36, and CEBPD [30]. Compared with their blood counterparts, TIMs express higher amounts of tissue-resident markers (NR4A1, NR4A2, and NLRP3), inflammatory cytokines, and chemokines (IL1B, CCL4, CXCL2, and CXCR4), cell growth regulators (AREG and EGR1), and nuclear factor κB (NF-κB) signaling genes (NFKB1 and NFKBIA) [25].…”

Section: Epigenetic Diversity Of Tams At the Single Cell Levelmentioning

confidence: 99%

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Section: Trends In Immunologymentioning

confidence: 99%

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“…Recent advances in deep learning (DL) have led to the rapid development of diagnostic and treatment support applications in various aspects of healthcare, including oncology [1]–[4]. The proposed applications of DL utilise a range of data modalities, including MRI scans [5], CT scans [6], histopathology slides [7], genomics [8], transcriptomics [9], [10], and most recently, integrated approaches with various data types [11], [12]. In general, studies using DL show excellent predictive performance, providing hope for successful translation into clinical practice [13], [14].…”

mentioning

confidence: 99%

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

MacDonald

Foley²,

Yap³

et al. 2022

Preprint

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Trust and transparency are critical for deploying deep learning (DL) models into the clinic. DL application poses generalisation obstacles since training/development datasets often have different data distributions to clinical/production datasets that can lead to incorrect predictions with underestimated uncertainty. To investigate this pitfall, we benchmarked one pointwise and three approximate Bayesian DL models used to predict cancer of unknown primary with three independent RNA-seq datasets covering 10,968 samples across 57 primary cancer types. Our results highlight simple and scalable Bayesian DL significantly improves the generalisation of uncertainty estimation (e.g., p- value = 0.0013 for calibration). Moreover, we demonstrate Bayesian DL substantially improves accuracy under data distributional shifts when utilising "uncertainty thresholding" by designing a prototypical metric that evaluates the expected (accuracy) loss when deploying models from development to production, which we call the Area between Development and Production curve (ADP). In summary, Bayesian DL is a hopeful avenue of research for generalising uncertainty, which improves performance, transparency, and therefore safety of DL models for deployment in real-world.

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Spatial whole transcriptome profiling of primary tumor from patients with metastatic prostate cancer

Salachan,

Rasmussen,

Ulhøi

et al. 2023

Intl Journal of Cancer

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Prostate cancer (PCa) is a highly heterogeneous disease in terms of its molecular makeup and clinical prognosis. The prostate tumor microenvironment (TME) is hypothesized to play an important role in driving disease aggressiveness, but precise mechanisms remain elusive. In our study, we used spatial transcriptomics to explore for the first time the spatial gene expression heterogeneity within primary prostate tumors from patients with metastatic disease. In total, we analyzed 5459 tissue spots from three PCa patients comprising castration‐resistant (CRPC) and neuroendocrine (NEPC) disease stages. Within CRPC, we identified a T cell cluster whose activity might be impaired by nearby regulatory T cells, potentially mediating the aggressive disease phenotype. Moreover, we identified Hallmark signatures of epithelial‐mesenchymal transition in a cancer‐associated fibroblast (CAF) cluster, indicating the aggressive characteristic of the primary TME leading to metastatic dissemination. Within NEPC, we identified active immune‐stroma cross‐talk exemplified by significant ligand‐receptor interactions between CAFs and M2 macrophages. Further, we identified a malignant cell population that was associated with the down‐regulation of an immune‐related gene signature. Lower expression of this signature was associated with higher levels of genomic instability in advanced PCa patients (SU2C cohort, n = 99) and poor recurrence free survival in early‐stage PCa patients (TCGA cohort, n = 395), suggesting prognostic biomarker potential. Taken together, our study reveals the importance of whole transcriptome profiling at spatial resolution for biomarker discovery and for advancing our understanding of tumor biology.

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Resolving the immune landscape of human prostate at a single-cell level in health and cancer

Cited by 48 publications

References 97 publications

Macrophage diversity in cancer revisited in the era of single-cell omics

Macrophage diversity in cancer revisited in the era of single-cell omics

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

Spatial whole transcriptome profiling of primary tumor from patients with metastatic prostate cancer

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