Search

Enabling the examination of cell-cell relationships in tissue, spatially resolved omics technologies have revolutionised our perspectives on cancer biology. Clinically, the development of immune checkpoint inhibitors (ICI) has advanced cancer therapeutics. However, a major challenge of effective implementation is the identification of predictive biomarkers of response. In this review we examine the potential added predictive value of spatial biomarkers of response to ICI beyond current clinical benchmarks., (© 2024. The Author(s).)

Background: Tumour-infiltrating CD8 + cytotoxic T cells confer favourable prognosis in colorectal cancer. The added prognostic value of other infiltrating immune cells is unclear and so we sought to investigate their prognostic value in two large clinical trial cohorts., Methods: We used multiplex immunofluorescent staining of tissue microarrays to assess the densities of CD8 + , CD20 + , FoxP3 + , and CD68 + cells in the intraepithelial and intrastromal compartments from tumour samples of patients with stage II-III colorectal cancer from the SCOT trial (ISRCTN59757862), which examined 3 months versus 6 months of adjuvant oxaliplatin-based chemotherapy, and from the QUASAR 2 trial (ISRCTN45133151), which compared adjuvant capecitabine with or without bevacizumab. Both trials included patients aged 18 years or older with an Eastern Cooperative Oncology Group performance status of 0-1. Immune marker predictors were analysed by multiple regression, and the prognostic and predictive values of markers for colorectal cancer recurrence-free interval by Cox regression were assessed using the SCOT cohort for discovery and QUASAR 2 cohort for validation., Findings: After exclusion of cases without tissue microarrays and with technical failures, and following quality control, we included 2340 cases from the SCOT trial and 1069 from the QUASAR 2 trial in our analysis. Univariable analysis of associations with recurrence-free interval in cases from the SCOT trial showed a strong prognostic value of intraepithelial CD8 (CD8 IE ) as a continuous variable (hazard ratio [HR] for 75th vs 25th percentile [75vs25] 0·73 [95% CI 0·68-0·79], p=2·5 × 10 -16 ), and of intrastromal FoxP3 (FoxP3 IS ; 0·71 [0·64-0·78], p=1·5 × 10 -13 ) but not as strongly in the epithelium (FoxP3 IE ; 0·89 [0·84-0·96], p=1·5 × 10 -4 ). Associations of other markers with recurrence-free interval were moderate. CD8 IE and FoxP3 IS retained independent prognostic value in bivariable and multivariable analysis, and, compared with either marker alone, a composite marker including both markers (CD8 IE -FoxP3 IS ) was superior when assessed as a continuous variable (adjusted [a]HR 75 vs 25 0·70 [95% CI 0·63-0·78], p=5·1 × 10 -11 ) and when categorised into low, intermediate, and high density groups using previously published cutpoints (aHR for intermediate vs high 1·68 [95% CI 1·29-2·20], p=1·3 × 10 -4 ; low vs high 2·58 [1·91-3·49], p=7·9 × 10 -10 ), with performance similar to the gold-standard Immunoscore. The prognostic value of CD8 IE -FoxP3 IS was confirmed in cases from the QUASAR 2 trial, both as a continuous variable (aHR 75 vs 25 0·84 [95% CI 0·73-0·96], p=0·012) and as a categorical variable for low versus high density (aHR 1·80 [95% CI 1·17-2·75], p=0·0071) but not for intermediate versus high (1·30 [0·89-1·88], p=0·17)., Interpretation: Combined evaluation of CD8 IE and FoxP3 IS could help to refine risk stratification in colorectal cancer. Investigation of FoxP3 IS cells as an immunotherapy target in colorectal cancer might be merited., Funding: Medical Research Council, National Institute for Health Research, Cancer Research UK, Swedish Cancer Society, Roche, and Promedica Foundation., Competing Interests: Declaration of interests DK has patents related to use of digital pathology algorithms, and serves as an advisor and has stock in Oxford Cancer Biomarkers. MPS has received honoraria from Merck, Server, Bayer, Takeda, and Amgen for meetings or lectures. WK has received royalties from Room4. DNC has participated in advisory boards for MSD and has received research funding on behalf of the TransSCOT consortium from HalioDx for analyses independent of this study. VHK has served as an invited speaker on behalf of Sharing Progress in Cancer Care and Indica Labs, and has received project-based research funding from The Image Analysis Group and Roche outside of the submitted work. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

The Swiss Digital Pathology Consortium (SDiPath) was founded in 2018 as a working group of the Swiss Society for Pathology with the aim of networking, training, and promoting digital pathology (DP) at a national level. Since then, two national surveys have been carried out on the level of knowledge, dissemination, use, and needs in DP, which have resulted in clear fields of action. In addition to organizing symposia and workshops, national guidelines were drawn up and an initiative for a national DP platform actively codesigned. With the growing use of digital image processing and artificial intelligence tools, continuous monitoring, evaluation, and exchange of experiences will be pursued, along with best practices., (© 2023. The Author(s).)

Tumor cell fraction (TCF) estimation is a common clinical task with well-established large interobserver variability. It thus provides an ideal test bed to evaluate potential impacts of employing a tumor cell fraction computer-aided diagnostic (TCFCAD) tool to support pathologists' evaluation. During a National Slide Seminar event, pathologists (n = 69) were asked to visually estimate TCF in 10 regions of interest (ROIs) from hematoxylin and eosin colorectal cancer images intentionally curated for diverse tissue compositions, cellularity, and stain intensities. Next, they re-evaluated the same ROIs while being provided a TCFCAD-created overlay highlighting predicted tumor vs nontumor cells, together with the corresponding TCF percentage. Participants also reported confidence levels in their assessments using a 5-tier scale, indicating no confidence to high confidence, respectively. The TCF ground truth (GT) was defined by manual cell-counting by experts. When assisted, interobserver variability significantly decreased, showing estimates converging to the GT. This improvement remained even when TCFCAD predictions deviated slightly from the GT. The standard deviation (SD) of the estimated TCF to the GT across ROIs was 9.9% vs 5.8% with TCFCAD (P < .0001). The intraclass correlation coefficient increased from 0.8 to 0.93 (95% CI, 0.65-0.93 vs 0.86-0.98), and pathologists stated feeling more confident when aided (3.67 ± 0.81 vs 4.17 ± 0.82 with the computer-aided diagnostic [CAD] tool). TCFCAD estimation support demonstrated improved scoring accuracy, interpathologist agreement, and scoring confidence. Interestingly, pathologists also expressed more willingness to use such a CAD tool at the end of the survey, highlighting the importance of training/education to increase adoption of CAD systems., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Multiplex immunofluorescence (mIF) imaging can provide comprehensive quantitative and spatial information for multiple immune markers for tumour immunoprofiling. However, application at scale to clinical trial samples sourced from multiple institutions is challenging due to pre-analytical heterogeneity. This study reports an analytical approach to the largest multi-parameter immunoprofiling study of clinical trial samples to date. We analysed 12,592 tissue microarray (TMA) spots from 3,545 colorectal cancers sourced from more than 240 institutions in two clinical trials (QUASAR 2 and SCOT) stained for CD4, CD8, CD20, CD68, FoxP3, pan-cytokeratin, and DAPI by mIF. TMA slides were multi-spectrally imaged and analysed by cell-based and pixel-based marker analysis. We developed an adaptive thresholding method to account for inter- and intra-slide intensity variation in TMA analysis. Applying this method effectively ameliorated inter- and intra-slide intensity variation improving the image analysis results compared with methods using a single global threshold. Correlation of CD8 data derived by our mIF analysis approach with single-plex chromogenic immunohistochemistry CD8 data derived from subsequent sections indicates the validity of our method (Spearman's rank correlation coefficients ρ between 0.63 and 0.66, p ≪ 0.01) as compared with the current gold standard analysis approach. Evaluation of correlation between cell-based and pixel-based analysis results confirms equivalency (ρ > 0.8, p ≪ 0.01, except for CD20 in the epithelial region) of both analytical approaches. These data suggest that our adaptive thresholding approach can enable analysis of mIF-stained clinical trial TMA datasets by digital pathology at scale for precision immunoprofiling., (© 2023 The Authors. The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

Metastatic colonization of distant organs accounts for over 90% of deaths related to solid cancers, yet the molecular determinants of metastasis remain poorly understood. Here, we unveil a mechanism of colonization in the aggressive basal-like subtype of breast cancer that is driven by the NAD + metabolic enzyme nicotinamide N-methyltransferase (NNMT). We demonstrate that NNMT imprints a basal genetic program into cancer cells, enhancing their plasticity. In line, NNMT expression is associated with poor clinical outcomes in patients with breast cancer. Accordingly, ablation of NNMT dramatically suppresses metastasis formation in pre-clinical mouse models. Mechanistically, NNMT depletion results in a methyl overflow that increases histone H3K9 trimethylation (H3K9me3) and DNA methylation at the promoters of PR/SET Domain-5 (PRDM5) and extracellular matrix-related genes. PRDM5 emerged in this study as a pro-metastatic gene acting via induction of cancer-cell intrinsic transcription of collagens. Depletion of PRDM5 in tumor cells decreases COL1A1 deposition and impairs metastatic colonization of the lungs. These findings reveal a critical activity of the NNMT-PRDM5-COL1A1 axis for cancer cell plasticity and metastasis in basal-like breast cancer., (© 2023 The Authors.)

Background: The coronavirus disease 2019 (COVID-19) pandemic resulted in unprecedented emphasis on infection control procedures; however, it is unknown whether the pandemic altered Clostridioides difficile infection (CDI) prevalence. This study investigated CDI prevalence before and during the COVID-19 pandemic in a national sample of United States (US) hospitals., Methods: This was a retrospective cohort study using the Premier Healthcare Database. Patients with laboratory-confirmed CDI from April 2019 through March 2020 (pre-COVID-19 period) and April 2020 through March 2021 (COVID-19 period) were included. CDI prevalence (CDI encounters per 10 000 total encounters) and inpatient outcomes (eg, mortality, hospital length of stay) were compared between pre-COVID-19 and COVID-19 periods using bivariable analyses or interrupted time series analysis., Results: A total of 25 992 CDI encounters were included representing 22 130 unique CDI patients. CDI prevalence decreased from the pre-COVID-19 to COVID-19 period (12.2 per 10 000 vs 8.9 per 10 000, P  < .0001), driven by a reduction in inpatient CDI prevalence (57.8 per 10 000 vs 49.4 per 10 000, P  < .0001); however, the rate ratio did not significantly change over time (RR, 1.04 [95% confidence interval, .90-1.20]). From the pre-COVID-19 to COVID-19 period, CDI patients experienced higher inpatient mortality (5.5% vs 7.4%, P  < .0001) and higher median encounter cost ($10 832 vs $12 862, P  < .0001)., Conclusions: CDI prevalence decreased during the COVID-19 pandemic in a national US sample, though at a rate similar to prior to the pandemic. CDI patients had higher inpatient mortality and encounter costs during the pandemic., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

CD8+ tumor-infiltrating T cells can be regarded as one of the most relevant predictive biomarkers in immune-oncology. Highly infiltrated tumors, referred to as inflamed (clinically "hot"), show the most favorable response to immune checkpoint inhibitors in contrast to tumors with a scarce immune infiltrate called immune desert or excluded (clinically "cold"). Nevertheless, quantitative and reproducible methods examining their prevalence within tumors are lacking. We therefore established a computational diagnostic algorithm to quantitatively measure spatial densities of tumor-infiltrating CD8+ T cells by digital pathology within the three known tumor compartments as recommended by the International Immuno-Oncology Biomarker Working Group in 116 prospective metastatic melanomas of the Swiss Tumor Profiler cohort. Workflow robustness was confirmed in 33 samples of an independent retrospective validation cohort. The introduction of the intratumoral tumor center compartment proved to be most relevant for establishing an immune diagnosis in metastatic disease, independent of metastatic site. Cut-off values for reproducible classification were defined and successfully assigned densities into the respective immune diagnostic category in the validation cohort with high sensitivity, specificity, and precision. We provide a robust diagnostic algorithm based on intratumoral and stromal CD8+ T-cell densities in the tumor center compartment that translates spatial densities of tumor-infiltrating CD8+ T cells into the clinically relevant immune diagnostic categories "inflamed", "excluded", and "desert". The consideration of the intratumoral tumor center compartment allows immune phenotyping in the clinically highly relevant setting of metastatic lesions, even if the invasive margin compartment is not captured in biopsy material., (© 2021. The Author(s).)

The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease., Competing Interests: Declaration of Interests B.S. is scientific co-founder and shareholder of Allcyte GmbH. L.P. and G.G. are listed as inventor on patents related to the 4i technology (WO 2019/207004; WO 2020/008071). G.R., K.-V.L., and S.G.S. are listed on a patent application related to single-cell analyses (European Patent Application No. 20170724.7). H.M. is on advisory boards for Bayer, Astra Zeneca, Janssen, Roche, and Merck. R.D. reports intermittent, project-focused consulting and/or advisory relationships with Novartis, Merck Sharp & Dohme (MSD), Bristol Myers Squibb (BMS), Roche, Amgen, Takeda, Pierre Fabre, Sun Pharma, Sanofi, Catalym, Second Genome, Regeneron, and Alligator outside the submitted work. G.R. is cofounder and on the Scientific Advisory Board of Computomics GmbH. M.P.L. is a co-founder and shareholder of Oncobit AG and receives research funding from Novartis, Roche, and Molecular Partners. The Tumor Profiler study is jointly funded by a public-private partnership involving F. Hoffmann-La Roche Ltd., ETH Zurich, University of Zurich, University Hospital Zurich, and University Hospital Basel., (Copyright © 2021. Published by Elsevier Inc.)

Future Medicine: Digital Pathology Abstract. Pathology is facing a paradigm shift. Digitization enables highly efficient, networked diagnostics and the simplified exchange of expert knowledge. Algorithms for image analysis and artificial intelligence have the potential to further increase the quality of diagnostics in pathology. Structured electronic reporting enables the continuous development of digital diagnostics and improves the communication between clinical disciplines. Here we identify and discuss the main trends that will shape digital pathology.