Supplementary Table S22 shows the marker genes of the different KPC fibroblasts sub-clusters

Supplementary Table S20 shows the marker genes of the different clusters in the fibroblast-enriched KPC analysis

Epithelial-to-mesenchymal transition (EMT) is associated with tumor initiation, metastasis, and drug resistance. However, the mechanisms underlying these associations are largely unknown. We studied several tumor types to identify the source of EMT gene expression signals and a potential mechanism of resistance to immuno-oncology treatment. Across tumor types, EMT-related gene expression was strongly associated with expression of stroma-related genes. Based on RNA sequencing of multiple patient-derived xenograft models, EMT-related gene expression was enriched in the stroma versus parenchyma. EMT-related markers were predominantly expressed by cancer-associated fibroblasts (CAFs), cells of mesenchymal origin which produce a variety of matrix proteins and growth factors. Scores derived from a 3-gene CAF transcriptional signature ( COL1A1 , COL1A2 , COL3A1 ) were sufficient to reproduce association between EMT-related markers and disease prognosis. Our results suggest that CAFs are the primary source of EMT signaling and have potential roles as biomarkers and targets for immuno-oncology therapies.

Supplementary Figure S1 shows the data quality and clustering metrics of the human PDAC single cell transcriptomes, Supplementary Figure S2 shows the distribution of sub-clusters of cells throughout the human dataset, Supplementary Figure S3 shows pathway analysis in human PDAC iCAFs and myCAFs, Supplementary Figure S4 shows the data quality and clustering metrics of the KPC mouse PDAC single cell transcriptomes, Supplementary Figure S5 shows pathway analysis in different KPC CAFs, Supplementary Figure S6 shows that Podoplanin is a pan-fibroblast marker in murine PDAC, Supplementary Figure S7 shows the validation of apCAFs in human PDAC, Supplementary Figure S8 shows the characterization of apCAFs in KPC and orthotopic transplantation models

Supplementary Table S14 shows GSEA of human PDAC myCAFs vs. iCAFs

Supplementary Table S13 shows marker genes of the different human fibroblasts

Supplementary Table S24 shows VIPER analysis of protein activity in KPC mouse CAFs

Supplementary Table S15 shows protein activity of signaling molecules in human CAFs, as analyzed by VIPER

Supplementary Table S6 shows marker genes of the different human ductal cell sub-clusters

Supplementary Table S10 shows marker genes of the different human T & NK cell sub-clusters

Supplementary Table S8 shows marker genes of the different human myeloid cell sub-clusters

Supplementary Table S16 shows protein activity of transcription factors in human CAFs, as analyzed by VIPER

Supplementary Table S23 shows GSEA of KPC mouse CAFs

Supplementary Table S4 shows marker genes of the different clusters in human PDAC scRNAseq

An integrated precision oncology approach that can guide the selection of personalized therapy is paramount to achieving improved outcomes in patients while maintaining responsible healthcare economics. Actionable precision oncology requires in vitro diagnostic tests that are sufficiently sensitive, accurate, reproducible, and reliable in the real-world setting with minimal risk of patient overtreatment or undertreatment. Liquid biopsies and specifically ctDNA based diagnostics provide a unique opportunity to optimize clinical decisions and thereby improve individual patient care. Such techniques could be particularly valuable in the adjuvant setting where multi-modal therapy (MMT) has curative potential. Multiple studies provide evidence that serial ctDNA monitoring can predict or detect recurrence earlier than conventional surveillance. However, as highlighted in the most recent ESMO ctDNA recommendations, the detection limit of current diagnostics to accurately report ctDNA fragments in peripheral blood remains a challenge. Importantly, it is currently unclear how to incorporate ctDNA as MRD biomarker or as a decision tool for treatment selection to improve cancer outcomes. GUIDE.MRD, a patient-centered precision oncology public-private partnership under the Innovative Health Initiative umbrella, is designed to improve the utility and clinical implementation of ctDNA as MRD biomarker and to evaluate the potential of novel decision tools to match patients to MMTs. Partners from academic, medical technology, pharmaceutical and patient advocacy sectors will join to (a) benchmark available ctDNA assays for sensitivity, specificity, and predictive value using reference material that closely mimic patient samples in the adjuvant setting, (b) to clinically validate the top performing ctDNA assay characteristics in patients with NSCLC, CRC and PDAC and develop patient-centric clinical implementation roadmaps, and (c) determine the utility of ctDNA assays as a prospective decision tool of clinical response and choice of MMT including novel combinations. Universitaetsklinikum Hamburg-Eppendorf. Innovative Health Initiative.

Supplementary Table S1 shows patient information, Supplementary Table S2 shows cell numbers analyzed from each human PDAC sample, Supplementary Table S3 shows statistics of the clustering analysis of the human PDAC single cell RNA sequencing, Supplementary Table S5 shows statistics of the human PDAC ductal cell analysis, Supplementary Table S7 shows statistics of the human PDAC myeloid cell analysis, Supplementary Table S9 shows statistics of the human PDAC T & NK cell analysis, Supplementary Table S11 shows cell numbers analyzed for fibroblast subtypes from each human PDAC sample, Supplementary Table S12 shows statistics of the human PDAC fibroblast analysis, Supplementary Table S17 shows statistics of all viable KPC mouse cell analysis, Supplementary Table S19 shows statistics of the KPC mouse fibroblast-enriched fraction analysis, Supplementary Table S21 shows statistics of the fibroblasts from KPC mouse fibroblast-enriched population

In patients (pts) with resectable melanoma, circulating tumor DNA (ctDNA) detection post-resection may be useful to inform disease state and recurrence risk. Post-resection pre-treatment plasma from 1127 pts with stage IIIB-D/IV resected melanoma treated with nivolumab + ipilimumab vs nivolumab in the phase 3 CheckMate 915 study (NCT03068455) was retrospectively evaluated for ctDNA status and level, using a tumor-guided, pt-specific panel of up to 200 variants (Invitae Personalized Cancer MonitoringÔ). Kaplan-Meier and Cox regression models were used to evaluate the association between ctDNA status and recurrence-free survival (RFS), alone and combined with baseline clinical factors and biomarkers, including interferon-γ, TMB, tumor PD-L1, CD8+ T cells, tumor thickness, ulceration, and lymph node involvement. Overall pre-treatment ctDNA prevalence was ∼16% (95% CI: 14%–18%) and similar across most baseline demographics. A trend of greater ctDNA+ prevalence in higher stage III substages of melanoma was observed (IIIB = 11% [35/333], IIIC = 18% [110/596], IIID = 41% [13/32]). Pre-treatment ctDNA was associated with an increased risk of recurrence (HR 1.87, 95% CI: 1.48–2.36; Table). No significant interaction between ctDNA status and treatment arm was seen (ratio of hazard ratios: 0.99, 95% CI: 0.63–1.57). Patients with ctDNA present exhibited a greater rate of recurrence, seen as early as week 13 of therapy (Table). Distant metastasis-free survival results were similar. In composite analyses, improved RFS prediction was observed after combining ctDNA with clinical factors and biomarkers.Table: 788ORFS probability by pre-treatment ctDNA status in CheckMate 915 treated patientsStatusNumber of pts (n)13-week RFS (95% CI)12-month RFS (95% CI)24-month RFS (95% CI)ctDNA+18372.3% (65–78.3)58.4% (50.7–65.3)45.5% (37.9–52.8)ctDNA-94491.7% (89.7–93.3)75.1% (72.1–77.8)65.4% (62.1–68.4) Open table in a new tab Pre-treatment ctDNA was associated with increased risk of early recurrence across treatment arms. ctDNA is a useful biomarker for combined analyses predicting outcome for adjuvant melanoma.

Transcriptomic analysis of the mammalian retinal pigment epithelium (RPE) aims to identify cellular networks that influence ocular development, maintenance, function, and disease. However, available evidence points to RPE cell heterogeneity within native tissue, which adds complexity to global transcriptomic analysis. Here, to assess cell heterogeneity, we performed single-cell RNA sequencing of RPE cells from two young adult male C57BL/6J mice. Following quality control to ensure robust transcript identification limited to cell singlets, we detected 13,858 transcripts among 2667 and 2846 RPE cells. Dimensional reduction by principal component analysis and uniform manifold approximation and projection revealed six distinct cell populations. All clusters expressed transcripts typical of RPE cells; the smallest (C1, containing 1–2% of total cells) exhibited the hallmarks of stem and/or progenitor (SP) cells. Placing C1–6 along a pseudotime axis suggested a relative decrease in melanogenesis and SP gene expression and a corresponding increase in visual cycle gene expression upon RPE maturation. K-means clustering of all detected transcripts identified additional expression patterns that may advance the understanding of RPE SP cell maintenance and the evolution of cellular metabolic networks during development. This work provides new insights into the transcriptome of the mouse RPE and a baseline for identifying experimentally induced transcriptional changes in future studies of this tissue.