Differentiation of fibroblasts is coupled with a loss of transcriptional regulatory programs. A, Trajectory analysis of fibroblast subclusters inferred by Slingshot. B, Gene expression of CAF marker genes plotted along the pseudotime trajectory leading to CAFs. Expression values in natural-log scale. C, RNA velocity analysis of fibroblast cells. D, Volcano plots of DE genes between fibroblast populations. Transcription factors that are DE between populations, with an adjusted P value <0.01 and average log2FC > 0.5 in absolute value, are indicated. E, Gene expression of selected transcription factors plotted along the pseudotime trajectory leading to CAFs. Expression values in natural-log scale. F, Top 20 candidate master regulators of transcriptional program altered between fibroblast populations identified by the master regulator analysis algorithm (MARINa). The targets of each transcription factor are shown in vertical bars (repressed genes are in blue, and activated genes in red) and are rank-sorted (x-axis) from the one most downregulated to the one most upregulated in the selected conditions: normal fibroblasts compared with transitional fibroblasts and transitional compared with CAFs. The heatmaps on the right side of each panel indicate inferred differential activity (Act) and differential expression (Exp) of the transcription factor. MARINa, MAster Regulator INference algorithm.

scRNA-seq of human pancreatic tissues. A, Schematic representation of the types of tissues employed in this study and the experimental pipeline for their analysis by scRNA-seq. B, UMAP visualization of single cells from all samples colored by cell type. C, UMAP projections of single cells from each tissue type (colored) contributing to the global UMAP (gray). D, Representative images of H&E-stained sections of each tissue type. Scale bar, 100 μm. E, Quantification of cell types within each tissue type. F, Quantification of cell populations (% from the total) along with the number of DE genes in each cell population between tissue types. UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction; H&E, hematoxylin and eosin.

Cell lines and patient-derived xenografts are essential to cancer research; however, the results derived from such models often lack clinical translatability, as they do not fully recapitulate the complex cancer biology. Identifying preclinical models that sufficiently resemble the biological characteristics of clinical tumors across different cancers is critically important. Here, we developed MOBER, Multi-Origin Batch Effect Remover method, to simultaneously extract biologically meaningful embeddings while removing confounder information. Applying MOBER on 932 cancer cell lines, 434 patient-derived tumor xenografts, and 11,159 clinical tumors, we identified preclinical models with greatest transcriptional fidelity to clinical tumors and models that are transcriptionally unrepresentative of their respective clinical tumors. MOBER allows for transformation of transcriptional profiles of preclinical models to resemble the ones of clinical tumors and, therefore, can be used to improve the clinical translation of insights gained from preclinical models. MOBER is a versatile batch effect removal method applicable to diverse transcriptomic datasets, enabling integration of multiple datasets simultaneously.

ABSTRACT:Cancer progression and response to therapy are inextricably reliant on the coevolution of a supportive tissue microenvironment. This is particularly evident in pancreatic ductal adenocarcinoma, a tumor type characterized by expansive and heterogeneous stroma. Herein, we employed single-cell RNA sequencing and spatial transcriptomics of normal, inflamed, and malignant pancreatic tissues to contextualize stromal dynamics associated with disease and treatment status, identifying temporal and spatial trajectories of fibroblast differentiation. Using analytical tools to infer cellular communication, together with a newly developed assay to annotate genomic alterations in cancer cells, we additionally explored the complex intercellular networks underlying tissue circuitry, highlighting a fibroblast-centric interactome that grows in strength and complexity in the context of malignant transformation. Our study yields new insights on the stromal remodeling events favoring the development of a tumor-supportive microenvironment and provides a powerful resource for the exploration of novel points of therapeutic intervention in pancreatic ductal adenocarcinoma. SIGNIFICANCE:Pancreatic cancer remains a high unmet medical need. Understanding the interactions between stroma and cancer cells in this disease may unveil new opportunities for therapeutic intervention.

TGFβ is a key mediator of gene expression in both cancer cells and CAFs. A and B, Circos plots depicting inferred ligand-to-target signaling between target genes (red) in fibroblasts (A) and cancer cells (B) and their associated paracrine (blue) or autocrine (yellow) ligands identified using NicheNet. Selected target genes represent those enriched in CAFs and transitional fibroblasts from untreated PDAC, relative to normal fibroblasts (A) or cancer cells relative to normal ductal cells (B). Within each panel, arrow colors indicate the population of cells expressing the ligand and their width indicate the ligand–receptor interaction weights. C, Dotplot displaying the expression of ligands identified in A and B in each cell population. The size of the dot represents the percentage of cells expressing that ligand, and color indicates the average expression level of the ligand across all cells within a cell population. D, Heatmap depicting the inferred contribution of cell populations to TGFβ signaling using CellChat. E, Hierarchical network diagram visualizing the inferred intercellular communication patterns for TGFβ signaling. Source and target cell populations are represented by solid and open circles, respectively. Line thickness is proportional to the communication probability between cell populations.

Abstract Metastases are the primary cause of cancer-related death, and improving the means of predicting and targeting their development is one of the major goals in cancer research. While surgical resection and neo-adjuvant therapy can cure well-confined primary tumors, our ability to effectively treat cancer is largely dependent on our capacity to interdict the process of metastasis. The recent accumulation of ‘omics data from metastatic tumors provides an unprecedented opportunity to develop machine learning models to predict the molecular changes during metastasis and explore the patterns of metastasis formation. With this in mind, we developed MetMapper, a deep learning model trained on primary and metastatic tumors from > 13 000 patients integrating data from 11 published data resources. MetMapper can predict the transcriptomic changes of a primary tumor when it metastasizes to different distant organs. The results were extensively validated using transcriptomics data from matched primary and metastatic tumor biopsies extracted from the same patients. Furthermore, MetMapper’s predictions revealed that the non-random patterns of cancer metastases can be partly explained by the degree of transcriptome reprogramming needed during metastasis: primary tumors tend to metastasize to organs that require minimal changes to their transcriptomes. Using MetMapper, we derived a metastatic potential score for patient tumors and demonstrate that this score can be used to stratify patients into high and low survival groups across different indications. The predicted metastatic potential of patient tumors significantly correlates with experimentally characterized metastatic potential of cancer cell lines. Additionally, by performing in-silico perturbations of genes and oncogenic pathways that can alter the metastatic potential of patient tumors, we identified genomic features that are highly associated with metastases to specific organs, some of which were reported by existing pan-cancer clinical sequencing studies. Our results demonstrate the utility of MetMapper as a novel AI-powered methodology for investigating mechanisms and patterns of metastatic dissemination, as well as forecasting metastatic outcomes of patient tumors. Citation Format: Gang Li, Evan Béal, Dean Sumner, Giorgio G. Galli, Viviana Cremasco, Joshua M. Korn, Frank Dondelinger, David Ruddy, Audrey Kauffmann, Slavica Dimitrieva. Predicting metastatic transcriptomes of patient tumors with deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 896.

Integration of single-cell and spatial transcriptomics reveals spatially defined fibroblast populations. A, Marker gene expression for fibroblast clusters overlayed on untreated PDAC sample HTB2779. Black box corresponds to that in whole mount image in B. B, Whole mount image and progressively higher magnifications of H&E-stained section of HTB2779. Scale bars, 2 mm (left), 300 μm (middle), and 60 μm (right). The locations of histologically defined cancer glands are indicated by black arrows. C, Marker gene expression for fibroblast clusters overlayed on NAT-PDAC sample HTB2903. Black box corresponds to that in whole mount image in D. D, Whole mount image and progressively higher magnifications of H&E-stained section of HTB2903. Scale bars, 2 mm (left), 300 μm (middle), and 60 μm (right). The locations of histologically defined cancer glands are indicated by black arrows. H&E, hematoxylin and eosin.

Concomitant inhibition of PI3Kβ, IGF1R and MAPK signaling are leading to full long-term pathway blockade. A) +B) Effects of treatment with the indicated inhibitors as single-agents or in combination on WM-266-4 (A) or RVH-421 (B) were evaluated by immunoblotting using phospho-specific or total target protein antibodies. PI3Kβi=rac-KIN-193, PI3Kαi=BYL719, IGF1Ri (A)=AEW541, IGF1Ri (B)=Figitumumab-like antibody, MEKi=MEK162

XML file - 99K, Kinase activity and selectivity for NVP-BGJ398; CCLE cell lines sensitive to NVP-BGJ398; GeneSet expression signatures; FGFR genetic alterations and concomitant mutations

PDF file, 53KB, A and B. 5x105 A2058 cells were seeded in 10 cm dishes and incubated for 24 h either with increasing amounts of BKM120 (A) or with 5 M of either GDC-0941 (B, top panel) or BEZ235 (B, bottom panel). Cells were then fixed, prepared as described for quantification of the population in the different phases of the cell cycle by fluorescence-activated cell sorting. G1, S and G2/M distribution for control untreated cells are described in the mean text and in Figure 4A. The activities of BKM120 on the cell cycle were plotted along to the inhibitory effects on pAkt levels (A).

PDF - 92K, NVP-BYL719 does not inhibit mTOR and PIKKs involved in DNA damage-repair processes. A. TSC1 -/- MEFs cells were grown in a 96-well format and treated for 1 h with increased concentrations of RAD001 or NVP-BYL719 (from 0.5 nmol/L to 10 ?mol/L in 1 third dilution steps) and immediately fixed. S235/236P-RPS6 levels were measured and IC50 determined with the Excel module XLfit. Background (no primary Ab incubated); BL, Baseline. B: TSC1 -/- MEFs cells were treated with increasing concentrations of NVP-BYL719 as indicated or RAD001 at 500 nmol/L or an equivalent DMSO concentration for 30 minutes. Levels of S235/236P-RPS6 and total RPS6 in protein- normalized lysates were detected by Western blot analyzis using an activation-state specific antibody, followed by incubation with species- specific HRP-labeled secondary antibody and signal development by ECL. C: 24 h post seeding, A549 cells were treated at the same time with Actinomycin D (Act D) at a concentration of 5 ?mol/L (an agent used to induce DNA damage), and with increasing concentrations of NVP-BYL719 as indicated or with the vehicle control (DMSO) for 1 h. Levels of S15P-p53 and tubulin in protein-normalized lysates were detected by Western blot analysis using an activation-state specific antibody, followed by incubation with species- specific HRP-labeled secondary antibody and signal development by ECL. D: 24 h post seeding, U2OS cells were pre-treated for 1 h with increased concentrations of NVP-BYL719 or KU55933 a specific small molecular mass inhibitor of ATM (Supplementary reference 1) at a concentration of 10 ?mol/L or with the vehicle control (DMSO). The cells were then irradiated with 15 Gy and re-incubated at 37 degrees C for 1 h and then lysed. Levels of S1981P-ATM in protein- normalized lysates were detected by Western blot analysis using an activation-state specific antibody, followed by incubation with species- specific HRP-labeled secondary antibody and signal development by ECL.

Supplementary Figure Legends, Table Legends

Supplementary Data from Exquisite Sensitivity to Dual BRG1/BRM ATPase Inhibitors Reveals Broad SWI/SNF Dependencies in Acute Myeloid Leukemia

Supplementary Figure S1 shows additional shRNA knockdown data in uveal melanoma cell lines. Supplementary Figure S2 shows additional dual BRG1/BRM knockdown data and BRG1 rescue data. Supplementary Figure S3 shows basal SWI/SNF subunit expression, additional caspase activity and viability data in compound treated and shRNA knockdown cell lines, and SWI/SNF mutations in uveal melanoma cells lines. Supplementary Figure S4 shows additional MITF knockdown data and further RNA-Seq and ATAC-Seq data set analyses. Supplementary Figure S5 shows target gene modulation by compound treatment in uveal melanoma cell lines and additional MITF overexpression rescue data. Supplementary Figure S6 shows genomic location of primers used in ChIP-qPCR experiment.

PDF file, 71KB, The data are scaled by the positive control (1M MG132) and the negative control (DMSO). The percentage of maximum activity (Amax) is represented in function of the crossing point (concentration in M at 50% of MG132 activity). Each data point represents a cell line; the vertical and horizontal lines represent, respectively, the median of the crossing point values (1.33M) and the median of the Amax values (-90.06%), of BKM120 across all cell lines. The populations of cell lines least responding to GDC-0941, among which some are sensitive to BKM120, are highlighted in green.

Additional method details for ATAC-Seq Data Analysis and ChIP-qPCR

Supplementary material and methods include the description for the bioanalytical method for HDM201 detection in plasma and tumor, the human and mouse gene expression analysis in vitro and in vivo, the live-cell quantification of cleaved-caspase activation, the western blot analysis, the immunohistochemistry, the splinkerette PCR for the amplification of transposon integration sites and the tumor sequencing, mapping of insertion sequences to the mouse genome and identification of common integration site, additional information on the shRNA screen and the tumor models and supplementary references. Supplementary figures include: • Fig S1: the SJSA-1 inhibition growth curves when treated with HDM201 at different doses and for different times and the data for MOLM-3. • Fig S2: the cumulative percentage of cleaved-caspase-3/7 positive cells over the time, the GI50 of HDM201, CGM097 or nutlin-3a on SJSA-1 cells and the cellular apoptosis, as judged by AUC of cleaved-caspase-3/7 positive cells, induced by these compounds. • Fig S3: the PK profile in plasma and tumor of HDM201 in SJSA-1 tumors-bearing rat after p.o. and i.v. treatment, the Bcl-xl mRNA levels in tumors after HDM201 treatment, representative images of SJSA-1 tumors stained with p53 and cleaved-caspase 3 antibodies after HDM201 treatment and the individual data for the efficacy experiment in SJSA-1 tumor-bearing rats. • Fig S4: the PD of HDM201 in PB tumor bearing nude mice after single dose administration. Supplementary tables include: • Table S1: Biochemical profile of HDM201. • Table S2: List of cell lines tested for their sensitivity to HDM201 (n=291) • Table S3: Contingency table indicating association between sensitivity to HDM201 and TP53 wild-type status. • Table S4: List of cell lines tested for their sensitivity to both MDM2 knock-down by shRNA and HDM201 (n=261) • Table S5: Contingency table indicating association between sensitivity to HDM201 and sensitivity to MDM2 shRNA. • Table S6: List of significant rescuer and sensitizer genes following both HDM201 treatment types • Table S7: Pharmacokinetic parameters for HDM201 after p.o. and i.v. dosing in rat. • Table S8: Summary of primary PK parameters for HDM201 daily regimen after single dose (Day 1) in patients. • Table S9: Summary of primary PK parameters for HDM201 daily regimen on Day 14 in patients. • Table S10: Summary of primary PK parameters for HDM201 q3w regimen after single dose in patients.

PDF file, 113KB, A. Effects of Paclitaxel and Nocodazole on Tubulin polymerization. Tubulin was mixed with either Paclitaxel (10 M), Nocodazole (10 M) or the DMSO control in the presence of GTP. The polymerization of monomeric tubulin into microtubule was started by transferring the reaction tubes from 4{degree sign}C to 37{degree sign}C, and monitored by the increase in absorbance (λ=340 nM) over a period of 60 min. B. Competition experiments of NVP-BKM120 with colchicine and podophyllotoxin by NMR spectroscopy. T1ρ relaxation of BKM120 in the presence of tubulin (50-fold excess of compound) remains unchanged after adding podophyllotoxin or colchicine, as emphasized by the drawn arrows. The spectra of the three compounds are shown in three colors at the bottom. C. Structures of GDC0941, BEZ235, Nocodazole and BKM120.

Median of the shRNAs and RSA values. File containing median counts and log fold changes of each gene from Supplementary Data File S1. According significance values of each gene as activator or sensitizer compared to DMSO control (see Methods) are included.

Details on materials and methods