Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

Stem cells in the adult pituitary are quiescent yet show acute activation upon tissue injury. The molecular mechanisms underlying this reaction are completely unknown. We applied single-cell transcriptomics to start unraveling the acute pituitary stem cell activation process as occurring upon targeted endocrine cell–ablation damage. This stem cell reaction was contrasted with the aging (middle-aged) pituitary, known to have lost damage-repair capacity. Stem cells in the aging pituitary show regressed proliferative activation upon injury and diminished in vitro organoid formation. Single-cell RNA sequencing uncovered interleukin-6 (IL-6) as being up-regulated upon damage, however only in young but not aging pituitary. Administering IL-6 to young mice promptly triggered pituitary stem cell proliferation, while blocking IL-6 or associated signaling pathways inhibited such reaction to damage. By contrast, IL-6 did not generate a pituitary stem cell activation response in aging mice, coinciding with elevated basal IL-6 levels and raised inflammatory state in the aging gland (inflammaging). Intriguingly, in vitro stem cell activation by IL-6 was discerned in organoid culture not only from young but also from aging pituitary, indicating that the aging gland’s stem cells retain intrinsic activatability in vivo, likely impeded by the prevailing inflammatory tissue milieu. Importantly, IL-6 supplementation strongly enhanced the growth capability of pituitary stem cell organoids, thereby expanding their potential as an experimental model. Our study identifies IL-6 as a pituitary stem cell activator upon local damage, a competence quenched at aging, concomitant with raised IL-6/inflammatory levels in the older gland. These insights may open the way to interfering with pituitary aging.

Single-cell RNA sequencing characterizes molecular pathways underlying inflammatory, metabolic, oxidative stress-mediated changes in Akimba model of diabetic retinopathy and identifies distinct functional subtypes of inflammatory & macroglial cells @Oxurion @LambrechtsDlab 2 ABSTRACT AIM: Diabetic retinopathy is a common complication of diabetes and a leading cause of visual impairment and blindness. Despite recent advances, our understanding of its pathophysiology remains incomplete. The aim of this study was to provide deeper insight into the complex network of molecular and cellular changes that underlie diabetic retinopathy by systematically mapping the transcriptional changes that occur in the different cellular compartments of the degenerating diabetic mouse retina. METHODS: Single-cell RNA sequencing was performed on retinal tissue from 12-week-old wild-type and Akimba (Ins2 Akita xVEGF +/-) mice, which are known to replicate features of clinical diabetic retinopathy. This resulted into transcriptome data for 9474 retinal cells, which could be annotated to 8 distinct retinal cell types. Using STRING analysis, we studied differentially expressed gene networks in neuronal, glial and immune cell compartments to create a comprehensive view on the pathological changes that occur in the Akimba retina. Using subclustering analysis, we further characterized macroglial and inflammatory cell subpopulations. Prominent findings were confirmed on protein level using immunohistochemistry, Western blotting and ELISA. RESULTS: At 12 weeks, the Akimba retina was found to display degeneration of rod photoreceptors and presence of inflammatory cells, identified by subclustering analysis as monocyte, macrophage and microglial populations. Analysis of differential expressed genes in the rod, cone, bipolar cell and macroglial compartments indicated changes in cell metabolism and ribosomal gene expression, gliosis, activation of immune system pathways and redox and metal ion dyshomeostasis. Experiments on protein level supported a metabolic shift from glycolysis to oxidative phosphorylation (GAPDH), activation of microglia/macrophages (Isolectin-B4), metal ion and oxidative stress response (metallothionein and heme oxygenase-1) and reactive macroglia (GFAP and S100) in the Akimba retina, as compared to wild-type mice. Our single-cell approach also indicates macroglial subpopulations with distinct fibrotic, inflammatory and gliotic profiles. CONCLUSION: Our study identifies molecular pathways underlying inflammatory, metabolic and oxidative stress-mediated changes in the Akimba mouse model of diabetic retinopathy and distinguishes distinct functional subtypes of inflammatory and macroglial cells.

Purpose: The European Organization for Research and Treatment of Cancer (EORTC) clinical phase II trial 90101 “CREATE” showed high antitumor activity of crizotinib, an inhibitor of anaplastic lymphoma kinase (ALK)/ROS1, in patients with advanced inflammatory myofibroblastic tumor (IMFT). However, recent findings suggested that other molecular targets in addition to ALK/ROS1 might also contribute to the sensitivity of this kinase inhibitor. We therefore performed an in-depth molecular characterization of archival IMFT tissue, collected from patients enrolled in this trial, with the aim to identify other molecular alterations that could play a role in the response to crizotinib. Experimental Design: Twenty-four archival IMFT samples were used for histopathological assessment and DNA/RNA evaluation to identify gene fusions, copy-number alterations (CNA), and mutations in the tumor tissue. Results were correlated with clinical parameters to assess a potential association between molecular findings and clinical outcomes. Results: We found 12 ALK fusions with 11 different partners in ALK-positive IMFT cases by Archer analysis whereas we did not identify any ROS1-rearranged tumor. One ALK-negative patient responding to crizotinib was found to have an ETV6–NTRK fusion in the tumor specimen. The CNA profile and mutational landscape of IMFT revealed extensive molecular heterogeneity. Loss of chromosome 19 (25% of cases) and PIK3CA mutations (9% of cases) were associated with shorter progression-free survival in patients receiving crizotinib. Conclusions: We identified multiple genetic alterations in archival IMFT material and provide further insight into the molecular profile of this ultra-rare, heterogeneous malignancy, which may potentially translate into novel treatment approaches for this orphan disease.

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41 a PDXNET consortium 42 b EurOPDX consortium 43 # These authors contributed equally to this work.44 § These authors jointly supervised this work. ABSTRACT 107Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical 108 studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution 109 during PDX engraftment and propagation, impacting the accuracy of PDX modeling of human 110 cancer. Here we exhaustively analyze copy number alterations (CNAs) in 1451 PDX and matched 111 patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing 112 and microarray data displayed substantially higher resolution and dynamic range than gene 113 expression-based inferences, and they also showed strong CNA conservation from PTs through 114 late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-115 late trios confirmed high-resolution CNA retention. We observed no significant enrichment of 116 cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between 117 patient and PDX tumors were comparable to variations in multi-region samples within patients. 118Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse 119 host. 121 MAIN 122A variety of models of human cancer have been used to study basic biological processes and 123 predict responses to treatment. For example, mouse models with genetically engineered 124 mutations in oncogenes and tumor suppressor genes have clarified the genetic and molecular 125 basis of tumor initiation and progression 1,2 , though responses sometimes differ between human 126 and mouse 3 . Cell lines have also been widely used to study cancer cells, but they lack the 127 heterogeneity and microenvironment of in vivo tumors and have shown limitations for predicting 128 clinical response 4 . Human tumors engrafted into transplant-compliant recipient mice (patient-129 derived xenografts, PDX) have advantages over prior systems for preclinical drug efficacy studies 130 because they allow researchers to directly study human cells and tissues in vivo 5-8 . Comparisons131 of genome characteristics and histopathology of primary tumors and xenografts of human breast 132 cancer 9-13 , ovarian cancer 14 , colorectal cancer 15 and lung cancer 16-18 , have demonstrated that the 133 biological properties of patient-derived tumors are largely preserved in xenografts. A growing body 134 of literature supports their use in cancer drug discovery and development 19-21 . 135A caveat to PDX models is that intratumoral evolution can occur during engraftment and 136 passaging 11,22-25 . Such evolution could potentially modify treatment response of PDXs with 137 respect to the patient tumors 23,26,27 , particularly if the evolution were to systematically alter cancer-138 related genes. This issue is related to multi-region comparisons of patient tumors 28-31 , for which 139 local mutational and immune infiltration variations have sugg...

Background Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy. Methods In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data. Results Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance. Conclusion A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

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