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Mutations in hundreds of genes have been associated with formation of human cancer, with different oncogenic lesions prevalent in different cancer types. Yet, the malignant phenotype is simple, characterized by unrestricted growth of cells that invade neighboring healthy tissue and in many cases metastasize to distant organs. One possible hypothesis explaining this dichotomy is that the cancer genes regulate a common set of target genes, which then function as master regulators of essential cancer phenotypes, such as growth, invasion and metastasis. To identify mechanisms that drive the most fundamental feature shared by all tumors – unrestricted cell proliferation – we used a multiomic approach to identify common transcriptional and posttranslational targets of major oncogenic pathways active in different cancer types, and combined this analysis with known regulators of the cell cycle. We identified translation and ribosome biogenesis as common targets of both transcriptional and posttranslational oncogenic pathways. By combining proteomic analysis of clinical samples with functional studies of cell cultures, we also establish NOLC1 as a key node whose convergent regulation both at transcriptional and posttranslational level is critical for tumor cell proliferation. Our results indicate that lineage-specific oncogenic pathways commonly regulate the same set of targets important for growth control, revealing novel key downstream nodes that could be targeted for cancer therapy or chemoprevention.

Ovarian high-grade serous carcinoma (HGSC) is typically diagnosed at an advanced stage, with multiple genetically heterogeneous clones existing in the tumors long before therapeutic intervention. Herein we characterized HGSC evolutionary states using whole-genome sequencing data from 214 samples of 55 HGSC patients in the prospective, longitudinal, multiregion DECIDER study. Comparison of the tissues revealed that site-of-origin samples have 70% more unique clones than the metastatic tumors or ascites. By integrating clonal composition and topology of HGSC tumors we discovered three evolutionary states that represent a continuum from genomically highly variable to stable tumors with significant association to treatment response. The states and their evolutionary trajectories were characterized by unique, targetable pathways, which were validated with RNA-seq data. Our study reveals that genomic heterogeneity is unaffected by the current standard-of-care and pinpoints effective treatment targets for each group. All genomics data are available via an interactive visualization platform for rapid exploration.

Hyaluronic acid (HA) is a biopolymer with wide applications in the field of medicine and cosmetics. Bacterial production of HA has a huge market globally. Certain species of Streptococcus are native producers of HA but they are pathogenic. Therefore, safer organisms such as L. lactis are engineered for HA production. However, there are challenges such as low yield, low molecular weight and polydispersity of HA obtained from these cultures. Optimisation of bioprocess parameters and downstream purification parameters are being addressed to overcome these challenges. We explore these problems from the perspective of microbial heterogeneity, since variations in phenotype affect the yield and properties of the product in a bioreactor. For this perspective, a method to quantitatively assess the occurrence of heterogenous phenotypes depending on the amount of HA produced at the single-cell level is required. Here, we evaluated for the first time the use of calcofluor white staining method combined with in vivo fluorescence confocal microscopy to quantify the heterogeneity in phenotypes of L. lactis cells engineered for HA production.From the microscopy image analysis, we found that the population harbours significant heterogeneity with respect to HA production and our novel approach successfully differentiates these phenotypes. Using the fluorescence intensity levels, first we were able to confidently differentiate cells not expressing HA (Host cells without HA genes for expression) from cells with genes for HA production (GJP2) and induced for expression, as there is a consistently two-fold higher level of expression in the GJP2 cells independently of the cell size. Further, this method revealed the occurrence of two different phenotypes in GJP2 cultures, one of a high-expression phenotype (40% of the population) and the other one of a low-expression (remaining 60% of the population), and it is the high expression phenotype that contributes to the increase in the HA expression of the GJP2 population compared with the host cells. Thus, it is essential to identify the extrinsic and intrinsic factors that can favour most of the cells in the population to switch and stabilise into the high-expression phenotype state in a bioreactor, for higher yield and possibly reduced heterogeneity of the product, such as polydispersity in chain lengths. For such optimisation studies, this in vivo method serves as a promising tool for rapid detection of phenotypes in the bioreactor samples under varying conditions, allowing fine tuning of the factors to stabilise high-expression phenotypes thereby maximizing the yield.Graphical Abstractdone.Key PointsCalcofluor staining successfully differentiated the phenotypes based on HA levels.This study revealed the occurrence of significant heterogeneity in HA expression.This method will aid for rapid optimization of factors for improved HA production.

Chemotherapy resistance is the greatest contributor to cancer mortality and the most urgent unmet challenge in oncology. In order to reveal transcriptomics changes due to platinum-based chemotherapy we analyzed single-cell RNA-seq data from fresh tissue samples taken at the time of diagnosis and after neoadjuvant chemotherapy from 11 high-grade serous ovarian cancer (HGSOC) patients. With a novel clustering method accounting for patient-specific variability and technical confounders, we identified 12 clusters. Of these, a stress-related transcriptional profile was enriched during chemotherapy and associated significantly to poor progression-free survival (PFS) and disease-free interval (DFI) in deconvoluted bulk RNA-seq data analysis of treatment-naive samples in TCGA cohort. Pan-cancer cell line analysis suggests that patients with high stress-related transcriptional profile may benefit from MEK1/2 inhibitors instead of platinum. Further, high proportion of stromal components and high interaction score between tumor and stromal suggest the tumor cells with high-stress profile actively interact with and potentially recruit stromal cells to their microenvironment already prior to chemotherapy, potentially facilitating protection from chemotherapeutic treatments. In summary, we have identified a stress-related transcriptional profile, which is present at the time of diagnosis, enriched during platinum treatment, independent predictor for poor PFS and DFI, and, based on in vitro data, targetable with MEK1/2 inhibitors.Translational relevanceWe discovered a stress-related transcriptional profile that is significantly enriched in fresh tissue samples after chemotherapy and is significantly associated with poor progression-free survival in an independent patient cohort. The survival association is independent of age, tumor purity or BRCAness. Therefore, this chemotherapy resistance associated profile is intrinsic and could thus be targeted already in treatment-naive patients. The translation potential of the stress-related transcriptomics profile was further supported by pan-cancer cell line analysis that showed that cell lines with high stress-related transcriptional profile are not affected by platinum, corroborating our results, whereas they were more sensitive to MEK1/2 inhibitors. Taken together, the stress-related transcriptional profile, quantifiable with a set of 35 marker genes, provides a basis for improved prediction of platinum response as well as novel avenues to treat this patient group more effectively.

MotivationSingle-cell proteomics technologies, such as mass cytometry, have enabled characterization of cell-to-cell variation and cell populations at a single cell resolution. These large amounts of data, however, require dedicated, interactive tools for translating the data into knowledge.ResultsWe present a comprehensive, interactive method called Cyto to streamline analysis of large-scale cytometry data. Cyto is a workflow-based open-source solution that automatizes the use of of state-of-the-art single-cell analysis methods with interactive visualization. We show the utility of Cyto by applying it to mass cytometry data from peripheral blood and high-grade serous ovarian cancer (HGSOC) samples. Our results show that Cyto is able to reliably capture the immune cell sub-populations from peripheral blood as well as cellular compositions of unique immune- and cancer cell subpopulations in HGSOC tumor and ascites samples.AvailabilityThe method is available as a Docker container at https://hub.docker.com/r/anduril/cyto and the user guide and source code are available at https://bitbucket.org/anduril-dev/cytoContactsampsa.hautaniemi@helsinki.fiSupplementary informationSupplementary material is available and FCS files are hosted at flowrepository.org/id/FR-FCM-Z2LW

A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell type specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell type specific expression through whole-genome sequencing and RNA in situ hybridization experiments. PRISM is freely available with full source code and documentation.

Many genes are spaced closely, allowing coordination without explicit control through shared regulatory elements and molecular interactions. We study the dynamics of a stochastic model of a gene-pair in a head-to-head configuration, sharing promoter elements, which accounts for the rate-limiting steps in transcription initiation. We find that only in specific regions of the parameter space of the rate-limiting steps is orderly coexpression exhibited, suggesting that successful cooperation between closely spaced genes requires the coevolution of compatible rate-limiting step configuration. The model predictions are validated using in vivo single-cell, single-RNA measurements of the dynamics of pairs of genes sharing promoter elements. Our results suggest that, in E. coli , the kinetics of the rate-limiting steps in active transcription can play a central role in shaping the dynamics of gene-pairs sharing promoter elements.