#human cell lines for developing some rna seq methods

[catboybiologist]

oh dear. it seems that i am a -quickly looks at bio- teeny tiny protozoan unknown to science i hope a beautiful woman studies me and learns my teeny tiny protozoan secrets.

Damn I hope there's a gorgeous protozoa researcher out there to fulfill those wishes bc every model organism I've worked with has been in animalia

[fiamh]

SESSION 3: INTEGRATED SYSTEMS AND NETWORK APPROACHES

Chaired by Jan Korbel. I did skip the first (sponsored) talk from Bio-Rad laboratories but made it for the first regular talk of the session. Christopher Plass couldn’t make it, unfortunately, but the talk title remained similar.

Epigenetic reprogramming in glioblastoma

Mario Suva (MGH) stepped in for Christopher.

Dissecting cellular state in glioblastoma: the most frequent brain tumor, peak incidence betwee 45 and 70 years, poor prognosis. Assumption of different cell populations, solid malignancy possibly following the cancer stem cell model with genetic and epigenetic changes (though disagreement on surface markers used to identify them). Cocktail targets used to induce pluripotency overlap with targets deregulating cancers. Describes an in vitro GBM stem cell model using neurospheres (matched pairs of stem cells, differentiated cells from the same patient, tested by their efficieny to induce tumors after injection into mice). Using H3K27ac as comprehensive readout, marking active regulatory elements in TPC (tumor propagating cells). Loci known to differentiate cells become active in differentiating cells.

Can use this readout to classify regulatory sites (active in stem cells, active in differentiating cells), check for genomic sequence enriched in regulators to predict transcription factors involved. Combine with RNA-Seq to overlap with differentially expressed TFs, came up with 19 candidates (validated by western, then functional test to see if TF expression confers phenotype). Introduce individual TFs in differentiated line, for some (POU3F2 for example) by themselves result in weak sphere formation. Complemented best factors with second TF, repeat with 3rd TF (time consuming, in many cases waited for a year for mice with injected cells to develop phenotype). None were developing tumors. Added helix-loop-helix factor which did the trick, cells induce tumours potently. [Might have missed what factors were used precisely, assume confidential still]. Tested epigenetic landscpae, TF mix re-actives the elements they predicted — not just phenotypic reversal but epigenetic reversal. Tried reverse approach knocking TFs down with shRNA, impairs self-renewal ability.

Moved back to human GBM to see how well the model transfers. Staining for TFs indicate subset of GBMs co-express all four TFs, mostly cells with cell surface markers for the stem cell phenotype. Used ChIP-Seq to map TF binding sites in genome, confirm preferential binding to stem cell program elements. Cross-talk between genetic mutations, epigenetic state that confer key properties in GBM.

Subsequently applied single-cell RNA-Seq to cells in primary glioblastoma, again because tumor tends to heterogeneous (at least four subtypes defined as well). Tried to distinguish infiltrating glia cells from cancer cells by single cell chromosome copy number variation as variant calling on single cells tricky. Averaged gene expression of 300 gene blocks based on location as markers, 420 cells with gain/loss events typical for GBM, 10 without (removed from study). Checked pattern of expression of surface receptors, turned out to be heterogeneous (PDGFRA, EGFR in particular subdivide in two classes). Re-arrangements of EGFR within the same tumour observed. Can arrange cells in a gradient by expression of stem cell signatures, but no discrete classes — a continuum. Core TF network found previously correlates with degree of stemness readout. Used in combination to classify into high/low stemness to identify other candidate regulators. [Tour de force to integrate different aspects of GBM biology, indeed.]

Interrogating regulatory networks to discover master regulators of tumor initiation, progression, and drug resistance

Talk from Andrea Califano (Columbia University) who starts on the genome (rather than beyond the genome). Number of statistical associations of genotypes to phenotype rapdily increasing, some fully penetrant (and often with no idea of their mechanism). Huge statistical problem to find useful links due to combinatorial problem (more genotype combinations than single cells on the planet), and without mechanistic clues unclear how the genotype causes phenotype. We lack the power to identify epistatic interactions.

Segue to model-based genomics. Treat the cell as a detector to understand how cells integrate different signals to build mechanistic understanding of genotypes/epigenotypes. Optimal to elucidate epistatic, synergistic interactions but the model needs to be accurate and reasonably comprehensive (see paper). Build models based on different tools to figure out transcriptional interactions, post-translational interactions, transcriptional interactions, identify master-regulators to get to a mechanism of action. Ran models for large number of different tumors with good success [I am too blind to read the tiny print on most of Andrea’s slides, unfortunately, so notes will be sparse here.] Process is to find some sort of ‘omics signature that distinguishes a particular phenotype transition (tumor/normal, resistant/non-resistant), then find targets of signature to unravel mechanism. Cancer progression driven by a relatively small number of master regulators, themselves dysregulated by a large number of upstream genetic and epigenetic changes. These can be unraveled, finding oncogene induced dependencies. Requires two states of the cell, find genes differentially expressed / methylated / anything, find most likely upstream regulators of singature, find master regulators that necessary and/or sufficient to cause the observed signature (and by extension the phenotypic change); usually a set or ranked list of candidate regulators. Can reverse / model the impact of changing these regulators to see if they replicate the signature.

Walks through a few examples next, e.g., reversal of glucocorticoid resistance in T-ALL or identify mesenchymal subtype in glioma. Walks through potential application for personalized medicine, particularly with regards to relapse/resistance. If master regulators implement bottlenecks then drugging these will reduce resistance as any upstream mutation (where they see most changes) will not be able to bypass the ‘drugged’ bottleneck. Clinical trial in breast cancer now ongoing to test that in practice targeting STAT3; modified algorithm for single patients based on single cell analysis to identify likely master regulators for disease progression. Targeting just some of the essential/central master regulators results in the collapse of the whole case/control signature. Find drugs to inverst master regulator signature (SYNGEN algorithm now in press). Applied to neuroendocrine tumours of the GI tract, ~1000 fresh frozen samples, generated regulatory model (ARCNe, RNA-Seq and Co) to identify likely master regulators with strong impat of 50 top hits (conserved in cell lines, xenografts). Validated with drugs targeted nodes/bottlenecks. Tested drugs for signature reversion by RNA-Seq, top hit again now in clinical trial.

Uncovering master regulators of oncogenic transformation by network analysis of the LINCS library of transcriptional signatures of cellular perturbations

Mario Medvedovic from the University of Cincinnati gives the first short selected talk of the session, introducing the LINCS Project and figuring out regulatory networks and functional networks from transcriptional profiles. LINCS generates perturbation signatures of given nodes resulting in a transcriptional signature / profile which can be used in reverse: given an expression profile what nodes are likely changed? A cube of different cell types x perturbations x assays: cancer cell lines, primary normal/diseased cells, differentiated iPSCs, etc.; drug/chemical perturbations, genetic changes, microenvironment changes, etc.; readouts are transcriptomic, proteomic, phenotypic assays, phosphoproteomic, etc.

LINCS L1000 Data (CMap III by the Golub lab) now consists of 77 cellular contexts, 20k reagents, 22k genetic perturbations. More than 1,3 million gene expression signatures generated. Use to integrate activity scores of regulatory networks using kernel methods to ‘summarize’ the relationships between nodes (genes) connected in PPI networks, determining the level of interaction between neighbouring nodes. Use results to predict mutated regulators in TCGA luminal A breast cancer data: define mutational signatures, use network to rank importance of mutated gene for the gene (works better than based on expression based networks alone). Transcriptionally regulated neighbour of a driver gene are informative.

Genomic, epigenomic and transcriptional analyses in a Tet-Myc driven mouse model of liver cancer­

Last talk given by Valerio Bianchi](http://www.iit.it/en/people/valerio-bianchi.html). Starts with an intro to the Myc gene and how they generated a liver-specific Myc overexpression system in mice under the control of Tet. Can induce tumor formation, reverse with Dox treatment. Number of controls (normal liver, different Myc switch-off times) while tracing RNA-Seq and ChIP-Seq data for Myc and various histone marks. Seems that Myc overexpression results in ‘invasion’ of already active promoters and enhancers. Myc-induced tumors show expression pattern similar to human hepatoblastomas, indicating the mouse model is a valid test environment.