Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance

MPAC: a computational framework for inferring cancer pathway activities from multi-omic data

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cancer (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC uses network relationships encoded in pathways using a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to prioritize proteins with potential clinical relevance. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

Inferring cellular and molecular processes in single-cell data with non-negative matrix factorization using Python, R and GenePattern Notebook implementations of CoGAPS

Non-negative matrix factorization (NMF) is an unsupervised learning method well suited to high-throughput biology. However, inferring biological processes from an NMF result still requires additional post hoc statistics and annotation for interpretation of learned features. Here, we introduce a suite of computational tools that implement NMF and provide methods for accurate and clear biological interpretation and analysis. A generalized discussion of NMF covering its benefits, limitations and open questions is followed by four procedures for the Bayesian NMF algorithm Coordinated Gene Activity across Pattern Subsets (CoGAPS). Each procedure will demonstrate NMF analysis to quantify cell state transitions in a public domain single-cell RNA-sequencing dataset. The first demonstrates PyCoGAPS, our new Python implementation that enhances runtime for large datasets, and the second allows its deployment in Docker. The third procedure steps through the same single-cell NMF analysis using our R CoGAPS interface. The fourth introduces a beginner-friendly CoGAPS platform using GenePattern Notebook, aimed at users with a working conceptual knowledge of data analysis but without a basic proficiency in the R or Python programming language. We also constructed a user-facing website to serve as a central repository for information and instructional materials about CoGAPS and its application programming interfaces. The expected timing to setup the packages and conduct a test run is around 15 min, and an additional 30 min to conduct analyses on a precomputed result. The expected runtime on the user's desired dataset can vary from hours to days depending on factors such as dataset size or input parameters.

A novel intronic circular RNA circFGFR1int2 up-regulates FGFR1 by recruiting transcriptional activators P65/FUS and suppressing miR-4687-5p to promote prostate cancer progression

Fibroblast growth factor receptor 1 (FGFR1) is a core component of the FGFs/FGFR pathway that activates multiple signalling pathways, including ERK1/2, PI3K/AKT, PLCγ, and NF-κB. Aberrant expression of FGFR1 due to gene amplification, chromosome rearrangement, point mutation, and epigenetic deregulations, have been reported in various cancers. FGFR1 overexpression has also been reported in prostate cancer (PCa), but the underlining mechanisms are not clear. Here we report a novel circular RNA, circFGFR1int2, derived from intron 2 of FGFR1 gene, which is overexpressed in PCa and associated with tumor progression. Importantly, we show that circFGFR1int2 facilitates FGFR1 transcription by recruiting transcription activators P65/FUS and by interacting with FGFR1 promoter. Moreover, we show that circFGFR1int2 suppresses post-transcriptional inhibitory effects of miR-4687-5p on FGFR1 mRNA. These mechanisms synergistically promote PCa cell growth, migration, and invasion. Overexpression of circFGFR1int2 is significantly correlated with higher tumor grade, Gleason score, and PSA level, and is a significant unfavorable prognosticator for CRPC-free survival (CFS) (RR = 3.277, 95% confidence interval: 1.192-9.009; P = 0.021). These findings unravelled novel mechanisms controlling FGFR1 gene expression by intronic circRNA and its potential clinicopathological utility as a diagnostic or therapeutic target.

Making In Vitro Tumor Models Whole Again

As a reductionist approach, patient-derived in vitro tumor models are inherently still too simplistic for personalized drug testing as they do not capture many characteristics of the tumor microenvironment (TME), such as tumor architecture and stromal heterogeneity. This is especially problematic for assessing stromal-targeting drugs such as immunotherapies in which the density and distribution of immune and other stromal cells determine drug efficacy. On the other end, in vivo models are typically costly, low-throughput, and time-consuming to establish. Ex vivo patient-derived tumor explant (PDE) cultures involve the culture of resected tumor fragments that potentially retain the intact  TME of the original tumor. Although developed decades ago, PDE cultures have not been widely adopted likely because of their low-throughput and poor long-term viability. However, with growing recognition of the importance of patient-specific TME in mediating drug response, especially in the field of immune-oncology, there is an urgent need to resurrect these holistic cultures. In this Review, the key limitations of patient-derived tumor explant cultures are outlined and technologies that have been developed or could be employed to address these limitations are discussed. Engineered holistic tumor explant cultures may truly realize the concept of personalized medicine for cancer patients.

RNA methylation and cellular response to oxidative stress-promoting anticancer agents

Disruption of the complex network that regulates redox homeostasis often underlies resistant phenotypes, which hinder effective and long-lasting cancer eradication. In addition, the RNA methylome-dependent control of gene expression also critically affects traits of cellular resistance to anti-cancer agents. However, few investigations aimed at establishing whether the epitranscriptome-directed adaptations underlying acquired and/or innate resistance traits in cancer could be implemented through the involvement of redox-dependent or -responsive signaling pathways. This is unexpected mainly because: i) the effectiveness of many anti-cancer approaches relies on their capacity to promote oxidative stress (OS); ii) altered redox milieu and reprogramming of mitochondrial function have been acknowledged as critical mediators of the RNA methylome-mediated response to OS. Here we summarize the current state of understanding on this topic, as well as we offer new perspectives that might lead to original approaches and strategies to delay or prevent the problem of refractory cancer and tumor recurrence.

Model-informed experimental design recommendations for distinguishing intrinsic and acquired targeted therapeutic resistance in head and neck cancer

The promise of precision medicine has been limited by the pervasive resistance to many targeted therapies for cancer. Inferring the timing (i.e., pre-existing or acquired) and mechanism (i.e., drug-induced) of such resistance is crucial for designing effective new therapeutics. This paper studies cetuximab resistance in head and neck squamous cell carcinoma (HNSCC) using tumor volume data obtained from patient-derived tumor xenografts. We ask if resistance mechanisms can be determined from this data alone, and if not, what data would be needed to deduce the underlying mode(s) of resistance. To answer these questions, we propose a family of mathematical models, with each member of the family assuming a different timing and mechanism of resistance. We present a method for fitting these models to individual volumetric data, and utilize model selection and parameter sensitivity analyses to ask: which member(s) of the family of models best describes HNSCC response to cetuximab, and what does that tell us about the timing and mechanisms driving resistance? We find that along with time-course volumetric data to a single dose of cetuximab, the initial resistance fraction and, in some instances, dose escalation volumetric data are required to distinguish among the family of models and thereby infer the mechanisms of resistance. These findings can inform future experimental design so that we can best leverage the synergy of wet laboratory experimentation and mathematical modeling in the study of novel targeted cancer therapeutics.

Subclone Eradication Analysis Identifies Targets for Enhanced Cancer Therapy and Reveals L1 Retrotransposition as a Dynamic Source of Cancer Heterogeneity

Treatment-eradicated cancer subclones have been reported in leukemia and have recently been detected in solid tumors. Here we introduce Differential Subclone Eradication and Resistance (DSER) analysis, a method developed to identify molecular targets for improved therapy by direct comparison of genomic features of eradicated and resistant subclones in pre- and posttreatment samples from a patient with BRCA2-deficient metastatic prostate cancer. FANCI and EYA4 were identified as candidate DNA repair-related targets for converting subclones from resistant to eradicable, and RNAi-mediated depletion of FANCI confirmed it as a potential target. The EYA4 alteration was associated with adjacent L1 transposon insertion during cancer evolution upon treatment, raising questions surrounding the role of therapy in L1 activation. Both carboplatin and enzalutamide turned on L1 transposon machinery in LNCaP and VCaP but not in PC3 and 22Rv1 prostate cancer cell lines. L1 activation in LNCaP and VCaP was inhibited by the antiretroviral drug azidothymidine. L1 activation was also detected postcastration in LuCaP 77 and LuCaP 105 xenograft models and postchemotherapy in previously published time-series transcriptomic data from SCC25 head and neck cancer cells. In conclusion, DSER provides an informative intermediate step toward effective precision cancer medicine and should be tested in future studies, especially those including dramatic but temporary metastatic tumor regression. L1 transposon activation may be a modifiable source of cancer genomic heterogeneity, suggesting the potential of leveraging newly discovered triggers and blockers of L1 activity to overcome therapy resistance. SIGNIFICANCE: Differential analysis of eradicated and resistant subclones following cancer treatment identifies that L1 activity associated with resistance is induced by current therapies and blocked by the antiretroviral drug azidothymidine.

Transfer learning between preclinical models and human tumors identifies a conserved NK cell activation signature in anti-CTLA-4 responsive tumors

BACKGROUND

Tumor response to therapy is affected by both the cell types and the cell states present in the tumor microenvironment. This is true for many cancer treatments, including immune checkpoint inhibitors (ICIs). While it is well-established that ICIs promote T cell activation, their broader impact on other intratumoral immune cells is unclear; this information is needed to identify new mechanisms of action and improve ICI efficacy. Many preclinical studies have begun using single-cell analysis to delineate therapeutic responses in individual immune cell types within tumors. One major limitation to this approach is that therapeutic mechanisms identified in preclinical models have failed to fully translate to human disease, restraining efforts to improve ICI efficacy in translational research.

METHOD

We previously developed a computational transfer learning approach called projectR to identify shared biology between independent high-throughput single-cell RNA-sequencing (scRNA-seq) datasets. In the present study, we test this algorithm's ability to identify conserved and clinically relevant transcriptional changes in complex tumor scRNA-seq data and expand its application to the comparison of scRNA-seq datasets with additional data types such as bulk RNA-seq and mass cytometry.

RESULTS

We found a conserved signature of NK cell activation in anti-CTLA-4 responsive mouse and human tumors. In human metastatic melanoma, we found that the NK cell activation signature associates with longer overall survival and is predictive of anti-CTLA-4 (ipilimumab) response. Additional molecular approaches to confirm the computational findings demonstrated that human NK cells express CTLA-4 and bind anti-CTLA-4 antibodies independent of the antibody binding receptor (FcR) and that similar to T cells, CTLA-4 expression by NK cells is modified by cytokine-mediated and target cell-mediated NK cell activation.

CONCLUSIONS

These data demonstrate a novel application of our transfer learning approach, which was able to identify cell state transitions conserved in preclinical models and human tumors. This approach can be adapted to explore many questions in cancer therapeutics, enhance translational research, and enable better understanding and treatment of disease.

The functional GRHL3-filaggrin axis maintains a tumor differentiation potential and influences drug sensitivity

Current therapies for treating heterogeneous cancers such as head and neck squamous cell carcinoma (HNSCC) are non-selective and are administered independent of response biomarkers. Therapy resistance subsequently emerges, resulting in increased cellular proliferation that is associated with loss of differentiation. Whether a cancer cell differentiation potential can dictate therapy responsiveness is still currently unknown. A multi-omic approach integrating whole-genome and whole-transcriptome sequencing with drug sensitivity was employed in a HNSCC mouse model, primary patients’ data, and human cell lines to assess the potential of functional differentiation in predicting therapy response. Interestingly, a subset of HNSCC with effective GRHL3-dependent differentiation was the most sensitive to inhibitors of PI3K/mTOR, c-Myc, and STAT3 signaling. Furthermore, we identified the GRHL3-differentiation target gene Filaggrin (FLG) as a response biomarker and more importantly, stratified HNSCC subsets as treatment resistant based on their FLG mutational profile. The loss of FLG in sensitive HNSCC resulted in a dramatic resistance to targeted therapies while the GRHL3-FLG signature predicted a favorable patient prognosis. This study provides evidence for a functional GRHL3-FLG tumor-specific differentiation axis that regulates targeted therapy response in HNSCC and establishes a rationale for clinical investigation of differentiation-paired targeted therapy in heterogeneous cancers.

PD-L1 is upregulated via BRD2 in head and neck squamous cell carcinoma models of acquired cetuximab resistance

BACKGROUND

Tumor models resistant to EGFR tyrosine kinase inhibitors or cisplatin express higher levels of the immune checkpoint molecule PD-L1. We sought to determine whether PD-L1 expression is elevated in head and neck squamous cell carcinoma (HNSCC) models of acquired cetuximab resistance and whether the expression is regulated by bromodomain and extraterminal domain (BET) proteins.

METHODS

Expression of PD-L1 was assessed in HNSCC cell line models of acquired cetuximab resistance. Proteolysis targeting chimera (PROTAC)- and RNAi-mediated targeting were used to assess the role of BET proteins.

RESULTS

Cetuximab-resistant HNSCC cells expressed elevated PD-L1 compared to cetuximab-sensitive controls. Treatment with the BET inhibitor JQ1, the BET PROTAC MZ1, or RNAi-mediated knockdown of BRD2 decreased PD-L1 expression. Knockdown of BRD2 also reduced the elevated levels of PD-L1 seen in a model of acquired cisplatin resistance.

CONCLUSIONS

PD-L1 is significantly elevated in HNSCC models of acquired cetuximab and cisplatin resistance where BRD2 is the primary regulator.

Forecasting cellular states: from descriptive to predictive biology via single-cell multiomics

As the single cell field races to characterize each cell type, state, and behavior, the complexity of the computational analysis approaches the complexity of the biological systems. Single cell and imaging technologies now enable unprecedented measurements of state transitions in biological systems, providing high-throughput data that capture tens-of-thousands of measurements on hundreds-of-thousands of samples. Thus, the definition of cell type and state is evolving to encompass the broad range of biological questions now attainable. To answer these questions requires the development of computational tools for integrated multi-omics analysis. Merged with mathematical models, these algorithms will be able to forecast future states of biological systems, going from statistical inferences of phenotypes to time course predictions of the biological systems with dynamic maps analogous to weather systems. Thus, systems biology for forecasting biological system dynamics from multi-omic data represents the future of cell biology empowering a new generation of technology-driven predictive medicine.

Precision Medicine Approaches to Overcome Resistance to Therapy in Head and Neck Cancers

Head and neck squamous cell carcinoma (HNSCC) is the sixth most incident cancer worldwide. More than half of HNSCC patients experience locoregional or distant relapse to treatment despite aggressive multimodal therapeutic approaches that include surgical resection, radiation therapy, and adjuvant chemotherapy. Before the arrival of immunotherapy, systemic chemotherapy was previously employed as the standard first-line protocol with an association of cisplatin or carboplatin plus 5-fluorouracil plus cetuximab (anti-EFGR antibody). Unfortunately, acquisition of therapy resistance is common in patients with HNSCC and often results in local and distant failure. Despite our better understanding of HNSCC biology, no other molecular-targeted agent has been approved for HNSCC. In this review, we outline the mechanisms of resistance to the therapeutic strategies currently used in HNSCC, discuss combination treatment strategies to overcome them, and summarize the therapeutic regimens that are presently being evaluated in early- and late-phase clinical trials.

Extracellular Vesicles Released by Tumor Endothelial Cells Spread Immunosuppressive and Transforming Signals Through Various Recipient Cells

Head and neck squamous cell carcinoma (HNSCC) has a high recurrence and metastatic rate with an unknown mechanism of cancer spread. Tumor inflammation is the most critical processes of cancer onset, growth, and metastasis. We hypothesize that the release of extracellular vesicles (EVs) by tumor endothelial cells (TECs) induce reprogramming of immune cells as well as stromal cells to create an immunosuppressive microenvironment that favor tumor spread. We call this mechanism as non-metastatic contagious carcinogenesis. Extracellular vesicles were collected from primary HNSCC-derived endothelial cells (TEC-EV) and were used for stimulation of peripheral blood mononuclear cells (PBMCs) and primary adipose mesenchymal stem cells (ASCs). Regulation of ASC gene expression was investigated by RNA sequencing and protein array. PBMC, stimulated with TEC-EV, were analyzed by enzyme-linked immunosorbent assay and fluorescence-activated cell sorting. We validated in vitro the effects of TEC-EV on ASCs or PBMC by measuring invasion, adhesion, and proliferation. We found and confirmed that TEC-EV were able to change ASC inflammatory gene expression signature within 24-48 h. TEC-EV were also able to enhance the secretion of TGF-β1 and IL-10 by PBMC and to increase T regulatory cell (Treg) expansion. TEC-EV carry specific proteins and RNAs that are responsible for Treg differentiation and immune suppression. ASCs and PBMC, treated with TEC-EV, enhanced proliferation, adhesion of tumor cells, and their invasion. These data indicate that TEC-EV exhibit a mechanism of non-metastatic contagious carcinogenesis that regulates tumor microenvironment and reprograms immune cells to sustain tumor growth and progression.

Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets

While the methods available for single-cell ATAC-seq analysis are well optimized for clustering cell types, the question of how to integrate multiple scATAC-seq data sets and/or sequencing modalities is still open. We present an analysis framework that enables such integration across scATAC-seq data sets by applying the CoGAPS Matrix Factorization algorithm and the projectR transfer learning program to identify common regulatory patterns across scATAC-seq data sets. We additionally integrate our analysis with scRNA-seq data to identify orthogonal evidence for transcriptional regulators predicted by scATAC-seq analysis. Using publicly available scATAC-seq data, we find patterns that accurately characterize cell types both within and across data sets. Furthermore, we demonstrate that these patterns are both consistent with current biological understanding and reflective of novel regulatory biology.

Integrated single-cell and bulk gene expression and ATAC-seq reveals heterogeneity and early changes in pathways associated with resistance to cetuximab in HNSCC-sensitive cell lines

Background

Identifying potential resistance mechanisms while tumour cells still respond to therapy is critical to delay acquired resistance.

Methods

We generated the first comprehensive multi-omics, bulk and single-cell data in sensitive head and neck squamous cell carcinoma (HNSCC) cells to identify immediate responses to cetuximab. Two pathways potentially associated with resistance were focus of the study: regulation of receptor tyrosine kinases by TFAP2A transcription factor, and epithelial-to-mesenchymal transition (EMT).

Results

Single-cell RNA-seq demonstrates heterogeneity, with cell-specific TFAP2A and VIM expression profiles in response to treatment and also with global changes to various signalling pathways. RNA-seq and ATAC-seq reveal global changes within 5 days of therapy, suggesting early onset of mechanisms of resistance; and corroborates cell line heterogeneity, with different TFAP2A targets or EMT markers affected by therapy. Lack of TFAP2A expression is associated with HNSCC decreased growth, with cetuximab and JQ1 increasing the inhibitory effect. Regarding the EMT process, short-term cetuximab therapy has the strongest effect on inhibiting migration. TFAP2A silencing does not affect cell migration, supporting an independent role for both mechanisms in resistance.

Conclusion

Overall, we show that immediate adaptive transcriptional and epigenetic changes induced by cetuximab are heterogeneous and cell type dependent; and independent mechanisms of resistance arise while tumour cells are still sensitive to therapy.

Interleukin 6 is increased in preclinical HNSCC models of acquired cetuximab resistance, but is not required for maintenance of resistance

The epidermal growth factor receptor inhibitor cetuximab is the only oncogene-targeted agent that has been FDA approved for the treatment of head and neck squamous cell carcinoma (HNSCC). Currently, there are no biomarkers used in the clinic to predict which HNSCC tumors will respond to cetuximab, and even in tumors that regress with treatment, acquired resistance occurs in the majority of cases. Though a number of mechanisms of acquired resistance to cetuximab have been identified in preclinical studies, no therapies targeting these resistance pathways have yet been effectively translated into the clinic. To address this unmet need, we examined the role of the cytokine interleukin 6 (IL-6) in acquired cetuximab resistance in preclinical models of HNSCC. We found that IL-6 secretion was increased in PE/CA-PJ49 cells that had acquired resistance to cetuximab compared to the parental cells from which they were derived. However, addition of exogenous IL-6 to parental cells did not promote cetuximab resistance, and inhibition of the IL-6 pathway did not restore cetuximab sensitivity in the cetuximab-resistant cells. Further examination of the IL-6 pathway revealed that expression of IL6R, which encodes a component of the IL-6 receptor, was decreased in cetuximab-resistant cells compared to parental cells, and that treatment of the cetuximab-resistant cells with exogenous IL-6 did not induce phosphorylation of signal transducer and activator of transcription 3, suggesting that the IL-6 pathway was functionally impaired in the cetuximab-resistant cells. These findings demonstrate that, even if IL-6 is increased in the context of cetuximab resistance, it is not necessarily required for maintenance of the resistant phenotype, and that targeting the IL-6 pathway may not restore sensitivity to cetuximab in cetuximab-refractory HNSCC.

Resveratrol Targets Urokinase-Type Plasminogen Activator Receptor Expression to Overcome Cetuximab-Resistance in Oral Squamous Cell Carcinoma

Drug resistance to anti-cancer agents is a major concern regarding the successful treatment of malignant tumors. Recent studies have suggested that acquired resistance to anti-epidermal growth factor receptor (EGFR) therapies such as cetuximab are in part caused by genetic alterations in patients with oral squamous cell carcinoma (OSCC). However, the molecular mechanisms employed by other complementary pathways that govern resistance remain unclear. In the current study, we performed gene expression profiling combined with extensive molecular validation to explore alternative mechanisms driving cetuximab-resistance in OSCC cells. Among the genes identified, we discovered that a urokinase-type plasminogen activator receptor (uPAR)/integrin β1/Src/FAK signal circuit converges to regulate ERK1/2 phosphorylation and this pathway drives cetuximab-resistance in the absence of EGFR overexpression or acquired EGFR activating mutations. Notably, the polyphenolic phytoalexin resveratrol, inhibited uPAR expression and consequently the signaling molecules ERK1/2 downstream of EGFR thus revealing additive effects on promoting OSCC cetuximab-sensitivity in vitro and in vivo. The current findings indicate that uPAR expression plays a critical role in acquired cetuximab resistance of OSCC and that combination therapy with resveratrol may provide an attractive means for treating these patients.

Adaptive Responses to Monotherapy in Head and Neck Cancer: Interventions for Rationale-Based Therapeutic Combinations

Most Phase II and III clinical trials in head and neck cancer (HNC) combine two or more treatment modalities, which are based, in part, on knowledge of the molecular mechanisms of innate and acquired resistance to monotherapy. In this review, we describe the range of tumor-cell autonomously derived (intrinsic) and tumor-microenvironment-derived (extrinsic) acquired-resistance mechanisms to various FDA-approved monotherapies for HNC. Specifically, we describe how tumor cells and the tumor microenvironment (TME) respond to radiation, chemotherapy, targeted therapy (cetuximab), and immunotherapies [programmed cell death 1 (PD-1) inhibitors] and adapt to the selective pressure of these monotherapies. Due to the diversity of adaptive responses to monotherapy, monitoring the response to treatment in patients is critical to understand the path that leads to resistance and to guide the optimal therapeutic drug combinations in the clinical setting. We envisage that applying such a rationale-based therapeutic strategy will improve treatment efficacy in HNC patients.

A Mechanism of Resistance to Antibody-Targeted Immune Attack

Targeted monoclonal antibody therapy is a promising therapeutic strategy for cancer, and antibody-dependent cell-mediated cytotoxicity (ADCC) represents a crucial mechanism underlying these approaches. The majority of patients have limited responses to monoclonal antibody therapy due to the development of resistance. Models of ADCC provide a system for uncovering immune-resistance mechanisms. We continuously exposed epidermal growth factor receptor (EGFR+) A431 cells to KIR-deficient NK92-CD16V effector cells and the anti-EGFR cetuximab. Persistent ADCC exposure yielded ADCC-resistant cells (ADCCR1) that, compared with control ADCC-sensitive cells (ADCCS1), exhibited reduced EGFR expression, overexpression of histone- and interferon-related genes, and a failure to activate NK cells, without evidence of epithelial-to-mesenchymal transition. These properties gradually reversed following withdrawal of ADCC selection pressure. The development of resistance was associated with lower expression of multiple cell-surface molecules that contribute to cell-cell interactions and immune synapse formation. Classic immune checkpoints did not modulate ADCC in this unique model system of immune resistance. We showed that the induction of ADCC resistance involves genetic and epigenetic changes that lead to a general loss of target cell adhesion properties that are required for the establishment of an immune synapse, killer cell activation, and target cell cytotoxicity.

Enter the Matrix: Factorization Uncovers Knowledge from Omics

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.