Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemia.

Acute myeloid leukemia (AML) is prevalent in both adult and pediatric patients. Despite advances in patient categorization, the heterogeneity of AML remains a challenge. Recent studies have explored the use of gene expression data to enhance AML diagnosis and prognosis, however, alternative approaches rooted in physics and chemistry may provide another level of insight into AML transformation. Utilizing publicly available databases, we analyze 884 human and mouse blood and bone marrow samples. We employ a personalized medicine strategy, combining state-transition theory and surprisal analysis, to assess the RNA transcriptome of individual patients. The transcriptome is transformed into physical parameters that represent each sample's steady state and the free energy change (FEC) from that steady state, which is the state with the lowest free energy.We found the transcriptome steady state was invariant across normal and AML samples. FEC, representing active molecular processes, varied significantly between samples and was used to create patient-specific barcodes to characterize the biology of the disease. We discovered that AML samples that were in a transition state had the highest FEC. This disease state may be characterized as the most unstable and hence the most therapeutically targetable since a change in free energy is a thermodynamic requirement for disease progression. We also found that distinct sets of ongoing processes may be at the root of otherwise similar clinical phenotypes, implying that our integrated analysis of transcriptome profiles may facilitate a personalized medicine approach to cure AML and restore a steady state in each patient.

Molecular Changes in Breast Cancer Induced by Radiation Therapy.

PURPOSE: Breast cancer treatments are based on prognostic clinicopathologic features that form the basis for therapeutic guidelines. Although the utilization of these guidelines has decreased breast cancer-associated mortality rates over the past three decades, they are not adequate for individualized therapy. Radiation therapy (RT) is the backbone of breast cancer treatment. Although a highly successful therapeutic modality clinically, from a biological perspective, preclinical studies have shown RT to have the potential to alter tumor cell phenotype, immunogenicity, and the surrounding microenvironment, potentially changing the behavior of cancer cells and resulting in a significant variation in RT response. This review presents the recent advances in revealing the complex molecular changes induced by RT in the treatment of breast cancer and highlights the complexities of translating this information into clinically relevant tools for improved prognostic insights and the revelation of novel approaches for optimizing RT. METHODS AND MATERIALS: Current literature was reviewed with a focus on recent advances made in the elucidation of tumor-associated radiation-induced molecular changes across molecular, genetic, and proteomic bases. This review was structured with the aim of providing an up-to-date overview over the very broad and complex subject matter of radiation-induced molecular changes and radioresistance, familiarizing the reader with the broader issue at hand. RESULTS: The subject of radiation-induced molecular changes in breast cancer has been broached from various physiological focal points including that of the immune system, immunogenicity and the abscopal effect, tumor hypoxia, breast cancer classification and subtyping, molecular heterogeneity, and molecular plasticity. It is becoming increasingly apparent that breast cancer clinical subtyping alone does not adequately account for variation in RT response or radioresistance. Multiple components of the tumor microenvironment and immune system, delivered RT dose and fractionation schedules, radiation-induced bystander effects, and intrinsic tumor physiology and heterogeneity all contribute to the resultant RT outcome. CONCLUSIONS: Despite recent advances and improvements in anticancer therapies, tumor resistance remains a significant challenge. As new analytical techniques and technologies continue to provide crucial insight into the complex molecular mechanisms of breast cancer and its treatment responses, it is becoming more evident that personalized anticancer treatment regimens may be vital in overcoming radioresistance.

Patient-specific signaling signatures predict optimal therapeutic combinations for triple negative breast cancer.

Triple negative breast cancer (TNBC) is a heterogeneous group of tumors which lack estrogen receptor, progesterone receptor, and HER2 expression. Targeted therapies have limited success in treating TNBC, thus a strategy enabling effective targeted combinations is an unmet need. To tackle these challenges and discover individualized targeted combination therapies for TNBC, we integrated phosphoproteomic analysis of altered signaling networks with patient-specific signaling signature (PaSSS) analysis using an information-theoretic, thermodynamic-based approach. Using this method on a large number of TNBC patient-derived tumors (PDX), we were able to thoroughly characterize each PDX by computing a patient-specific set of unbalanced signaling processes and assigning a personalized therapy based on them. We discovered that each tumor has an average of two separate processes, and that, consistent with prior research, EGFR is a major core target in at least one of them in half of the tumors analyzed. However, anti-EGFR monotherapies were predicted to be ineffective, thus we developed personalized combination treatments based on PaSSS. These were predicted to induce anti-EGFR responses or to be used to develop an alternative therapy if EGFR was not present.In-vivo experimental validation of the predicted therapy showed that PaSSS predictions were more accurate than other therapies. Thus, we suggest that a detailed identification of molecular imbalances is necessary to tailor therapy for each TNBC. In summary, we propose a new strategy to design personalized therapy for TNBC using pY proteomics and PaSSS analysis. This method can be applied to different cancer types to improve response to the biomarker-based treatment.

Understanding Glioblastoma Signaling, Heterogeneity, Invasiveness, and Drug Delivery Barriers.

The most prevalent and aggressive type of brain cancer, namely, glioblastoma (GBM), is characterized by intra- and inter-tumor heterogeneity and strong spreading capacity, which makes treatment ineffective. A true therapeutic answer is still in its infancy despite various studies that have made significant progress toward understanding the mechanisms behind GBM recurrence and its resistance. The primary causes of GBM recurrence are attributed to the heterogeneity and diffusive nature; therefore, monitoring the tumor's heterogeneity and spreading may offer a set of therapeutic targets that could improve the clinical management of GBM and prevent tumor relapse. Additionally, the blood-brain barrier (BBB)-related poor drug delivery that prevents effective drug concentrations within the tumor is discussed. With a primary emphasis on signaling heterogeneity, tumor infiltration, and computational modeling of GBM, this review covers typical therapeutic difficulties and factors contributing to drug resistance development and discusses potential therapeutic approaches.

Paclitaxel Delivery to the Brain for Glioblastoma Treatment.

The development of paclitaxel-loaded polymeric nanoparticles for the treatment of brain tumors was investigated. Poly(lactide-glycolide) (PLGA) nanoparticles containing 10% w/w paclitaxel with a particle size of 216 nm were administered through intranasal and intravenous routes to male Sprague-Dawley rats at a dose of 5 mg/kg. Both routes of administration showed appreciable accumulation of paclitaxel in brain tissue, liver, and kidney without any sign of toxicity. The anti-proliferative effect of the nanoparticles on glioblastoma tumor cells was comparable to that of free paclitaxel.

The immunogenic radiation and new players in immunotherapy and targeted therapy for head and neck cancer.

Although treatment modalities for head and neck cancer have evolved considerably over the past decades, survival rates have plateaued. The treatment options remained limited to definitive surgery, surgery followed by fractionated radiotherapy with optional chemotherapy, and a definitive combination of fractionated radiotherapy and chemotherapy. Lately, immunotherapy has been introduced as the fourth modality of treatment, mainly administered as a single checkpoint inhibitor for recurrent or metastatic disease. While other regimens and combinations of immunotherapy and targeted therapy are being tested in clinical trials, adapting the appropriate regimens to patients and predicting their outcomes have yet to reach the clinical setting. Radiotherapy is mainly regarded as a means to target cancer cells while minimizing the unwanted peripheral effect. Radiotherapy regimens and fractionation are designed to serve this purpose, while the systemic effect of radiation on the immune response is rarely considered a factor while designing treatment. To bridge this gap, this review will highlight the effect of radiotherapy on the tumor microenvironment locally, and the immune response systemically. We will review the methodology to identify potential targets for therapy in the tumor microenvironment and the scientific basis for combining targeted therapy and radiotherapy. We will describe a current experience in preclinical models to test these combinations and propose how challenges in this realm may be faced. We will review new players in targeted therapy and their utilization to drive immunogenic response against head and neck cancer. We will outline the factors contributing to head and neck cancer heterogeneity and their effect on the response to radiotherapy. We will review in-silico methods to decipher intertumoral and intratumoral heterogeneity and how these algorithms can predict treatment outcomes. We propose that (a) the sequence of surgery, radiotherapy, chemotherapy, and targeted therapy should be designed not only to annul cancer directly, but to prime the immune response. (b) Fractionation of radiotherapy and the extent of the irradiated field should facilitate systemic immunity to develop. (c) New players in targeted therapy should be evaluated in translational studies toward clinical trials. (d) Head and neck cancer treatment should be personalized according to patients and tumor-specific factors.

Overexpressed kinetochore genes are used by cancer cells as genome destabilizers and transformation catalysts.

Cancer cells have an altered transcriptome, which contributes to their abnormal behavior. Many tumors have high levels of kinetochore genes, which play important roles in genome stability. This overexpression could be utilized to destabilize cancer cell genomes, however this has not been proven specifically. We investigated the link between kinetochore gene overexpression, chromosomal number variations (CNVs) and genomic instability. Data on RNA expression and CNV from 12 different cancer types were evaluated using information theory. In all cancer types, we looked at the relationship between RNA expression and CNVs. Kinetochore gene expression was found to be substantially linked with CNV levels. In all cancer types, with the exception of thyroid cancer, highly expressed kinetochore genes were enriched in the most dominant cancer-specific co-expression subnetworks characterizing the largest patient subgroups. Except for thyroid cancer, kinetochore inner protein CENPA was among the transcripts most strongly associated with CNV values in all cancer types studied, with significantly higher expression levels in patients with high CNVs than in patients with low CNVs. CENPA function was investigated further in cell models by transfecting genomically stable (HCT116) and unstable (MCF7 and HT29) cancer cell lines using CENPA overexpression vectors. This overexpression increased the number of abnormal cell divisions in the stable cancer cell line HCT116 and, to a lesser extent, in the unstable cell lines MCF7 and HT29. Overexpression improved anchorage-independent growth properties of all cell lines. Our findings suggest that overexpression of kinetochore genes in general, and CENPA in particular, can cause genomic instability and cancer progression.

HU308 Mitigates Osteoarthritis by Stimulating Sox9-Related Networks of Carbohydrate Metabolism.

Osteoarthritis (OA) is characterized by progressive, irreversible erosion of articular cartilage accompanied by severe pain and immobility. This study aimed to assess the effect and mechanism of action of HU308, a selective cannabinoid receptor type 2 (CB2) agonist, in preventing OA-related joint damage. To test the assumption that HU308 could prevent OA-related joint damage, Cnr2 null mice and wild type (WT) mice were aged to reach 20 months and analyzed for joint structural features. OA was induced in WT mice via a post-traumatic procedure or aging, followed by HU308 local (intra-articular) or systemic (intraperitoneal) administration, respectively. Additional analyses of time and dose courses for HU308 were carried out in human primary chondrocytes, analyzed by RNA sequencing, RT-PCR, chromatin immunoprecipitation, and immunoblotting. Our results showed that Cnr2 null mice exhibited enhanced age-related OA severity and synovitis compared to age-matched WT mice. Systemic administration of HU308 to 16-month-old mice improved pain sensitivity and maintained joint integrity, which was consistent with the intra-articular administration of HU308 in post-traumatic OA mice. When assessing human chondrocytes treated with HU308, we uncovered a dose- and time-related increase in ACAN and COL2A1 expression, which was preceded by increased SOX9 expression due to pCREB transcriptional activity. Finally, transcriptomic analysis of patient-derived human chondrocytes identified patient subpopulations exhibiting HU308-responsive trends as judged by enhanced SOX9 expression, accompanied by enriched gene networks related to carbohydrate metabolism. Collectively, the results showed that HU308 reduced trauma and age-induced OA via CB2-pCREB dependent activation of SOX9, contributing to augmented gene networks related to carbohydrate metabolism. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance.

BACKGROUND: Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance. METHODS: In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell. RESULTS: Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT). CONCLUSIONS: We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.

Overcoming resistance to EGFR monotherapy in HNSCC by identification and inhibition of individualized cancer processes.

Therapeutic strategies for advanced head and neck squamous carcinoma (HNSCC) consist of multimodal treatment, including Epidermal Growth Factor Receptor (EGFR) inhibition, immune-checkpoint inhibition, and radio (chemo) therapy. Although over 90% of HNSCC tumors overexpress EGFR, attempts to replace cytotoxic treatments with anti-EGFR agents have failed due to alternative signaling pathways and inter-tumor heterogeneity. Methods: Using protein expression data obtained from hundreds of HNSCC tissues and cell lines we compute individualized signaling signatures using an information-theoretic approach. The approach maps each HNSCC malignancy according to the protein-protein network reorganization in every tumor. We show that each patient-specific signaling signature (PaSSS) includes several distinct altered signaling subnetworks. Based on the resolved PaSSSs we design personalized drug combinations. Results: We show that simultaneous targeting of central hub proteins from each altered subnetwork is essential to selectively enhance the response of HNSCC tumors to anti-EGFR therapy and inhibit tumor growth. Furthermore, we demonstrate that the PaSSS-based drug combinations lead to induced expression of T cell markers and IFN-γ secretion, pointing to higher efficiency of the immune response. Conclusion: The PaSSS-based approach advances our understanding of how individualized therapies should be tailored to HNSCC tumors.

Drug-Induced Resistance and Phenotypic Switch in Triple-Negative Breast Cancer Can Be Controlled via Resolution and Targeting of Individualized Signaling Signatures.

Triple-negative breast cancer (TNBC) is an aggressive subgroup of breast cancers which is treated mainly with chemotherapy and radiotherapy. Epidermal growth factor receptor (EGFR) was considered to be frequently expressed in TNBC, and therefore was suggested as a therapeutic target. However, clinical trials of EGFR inhibitors have failed. In this study, we examine the relationship between the patient-specific TNBC network structures and possible mechanisms of resistance to anti-EGFR therapy. Using an information-theoretical analysis of 747 breast tumors from the TCGA dataset, we resolved individualized protein network structures, namely patient-specific signaling signatures (PaSSS) for each tumor. Each PaSSS was characterized by a set of 1-4 altered protein-protein subnetworks. Thirty-one percent of TNBC PaSSSs were found to harbor EGFR as a part of the network and were predicted to benefit from anti-EGFR therapy as long as it is combined with anti-estrogen receptor (ER) therapy. Using a series of single-cell experiments, followed by in vivo support, we show that drug combinations which are not tailored accurately to each PaSSS may generate evolutionary pressure in malignancies leading to an expansion of the previously undetected or untargeted subpopulations, such as ER+ populations. This corresponds to the PaSSS-based predictions suggesting to incorporate anti-ER drugs in certain anti-TNBC treatments. These findings highlight the need to tailor anti-TNBC targeted therapy to each PaSSS to prevent diverse evolutions of TNBC tumors and drug resistance development.

Explant Modeling of the Immune Environment of Head and Neck Cancer.

Patients exhibit distinct responses to immunotherapies that are thought to be linked to their tumor immune environment. However, wide variations in outcomes are also observed in patients with matched baseline tumor environments, indicating that the biological response to treatment is not currently predictable using a snapshot analysis. To investigate the relationship between the immune environment of tumors and the biological response to immunotherapies, we characterized four murine head and neck squamous cell carcinoma (HNSCC) models on two genetic backgrounds. Using tumor explants from those models, we identified correlations between the composition of infiltrating immune cells and baseline cytokine profiles prior to treatment. Following treatment with PD-1 blockade, CTLA-4 blockade, or OX40 stimulation, we observed inter-individual variability in the response to therapy between genetically identical animals bearing the same tumor. These distinct biological responses to treatment were not linked to the initial tumor immune environment, meaning that outcome would not be predictable from a baseline analysis of the tumor infiltrates. We similarly performed the explant assay on patient HNSCC tumors and found significant variability between the baseline environment of the tumors and their response to therapy. We propose that tumor explants provide a rapid biological assay to assess response to candidate immunotherapies that may allow matching therapies to individual patient tumors. Further development of explant approaches may allow screening and monitoring of treatment responses in HNSCC.

Heparanase: a potential marker of worse prognosis in estrogen receptor-positive breast cancer.

Heparanase promotes tumor growth in breast tumors. We now evaluated heparanase protein and gene-expression status and investigated its impact on disease-free survival in order to gain better insight into the role of heparanase in ER-positive (ER+) breast cancer prognosis and to clarify its role in cell survival following chemotherapy. Using pooled analysis of gene-expression data, we found that heparanase was associated with a worse prognosis in estrogen receptor-positive (ER+) tumors (log-rank p < 10-10) and predictive to chemotherapy resistance (interaction p = 0.0001) but not hormonal therapy (Interaction p = 0.62). These results were confirmed by analysis of data from a phase III, prospective randomized trial which showed that heparanase protein expression is associated with increased risk of recurrence in ER+ breast tumors (log-rank p = 0.004). In vitro experiments showed that heparanase promoted tumor progression and increased cell viability via epithelial-mesenchymal transition, stemness, and anti-apoptosis pathways in luminal breast cancer. Taken together, our results demonstrated that heparanase is associated with worse outcomes and increased cell viability in ER+ BC.

Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures.

Despite huge investments and major efforts to develop remedies for Alzheimer's disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1-3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches.

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient-specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient-specific combined therapy should be designed. Large-scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.

Dissecting the role of crosstalk between glioblastoma subpopulations in tumor cell spreading.

Glioblastoma (GBM) is a highly infiltrative brain cancer, which is thus difficult to operate. GBM cells frequently harbor Epidermal Growth Factor Receptor amplification (EGFRwt) and/or activating mutation (EGFRvIII), generating at least two different cellular subpopulations within the tumor. We examined the relationship between the diffusive architectures of GBM tumors and the paracrine interactions between those subpopulations. Our aim was to shed light on what drives GBM cells to reach large cell-cell distances, and whether this characteristic can be manipulated. We established a methodology that quantifies the infiltration abilities of cancer cells through computation of cell-cell separation distance distributions in 3D. We found that aggressive EGFRvIII cells modulate the migration and infiltrative properties of EGFRwt cells. EGFRvIII cells secrete HGF and IL6, leading to enhanced activity of Src protein in EGFRwt cells, and rendering EGFRwt cells higher velocity and augmented ability to spread. Src inhibitor, dasatinib, at low non-toxic concentrations, reduced the infiltrative properties of EGFRvIII/EGFRwt neurospheres. Furthermore, dasatinib treatment induced compact multicellular microstructure packing of EGFRvIII/EGFRwt cells, impairing their ability to spread. Prevention of cellular infiltration or induction of compact microstructures may assist the detection of GBM tumors and tumor remnants in the brains and improve their surgical removal.

Decoding cancer heterogeneity: studying patient-specific signaling signatures towards personalized cancer therapy.

The past years have witnessed a rapid increase in the amount of large-scale tumor datasets. The challenge has now become to find a way to obtain useful information from these masses of data that will allow to determine which combination of FDA-approved drugs is best suited to treat the specific tumor. Various statistical analyses are being developed to extract significant signals from cancer datasets. However, tumors are still being assigned to pre-defined categories (breast luminal A, triple negative, etc.), conceptually contradicting the vast heterogeneity that is known to exist among tumors, and likely overlooking unique tumors that must be addressed and treated individually. We present herein an approach based on information theory that, rather than searches for what makes a tumor similar to other tumors, addresses tumors individually and unbiasedly, and impartially decodes the critical patient-specific molecular network reorganization in every tumor. Methods: Using a large dataset obtained from ~3500 tumors of 11 types we decipher the altered protein network structure in each tumor, namely the patient-specific signaling signature. Each signature can harbor several altered protein subnetworks. We suggest that simultaneous targeting of central proteins from every altered subnetwork is essential to efficiently disturb the altered signaling in each tumor. We experimentally validate our ability to dissect sample-specific signaling signatures and to rationally design personalized drug combinations. Results: We unraveled a surprisingly simple order that underlies the extreme apparent complexity of tumor tissues, demonstrating that only 17 altered protein subnetworks characterize ~3500 tumors of 11 types. Each tumor was described by a specific subset of 1-4 subnetworks out of 17, i.e. a tumor-specific altered signaling signature. We show that the majority of tumor-specific signaling signatures are extremely rare, and are shared by only 5 tumors or less, supporting a personalized, comprehensive study of tumors in order to design the optimal combination therapy for every patient. We validate the results by confirming that the processes identified in the 11 original cancer types characterize patients harboring a different cancer type as well. We show experimentally, using different cancer cell lines, that the individualized combination therapies predicted by us achieved higher rates of killing than the clinically prescribed treatments. Conclusions: We present a new strategy to deal with the inter-tumor heterogeneity and to break down the high complexity of cancer systems into simple, easy to crack, patient-specific signaling signatures that guide the rational design of personalized drug therapies.

Personalized disease signatures through information-theoretic compaction of big cancer data.

Every individual cancer develops and grows in its own specific way, giving rise to a recognized need for the development of personalized cancer diagnostics. This suggested that the identification of patient-specific oncogene markers would be an effective diagnostics approach. However, tumors that are classified as similar according to the expression levels of certain oncogenes can eventually demonstrate divergent responses to treatment. This implies that the information gained from the identification of tumor-specific biomarkers is still not sufficient. We present a method to quantitatively transform heterogeneous big cancer data to patient-specific transcription networks. These networks characterize the unbalanced molecular processes that deviate the tissue from the normal state. We study a number of datasets spanning five different cancer types, aiming to capture the extensive interpatient heterogeneity that exists within a specific cancer type as well as between cancers of different origins. We show that a relatively small number of altered molecular processes suffices to accurately characterize over 500 tumors, showing extreme compaction of the data. Every patient is characterized by a small specific subset of unbalanced processes. We validate the result by verifying that the processes identified characterize other cancer patients as well. We show that different patients may display similar oncogene expression levels, albeit carrying biologically distinct tumors that harbor different sets of unbalanced molecular processes. Thus, tumors may be inaccurately classified and addressed as similar. These findings highlight the need to expand the notion of tumor-specific oncogenic biomarkers to patient-specific, comprehensive transcriptional networks for improved patient-tailored diagnostics.

Intercellular signaling through secreted proteins induces free-energy gradient-directed cell movement.

Controlling cell migration is important in tissue engineering and medicine. Cell motility depends on factors such as nutrient concentration gradients and soluble factor signaling. In particular, cell-cell signaling can depend on cell-cell separation distance and can influence cellular arrangements in bulk cultures. Here, we seek a physical-based approach, which identifies a potential governed by cell-cell signaling that induces a directed cell-cell motion. A single-cell barcode chip (SCBC) was used to experimentally interrogate secreted proteins in hundreds of isolated glioblastoma brain cancer cell pairs and to monitor their relative motions over time. We used these trajectories to identify a range of cell-cell separation distances where the signaling was most stable. We then used a thermodynamics-motivated analysis of secreted protein levels to characterize free-energy changes for different cell-cell distances. We show that glioblastoma cell-cell movement can be described as Brownian motion biased by cell-cell potential. To demonstrate that the free-energy potential as determined by the signaling is the driver of motion, we inhibited two proteins most involved in maintaining the free-energy gradient. Following inhibition, cell pairs showed an essentially random Brownian motion, similar to the case for untreated, isolated single cells.

A Thermodynamic-Based Interpretation of Protein Expression Heterogeneity in Different Glioblastoma Multiforme Tumors Identifies Tumor-Specific Unbalanced Processes.

We describe a thermodynamic-motivated, information theoretic analysis of proteomic data collected from a series of 8 glioblastoma multiforme (GBM) tumors. GBMs are considered here as prototypes of heterogeneous cancers. That heterogeneity is viewed here as manifesting in different unbalanced biological processes that are associated with thermodynamic-like constraints. The analysis yields a molecular description of a stable steady state that is common across all tumors. It also resolves molecular descriptions of unbalanced processes that are shared by several tumors, such as hyperactivated phosphoprotein signaling networks. Further, it resolves unbalanced processes that provide unique classifiers of tumor subgroups. The results of the theoretical interpretation are compared against those of statistical multivariate methods and are shown to provide a superior level of resolution for identifying unbalanced processes in GBM tumors. The identification of specific constraints for each GBM tumor suggests tumor-specific combination therapies that may reverse this imbalance.