Metabolic Synergy between Human Symbionts Bacteroides and Methanobrevibacter

Trophic interactions between microbes are postulated to determine whether a host microbiome is healthy or causes predisposition to disease. Two abundant taxa, the Gram-negative heterotrophic bacterium Bacteroides thetaiotaomicron and the methanogenic archaeon Methanobrevibacter smithii, are proposed to have a synergistic metabolic relationship. Both organisms play vital roles in human gut health; B. thetaiotaomicron assists the host by fermenting dietary polysaccharides, whereas M. smithii consumes end-stage fermentation products and is hypothesized to relieve feedback inhibition of upstream microbes such as B. thetaiotaomicron. To study their metabolic interactions, we defined and optimized a coculture system and used software testing techniques to analyze growth under a range of conditions representing the nutrient environment of the host. We verify that B. thetaiotaomicron fermentation products are sufficient for M. smithii growth and that accumulation of fermentation products alters secretion of metabolites by B. thetaiotaomicron to benefit M. smithii. Studies suggest that B. thetaiotaomicron metabolic efficiency is greater in the absence of fermentation products or in the presence of M. smithii. Under certain conditions, B. thetaiotaomicron and M. smithii form interspecies granules consistent with behavior observed for syntrophic partnerships between microbes in soil or sediment enrichments and anaerobic digesters. Furthermore, when vitamin B12, hematin, and hydrogen gas are abundant, coculture growth is greater than the sum of growth observed for monocultures, suggesting that both organisms benefit from a synergistic mutual metabolic relationship. IMPORTANCE The human gut functions through a complex system of interactions between the host human tissue and the microbes which inhabit it. These diverse interactions are difficult to model or examine under controlled laboratory conditions. We studied the interactions between two dominant human gut microbes, B. thetaiotaomicron and M. smithii, using a seven-component culturing approach that allows the systematic examination of the metabolic complexity of this binary microbial system. By combining high-throughput methods with machine learning techniques, we were able to investigate the interactions between two dominant genera of the gut microbiome in a wide variety of environmental conditions. Our approach can be broadly applied to studying microbial interactions and may be extended to evaluate and curate computational metabolic models. The software tools developed for this study are available as user-friendly tutorials in the Department of Energy KBase.

Subjective Information and Survival in a Simulated Biological System

Information transmission and storage have gained traction as unifying concepts to characterize biological systems and their chances of survival and evolution at multiple scales. Despite the potential for an information-based mathematical framework to offer new insights into life processes and ways to interact with and control them, the main legacy is that of Shannon’s, where a purely syntactic characterization of information scores systems on the basis of their maximum information efficiency. The latter metrics seem not entirely suitable for biological systems, where transmission and storage of different pieces of information (carrying different semantics) can result in different chances of survival. Based on an abstract mathematical model able to capture the parameters and behaviors of a population of single-celled organisms whose survival is correlated to information retrieval from the environment, this paper explores the aforementioned disconnect between classical information theory and biology. In this paper, we present a model, specified as a computational state machine, which is then utilized in a simulation framework constructed specifically to reveal emergence of a “subjective information”, i.e., trade-off between a living system’s capability to maximize the acquisition of information from the environment, and the maximization of its growth and survival over time. Simulations clearly show that a strategy that maximizes information efficiency results in a lower growth rate with respect to the strategy that gains less information but contains a higher meaning for survival.

Molecular Communications in Viral Infections Research: Modeling, Experimental Data, and Future Directions

Hundreds of millions of people worldwide are affected by viral infections each year, and yet, several of them neither have vaccines nor effective treatment during and post-infection. This challenge has been highlighted by the COVID-19 pandemic, showing how viruses can quickly spread and impact society as a whole. Novel interdisciplinary techniques must emerge to provide forward-looking strategies to combat viral infections, as well as possible future pandemics. In the past decade, an interdisciplinary area involving bioengineering, nanotechnology and information and communication technology (ICT) has been developed, known as Molecular Communications. This new emerging area uses elements of classical communication systems to molecular signalling and communication found inside and outside biological systems, characterizing the signalling processes between cells and viruses. In this paper, we provide an extensive and detailed discussion on how molecular communications can be integrated into the viral infectious diseases research, and how possible treatment and vaccines can be developed considering molecules as information carriers. We provide a literature review on molecular communications models for viral infection (intra-body and extra-body), a deep analysis on their effects on immune response, how experimental can be used by the molecular communications community, as well as open issues and future directions.

Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning

MOTIVATION

Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic modifications and genetic variations. Computational modeling, as an essential research method, has generated promising testable quantitative models that represent complex interplay among different gene regulatory mechanisms based on these data in many biological systems. However, given the dynamic changes of interactome in chaotic systems such as cancers, and the dramatic growth of heterogeneous data on this topic, such promise has encountered unprecedented challenges in terms of model complexity and scalability. In this study, we introduce a new integrative machine learning approach that can infer multifaceted gene regulations in cancers with a particular focus on microRNA regulation. In addition to new strategies for data integration and graphical model fusion, a supervised deep learning model was integrated to identify conditional microRNA-mRNA interactions across different cancer stages.

RESULTS

In a case study of human breast cancer, we have identified distinct gene regulatory networks associated with four progressive stages. The subsequent functional analysis focusing on microRNA-mediated dysregulation across stages has revealed significant changes in major cancer hallmarks, as well as novel pathological signaling and metabolic processes, which shed light on microRNAs' regulatory roles in breast cancer progression. We believe this integrative model can be a robust and effective discovery tool to understand key regulatory characteristics in complex biological systems.

AVAILABILITY

http://sbbi-panda.unl.edu/pin/.

Nanonetworks in Biomedical Applications

By interconnecting nanomachines and forming nanonetworks, the capacities of single nanomachines are expected to be enhanced, as the ensuing information exchange will allow them to cooperate towards a common goal. Nowadays, systems normally use electromagnetic signals to encode, send and receive information, however, in a novel communication paradigm, molecular transceivers, channel models or protocols use molecules. This article presents the current developments in nanomachines along with their future architecture to better understand nanonetwork scenarios in biomedical applications. Furthermore, to highlight the communication needs between nanomachines, two applications for nanonetworks are also presented: i) a new networking paradigm, called the Internet of NanoThings, that allows nanoscale devices to interconnect with existing communication networks, and ii) Molecular Communication, where the propagation of chemical compounds like drug particles, carry out the information exchange.

Computational Models for Trapping Ebola Virus Using Engineered Bacteria

The outbreak of the Ebola virus in recent years has resulted in numerous research initiatives to seek new solutions to contain the virus. A number of approaches that have been investigated include new vaccines to boost the immune system. An alternative post-exposure treatment is presented in this paper. The proposed approach for clearing the Ebola virus can be developed through a microfluidic attenuator, which contains the engineered bacteria that traps Ebola flowing through the blood onto its membrane. The paper presents the analysis of the chemical binding force between the virus and a genetically engineered bacterium considering the opposing forces acting on the attachment point, including hydrodynamic tension and drag force. To test the efficacy of the technique, simulations of bacterial motility within a confined area to trap the virus were performed. More than 60 percent of the displaced virus could be collected within 15 minutes. While the proposed approach currently focuses on in vitro environments for trapping the virus, the system can be further developed into a future treatment system whereby blood can be cycled out of the body into a microfluidic device that contains the engineered bacteria to trap viruses.

BioSIMP: Using Software Testing Techniques for Sampling and Inference in Biological Organisms

Years of research in software engineering has given us novel ways to reason about, test, and predict the behavior of complex software systems that contain hundreds of thousands of lines of code. Many of these techniques have been inspired by nature such as genetic algorithms, swarm intelligence, and ant colony optimization. In this paper we reverse the direction and present BioSIMP, a process that models and predicts the behavior of biological organisms to aid in the emerging field of systems biology. It utilizes techniques from testing and modeling of highly-configurable software systems. Using both experimental and simulation data we show that BioSIMP can find important environmental factors in two microbial organisms. However, we learn that in order to fully reason about the complexity of biological systems, we will need to extend existing or create new software engineering techniques.

Subthreshold linear modeling of dendritic trees: a computational approach

The design of communication systems based on the transmission of information through neurons is envisioned as a key technology for the pervasive interconnection of future wearable and implantable devices. While previous literature has mainly focused on modeling propagation of electrochemical spikes carrying natural information through the nervous system, in recent work the authors of this paper proposed the so-called subthreshold electrical stimulation as a viable technique to propagate artificial information through neurons. This technique promises to limit the interference with natural communication processes, and it can be successfully approximated with linear models. In this paper, a novel model is proposed to account for the subthreshold stimuli propagation from the dendritic tree to the soma of a neuron. A computational approach is detailed to obtain this model for a given realistic 3D dendritic tree with an arbitrary morphology. Numerical results from the model are obtained over a stimulation signal bandwidth of 1KHz, and compared with the results of a simulation through the NEURON software.

Abstract P1-07-09: A multi-OMIC analysis to explore the impact of “actionable” genomic alterations on protein pathway activation: Clinical implication for precision medicine in metastatic breast cancer

Background: While genomic alterations are central players in tumor progression, proteins are the targets for precision therapy. The degree by which “actionable” genomic alterations translate into activated/altered proteins and pathway is still under investigation. Using a multi-OMIC approach from the SideOut 2 metastatic breast cancer (MBC) trial, this study explored the concordance between selected “actionable” genomic alterations and protein expression/activation.

Methods: Snap frozen biopsies from 29 MBC patients enrolled in a prospective phase II trial were used for this analysis. Exome WES and RNASeq data was processed using an in-house developed pipeline and identified amplification of CCND1 (6/29), FGFR1 (4/29), and FGF 3, 4, 5, and 19 (4/29) as some of most frequent “actionable” genomic alterations in our MBC cohort. Signaling analysis of the 29 cases was performed using Reverse Phase Protein Microarray coupled with Laser Capture Microdissection. Protein expression/phosphorylation was measured in a continuous scale and classified based on quartile distribution. Concordance between CCND1 amplification and Cyclin D1 expression, along with the activation of FOXM1 T600 and Rb S780, was explored. Amplification of the FGFR1 locus or its ligands was correlated with the level of activation/phosphorylation of FGFR1 Y653/654.

Results: While Cyclin D1 protein expression was greater than the population mean for 4/6 (67%) patients with CCND1 amplification, only 2/6 (33%) patients with CCND1 amplification had Cyclin D1 level within the top quartile of the population (n=29). FOXM1 T600 activation was independent from CCND1 amplification, with high levels of FOXM1 T600 predominantly in the CCND1 wild-type population. Only 1/6 (17%) patients with CCND1 amplification had FOXM1 T600 level similar to the top quartile of the population while a second patient was above the population median. Activation of Rb S780 was above the population median, but below the top quartile, in 2/6 (33%) CCND1 amplified patients. Similarly, none of the patients with activation of FGFR Y653/654 equal to the top quartile harbored an FGFR1 amplification. Only 1/4 (25%) patients carrying an FGFR1 amplification had an activation of FGFR Y653/654 above the population median. Similarly, 1/4 (25%) patients with FGF ligand amplification showed FGFR Y653/654 level within the top quartile while three patients had FGFR Y653/654 activation below the population median. No significant results were found between proteomic (below/above the median) and genomic characteristics by Fisher test (p>0.05).

Conclusion: Molecular genotyping of “actionable” cancer targets alone may be insufficient in predicting whether the actual drug target protein is expressed and/or activated in any given patient's tumor. Although these results need further validation, the combination of genomic and proteomic data may represent a more informative approach for identifying real molecular drivers of individual lesions as well as “actionable” protein/phosphoprotein targets in the absence of genomic events. Multi-OMIC approaches may lead to more effective stratification in precision medicine trials.

Citation Format: Pierobon M, Wong S, Reeded A, Anthony S, Robert N, Northfelt DW, Jahanzeb M, Vocila L, Wulfkuhle J, Dunetz B, Aldrich J, Byron S, Craig D, Liotta L, Carpten J, Petricoin EF. A multi-OMIC analysis to explore the impact of “actionable” genomic alterations on protein pathway activation: Clinical implication for precision medicine in metastatic breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-07-09.

Abstract P2-05-21: The AKT-mTOR pathway as a potential organ-specific drug target signature of hepatic metastases from breast cancer

Background: The identification of organ-specific targetable signatures may help design more effective treatment for patients with metastatic breast cancer (MBC). We took a multi-OMIC approach to assess whether the AKT-mTOR pathway is globally activated during metastatic progression or whether it represents an organ-specific target.

Methods: Snap frozen biopsies from 25 MBC patients enrolled in a prospective phase II trial were used. Sites of metastasis were classified as liver (n=8) and others (n=17), the latter including cutaneous, lung, lymph nodes, and intra-abdominal lesions. Signaling analysis of the 25 cases was performed using Reverse Phase Protein Microarray (RPPA) coupled with Laser Capture Microdissection. Activation of the AKT-mTOR pathway was quantified as phosphorylation of AKT (S473) and the mTOR target p70S6 (T389). Matched exome (WES) and RNASeq data were available for 17 of 25 patients, five with liver metastases. Sequencing data was processed using an in-house developed pipeline to identify somatic events including coding mutations, copy number alterations, gene fusions, and differential expression. Activation of the AKT-mTOR pathway and sequencing data were compared between hepatic and non-hepatic lesions using an integrated RPPA and genomic approach.

Results: Among liver metastases, AKT was activated in 4 of the 8 (50.0%) patients, while 6 of the 8 cases (75.0%) showed activation of p70S6. Sequencing data revealed mutation of PIK3CA in 4 of the 5 liver metastases (80.0%). Three of the PIK3CA mutated specimens with catalytic domain mutations (codons 1023 and 147) demonstrated co-activation of AKT and p70S6, while the fourth case, containing a helical domain mutation (E542K), had activation of p70S6 only. The PIK3CA wild-type liver metastasis demonstrated low activation of AKT and p70S6. For non-hepatic metastases AKT was activated in 2 of the 17 cases (11.8%) and p70S6 in 5 of the 17 patients (29.4%).

Discussion: Although these results need further validation, activation of the AKT-mTOR pathway appears to be a hepatic specific signature in MBC and could be used for the selection of targeted agents for hepatic lesions.

Citation Format: Pierobon M, Wong S, Reeder A, Anthony SP, Robert NJ, Northfelt DW, Jahanzeb M, Vocila L, Wulfkuhle J, Dunetz B, Aldrich J, Byron S, Craig D, Liotta L, Petricoin EF, Carpten J. The AKT-mTOR pathway as a potential organ-specific drug target signature of hepatic metastases from breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P2-05-21.

Pilot phase I/II personalized therapy trial for metastatic colorectal cancer: evaluating the feasibility of protein pathway activation mapping for stratifying patients to therapy with imatinib and panitumumab

This nonrandomized phase I/II trial assessed the efficacy/tolerability of imatinib plus panitumumab in patients affected by metastatic colorectal cancer (mCRC) after stratification to treatment by selection of activated imatinib drug targets using reverse-phase protein array (RPPA). mCRC patients presenting with a biopsiable liver metastasis were enrolled. Allocation to the experimental and control arms was established using functional pathway activation mapping of c-Kit, PDGFR, and c-Abl phosphorylation by RPPA. The experimental arm received run-in escalation therapy with imatinib followed by panitumumab. The control arm received panitumumab alone. Seven patients were enrolled in the study. For three of the seven patients, sequential pre- and post-treatment biopsies were used to evaluate the effect of the therapeutic compounds on the drug targets and substrates. A decrease in the activation level of the drug targets and downstream substrates was observed in two of three patients. Combination therapy increased the activation of the AKT-mTOR pathway and several receptor tyrosine kinases. This study proposes a novel methodology for stratifying patients to personalized treatment based on the activation level of the drug targets. This workflow provides the ability to monitor changes in the signaling pathways after the administration of targeted therapies and to identify compensatory mechanisms.

Individualized therapy for metastatic colorectal cancer

Systemic therapeutic efficacy is central to determining the outcome of patients with metastatic colorectal cancer (CRC). In these patients, there is a critical need for predictive biomarkers to optimize efficacy whilst minimizing toxicity. The integration of a new generation of molecularly targeted drugs into the treatment of CRC, coupled with the development of sophisticated technologies for individual tumours as well as patient molecular profiling, underlines the potential for personalized medicine. In this review, we focus on the latest progress made within the genomic and proteomic fields, concerning predictive biomarkers for individualized therapy in metastatic CRC.

Chemoresistant colorectal cancer cells and cancer stem cells mediate growth and survival of bystander cells

BACKGROUND

Recent studies suggest that cancer stem cells (CSCs) mediate chemoresistance, but interestingly, only a small percentage of cells in a resistant tumour are CSCs; this suggests that non-CSCs survive by other means. We hypothesised that chemoresistant colorectal cancer (CRC) cells generate soluble factors that enhance survival of chemonaive tumour cells.

METHODS

Chemoresistant CRC cells were generated by serial passage in oxaliplatin (Ox cells). Conditioned media (CM) was collected from parental and oxaliplatin-resistant (OxR) cells. CRC cells were treated with CM and growth and survival were assessed. Tumour growth rates were determined in nude mice after cells were treated with CM. Mass spectrometry (MS) identified proteins in CM. Reverse phase protein microarray assays determined signalling effects of CM in parental cells.

RESULTS

Oxaliplatin-resistant CM increased survival of chemo-naive cells. CSC CM also increased growth of parental cells. Parental and OxR mixed tumours grew larger than tumours composed of parental or OxR cells alone. Mass spectrometry detected unique survival-promoting factors in OxR CM compared with parental CM. Cells treated with OxR CM demonstrated early phosphorylation of EGFR and MEK1, with later upregulation of total Akt .We identified progranulin as a potential mediator of chemoresistance.

CONCLUSION

Chemoresistant tumour cells and CSCs may promote resistance through soluble factors that mediate survival in otherwise chemosensitive tumour cells.

Discovery of a new phospho-HER2+/FISH- molecular subtype of human breast cancer by functional pathway mapping

11009

Background: Effective treatment of breast cancer through the targeting of the HER2 protein in human breast cancer represents a major advance in oncology, yet the identification of responders has relied on non-quantitative methods (IHC) that measure the erbB2 protein or indirect genomic analysis (FISH) of the erbB2 gene that cannot predict protein pathway activation status. We utilized a new quantitative protein microarray assay to measure total and phosphorylated HER2 in the context of broad-scale EGFR signal pathway mapping in order to generate a new molecular characterization scheme for human breast cancer. Methods: Pure tumor epithelium from 149 frozen pre-treatment human breast cancer tissue specimens (from the I-SPY TRIAL: CALGB 150007/150012, ACRIN 6657) were procured via Laser Capture Microdissection and protein pathway mapping was performed using Reverse Phase Protein Microarrays (RPMA) whereby the activation of 40 key signaling proteins was quantitatively measured at once. Results: While phospho-HER2 and total HER2 as measured by RPMA had excellent concordance with FISH (95%) and IHC (94%), of the 63 cases where both clinical FISH and IHC status of c-erbB2 were known, we discovered that 5/45 (11%) of the FISH-/IHC- cases had phosphorylated HER2 levels as high or higher than the FISH+/IHC positive patients. These results were confirmed and validated by independent analysis with quantitative Western Blot using a separate biopsy specimen from the same patients. Conclusions: A new molecular phenotype of human breast cancer has been identified whereby total levels of HER2 are low yet levels of the phosphorlyated receptor are very high. This molecular phenotype is not detectable by FISH analysis nor by measurement of the total HER2 protein itself. Given the central importance of phosphorylation on effective signal transduction of the EGFR family, we are planning to determine the clinical significance of this finding by retrospective analysis of banked material with outcome, and a prospective clinical trial in I-SPY 2. Support: ACRIN U01 CA079778 ; CALGB CA31964, CA33601; NCI SPORE CA58207.

[Table: see text]

Semi-quantitative protein analysis of HER2 and ER levels in human breast cancer reveals broad expression ranges within HER2+ and ER+ phenotypes

11014

Background: ER and HER2 measurements underpin a majority of breast cancer clinical testing, yet determination of ER and HER2 protein levels are routinely performed using subjective approaches. We have developed a semi-quantitative calibrated protein microarray, the Reverse Phase Protein Microarray (RPMA), to more adequately determine accurate and precise protein expression levels in clinical tissue specimens. We utilized this method to determine HER2 and ER expression levels and compared the results to those values reported from the clinical laboratory. Methods: Pure tumor epithelium from 149 frozen pre-treatment human breast cancer tissue specimens (from the I-SPY TRIAL: CALGB 150007/150012, ACRIN 6657) were procured via Laser Capture Microdissection and protein pathway mapping was performed whereby the ER (N=112) and HER2 (N= 118) levels were directly measured and compared with reported IHC values. Results: Overall, RPMA measurements of HER2 had excellent correlation with IHC and FISH HER2 determination with no IHC or FISH false positives (88/88). 7 out of 30 of the FISH/IHC+ cases were found by RPMA to have HER2 values as low or lower than the FISH/IHC- cases with the HER2+ population having RPMA expression levels that varied by as much as 10-fold. There was less concordance between RPMA ER values and IHC ER values. Detailed analysis of a more homogeneous ER+ population (Allred =8) revealed a large dynamic range of expression of ER (10–20 fold) by RPMA. The HER2 and ER RPMA results were independently validated by Western Blot using a separate biopsy. Conclusions: Discreet protein expression values obtained by RPMA for ER and HER2 reveal distinctly broad expression values, with as much as 10–20 fold dynamic range in expression, even in a homogeneous (e.g. Allred score of 8) population. Such dynamic differences in protein expression may produce dramatic effects in therapeutic response rate. These findings, if found to be clinically useful, point to a potential need for more fine-tuned protein expression determination by quantitative high-throughput technologies for patient stratification even for standard-of-care FDA approved therapies.

[Table: see text]

Use of a prognostic pathway signature for colorectal cancer comprised of EGFR/COX2 and imatinib drug target activation to predict occult metastasis in M0 CRC

4042

Background: Development of distant metastasis is the main cause of death among CRC patients. Approximately 30% of CRC patients initially staged M0-N0 die from tumor recurrence. Previously, we determined that members of EGFR/PDGFR/cAbl/cKit pathways were hyperactivated in hepatic synchronous CRC metastasis compared to primary tumor. In order to determine if this signature was a distinguished repertoire of the early stage primary tumor, we analyzed 58 CRC M0 at the moment of the diagnosis that upon 5 yr follow-up had differing disease progressions. Methods: All tissues were immediately snap frozen after surgery. Reverse phase protein microarray (RPMA) was performed using microdissected material to generate multiplexed pathway profiling. For each sample 75 different endpoints were analyzed. Results: Statistical comparison of the 75 endpoints in 8 M0 patients who progressed to M1 and 50 patients who remained M0 regardless of initial staging, revealed a number of signaling proteins whose activation/phosphorylation were elevated and subsumed in a linked pathway. Specifically COX2 and c-Kit/PDGFR/Notch were highly activated in the 8 patients with occult metastasis. A prognostic pathway signature comprised of 13 interlinked molecules was developed. Univariate, ROC and Kaplan-Meier analysis of this signature revealed a statistically significant prognostic signature with an AUC of 0.87 and a 95% confidence interval. Conclusions: A signaling portrait of 13 interlinked proteins provided a strong prognostic indicator for metastasis regardless of stage. This signature was comprised of the phosphorylation/activation of growth factor receptors, including the entire suite of Gleevec targets. A large number of these prognostic signature components were previously found activated in the metastatic lesions themselves which indicates a potential functional role of this linked protein network in metastatic progression and maintenance. If validated in larger study sets, clinical trials to test Gleevec therapy combined with Cox2 /EGFR inhibitors to prevent/delay development of distant metastasis in patients with M0 should be considered.

[Table: see text]

Personalized therapy for metastatic colorectal cancer: A closer possibility?

4131

Background: Colorectal cancer (CRC) is the second leading cause of cancer related death in the Western world, and survival rate is closely associated with the development of metastases. Personalized targeted therapies promise to have a dramatic impact on the treatment of cancer over the next decade. The molecular fingerprint of a patient’s tumor is the basis for specific targeted therapy. Most often, we are not measuring what we are treating. If we choose therapy based on the primary tumor, but we are treating the metastasis, we are likely giving the wrong therapy if the two microenvironments are not equivalent. In this study we employed reverse phase protein microarrays (RPPA) to compare the protein kinases signal pathway derangements in the primary CRC and in its synchronous liver metastasis. Methods: Pure cell populations of 34 cases of patient-matched CRC and hepatic metastases (collected at the same surgical time) were isolated through laser capture microdissection and then lysed. The lysed cells were evaluated using RPPA technology that allowed us to analyze the activation status of 80 different kinases. Data analysis was performed using commercially available software. Results: Of the 80 kinases only 20 endpoints were significantly (p< 0.05) altered between the two populations. These endpoints were contained within just a few signaling pathways, including the PI3K-AKT prosurvival pathway and the c-kit/PDGFr/c-abl growth factor pathway. We noted a significant increase in phosphorylation of AKT along with a decrease in phosphorylation of PTEN in the liver metastasis compared to the matched primary tumors. This is in keeping with what is known about AKT since phosphorylation of PTEN serves to destabilize the protein, which serves as a natural upstream suppressor of AKT kinase. Conclusions: Specific cell signaling pathways, such as the PI3K-AKT and the c-kit/PDGFr/c-abl growth factor signaling pathway, are significantly altered and activated in hepatic metastasis compared to the primary colorectal site. Since the data reveals elevation in kinase activity increases on a pathway-wide level, a rational hypothesis can be developed whereby combinations of drugs such as an AKT- mTOR inhibitor and/or Gleevec may be an effective and novel therapeutic strategy for the treatment of metastatic CRC.

No significant financial relationships to disclose.

Alterations in molecular networks of metastatic colorectal carcinoma reveal organ-specific signatures: Implications for targeted therapy of metastatic disease

3532

Background: Proteomic analysis of aberrant protein kinase activity is poised to provide crucial knowledge that could drive molecular-targeted therapeutics and personalized medicine. Many cancers are detected at late stages when metastasis has already occurred. Knowledge about the molecular derangements in the metastatic lesion is crucial for the rational selection of therapeutics. Very little is known about the signaling networks in the metastatic microenvironment. We employed reverse phase protein microarrays coupled to laser capture microdissection for a multiplexed phosphoproteomic fingerprint of colorectal metastatic disease to begin to understand the molecular functional changes that occur upon metastasis. Methods: 68 frozen cases of patient-matched colorectal cancer and hepatic metastasis, 15 cases of pulmonary metastasis, and 27 cases of hepatic metastasis of other primary cancers including breast, melanoma, pancreatic, ovarian, and stomach cancers (all taken at the same time at surgery), were subjected to laser capture microdissection. Procured tumor epithelia (20,000 cells per sample), were lysed and subjected to reverse phase protein microarray analysis. Using this technique, we measured the phosphorylation state of 75 kinase substrates. Molecular network analysis was performed using commercially available software. Results: Our results indicate that, unlike analysis of gene microarray data, we observe a significant difference between the molecular networks of activated kinase substrates within the metastatic lesion compared to the patient-matched primary tumor. In fact, despite overall patient-specific heterogeneity of the portraits, organ specific signatures that were independent of the primary origin of the tumor were identified. Conculsions: Effective treatment in the new era of personalized targeted therapeutics will require the ability to understand the functional activation of cellular signaling pathways since these are the drug targets themselves. Our results indicate that treatment of metastatic disease, and patient stratification for matching with the appropriate therapy may be organ-specific and not predicated upon the primary site of the disease.

No significant financial relationships to disclose.