Computational Biological Modeling Identifies PD-(L)1 Immunotherapy Sensitivity Among Molecular Subgroups of KRAS-Mutated Non-Small-Cell Lung Cancer.

PURPOSE: KRAS-mutated (KRASMUT) non-small-cell lung cancer (NSCLC) is emerging as a heterogeneous disease defined by comutations, which may confer differential benefit to PD-(L)1 immunotherapy. In this study, we leveraged computational biological modeling (CBM) of tumor genomic data to identify PD-(L)1 immunotherapy sensitivity among KRASMUT NSCLC molecular subgroups. MATERIALS AND METHODS: In this multicohort retrospective analysis, the genotype clustering frequency ranked method was used for molecular clustering of tumor genomic data from 776 patients with KRASMUT NSCLC. These genomic data were input into the CBM, in which customized protein networks were characterized for each tumor. The CBM evaluated sensitivity to PD-(L)1 immunotherapy using three metrics: programmed death-ligand 1 expression, dendritic cell infiltration index (nine chemokine markers), and immunosuppressive biomarker expression index (14 markers). RESULTS: Genotype clustering identified eight molecular subgroups and the CBM characterized their shared cancer pathway characteristics: KRASMUT/TP53MUT, KRASMUT/CDKN2A/B/CMUT, KRASMUT/STK11MUT, KRASMUT/KEAP1MUT, KRASMUT/STK11MUT/KEAP1MUT, KRASMUT/PIK3CAMUT, KRAS MUT/ATMMUT, and KRASMUT without comutation. CBM identified PD-(L)1 immunotherapy sensitivity in the KRASMUT/TP53MUT, KRASMUT/PIK3CAMUT, and KRASMUT alone subgroups and resistance in the KEAP1MUT containing subgroups. There was insufficient genomic information to elucidate PD-(L)1 immunotherapy sensitivity by the CBM in the KRASMUT/CDKN2A/B/CMUT, KRASMUT/STK11MUT, and KRASMUT/ATMMUT subgroups. In an exploratory clinical cohort of 34 patients with advanced KRASMUT NSCLC treated with PD-(L)1 immunotherapy, the CBM-assessed overall survival correlated well with actual overall survival (r = 0.80, P < .001). CONCLUSION: CBM identified distinct PD-(L)1 immunotherapy sensitivity among molecular subgroups of KRASMUT NSCLC, in line with previous literature. These data provide proof-of-concept that computational modeling of tumor genomics could be used to expand on hypotheses from clinical observations of patients receiving PD-(L)1 immunotherapy and suggest mechanisms that underlie PD-(L)1 immunotherapy sensitivity.

Prediction of Outcomes with a Computational Biology Model in Newly Diagnosed Glioblastoma Patients Treated with Radiation Therapy and Temozolomide.

PURPOSE: Precision medicine has been most successful in targeting single mutations, but personalized medicine using broader genomic tumor profiles for individual patients is less well developed. We evaluate a genomics-informed computational biology model (CBM) to predict outcomes from standard treatments and to suggest novel therapy recommendations in glioblastoma (GBM). METHODS AND MATERIALS: In this retrospective study, 98 patients with newly diagnosed GBM undergoing surgery followed by radiation therapy and temozolomide at a single institution with available genomic data were identified. Incorporating mutational and copy number aberration data, a CBM was used to simulate the response of GBM tumor cells and generate efficacy predictions for radiation therapy (RTeff) and temozolomide (TMZeff). RTeff and TMZeff were evaluated for association with overall survival and progression-free survival in a Cox regression model. To demonstrate a CBM-based individualized therapy strategy, treatment recommendations were generated for each patient by testing a panel of 45 central nervous system-penetrant US Food and Drug Administration-approved agents. RESULTS: High RTeff scores were associated with longer survival on univariable analysis (P < .001), which persisted after controlling for age, extent of resection, performance status, MGMT, and IDH status (P = .017). High RTeff patients had a longer overall survival compared with low RTeff patients (median, 27.7 vs 14.6 months). High TMZeff was also associated with longer survival on univariable analysis (P = .007) but did not hold on multivariable analysis, suggesting an interplay with MGMT status. Among predictions of the 3 most efficacious combination therapies for each patient, only 2.4% (7 of 294) of 2-drug recommendations produced by the CBM included TMZ. CONCLUSIONS: CBM-based predictions of RT and TMZ effectiveness were associated with survival in patients with newly diagnosed GBM treated with those therapies, suggesting a possible predictive utility. Furthermore, the model was able to suggest novel individualized monotherapies and combinations. Prospective evaluation of such a personalized treatment strategy in clinical trials is needed.

Computational Models Accurately Predict Multi-Cell Biomarker Profiles in Inflammation and Cancer.

Individual computational models of single myeloid, lymphoid, epithelial, and cancer cells were created and combined into multi-cell computational models and used to predict the collective chemokine, cytokine, and cellular biomarker profiles often seen in inflamed or cancerous tissues. Predicted chemokine and cytokine output profiles from multi-cell computational models of gingival epithelial keratinocytes (GE KER), dendritic cells (DC), and helper T lymphocytes (HTL) exposed to lipopolysaccharide (LPS) or synthetic triacylated lipopeptide (Pam3CSK4) as well as multi-cell computational models of multiple myeloma (MM) and DC were validated using the observed chemokine and cytokine responses from the same cell type combinations grown in laboratory multi-cell cultures with accuracy. Predicted and observed chemokine and cytokine responses of GE KER + DC + HTL exposed to LPS and Pam3CSK4 matched 75% (15/20, p = 0.02069) and 80% (16/20, P = 0.005909), respectively. Multi-cell computational models became 'personalized' when cell line-specific genomic data were included into simulations, again validated with the same cell lines grown in laboratory multi-cell cultures. Here, predicted and observed chemokine and cytokine responses of MM cells lines MM.1S and U266B1 matched 75% (3/4) and MM.1S and U266B1 inhibition of DC marker expression in co-culture matched 100% (6/6). Multi-cell computational models have the potential to identify approaches altering the predicted disease-associated output profiles, particularly as high throughput screening tools for anti-inflammatory or immuno-oncology treatments of inflamed multi-cellular tissues and the tumor microenvironment.

A genomics-informed computational biology platform prospectively predicts treatment responses in AML and MDS patients.

Patients with myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML) are generally older and have more comorbidities. Therefore, identifying personalized treatment options for each patient early and accurately is essential. To address this, we developed a computational biology modeling (CBM) and digital drug simulation platform that relies on somatic gene mutations and gene CNVs found in malignant cells of individual patients. Drug treatment simulations based on unique patient-specific disease networks were used to generate treatment predictions. To evaluate the accuracy of the genomics-informed computational platform, we conducted a pilot prospective clinical study (NCT02435550) enrolling confirmed MDS and AML patients. Blinded to the empirically prescribed treatment regimen for each patient, genomic data from 50 evaluable patients were analyzed by CBM to predict patient-specific treatment responses. CBM accurately predicted treatment responses in 55 of 61 (90%) simulations, with 33 of 61 true positives, 22 of 61 true negatives, 3 of 61 false positives, and 3 of 61 false negatives, resulting in a sensitivity of 94%, a specificity of 88%, and an accuracy of 90%. Laboratory validation further confirmed the accuracy of CBM-predicted activated protein networks in 17 of 19 (89%) samples from 11 patients. Somatic mutations in the TET2, IDH1/2, ASXL1, and EZH2 genes were discovered to be highly informative of MDS response to hypomethylating agents. In sum, analyses of patient cancer genomics using the CBM platform can be used to predict precision treatment responses in MDS and AML patients.

A feedforward relationship between active Rac1 and phosphorylated Bcl-2 is critical for sustaining Bcl-2 phosphorylation and promoting cancer progression.

Active GTPase-Rac1 is associated with cellular processes involved in carcinogenesis and expression of Bcl-2 endows cells with the ability to evade apoptosis. Here we provide evidence that active Rac1 and Bcl-2 work in a positive feedforward loop to promote sustained phosphorylation of Bcl-2 at serine-70 (S70pBcl-2), which stabilizes its anti-apoptotic activity. Pharmacological and genetic inactivation of Rac1 prevent interaction with Bcl-2 and reduce S70pBcl-2. Similarly, BH3-mimetic inhibitors of Bcl-2 could disrupt Rac1-Bcl-2 interaction and reduce S70pBcl-2. This effect of active Rac1 could also be rescued by scavengers of intracellular superoxide (O2.-), thus implicating NOX-activating activity of Rac1 in promoting S70pBcl-2. Moreover, active Rac1-mediated redox-dependent S70pBcl-2 involves the inhibition of phosphatase PP2A holoenzyme assembly. Sustained S70pBcl-2 in turn secures Rac1/Bcl-2 interaction. Importantly, inhibiting Rac1 activity, scavenging O2.- or employing BH3-mimetic inhibitor significantly reduced S70pBcl-2-mediated survival in cancer cells. Notably, Rac1 expression, and its interaction with Bcl-2, positively correlate with S70pBcl-2 levels in patient-derived lymphoma tissues and with advanced stage lymphoma and melanoma. Together, we provide evidence of a positive feedforward loop involving active Rac1, S70pBcl-2 and PP2A, which could have potential diagnostic, prognostic and therapeutic implications.

Sequential azacitidine and lenalidomide for patients with relapsed and refractory acute myeloid leukemia: Clinical results and predictive modeling using computational analysis.

BACKGROUND: Patients with relapsed and refractory (R/R) acute myeloid leukemia (AML) have limited treatment options. Genomically-defined personalized therapies are only applicable for a minority of patients. Therapies without identifiable targets can be effective but patient selection is challenging. The sequential combination of azacitidine with high-dose lenalidomide has shown activity; we aimed to determine the efficacy of this genomically-agnostic regimen in patients with R/R AML, with the intention of applying sophisticated methods to predict responders. METHODS: Thirty-seven R/R AML/myelodysplastic syndrome patients were enrolled in a phase 2 study of azacitidine with lenalidomide. The primary endpoint was complete remission (CR) and CR with incomplete blood count recovery (CRi) rate. A computational biological modeling (CBM) approach was applied retrospectively to predict outcomes based on the understood mechanisms of azacitidine and lenalidomide in the setting of each patients' disease. FINDINGS: Four of 37 patients (11%) had a CR/CRi; the study failed to meet the alternative hypothesis. Significant toxicity was observed in some cases, with three treatment-related deaths and a 30-day mortality rate of 14%. However, the CBM method predicted responses in 83% of evaluable patients, with a positive and negative predictive value of 80% and 89%, respectively. INTERPRETATION: Sequential azacitidine and high-dose lenalidomide is effective in a minority of R/R AML patients; it may be possible to predict responders at the time of diagnosis using a CBM approach. More efforts to predict responses in non-targeted therapies should be made, to spare toxicity in patients unlikely to respond and maximize treatments for those with limited options.

Predicting response to BET inhibitors using computational modeling: A BEAT AML project study.

Despite advances in understanding the molecular pathogenesis of acute myeloid leukaemia (AML), overall survival rates remain low. The ability to predict treatment response based on individual cancer genomics using computational modeling will aid in the development of novel therapeutics and personalize care. Here, we used a combination of genomics, computational biology modeling (CBM), ex vivo chemosensitivity assay, and clinical data from 100 randomly selected patients in the Beat AML project to characterize AML sensitivity to a bromodomain (BRD) and extra-terminal (BET) inhibitor. Computational biology modeling was used to generate patient-specific protein network maps of activated and inactivated protein pathways translated from each genomic profile. Digital drug simulations of a BET inhibitor (JQ1) were conducted by quantitatively measuring drug effect using a composite AML disease inhibition score. 93% of predicted disease inhibition scores matched the associated ex vivo IC50 value. Sensitivity and specificity of CBM predictions were 97.67%, and 64.29%, respectively. Genomic predictors of response were identified. Patient samples harbouring chromosomal aberrations del(7q) or -7, +8, or del(5q) and somatic mutations causing ERK pathway dysregulation, responded to JQ1 in both in silico and ex vivo assays. This study shows how a combination of genomics, computational modeling and chemosensitivity testing can identify network signatures associating with treatment response and can inform priority populations for future clinical trials of BET inhibitors.

Computational modeling of early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) to identify personalized therapy using genomics.

Early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) is an aggressive hematological malignancy for which optimal therapeutic approaches are poorly characterized. Using computational biology modeling (CBM) in conjunction with genomic data from cell lines and individual patients, we generated disease-specific protein network maps that were used to identify unique characteristics associated with the mutational profiles of ETP-ALL compared to non-ETP-ALL (T-ALL) cases and simulated cellular responses to a digital library of FDA-approved and investigational agents. Genomics-based classification of ETP-ALL patients using CBM had a prediction sensitivity and specificity of 93% and 87%, respectively. This analysis identified key genomic and pathway characteristics that are distinct in ETP-ALL including deletion of nucleophosmin-1 (NPM1), mutations of which are used to direct therapeutic decisions in acute myeloid leukemia. Computational simulations based on mutational profiles of 62 ETP-ALL patient models identified 87 unique targeted combination therapies in 56 of the 62 patients despite actionable mutations being present in only 37% of ETP-ALL patients. Shortlisted two-drug combinations were predicted to be synergistic in 11 profiles and were validated by in vitro chemosensitivity assays. In conclusion, computational modeling was able to identify unique biomarkers and pathways for ETP-ALL, and identify new drug combinations for potential clinical testing.

Correction to: Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy.

It has been highlighted that in the original manuscript [1] Table S3 'An example of the predictive computational modeling process. Specific details on an annexure section of the PD-L1 pathway show the step-by-step reactions, mechanisms, and reaction equations that occur. Such reactions also occurred in all of the other pathways' was omitted and did not appear in the Additional files and that the Additional files were miss-numbered thereafter. This Correction shows the correct and incorrect Additional files. The original article has been updated.

Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy.

BACKGROUND: Programmed Death Ligand 1 (PD-L1) is a co-stimulatory and immune checkpoint protein. PD-L1 expression in non-small cell lung cancers (NSCLC) is a hallmark of adaptive resistance and its expression is often used to predict the outcome of Programmed Death 1 (PD-1) and PD-L1 immunotherapy treatments. However, clinical benefits do not occur in all patients and new approaches are needed to assist in selecting patients for PD-1 or PD-L1 immunotherapies. Here, we hypothesized that patient tumor cell genomics influenced cell signaling and expression of PD-L1, chemokines, and immunosuppressive molecules and these profiles could be used to predict patient clinical responses. METHODS: We used a recent dataset from NSCLC patients treated with pembrolizumab. Deleterious gene mutational profiles in patient exomes were identified and annotated into a cancer network to create NSCLC patient-specific predictive computational simulation models. Validation checks were performed on the cancer network, simulation model predictions, and PD-1 match rates between patient-specific predicted and clinical responses. RESULTS: Expression profiles of these 24 chemokines and immunosuppressive molecules were used to identify patients who would or would not respond to PD-1 immunotherapy. PD-L1 expression alone was not sufficient to predict which patients would or would not respond to PD-1 immunotherapy. Adding chemokine and immunosuppressive molecule expression profiles allowed patient models to achieve a greater than 85.0% predictive correlation among predicted and reported patient clinical responses. CONCLUSIONS: Our results suggested that chemokine and immunosuppressive molecule expression profiles can be used to accurately predict clinical responses thus differentiating among patients who would and would not benefit from PD-1 or PD-L1 immunotherapies.

Human beta defensin 3 alters matrix metalloproteinase production in human dendritic cells exposed to Porphyromonas gingivalis hemagglutinin B.

BACKGROUND: Matrix metalloproteinases (MMPs) are zinc- or calcium-dependent proteinases involved in normal maintenance of extracellular matrix. When elevated, they contribute to the tissue destruction seen in periodontal disease. Recently, we found that human beta defensin 3 (HBD3), a cationic antimicrobial peptide, alters chemokine and proinflammatory cytokine responses in human myeloid dendritic cells exposed to Porphyromonas gingivalis hemagglutinin B (HagB). In this study, the hypotheses that HagB induces MMP production in dendritic cells and that HBD3 mixed with HagB prior to treatment alters HagB-induced MMP profiles were tested. METHODS: Dendritic cells were exposed to 0.2 µM HagB alone and HagB + HBD3 (0.2 or 2.0 µM) mixtures. After 16 hours, concentrations of MMPs in cell culture media were determined with commercial multiplex fluorescent bead-based immunoassays. An integrated cell network was used to identify potential HagB-induced signaling pathways in dendritic cells leading to the production of MMPs. RESULTS: 0.2 µM HagB induced MMP1, -2, -7, -9, and -12 responses in dendritic cells. 0.2 µM HBD3 enhanced the HagB-induced MMP7 response (P < 0.05) and 2.0 µM HBD3 attenuated HagB-induced MMP1, -7, and -9 responses (P < 0.05). The MMP12 response was not affected. In the predicted network, MMPs are produced via activation of multiple pathways. Signals converge to activate numerous transcription factors, which transcribe different MMPs. CONCLUSION: HagB was an MMP stimulus and HBD3 was found to decrease HagB-induced MMP1, -7, and -9 responses in dendritic cells at 16 hours, an observation that suggests HBD3 can alter microbial antigen-induced production of MMPs.

Ex-vivo sensitivity profiling to guide clinical decision making in acute myeloid leukemia: A pilot study.

A precision medicine approach is appealing for use in AML due to ease of access to tumor samples and the significant variability in the patients' response to treatment. Attempts to establish a precision medicine platform for AML, however, have been unsuccessful, at least in part due to the use of small compound panels and having relatively slow turn over rates, which restricts the scope of treatment and delays its onset. For this pilot study, we evaluated a cohort of 12 patients with refractory AML using an ex vivo drug sensitivity testing (DST) platform. Purified AML blasts were screened with a panel of 215 FDA-approved compounds and treatment response was evaluated after 72h of exposure. Drug sensitivity scoring was reported to the treating physician, and patients were then treated with either DST- or non-DST guided therapy. We observed survival benefit of DST-guided therapy as compared to the survival of patients treated according to physician recommendation. Three out of four DST-treated patients displayed treatment response, while all of the non-DST-guided patients progressed during treatment. DST rapidly and effectively provides personalized treatment recommendations for patients with refractory AML.

Cell genomics and immunosuppressive biomarker expression influence PD-L1 immunotherapy treatment responses in HNSCC-a computational study.

OBJECTIVES: Programmed death-ligand 1 (PD-L1) expression is correlated with objective response rates to PD-1 and PD-L1 immunotherapies. However, both immunotherapies have only demonstrated 12%-24.8% objective response rates in patients with head and neck squamous cell carcinoma (HNSCC), demonstrating a need for a more accurate method to identify those who will respond before their therapy. Immunohistochemistry to detect PD-L1 reactivity in tumors can be challenging, and additional methods are needed to predict and confirm PD-L1 expression. Here, we hypothesized that HNSCC tumor cell genomics influences cell signaling and downstream effects on immunosuppressive biomarkers and that these profiles can predict patient clinical responses. STUDY DESIGN: We identified deleterious gene mutations in SCC4, SCC15, and SCC25 and created cell line-specific predictive computational simulation models. The expression of 24 immunosuppressive biomarkers were then predicted and used to sort cell lines into those that would respond to PD-L1 immunotherapy and those that would not. RESULTS: SCC15 and SCC25 were identified as cell lines that would respond to PD-L1 immunotherapy treatment and SCC4 was identified as a cell line that would not likely respond to PD-L1 immunotherapy treatment. CONCLUSIONS: This approach, when applied to HNSCC cells, has the ability to predict PD-L1 expression and predict PD-1- or PD-L1-targeted treatment responses in these patients.

Computational drug treatment simulations on projections of dysregulated protein networks derived from the myelodysplastic mutanome match clinical response in patients.

Although the majority of MDS patients fail to achieve clinical improvement to approved therapies, some patients benefit from treatment. Predicting patient response prior to therapy would improve treatment effectiveness, avoid treatment-related adverse events and reduce healthcare costs. Three separate cohorts of MDS patients were used to simulate drug response to lenalidomide alone, hypomethylating agent (HMA) alone, or HMA plus lenalidomide. Utilizing a computational biology program, genomic abnormalities in each patient were used to create an intracellular pathway map that was then used to screen for drug response. In the lenalidomide treated cohort, computer modeling correctly matched clinical responses in 37/46 patients (80%). In the second cohort, 15 HMA patients were modeled and correctly matched to responses in 12 (80%). In the third cohort, computer modeling correctly matched responses in 10/10 patients (100%). This computational biology network approach identified GGH overexpression as a potential resistance factor to HMA treatment and paradoxical activation of beta-catenin (through Csnk1a1 inhibition) as a resistance factor to lenalidomide treatment. We demonstrate that a computational technology is able to map the complexity of the MDS mutanome to simulate and predict drug response. This tool can improve understanding of MDS biology and mechanisms of drug sensitivity and resistance.

Predicting PD-L1 expression on human cancer cells using next-generation sequencing information in computational simulation models.

PURPOSE: Interaction of the programmed death-1 (PD-1) co-receptor on T cells with the programmed death-ligand 1 (PD-L1) on tumor cells can lead to immunosuppression, a key event in the pathogenesis of many tumors. Thus, determining the amount of PD-L1 in tumors by immunohistochemistry (IHC) is important as both a diagnostic aid and a clinical predictor of immunotherapy treatment success. Because IHC reactivity can vary, we developed computational simulation models to accurately predict PD-L1 expression as a complementary assay to affirm IHC reactivity. METHODS: Multiple myeloma (MM) and oral squamous cell carcinoma (SCC) cell lines were modeled as examples of our approach. Non-transformed cell models were first simulated to establish non-tumorigenic control baselines. Cell line genomic aberration profiles, from next-generation sequencing (NGS) information for MM.1S, U266B1, SCC4, SCC15, and SCC25 cell lines, were introduced into the workflow to create cancer cell line-specific simulation models. Percentage changes of PD-L1 expression with respect to control baselines were determined and verified against observed PD-L1 expression by ELISA, IHC, and flow cytometry on the same cells grown in culture. RESULT: The observed PD-L1 expression matched the predicted PD-L1 expression for MM.1S, U266B1, SCC4, SCC15, and SCC25 cell lines and clearly demonstrated that cell genomics play an integral role by influencing cell signaling and downstream effects on PD-L1 expression. CONCLUSION: This concept can easily be extended to cancer patient cells where an accurate method to predict PD-L1 expression would affirm IHC results and improve its potential as a biomarker and a clinical predictor of treatment success.

Identification of myeloproliferative neoplasm drug agents via predictive simulation modeling: assessing responsiveness with micro-environment derived cytokines.

Previous studies have shown that the bone marrow micro-environment supports the myeloproliferative neoplasms (MPN) phenotype including via the production of cytokines that can induce resistance to frontline MPN therapies. However, the mechanisms by which this occurs are poorly understood. Moreover, the ability to rapidly identify drug agents that can act as adjuvants to existing MPN frontline therapies is virtually non-existent. Here, using a novel predictive simulation approach, we sought to determine the effect of various drug agents on MPN cell lines, both with and without the micro-environment derived inflammatory cytokines. We first created individual simulation models for two representative MPN cell lines; HEL and SET-2, based on their genomic mutation and copy number variation (CNV) data. Running computational simulations on these virtual cell line models, we identified a synergistic effect of two drug agents on cell proliferation and viability; namely, the Jak2 kinase inhibitor, G6, and the Bcl-2 inhibitor, ABT737. IL-6 did not show any impact on the cells due to the predicted lack of IL-6 signaling within these cells. Interestingly, TNFα increased the sensitivity of the single drug agents and their use in combination while IFNγ decreased the sensitivity. In summary, this study predictively identified two drug agents that reduce MPN cell viability via independent mechanisms that was prospectively validated. Moreover, their efficacy is either potentiated or inhibited, by some of the micro-environment derived cytokines. Lastly, this study has validated the use of this simulation based technology to prospectively determine such responses.

AshwaMAX and Withaferin A inhibits gliomas in cellular and murine orthotopic models.

Glioblastoma multiforme (GBM) is an aggressive, malignant cancer Johnson and O'Neill (J Neurooncol 107: 359-364, 2012). An extract from the winter cherry plant (Withania somnifera ), AshwaMAX, is concentrated (4.3 %) for Withaferin A; a steroidal lactone that inhibits cancer cells Vanden Berghe et al. (Cancer Epidemiol Biomark Prev 23: 1985-1996, 2014). We hypothesized that AshwaMAX could treat GBM and that bioluminescence imaging (BLI) could track oral therapy in orthotopic murine models of glioblastoma. Human parietal-cortical glioblastoma cells (GBM2, GBM39) were isolated from primary tumors while U87-MG was obtained commercially. GBM2 was transduced with lentiviral vectors that express Green Fluorescent Protein (GFP)/firefly luciferase fusion proteins. Mutational, expression and proliferative status of GBMs were studied. Intracranial xenografts of glioblastomas were grown in the right frontal regions of female, nude mice (n = 3-5 per experiment). Tumor growth was followed through BLI. Neurosphere cultures (U87-MG, GBM2 and GBM39) were inhibited by AshwaMAX at IC50 of 1.4, 0.19 and 0.22 µM equivalent respectively and by Withaferin A with IC50 of 0.31, 0.28 and 0.25 µM respectively. Oral gavage, every other day, of AshwaMAX (40 mg/kg per day) significantly reduced bioluminescence signal (n = 3 mice, p < 0.02, four parameter non-linear regression analysis) in preclinical models. After 30 days of treatment, bioluminescent signal increased suggesting onset of resistance. BLI signal for control, vehicle-treated mice increased and then plateaued. Bioluminescent imaging revealed diffuse growth of GBM2 xenografts. With AshwaMAX, GBM neurospheres collapsed at nanomolar concentrations. Oral treatment studies on murine models confirmed that AshwaMAX is effective against orthotopic GBM. AshwaMAX is thus a promising candidate for future clinical translation in patients with GBM.

Overexpression of Bcl-2 induces STAT-3 activation via an increase in mitochondrial superoxide.

We recently reported a novel interaction between Bcl-2 and Rac1 and linked that to the ability of Bcl-2 to induce a pro-oxidant state in cancer cells. To gain further insight into the functional relevance of this interaction, we utilized computer simulation based on the protein pathway dynamic network created by Cellworks Group Inc. STAT3 was identified among targets that positively correlated with Rac1 and/or Bcl-2 expression levels. Validating this, the activation level of STAT3, as marked by p-Tyr705, particularly in the mitochondria, was significantly higher in Bcl-2-overexpressing cancer cells. Bcl-2-induced STAT3 activation was a function of GTP-loaded Rac1 and NADPH oxidase (Nox)-dependent increase in intracellular superoxide (O2•-). Furthermore, ABT199, a BH-3 specific inhibitor of Bcl-2, as well as silencing of Bcl-2 blocked STAT3 phosphorylation. Interestingly, while inhibiting intracellular O2•- blocked STAT3 phosphorylation, transient overexpression of wild type STAT3 resulted in a significant increase in mitochondrial O2•- production, which was rescued by the functional mutants of STAT3 (Y705F). Notably, a strong correlation between the expression and/or phosphorylation of STAT3 and Bcl-2 was observed in primary tissues derived from patients with different sub-sets of B cell lymphoma. These data demonstrate the presence of a functional crosstalk between Bcl-2, Rac1 and activated STAT3 in promoting a permissive redox milieu for cell survival. Results also highlight the potential utility of a signature involving Bcl-2 overexpression, Rac1 activation and STAT3 phosphorylation for stratifying clinical lymphomas based on disease severity and chemoresistance.

Personalization of cancer treatment using predictive simulation.

BACKGROUND: The personalization of cancer treatments implies the reconsideration of a one-size-fits-all paradigm. This move has spawned increased use of next generation sequencing to understand mutations and copy number aberrations in cancer cells. Initial personalization successes have been primarily driven by drugs targeting one patient-specific oncogene (e.g., Gleevec, Xalkori, Herceptin). Unfortunately, most cancers include a multitude of aberrations, and the overall impact on cancer signaling and metabolic networks cannot be easily nullified by a single drug. METHODS: We used a novel predictive simulation approach to create an avatar of patient cancer cells using point mutations and copy number aberration data. Simulation avatars of myeloma patients were functionally screened using various molecularly targeted drugs both individually and in combination to identify drugs that are efficacious and synergistic. Repurposing of drugs that are FDA-approved or under clinical study with validated clinical safety and pharmacokinetic data can provide a rapid translational path to the clinic. High-risk multiple myeloma patients were modeled, and the simulation predictions were assessed ex vivo using patient cells. RESULTS: Here, we present an approach to address the key challenge of interpreting patient profiling genomic signatures into actionable clinical insights to make the personalization of cancer therapy a practical reality. Through the rational design of personalized treatments, our approach also targets multiple patient-relevant pathways to address the emergence of single therapy resistance. Our predictive platform identified drug regimens for four high-risk multiple myeloma patients. The predicted regimes were found to be effective in ex vivo analyses using patient cells. CONCLUSIONS: These multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches.