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.

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.

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.

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.

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.

PI3KCA plays a major role in multiple myeloma and its inhibition with BYL719 decreases proliferation, synergizes with other therapies and overcomes stroma-induced resistance.

The phosphatidylinositide 3-kinase (PI3K) pathway is activated and correlated with drug resistance in multiple myeloma (MM). In the present study we investigated the role of PI3KCA (PI3K-α) in the progression and drug resistance in MM. We showed that the gene expression of PI3KCA isoform was higher in MM compared to normal subjects. BYL719, a novel and specific PI3KCA inhibitor inhibited the survival of primary MM cells and cell lines but not normal peripheral blood mononuclear cells. BYL719 induced the apoptosis of MM cells and inhibited their cell cycle by causing G1 arrest. BYL719 inhibited PI3K signalling, decreased proliferation and cells cycle signalling, and induced apoptosis signalling in MM cells. Finally, BYL719 synergized with bortezomib and carfilzomib, and overcame drug resistance induced by bone marrow stroma. These results were confirmed using in silico simulation of MM cell lines, BYL719 and bortezomib, and showed similar trends in survival, proliferation, apoptosis, cell signalling and synergy with drugs. In conclusion, PI3KCA plays a major role in proliferation and drug resistance of MM cells, the effects of which were inhibited with BYL719. These results provide a preclinical basis for a future clinical trial of BYL719 in MM as a single agent or in combination with other drugs.

ß-Caryophyllene oxide inhibits constitutive and inducible STAT3 signaling pathway through induction of the SHP-1 protein tyrosine phosphatase.

Constitutive activation of STAT3 is frequently observed and closely linked with proliferation, survival, invasion, metastasis and angiogenesis in tumor cells. In the present study, we investigated whether ß-caryophyllene oxide (CPO), a sesquiterpene isolated primarily from the essential oils of medicinal plants such as guava (Psidium guajava), and oregano (Origanum vulgare L.), can mediate its effect through interference with the STAT3 activation pathway in cancer cells. The effect of CPO on STAT3 activation, associated protein kinases and phosphatase, STAT3-regulated gene products and apoptosis was investigated using both functional proteomics tumor pathway technology platform and different tumor cell lines. We found that CPO suppressed constitutive STAT3 activation in multiple myeloma (MM), breast and prostate cancer cell lines, with a significant dose- and time-dependent effects observed in MM cells. The suppression was mediated through the inhibition of activation of upstream kinases c-Src and JAK1/2. Also, vanadate treatment reversed CPO-induced down-regulation of STAT3, suggesting the involvement of a tyrosine phosphatase. Indeed, we found that CPO induced the expression of tyrosine phosphatase SHP-1 that correlated with the down-regulation of constitutive STAT3 activation. Interestingly, deletion of SHP-1 gene by siRNA abolished the ability of CPO to inhibit STAT3 activation. The inhibition of STAT3 activation by CPO inhibited proliferation, induced apoptosis and abrogated the invasive potential of tumor cells. Our results suggest for the first time that CPO is a novel blocker of STAT3 signaling cascade and thus has an enormous potential for the treatment of various cancers harboring constitutively activated STAT3.

Novel anti-glioblastoma agents and therapeutic combinations identified from a collection of FDA approved drugs.

BACKGROUND: Glioblastoma (GBM) is a therapeutic challenge, associated with high mortality. More effective GBM therapeutic options are urgently needed. Hence, we screened a large multi-class drug panel comprising the NIH clinical collection (NCC) that includes 446 FDA-approved drugs, with the goal of identifying new GBM therapeutics for rapid entry into clinical trials for GBM. METHODS: Screens using human GBM cell lines revealed 22 drugs with potent anti-GBM activity, including serotonergic blockers, cholesterol-lowering agents (statins), antineoplastics, anti-infective, anti-inflammatories, and hormonal modulators. We tested the 8 most potent drugs using patient-derived GBM cancer stem cell-like lines. Notably, the statins were active in vitro; they inhibited GBM cell proliferation and induced cellular autophagy. Moreover, the statins enhanced, by 40-70 fold, the pro-apoptotic activity of irinotecan, a topoisomerase 1 inhibitor currently used to treat a variety of cancers including GBM. Our data suggest that the mechanism of action of statins was prevention of multi-drug resistance protein MDR-1 glycosylation. This drug combination was synergistic in inhibiting tumor growth in vivo. Compared to animals treated with high dose irinotecan, the drug combination showed significantly less toxicity. RESULTS: Our data identifies a novel combination from among FDA-approved drugs. In addition, this combination is safer and well tolerated compared to single agent irinotecan. CONCLUSIONS: Our study newly identifies several FDA-approved compounds that may potentially be useful in GBM treatment. Our findings provide the basis for the rational combination of statins and topoisomerase inhibitors in GBM.

In silico modeling predicts drug sensitivity of patient-derived cancer cells.

BACKGROUND: Glioblastoma (GBM) is an aggressive disease associated with poor survival. It is essential to account for the complexity of GBM biology to improve diagnostic and therapeutic strategies. This complexity is best represented by the increasing amounts of profiling ("omics") data available due to advances in biotechnology. The challenge of integrating these vast genomic and proteomic data can be addressed by a comprehensive systems modeling approach. METHODS: Here, we present an in silico model, where we simulate GBM tumor cells using genomic profiling data. We use this in silico tumor model to predict responses of cancer cells to targeted drugs. Initially, we probed the results from a recent hypothesis-independent, empirical study by Garnett and co-workers that analyzed the sensitivity of hundreds of profiled cancer cell lines to 130 different anticancer agents. We then used the tumor model to predict sensitivity of patient-derived GBM cell lines to different targeted therapeutic agents. RESULTS: Among the drug-mutation associations reported in the Garnett study, our in silico model accurately predicted ~85% of the associations. While testing the model in a prospective manner using simulations of patient-derived GBM cell lines, we compared our simulation predictions with experimental data using the same cells in vitro. This analysis yielded a ~75% agreement of in silico drug sensitivity with in vitro experimental findings. CONCLUSIONS: These results demonstrate a strong predictability of our simulation approach using the in silico tumor model presented here. Our ultimate goal is to use this model to stratify patients for clinical trials. By accurately predicting responses of cancer cells to targeted agents a priori, this in silico tumor model provides an innovative approach to personalizing therapy and promises to improve clinical management of cancer.

Elastin sequences trigger transient proinflammatory responses by human dermal fibroblasts.

Following penetrating injury of the skin, a highly orchestrated and overlapping sequence of events helps to facilitate wound resolution. Inflammation is a hallmark that is initiated early, but the reciprocal relationship between cells and matrix molecules that triggers and maintains inflammation is poorly appreciated. Elastin is enriched in the deep dermis of skin. We propose that deep tissue injury encompasses elastin damage, yielding solubilized elastin that triggers inflammation. As dermal fibroblasts dominate the deep dermis, this means that a direct interaction between elastin sequences and fibroblasts would reveal a proinflammatory signature. Tropoelastin was used as a surrogate for elastin sequences. Tropoelastin triggered fibroblast expression of the metalloelastase MMP-12, which is normally expressed by macrophages. MMP-12 expression increased 1056 ± 286-fold by 6 h and persisted for 24 h. Chemokine expression was more transient, as chemokine C-X-C motif ligand 8 (CXCL8), CXCL1, and CXCL5 transcripts increased 11.8 ± 2.6-, 10.2 ± 0.4-, and 8593 ± 996-fold, respectively, by 6-12 h and then decreased. Through the use of specific inhibitors and protein truncation, we found that transduction of the tropoelastin signal was mediated by the fibroblast elastin binding protein (EBP). In silico modeling using a predictive computational fibroblast model confirmed the up-regulation, and simulations revealed PKA as a key part of the signaling circuit. We tested this prediction with 1 µM PKA inhibitor H-89 and found that 2 h of exposure correspondingly reduced expression of MMP-12 (63.9±12.3%) and all chemokine markers, consistent with the levels seen with EBP inhibition, and validated PKA as a novel node and druggable target to ameliorate the proinflammatory state. A separate trigger that utilized C-terminal RKRK of tropoelastin reduced marker expression to 65.0-76.5% and suggests the parallel involvement of integrin αVß3. We propose that the solubilization of elastin as a result of dermal damage leads to rapid chemokine up-regulation by fibroblasts that is quenched when exposed elastin is removed by MMP-12.

Isorhamnetin inhibits proliferation and invasion and induces apoptosis through the modulation of peroxisome proliferator-activated receptor γ activation pathway in gastric cancer.

Gastric cancer (GC) is a lethal malignancy and the second most common cause of cancer-related deaths. Although treatment options such as chemotherapy, radiotherapy, and surgery have led to a decline in the mortality rate due to GC, chemoresistance remains as one of the major causes for poor prognosis and high recurrence rate. In this study, we investigated the potential effects of isorhamnetin (IH), a 3'-O-methylated metabolite of quercetin on the peroxisome proliferator-activated receptor γ (PPAR-γ) signaling cascade using proteomics technology platform, GC cell lines, and xenograft mice model. We observed that IH exerted a strong antiproliferative effect and increased cytotoxicity in combination with chemotherapeutic drugs. IH also inhibited the migratory/invasive properties of GC cells, which could be reversed in the presence of PPAR-γ inhibitor. We found that IH increased PPAR-γ activity and modulated the expression of PPAR-γ regulated genes in GC cells. Also, the increase in PPAR-γ activity was reversed in the presence of PPAR-γ-specific inhibitor and a mutated PPAR-γ dominant negative plasmid, supporting our hypothesis that IH can act as a ligand of PPAR-γ. Using molecular docking analysis, we demonstrate that IH formed interactions with seven polar residues and six nonpolar residues within the ligand-binding pocket of PPAR-γ that are reported to be critical for its activity and could competitively bind to PPAR-γ. IH significantly increased the expression of PPAR-γ in tumor tissues obtained from xenograft model of GC. Overall, our findings clearly indicate that antitumor effects of IH may be mediated through modulation of the PPAR-γ activation pathway in GC.

Honokiol inhibits signal transducer and activator of transcription-3 signaling, proliferation, and survival of hepatocellular carcinoma cells via the protein tyrosine phosphatase SHP-1.

The activation of signal transducers and activators of transcription 3 (STAT3) has been closely linked with the proliferation, survival, invasion, and angiogenesis of hepatocellular carcinoma (HCC) and represents an attractive target for therapy. In the present report, we investigated whether honokiol mediates its effect through interference with the STAT3 activation pathway. The effect of honokiol on STAT3 activation, associated protein kinases, and phosphatase, STAT3-regulated gene products and apoptosis was investigated using both functional proteomics tumor pathway technology platform and different HCC cell lines. We found that honokiol inhibited both constitutive and inducible STAT3 activation in a dose- and time-dependent manner in HCC cells. The suppression was mediated through the inhibition of activation of upstream kinases c-Src, Janus-activated kinase 1, and Janus-activated kinase 2. Vanadate treatment reversed honokiol-induced down-regulation of STAT3, suggesting the involvement of a tyrosine phosphatase. Indeed, we found that honokiol induced the expression of tyrosine phosphatase SHP-1 that correlated with the down-regulation of constitutive STAT3 activation. Moreover, deletion of SHP-1 gene by siRNA abolished the ability of honokiol to inhibit STAT3 activation. The inhibition of STAT3 activation by honokiol led to the suppression of various gene products involved in proliferation, survival, and angiogenesis. Finally, honokiol inhibited proliferation and significantly potentiated the apoptotic effects of paclitaxel and doxorubicin in HCC cells. Overall, the results suggest that honokiol is a novel blocker of STAT3 activation and may have a great potential for the treatment of HCC and other cancers.

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.

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.

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.

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.

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.

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.

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.