Personalization of cancer treatment using predictive simulation

Targeting chromosome 12q amplification in relapsed glioblastoma: the use of computational biological modeling to identify effective therapy-a case report

Background

Relapsed glioblastoma (GBM) is often an imminently fatal condition with limited therapeutic options. Computation biological modeling, i.e., biosimulation, of comprehensive genomic information affords the opportunity to create a disease avatar that can be interrogated in silico with various drug combinations to identify the most effective therapies.

Case Description

We report the outcome of a GBM patient with chromosome 12q amplification who achieved substantial disease remission from a novel therapy using this approach. Following next generation sequencing (NGS) was performed on the tumor specimen. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotype. In silico responses to various drug combinations were biosimulated in the disease network. Efficacy scores representing the computational effect of treatment for each strategy were generated and compared to each other to ascertain the differential benefit in drug response from various regimens. Biosimulation identified CDK4/6 inhibitors, nelfinavir and leflunomide to be effective agents singly and in combination. Upon receiving this treatment, the patient achieved a prompt and clinically meaningful remission lasting 6 months.

Conclusions

Biosimulation has utility to identify active treatment combinations, stratify treatment options and identify investigational agents relevant to patients' comprehensive genomic abnormalities. Additionally, the combination of abemaciclib and nelfinavir appear promising for GBM and potentially other cancers harboring chromosome 12q amplification.

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

PURPOSE

KRAS-mutated (KRAS^MUT) 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 KRAS^MUT 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 KRAS^MUT 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: KRAS^MUT/TP53^MUT, KRAS^MUT/CDKN2A/B/C^MUT, KRAS^MUT/STK11^MUT, KRAS^MUT/KEAP1^MUT, KRAS^MUT/STK11^MUT/KEAP1^MUT, KRAS^MUT/PIK3CA^MUT, KRAS ^MUT/ATM^MUT, and KRAS^MUT without comutation. CBM identified PD-(L)1 immunotherapy sensitivity in the KRAS^MUT/TP53^MUT, KRAS^MUT/PIK3CA^MUT, and KRAS^MUT alone subgroups and resistance in the KEAP1^MUT containing subgroups. There was insufficient genomic information to elucidate PD-(L)1 immunotherapy sensitivity by the CBM in the KRAS^MUT/CDKN2A/B/C^MUT, KRAS^MUT/STK11^MUT, and KRAS^MUT/ATM^MUT subgroups. In an exploratory clinical cohort of 34 patients with advanced KRAS^MUT 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 KRAS^MUT 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.

Everolimus in the treatment of metastatic thymic epithelial tumors

INTRODUCTION

There is emerging evidence to support the use of mTOR inhibitor everolimus in patients with advanced, relapsed-refractory thymic epithelial tumors (TETs). However, patient selection and identifying predictive biomarkers of response remains a challenge. Here, we describe a single-center experience with everolimus in patients with TETs and provide detailed molecular analysis of their thymic tumors.

MATERIALS AND METHODS

Data on all patients with advanced TETs who were prescribed everolimus at Stanford University were retrospectively assessed. Time to treatment failure (TTF) and overall survival (OS) were calculated. STAMP, a 130-gene targeted next generation sequencing (NGS) panel, was performed on each tumor sample.

RESULTS

Twelve patients with thymoma (T) and three with thymic carcinoma (TC) treated with everolimus were included. Patients had been heavily pre-treated with an average of three prior lines of therapy. Three patients discontinued treatment due to adverse events. The average TTF was 14.7 months in T and 2.6 months in TC with median OS of 27.6 months in the entire cohort (NR T and 5.3 months TC). Two patients with paraneoplastic autoimmune diseases had improvement in autoimmunity on everolimus. Pathogenic mutations were observed in 4/15 (27 %) of patients and includedTP53, KEAP1 and CDKN2A. Several variants of unknown significance in key genes responsible for modulating tumor response to mTOR inhibition were also found.

CONCLUSION

As previously reported in a prospective trial, patients with previously treated advanced TETs appear to benefit from everolimus in this single institution cohort. Moreover, there was a manageable toxicity profile and no cases of everolimus-induced pneumonitis. A targeted NGS panel revealed several pathogenic mutations but there was no association between detectable tumor mutations and time to treatment failure in this cohort.

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.

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.

Drug Repurposing in the Development of Anticancer Agents

BACKGROUND

Research into repositioning known drugs to treat cancer other than the originally intended disease continues to grow and develop, encouraged in part, by several recent success stories. Many of the studies in this article are geared towards repurposing generic drugs because additional clinical trials are relatively easy to perform and the drug safety profiles have previously been established.

OBJECTIVE

This review provides an overview of anticancer drug development strategies which is one of the important areas of drug restructuring.

METHODS

Repurposed drugs for cancer treatments are classified by their pharmacological effects. The successes and failures of important repurposed drugs as anticancer agents are evaluated in this review.

RESULTS AND CONCLUSION

Drugs could have many off-target effects, and can be intelligently repurposed if the off-target effects can be employed for therapeutic purposes. In cancer, due to the heterogeneity of the disease, often targets are quite diverse, hence a number of already known drugs that interfere with these targets could be deployed or repurposed with appropriate research and development.

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.

Optimizing drug development in oncology by clinical trial simulation: Why and how?

In therapeutic research, the safety and efficacy of pharmaceutical products are necessarily tested on humans via clinical trials after an extensive and expensive preclinical development period. Methodologies such as computer modeling and clinical trial simulation (CTS) might represent a valuable option to reduce animal and human assays. The relevance of these methods is well recognized in pharmacokinetics and pharmacodynamics from the preclinical phase to postmarketing. However, they are barely used and are poorly regarded for drug approval, despite Food and Drug Administration and European Medicines Agency recommendations. The generalization of CTS could be greatly facilitated by the availability of software for modeling biological systems, by clinical trial studies and hospital databases. Data sharing and data merging raise legal, policy and technical issues that will need to be addressed. Development of future molecules will have to use CTS for faster development and thus enable better patient management. Drug activity modeling coupled with disease modeling, optimal use of medical data and increased computing speed should allow this leap forward. The realization of CTS requires not only bioinformatics tools to allow interconnection and global integration of all clinical data but also a universal legal framework to protect the privacy of every patient. While recognizing that CTS can never replace 'real-life' trials, they should be implemented in future drug development schemes to provide quantitative support for decision-making. This in silico medicine opens the way to the P4 medicine: predictive, preventive, personalized and participatory.

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.

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.

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.

Novel therapeutic targets in Waldenstrom macroglobulinemia

Understanding of molecular mechanisms that drive Waldenstrom macroglobulinemia (WM) cell survival are rapidly evolving. This review briefly highlights emerging "WM-relevant" targets; for which therapeutic strategies are currently being investigated in preclinical and clinical studies. With the discovery of MYD88_L265P signaling and remarkable activity of ibrutinib in WM, other targets within the B-cell receptor pathway are now being focused on for therapeutic intervention. Additional targets which play a role in WM cell survival include TLR7, 8 and 9, proteasome-associated deubiquitinating enzymes (USP14 and UCHL5), XPO1/CRM1 and AURKA. New drugs for established targets are also discussed. Lastly, we spotlight 3 highly innovative WM-specific therapies: MYD88 peptide inhibitors, MYD88_L265P-directed immune activation and CD19-directed chimeric antigen receptor T-cell therapy, which are in various stages of development. Indeed, treatment of WM is poised to undergo a paradigm shift in the coming years towards highly disease-driven and more personalized therapeutic modalities with curative intent.

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.

Single-cell Transcriptome Study as Big Data

The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies.

Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review)

The existing tumor heterogeneity and the complexity of cancer cell biology critically demand powerful translational tools with which to support interdisciplinary efforts aiming to advance personalized cancer medicine decisions in drug development and clinical practice. The development of physiologically based pharmacokinetic (PBPK) models to predict the effects of drugs in the body facilitates the clinical translation of genomic knowledge and the implementation of in vivo pharmacology experience with pharmacogenomics. Such a direction unequivocally empowers our capacity to also make personalized drug dosage scheme decisions for drugs, including molecularly targeted agents and innovative nanoformulations, i.e. in establishing pharmacotyping in prescription. In this way, the applicability of PBPK models to guide individualized cancer therapeutic decisions of broad clinical utility in nanomedicine in real-time and in a cost-affordable manner will be discussed. The latter will be presented by emphasizing the need for combined efforts within the scientific borderlines of genomics with nanotechnology to ensure major benefits and productivity for nanomedicine and personalized medicine interventions.

Conserved molecular mechanisms underlying the effects of small molecule xenobiotic chemotherapeutics on cells

For proper determination of the apoptotic potential of chemoxenobiotics in synergism, it is important to understand the modes, levels and character of interactions of chemoxenobiotics with cells in the context of predicted conserved biophysical properties. Chemoxenobiotic structures are studied with respect to atom distribution over molecular space, the predicted overall octanol-to-water partition coefficient (Log OWPC; unitless) and molecular size viz a viz van der Waals diameter (vdWD). The Log OWPC-to-vdWD (nm^-1 ) parameter is determined, and where applicable, hydrophilic interacting moiety/core-to-vdWD (nm^-1 ) and lipophilic incorporating hydrophobic moiety/core-to-vdWD (nm^-1 ) parameters of their part-structures are determined. The cellular and sub-cellular level interactions of the spectrum of xenobiotic chemotherapies have been characterized, for which a classification system has been developed based on predicted conserved biophysical properties with respect to the mode of chemotherapeutic effect. The findings of this study are applicable towards improving the effectiveness of existing combination chemotherapy regimens and the predictive accuracy of personalized cancer treatment algorithms as well as towards the selection of appropriate novel xenobiotics with the potential to be potent chemotherapeutics for dendrimer nanoparticle-based effective transvascular delivery.