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

Decision model for durable clinical benefit from front- or late-line immunotherapy alone or with chemotherapy in non-small cell lung cancer

BACKGROUND

Predictive biomarkers and models of immune checkpoint inhibitors (ICIs) have been extensively studied in non-small cell lung cancer (NSCLC). However, evidence for many biomarkers remains inconclusive, and the opaqueness of machine learning models hinders practicality. We aimed to provide compelling evidence for biomarkers and develop a transparent decision tree model.

METHODS

We consolidated data from 3,288 ICI-treated patients with NSCLC across real-world multicenter, public cohorts and the Choice-01 trial (ClinicalTrials.gov: NCT03856411). Over 50 features were examined for predicting durable clinical benefits (DCBs) from ICIs. Noteworthy biomarkers were identified to establish a decision tree model. Additionally, we explored the tumor microenvironment and peripheral CD8+ programmed death-1 (PD-1)+ T cell receptor (TCR) profiles.

FINDINGS

Multivariate logistic regression analysis identified tumor histology, PD-ligand 1 (PD-L1) expression, tumor mutational burden, line, and regimen of ICI treatment as significant factors. Mutation subtypes of EGFR, KRAS, KEAP1, STK11, and disruptive TP53 mutations were associated with DCB. The decision tree (DT10) model, using the ten clinicopathological and genomic markers, showed superior performance in predicting DCB in the training set (area under the curve [AUC] = 0.82) and consistently outperformed other models in test sets. DT10-predicted-DCB patients manifested longer survival, an enriched inflamed tumor immune phenotype (67%), and higher peripheral TCR diversity, whereas the DT10-predicted-NDB (non-durable benefit) group showed an enriched desert immune phenotype (86%) and higher peripheral TCR clonality.

CONCLUSIONS

The model effectively predicted DCB after front-/subsequent-line ICI treatment, with or without chemotherapy, for squamous and non-squamous lung cancer, offering clinicians valuable insights into efficacy prediction using cost-effective variables.

FUNDING

This study was supported by the National Key R&D Program of China.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Multi-omics indicators of long-term survival benefits after immune checkpoint inhibitor therapy

Molecular indicators of long-term survival (LTS) in response to immune-checkpoint inhibitor (ICI) treatment have the potential to provide both mechanistic and therapeutic insights. In this study, we construct predictive models of LTS following ICI therapy based on data from 158 clinical trials involving 21,023 patients of 25 cancer types with available 1-year overall survival (OS) rates. We present evidence for the use of 1-year OS rate as a surrogate for LTS. Based on these and corresponding TCGA multi-omics data, total neoantigen, metabolism score, CD8+ T cell, and MHC_score were identified as predictive biomarkers. These were integrated into a Gaussian process regression model that estimates "long-term survival predictive score of immunotherapy" (iLSPS). We found that iLSPS outperformed the predictive capabilities of individual biomarkers and successfully predicted LTS of patient groups with melanoma and lung cancer. Our study explores the feasibility of modeling LTS based on multi-omics indicators and machine-learning methods.

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.

Association of Tumor Mutational Burden and Immune Gene Expression with Response to PD-1 Blockade by Sasanlimab Across Tumor Types and Routes of Administration

Background

Sasanlimab is a monoclonal antibody that binds to the programmed cell death receptor 1 (PD-1). Anti-PD-1 monoclonal antibodies have improved patient clinical outcomes; however, not all treated patients derive clinical benefit. Further insights on potential biomarkers beyond PD-L1 expression levels would help to identify the patients most likely to respond to treatment.

Objective

This study evaluated tumor biopsies from patients treated with intravenous or subcutaneous sasanlimab to identify biomarkers of response and characterize pharmacodynamic activity.

Methods

Anti-PD-1/PD-ligand 1 (PD-L1)-naive patients with advanced solid tumors received sasanlimab intravenously at 1, 3, or 10 mg/kg every 3 weeks (n = 23) or subcutaneously at 300 mg every 4 weeks (n = 15). Best tumor percentage change from baseline was determined by RECIST. Whole-exome DNA and RNA sequencing were performed in tumor samples collected from treated patients at protocol-defined timepoints. PD-L1 and CD8 protein expression were evaluated in tumor biopsies by immunohistochemistry. Associations with response were assessed by linear regression analysis.

Results

Baseline tumor mutational burden (TMB), as well as PD-L1 and CD8 expression, were significantly associated with response to sasanlimab across the multiple dose levels, routes of administration, and range of tumor types evaluated. TMB is an independent biomarker from the various tumor inflammatory genes and signatures evaluated. Gene set enrichment analysis showed that higher baseline expression levels of genes related to the interferon-γ and PD-1 signaling pathways and the cell cycle were significantly associated with response to sasanlimab across tumor types. No differences were observed between routes of administration with regard to response to sasanlimab for the biomarkers of interest (TMB, PD-L1, CD8, and interferon-γ signature). Evaluation of pharmacodynamic changes showed increased tumor expression of genes enriched in adaptive immune response pathways.

Conclusions

Our findings indicate an active, immunomodulatory mechanism for the anti-PD-1 antibody sasanlimab across different tumor types and routes of administration.

Trial Registration

ClinicalTrials.gov identifier NCT02573259; registered October 2015.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11523-021-00833-2.

The Combination of Radiotherapy With Immunotherapy and Potential Predictive Biomarkers for Treatment of Non-Small Cell Lung Cancer Patients

Radiotherapy is an effective local treatment modality of NSCLC. Its capabilities of eliminating tumor cells by inducing double strand DNA (dsDNA) damage and modulating anti-tumor immune response in irradiated and nonirradiated sites have been elucidated. The novel ICIs therapy has brought hope to patients resistant to traditional treatment methods, including radiotherapy. The integration of radiotherapy with immunotherapy has shown improved efficacy to control tumor progression and prolong survival in NSCLC. In this context, biomarkers that help choose the most effective treatment modality for individuals and avoid unnecessary toxicities caused by ineffective treatment are urgently needed. This article summarized the effects of radiation in the tumor immune microenvironment and the mechanisms involved. Outcomes of multiple clinical trials investigating immuno-radiotherapy were also discussed here. Furthermore, we outlined the emerging biomarkers for the efficacy of PD-1/PD-L1 blockades and radiation therapy and discussed their predictive value in NSCLC.

Genomic profiling of solid tumors harboring BRD4-NUT and response to immune checkpoint inhibitors

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NUT carcinoma is a rare but aggressive solid tumor that can be diagnosed by presence of the BRD4-NUT fusion.

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This series presents 31 cases of solid tumors that harbor BRD4-NUT but often carry other diagnoses such NSCLC—NOS and NSCLC-SCC.

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Despite lack of PD-L1 expression and a low tumor mutational burden, two index cases responded to either nivolumab or atezolizumab+chemotherapy with partial response or better with 4–5 month duration of response.

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The unexpected response to checkpoint inhibitors could be explained by a very high affinity of the fusion peptide at the junction of BRD4 and NUT to the MHC complex as recently suggested for an exceptional response to an immune checkpoint inhibitor in a fusion bearing low TMB, low PD-L1 expression head and neck carcinoma.

Background

The translocation t(15:19) produces the oncogenic BRD4-NUT fusion which is pathognomonic for NUT carcinoma (NC), which is a rare, but extremely aggressive solid tumor. Comprehensive genomic profiling (CGP) by hybrid-capture based next generation sequencing of 186+ genes of a cohort of advanced cancer cases with a variety of initial diagnoses harboring BRD4-NUT may shed further insight into the biology of these tumors and possible options for targeted treatment.

Case presentation

Thirty-one solid tumor cases harboring a BRD4-NUT translocation are described, with only 16% initially diagnosed as NC and the remainder carrying other diagnoses, most commonly NSCLC—NOS (22%) and lung squamous cell carcinoma (NSCLC-SCC) (16%). The cohort was all microsatellite stable and harbored a low Tumor Mutational Burden (TMB, mean 1.7 mut/mb, range 0–4). In two index cases, patients treated with immune checkpoint inhibitors (ICPI) had unexpected partial or better responses of varying duration. Notably, four cases – including the two index cases - were negative for PD-L1 expression. Neo-antigen prediction for BRD4-NUT and then affinity modeling of the peptide-MHC (pMHC) complex for an assessable index case predicted very high affinity binding, both on a ranked (99.9%) and absolute (33 nM) basis.

Conclusions

CGP identifies BRD4-NUT fusions in advanced solid tumors which carry a broad range of initial diagnoses and which should be re-diagnosed as NC per guidelines. A hypothesized mechanism underlying responses to ICPI in the low TMB, PD-L1 negative index cases is the predicted high affinity of the BRD4-NUT fusion peptide to MHC complexes. Further study of pMHC affinity and response to immune checkpoint inhibitors in patients with NC harboring BRD4-NUT is needed to validate this therapeutic hypothesis.

Correlation of genomic alterations and PD-L1 expression in thymoma

Thymic epithelial tumors (TETs) include several anterior mediastinal malignant tumours: thymomas, thymic carcinomas and thymic neuroendocrine cancers. There is significant variety in the biologic features and clinical course of TETs and many attempts have been made to identify target genes for successful therapy of TETs. Next generation sequencing (NGS) represents a huge advancement in diagnostics and these new molecular technologies revealed that thymic neoplasms have the lowest tumor mutation burden among all adult malignant tumours with a different pattern of molecular aberrations in thymomas and thymic carcinomas. As for the PD-L1 expression in tumor cells in thymoma and thymic carcinoma, it varies a lot in published studies, with findings of PD-L1 expression from 23% to 92% in thymoma and 36% to 100% in thymic carcinoma. When correlated PD-L1 expression with disease stage some controversial results were obtained, with no association with tumor stage in most studies. This is, at least in part, explained by the fact that several diverse PD-L1 immunohistochemical tests were used in each trial, with four different antibodies (SP142, SP263, 22C3, and 28-8), different definition of PD-L1 positivity and cutoff values throughout the studies as well. There is a huge interest in using genomic features to produce predictive genomic-based immunotherapy biomarkers, particularly since recent data suggest that certain tumor-specific genomic alterations, either individually or in combination, appear to influence immune checkpoint activity and better responses as the outcome, so as such in some cancer types they may complement existing biomarkers to improve the selection criteria for immunotherapy.

Accelerating the Development of Personalized Cancer Immunotherapy by Integrating Molecular Patients' Profiles with Dynamic Mathematical Models

We review the evolution, achievements, and limitations of the current paradigm shift in medicine, from the "one-size-fits-all" model to "Precision Medicine." Precision, or personalized, medicine-tailoring the medical treatment to the personal characteristics of each patient-engages advanced statistical methods to evaluate the relationships between static patient profiling (e.g., genomic and proteomic), and a simple clinically motivated output (e.g., yes/no responder). Today, precision medicine technologies that have facilitated groundbreaking advances in oncology, notably in cancer immunotherapy, are approaching the limits of their potential, mainly due to the scarcity of methods for integrating genomic, proteomic and clinical patient information. A different approach to treatment personalization involves methodologies focusing on the dynamic interactions in the patient-disease-drug system, as portrayed in mathematical modeling. Achievements of this scientific approach, in the form of algorithms for predicting personal disease dynamics in individual patients under immunotherapeutic drugs, are reviewed as well. The contribution of the dynamic approaches to precision medicine is limited, at present, due to insufficient applicability and validation. Yet, the time is ripe for amalgamating together these two approaches, for maximizing their joint potential to personalize and improve cancer immunotherapy. We suggest the roadmap toward achieving this goal, technologically, and urge clinicians, pharmacologists, and computational biologists to join forces along the pharmaco-clinical track of this development.

Primary pulmonary lymphoepithelioma-like carcinoma is characterized by high PD-L1 expression, but low tumor mutation burden

Primary pulmonary lymphoepithelioma-like carcinoma (PLELC) is a rare subtype of non-small cell lung cancer (NSCLC). There are few reported studies on the relationship between programmed death ligand-1 (PD-L1) expression and genomics features of this distinct NSCLC subtype. Our study aimed to investigate the expression levels of PD-L1 to determine their clinical value and to identify genetic alterations in PLELC. Fifty-nine PLELC patients, whose clinical information and pathology results were available, were included in this study. Immunohistochemical analysis of PD-L1 was performed in all cases. Specimens of 37 PLELCs and 3 metastatic nasopharyngeal carcinomas (NPCs) of the lung, resected within the previous 3 years, were chosen for mutation analysis, using next-generation sequencing of 425 genes. PLELC patients in the present study were mainly non-smoking females, with a high frequency of PD-L1 positivity in their tumors. Positivity rates were 96.6 %, 91.5 %, 83.1 %, and 61.0 % at tumor proportion scores (TPSs)≥ 1%, 5%, 10 %, and 50 %, respectively. Moreover, we observed that PD-L1 expression was higher in specimens stored for ≤ 3 years and in tumor cells with vesicular nucleus morphology at a TPS ≥ 50 %. Mutation analysis suggested a relatively high frequency of TP53 mutations and MCL1 copy number variation, but low tumor mutation burden (TMB) (ranging from 0 to 6.9, median of 1.1 mutation per megabase) and similarity of gene alteration with NPCs. However, no specific germline mutation was detected in PLELC patients. Additionally, survival analysis showed that patients in the early stages (stage I and II) had higher progression-free survival rates (P = 0.035) and those with tumors containing obvious stroma fibrosis tended to have worse prognosis (P =  0.008). However only stage was shown to be the independent prognostic factor (P = 0.008, HR=4.807, 95 %CI:1.508-15.323).PLELC is a subtype of lung cancer with distinct clinicopathological and genetic features, especially characterized by high PD-L1 expression and low TMB.

Precision Cardio-Oncology: a Systems-Based Perspective on Cardiotoxicity of Tyrosine Kinase Inhibitors and Immune Checkpoint Inhibitors

Cancer therapies have been evolving from conventional chemotherapeutics to targeted agents. This has fulfilled the hope of greater efficacy but unfortunately not of greater safety. In fact, a broad spectrum of toxicities can be seen with targeted therapies, including cardiovascular toxicities. Among these, cardiomyopathy and heart failure have received greatest attention, given their profound implications for continuation of cancer therapies and cardiovascular morbidity and mortality. Prediction of risk has always posed a challenge and even more so with the newer targeted agents. The merits of accurate risk prediction, however, are very evident, e.g. facilitating treatment decisions even before the first dose is given. This is important for agents with a long half-life and high potential to induced life-threatening cardiac complications, such as myocarditis with immune checkpoint inhibitors. An opportunity to address these needs in the field of cardio-oncology is provided by the expanding repertoire of "-omics" and other tools in precision medicine and their integration in a systems biology approach. This may allow for new insights into patho-mechanisms and the creation of more precise and cost-effective risk prediction tools with the ultimate goals of improved therapy decisions and prevention of cardiovascular complications. Herein, we explore this topic as a future approach to translating the complexity of cardio-oncology to the reality of patient care.

The Pleiotropic Role of the KEAP1/NRF2 Pathway in Cancer

The unregulated proliferative capacity of many tumors is dependent on dysfunctional nutrient utilization and ROS (reactive oxygen species) signaling to sustain a deranged metabolic state. Although it is clear that cancers broadly rely on these survival and signaling pathways, how they achieve these aims varies dramatically. Mutations in the KEAP1/NRF2 pathway represent a potent cancer adaptation to exploit native cytoprotective pathways that involve both nutrient metabolism and ROS regulation. Despite activating these advantageous processes, mutations within KEAP1/ NRF2 are not universally selected for across cancers and instead appear to interact with particular tumor driver mutations and tissues of origin. Here, we highlight the relationship between the KEAP1/NRF2 signaling axis and tumor biology with a focus on genetic mutation, metabolism, immune regulation, and treatment implications and opportunities. Understanding the dysregulation of KEAP1 and NRF2 provides not only insight into a commonly mutated tumor suppressor pathway but also a window into the factors dictating the development and evolution of many cancers.

Biomarkers for immune checkpoint inhibition in non-small cell lung cancer (NSCLC)

The emergence of immunotherapy has dramatically changed how non-small cell lung cancer is treated, and longer survival is now possible for some patients, even those with advanced disease. Although some patients achieve durable responses to checkpoint blockade, not all experience such benefits, and some suffer from significant immunotoxicities. Given this, biomarkers that predict response to therapy are essential, and testing for tumor programmed death ligand 1(PD-L1) expression is the current standard. The extent of PD-L1 expression determined by immunohistochemistry (IHC) has demonstrated a correlation with treatment response, although limitations with this marker exist. Recently, tumor mutational burden has emerged as an alternative biomarker, and studies have demonstrated its utility, irrespective of the PD-L1 level of a tumor. Gene expression signatures, tumor genotype (such as the presence of an oncogenic driver mutation), as well as the density of tumor-infiltrating lymphocytes in the tumor microenvironment also seem to affect response to immunotherapy and are being researched. Peripheral serum markers are being studied, and some have demonstrated predictive ability, although most are still investigational and need prospective validation. In the current article, the authors review the biomarker PD-L1 as well as other emerging and investigational tissue-based and serum-based markers that have potential to better predict responders to immunotherapy.

Prediction of PD-L1 Expression in Neuroblastoma via Computational Modeling

Immunotherapy is a promising new therapeutic approach for neuroblastoma (NBM): an anti-GD2 vaccine combined with orally administered soluble beta-glucan is undergoing a phase II clinical trial and nivolumab and ipilimumab are being tested in recurrent and refractory tumors. Unfortunately, predictive biomarkers of response to immunotherapy are currently not available for NBM patients. The aim of this study was to create a computational network model simulating the different intracellular pathways involved in NBM, in order to predict how the tumor phenotype may be influenced to increase the sensitivity to anti-programmed cell death-ligand-1 (PD-L1)/programmed cell death-1 (PD-1) immunotherapy. The model runs on COPASI software. In order to determine the influence of intracellular signaling pathways on the expression of PD-L1 in NBM, we first developed an integrated network of protein kinase cascades. Michaelis-Menten kinetics were associated to each reaction in order to tailor the different enzymes kinetics, creating a system of ordinary differential equations (ODEs). The data of this study offers a first tool to be considered in the therapeutic management of the NBM patient undergoing immunotherapeutic treatment.

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.

MicroRNAs aid the assessment of programmed death ligand 1 expression in patients with non-small cell lung cancer

The qualification of patients with non-small cell lung cancer (NSCLC) for anti-programmed cell death 1 (PD-1) or anti-programmed death ligand 1 (PD-L1) antibody therapy is based on an immunohistochemistry (IHC) assessment of PD-L1 expression. Immunological checkpoint inhibitors improve the overall survival of patients with expression of PD-L1; however certain PD-L1-negative patients may also benefit from immunotherapy. This indicates the requirement for novel predictive factors for the qualification of immunotherapy. It is also necessary to understand the mechanisms that effect the expression of PD-L1 in tumor cells. The expression of PD-L1 in 47 formalin-fixed, paraffin-embedded, NSCLC specimens was assessed using IHC and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The expression of 8 microRNAs (miRNAs, miRs) complementary to PD-L1-mRNA was also evaluated using RT-qPCR. A positive correlation was revealed between the expression level of PD-L1-mRNA and 2 miRs, miR-141 (R=0.533; P=0.0029) and miR-1184 (R=0.463; P=0.049). There was also a positive correlation between the percentage of PD-L1-positive tumor cells and the expression levels of miR-141 (R=0.441; P=0.0024), miR-200b (R=0.372; P=0.011) and miR-429 (R=0.430; P=0.0028), and between the percentage of the tumor area with immune cell infiltration and the expression levels of miR-141 (R=0.333; P=0.03) and miR-200b (R=0.312; P=0.046). Additionally, the percentage of tumor cells expressing PD-L1 positively correlated with miR-141 expression (R=0.407; P=0.0055). Correlations between the expression of the investigated miRs (particularly miR-141) and PD-L1 indicated that miRs may regulate PD-L1 expression at a post-transcriptional level.

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.

Immune profiling reveals the diverse nature of the immune response in NSCLC and reveals signaling pathways that may influence the anti-tumor immune response

Recent FDA approvals of immunotherapy for NSCLC provide patients new treatment options, and these approvals also highlight the importance of the immune response in cancer treatment. While immunotherapy provides patients a new treatment option, the therapy is effective in less than half of the treated patients. To attain greater insight into the tumor-immune microenvironment, NSCLC tumors were analyzed by IHC and RNA-seq. IHC was used to identify NSCLC tumors that contain low, moderate, or high levels of CD8+ positive cells as a manifestation of an active anti-tumor immune response. Gene expression analysis identified an emergent gene signature that is associated with high and moderate levels of CD8 in NSCLC. In addition, the NSCLC tumors also express a unique combination of genes that may indicate complex anti-tumor immune responses (INFG-related genes, STATs, CXCL9, OX40, PD-L1, PD-L2, IDO1, and CD47). Several NSCLC tumors also express the immune checkpoint PD-L1 and at least one additional immune inhibitory molecule (IDO1, PD-L2, or others), which may explain the lack of a therapeutic response to treatments that disrupt only one immune checkpoint pathway.