The Role of Intratumor Heterogeneity in the Response of Metastatic Non-Small Cell Lung Cancer to Immune Checkpoint Inhibitors

Multimodal Imaging Approach for Tumor Treatment Response Evaluation in the Era of Immunotherapy

ABSTRACT

Immunotherapy is likely the most remarkable advancement in lung cancer treatment during the past decade. Although immunotherapy provides substantial benefits, their therapeutic responses differ from those of conventional chemotherapy and targeted therapy, and some patients present unique immunotherapy response patterns that cannot be judged under the current measurement standards. Therefore, the response monitoring of immunotherapy can be challenging, such as the differentiation between real response and pseudo-response. This review outlines the various tumor response patterns to immunotherapy and discusses methods for quantifying computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (PET) in the field of lung cancer. Emerging technologies in magnetic resonance imaging (MRI) and non-FDG PET tracers are also explored. With immunotherapy responses, the role for imaging is essential in both anatomical radiological responses (CT/MRI) and molecular changes (PET imaging). Multiple aspects must be considered when assessing treatment responses using CT and PET. Finally, we introduce multimodal approaches that integrate imaging and nonimaging data, and we discuss future directions for the assessment and prediction of lung cancer responses to immunotherapy.

Novel strategies for modulating the gut microbiome for cancer therapy

Recent advancements in genomics, transcriptomics, and metabolomics have significantly advanced our understanding of the human gut microbiome and its impact on the efficacy and toxicity of anti-cancer therapeutics, including chemotherapy, immunotherapy, and radiotherapy. In particular, prebiotics, probiotics, and postbiotics are recognized for their unique properties in modulating the gut microbiota, maintaining the intestinal barrier, and regulating immune cells, thus emerging as new cancer treatment modalities. However, clinical translation of microbiome-based therapy is still in its early stages, facing challenges to overcome physicochemical and biological barriers of the gastrointestinal tract, enhance target-specific delivery, and improve drug bioavailability. This review aims to highlight the impact of prebiotics, probiotics, and postbiotics on the gut microbiome and their efficacy as cancer treatment modalities. Additionally, we summarize recent innovative engineering strategies designed to overcome challenges associated with oral administration of anti-cancer treatments. Moreover, we will explore the potential benefits of engineered gut microbiome-modulating approaches in ameliorating the side effects of immunotherapy and chemotherapy.

Dynamical modeling of proliferative-invasive plasticity and IFNγ signaling in melanoma reveals mechanisms of PD-L1 expression heterogeneity

BACKGROUND

Phenotypic heterogeneity of melanoma cells contributes to drug tolerance, increased metastasis, and immune evasion in patients with progressive disease. Diverse mechanisms have been individually reported to shape extensive intra-tumor and inter-tumor phenotypic heterogeneity, such as IFNγ signaling and proliferative to invasive transition, but how their crosstalk impacts tumor progression remains largely elusive.

METHODS

Here, we integrate dynamical systems modeling with transcriptomic data analysis at bulk and single-cell levels to investigate underlying mechanisms behind phenotypic heterogeneity in melanoma and its impact on adaptation to targeted therapy and immune checkpoint inhibitors. We construct a minimal core regulatory network involving transcription factors implicated in this process and identify the multiple 'attractors' in the phenotypic landscape enabled by this network. Our model predictions about synergistic control of PD-L1 by IFNγ signaling and proliferative to invasive transition were validated experimentally in three melanoma cell lines-MALME3, SK-MEL-5 and A375.

RESULTS

We demonstrate that the emergent dynamics of our regulatory network comprising MITF, SOX10, SOX9, JUN and ZEB1 can recapitulate experimental observations about the co-existence of diverse phenotypes (proliferative, neural crest-like, invasive) and reversible cell-state transitions among them, including in response to targeted therapy and immune checkpoint inhibitors. These phenotypes have varied levels of PD-L1, driving heterogeneity in immunosuppression. This heterogeneity in PD-L1 can be aggravated by combinatorial dynamics of these regulators with IFNγ signaling. Our model predictions about changes in proliferative to invasive transition and PD-L1 levels as melanoma cells evade targeted therapy and immune checkpoint inhibitors were validated in multiple RNA-seq data sets from in vitro and in vivo experiments.

CONCLUSION

Our calibrated dynamical model offers a platform to test combinatorial therapies and provide rational avenues for the treatment of metastatic melanoma. This improved understanding of crosstalk among PD-L1 expression, proliferative to invasive transition and IFNγ signaling can be leveraged to improve the clinical management of therapy-resistant and metastatic melanoma.

Dynamical modelling of proliferative-invasive plasticity and IFNγ signaling in melanoma reveals mechanisms of PD-L1 expression heterogeneity

Phenotypic heterogeneity of melanoma cells contributes to drug tolerance, increased metastasis, and immune evasion in patients with progressive disease. Diverse mechanisms have been individually reported to shape extensive intra- and inter-tumoral phenotypic heterogeneity, such as IFNγ signaling and proliferative to invasive transition, but how their crosstalk impacts tumor progression remains largely elusive. Here, we integrate dynamical systems modeling with transcriptomic data analysis at bulk and single-cell levels to investigate underlying mechanisms behind phenotypic heterogeneity in melanoma and its impact on adaptation to targeted therapy and immune checkpoint inhibitors. We construct a minimal core regulatory network involving transcription factors implicated in this process and identify the multiple "attractors" in the phenotypic landscape enabled by this network. Our model predictions about synergistic control of PD-L1 by IFNγ signaling and proliferative to invasive transition were validated experimentally in three melanoma cell lines - MALME3, SK-MEL-5 and A375. We demonstrate that the emergent dynamics of our regulatory network comprising MITF, SOX10, SOX9, JUN and ZEB1 can recapitulate experimental observations about the co-existence of diverse phenotypes (proliferative, neural crest-like, invasive) and reversible cell-state transitions among them, including in response to targeted therapy and immune checkpoint inhibitors. These phenotypes have varied levels of PD-L1, driving heterogeneity in immune-suppression. This heterogeneity in PD-L1 can be aggravated by combinatorial dynamics of these regulators with IFNγ signaling. Our model predictions about changes in proliferative to invasive transition and PD-L1 levels as melanoma cells evade targeted therapy and immune checkpoint inhibitors were validated in multiple data sets from in vitro and in vivo experiments. Our calibrated dynamical model offers a platform to test combinatorial therapies and provide rational avenues for the treatment of metastatic melanoma. This improved understanding of crosstalk among PD-L1 expression, proliferative to invasive transition and IFNγ signaling can be leveraged to improve the clinical management of therapy-resistant and metastatic melanoma.

Novel Genetic Subtypes of Urothelial Carcinoma With Differential Outcomes on Immune Checkpoint Blockade

PURPOSE

Immune checkpoint blockade (ICB) therapy has significantly improved clinical outcomes in bladder cancer. Identification of correlates of benefit is critical to select appropriate therapy for individual patients.

METHODS

To reveal genetic variables associated with benefit from ICB, we performed whole-exome sequencing on tumor specimens from 88 patients with advanced bladder cancer treated with ICB.

RESULTS

We identified several genetic factors that correlated with progression-free and overall survival after ICB therapy including ARID1A mutation, tumor mutational burden, intratumoral heterogeneity, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome (immune dN/dS), and tumor cell purity. In addition, we noted that neutrophil-to-lymphocyte ratio and smoking history were negatively associated with overall survival. These genetic characteristics define four molecular subtypes demonstrating differential sensitivity to ICB. We validated the association of these four subtypes with clinical benefit from ICB in an independent cohort (IMvigor210). Finally, we showed that these molecular subtypes also correlate with outcome, although with distinct relationships, among patients not treated with ICB using The Cancer Genome Atlas (TCGA) bladder cancer cohort. Using parallel RNA sequencing data, the subtypes were also shown to correlate with immune infiltration and inflammation, respectively, in the IMvigor210 and TCGA cohorts.

CONCLUSION

Together, our study defines molecular subgroups of bladder cancer that influence benefit from ICB.

Evaluation of Blood Tumor Mutation Burden for the Efficacy of Second-Line Atezolizumab Treatment in Non-Small Cell Lung Cancer: BUDDY Trial

This study aimed to investigate the feasibility of blood-based biomarkers, including blood tumor mutation burden (bTMB), to predict atezolizumab efficacy in relapsed and advanced non-small cell lung cancer (NSCLC). Stage IV NSCLC patients who had previously received platinum-doublet chemotherapy were recruited and received 1200 mg of atezolizumab every three weeks. Blood was collected to obtain plasma cell-free DNA (cfDNA) before the first cycle (C0) and at the fourth cycle (C4). bTMB was measured by CT-ULTRA in patients with cfDNA over 10 ng. The objective response rate (ORR) of the enrolled 100 patients was 10%, and there was no difference in ORR according to bTMB (cutoff: 11.5 muts/Mb) at C0 (high bTMB: 8.1% vs. low bTMB: 11.1%). However, the C4/C0 bTMB ratio was significantly lower in the durable clinical benefit (DCB) patients. The cfDNA concentration at C0, the C4/C0 ratio of the cfDNA concentration, the highest variant allele frequency (hVAF), and the VAF standard deviation (VAFSD) were significantly lower in the DCB patients. In the multivariate analysis, a high cfDNA concentration at C0 (cutoff: 8.6 ng/mL) and a C4/C0 bTMB ratio greater than 1 were significantly associated with progression-free survival. These results suggest that baseline levels and dynamic changes of blood-based biomarkers (bTMB, cfDNA concentration, and VAFSD) could predict atezolizumab efficacy in previously treated NSCLC patients.

Transcriptional Heterogeneity of Cellular Senescence in Cancer

Cellular senescence plays a paradoxical role in tumorigenesis through the expression of diverse senescence-associated (SA) secretory phenotypes (SASPs). The heterogeneity of SA gene expression in cancer cells not only promotes cancer stemness but also protects these cells from chemotherapy. Despite the potential correlation between cancer and SA biomarkers, many transcriptional changes across distinct cell populations remain largely unknown. During the past decade, single-cell RNA sequencing (scRNA-seq) technologies have emerged as powerful experimental and analytical tools to dissect such diverse senescence-derived transcriptional changes. Here, we review the recent sequencing efforts that successfully characterized scRNA-seq data obtained from diverse cancer cells and elucidated the role of senescent cells in tumor malignancy. We further highlight the functional implications of SA genes expressed specifically in cancer and stromal cell populations in the tumor microenvironment. Translational research leveraging scRNA-seq profiling of SA genes will facilitate the identification of novel expression patterns underlying cancer susceptibility, providing new therapeutic opportunities in the era of precision medicine.

Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model

Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front.

Chemokines and NSCLC: Emerging role in prognosis, heterogeneity, and therapeutics

Lung cancer persists to contribute to one-quarter of cancer-associated deaths. Among the different histologies, non-small cell lung cancer (NSCLC) alone accounts for 85% of the cases. The development of therapies involving immune checkpoint inhibitors and angiogenesis inhibitors has increased patients' survival probability and reduced mortality rates. Developing targeted therapies against essential genetic alterations also translates to better treatment strategies. But the benefits still seem farfetched due to the development of drug resistance and refractory tumors. In this review, we have highlighted the interplay of different tumor microenvironment components, essentially discussing the chemokine families (CC, CXC, C, and CX3C) that regulate the tumor biology in NSCLC and promote tumor growth, metastasis, and associated heterogeneity. The development of therapeutics and prognostic markers is a complex and multipronged approach. However, some essential chemokines can act as critical players for being considered potential prognostic markers and therapeutic targets.

Deep Downregulation of PD-L1 by Caged Peptide-Conjugated AIEgen/miR-140 Nanoparticles for Enhanced Immunotherapy

Downregulating programmed cell death ligand 1(PD-L1) protein levels in tumor cells is an effective way to achieve immune system activation for oncology treatment, but current strategies are inadequate. Here, we design a caged peptide-AIEgen probe (GCP) to self-assemble with miR-140 forming GCP/miR-140 nanoparticles. After entering tumor cells, GCP/miR-140 disassembles in the presence of Cathepsin B (CB) and releases caged GO203 peptide, miR-140 and PyTPA. Peptide decages in the highly reductive intracellular environment and binds to mucin 1 (MUC1), thereby downregulating the expression of PD-L1. Meanwhile, miR-140 reduces PD-L1 expression by targeting downregulation of PD-L1 mRNA. Under the action of PyTPA-mediated photodynamic therapy (PDT), tumor-associated antigens are released, triggering immune cell attack on tumor cells. This multiple mechanism-based strategy of deeply downregulating PD-L1 in tumor cells activates the immune system and thus achieves effective immunotherapy.

Immunotherapy in Lung Cancer: Current Landscape and Future Directions

Over the past decade, lung cancer treatment has undergone a major paradigm shift. A greater understanding of lung cancer biology has led to the development of many effective targeted therapies as well as of immunotherapy. Immune checkpoint inhibitors (ICIs) have shown tremendous benefit in the treatment of non-small cell lung cancer (NSCLC) and are now being used as first-line therapies in metastatic disease, consolidation therapy following chemoradiation in unresectable locally advanced disease, and adjuvant therapy following surgical resection and chemotherapy in resectable disease. Despite these benefits, predicting who will respond to ICIs has proven to be difficult and there remains a need to discover new predictive immunotherapy biomarkers. Furthermore, resistance to ICIs in lung cancer is frequent either because of a lack of response or disease progression after an initial response. The utility of ICIs in the treatment of small cell lung cancer (SCLC) remains limited to first-line treatment of extensive stage disease in combination with chemotherapy with modest impact on overall survival. It is thus important to explore and exploit additional targets to reap the full benefits of immunotherapy in the treatment of lung cancer. Here, we will summarize the current state of immunotherapy in lung cancer, discuss novel targets, and explore the intersection between DNA repair defects and immunotherapy.

Resistance to Immunotherapy: Mechanisms and Means for Overcoming

Immune checkpoint blockade transformed cancer therapy during the last decade. However, durable responses remain uncommon, early and late relapses occur over the course of treatment, and many patients with PD-L1-expressing tumors do not respond to PD-(L)1 blockade. In addition, while some malignancies exhibit inherent resistance to treatment, others develop adaptations that allow them to evade antitumor immunity after a period of response. It is crucial to understand the pathophysiology of the tumor-immune system interplay and the mechanisms of immune escape in order to circumvent primary and acquired resistance. Here we provide an outline of the most well-defined mechanisms of resistance and shed light on ongoing efforts to reinvigorate immunoreactivity.

Heterogeneity and evolution of tumour immune microenvironment in metastatic gastroesophageal adenocarcinoma

BACKGROUND

Tumour immune microenvironment heterogeneity is prevalent in numerous cancers and can negatively impact immunotherapy response. Immune heterogeneity and evolution in gastroesophageal adenocarcinoma (GEA) have not been studied in the past.

METHODS

Together with a multi-region sampling of normal, primary and metastatic tissues, we performed whole exome sequencing, TCR sequencing as well as immune cell infiltration estimation through deconvolution of gene expression signals.

RESULTS

We discovered high TCR repertoire and immune cell infiltration heterogeneity among metastatic sites, while they were homogeneous among primary and normal samples. Metastatic sites shared high levels of abundant TCR clonotypes with blood, indicating immune surveillance via blood. Metastatic sites also had low levels of tumour-eliminating immune cells and were undergoing heavy immunomodulation compared to normal and primary tumour tissues. There was co-evolution of neo-antigen and TCR repertoire, but only in patients with late diverging mutational evolution. Co-evolution of TCR repertoire and immune cell infiltration was seen in all except one patient.

CONCLUSIONS

Our findings revealed immune heterogeneity and co-evolution in GEA, which may inform immunotherapy decision-making.

tRNA Metabolism and Lung Cancer: Beyond Translation

Lung cancer, one of the most malignant tumors, has extremely high morbidity and mortality, posing a serious threat to global health. It is an urgent need to fully understand the pathogenesis of lung cancer and provide new ideas for its treatment. Interestingly, accumulating evidence has identified that transfer RNAs (tRNAs) and tRNA metabolism–associated enzymes not only participate in the protein translation but also play an important role in the occurrence and development of lung cancer. In this review, we summarize the different aspects of tRNA metabolism in lung cancer, such as tRNA transcription and mutation, tRNA molecules and derivatives, tRNA-modifying enzymes, and aminoacyl-tRNA synthetases (ARSs), aiming at a better understanding of the pathogenesis of lung cancer and providing new therapeutic strategies for it.

Failure of Immunotherapy-The Molecular and Immunological Origin of Immunotherapy Resistance in Lung Cancer

Immune checkpoint inhibitors (ICIs) have a huge impact on clinical treatment results in non-small cell lung cancer (NSCLC). Blocking antibodies targeting programmed cell death protein 1 (PD-1), programmed cell death protein ligand 1 (PD-L1) or CTLA-4 (cytotoxic T cell antigen 4) have been developed and approved for the treatment of NSCLC patients. However, a large number of patients develop resistance to this type of treatment. Primary and secondary immunotherapy resistance are distinguished. No solid biomarkers are available that are appropriate to predict the unique sensitivity to immunotherapy. Knowledge of predictive markers involved in treatment resistance is fundamental for planning of new treatment combinations. Scientists focused research on the use of immunotherapy as an essential treatment in combination with other therapy strategies, which could increase cancer immunogenicity by generating tumor cells death and new antigen release as well as by targeting other immune checkpoints and tumor microenvironment. In the present review, we summarize the current knowledge of molecular bases underlying immunotherapy resistance and discuss the capabilities and the reason of different therapeutic combinations.

Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies

Immune profiling of formalin-fixed, paraffin-embedded tissues using multiplex immunofluorescence (mIF) staining and image analysis methodology allows for the study of several biomarkers on a single slide. The pathology quality control (PQC) for tumor tissue immune profiling using digital image analysis of core needle biopsies is an important step in any laboratory to avoid wasting time and materials. Although there are currently no established inclusion and exclusion criteria for samples used in this type of assay, a PQC is necessary to achieve accurate and reproducible data. We retrospectively reviewed PQC data from hematoxylin and eosin (H&E) slides and from mIF image analysis samples obtained during 2019. We reviewed a total of 931 reports from core needle biopsy samples; 123 (13.21%) were excluded during the mIF PQC. The most common causes of exclusion were the absence of malignant cells or fewer than 100 malignant cells in the entire section (n = 42, 34.15%), tissue size smaller than 4 × 1 mm (n = 16, 13.01%), fibrotic tissue without inflammatory cells (n = 12, 9.76%), and necrotic tissue (n = 11, 8.94%). Baseline excluded samples had more fibrosis (90 vs 10%) and less necrosis (5 vs 90%) compared with post-treatment excluded samples. The most common excluded organ site of the biopsy was the liver (n = 19, 15.45%), followed by soft tissue (n = 17, 13.82%) and the abdominal region (n = 15, 12.20%). We showed that the PQC is an important step for image analysis and that the absence of malignant cells is the most limiting sample characteristic for mIF image analysis. We also discuss other challenges that pathologists need to consider to report reliable and reproducible image analysis data.

Comprehensive analysis of tumor microenvironment and identification of an immune signature to predict the prognosis and immunotherapeutic response in lung squamous cell carcinoma

Background

Tumor mutation burden (TMB) and immune microenvironment are important determinants of prognosis and immunotherapeutic efficacy for cancer patients. The aim of the present study was to develop an immune signature to effectively predict prognosis and immunotherapeutic response in patients with lung squamous cell carcinoma (LUSC).

Methods

TMB and immune microenvironment characteristics were comprehensively analyzed by multi-omics data in LUSC. The immune signature was further constructed and validated in multiple independent datasets by LASSO Cox regression analysis. Next, the value of immune signature in predicting the response of immunotherapy was evaluated. Finally, the possible mechanism of immune signature was also investigated.

Results

A novel immune signature based on 5 genes was constructed and validated to predict the prognosis of LUSC patients. These genes were filamin-C, Rho family GTPase 1, interleukin 4-induced gene-1, transglutaminase 2, and prostaglandin I2 synthase. High-risk patients had significantly poorer survival than low-risk patients. A nomogram was also developed based on the immune signature and tumor stage, which showed good application. Furthermore, we found that the immune signature had a significant correlation with immune checkpoint, microsatellite instability, tumor infiltrating lymphocytes, cytotoxic activity scores, and T-cell-inflamed score, suggesting low-risk patients are more likely to benefit from immunotherapy. Finally, functional enrichment and pathway analyses revealed several significantly enriched immune-related biological processes and metabolic pathways.

Conclusions

In the present study, we developed a novel immune signature that could predict prognosis and immunotherapeutic response in LUSC patients. The results not only help identify LUSC patients with poor survival, but also increase our understanding of the immune microenvironment and immunotherapy in LUSC.

Discordance in PD-L1 scores on repeat testing of non-small cell lung carcinomas

INTRODUCTION

PD-L1 expression may be used as a biomarker predictive of non-small cell lung carcinoma (NSCLC) response to PD-L1 inhibitor treatment. Spatial and temporal heterogeneity in PD-L1 expression and variation in PD-L1 test interpretation may contribute to differences in PD-L1 test results between samples of the same patient's disease.

METHODS

Retrospective chart review identified 77 NSCLC patients with 22C3 PharmDx PD-L1 assays performed on two different tumor samples. Patients clinically suspected to have two separate primaries were excluded. PD-L1 test results in different score categories (<1%, 1-49% and ≥50%) were considered discordant. Clinical and pathologic factors associated with discordance were assessed.

RESULTS

28 (36%) of the 77 cases had discordant PD-L1 scores between samples. Patients with an initial test result of 1-49% were most likely to have a discordant second test result. Specimen type (cytology, small biopsy or resection), specimen site (lung, lymph node, pleura/pleural effusion or distant metastasis), time between specimen collection, and treatment between specimen collection were not significantly associated with the rate of discordance.

CONCLUSIONS

Repeat PD-L1 testing of the same patient's NSCLC results frequently resulted in discordant test results, independent of whether the samples differed in clinical or pathologic factors. This discordance rate underscores the extent to which PD-L1 levels are heterogeneous and difficult to accurately represent with a single test value. Further study of the predictive value of PD-L1 scores in cases with discordant results is needed.