Multiregion gene expression profiling reveals heterogeneity in molecular subtypes and immunotherapy response signatures in lung cancer

Spatial and multiomics analysis of human and mouse lung adenocarcinoma precursors reveals TIM-3 as a putative target for precancer interception

How tumor microenvironment shapes lung adenocarcinoma (LUAD) precancer evolution remains poorly understood. Spatial immune profiling of 114 human LUAD and LUAD precursors reveals a progressive increase of adaptive response and a relative decrease of innate immune response as LUAD precursors progress. The immune evasion features align the immune response patterns at various stages. TIM-3-high features are enriched in LUAD precancers, which decrease in later stages. Furthermore, single-cell RNA sequencing (scRNA-seq) and spatial immune and transcriptomics profiling of LUAD and LUAD precursor specimens from 5 mouse models validate high TIM-3 features in LUAD precancers. In vivo TIM-3 blockade at precancer stage, but not at advanced cancer stage, decreases tumor burden. Anti-TIM-3 treatment is associated with enhanced antigen presentation, T cell activation, and increased M1/M2 macrophage ratio. These results highlight the coordination of innate and adaptive immune response/evasion during LUAD precancer evolution and suggest TIM-3 as a potential target for LUAD precancer interception.

Advances in microfluidic platforms for tumor cell phenotyping: from bench to bedside

Heterogeneities among tumor cells significantly contribute towards cancer progression and therapeutic inefficiency. Hence, understanding the nature of cancer through liquid biopsies and isolation of circulating tumor cells (CTCs) has gained considerable interest over the years. Microfluidics has emerged as one of the most popular platforms for performing liquid biopsy applications. Various label-free and labeling techniques using microfluidic platforms have been developed, the majority of which focus on CTC isolation from normal blood cells. However, sorting and profiling of various cell phenotypes present amongst those CTCs is equally important for prognostics and development of personalized therapies. In this review, firstly, we discuss the biophysical and biochemical heterogeneities associated with tumor cells and CTCs which contribute to cancer progression. Moreover, we discuss the recently developed microfluidic platforms for sorting and profiling of tumor cells and CTCs. These techniques are broadly classified into biophysical and biochemical phenotyping methods. Biophysical methods are further classified into mechanical and electrical phenotyping. While biochemical techniques have been categorized into surface antigen expressions, metabolism, and chemotaxis-based phenotyping methods. We also shed light on clinical studies performed with these platforms over the years and conclude with an outlook for the future development in this field.

A probabilistic modeling framework for genomic networks incorporating sample heterogeneity

Probabilistic graphical models are powerful tools to quantify, visualize, and interpret network dependencies in complex biological systems such as high-throughput -omics. However, many graphical models assume sample homogeneity, limiting their effectiveness. We propose a flexible Bayesian approach called graphical regression (GraphR), which (1) incorporates sample heterogeneity at different scales through a regression-based formulation, (2) enables sparse sample-specific network estimation, (3) identifies and quantifies potential effects of heterogeneity on network structures, and (4) achieves computational efficiency via variational Bayes algorithms. We illustrate the comparative efficiency of GraphR against existing state-of-the-art methods in terms of network structure recovery and computational cost across multiple settings. We use GraphR to analyze three multi-omic and spatial transcriptomic datasets to investigate inter- and intra-sample molecular networks and delineate biological discoveries that otherwise cannot be revealed by existing approaches. We have developed a GraphR R package along with an accompanying Shiny App that provides comprehensive analysis and dynamic visualization functions.

A novel computational model ITHCS for enhanced prognostic risk stratification in ESCC by correcting for intratumor heterogeneity

Intratumor heterogeneity significantly challenges the accuracy of existing prognostic models for esophageal squamous cell carcinoma (ESCC) by introducing biases related to the varied genetic and molecular landscapes within tumors. Traditional models, relying on single-sample, single-region bulk RNA sequencing, fall short of capturing the complexity of intratumor heterogeneity. To fill this gap, we developed a computational model for intratumor heterogeneity corrected signature (ITHCS) by employing both multiregion bulk and single-cell RNA sequencing to pinpoint genes that exhibit consistent expression patterns across different tumor regions but vary significantly among patients. Utilizing these genes, we applied multiple machine-learning algorithms for sophisticated feature selection and model construction. The ITHCS model significantly outperforms existing prognostic indicators in accuracy and generalizability, markedly reducing sampling biases caused by intratumor heterogeneity. This improvement is especially notable in the prognostic assessment of early-stage ESCC patients, where the model exhibits exceptional predictive power. Additionally, we found that the risk score based on ITHCS may be associated with epithelial-mesenchymal transition characteristics, indicating that high-risk patients may exhibit a diminished efficacy to immunotherapy.

Multiregional transcriptomic profiling provides improved prognostic insight in localized non-small cell lung cancer

Lung Cancer remains the leading cause of cancer deaths in the USA and worldwide. Non-small cell lung cancer (NSCLC) harbors high transcriptomic intratumor heterogeneity (RNA-ITH) that limits the reproducibility of expression-based prognostic models. In this study, we used multiregional RNA-seq data (880 tumor samples from 350 individuals) from both public (TRACERx) and internal (MDAMPLC) cohorts to investigate the effect of RNA-ITH on prognosis in localized NSCLC at the gene, signature, and tumor microenvironment levels. At the gene level, the maximal expression of hazardous genes (expression negatively associated with survival) but the minimal expression of protective genes (expression positively associated with survival) across different regions within a tumor were more prognostic than the average expression. Following that, we examined whether multiregional expression profiling can improve the performance of prognostic signatures. We investigated 11 gene signatures collected from previous publications and one signature developed in this study. For all of them, the prognostic prediction accuracy can be significantly improved by converting the regional expression of signature genes into sample-specific expression with a simple function-taking the maximal expression of hazardous genes and the minimal expression of protective genes. In the tumor microenvironment, we found a similar rule also seems applicable to immune ITH. We calculated the infiltration levels of major immune cell types in each region of a sample based on expression deconvolution. Prognostic analysis indicated that the region with the lowest infiltration level of protective or highest infiltration level of hazardous immune cells determined the prognosis of NSCLC patients. Our study highlighted the impact of RNA-ITH on the prognostication of NSCLC, which should be taken into consideration to optimize the design and application of expression-based prognostic biomarkers and models. Multiregional assays have the great potential to significantly improve their applications to prognostic stratification.

Deciphering the heterogeneity of neutrophil cells within circulation and the lung cancer microenvironment pre- and post-operation

Neutrophils play a crucial role in the immune system within tumor microenvironment. At present, numerous studies have explored the changes of neutrophils' automatic killing effect and cellular communication with other immune cells under pathological conditions through single-cell sequencing. However, there remains a lack of definite conclusion about the identification criteria of neutrophil subgroups. Here, we collected tumor and para-carcinoma tissues, pre- and postoperative blood from patients with non-small cell lung cancer (NSCLC), and performed single-cell RNA (scRNA) sequencing to evaluate the distribution of neutrophil subgroups. We have developed a computational method of over expression rate (OER) to evaluate the specificity of neutrophil subgroups, in order to target gene panels with potential clinical application value. In addition, OER was used to evaluate specificity of neutrophil subsets in healthy people and patients with various diseases to further validate the feasibility of this evaluation system. As a result, we found the specificity of Neu_ c1_ IL1B and Neu_ c2_ cxcr4 (low) in postoperative blood has increased, while that of IL-7R + neutrophils has decreased, indicating that these groups of cells possibly differentiated or migrated to other subgroups in the state of lung cancer. In addition, seven gene panels (Neu_c3_CST7, RSAD2_Neu, S100A2/Pabpc1_Neu, ISG15/Ifit3_Neu, CD74_Neu, PTGS2/Actg1_Neu, SPP1_Neu) were high specific in all the four NSCLC-associated samples, meaning that changes in the percentage of these cell populations would have a high degree of confidence in assessing changes of disease status. In conclusion, combined consideration of the distribution characteristics of neutrophil subgroups could help evaluate the diagnosis and prognosis of NSCLC.

Logic-gated tumor-microenvironment nanoamplifier enables targeted delivery of CRISPR/Cas9 for multimodal cancer therapy

Recent innovations in nanomaterials inspire abundant novel tumor-targeting CRISPR-based gene therapies. However, the therapeutic efficiency of traditional targeted nanotherapeutic strategies is limited by that the biomarkers vary in a spatiotemporal-dependent manner with tumor progression. Here, we propose a self-amplifying logic-gated gene editing strategy for gene/H2O2-mediated/starvation multimodal cancer therapy. In this approach, a hypoxia-degradable covalent-organic framework (COF) is synthesized to coat a-ZIF-8 in which glucose oxidase (GOx) and CRISPR system are packaged. To intensify intracellular redox dyshomeostasis, DNAzymes which can cleave catalase mRNA are loaded as well. When the nanosystem gets into the tumor, the weakly acidic and hypoxic microenvironment degrades the ZIF-8@COF to activate GOx, which amplifies intracellular H+ and hypoxia, accelerating the nanocarrier degradation to guarantee available CRISPR plasmid and GOx release in target cells. These tandem reactions deplete glucose and oxygen, leading to logic-gated-triggered gene editing as well as synergistic gene/H2O2-mediated/starvation therapy. Overall, this approach highlights the biocomputing-based CRISPR delivery and underscores the great potential of precise cancer therapy.

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.

A transcriptomic intratumour heterogeneity-free signature overcomes sampling bias in prognostic risk classification for hepatocellular carcinoma

Background & Aims

Intratumour heterogeneity (ITH) fosters the vulnerability of RNA expression-based biomarkers derived from a single biopsy to tumour sampling bias, and is regarded as an unaddressed confounding factor for patient precision stratification using molecular biomarkers. This study aimed to identify an ITH-free predictive biomarker in hepatocellular carcinoma (HCC).

Methods

We interrogated the confounding effect of ITH on performance of molecular biomarkers and quantified transcriptomic heterogeneity utilising three multiregional HCC transcriptome datasets involving 142 tumoural regions from 30 patients. A de novo strategy based on the heterogeneity metrics was devised to develop a surveillant biomarker (a utility gadget using RNA; AUGUR) using three datasets involving 715 liver samples from 509 patients with HCC. The performance of AUGUR was assessed in seven cross-platform HCC cohorts that encompassed 1,206 patients.

Results

An average discordance rate of 39.9% at the level of individual patients was observed applying 13 published prognostic signatures to classify tumour regions. We partitioned genes into four heterogeneity quadrants, from which we developed and validated a reproducible robust ITH-free expression signature AUGUR that showed significant positive associations with adverse features of HCC. High AUGUR risk increased the risk of disease progression and mortality independent of established clinicopathological indices, which maintained concordance across seven cohorts. Moreover, AUGUR compared favourably to the discriminative ability, prognostic accuracy, and patient risk concordant rates of 13 published signatures. Finally, a well-calibrated predictive nomogram integrating AUGUR and tumour-node-metastasis (TNM) stage was established, which generated a numerical probability of mortality.

Conclusions

We constructed and validated an ITH-free AUGUR and nomogram that overcame sampling bias and provided reliable prognostic information for patients with HCC.

Impact and Implications

Intratumour heterogeneity (ITH) is prevalent in hepatocellular carcinoma (HCC), and is regarded as an unaddressed confounding factor for biomarker design and application. We examined the confounding effect of transcriptomic ITH in patient risk classification, and found existing molecular biomarkers of HCC were vulnerable to tumour sampling bias. We then developed an ITH-free expression biomarker (a utility gadget using RNA; AUGUR) that overcame clinical sampling bias and maintained prognostic reproducibility and generalisability across multiple HCC patient cohorts from different commercial platforms. Furthermore, we established and validated a well-calibrated nomogram based on AUGUR and tumour-node-metastasis (TNM) stage that provided an individualised prognostic information for patients with HCC.

Analysis of immune subtypes in non-small-cell lung cancer based on TCGA database

Immunotherapy is one of the main therapeutic approaches for non-small-cell lung cancer (NSCLC). Based on the poor response of immunotherapy, it is crucial to determine the most accurate and widespread predictive characteristics of NSCLC. We retrieved lung squamous cell carcinoma and lung adenocarcinoma gene expression profiles and clinical data from the cancer genome atlas database and classified them into 3 subtypes based on 29 immune gene sets. Combined with previous studies, the expression differences of related pathways and genes in different subtypes were analyzed. We classified them into 3 subtypes: Immunity High, Immunity Medium, and Immunity Low. Immunity High had the strongest immune cell infiltration and antitumor immune activity. Gene ontology enrichment analyses revealed enriched immune-related signaling pathways in lung squamous cell carcinoma. The hyperactivation of cancer-related pathways did not occur in any NSCLC. In addition, the Hippo signaling pathway was negatively correlated with immune signature, whereas epithelial-to-mesenchymal transition was positively correlated. In addition, we found significant differences in immune signatures between males and females; however, no correlation was observed with other clinical data. The identification of NSCLC subtypes based on immune signatures has potential clinical implications for NSCLC treatment.

Diagnostic classification of childhood cancer using multiscale transcriptomics

The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.

Gene expression prediction based on neighbour connection neural network utilizing gene interaction graphs

Having observed that gene expressions have a correlation, the Library of Integrated Network-based Cell-Signature program selects 1000 landmark genes to predict the remaining gene expression value. Further works have improved the prediction result by using deep learning models. However, these models ignore the latent structure of genes, limiting the accuracy of the experimental results. We therefore propose a novel neural network named Neighbour Connection Neural Network(NCNN) to utilize the gene interaction graph information. Comparing to the popular GCN model, our model incorperates the graph information in a better manner. We validate our model under two different settings and show that our model promotes prediction accuracy comparing to the other models.

Analysis of Immune Intratumor Heterogeneity Highlights Immunoregulatory and Coinhibitory Lymphocytes as Hallmarks of Recurrence in Stage I Non-Small Cell Lung Cancer

Our understanding of the molecular mechanisms underlying postsurgical recurrence of non-small cell lung cancer (NSCLC) is rudimentary. Molecular and T cell repertoire intratumor heterogeneity (ITH) have been reported to be associated with postsurgical relapse; however, how ITH at the cellular level impacts survival is largely unknown. Here we report the analysis of 2880 multispectral images representing 14.2% to 27% of tumor areas from 33 patients with stage I NSCLC, including 17 cases (relapsed within 3 years after surgery) and 16 controls (without recurrence ≥5 years after surgery) using multiplex immunofluorescence. Spatial analysis was conducted to quantify the minimum distance between different cell types and immune cell infiltration around malignant cells. Immune ITH was defined as the variance of immune cells from 3 intratumor regions. We found that tumors from patients having relapsed display different immune biology compared with nonrecurrent tumors, with a higher percentage of tumor cells and macrophages expressing PD-L1 (P =.031 and P =.024, respectively), along with an increase in regulatory T cells (Treg) (P =.018), antigen-experienced T cells (P =.025), and effector-memory T cells (P =.041). Spatial analysis revealed that a higher level of infiltration of PD-L1+ macrophages (CD68+PD-L1+) or antigen-experienced cytotoxic T cells (CD3+CD8+PD-1+) in the tumor was associated with poor overall survival (P =.021 and P =.006, respectively). A higher degree of Treg ITH was associated with inferior recurrence-free survival regardless of tumor mutational burden (P =.022), neoantigen burden (P =.021), genomic ITH (P =.012) and T cell repertoire ITH (P =.001). Using multiregion multiplex immunofluorescence, we characterized ITH at the immune cell level along with whole exome and T cell repertoire sequencing from the same tumor regions. This approach highlights the role of immunoregulatory and coinhibitory signals as well as their spatial distribution and ITH that define the hallmarks of tumor relapse of stage I NSCLC.

Therapy sculpts the complex interplay between cancer and the immune system during tumour evolution

Cancer development is an evolutionary process. A key selection pressure is exerted by therapy, one of the few players in cancer evolution that can be controlled. As such, an understanding of how treatment acts to sculpt the tumour and its microenvironment and how this influences a tumour's subsequent evolutionary trajectory is critical. In this review, we examine cancer evolution and intra-tumour heterogeneity in the context of therapy. We focus on how radiotherapy, chemotherapy and immunotherapy shape both tumour development and the environment in which tumours evolve and how resistance can develop or be selected for during treatment.

RAS oncogenic activity predicts response to chemotherapy and outcome in lung adenocarcinoma

Activating mutations in KRAS occur in 32% of lung adenocarcinomas (LUAD). Despite leading to aggressive disease and resistance to therapy in preclinical studies, the KRAS mutation does not predict patient outcome or response to treatment, presumably due to additional events modulating RAS pathways. To obtain a broader measure of RAS pathway activation, we developed RAS84, a transcriptional signature optimised to capture RAS oncogenic activity in LUAD. We report evidence of RAS pathway oncogenic activation in 84% of LUAD, including 65% KRAS wild-type tumours, falling into four groups characterised by coincident alteration of STK11/LKB1, TP53 or CDKN2A, suggesting that the classifications developed when considering only KRAS mutant tumours have significance in a broader cohort of patients. Critically, high RAS activity patient groups show adverse clinical outcome and reduced response to chemotherapy. Patient stratification using oncogenic RAS transcriptional activity instead of genetic alterations could ultimately assist in clinical decision-making.

Transcriptomics and solid tumors: The next frontier in precision cancer medicine

Transcriptomics, which encompasses assessments of alternative splicing and alternative polyadenylation, identification of fusion transcripts, explorations of noncoding RNAs, transcript annotation, and discovery of novel transcripts, is a valuable tool for understanding cancer mechanisms and identifying biomarkers. Recent advances in high-throughput technologies have enabled large-scale gene expression profiling. Importantly, RNA expression profiling of tumor tissue has been successfully used to determine clinically actionable molecular alterations. The WINTHER precision medicine clinical trial was the first prospective trial in diverse solid malignancies that assessed both genomics and transcriptomics to match treatments to specific molecular alterations. The use of transcriptome analysis in WINTHER and other trials increased the number of targetable -omic changes compared to genomic profiling alone. Other applications of transcriptomics involve the evaluation of tumor and circulating noncoding RNAs as predictive and prognostic biomarkers, the improvement of risk stratification by the use of prognostic and predictive multigene assays, the identification of fusion transcripts that drive tumors, and an improved understanding of the impact of DNA changes as some genomic alterations are silenced at the RNA level. Finally, RNA sequencing and gene expression analysis have been incorporated into clinical trials to identify markers predicting response to immunotherapy. Many issues regarding the complexity of the analysis, its reproducibility and variability, and the interpretation of the results still need to be addressed. The integration of transcriptomics with genomics, proteomics, epigenetics, and tumor immune profiling will improve biomarker discovery and our understanding of disease mechanisms and, thereby, accelerate the implementation of precision oncology.

The clonal expression genes associated with poor prognosis of liver cancer

The extensive spatial genomic intratumor heterogeneity (ITH) in liver cancer hindered treatment development and limited biomarker design. Early events that drive tumor malignant transformation in tumor founder cells are clonally present in all tumor cell populations, which provide stable biomarkers for the localization of tumor cells and patients’ prognosis. In the present study, we identified the recurrently clonal somatic mutations and copy number alterations (CNAs) (893 clonal somatic mutations and 6,617 clonal CNAs) in 353 liver cancer patients from The Cancer Genome Atlas (TCGA) and evaluated their prognosis potential. We showed that prognosis-related clonal alterations might play essential roles in tumor evolution. We identified 32 prognosis related clonal alterations differentially expressed between paired normal and tumor samples, that their expression was cross-validated by three independent cohorts (50 paired samples in TCGA, 149 paired samples in GSE76297, and 9 paired samples in SUB6779164). These clonal expression alterations were also significantly correlated with clinical phenotypes. Using stepwise regression, we identified five (UCK2, EFNA4, KPAN2, UBE2T, and KIF14) and six (MCM10, UCK2, IQGAP3, EFNA4, UBE2T, and KPNA2) clonal expression alterations for recurrence and survival model construction, respectively. Furthermore, in 10 random repetitions, we showed strong applicability of the multivariate Cox regression models constructed based on the clonal expression genes, which significantly predicted the outcomes of the patients in all the training and validation sets. Taken together, our work may provide a new avenue to overcome spatial ITH and refine biomarker design across cancer types.

Challenges and Opportunities for Immunoprofiling Using a Spatial High-Plex Technology: The NanoString GeoMx® Digital Spatial Profiler

Characterization of the tumor microenvironment through immunoprofiling has become an essential resource for the understanding of the complex immune cell interactions and the assessment of biomarkers for prognosis and prediction of immunotherapy response; however, these studies are often limited by tissue heterogeneity and sample size. The nanoString GeoMx^® Digital Spatial Profiler (DSP) is a platform that allows high-plex profiling at the protein and RNA level, providing spatial and temporal assessment of tumors in frozen or formalin-fixed paraffin-embedded limited tissue sample. Recently, high-impact studies have shown the feasibility of using this technology to identify biomarkers in different settings, including predictive biomarkers for immunotherapy in different tumor types. These studies showed that compared to other multiplex and high-plex platforms, the DSP can interrogate a higher number of biomarkers with higher throughput; however, it does not provide single-cell resolution, including co-expression of biomarker or spatial information at the single-cell level. In this review, we will describe the technical overview of the platform, present current evidence of the advantages and limitations of the applications of this technology, and provide important considerations for the experimental design for translational immune-oncology research using this tissue-based high-plex profiling approach.

Assessing dynamic metabolic heterogeneity in non-small cell lung cancer patients via ultra-high sensitivity total-body [18F]FDG PET/CT imaging: quantitative analysis of [18F]FDG uptake in primary tumors and metastatic lymph nodes

PURPOSE

This study aimed to quantitatively assess [¹⁸F]FDG uptake in primary tumor (PT) and metastatic lymph node (mLN) in newly diagnosed non-small cell lung cancer (NSCLC) using the total-body [¹⁸F]FDG PET/CT and to characterize the dynamic metabolic heterogeneity of NSCLC.

METHODS

The 60-min dynamic total-body [¹⁸F]FDG PET/CT was performed before treatment. The PTs and mLNs were manually delineated. An unsupervised K-means classification method was used to cluster patients based on the imaging features of PTs. The metabolic features, including Patlak-Ki, Patlak-Intercept, SUV_mean, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and textural features, were extracted from PTs and mLNs. The targeted next-generation sequencing of tumor-associated genes was performed. The expression of Ki67, CD3, CD8, CD34, CD68, and CD163 in PTs was determined by immunohistochemistry.

RESULTS

A total of 30 patients with stage IIIA-IV NSCLC were enrolled. Patients were divided into fast dynamic FDG metabolic group (F-DFM) and slow dynamic FDG metabolic group (S-DFM) by the unsupervised K-means classification of PTs. The F-DFM group showed significantly higher Patlak-Ki (P < 0.001) and SUV_mean (P < 0.001) of PTs compared with the S-DFM group, while no significant difference was observed in Patlak-Ki and SUV_mean of mLNs between the two groups. The texture analysis indicated that PTs in the S-DFM group were more heterogeneous in FDG uptake than those in the F-DFM group. Higher T cells (CD3⁺/CD8⁺) and macrophages (CD68⁺/CD163⁺) infiltration in the PTs were observed in the F-DFM group. No significant difference was observed in tumor mutational burden between the two groups.

CONCLUSION

The dynamic total-body [¹⁸F]FDG PET/CT stratified NSCLC patients into the F-DFM and S-DFM groups, based on Patlak-Ki and SUV_mean of PTs. PTs in the F-DFM group seemed to be more homogenous in terms of [¹⁸F]FDG uptake than those in the S-DFM group. The higher infiltrations of T cells and macrophages were observed in the F-DFM group, which suggested a potential benefit from immunotherapy.

Oncogenic Alterations in Histologically Negative Lymph Nodes Are Associated with Prognosis of Patients with Stage I Lung Adenocarcinoma

Simple Summary

Lymph nodes (LNs) metastasis is one of the most important factors affecting the outcome of non-small cell lung. The aim of this study is to explore whether presence of oncogenic alterations in histologically-negative lymph nodes (LNs) can be of prognostic significance in stage I lung adenocarcinoma (LUAD). We confirmed that presence of oncogenic alterations in regional LN may be associated with higher risks of postsurgical recurrence of Stage I LUAD, particularly for certain molecular subgroups. These results warranted future studies on larger cohort of NSCLC patients using more comprehensive cancer gene panels to establish the clinical impact of molecular LN occult metastasis for localized NSCLC and identify Stage I patients at high risks for recurrence for appropriate adjuvant therapy.

Abstract

Background: Survival of patients with stage I non-small cell lung cancer (NSCLC) varies greatly. We sought to explore whether presence of oncogenic alterations in histologically-negative lymph nodes (LNs) can be of prognostic significance in stage I lung adenocarcinoma (LUAD). Methods: Genomic analysis of oncogenic alterations was applied to 123 stage I LUAD tumors. The same genomic variants identified in primary tumors were examined in corresponding histologically-negative LNs. Results: A total of 102 (82.9%) patients had at least one canonical oncogenic alteration detected in primary tumors, and 57 LNs from 12 patients (11.8%) were found to carry the identical oncogenic alterations detected in the corresponding primary tumor tissues, including EGFR mutations (six cases), KRAS mutations (three cases), ALK fusion (one case), BRAF mutation (one case) and HER2 & NRAS co-mutations (one case). None of these LNs was found to have occult tumor cells by routine pathological assessment or immunohistochemistry staining using antibodies against pan-cytokeratins (AE1/AE3) and the epithelial marker Ber-EP4. The detection rate of oncogenenic alterations in LN was significantly higher in RAS-mutant tumors than EGFR mutant tumors (36.36% verse 7.41%, p = 0.017). Patients with oncogenic alterations in LN showed inferior disease-free survival (DFS, p = 0.025) and overall survival (OS, p = 0.027). Furthermore, patients with RAS-mutations detected in LN had the worst DFS and OS (p = 0.001). Among the 11 patients with RAS mutation in primary tumors, DFS and OS in the four patients with mutations detected in LN were significantly shorter than the remaining seven patients without mutations LN (DFS, p = 0.001, OS, p = 0.002). Conclusions: Genomic analysis has the potential to detect oncogenic alterations in regional LNs for localized LUAD and presence of oncogenic alterations in regional LN may be associated with inferior clinical outcome of stage I LUAD, particularly for certain molecular subgroups. ClinicalTrials.gov ID NCT04266691

Transcriptome analysis reveals upregulation of immune response pathways at the invasive tumour front of metastatic seminoma germ cell tumours

Background

Testicular germ cell tumours (TGCTs) have a high metastasis rate. However, the mechanisms related to their invasion, progression and metastasis are unclear. Therefore, we investigated gene expression changes that might be linked to metastasis in seminomatous testicular germ cell tumour (STGCT) patients.

Methods

Defined areas [invasive tumour front (TF) and tumour centre (TC)] of non-metastatic (with surveillance and recurrence-free follow-up >2 years) and metastatic STGCTs were collected separately using laser capture microdissection. The expression of 760 genes related to tumour progression and metastasis was analysed using nCounter technology and validated with quantitative real-time PCR and enzyme-linked immunosorbent assay.

Results

Distinct gene expression patterns were observed in metastatic and non-metastatic seminomas with respect to both the TF and TC. Comprehensive pathway analysis showed enrichment of genes related to tumour functions such as inflammation, angiogenesis and metabolism at the TF compared to the TC. Remarkably, prominent inflammatory and cancer-related pathways, such as interleukin-6 (IL-6) signalling, integrin signalling and nuclear factor-κB signalling, were significantly upregulated in the TF of metastatic vs non-metastatic tumours.

Conclusions

IL-6 signalling was the most significantly upregulated pathway in metastatic vs non-metastatic tumours and therefore could constitute a therapeutic target for future personalised therapy. In addition, this is the first study showing intra- and inter-tumour heterogeneity in STGCT.

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.

The histologic phenotype of lung cancers is associated with transcriptomic features rather than genomic characteristics

Histology plays an essential role in therapeutic decision-making for lung cancer patients. However, the molecular determinants of lung cancer histology are largely unknown. We conduct whole-exome sequencing and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histology. A median of 68.9% of point mutations and 83% of copy number aberrations are shared between different histologic components within the same tumors. Furthermore, different histologic components within the tumors demonstrate similar subclonal architecture. On the other hand, transcriptomic profiling reveals shared pathways between the same histologic subtypes from different patients, which is supported by the analyses of the transcriptomic data from 141 cell lines and 343 lung cancers of different histologic subtypes. These data derived from mixed histologic subtypes in the setting of identical genetic background and exposure history support that the histologic fate of lung cancer cells is associated with transcriptomic features rather than the genomic profiles in most tumors.

The molecular determinants of lung cancer histologic subtypes are not well understood. Here the authors analyze lung cancers of mixed histology and find that histologic subtypes are associated with transcriptomic features rather than genomic profiles in most tumors.

Cold and heterogeneous T cell repertoire is associated with copy number aberrations and loss of immune genes in small-cell lung cancer

Small-cell lung cancer (SCLC) is speculated to harbor complex genomic intratumor heterogeneity (ITH) associated with high recurrence rate and suboptimal response to immunotherapy. Here, using multi-region whole exome/T cell receptor (TCR) sequencing as well as immunohistochemistry, we reveal a rather homogeneous mutational landscape but extremely cold and heterogeneous TCR repertoire in limited-stage SCLC tumors (LS-SCLCs). Compared to localized non-small cell lung cancers, LS-SCLCs have similar predicted neoantigen burden and genomic ITH, but significantly colder and more heterogeneous TCR repertoire associated with higher chromosomal copy number aberration (CNA) burden. Furthermore, copy number loss of IFN-γ pathway genes is frequently observed and positively correlates with CNA burden. Higher mutational burden, higher T cell infiltration and positive PD-L1 expression are associated with longer overall survival (OS), while higher CNA burden is associated with shorter OS in patients with LS-SCLC.

Harnessing the predictive power of preclinical models for oncology drug development

Recent progress in understanding the molecular basis of cellular processes, identification of promising therapeutic targets and evolution of the regulatory landscape makes this an exciting and unprecedented time to be in the field of oncology drug development. However, high costs, long development timelines and steep rates of attrition continue to afflict the drug development process. Lack of predictive preclinical models is considered one of the key reasons for the high rate of attrition in oncology. Generating meaningful and predictive results preclinically requires a firm grasp of the relevant biological questions and alignment of the model systems that mirror the patient context. In doing so, the ability to conduct both forward translation, the process of implementing basic research discoveries into practice, as well as reverse translation, the process of elucidating the mechanistic basis of clinical observations, greatly enhances our ability to develop effective anticancer treatments. In this Review, we outline issues in preclinical-to-clinical translatability of molecularly targeted cancer therapies, present concepts and examples of successful reverse translation, and highlight the need to better align tumour biology in patients with preclinical model systems including tracking of strengths and weaknesses of preclinical models throughout programme development.

Intratumor heterogeneity: the hidden barrier to immunotherapy against MSI tumors from the perspective of IFN-γ signaling and tumor-infiltrating lymphocytes

In this era of precision medicine, with the help of biomarkers, immunotherapy has significantly improved prognosis of many patients with malignant tumor. Deficient mismatch repair (dMMR)/microsatellite instability (MSI) status is used as a biomarker in clinical practice to predict favorable response to immunotherapy and prognosis. MSI is an important characteristic which facilitates mutation and improves the likelihood of a favorable response to immunotherapy. However, many patients with dMMR/MSI still respond poorly to immunotherapies, which partly results from intratumor heterogeneity propelled by dMMR/MSI. In this review, we discuss how dMMR/MSI facilitates mutations in tumor cells and generates intratumor heterogeneity, especially through type II interferon (IFN-γ) signaling and tumor-infiltrating lymphocytes (TILs). We discuss the mechanism of immunotherapy from the perspective of dMMR/MSI, molecular pathways and TILs, and we discuss how intratumor heterogeneity hinders the therapeutic effect of immunotherapy. Finally, we summarize present techniques and strategies to look at the tumor as a whole to design personalized regimes and achieve favorable prognosis.

A comprehensive map of alternative polyadenylation in African American and European American lung cancer patients

Deciphering the post-transcriptional mechanisms (PTM) regulating gene expression is critical to understand the dynamics underlying transcriptomic regulation in cancer. Alternative polyadenylation (APA)-regulation of mRNA 3'UTR length by alternating poly(A) site usage-is a key PTM mechanism whose comprehensive analysis in cancer remains an important open challenge. Here we use a method and analysis pipeline that sequences 3'end-enriched RNA directly to overcome the saturation limitation of traditional 5'-3' based sequencing. We comprehensively map the APA landscape in lung cancer in a cohort of 98 tumor/non-involved tissues derived from European American and African American patients. We identify a global shortening of 3'UTR transcripts in lung cancer, with notable functional implications on the expression of both coding and noncoding genes. We find that APA of non-coding RNA transcripts (long non-coding RNAs and microRNAs) is a recurrent event in lung cancer and discover that the selection of alternative polyA sites is a form of non-coding RNA expression control. Our results indicate that mRNA transcripts from EAs are two times more likely than AAs to undergo APA in lung cancer. Taken together, our findings comprehensively map and identify the important functional role of alternative polyadenylation in determining transcriptomic heterogeneity in lung cancer.

Clinical Implications of Inter- and Intra-Tumor Heterogeneity of Immune Cell Markers in Lung Cancer

BACKGROUND

Immune cell transcriptome signatures have been widely used to study the lung tumor microenvironment (TME). However, it is unclear to what extent the immune cell composition in the lung TME varies across histological and molecular subtypes (inter-tumor heterogeneity or Inter-TH) and within tumors (intratumor heterogeneity or ITH), and whether ITH has any prognostic relevance.

METHODS

Using RNA sequencing in 269 tumor samples from 160 lung cancer patients we quantified the Inter-TH of immune gene expression and immune cell abundance and evaluated the association of median immune cell abundance with clinical/pathological features and overall survival. In 39 tumors with 132 multi-region samples, we also analyzed the ITH of immune cell abundance in relation to overall survival using a variance-weighted multivariate Cox model not biased by the number of samples per tumor.

RESULTS

Substantial Inter-TH of 14 immune cell types was observed even within the same histological and molecular subtypes, but early-stage tumors had higher lymphocyte infiltration across all tumor types. In multi-region samples, an unbiased estimate of low ITH of overall immune cell composition (hazard ratio [HR] = 0.40, 95% confidence interval [CI] = 0.21 to 0.78; P = .007), dendritic cells (HR = 0.24, 95% CI = 0.096 to 0.58; P = .002) and macrophages (HR = 0.50, 95% CI = 0.30 to 0.84; P = .009) was strongly associated with poor survival. The ITH of three markers, including CD163 and CD68 (macrophages) and CCL13 (dendritic cells), was enough to characterize the ITH of the corresponding immune cell abundances and its association with overall survival.

CONCLUSION

This study suggests that lack of immune cell diversity may facilitate tumor evasion and progression. ITH inferred from CCL13, CD163 and CD68 could be used as a prognostic tool in clinical practice.

PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity

BACKGROUND

Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking.

RESULTS

We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module.

CONCLUSIONS

PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.

Immune evolution from preneoplasia to invasive lung adenocarcinomas and underlying molecular features

The mechanism by which anti-cancer immunity shapes early carcinogenesis of lung adenocarcinoma (ADC) is unknown. In this study, we characterize the immune contexture of invasive lung ADC and its precursors by transcriptomic immune profiling, T cell receptor (TCR) sequencing and multiplex immunofluorescence (mIF). Our results demonstrate that anti-tumor immunity evolved as a continuum from lung preneoplasia, to preinvasive ADC, minimally-invasive ADC and frankly invasive lung ADC with a gradually less effective and more intensively regulated immune response including down-regulation of immune-activation pathways, up-regulation of immunosuppressive pathways, lower infiltration of cytotoxic T cells (CTLs) and anti-tumor helper T cells (Th), higher infiltration of regulatory T cells (Tregs), decreased T cell clonality, and lower frequencies of top T cell clones in later-stages. Driver mutations, chromosomal copy number aberrations (CNAs) and aberrant DNA methylation may collectively impinge host immune responses and facilitate immune evasion, promoting the outgrowth of fit subclones in preneoplasia into dominant clones in invasive ADC.

The role of tumor heterogeneity in immune-tumor interactions

The development of cancer stems from genetic instability and changes in genomic sequences, and hence, the heterogeneity exhibited by tumors is integral to the nature of cancer itself. Tumor heterogeneity can be further altered by factors that are not cancer cell intrinsic, i.e., by the microenvironment, including the patient's immune responses to tumors and administered therapies (immunotherapies, chemotherapies, and/or radiation therapies). The focus of this review is the impact of tumor heterogeneity on the interactions between immune cells and the tumor, taking into account that heterogeneity can exist at several levels. These levels include heterogeneity within an individual tumor, within an individual patient (particularly between the primary tumor and metastatic lesions), among the subtypes of a specific type of cancer, or within cancers that originate from different tissues. Because of the potential for immunity (either the natural immune system or via immunotherapeutics) to halt the progression of cancer, major clinical significance exists in understanding the impact of tumor heterogeneity on the associations between immune cells and tumor cells. Increased knowledge of why, whether, and how immune-tumor interactions occur provides the means to guide these interactions and improve outcomes for patients.

On transformative adaptive activation functions in neural networks for gene expression inference

Gene expression profiling was made more cost-effective by the NIH LINCS program that profiles only ∼1, 000 selected landmark genes and uses them to reconstruct the whole profile. The D-GEX method employs neural networks to infer the entire profile. However, the original D-GEX can be significantly improved. We propose a novel transformative adaptive activation function that improves the gene expression inference even further and which generalizes several existing adaptive activation functions. Our improved neural network achieves an average mean absolute error of 0.1340, which is a significant improvement over our reimplementation of the original D-GEX, which achieves an average mean absolute error of 0.1637. The proposed transformative adaptive function enables a significantly more accurate reconstruction of the full gene expression profiles with only a small increase in the complexity of the model and its training procedure compared to other methods.

Transcriptomic and proteomic intra-tumor heterogeneity of colorectal cancer varies depending on tumor location within the colorectum

Background

Intra-tumor heterogeneity (ITH) of colorectal cancer (CRC) complicates molecular tumor classification, such as transcriptional subtyping. Differences in cellular states, biopsy cell composition, and tumor microenvironment may all lead to ITH. Here we analyze ITH at the transcriptomic and proteomic levels to ascertain whether subtype discordance between multiregional biopsies reflects relevant biological ITH or lack of classifier robustness. Further, we study the impact of tumor location on ITH.

Methods

Multiregional biopsies from stage II and III CRC tumors were analyzed by RNA sequencing (41 biopsies, 14 tumors) and multiplex immune protein analysis (89 biopsies, 29 tumors). CRC subtyping was performed using consensus molecular subtypes (CMS), CRC intrinsic subtypes (CRIS), and TUMOR types. ITH-scores and network maps were defined to determine the origin of heterogeneity. A validation cohort was used with one biopsy per tumor (162 tumors).

Results

Overall, inter-tumor transcriptional variation exceeded ITH, and subtyping calls were frequently concordant between multiregional biopsies. Still, some tumors had high transcriptional ITH and were classified discordantly. Subtyping of proximal MSS tumors were discordant for 50% of the tumors, this ITH was related to differences in the microenvironment. Subtyping of distal MSS tumors were less discordant, here the ITH was more cancer-cell related. The subtype discordancy reflected actual molecular ITH within the tumors. The relevance of the subtypes was reflected at protein level where several inflammation markers were significantly increased in immune related transcriptional subtypes, which was verified in an independent cohort (Wilcoxon rank sum test; p<0.05). Unsupervised hierarchical clustering of the protein data identified large ITH at protein level; as the multiregional biopsies clustered together for only 9 out of 29 tumors.

Conclusion

Our transcriptomic and proteomic analyses show that the tumor location along the colorectum influence the ITH of CRC, which again influence the concordance of subtyping.

On tower and checkerboard neural network architectures for gene expression inference

BACKGROUND

One possible approach how to economically facilitate gene expression profiling is to use the L1000 platform which measures the expression of ∼1,000 landmark genes and uses a computational method to infer the expression of another ∼10,000 genes. One such method for the gene expression inference is a D-GEX which employs neural networks.

RESULTS

We propose two novel D-GEX architectures that significantly improve the quality of the inference by increasing the capacity of a network without any increase in the number of trained parameters. The architectures partition the network into individual towers. Our best proposed architecture - a checkerboard architecture with a skip connection and five towers - together with minor changes in the training protocol improves the average mean absolute error of the inference from 0.134 to 0.128.

CONCLUSIONS

Our proposed approach increases the gene expression inference accuracy without increasing the number of weights of the model and thus without increasing the memory footprint of the model that is limiting its usage.

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

Immune checkpoint inhibitors (ICIs) represent one of the most promising therapeutic approaches in metastatic non-small cell lung cancer (M-NSCLC). Unfortunately, approximately 50–75% of patients do not respond to this treatment modality. Intratumor heterogeneity (ITH) at the genetic and phenotypic level is considered as a major cause of anticancer therapy failure, including resistance to ICIs. Recent observations suggest that spatial heterogeneity in the composition and spatial organization of the tumor microenvironment plays a major role in the response of M-NSCLC patients to ICIs. In this mini review, we first present a brief overview of the use of ICIs in M-NSCLC. We then discuss the role of genetic and non-genetic ITH on the efficacy of ICIs in patients with M-NSCLC.

Multiomics profiling of primary lung cancers and distant metastases reveals immunosuppression as a common characteristic of tumor cells with metastatic plasticity

BACKGROUND

Metastasis is the primary cause of cancer mortality accounting for 90% of cancer deaths. Our understanding of the molecular mechanisms driving metastasis is rudimentary.

RESULTS

We perform whole exome sequencing (WES), RNA sequencing, methylation microarray, and immunohistochemistry (IHC) on 8 pairs of non-small cell lung cancer (NSCLC) primary tumors and matched distant metastases. Furthermore, we analyze published WES data from 35 primary NSCLC and metastasis pairs, and transcriptomic data from 4 autopsy cases with metastatic NSCLC and one metastatic lung cancer mouse model. The majority of somatic mutations are shared between primary tumors and paired distant metastases although mutational signatures suggest different mutagenesis processes in play before and after metastatic spread. Subclonal analysis reveals evidence of monoclonal seeding in 41 of 42 patients. Pathway analysis of transcriptomic data reveals that downregulated pathways in metastases are mainly immune-related. Further deconvolution analysis reveals significantly lower infiltration of various immune cell types in metastases with the exception of CD4+ T cells and M2 macrophages. These results are in line with lower densities of immune cells and higher CD4/CD8 ratios in metastases shown by IHC. Analysis of transcriptomic data from autopsy cases and animal models confirms that immunosuppression is also present in extracranial metastases. Significantly higher somatic copy number aberration and allelic imbalance burdens are identified in metastases.

CONCLUSIONS

Metastasis is a molecularly late event, and immunosuppression driven by different molecular events, including somatic copy number aberration, may be a common characteristic of tumors with metastatic plasticity.

Evolution of Genomic and T-cell Repertoire Heterogeneity of Malignant Pleural Mesothelioma Under Dasatinib Treatment

PURPOSE

Malignant pleural mesothelioma (MPM) is considered an orphan disease with few treatment options. Despite multimodality therapy, the majority of MPMs recur and eventually become refractory to any systemic treatment. One potential mechanism underlying therapeutic resistance may be intratumor heterogeneity (ITH), making MPM challenging to eradicate. However, the ITH architecture of MPM and its clinical impact have not been well studied.

EXPERIMENTAL DESIGN

We delineated the immunogenomic ITH by multiregion whole-exome sequencing and T-cell receptor (TCR) sequencing of 69 longitudinal MPM specimens from nine patients with resectable MPM, who were treated with dasatinib.

RESULTS

The median total mutation burden before dasatinib treatment was 0.65/Mb, similar with that of post-dasatinib treatment (0.62/Mb). The median proportion of mutations shared by any given pair of two tumor regions within the same tumors was 80% prior to and 83% post-dasatinib treatment indicating a relatively homogenous genomic landscape. T-cell clonality, a parameter indicating T-cell expansion and reactivity, was significantly increased in tumors after dasatinib treatment. Furthermore, on average, 82% of T-cell clones were restricted to individual tumor regions, with merely 6% of T-cell clones shared by all regions from the same tumors indicating profound TCR heterogeneity. Interestingly, patients with higher T-cell clonality and higher portion of T cells present across all tumor regions in post-dasatinib-treated tumors had significantly longer survival.

CONCLUSIONS

Despite the homogeneous genomic landscape, the TCR repertoire is extremely heterogeneous in MPM. Dasatinib may potentially induce T-cell response leading to improved survival.

An algorithm to quantify intratumor heterogeneity based on alterations of gene expression profiles

Intratumor heterogeneity (ITH) is a biomarker of tumor progression, metastasis, and immune evasion. Previous studies evaluated ITH mostly based on DNA alterations. Here, we developed a new algorithm (DEPTH) for quantifying ITH based on mRNA alterations in the tumor. DEPTH scores displayed significant correlations with ITH-associated features (genomic instability, tumor advancement, unfavorable prognosis, immunosuppression, and drug response). Compared to DNA-based ITH scores (EXPANDS, PhyloWGS, MATH, and ABSOLUTE), DEPTH scores had stronger correlations with antitumor immune signatures, cell proliferation, stemness, tumor advancement, survival prognosis, and drug response. Compared to two other mRNA-based ITH scores (tITH and sITH), DEPTH scores showed stronger and more consistent associations with genomic instability, unfavorable tumor phenotypes and clinical features, and drug response. We further validated the reliability and robustness of DEPTH in 50 other datasets. In conclusion, DEPTH may provide new insights into tumor biology and potential clinical implications for cancer prognosis and treatment.

Programmed Death-Ligand 1 Heterogeneity and Its Impact on Benefit From Immune Checkpoint Inhibitors in NSCLC

INTRODUCTION

Programmed death-ligand 1 (PD-L1) expression may vary in different disease sites and at different time points of the disease course. We aimed to investigate PD-L1 heterogeneity and its usefulness as a predictive value for immune checkpoint inhibitor (ICI) therapy in patients with NSCLC.

METHODS

PD-L1 expression was analyzed in 1398 patients with NSCLC. The predictive value of PD-L1 for ICIs in 398 patients with metastatic NSCLC was assessed.

RESULTS

PD-L1 was significantly associated with biopsy sites (p = 0.004). Adrenal, liver, and lymph node (LN) metastases had the highest PD-L1 expression as a continuous variable and at 1% or 50% cutoff. PD-L1 expression was lower in bone and brain metastases. Among 112 patients with two specimens tested, 55 (49%) had major changes in PD-L1 falling into different clinically relevant categories (<1%, 1%-49%, ≥50%) at different time points. Previous ICI therapy was associated with significant decrease in PD-L1 compared with treatment-naive counterparts (p = 0.015). Patients with metastatic NSCLC treated with ICI (n = 398) were divided into three cohorts on the basis of biopsy sites: lung (n = 252), LN (n = 85), and distant metastasis (n = 61). Higher PD-L1 in lung or distant metastasis specimens was associated with higher response rate, longer progression-free survival, and overall survival. However, PD-L1 in LN biopsies was not associated with either response or survival.

CONCLUSIONS

PD-L1 varies substantially across different anatomical sites and changes during the clinical course. PD-L1 from different biopsy sites may have different predictive values for benefit from ICIs in NSCLC.

Multi-Institutional Prospective Validation of Prognostic mRNA Signatures in Early Stage Squamous Lung Cancer (Alliance)

INTRODUCTION

Surgical resection is curative for some patients with early lung squamous cell carcinoma. Staging and clinical factors do not adequately predict recurrence risk. We sought to validate the discriminative performance of proposed prognostic gene expression signatures at a level of rigor sufficient to support clinical use.

METHODS

The two-stage validation used independent core laboratories, objective quality control standards, locked test parameters, and large multi-institutional specimen and data sets. The first stage validation confirmed a signature's ability to stratify patient survival. The second-stage validation determined which signature(s) optimally improved risk discrimination when added to baseline clinical predictors. Participants were prospectively enrolled in institutional (cohort I) or cooperative group (cohort II) biospecimen and data collection protocols. All cases underwent a central review of clinical, pathologic, and biospecimen parameters using objective criteria to determine final inclusion (cohort I: n = 249; cohort II: n = 234). Primary selection required that a signature significantly predict a 3-year survival after surgical resection in cohort I. Signatures meeting this criterion were further tested in cohort II, comparing risk prediction using baseline risk factors alone versus in combination with the signature.

RESULTS

Male sex, advanced age, and higher stage were associated with shorter survival in cohort I and established a baseline clinical model. Of the three signatures validated in cohort I, one signature was validated in cohort II and statistically significantly enhanced the prognosis relative to the baseline model (C-index difference 0.122; p < 0.05).

CONCLUSIONS

These results represent the first rigorous validation of a test appropriate to direct adjuvant treatment or clinical trials for patients with lung squamous cell carcinoma.

The roles of RNA in DNA double-strand break repair

Effective DNA repair is essential for cell survival: a failure to correctly repair damage leads to the accumulation of mutations and is the driving force for carcinogenesis. Multiple pathways have evolved to protect against both intrinsic and extrinsic genotoxic events, and recent developments have highlighted an unforeseen critical role for RNA in ensuring genome stability. It is currently unclear exactly how RNA molecules participate in the repair pathways, although many models have been proposed and it is possible that RNA acts in diverse ways to facilitate DNA repair. A number of well-documented DNA repair factors have been described to have RNA-binding capacities and, moreover, screens investigating DNA-damage repair mechanisms have identified RNA-binding proteins as a major group of novel factors involved in DNA repair. In this review, we integrate some of these datasets to identify commonalities that might highlight novel and interesting factors for future investigations. This emerging role for RNA opens up a new dimension in the field of DNA repair; we discuss its impact on our current understanding of DNA repair processes and consider how it might influence cancer progression.

Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer

No predictive biomarkers can robustly identify patients with non-small cell lung cancer (NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes ("delta") in the radiomic texture (DelRADx) of CT patterns both within and outside tumor nodules before and after two to three cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 patients with NSCLC at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted, and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST-derived response. Association of delta-radiomic risk score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an AUC of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, and 0.85 and 0.81 in D2 and D3 DRS was associated with OS [HR: 1.64; 95% confidence interval (CI), 1.22-2.21; P = 0.0011; C-index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in patients with NSCLC.

A clonal expression biomarker associates with lung cancer mortality

An aim of molecular biomarkers is to stratify patients with cancer into disease subtypes predictive of outcome, improving diagnostic precision beyond clinical descriptors such as tumor stage1. Transcriptomic intratumor heterogeneity (RNA-ITH) has been shown to confound existing expression-based biomarkers across multiple cancer types2-6. Here, we analyze multi-region whole-exome and RNA sequencing data for 156 tumor regions from 48 patients enrolled in the TRACERx study to explore and control for RNA-ITH in non-small cell lung cancer. We find that chromosomal instability is a major driver of RNA-ITH, and existing prognostic gene expression signatures are vulnerable to tumor sampling bias. To address this, we identify genes expressed homogeneously within individual tumors that encode expression modules of cancer cell proliferation and are often driven by DNA copy-number gains selected early in tumor evolution. Clonal transcriptomic biomarkers overcome tumor sampling bias, associate with survival independent of clinicopathological risk factors, and may provide a general strategy to refine biomarker design across cancer types.

Addressing cellular heterogeneity in tumor and circulation for refined prognostication

Delineation of intratumor heterogeneity (ITH) has been a subject of growing interest for defining and tracking the evolution of cancer. Yet, the clinical consequences of such ITH on risk prediction remain unclear. Here we show ITH-driven variance on patient stratification and argue that the level of ITH of individual genes should be considered when developing single sector-based prognostic multigene tests (MGTs) in non–small-cell lung cancer (NSCLC). Single-cell molecular analysis of enriched, patient-derived circulating tumor cells (CTCs) further revealed predictive biomarkers for metastatic risk. Through systematic analysis of genes implicated in multiple steps of the metastatic spectrum, we demonstrate that the refined signatures achieve superior accuracy in identifying patients with early-stage disease at high risk of recurrence of NSCLC.

Despite pronounced genomic and transcriptomic heterogeneity in non–small-cell lung cancer (NSCLC) not only between tumors, but also within a tumor, validation of clinically relevant gene signatures for prognostication has relied upon single-tissue samples, including 2 commercially available multigene tests (MGTs). Here we report an unanticipated impact of intratumor heterogeneity (ITH) on risk prediction of recurrence in NSCLC, underscoring the need for a better genomic strategy to refine prognostication. By leveraging label-free, inertial-focusing microfluidic approaches in retrieving circulating tumor cells (CTCs) at single-cell resolution, we further identified specific gene signatures with distinct expression profiles in CTCs from patients with differing metastatic potential. Notably, a refined prognostic risk model that reconciles the level of ITH and CTC-derived gene expression data outperformed the initial classifier in predicting recurrence-free survival (RFS). We propose tailored approaches to providing reliable risk estimates while accounting for ITH-driven variance in NSCLC.

Interactive online consensus survival tool for esophageal squamous cell carcinoma prognosis analysis

Esophageal squamous cell carcinoma (ESCC) is one of the most common types of cancer worldwide. However, operative diagnostic and prognostic systems for ESCC remain to be established. To improve assessment of the prognosis for patients with ESCC, the present study developed an online consensus survival tool for ESCC, termed OSescc. OSescc was built using 264 ESCC cases with gene expression data and relevant clinical information obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Kaplan-Meier survival plots with hazard ratios and P-values were generated by OSescc to predict the association between potential biomarkers and relapse free survival and overall survival. In addition, the current study integrated a function by which one could assess the prognosis based on an individual probe or the mean value of multiple probes for each gene, which helped improve the evaluation of the validity and reliability of the potential prognosis biomarkers. OSescc can be accessed at bioinfo.henu.edu.cn/DBList.jsp.

Non-canonical cMet regulation by vimentin mediates Plk1 inhibitor-induced apoptosis

To address the need for improved systemic therapy for non-small-cell lung cancer (NSCLC), we previously demonstrated that mesenchymal NSCLC was sensitive to polo-like kinase (Plk1) inhibitors, but the mechanisms of resistance in epithelial NSCLC remain unknown. Here, we show that cMet was differentially regulated in isogenic pairs of epithelial and mesenchymal cell lines. Plk1 inhibition inhibits cMet phosphorylation only in mesenchymal cells. Constitutively active cMet abrogates Plk1 inhibitor-induced apoptosis. Likewise, cMet silencing or inhibition enhances Plk1 inhibitor-induced apoptosis. Cells with acquired resistance to Plk1 inhibitors are more epithelial than their parental cells and maintain cMet activation after Plk1 inhibition. In four animal NSCLC models, mesenchymal tumors were more sensitive to Plk1 inhibition alone than were epithelial tumors. The combination of cMet and Plk1 inhibition led to regression of tumors that did not regrow when drug treatment was stopped. Plk1 inhibition did not affect HGF levels but did decrease vimentin phosphorylation, which regulates cMet phosphorylation via β1-integrin. This research defines a heretofore unknown mechanism of ligand-independent activation of cMet downstream of Plk1 and an effective combination therapy.