Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types.

The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8887 TCGA, and 1261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n = 1261) including our lung cancer cohort (n = 497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.

CRISPR-Cas9 screening identifies KRAS-induced COX-2 as a driver of immunotherapy resistance in lung cancer.

Oncogenic KRAS impairs anti-tumor immune responses. As effective strategies to combine KRAS inhibitors and immunotherapies have so far proven elusive, a better understanding of how oncogenic KRAS drives immune evasion is needed to identify approaches that could sensitize KRAS-mutant lung cancer to immunotherapy. In vivo CRISPR-Cas9 screening in an immunogenic murine lung cancer model identified mechanisms by which oncogenic KRAS promotes immune evasion, most notably via upregulation of immunosuppressive cyclooxygenase-2 (COX-2) in cancer cells. Oncogenic KRAS potently induced COX-2 in both mouse and human lung cancer, which was suppressed using KRAS inhibitors. COX-2 acted via prostaglandin E2 (PGE2) to promote resistance to immune checkpoint blockade (ICB) in lung adenocarcinoma. Targeting COX-2/PGE2 remodeled the tumor microenvironment by inducing pro-inflammatory polarization of myeloid cells and influx of activated cytotoxic CD8+ T cells, which increased the efficacy of ICB. Restoration of COX-2 expression contributed to tumor relapse after prolonged KRAS inhibition. These results provide the rationale for testing COX-2/PGE2 pathway inhibitors in combination with KRASG12C inhibition or ICB in patients with KRAS-mutant lung cancer.

Antibodies against endogenous retroviruses promote lung cancer immunotherapy.

B cells are frequently found in the margins of solid tumours as organized follicles in ectopic lymphoid organs called tertiary lymphoid structures (TLS)1,2. Although TLS have been found to correlate with improved patient survival and response to immune checkpoint blockade (ICB), the underlying mechanisms of this association remain elusive1,2. Here we investigate lung-resident B cell responses in patients from the TRACERx 421 (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy) and other lung cancer cohorts, and in a recently established immunogenic mouse model for lung adenocarcinoma3. We find that both human and mouse lung adenocarcinomas elicit local germinal centre responses and tumour-binding antibodies, and further identify endogenous retrovirus (ERV) envelope glycoproteins as a dominant anti-tumour antibody target. ERV-targeting B cell responses are amplified by ICB in both humans and mice, and by targeted inhibition of KRAS(G12C) in the mouse model. ERV-reactive antibodies exert anti-tumour activity that extends survival in the mouse model, and ERV expression predicts the outcome of ICB in human lung adenocarcinoma. Finally, we find that effective immunotherapy in the mouse model requires CXCL13-dependent TLS formation. Conversely, therapeutic CXCL13 treatment potentiates anti-tumour immunity and synergizes with ICB. Our findings provide a possible mechanistic basis for the association of TLS with immunotherapy response.

Lung adenocarcinoma promotion by air pollutants.

A complete understanding of how exposure to environmental substances promotes cancer formation is lacking. More than 70 years ago, tumorigenesis was proposed to occur in a two-step process: an initiating step that induces mutations in healthy cells, followed by a promoter step that triggers cancer development1. Here we propose that environmental particulate matter measuring ≤2.5 µm (PM2.5), known to be associated with lung cancer risk, promotes lung cancer by acting on cells that harbour pre-existing oncogenic mutations in healthy lung tissue. Focusing on EGFR-driven lung cancer, which is more common in never-smokers or light smokers, we found a significant association between PM2.5 levels and the incidence of lung cancer for 32,957 EGFR-driven lung cancer cases in four within-country cohorts. Functional mouse models revealed that air pollutants cause an influx of macrophages into the lung and release of interleukin-1ß. This process results in a progenitor-like cell state within EGFR mutant lung alveolar type II epithelial cells that fuels tumorigenesis. Ultradeep mutational profiling of histologically normal lung tissue from 295 individuals across 3 clinical cohorts revealed oncogenic EGFR and KRAS driver mutations in 18% and 53% of healthy tissue samples, respectively. These findings collectively support a tumour-promoting role for  PM2.5 air pollutants  and provide impetus for public health policy initiatives to address air pollution to reduce disease burden.

Intracellular Adhesion Molecule-1 Improves Responsiveness to Immune Checkpoint Inhibitor by Activating CD8+ T Cells.

Immune checkpoint inhibitor (ICI) clinically benefits cancer treatment. However, the ICI responses are only achieved in a subset of patients, and the underlying mechanisms of the limited response remain unclear. 160 patients with non-small cell lung cancer treated with anti-programmed cell death protein-1 (anti-PD-1) or anti-programmed death ligand-1 (anti-PD-L1) are analyzed to understand the early determinants of response to ICI. It is observed that high levels of intracellular adhesion molecule-1 (ICAM-1) in tumors and plasma of patients are associated with prolonged survival. Further reverse translational studies using murine syngeneic tumor models reveal that soluble ICAM-1 (sICAM-1) is a key molecule that increases the efficacy of anti-PD-1 via activation of cytotoxic T cells. Moreover, chemokine (CXC motif) ligand 13 (CXCL13) in tumors and plasma is correlated with the level of ICAM-1 and ICI efficacy, suggesting that CXCL13 might be involved in the ICAM-1-mediated anti-tumor pathway. Using sICAM-1 alone and in combination with anti-PD-1 enhances anti-tumor efficacy in anti-PD-1-responsive tumors in murine models. Notably, combinatorial therapy with sICAM-1 and anti-PD-1 converts anti-PD-1-resistant tumors to responsive ones in a preclinical study. These findings provide a new immunotherapeutic strategy for treating cancers using ICAM-1.

MHC II immunogenicity shapes the neoepitope landscape in human tumors.

Despite advances in predicting physical peptide-major histocompatibility complex I (pMHC I) binding, it remains challenging to identify functionally immunogenic neoepitopes, especially for MHC II. By using the results of >36,000 immunogenicity assay, we developed a method to identify pMHC whose structural alignment facilitates T cell reaction. Our method predicted neoepitopes for MHC II and MHC I that were responsive to checkpoint blockade when applied to >1,200 samples of various tumor types. To investigate selection by spontaneous immunity at the single epitope level, we analyzed the frequency spectrum of >25 million mutations in >9,000 treatment-naive tumors with >100 immune phenotypes. MHC II immunogenicity specifically lowered variant frequencies in tumors under high immune pressure, particularly with high TCR clonality and MHC II expression. A similar trend was shown for MHC I neoepitopes, but only in particular tissue types. In summary, we report immune selection imposed by MHC II-restricted natural or therapeutic T cell reactivity.

Transcriptional upregulation of CXCL13 is correlated with a favorable response to immune checkpoint inhibitors in lung adenocarcinoma.

BACKGROUND: The chemokine CXCL13 is known to influence local anti-tumor immunity by recruiting immune cells and forming tertiary lymphoid structures (TLS). It has been hypothesized that TLS, led by the expression of CXCL13, could be a predictive or prognostic biomarker for immunotherapy. We investigated the predictive value of CXCL13 to immune checkpoint inhibitors (ICI) in lung adenocarcinoma. METHODS: We constructed an exploratory dataset (n = 63) and a validation dataset (n = 57) in metastatic lung adenocarcinoma patients treated with ICI. Based on the clinical response, the difference in gene expression profile, including CXCL13, was evaluated. RESULTS: From the exploratory dataset, CXCL13 expression was significantly upregulated in the ICI responders (p = 0.002). Survival analysis using a cut-off value of the median expression value of CXCL13 showed prolonged progression-free survival (PFS) (p = 0.004) and overall survival (OS) (p = 0.007). CXCL13 expression was correlated with other immune response genes, such as GZMA, CD8A, IFNG, PRF1, TLS-related gene sets and its receptor, CXCR5. Notably, subgroup analyses based on CXCL13 expression and CD8A showed that CXCL13-upregulated patients demonstrated comparably prolonged survival regardless of CD8A expression. In the validation dataset, CXCL13 upregulation also demonstrated a significant prolongation of both PFS (p = 0.050) and OS (p = 0.026). CONCLUSION: We observed that CXCL13 upregulation is correlated to better ICI response in lung adenocarcinoma. Our results support that CXCL13 could be an important chemokine in shaping the immunoactive tumor microenvironment which affects the anti-tumor effect of ICI.

High-Resolution Profiling of Lung Adenocarcinoma Identifies Expression Subtypes with Specific Biomarkers and Clinically Relevant Vulnerabilities.

Lung adenocarcinoma (LUAD) is one of the most common cancer types and has various treatment options. Better biomarkers to predict therapeutic response are needed to guide choice of treatment modality and to improve precision medicine. Here, we used a consensus hierarchical clustering approach on 509 LUAD cases from The Cancer Genome Atlas to identify five robust LUAD expression subtypes. Genomic and proteomic data from patient samples and cell lines was then integrated to help define biomarkers of response to targeted therapies and immunotherapies. This approach defined subtypes with unique proteogenomic and dependency profiles. Subtype 4 (S4)-associated cell lines exhibited specific vulnerability to loss of CDK6 and CDK6-cyclin D3 complex gene (CCND3). Subtype 3 (S3) was characterized by dependency on CDK4, immune-related expression patterns, and altered MET signaling. Experimental validation showed that S3-associated cell lines responded to MET inhibitors, leading to increased expression of programmed death-ligand 1 (PD-L1). In an independent real-world patient dataset, patients with S3 tumors were enriched with responders to immune checkpoint blockade. Genomic features in S3 and S4 were further identified as biomarkers for enabling clinical diagnosis of these subtypes. Overall, our consensus hierarchical clustering approach identified robust tumor expression subtypes, and our subsequent integrative analysis of genomics, proteomics, and CRISPR screening data revealed subtype-specific biology and vulnerabilities. These LUAD expression subtypes and their biomarkers could help identify patients likely to respond to CDK4/6, MET, or PD-L1 inhibitors, potentially improving patient outcome. SIGNIFICANCE: Integrative analysis of multiomic and drug dependency data uncovers robust lung adenocarcinoma expression subtypes with unique therapeutic vulnerabilities and subtype-specific biomarkers of response.

Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non-Small-Cell Lung Cancer.

PURPOSE: Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI). METHODS: We have developed an artificial intelligence (AI)-powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non-small-cell lung cancer (NSCLC). RESULTS: Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists (P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP. CONCLUSION: The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.

Synthetic lethality-mediated precision oncology via the tumor transcriptome.

Precision oncology has made significant advances, mainly by targeting actionable mutations in cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome to guide patient treatment. Here, we introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a precision oncology framework harnessing genetic interactions to predict patient response to cancer therapy from the tumor transcriptome. SELECT is tested on a broad collection of 35 published targeted and immunotherapy clinical trials from 10 different cancer types. It is predictive of patients' response in 80% of these clinical trials and in the recent multi-arm WINTHER trial. The predictive signatures and the code are made publicly available for academic use, laying a basis for future prospective clinical studies.

Clinical advantage of targeted sequencing for unbiased tumor mutational burden estimation in samples with low tumor purity.

BACKGROUND: Tumor mutational burden (TMB) measurement is limited by low tumor purity of samples, which can influence prediction of the immunotherapy response, particularly when using whole-exome sequencing-based TMB (wTMB). This issue could be overcome by targeted panel sequencing-based TMB (pTMB) with higher depth of coverage, which remains unexplored. METHODS: We comprehensively reanalyzed four public datasets of immune checkpoint inhibitor (ICI)-treated cohorts (adopting pTMB or wTMB) to test each biomarker's predictive ability for low purity samples (cut-off: 30%). For validation, paired genomic profiling with the same tumor specimens was performed to directly compare wTMB and pTMB in patients with breast cancer (paired-BRCA, n=165) and ICI-treated patients with advanced non-small-cell lung cancer (paired-NSCLC, n=156). RESULTS: Low tumor purity was common (range 30%-45%) in real-world samples from ICI-treated patients. In the survival analyzes of public cohorts, wTMB could not predict the clinical benefit of immunotherapy when tumor purity was low (log-rank p=0.874), whereas pTMB could effectively stratify the survival outcome (log-rank p=0.020). In the paired-BRCA and paired-NSCLC cohorts, pTMB was less affected by tumor purity, with significantly more somatic variants identified at low allele frequency (p<0.001). We found that wTMB was significantly underestimated in low purity samples with a large proportion of clonal variants undetected by whole-exome sequencing. Interestingly, pTMB more accurately predicted progression-free survival (PFS) after immunotherapy than wTMB owing to its superior performance in the low tumor purity subgroup (p=0.054 vs p=0.358). Multivariate analysis revealed pTMB (p=0.016), but not wTMB (p=0.32), as an independent predictor of PFS even in low-purity samples. The net reclassification index using pTMB was 21.7% in the low-purity subgroup (p=0.016). CONCLUSIONS: Our data suggest that TMB characterization with targeted deep sequencing might have potential strength in predicting ICI responsiveness due to its enhanced sensitivity for hard-to-detect variants at low-allele fraction. Therefore, pTMB could act as an invaluable biomarker in the setting of both clinical trials and practice outside of trials based on its reliable performance in mitigating the purity-related bias.

Earlier-Phased Cancer Immunity Cycle Strongly Influences Cancer Immunity in Operable Never-Smoker Lung Adenocarcinoma.

Exome and transcriptome analyses of clinically homogeneous early-stage never-smoker female patients with lung adenocarcinoma were performed to understand tumor-T cell interactions and immune escape points. Using our novel gene panels of eight functional categories in the cancer-immunity cycle, three distinct subgroups were identified in this immune checkpoint blockade-refractory cohort by defective gene expression in two major domains, i.e., type I interferon production/signaling pathway and antigen-presenting machinery. Our approach could play a critical role in understanding immune evasion mechanisms, developing a method for effective selection of rare immune checkpoint blockade responders, and finding new treatment strategies.

Metabolic radiogenomics in lung cancer: associations between FDG PET image features and oncogenic signaling pathway alterations.

This study investigated the associations between image features extracted from tumor 18F-fluorodeoxyglucose (FDG) uptake and genetic alterations in patients with lung cancer. A total of 137 patients (age, 62.7 ± 10.2 years) who underwent FDG positron emission tomography/computed tomography (PET/CT) and targeted deep sequencing analysis for a tumor lesion, comprising 61 adenocarcinoma (ADC), 31 squamous cell carcinoma (SQCC), and 45 small cell lung cancer (SCLC) patients, were enrolled in this study. From the tumor lesions, 86 image features were extracted, and 381 genes were assessed. PET features were associated with genetic mutations: 41 genes with 24 features in ADC; 35 genes with 22 features in SQCC; and 43 genes with 25 features in SCLC (FDR < 0.05). Clusters based on PET features showed an association with alterations in oncogenic signaling pathways: Cell cycle and WNT signaling pathways in ADC (p = 0.023, p = 0.035, respectively); Cell cycle, p53, and WNT in SQCC (p = 0.045, 0.009, and 0.029, respectively); and TGFß in SCLC (p = 0.030). In addition, SUVpeak and SUVmax were associated with a mutation of the TGFß signaling pathway in ADC (FDR = 0.001, < 0.001). In this study, PET image features had significant associations with alterations in genes and oncogenic signaling pathways in patients with lung cancer.

Dysregulation of cancer genes by recurrent intergenic fusions.

BACKGROUND: Gene fusions have been studied extensively, as frequent drivers of tumorigenesis as well as potential therapeutic targets. In many well-known cases, breakpoints occur at two intragenic positions, leading to in-frame gene-gene fusions that generate chimeric mRNAs. However, fusions often occur with intergenic breakpoints, and the role of such fusions has not been carefully examined. RESULTS: We analyze whole-genome sequencing data from 268 patients to catalog gene-intergenic and intergenic-intergenic fusions and characterize their impact. First, we discover that, in contrast to the common assumption, chimeric oncogenic transcripts-such as those involving ETV4, ERG, RSPO3, and PIK3CA-can be generated by gene-intergenic fusions through splicing of the intervening region. Second, we find that over-expression of an upstream or downstream gene by a fusion-mediated repositioning of a regulatory sequence is much more common than previously suspected, with enhancers sometimes located megabases away. We detect a number of recurrent fusions, such as those involving ANO3, RGS9, FUT5, CHI3L1, OR1D4, and LIPG in breast; IGF2 in colon; ETV1 in prostate; and IGF2BP3 and SIX2 in thyroid cancers. CONCLUSION: Our findings elucidate the potential oncogenic function of intergenic fusions and highlight the wide-ranging consequences of structural rearrangements in cancer genomes.

Genomic scoring to determine clinical benefit of immunotherapy by targeted sequencing.

AIMS: Immune checkpoint inhibitors (ICIs) induce durable responses, but their clinical benefits apply to only a subset of patients. Therefore, precisely predicting a patient's response before ICI treatment is crucial. METHODS: A total of 248 patients with anti-Programmed cell death protein 1/Programmed death-ligand 1 (PD1/PD-L1)-treated advanced non-small cell lung cancer were enrolled, and clinical outcomes were collected with a minimum 6-month follow-up period. Tumour tissues were used for PD-L1 staining, targeted sequencing of 380 cancer-related genes and whole-exome sequencing (WES). RESULTS: The tumour mutation burden (TMB) obtained from targeted sequencing was higher among patients with a partial response (PR) than those with progressive disease (PD)/stable disease (SD) (P = 0.01) and in those with durable clinical benefit (DCB) than nondurable benefit (NDB) (P = 0.05). The somatic copy number alteration (SCNA) was lower in patients with a PR than those with PD/SD (P = 0.02) and in those with DCB than NDB (P = 0.02). The accuracy of the TMB and SCNA results from the targeted sequencing was confirmed by testing the correlation of the TMB and SCNA results from the targeted sequencing against those results from WES (r = 0.87, r = 0.62, respectively). To improve prediction score, TMB, SCNA and PD-L1 were integrated. New prediction scores reached Area under the ROC Curve (AUC) = 0.71 from TMB (AUC = 0.63), SCNA (AUC = 0.52) or PD-L1 (AUC = 0.57) with our cohort, and validation set from other cohorts also showed improved prediction scores with our new model. CONCLUSION: We report TMB, SCNA and PD-L1 as ICI biomarkers. Combining all these factors improved the prediction accuracy of ICI response compared with using individual factors. Tumour molecular features, TMB and SCNA, were efficiently obtained by targeted sequencing.

Comprehensive Clinical and Genetic Characterization of Hyperprogression Based on Volumetry in Advanced Non-Small Cell Lung Cancer Treated With Immune Checkpoint Inhibitor.

INTRODUCTION: Hyperprogressive disease (HPD), characterized by accelerated tumor progression, has been proposed as a new pattern of progression after immune checkpoint inhibitor (ICI) treatment. The aim of this study was to describe the characteristics of HPD and investigate its predictive markers. METHODS: Clinical and radiological findings of 335 patients with advanced NSCLC treated with ICI monotherapy were retrospectively analyzed. Radiological data were quantitatively and longitudinally analyzed for tumor size and volume by comparing baseline and follow-up computerized tomography results. The findings were matched with individual genomic profiles generated by deep sequencing of 380 genes. RESULTS: Among 135 patients with progressive disease (PD), as assessed by the Response Evaluation Criteria in Solid Tumors version 1.1 criteria, 48 (14.3% of all patients and 35.6% of those with PD) and 44 (13.1% of all patients and 32.6% among those with PD) were found to have HPD by volumetry (HPDV) and assessed by RECIST 1.1 (HPDR), respectively. Patients with HPDV were associated with significantly inferior overall survival (OS) versus that of patients without HPDV with PD (median OS = 4.7 months [95% confidence interval: 3.5-11.9)] versus 7.9 months [95% confidence interval: 6.0-13.5] [p = 0.004]); OS did not differ between patients without and without HPDR. HPDV status was an independent factor in OS. A derived neutrophil-to-lymphocyte ratio greater than 4 and lactate dehydrogenase level greater than the upper limit of normal were significantly associated with HPDV. Moreover, we identified coinciding KRAS and serine/threonine kinase 11 gene (STK11) mutations in the cohort of patients with HPDV (three of 16), whereas none were found in the cohort of patients without HPDV (zero of 28). CONCLUSIONS: Defining HPD treated with ICI on the basis of volumetric measurement is more precise than is defining it on the basis of one-dimensional analysis. Pre-ICI derived neutrophil-to-lymphocyte ratio, lactate dehydrogenase level, and concurrence of STK11 and KRAS mutations could thus be used as potential biomarkers for HPD prediction.

Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer.

PURPOSE: This study investigated the correlations between parameters of 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography (PET) scan and indices of genetic properties, heterogeneity index (HI), and tumor mutation burden (TMB), in patients with lung cancer. METHODS: We produced 106 PET indices for each tumor site that underwent genomic analysis in a total of 176 study subjects (age, 62.0 ± 10.0 y; males, 68.2%), comprising 101 adenocarcinoma (ADC), 29 squamous cell carcinoma (SQCC), and 46 small cell lung cancer (SCLC) patients. We then examined the correlations of the PET parameters with genetic properties of HI and TMB, according to pathology and tumor site. RESULTS: Comparisons between PET parameters and the genetic properties with false discovery rate (FDR) correction revealed that the surface standard uptake value (SUV) entropy of SUV statistics had a significant correlation with HI only in patients with SCLC who underwent a genetic test in lymph nodes (r = 0.592, p = 0.028), whereas PET parameters did not show a significant correlation with HI or TMB in patients with SCLC who underwent a genetic test in lung tissue. In patients with ADC and SQCC, there was no significant correlation between PET parameters and the genetic properties. Although SUVmax showed raw p values less than 0.05 in correlation with HI (r = 0.315, raw p = 0.048) and TMB (r = 0.206, raw p = 0.043) in ADC, and SUVpeak had a raw p value less than 0.05 in correlation with HI (r = 0.394, raw p = 0.046) in SQCC, these parameters were not significant when corrected by FDR. CONCLUSIONS: In this study, surface SUV entropy had a significant correlation with HI in SCLC. Regarding other PET parameters and tumors, no significant correlation with genetic parameters existed.

Tumor Heterogeneity Predicts Metastatic Potential in Colorectal Cancer.

Purpose: Tumors continuously evolve to maintain growth; secondary mutations facilitate this process, resulting in high tumor heterogeneity. In this study, we compared mutations in paired primary and metastatic colorectal cancer tumor samples to determine whether tumor heterogeneity can predict tumor metastasis.Experimental Design: Somatic variations in 46 pairs of matched primary-liver metastatic tumors and 42 primary tumors without metastasis were analyzed by whole-exome sequencing. Tumor clonality was estimated from single-nucleotide and copy-number variations. The correlation between clinical parameters of patients and clonal heterogeneity in liver metastasis was evaluated.Results: Tumor heterogeneity across colorectal cancer samples was highly variable; however, a high degree of tumor heterogeneity was associated with a worse disease-free survival. Highly heterogeneous primary colorectal cancer was correlated with a higher rate of liver metastasis. Recurrent somatic mutations in APC, TP53, and KRAS were frequently detected in highly heterogeneous colorectal cancer. The variant allele frequency of these mutations was high, while somatic mutations in other genes such as PIK3CA and NOTCH1 were low. The number and distribution of primary colorectal cancer subclones were preserved in metastatic tumors.Conclusions: Heterogeneity of primary colorectal cancer tumors can predict the potential for liver metastasis and thus, clinical outcome of patients. Clin Cancer Res; 23(23); 7209-16. ©2017 AACR.