Global and targeted serum metabolic profiling of colorectal cancer progression.

BACKGROUND: Patients with colorectal adenoma polyps (PLPs) are at higher risk for developing colorectal cancer (CRC). However, the development of improved and robust biomarkers to enable the screening, surveillance, and early detection of PLPs and CRC continues to be a challenge. The aim of this study was to identify biomarkers of progression to CRC through metabolomic profiling of human serum samples with a multistage approach. METHODS: Metabolomic profiling was conducted with the Metabolon platform for 30 CRC patients, 30 PLP patients, and 30 control subjects, and this was followed by the targeted validation of the top metabolites in an additional set of 50 CRC patients, 50 PLP patients, and 50 controls with liquid chromatography-tandem mass spectrometry. Unconditional multivariate logistic regression models, adjusted for covariates, were used to evaluate associations with PLP and CRC risk. RESULTS: For the discovery phase, 404 serum metabolites were detected, with 50 metabolites showing differential levels between CRC patients, PLP patients, and controls (P for trend < .05). After validation, the 3 top metabolites (xanthine, hypoxanthine, and d-mannose) were validated: lower levels of xanthine and hypoxanthine and higher levels of d-mannose were found in PLP and CRC cases versus controls. A further exploratory analysis of metabolic pathways revealed key roles for the urea cycle and caffeine metabolism associated with PLP and CRC risk. In addition, a joint effect of the top metabolites with smoking and a significant interaction with the body mass index were observed. An analysis of the ratio of hypoxanthine levels to xanthine levels indicated an association with CRC progression. CONCLUSIONS: These results suggest the potential utility of circulating metabolites as novel biomarkers for the early detection of CRC. Cancer 2017;123:4066-74. © 2017 American Cancer Society.

Identification of Serum Markers of Esophageal Adenocarcinoma by Global and Targeted Metabolic Profiling.

BACKGROUND & AIMS: We aimed to identify new serum biomarkers of esophageal adenocarcinoma (EAC). METHODS: We performed metabolomic analyses of serum samples from 2 sets of case-control pairs in the discovery phase, each consisting of 30 patients with histologically confirmed EAC (cases) from the University of Texas MD Anderson Cancer Center and 30 matched subjects without EAC (controls). We identified metabolites whose levels differed significantly between cases and controls and validated those with the greatest difference in an analysis of 321 EAC cases and 331 controls. We generated a metabolite risk score (MRS) for the metabolites. RESULTS: The levels of 64 metabolites differed significantly between EAC cases and controls in the discovery phase. The metabolites with the greatest difference were amino acid L-proline (LP), ketone body 3-hydroxybutyrate (BHBA), and carbohydrate D-mannose (DM); these differences were confirmed in the validation set. Cases had lower mean levels of LP than controls (22.78 ± 6.79 µg/mL vs 28.24 ± 8.64 µg/mL; P < .001) and higher levels of BHBA (18.06 ± 17.84 µg/mL vs 7.73 ± 9.92 µg/mL; P < .001) and DM (9.87 ± 4.28 µg/mL vs 6.28 ± 3.61 µg/mL; P < .001). Levels of DM were significantly higher in patients with late-stage EAC than early-stage EAC (10.61 ± 4.79 µg/mL vs 8.97 ± 3.36 µg/mL; P = .005). Higher levels of LP were associated with significant decrease in risk of EAC (odds ratio [OR], 0.26; 95% confidence interval [CI], 0.18-0.38). A significant increase in risk of EAC was associated with higher levels of BHBA (OR, 4.05; 95% CI, 2.84-5.78) and DM (OR, 7.04; 95% CI, 4.79-10.34). Levels of all 3 metabolites associated with EAC risk in a dose response manner; the level of risk conferred by the metabolites increased jointly with smoking status and body mass index. Individuals with high MRS had significant (7.76-fold) increase in risk of EAC vs those with low MRS. Smokers with high MRS had the greatest risk of EAC (OR, 23.40; 95% CI, 10.95-50.00), compared with never smokers with low MRS. CONCLUSIONS: On the basis of a case vs control metabolic profile analysis, levels of LP, BHBA, and DM are associated with risk of EAC. These markers might be used as risk and prognostic factors for patients with EAC.

Determination of proline in human serum by a robust LC-MS/MS method: application to identification of human metabolites as candidate biomarkers for esophageal cancer early detection and risk stratification.

Altered serum proline levels are related to cancer metabolism. This study developed and validated a LC-MS/MS method to analyze proline in human serum. Surrogate blank serum, coupled with stable isotope l-proline-(13) C5 ,(15) N as internal standard, was used for generating standard curves ranging from 2.5 to 100 µg/mL. Proline was extracted from serum samples using methanol. A Phenomenex Lux 5u Cellulose-1 column (250 × 4.6 mm) was used for chromatographic separation with 40% methanol in 0.05% formic acid aqueous solution as a mobile phase. Mass detection was performed under positive ionization electrospray. Intra- and inter-day accuracy and precision were <10%. The extraction recovery and matrix factor were 99.17 and 1.47%, respectively. Our study showed that the chiral column had high specificity and selectivity for separating proline from serum components. The assay was successfully applied for the quantification of human serum proline levels from 30 esophageal cancer patients and 30 healthy volunteers. Statistical analyses showed significantly lower levels of serum proline in the patients as compared with the healthy volunteers (p-value = 0.011). We report here a simple, specific and reproducible LC-MS/MS method for the quantification of proline in human serum as a potential screening biomarker for esophageal cancer.

Genomic Staging of Multifocal Lung Squamous Cell Carcinomas Is Independent of the Comprehensive Morphologic Assessment.

INTRODUCTION: Morphologic and molecular data for staging of multifocal lung squamous cell carcinomas (LSCCs) are limited. In this study, whole exome sequencing (WES) was used as the gold standard to determine whether multifocal LSCC represented separate primary lung cancers (SPLCs) or intrapulmonary metastases (IPMs). Genomic profiles were compared with the comprehensive morphologic assessment. METHODS: WES was performed on 20 tumor pairs of multifocal LSCC and matched normal lymph nodes using the Illumina NovaSeq6000 S4-Xp (Illumina, San Diego, CA). WES clonal and subclonal analysis data were compared with histologic assessment by 16 thoracic pathologists. In addition, the immune gene profiling of the study cases was characterized by the HTG EdgeSeq Precision Immuno-Oncology Panel. RESULTS: By WES data, 11 cases were classified as SPLC and seven cases as IPM. Two cases were technically suboptimal. Analysis revealed marked genomic and immunogenic heterogeneity, but immune gene expression profiles highly correlated with mutation profiles. Tumors classified as IPM have a large number of shared mutations (ranging from 33.5% to 80.7%). The agreement between individual morphologic assessments for each case and WES was 58.3%. One case was unanimously interpreted morphologically as IPM and was in agreement with WES. In a further 17 cases, the number of pathologists whose morphologic interpretation was in agreement with WES ranged from two (one case) to 15 pathologists (one case) per case. Pathologists showed a fair interobserver agreement in the morphologic staging of multiple LSCCs, with an overall kappa of 0.232. CONCLUSIONS: Staging of multifocal LSCC based on morphologic assessment is unreliable. Comprehensive genomic analyses should be adopted for the staging of multifocal LSCC.

MicroRNA signatures and treatment response in patients with advanced classical Hodgkin lymphoma.

Although specific microRNA (miRNA) signatures in classical Hodgkin lymphoma (cHL) have been proposed, their relationship with clinical outcome remains unclear. Despite treatment advances, a substantial subset of patients with advanced cHL are refractory to standard therapies based on adriamycin and its variants. Global miRNA expression data of 29 advanced cHL patients and five cHL-derived cell lines were used to identify profiles from Hodgkin-Reed-Sternberg (HRS) cells and their non-tumoural microenvironment. A cHL-miRNA signature was identified with 234 miRNAs differentially expressed. A subset of these miRNAs was associated with outcome and selected for study in an independent set of 168 cHL samples using quantitative reverse transcription polymerase chain reaction. Multivariate Cox regression analyses including cross-validation with failure-free survival (FFS) as clinical endpoint revealed a miRNA signature with MIR21, MIR30E, MIR30D and MIR92B* that identified two risk-groups with significant differences in 5-year FFS (81% vs. 35.7%; P < 0.001). Additionally, functional silencing of MIR21 and MIR30D in L428 cells showed increased sensitivity to doxorubicin-induced apoptosis, pointing towards abnormalities of mitochondrial intrinsic and TP53-CDKN1A pathways as related to miRNA deregulation in cHL. These results suggest that clinical outcome in cHL is associated with a specific miRNA signature. Moreover, functional analyses suggest a role for MIR21 and MIR30D in cHL pathogenesis and therapeutic resistance.

MicroRNAs as prognostic markers in indolent primary cutaneous B-cell lymphoma.

Indolent primary cutaneous B-cell lymphoma is a group of malignant lymphomas comprising marginal zone B-cell lymphoma and centrofollicular B-cell lymphoma. Relapse rate of these tumors is close to 40%, and identifying those patients who are likely to progress remains a challenge. The aim of this study was to characterize the microRNA (miRNA) expression profile of a series of primary cutaneous B-cell lymphomas and correlate with histological and clinical findings. We studied a series of 68 patients with primary cutaneous B-cell lymphomas (30 cutaneous marginal-zone B-cell lymphomas and 38 primary cutaneous centrofollicular lymphomas). A set of 11 miRNAs associated with the differentiation stage of B cells was quantified by real-time PCR, using RNA extracted from formalin-fixed, paraffin-embedded tissue diagnostic samples. Relevant clinical variables were retrieved in a subset of 57 patients (28 cutaneous marginal zone B-cell lymphomas and 29 primary cutaneous centrofollicular lymphomas). miR-150 was upregulated in cutaneous marginal zone B-cell lymphomas relative to primary cutaneous centrofollicular lymphoma samples (false discovery rate <0.05). miR-155 and miR-150 expression levels were associated with progression-free survival in a univariate Cox regression analysis (P<0.1). After stratification by histological subtype, low-expression levels of miR-155 and miR-150 were both associated with shorter progression-free survival only in primary cutaneous marginal zone B-cell lymphomas cases (log-rank test, P<0.05). In summary, miRNA expression analysis can be used as a tool for diagnosis and outcome prognosis in indolent primary cutaneous B-cell lymphoma.

miRNA expression in diffuse large B-cell lymphoma treated with chemoimmunotherapy.

Diffuse large B-cell lymphoma (DLBCL) prognostication requires additional biologic markers. miRNAs may constitute markers for cancer diagnosis, outcome, or therapy response. In the present study, we analyzed the miRNA expression profile in a retrospective multicenter series of 258 DLBCL patients uniformly treated with chemoimmunotherapy. Findings were correlated with overall survival (OS) and progression-free survival (PFS). miRNA and gene-expression profiles were studied using microarrays in an initial set of 36 cases. A selection of miRNAs associated with either DLBCL molecular subtypes (GCB/ABC) or clinical outcome were studied by multiplex RT-PCR in a test group of 240 cases with available formalin-fixed, paraffin-embedded (FFPE) diagnostic samples. The samples were divided into a training set (123 patients) and used to derive miRNA-based and combined (with IPI score) Cox regression models in an independent validation series (117 patients). Our model based on miRNA expression predicts OS and PFS and improves upon the predictions based on clinical variables. Combined models with IPI score identified a high-risk group of patients with a 2-year OS and a PFS probability of < 50%. In summary, a precise miRNA signature is associated with poor clinical outcome in chemoimmunotherapy-treated DLBCL patients. This information improves upon IPI-based predictions and identifies a subgroup of candidate patients for alternative therapeutic regimens.

Clinical significance of both tumor and stromal expression of components of the IL-1 and TNF-α signaling pathways in prostate cancer.

IL-1 and TNF-α, the two major proinflammatory cytokines, have been involved in initiation and progression of several malignancies. They could influence the biological behavior of prostatic tumors and patient outcome, and could be useful as prognostic factors. This study evaluated the prognostic capability for biochemical progression after radical prostatectomy of expression of IL-1, TNF-α and related signaling components, in the tumor and surrounding stroma, as well as its correlation with other clinicopathological features. Expression of IL-1α, IL-1ß, IL-1Ra, IL-1RI, IL-1RII, IRAK-1, TRAF6, TNF-α, TNFRI and TRAF2 was analyzed by immunohistochemistry in radical prostatectomy samples from 93 prostate cancer patients. Spearman's test, Kaplan-Meier curves, and univariate and multivariate Cox proportional hazard regression analyses were performed. Expression of TNF-α, TNFRI, TRAF2, ILRI, IRAK-1 and TRAF6 correlated with at least one clinicopathological feature (clinical T stage, pathological T stage, preoperative serum PSA or Gleason score). Increased tumor expression of TNF-α, TNFRI and IL-1RI, and reduced tumor expression of IRAK-1 were significantly correlated with a poor prognosis in univariate analysis. Reduced stromal expression of IL-1ß and IL-1RII, and increased stromal expression of IRAK-1 were also adverse prognostic factors in univariate analysis. Remarkably, tumor IL-1ß and stromal IL-1RII and IRAK-1 remained as independent prognostic factors after adjustment for preoperative serum PSA, pathological T stage and Gleason score in multivariate Cox models. Our results suggest that prostatic expression of TNF-α, IL-1ß and related signaling proteins (TNFRI, IL-1RI, IL-1RII and IRAK-1) predicts clinical outcome in prostate cancer, and support the involvement of TNF-α and IL-1ß signaling in prostate cancer progression.

International Association for the Study of Lung Cancer Study of Reproducibility in Assessment of Pathologic Response in Resected Lung Cancers After Neoadjuvant Therapy.

INTRODUCTION: Pathologic response has been proposed as an early clinical trial end point of survival after neoadjuvant treatment in clinical trials of NSCLC. The International Association for the Study of Lung Cancer (IASLC) published recommendations for pathologic evaluation of resected lung cancers after neoadjuvant therapy. The aim of this study was to assess pathologic response interobserver reproducibility using IASLC criteria. METHODS: An international panel of 11 pulmonary pathologists reviewed hematoxylin and eosin-stained slides from the lung tumors of resected NSCLC from 84 patients who received neoadjuvant immune checkpoint inhibitors in six clinical trials. Pathologic response was assessed for percent viable tumor, necrosis, and stroma. For each slide, tumor bed area was measured microscopically, and pre-embedded formulas calculated unweighted and weighted major pathologic response (MPR) averages to reflect variable tumor bed proportion. RESULTS: Unanimous agreement among pathologists for MPR was observed in 68 patients (81%), and inter-rater agreement (IRA) was 0.84 (95% confidence interval [CI]: 0.76-0.92) and 0.86 (95% CI: 0.79-0.93) for unweighted and weighted averages, respectively. Overall, unweighted and weighted methods did not reveal significant differences in the classification of MPR. The highest concordance by both methods was observed for cases with more than 95% viable tumor (IRA = 0.98, 95% CI: 0.96-1) and 0% viable tumor (IRA = 0.94, 95% CI: 0.89-0.98). The most common reasons for discrepancies included interpretations of tumor bed, presence of prominent stromal inflammation, distinction between reactive and neoplastic pneumocytes, and assessment of invasive mucinous adenocarcinoma. CONCLUSIONS: Our study revealed excellent reliability in cases with no residual viable tumor and good reliability for MPR with the IASLC recommended less than or equal to 10% cutoff for viable tumor after neoadjuvant therapy.

A molecular risk score based on 4 functional pathways for advanced classical Hodgkin lymphoma.

Despite improvement in the treatment of advanced classical Hodgkin lymphoma, approximately 30% of patients relapse or die as result of the disease. Current predictive systems, determined by clinical and analytical parameters, fail to identify these high-risk patients accurately. We took a multistep approach to design a quantitative reverse-transcription polymerase chain reaction assay to be applied to routine formalin-fixed paraffin-embedded samples, integrating genes expressed by the tumor cells and their microenvironment. The significance of 30 genes chosen on the basis of previously published data was evaluated in 282 samples (divided into estimation and validation sets) to build a molecular risk score to predict failure. Adequate reverse-transcription polymerase chain reaction profiles were obtained from 262 of 282 cases (92.9%). Best predictor genes were integrated into an 11-gene model, including 4 functional pathways (cell cycle, apoptosis, macrophage activation, and interferon regulatory factor 4) able to identify low- and high-risk patients with different rates of 5-year failure-free survival: 74% versus 44.1% in the estimation set (P < .001) and 67.5% versus 45.0% in the validation set (P = .022). This model can be combined with stage IV into a final predictive model able to identify a group of patients with very bad outcome (5-year failure-free survival probability, 25.2%).

Immunohistochemical markers for tumor associated macrophages and survival in advanced classical Hodgkin's lymphoma.

A subset of patients with advanced classical Hodgkin's lymphoma is refractory to standard therapies. Therefore, it is relevant to identify new biologically-based prognostic markers. Recently, tumor associated macrophages have been proposed as a factor that predicts survival, although contradictory results have also been reported. Here we analyzed four macrophage markers (CD68, CD163, LYZ, and STAT1) using immunohistochemistry and automated quantification, in two independent series of advanced classical Hodgkin's lymphoma (n=266 and 103 patients, respectively). Our results did not confirm that specific macrophage immunohistochemical markers could be used as surrogates for gene expression profiling studies. Survival analyses did not show correlation between CD163, LYZ or STAT1 and either failure-free or disease-specific survival. There was an association between CD68 and disease-specific survival, but it was not consistent in both series. In conclusion, individual tumor associated macrophage markers cannot be used to predict outcome before technical standardization and prospective validation in independent series of patients with comparable stages and treatments.

Shared Nearest Neighbors Approach and Interactive Browser for Network Analysis of a Comprehensive Non-Small-Cell Lung Cancer Data Set.

PURPOSE: Advances in biological measurement technologies are enabling large-scale studies of patient cohorts across multiple omics platforms. Holistic analysis of these data can generate actionable insights for translational research and necessitate new approaches for data integration and mining. METHODS: We present a novel approach for integrating data across platforms on the basis of the shared nearest neighbors algorithm and use it to create a network of multiplatform data from the immunogenomic profiling of non-small-cell lung cancer project. RESULTS: Benchmarking demonstrates that the shared nearest neighbors-based network approach outperforms a traditional gene-gene network in capturing established interactions while providing new ones on the basis of the interplay between measurements from different platforms. When used to examine patient characteristics of interest, our approach provided signatures associated with and new leads related to recurrence and TP53 oncogenotype. CONCLUSION: The network developed offers an unprecedented, holistic view into immunogenomic profiling of non-small-cell lung cancer, which can be explored through the accompanying interactive browser that we built.

Differential expression of JAK2 and Src kinase genes in response to hydroxyurea treatment in polycythemia vera and essential thrombocythemia.

This study investigates the differential gene expression profile of JAK2(V617F)-positive myeloproliferative neoplasm (MPN) patients, with and without response to hydroxyurea (HU) treatment. Twenty-one polycythemia vera, 28 essential thrombocythemia, eight secondary erythrocytosis, and 30 controls were studied. Thirty-four genes were overexpressed in patients who did not respond to HU. Of these, some participate in proliferative pathways: MAPK, AKT, Src kinase (SFK), and JAK2 pathway. JAK2 allele burden was similar between groups of responders and nonresponder. A molecular fingerprint distinguishes JAK2(V617F)-positive MPN patients without response to HU treatment, with overexpression of JAK2, MAPK14, PIK3CA, and SFK genes.

Multiplex Tissue Imaging Harmonization: A Multicenter Experience from CIMAC-CIDC Immuno-Oncology Biomarkers Network.

PURPOSE: The Cancer Immune Monitoring and Analysis Centers - Cancer Immunologic Data Commons (CIMAC-CIDC) network supported by the NCI Cancer Moonshot initiative was established to provide correlative analyses for clinical trials in cancer immunotherapy, using state-of-the-art technology. Fundamental to this initiative is implementation of multiplex IHC assays to define the composition and distribution of immune infiltrates within tumors in the context of their potential role as biomarkers. A critical unanswered question involves the relative fidelity of such assays to reliably quantify tumor-associated immune cells across different platforms. EXPERIMENTAL DESIGN: Three CIMAC sites compared across their laboratories: (i) image analysis algorithms, (ii) image acquisition platforms, (iii) multiplex staining protocols. Two distinct high-dimensional approaches were employed: multiplexed IHC consecutive staining on single slide (MICSSS) and multiplexed immunofluorescence (mIF). To eliminate variables potentially impacting assay performance, we completed a multistep harmonization process, first comparing assay performance using independent protocols followed by the integration of laboratory-specific protocols and finally, validating this harmonized approach in an independent set of tissues. RESULTS: Data generated at the final validation step showed an intersite Spearman correlation coefficient (r) of ≥0.85 for each marker within and across tissue types, with an overall low average coefficient of variation ≤0.1. CONCLUSIONS: Our results support interchangeability of protocols and platforms to deliver robust, and comparable data using similar tissue specimens and confirm that CIMAC-CIDC analyses may therefore be used with confidence for statistical associations with clinical outcomes largely independent of site, antibody selection, protocol, and platform across different sites.

Pretreatment Tissue TCR Repertoire Evenness Is Associated with Complete Pathologic Response in Patients with NSCLC Receiving Neoadjuvant Chemoimmunotherapy.

PURPOSE: Characterization of the T-cell receptor (TCR) repertoire may be a promising source for predictive biomarkers of pathologic response to immunotherapy in locally advanced non-small cell lung cancer (NSCLC). EXPERIMENTAL DESIGN: In this study, next-generation TCR sequencing was performed in peripheral blood and tissue samples of 40 patients with NSCLC, before and after neoadjuvant chemoimmunotherapy (NADIM clinical trial, NCT03081689), considering their complete pathologic response (CPR) or non-CPR. Beyond TCR metrics, tissue clones were ranked by their frequency and spatiotemporal evolution of top 1% clones was determined. RESULTS: We have found a positive association between an uneven TCR repertoire in tissue samples at diagnosis and CPR at surgery. Moreover, TCR most frequently ranked clones (top 1%) present in diagnostic biopsies occupied greater frequency in the total clonal space of CPR patients, achieving an AUC ROC to identify CPR patients of 0.967 (95% confidence interval, 0.897-1.000; P = 0.001), and improving the results of PD-L1 tumor proportion score (TPS; AUC = 0.767; P = 0.026) or tumor mutational burden (TMB; AUC = 0.550; P = 0.687). Furthermore, tumors with high pretreatment top 1% clonal space showed similar immune cell populations but a higher immune reactive gene expression profile. Finally, the selective expansion of pretreatment tissue top 1% clones in peripheral blood of CPR patients suggests also a peripheral immunosurveillance, which could explain the high survival rate of these patients. CONCLUSIONS: We have identified two parameters derived from TCR repertoire analysis that could outperform PD-L1 TPS and TMB as predictive biomarkers of CPR after neoadjuvant chemoimmunotherapy, and unraveled possible mechanisms of CPR involving enhanced tumor immunogenicity and peripheral immunosurveillance.

Single-Cell Expression Landscape of SARS-CoV-2 Receptor ACE2 and Host Proteases in Normal and Malignant Lung Tissues from Pulmonary Adenocarcinoma Patients.

The novel coronavirus SARS-CoV-2 is the causative agent of the COVID-19 pandemic. Severely symptomatic COVID-19 is associated with lung inflammation, pneumonia, and respiratory failure, thereby raising concerns of elevated risk of COVID-19-associated mortality among lung cancer patients. Angiotensin-converting enzyme 2 (ACE2) is the major receptor for SARS-CoV-2 entry into lung cells. The single-cell expression landscape of ACE2 and other SARS-CoV-2-related genes in pulmonary tissues of lung cancer patients remains unknown. We sought to delineate single-cell expression profiles of ACE2 and other SARS-CoV-2-related genes in pulmonary tissues of lung adenocarcinoma (LUAD) patients. We examined the expression levels and cellular distribution of ACE2 and SARS-CoV-2-priming proteases TMPRSS2 and TMPRSS4 in 5 LUADs and 14 matched normal tissues by single-cell RNA-sequencing (scRNA-seq) analysis. scRNA-seq of 186,916 cells revealed epithelial-specific expression of ACE2, TMPRSS2, and TMPRSS4. Analysis of 70,030 LUAD- and normal-derived epithelial cells showed that ACE2 levels were highest in normal alveolar type 2 (AT2) cells and that TMPRSS2 was expressed in 65% of normal AT2 cells. Conversely, the expression of TMPRSS4 was highest and most frequently detected (75%) in lung cells with malignant features. ACE2-positive cells co-expressed genes implicated in lung pathobiology, including COPD-associated HHIP, and the scavengers CD36 and DMBT1. Notably, the viral scavenger DMBT1 was significantly positively correlated with ACE2 expression in AT2 cells. We describe normal and tumor lung epithelial populations that express SARS-CoV-2 receptor and proteases, as well as major host defense genes, thus comprising potential treatment targets for COVID-19 particularly among lung cancer patients.

Immune Profiling Mass Cytometry Assay Harmonization: Multicenter Experience from CIMAC-CIDC.

PURPOSE: The Cancer Immune Monitoring and Analysis Centers - Cancer Immunologic Data Commons (CIMAC-CIDC) Network is supported by the NCI to identify biomarkers of response to cancer immunotherapies across clinical trials using state-of-the-art assays. A primary platform for CIMAC-CIDC studies is cytometry by time of flight (CyTOF), performed at all CIMAC laboratories. To ensure the ability to generate comparable CyTOF data across labs, a multistep cross-site harmonization effort was undertaken. EXPERIMENTAL DESIGN: We first harmonized standard operating procedures (SOPs) across the CIMAC sites. Because of a new acquisition protocol comparing original narrow- or new wide-bore injector introduced by the vendor (Fluidigm), we also tested this protocol across sites before finalizing the harmonized SOP. We then performed cross-site assay harmonization experiments using five shared cryopreserved and one lyophilized internal control peripheral blood mononuclear cell (PBMC) with a shared lyophilized antibody cocktail consisting of 14 isotype-tagged antibodies previously validated, plus additional liquid antibodies. These reagents and samples were distributed to the CIMAC sites and the data were centrally analyzed by manual gating and automated methods (Astrolabe). RESULTS: Average coefficients of variation (CV) across sites for each cell population were reported and compared with a previous multisite CyTOF study. We reached an intersite CV of under 20% for most cell subsets, very similar to a previously published study. CONCLUSIONS: These results establish the ability to reproduce CyTOF data across sites in multicenter clinical trials, and also highlight the importance of quality control procedures, such as the use of spike-in control samples, for tracking variability in this assay.

Cross-Site Concordance Evaluation of Tumor DNA and RNA Sequencing Platforms for the CIMAC-CIDC Network.

PURPOSE: Whole-exome (WES) and RNA sequencing (RNA-seq) are key components of cancer immunogenomic analyses. To evaluate the consistency of tumor WES and RNA-seq profiling platforms across different centers, the Cancer Immune Monitoring and Analysis Centers (CIMAC) and the Cancer Immunologic Data Commons (CIDC) conducted a systematic harmonization study. EXPERIMENTAL DESIGN: DNA and RNA were centrally extracted from fresh frozen and formalin-fixed paraffin-embedded non-small cell lung carcinoma tumors and distributed to three centers for WES and RNA-seq profiling. In addition, two 10-plex HapMap cell line pools with known mutations were used to evaluate the accuracy of the WES platforms. RESULTS: The WES platforms achieved high precision (> 0.98) and recall (> 0.87) on the HapMap pools when evaluated on loci using > 50× common coverage. Nonsynonymous mutations clustered by tumor sample, achieving an index of specific agreement above 0.67 among replicates, centers, and sample processing. A DV200 > 24% for RNA, as a putative presequencing RNA quality control (QC) metric, was found to be a reliable threshold for generating consistent expression readouts in RNA-seq and NanoString data. MedTIN > 30 was likewise assessed as a reliable RNA-seq QC metric, above which samples from the same tumor across replicates, centers, and sample processing runs could be robustly clustered and HLA typing, immune infiltration, and immune repertoire inference could be performed. CONCLUSIONS: The CIMAC collaborating laboratory platforms effectively generated consistent WES and RNA-seq data and enable robust cross-trial comparisons and meta-analyses of highly complex immuno-oncology biomarker data across the NCI CIMAC-CIDC Network.

Resolving the Spatial and Cellular Architecture of Lung Adenocarcinoma by Multiregion Single-Cell Sequencing.

Little is known of the geospatial architecture of individual cell populations in lung adenocarcinoma (LUAD) evolution. Here, we perform single-cell RNA sequencing of 186,916 cells from five early-stage LUADs and 14 multiregion normal lung tissues of defined spatial proximities from the tumors. We show that cellular lineages, states, and transcriptomic features geospatially evolve across normal regions to LUADs. LUADs also exhibit pronounced intratumor cell heterogeneity within single sites and transcriptional lineage-plasticity programs. T regulatory cell phenotypes are increased in normal tissues with proximity to LUAD, in contrast to diminished signatures and fractions of cytotoxic CD8+ T cells, antigen-presenting macrophages, and inflammatory dendritic cells. We further find that the LUAD ligand-receptor interactome harbors increased expression of epithelial CD24, which mediates protumor phenotypes. These data provide a spatial atlas of LUAD evolution, and a resource for identification of targets for its treatment. SIGNIFICANCE: The geospatial ecosystem of the peripheral lung and early-stage LUAD is not known. Our multiregion single-cell sequencing analyses unravel cell populations, states, and phenotypes in the spatial and ecologic evolution of LUAD from the lung that comprise high-potential targets for early interception.This article is highlighted in the In This Issue feature, p. 2355.

Network for Biomarker Immunoprofiling for Cancer Immunotherapy: Cancer Immune Monitoring and Analysis Centers and Cancer Immunologic Data Commons (CIMAC-CIDC).

PURPOSE: Immunoprofiling to identify biomarkers and integration with clinical trial outcomes are critical to improving immunotherapy approaches for patients with cancer. However, the translational potential of individual studies is often limited by small sample size of trials and the complexity of immuno-oncology biomarkers. Variability in assay performance further limits comparison and interpretation of data across studies and laboratories. EXPERIMENTAL DESIGN: To enable a systematic approach to biomarker identification and correlation with clinical outcome across trials, the Cancer Immune Monitoring and Analysis Centers and Cancer Immunologic Data Commons (CIMAC-CIDC) Network was established through support of the Cancer MoonshotSM Initiative of the National Cancer Institute (NCI) and the Partnership for Accelerating Cancer Therapies (PACT) with industry partners via the Foundation for the NIH. RESULTS: The CIMAC-CIDC Network is composed of four academic centers with multidisciplinary expertise in cancer immunotherapy that perform validated and harmonized assays for immunoprofiling and conduct correlative analyses. A data coordinating center (CIDC) provides the computational expertise and informatics platforms for the storage, integration, and analysis of biomarker and clinical data. CONCLUSIONS: This overview highlights strategies for assay harmonization to enable cross-trial and cross-site data analysis and describes key elements for establishing a network to enhance immuno-oncology biomarker development. These include an operational infrastructure, validation and harmonization of core immunoprofiling assays, platforms for data ingestion and integration, and access to specimens from clinical trials. Published in the same volume are reports of harmonization for core analyses: whole-exome sequencing, RNA sequencing, cytometry by time of flight, and IHC/immunofluorescence.