An atlas of epithelial cell states and plasticity in lung adenocarcinoma.

Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention.

Genome-wide methylation profiling identifies a novel gene signature for patients with synchronous colorectal cancer.

BACKGROUND: There are no robust tools for the diagnosis of synchronous colorectal cancer (SyCRC). Herein, we developed the first methylation signature to identify and characterise patients with SyCRC. METHODS: For biomarker discovery, we analysed the genome-wide methylation profiles of 16 SyCRC and 18 solitary colorectal cancer (SoCRC) specimens. We thereafter established a methylation signature risk-scoring model to identify SyCRC in an independent cohort of 38 SyCRC and 42 SoCRC patients. In addition, we evaluated the prognostic value of the identified methylation profile. RESULTS: We identified six differentially methylated CpG probes/sites that distinguished SyCRC from SoCRC. In the validation cohort, we developed a methylation panel that identified patients with SyCRC from not only larger tumour (AUC = 0.91) but also the paired remaining tumour (AUC = 0.93). Moreover, high risk scores of our panel were associated with the development of metachronous CRC among patients with SyCRC (AUC = 0.87) and emerged as an independent predictor for relapse-free survival (hazard ratio = 2.72; 95% CI = 1.12-6.61). Furthermore, the risk stratification model which combined with clinical risk factors was a diagnostic predictor of recurrence (AUC = 0.90). CONCLUSIONS: Our novel six-gene methylation panel robustly identifies patients with SyCRC, which has the clinical potential to improve the diagnosis and management of patients with CRC.

A DNA Methylation-based Epigenetic Signature for the Identification of Lymph Node Metastasis in T1 Colorectal Cancer.

OBJECTIVE: This study aimed to unravel the lymph node metastasis (LNM)-related methylated DNA (mDNA) landscape and develop a mDNA signature to identify LNM in patients with T1 colorectal cancers (T1 CRC). BACKGROUND: Considering the invasiveness of T1 CRC, current guidelines recommend endoscopic resection in patients with LNM-negative, and radical surgical resection only for high-risk LNM-positive patients. Unfortunately, the clinicopathological criteria for LNM risk stratification are imperfect, resulting in frequent misdiagnosis leading to unnecessary radical surgeries and postsurgical complications. METHODS: We conducted genome-wide methylation profiling of 39 T1 CRC specimens to identify differentially methylated CpGs between LNM-positive and LNM-negative, and performed quantitative pyrosequencing analysis in 235 specimens from 3 independent patient cohorts, including 195 resected tissues (training cohort: n=128, validation cohort: n=67) and 40 pretreatment biopsies. RESULTS: Using logistic regression analysis, we developed a 9-CpG signature to distinguish LNM-positive versus LNM-negative surgical specimens in the training cohort [area under the curve (AUC)=0.831, 95% confidence interval (CI)=0.755-0.892; P <0.0001], which was subsequently validated in additional surgical specimens (AUC=0.825; 95% CI=0.696-0.955; P =0.003) and pretreatment biopsies (AUC=0.836; 95% CI=0.640-1.000, P =0.0036). This diagnostic power was further improved by combining the signature with conventional clinicopathological features. CONCLUSIONS: We established a novel epigenetic signature that can robustly identify LNM in surgical specimens and even pretreatment biopsies from patients with T1 CRC. Our signature has strong translational potential to improve the selection of high-risk patients who require radical surgery while sparing others from its complications and expense.

Genome-wide association studies and CRISPR/Cas9-mediated gene editing identify regulatory variants influencing eyebrow thickness in humans.

Hair plays an important role in primates and is clearly subject to adaptive selection. While humans have lost most facial hair, eyebrows are a notable exception. Eyebrow thickness is heritable and widely believed to be subject to sexual selection. Nevertheless, few genomic studies have explored its genetic basis. Here, we performed a genome-wide scan for eyebrow thickness in 2961 Han Chinese. We identified two new loci of genome-wide significance, at 3q26.33 near SOX2 (rs1345417: P = 6.51×10(-10)) and at 5q13.2 near FOXD1 (rs12651896: P = 1.73×10(-8)). We further replicated our findings in the Uyghurs, a population from China characterized by East Asian-European admixture (N = 721), the CANDELA cohort from five Latin American countries (N = 2301), and the Rotterdam Study cohort of Dutch Europeans (N = 4411). A meta-analysis combining the full GWAS results from the three cohorts of full or partial Asian descent (Han Chinese, Uyghur and Latin Americans, N = 5983) highlighted a third signal of genome-wide significance at 2q12.3 (rs1866188: P = 5.81×10(-11)) near EDAR. We performed fine-mapping and prioritized four variants for further experimental verification. CRISPR/Cas9-mediated gene editing provided evidence that rs1345417 and rs12651896 affect the transcriptional activity of the nearby SOX2 and FOXD1 genes, which are both involved in hair development. Finally, suitable statistical analyses revealed that none of the associated variants showed clear signals of selection in any of the populations tested. Contrary to popular speculation, we found no evidence that eyebrow thickness is subject to strong selective pressure.

Meta-analysis of genome-wide association studies identifies 8 novel loci involved in shape variation of human head hair.

Shape variation of human head hair shows striking variation within and between human populations, while its genetic basis is far from being understood. We performed a series of genome-wide association studies (GWASs) and replication studies in a total of 28 964 subjects from 9 cohorts from multiple geographic origins. A meta-analysis of three European GWASs identified 8 novel loci (1p36.23 ERRFI1/SLC45A1, 1p36.22 PEX14, 1p36.13 PADI3, 2p13.3 TGFA, 11p14.1 LGR4, 12q13.13 HOXC13, 17q21.2 KRTAP, and 20q13.33 PTK6), and confirmed 4 previously known ones (1q21.3 TCHH/TCHHL1/LCE3E, 2q35 WNT10A, 4q21.21 FRAS1, and 10p14 LINC00708/GATA3), all showing genome-wide significant association with hair shape (P < 5e-8). All except one (1p36.22 PEX14) were replicated with nominal significance in at least one of the 6 additional cohorts of European, Native American and East Asian origins. Three additional previously known genes (EDAR, OFCC1, and PRSS53) were confirmed at the nominal significance level. A multivariable regression model revealed that 14 SNPs from different genes significantly and independently contribute to hair shape variation, reaching a cross-validated AUC value of 0.66 (95% CI: 0.62-0.70) and an AUC value of 0.64 in an independent validation cohort, providing an improved accuracy compared with a previous model. Prediction outcomes of 2504 individuals from a multiethnic sample were largely consistent with general knowledge on the global distribution of hair shape variation. Our study thus delivers target genes and DNA variants for future functional studies to further evaluate the molecular basis of hair shape in humans.

A landscape of synthetic viable interactions in cancer.

Synthetic viability, which is defined as the combination of gene alterations that can rescue the lethal effects of a single gene alteration, may represent a mechanism by which cancer cells resist targeted drugs. Approaches to detect synthetic viable (SV) interactions in cancer genome to investigate drug resistance are still scarce. Here, we present a computational method to detect synthetic viability-induced drug resistance (SVDR) by integrating the multidimensional data sets, including copy number alteration, whole-exome mutation, expression profile and clinical data. SVDR comprehensively characterized the landscape of SV interactions across 8580 tumors in 32 cancer types by integrating The Cancer Genome Atlas data, small hairpin RNA-based functional experimental data and yeast genetic interaction data. We revealed that the SV interactions are favorable to cells and can predict clinical prognosis for cancer patients, which were robustly observed in an independent data set. By integrating the cancer pharmacogenomics data sets from Cancer Cell Line Encyclopedia (CCLE) and Broad Cancer Therapeutics Response Portal, we have demonstrated that SVDR enables drug resistance prediction and exhibits high reliability between two databases. To our knowledge, SVDR is the first genome-scale data-driven approach for the identification of SV interactions related to drug resistance in cancer cells. This data-driven approach lays the foundation for identifying the genomic markers to predict drug resistance and successfully infers the potential drug combination for anti-cancer therapy.

A genome-wide association study identifies FSHR rs2300441 associated with follicle-stimulating hormone levels.

Follicle-stimulating hormone (FSH) and luteinizing hormone (LH) play critical roles in female reproduction, while the underlying genetic basis is poorly understood. Genome-wide association studies (GWASs) of FSH and LH levels were conducted in 2590 Chinese females including 1882 polycystic ovary syndrome (PCOS) cases and 708 controls. GWAS for FSH level identified multiple variants at FSHR showing genome-wide significance with the top variant (rs2300441) located in the intron of FSHR. The A allele of rs2300441 led to a reduced level of FSH in the PCOS group (ß = -.43, P = 6.70 × 10-14 ) as well as in the control group (ß = -.35, P = 6.52 × 10-4 ). In the combined sample, this association was enhanced after adjusting for the PCOS status (before: ß = -.38, P = 1.77 × 10-13 ; after: ß = -.42, P = 3.33 × 10-16 ), suggesting the genetic effect is independent of the PCOS status. The rs2300441 explained sevenfold higher proportion of the FSH variance than the total variance explained by the two previously reported FSHR missense variants (rs2300441 R2 = 1.40% vs rs6166 R2 = 0.17%, rs6165 R2 = 0.03%). GWAS for LH did not identify any genome-wide significant associations. In conclusion, we identified genome-wide significant association between variants in FSHR and circulating FSH first, with the top associated variant rs2300441 might be a primary contributor at the population level.

Differential expression analysis at the individual level reveals a lncRNA prognostic signature for lung adenocarcinoma.

BACKGROUND: Deregulations of long non-coding RNAs (lncRNAs) have been implicated in cancer initiation and progression. Current methods can only capture differential expression of lncRNAs at the population level and ignore the heterogeneous expression of lncRNAs in individual patients. METHODS: We propose a method (LncRIndiv) to identify differentially expressed (DE) lncRNAs in individual cancer patients by exploiting the disrupted ordering of expression levels of lncRNAs in each disease sample in comparison with stable normal ordering. LncRIndiv was applied to lncRNA expression profiles of lung adenocarcinoma (LUAD). Based on the expression profile of LUAD individual-level DE lncRNAs, we used a forward selection procedure to identify prognostic signature for stage I-II LUAD patients without adjuvant therapy. RESULTS: In both simulated data and real pair-wise cancer and normal sample data, LncRIndiv method showed good performance. Based on the individual-level DE lncRNAs, we developed a robust prognostic signature consisting of two lncRNA (C1orf132 and TMPO-AS1) for stage I-II LUAD patients without adjuvant therapy (P = 3.06 × 10-6, log-rank test), which was confirmed in two independent datasets of GSE50081 (P = 1.82 × 10-2, log-rank test) and GSE31210 (P = 7.43 × 10-4, log-rank test) after adjusting other clinical factors such as smoking status and stages. Pathway analysis showed that TMPO-AS1 and C1orf132 could affect the prognosis of LUAD patients through regulating cell cycle and cell adhesion. CONCLUSIONS: LncRIndiv can successfully detect DE lncRNAs in individuals and be applied to identify prognostic signature for LUAD patients.

Autophagy-related prognostic signature for breast cancer.

Autophagy is a process that degrades intracellular constituents, such as long-lived or damaged proteins and organelles, to buffer metabolic stress under starvation conditions. Deregulation of autophagy is involved in the progression of cancer. However, the predictive value of autophagy for breast cancer prognosis remains unclear. First, based on gene expression profiling, we found that autophagy genes were implicated in breast cancer. Then, using the Cox proportional hazard regression model, we detected autophagy prognostic signature for breast cancer in a training dataset. We identified a set of eight autophagy genes (BCL2, BIRC5, EIF4EBP1, ERO1L, FOS, GAPDH, ITPR1 and VEGFA) that were significantly associated with overall survival in breast cancer. The eight autophagy genes were assigned as a autophagy-related prognostic signature for breast cancer. Based on the autophagy-related signature, the training dataset GSE21653 could be classified into high-risk and low-risk subgroups with significantly different survival times (HR = 2.72, 95% CI = (1.91, 3.87); P = 1.37 × 10(-5)). Inactivation of autophagy was associated with shortened survival of breast cancer patients. The prognostic value of the autophagy-related signature was confirmed in the testing dataset GSE3494 (HR = 2.12, 95% CI = (1.48, 3.03); P = 1.65 × 10(-3)) and GSE7390 (HR = 1.76, 95% CI = (1.22, 2.54); P = 9.95 × 10(-4)). Further analysis revealed that the prognostic value of the autophagy signature was independent of known clinical prognostic factors, including age, tumor size, grade, estrogen receptor status, progesterone receptor status, ERBB2 status, lymph node status and TP53 mutation status. Finally, we demonstrated that the autophagy signature could also predict distant metastasis-free survival for breast cancer.

Evolutionary fingerprints of EMT in pancreatic cancers.

Mesenchymal plasticity has been extensively described in advanced and metastatic epithelial cancers; however, its functional role in malignant progression, metastatic dissemination and therapy response is controversial. More importantly, the role of epithelial mesenchymal transition (EMT) and cell plasticity in tumor heterogeneity, clonal selection and clonal evolution is poorly understood. Functionally, our work clarifies the contribution of EMT to malignant progression and metastasis in pancreatic cancer. We leveraged ad hoc somatic mosaic genome engineering, lineage tracing and ablation technologies and dynamic genetic reporters to trace and ablate tumor-specific lineages along the phenotypic spectrum of epithelial to mesenchymal plasticity. The experimental evidences clarify the essential contribution of mesenchymal lineages to pancreatic cancer evolution and metastatic dissemination. Spatial genomic analysis combined with single cell transcriptomic and epigenomic profiling of epithelial and mesenchymal lineages reveals that EMT promotes with the emergence of chromosomal instability (CIN). Specifically tumor lineages with mesenchymal features display highly conserved patterns of genomic evolution including complex structural genomic rearrangements and chromotriptic events. Genetic ablation of mesenchymal lineages robustly abolished these mutational processes and evolutionary patterns, as confirmed by cross species analysis of pancreatic and other human epithelial cancers. Mechanistically, we discovered that malignant cells with mesenchymal features display increased chromatin accessibility, particularly in the pericentromeric and centromeric regions, which in turn results in delayed mitosis and catastrophic cell division. Therefore, EMT favors the emergence of high-fitness tumor cells, strongly supporting the concept of a cell-state, lineage-restricted patterns of evolution, where cancer cell sub-clonal speciation is propagated to progenies only through restricted functional compartments. Restraining those evolutionary routes through genetic ablation of clones capable of mesenchymal plasticity and extinction of the derived lineages completely abrogates the malignant potential of one of the most aggressive form of human cancer.

Evolution of immune and stromal cell states and ecotypes during gastric adenocarcinoma progression.

Understanding tumor microenvironment (TME) reprogramming in gastric adenocarcinoma (GAC) progression may uncover novel therapeutic targets. Here, we performed single-cell profiling of precancerous lesions, localized and metastatic GACs, identifying alterations in TME cell states and compositions as GAC progresses. Abundant IgA+ plasma cells exist in the premalignant microenvironment, whereas immunosuppressive myeloid and stromal subsets dominate late-stage GACs. We identified six TME ecotypes (EC1-6). EC1 is exclusive to blood, while EC4, EC5, and EC2 are highly enriched in uninvolved tissues, premalignant lesions, and metastases, respectively. EC3 and EC6, two distinct ecotypes in primary GACs, associate with histopathological and genomic characteristics, and survival outcomes. Extensive stromal remodeling occurs in GAC progression. High SDC2 expression in cancer-associated fibroblasts (CAFs) is linked to aggressive phenotypes and poor survival, and SDC2 overexpression in CAFs contributes to tumor growth. Our study provides a high-resolution GAC TME atlas and underscores potential targets for further investigation.

Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance.

Tumor-infiltrating T cells offer a promising avenue for cancer treatment, yet their states remain to be fully characterized. Here we present a single-cell atlas of T cells from 308,048 transcriptomes across 16 cancer types, uncovering previously undescribed T cell states and heterogeneous subpopulations of follicular helper, regulatory and proliferative T cells. We identified a unique stress response state, TSTR, characterized by heat shock gene expression. TSTR cells are detectable in situ in the tumor microenvironment across various cancer types, mostly within lymphocyte aggregates or potential tertiary lymphoid structures in tumor beds or surrounding tumor edges. T cell states/compositions correlated with genomic, pathological and clinical features in 375 patients from 23 cohorts, including 171 patients who received immune checkpoint blockade therapy. We also found significantly upregulated heat shock gene expression in intratumoral CD4/CD8+ cells following immune checkpoint blockade treatment, particularly in nonresponsive tumors, suggesting a potential role of TSTR cells in immunotherapy resistance. Our well-annotated T cell reference maps, web portal and automatic alignment/annotation tool could provide valuable resources for T cell therapy optimization and biomarker discovery.

[Quantitative analysis of DNA methylation by pyrosequencing and MassARRAY technique for age estimation: a comparative study].

OBJECTIVE: To study the difference in age estimation based on quantitative analysis of DNA methylation by MassARRAY and pyrosequencing techniques. METHODS: The methylation levels of 9 CpG sites from two independent whole blood sample sets (containing 65 and 62 samples) were detected using MassARRAY and pyrosequencing techniques. Z-score transformation was used to remove the batch effects of different techniques, and a linear regression model was used for age prediction. RESULTS: For age prediction using the MassARRAY system, the 65 samples showed a mean absolute difference (MAD) of 2.49 years before Z-score transformation of the data and 2.44 years after the transformation, similar to the results in the 62 samples (MAD of 3.36 years before and 3.42 years after Z-score transformation). For data typed from pyrosequencing, the 65 samples showed a MAD of 4.20 years before and 2.76 years after data Z-score transformation, also similar to the results in the 62 samples (MAD of 3.92 years before and 3.63 years after data transformation). CONCLUSIONS: Z-score transformation can effectively reduce the system batch effect between MassARRAY and pyrosequencing. Data from the MassARRAY system allows direct age estimation without further data processing, while the pyrosequencing data may increase the error in age estimation, which can be corrected by Z-score transformation of the data.

Validation of methylation-based forensic age estimation in time-series bloodstains on FTA cards and gauze at room temperature conditions.

We previously proposed a prediction model consisting of 9 CpG sites for forensic age estimation with high practical potentials in Chinese males. Here, we further evaluated the performance of this prediction model in two independent batches of time-series bloodstain samples naturally exposed to room temperature conditions. The first batch consists of 30 Han Chinese males (18-59 years of age) whose peripheral blood was converted into bloodstains on Flinders Technology Association (FTA) cards and naturally exposed to room temperature conditions for different time points up to 3 months. The second batch consists of 99 Han Chinese males (21-66 years of age) whose peripheral blood was divided into 3 replicates, converted into bloodstains on gauze, and naturally exposed to room temperature conditions for 3 months. For each time point and each replicate, the methylation levels at the 9 CpG sites were detected using the EpiTYPER system. Applying the 9-CpG age prediction model to these bloodstain samples resulted in highly accurate age predictions for all time points and replicates (0.81

Systematic feature selection improves accuracy of methylation-based forensic age estimation in Han Chinese males.

Estimating individual age from biomarkers may provide key information facilitating forensic investigations. Recent progress has shown DNA methylation at age-associated CpG sites as the most informative biomarkers for estimating the individual age of an unknown donor. Optimal feature selection plays a critical role in determining the performance of the final prediction model. In this study we investigate methylation levels at 153 age-associated CpG sites from 21 previously reported genomic regions using the EpiTYPER system for their predictive power on individual age in 390 Han Chinese males ranging from 15 to 75 years of age. We conducted a systematic feature selection using a stepwise backward multiple linear regression analysis as well as an exhaustive searching algorithm. Both approaches identified the same subset of 9 CpG sites, which in linear combination provided the optimal model fitting with mean absolute deviation (MAD) of 2.89 years of age and explainable variance (R2) of 0.92. The final model was validated in two independent Han Chinese male samples (validation set 1, N = 65, MAD = 2.49, R2 = 0.95, and validation set 2, N = 62, MAD = 3.36, R2 = 0.89). Other competing models such as support vector machine and artificial neural network did not outperform the linear model to any noticeable degree. The validation set 1 was additionally analyzed using Pyrosequencing technology for cross-platform validation and was termed as validation set 3. Directly applying our model, in which the methylation levels were detected by the EpiTYPER system, to the data from pyrosequencing technology showed, however, less accurate results in terms of MAD (validation set 3, N = 65 Han Chinese males, MAD = 4.20, R2 = 0.93), suggesting the presence of a batch effect between different data generation platforms. This batch effect could be partially overcome by a z-score transformation (MAD = 2.76, R2 = 0.93). Overall, our systematic feature selection identified 9 CpG sites as the optimal subset for forensic age estimation and the prediction model consisting of these 9 markers demonstrated high potential in forensic practice. An age estimator implementing our prediction model allowing missing markers is freely available at http://liufan.big.ac.cn/AgePrediction.

The BRCA1/2-directed miRNA signature predicts a good prognosis in ovarian cancer patients with wild-type BRCA1/2.

Ovarian cancer patients carrying alterations (i.e., germline mutations, somatic mutations, hypermethylations and/or deletions) of BRCA1 or BRCA2 (BRCA1/2) have a better prognosis than BRCA1/2 alteration non-carriers. However, patients with wild-type BRCA1/2 may also have a favorable prognosis as a result of other mechanisms that remain poorly elucidated, such as the deregulation of miRNAs. We therefore sought to identify BRCA1/2-directed miRNA signatures that have prognostic value in ovarian cancer patients with wild-type BRCA1/2 and study how the deregulation of miRNAs impacts the prognosis of patients treated with platinum-based chemotherapy. By analyzing multidimensional datasets of ovarian cancer patients from the TCGA data portal, we identified three miRNAs (hsa-miR-146a, hsa-miR-148a and hsa-miR-545) that target BRCA1/2 and were associated with overall survival and progression-free survival in patients with wild-type BRCA1/2. By analyzing the expression profiles and Gene Ontology functional enrichment, we found that carriers of BRCA1/2 alterations and patients with miRNA deregulation shared a common mechanism, regulation of the DNA repair-related pathways, that affects the prognosis of ovarian cancer patients. Our work highlights that a proportion of patients with wild-type BRCA1/2 ovarian cancers benefit from platinum-based chemotherapy and that the patients who benefit could be predicted from BRCA1/2-directed miRNA signature.