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Tumor-infiltrating B and plasma cells (TIB) are prevalent in lung adenocarcinoma (LUAD); however, they are poorly characterized. We performed paired single-cell RNA and B-cell receptor (BCR) sequencing of 16 early-stage LUADs and 47 matching multiregion normal tissues. By integrative analysis of ∼50,000 TIBs, we define 12 TIB subsets in the LUAD and adjacent normal ecosystems and demonstrate extensive remodeling of TIBs in LUADs. Memory B cells and plasma cells (PC) were highly enriched in tumor tissues with more differentiated states and increased frequencies of somatic hypermutation. Smokers exhibited markedly elevated PCs and PCs with distinct differentiation trajectories. BCR clonotype diversity increased but clonality decreased in LUADs, smokers, and with increasing pathologic stage. TIBs were mostly localized within CXCL13+ lymphoid aggregates, and immune cell sources of CXCL13 production evolved with LUAD progression and included elevated fractions of CD4 regulatory T cells. This study provides a spatial landscape of TIBs in early-stage LUAD.Significance:While TIBs are highly enriched in LUADs, they are poorly characterized. This study provides a much-needed understanding of the transcriptional, clonotypic states and phenotypes of TIBs, unraveling their potential roles in the immunopathology of early-stage LUADs and constituting a road map for the development of TIB-targeted immunotherapies for the treatment of this morbid malignancy.This article is highlighted in the In This Issue feature, p. 2483

Supplementary Data from The Single-Cell Immunogenomic Landscape of B and Plasma Cells in Early-Stage Lung Adenocarcinoma

Supplementary Figure from The Single-Cell Immunogenomic Landscape of B and Plasma Cells in Early-Stage Lung Adenocarcinoma

Tumor-infiltrating B and plasma cells (TIB) are prevalent in lung adenocarcinoma (LUAD); however, they are poorly characterized. We performed paired single-cell RNA and B-cell receptor (BCR) sequencing of 16 early-stage LUADs and 47 matching multiregion normal tissues. By integrative analysis of ∼50,000 TIBs, we define 12 TIB subsets in the LUAD and adjacent normal ecosystems and demonstrate extensive remodeling of TIBs in LUADs. Memory B cells and plasma cells (PC) were highly enriched in tumor tissues with more differentiated states and increased frequencies of somatic hypermutation. Smokers exhibited markedly elevated PCs and PCs with distinct differentiation trajectories. BCR clonotype diversity increased but clonality decreased in LUADs, smokers, and with increasing pathologic stage. TIBs were mostly localized within CXCL13+ lymphoid aggregates, and immune cell sources of CXCL13 production evolved with LUAD progression and included elevated fractions of CD4 regulatory T cells. This study provides a spatial landscape of TIBs in early-stage LUAD.Significance:While TIBs are highly enriched in LUADs, they are poorly characterized. This study provides a much-needed understanding of the transcriptional, clonotypic states and phenotypes of TIBs, unraveling their potential roles in the immunopathology of early-stage LUADs and constituting a road map for the development of TIB-targeted immunotherapies for the treatment of this morbid malignancy.This article is highlighted in the In This Issue feature, p. 2483

Intratumoral heterogeneity (ITH) is a fundamental property of cancer; however, the origins of ITH remain poorly understood. We performed single-cell transcriptome profiling of peritoneal carcinomatosis (PC) from 15 patients with gastric adenocarcinoma (GAC), constructed a map of 45,048 PC cells, profiled the transcriptome states of tumor cell populations, incisively explored ITH of malignant PC cells and identified significant correlates with patient survival. The links between tumor cell lineage/state compositions and ITH were illustrated at transcriptomic, genotypic, molecular and phenotypic levels. We uncovered the diversity in tumor cell lineage/state compositions in PC specimens and defined it as a key contributor to ITH. Single-cell analysis of ITH classified PC specimens into two subtypes that were prognostically independent of clinical variables, and a 12-gene prognostic signature was derived and validated in multiple large-scale GAC cohorts. The prognostic signature appears fundamental to GAC carcinogenesis and progression and could be practical for patient stratification.

Introduction: Gastric adenocarcinoma (GAC), a global health burden, lacks detail understanding of the evolution-driven cellular/molecular programs that lead to GAC tumorigenesis followed by progression/metastases. How the TME is orchestrated by precancerous lesions, primary GAC, and in metastatic niches, when well understood, may propel us into an entirely new dimension with the hopes of novel therapeutics. However, only a few studies have investigated the immune/stromal subtypes of GAC with the limitation of scope, cohort size, and/or depth or mainly focused on the primary GACs. Here, we present an atlas of transcriptionally diverse TME across the full continuum of GAC by including peripheral blood, normal gastric tissues, premalignant lesions, localized, and metastatic GACs. Methods: We performed a comprehensive single-cell profiling of 68 specimens collected from 43 subjects including a total of 77,392 high-quality cells which revealed 62 unique cell states uncovering varying profiles. We defined alterations in TMEs that underscore initiation of tumorigenesis to eventual progression. Results: We found a striking preponderance of B lineage cells, primarily the IgA+ plasma cells, in TMEs of the precancerous lesions, whereas 3 immunosuppressive myeloid subsets dominated in advanced GACs. Fractions of GZMK+ effector CD8 T cells and progenitor exhausted CD8 T cells gradually increased as GACs progressed to advanced stages. In addition, our analysis revealed extensive stromal remodeling along the GAC continuum, which may have contributed to enhanced angiogenesis and immune suppressive signaling. The observations in the primary tumors could be validated in an independent scRNA-seq dataset. Notably, we uncovered 3 unique TME interactomes and defined 6 cellular environtypes inhabited by 62 TME cell subsets giving GAC to a novel landscape not yet defined. The two distinct environtypes in GAC primaries are validated in three independent large-scale GAC cohorts, giving credence and definition to previously established histopathological variables, genomic/molecular subtypes and clinical outcomes. The analysis of tumor associated stromal cells discovered SDC2 as an exploitable target to pursue. SDC2 was abundant in cancer associated fibroblasts (CAFs), and the abundance is validated in 3 independent single-cell GAC cohorts as well as at the protein level. SDC2 expression was significantly higher in advanced (vs. early) stages and diffuse (vs. intestinal) type of GAC, and SDC2 overexpression was associated with shorter survival in all 5 large-scale GAC cohorts. Lastly, we assessed the functional effects of SDC2 expression in CAFs on tumor growth in vivo in xenograft models and found SDC2 overexpression in CAFs contributes to tumor growth. Conclusion: This study provides an atlas of GAC TMEs from tumorigenesis to advanced GAC that could be further developed for novel therapeutics but also serves as a community resource. Citation Format: Ruiping Wang, Shumei Song, Jiangjiang Qin, Katsuhiro Yoshimura, Fuduan Peng, Yanshuo Chu, Yuan Li, Yibo Fan, Jiankang Jin, Minghao Dang, Enyu Dai, Guangsheng Pei, Guangchun Han, Yating Li, Deyali Chatterjee, Melissa P. Pizzi, Ailing W. Scott, Ghia Tatlonghari, Xinmiao Yan, Matheus Da Silva Sewastjanow, Ahmed Adel Fouad Abdelhakeem, Pawel K. Mazur, Xiangdong Cheng, Jaffer A. Ajani, Linghua Wang. Evolution of immune and stromal cell states during the gastric cancer continuum [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1194.

Introduction: DNA replication is a tightly regulated, multi-step process involving licensing and replication factors that faithfully orchestrate a single duplication of the entire genome exclusively during S phase of the cell cycle. Although the mechanisms that prevent uncoordinated DNA replication are well characterized, it remains unclear how cells counteract its potentially harmful effects when it does occur, such as in cancer cells driven by aberrant oncogenic signaling and loss of cell cycle checkpoints. Here, we identify DPY30, an understudied component of all COMPASS complexes, as a modulator of DNA replication. We found upregulated DPY30 expression in pancreatic ductal adenocarcinoma (PDAC) as well as a correlation between increasing DPY30 expression level and higher tumor grade and poor clinical outcomes. Experimental procedure: Human- and mouse-derived PDAC cells silenced or knockout for DPY30 expression were generated and characterized for their in vivo growth in recipient mice with increasing level of immune pressure. Chromatin immunoprecipitation followed by massive parallel sequencing was used to measure the epigenetic landscape in DPY30 or WDR5 knockdown cells. Unbiased proteomics and transcriptomics analyses were performed to elucidate molecular mechanisms. Results: The canonical function of COMPASS complexes is to regulate histone methylation. Unexpectedly, DPY30 loss did not affect histone methylation patterns compared to WDR5 loss, the core member of the COMPASS-like complex. This prompted unbiased proteomics analysis, which showed that DPY30 interacts with members of the DNA helicase, possibly stabilizing the DNA replication fork progression. We found that DPY30 overexpression, by preventing Geminin ubiquitylation, strongly stabilizes its expression, which in turn suppresses Cdt1 activity in late S-phase, buffering the risk of uncoordinated DNA replication in G2/M phase. Consistently, DPY30 loss resulted in DNA re-replication and chromosomal instability, as manifested by DNA fiber and metaphasic spread. In vivo, whole-exome sequencing analysis of DPY30-knockout tumors revealed that DPY30 loss markedly increased mutational burden in immunodeficient mice. In syngeneic models, DPY30 loss strongly inhibited tumor growth, prolonged survival, and increased T cell infiltration of tumors. Moreover, the immune system selectively cleared tumor cells with complex karyotypes, and we observed improved anti-tumor efficacy with DPY30 knockdown and anti-PD-1 treatment compared to either condition alone. Conclusions: In summary, we have identified a previously undescribed function for DPY30 to stabilize the replisome machinery and prevent excessive DNA replication. Our findings indicate that, in PDAC, DPY30 promotes genome stability, thus providing a rationale for targeting DPY30 or its effector proteins in combination with immune-checkpoint inhibitors. Citation Format: Francesca Citron, Luigi Perelli, Yanshuo Chu, Sanjana Srinivasan, I-Lin Ho, Er-Yen Yen, Rutvi Shah, Sergio Attanasio, Luis Castillo Montanez, Mauro Di Pilato, Shan Jiang, Wantong Yao, Sisi Gao, Angela Deem, Giannicola Genovese, Linghua Wang, Virginia Giuliani, Andrea Viale, Giulio F. Draetta. DPY30 loss leads to DNA re-replication and immunoediting in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 303.

Immune checkpoint inhibitors are associated with immune-related adverse events (irAEs), including arthritis (arthritis-irAE). Management of arthritis-irAE is challenging because immunomodulatory therapy for arthritis should not impede antitumor immunity. Understanding of the mechanisms of arthritis-irAE is critical to overcome this challenge, but the pathophysiology remains unknown. Here, we comprehensively analyze peripheral blood and/or synovial fluid samples from 20 patients with arthritis-irAE, and unmask a prominent Th1-CD8+ T cell axis in both blood and inflamed joints. CX3CR1hi CD8+ T cells in blood and CXCR3hi CD8+ T cells in synovial fluid, the most clonally expanded T cells, significantly share TCR repertoires. The migration of blood CX3CR1hi CD8+ T cells into joints is possibly mediated by CXCL9/10/11/16 expressed by myeloid cells. Furthermore, arthritis after combined CTLA-4 and PD-1 inhibitor therapy preferentially has enhanced Th17 and transient Th1/Th17 cell signatures. Our data provide insights into the mechanisms, predictive biomarkers, and therapeutic targets for arthritis-irAE.

9523 Background: Treatment with BRAF+/-MEK inhibition (BRAF+/-MEKi) has revolutionized treatment in melanoma and other cancers, but resistance is common and innovative treatment strategies are needed. Sexual dimorphism in response to BRAF+/-MEKi have been noted, but mechanisms behind this are poorly understood and hormonal modulation has not been well-studied in this setting. Methods: We examined outcomes by sex in five clinical cohorts of patients (pts) (total n = 792, 362 female, 430 male) with BRAF-mutated melanoma who were treated with BRAF/MEKi in either the neoadjuvant or metastatic setting. Rates of major pathologic response (MPR), clinical benefit (CB), progression free survival (PFS) relapse-free survival (RFS) and overall survival (OS) were assessed. Translational research studies were performed on available pre- and on-treatment tumor samples (n = 27 pts) including RNA sequencing and profiling androgen receptor (AR) expression. Parallel studies were performed in preclinical models to assess the effect of sex and AR modulation on response to BRAF+/-MEKi. Results: In this study, improved rates of MPR, CB, PFS and OS were observed in female vs male pts. Specifically, female patients treated with neoadjuvant BRAF+MEKi showed significantly higher rates of MPR (66% v. 14%, p = 0.001), and improved RFS (64% versus 32% at 2 years, p = 0.021) vs male pts in the neoadjuvant setting (n = 51). These findings were not observed in a 2nd smaller trial of pts (n = 35), but were validated in a cohort of pts with unresectable metastatic melanoma treated with BRAF+MEKi (n = 69), with significantly higher rates of CB (80% v. 68%, p = 0.022) and PFS (12 v. 7 months, p = 0.003) in female vs male pts. Data from several published trials was analyzed (COMBI-D and METRIC trials), demonstrating improved PFS/OS at 2 years in female vs male pts treated with combined BRAF/MEKi (n = 211; p = 0.03 and, p = 0.04) and in female vs male pts treated with MEKi monotherapy (n = 206; p = 0.04 and p = 0.002), but not in female vs male pts treated with BRAFi monotherapy (n = 211; p = 0.21 and 0.095). Significantly higher expression AR expression was observed in available on- vs pre-treatment samples from male pts (p = 0.01), suggesting that treatment with BRAF/MEKi may induce AR expression in tumors. Findings were recapitulated in several preclinical models, and treatment with pharmacologic inhibitors of AR signaling (enzalutamide) in combination with BRAF/MEKi was associated with significantly enhanced anti-tumor activity in both male and female mice (p = 0.003 and p < 0.0001). Conversely, systemic treatment with testosterone was associated with significantly impaired tumor control in male and female mice (p = 0.021 and < 0.001). Conclusions: These data suggest that AR blockade may promote BRAF/MEKi response in melanoma, warranting further investigation in clinical trials. The impact of AR signaling, and modulation should be studied in MAPK-targeted therapy across other cancer types.

State-of-the-art next-generation sequencing (NGS)-based subclonal reconstruction methods perform poorly on somatic copy number alternations (SCNAs), due to not only it needs to simultaneously estimate the subclonal population frequency and the absolute copy number for each SCNA, but also there exist complex bias and noise in the tumor and its paired normal sequencing data. Both existing NGS-based SCNA detection methods and SCNA’s subclonal population frequency inferring tools use the read count on radio (RCR) of tumor to its paired normal as the key feature of tumor sequencing data; however, the sequencing error and bias have great impact on RCR, which leads to a large number of redundant SCNA segments that make the subsequent process of SCNA’s subclonal population frequency inferring and subclonal reconstruction time-consuming and inaccurate. We perform a mathematical analysis of the solution number of SCNA’s subclonal frequency, and we propose a computational algorithm to reduce the impact of false breakpoints based on it. We construct a new probability model that incorporates the RCR bias correction algorithm, and by stringing it with the false breakpoint filtering algorithm, we construct a whole SCNA’s subclonal population reconstruction pipeline. The experimental result shows that our pipeline outperforms the existing subclonal reconstruction programs both on simulated data and TCGA data. Source code is publicly available as a Python package at https://github.com/dustincys/msphy-SCNAClonal.

Large amounts of genome data are publicly available due to the high-throughput sequencing technologies developed in recent years. This availability raises a major concern about data storage costs, given that an effective and efficient compression algorithm for genome data remains an unresolved challenge in genomic data studies. In this paper, we propose a compression method, DeepDNA, that is a hybrid convolutional and recurrent deep neural network for compressing human genome data. In the DeepDNA model, the convolutional layer captures the genome's local features, while the recurrent layer captures long-term dependencies for estimating the next base probabilities in the genomic sequence. The experimental results on human mitochondrial genome datasets show the effectiveness of the DeepDNA method.The code for DeepDNA is available at https://github.com/rongiiewang/deepDNA.

When using next generation sequencing (NGS) data of tumor and its paired normal to detect somatic copy number alteration (SCNA) regions, the imbalance read’s PCR amplification process and system noise introduced by the twice sequencing procedures cause the result of existing algorithms to contain many false positive breakpoints and SCNA regions. These false positive breakpoints and SCNA regions further affect SCNA based subclonal population inferring tool that uses these SCNA regions as input. We present p-SCNAClonal, a tool that improves tumor subclonal population inferring by merging the SCNA regions according to the read count and B-allele frequency (BAF) information to reduce the number of false positive SCNA breakpoints. p-SCNAClonal then locates the baseline SCNA (not containing any SCNAs) and infers the absolute copy number of SCNA and its subclonal populations through a probabilistic model. We show p-SCNAClonal’s superiority to existing SCNA based subclonal population inferring method. p-SCNAClonal is publicly available as a Python package at https://github.com/Billy-Nie/pSCNAClonal

Relative terms often appear together in the literature. Methods have been presented for weighting relativity of pairwise terms by their co-occurring literature and inferring new relationship. Terms in the literature are also in the directed acyclic graph of ontologies, such as Gene Ontology and Disease Ontology. Therefore, semantic association between terms may help for establishing relativities between terms in literature. However, current methods do not use these associations. In this paper, an adjusted R-scaled score (ARSS) based on information content (ARSSIC) method is introduced to infer new relationship between terms. First, set inclusion relationship between terms of ontology was exploited to extend relationships between these terms and literature. Next, the ARSS method was presented to measure relativity between terms across ontologies according to these extensional relationships. Then, the ARSSIC method using ratios of information shared of term's ancestors was designed to infer new relationship between terms across ontologies. The result of the experiment shows that ARSS identified more pairs of statistically significant terms based on corresponding gene sets than other methods. And the high average area under the receiver operating characteristic curve (0.9293) shows that ARSSIC achieved a high true positive rate and a low false positive rate. Data is available at http://mlg.hit.edu.cn/ARSSIC/.

Modeling and Correct the GC bias of tumor and normal WGS data for SCNA based tumor subclonal population inferring. (PDF 2570 kb)

DNA methylation has been identified to be widely associated to complex diseases. Among biological platforms to profile DNA methylation in human, the Illumina Infinium HumanMethylation450 BeadChip (450K) has been accepted as one of the most efficient technologies. However, challenges exist in analysis of DNA methylation data generated by this technology due to widespread biases. Here we proposed a generalized framework for evaluating data analysis methods for Illumina 450K array. This framework considers the following steps towards a successful analysis: importing data, quality control, within-array normalization, correcting type bias, detecting differentially methylated probes or regions and biological interpretation. We evaluated five methods using three real datasets, and proposed outperform methods for the Illumina 450K array data analysis.

Somatic copy number alternations (SCNAs) can be utilized to infer tumor subclonal populations in whole genome seuqncing studies, where usually their read count ratios between tumor-normal paired samples serve as the inferring proxy. We found that, in a GC study, the GC contents and read count ratios on SCNA segments present a Log linear biased pattern. However, currently no subclonal inferring tools take into account this information. We provide Pre-SCNAClonal, a comprehensive GC bias correction tool for inferring tumor subclonal populations based on SCNAs. Results show that Pre-SCNAClonal could effectively and robustly correct the GC bias and improve the performance of the SCNAs based tumor subclonal population inferring tools. Pre-SCNAClonal could be strung together with the SCNAs based subclonal population inferring tool as a pipeline or run individually as needed. Pre-SCNAClonal is publicly available as a Python package: https://github.com/dustincys/Pre-SCNAClonal

Discovering similar diseases is very helpful for revealing the pathogenesis of diseases and making direction in drug use. And related diseases are often triggered by disease-related genes. Therefore, function interaction networks structured by disease-related genes are suitable for measurement of disease similarity, and some methods have utilized the advantage of function interaction of disease-related genes. However, all of them were developed by using a single gene functional network, some of them ignoring the effect of non-neighbour nodes in a functional interaction network. In this study, we propose a new method, FNSemSim, for computing relatedness between diseases by fusing two protein networks, which could be utilized fully based on random walk with restart (RWR). And a benchmark set of similar disease pairs are used to assess the performance of FNSemSim. As a result, FNSemSim achieved a very good performance with a high AUC (area under the receiver operating characteristic curve) reached 98.7%. Furthermore, we further studied the impact of different data sources, including function interaction networks and disease-related genes databases. It was found that the quality of the data sources has a greater impact on the performance of disease similarity calculation than the size of the data source, and utilizing function interaction networks and gene-disease association data could improve the performance of FNSemSim.

Next-generation sequencing (NGS) and the third generation sequencing (TGS) have recently allowed us to develop algorithms to quantitatively dissect the extent of heterogeneity within a tumour, resolve cancer evolution history and identify the somatic variations and aneuploidy events. Simulation of tumor NGS and TGS data serves as a powerful and cost-effective approach for benchmarking these algorithms, however, there is no available tool that could simulate all the distinct subclonal genomes with diverse aneuploidy events and somatic variations according to the given tumor evolution history. We provide a simulation package, Pysubsim-tree, which could simulate the tumor genomes according to their evolution history defined by the somatic variations and aneuploidy events. Pysubsim-tree is free, open source, available at: https://github.com/dustincys/pysubsimtree

Tumours contain multiple, genetically diverse subclonal populations of cells that have evolved from a single progenitor population. Currently, next-generation sequencing (NGS) and the third generation sequencing (TGS) have recently allowed us to develop algorithms to quantitatively dissect the extent of heterogeneity within a tumour, resolve cancer evolution history and identify the somatic variations and aneuploidy events with subclonal frequency. However, existing tumour NGS data has no ground truth annotation to validate all these NGS based tumour analysis algorithms. To benchmark these algorithms, a powerful tumour genome simulation tool which could simulate all the distinct subclonal genomes with diverse aneuploidy events and somatic variations according to the given tumour evolution history is in need. We provide a simulation package, Pysubsim-tree, which could simulate the tumour genomes according to their evolution history defined by the somatic variations and aneuploidy events. Pysubsim-tree is free, open source, available at: https://github.com/dustincys/pysubsimtree.

Protein-protein interactions (PPIs) are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. This paper aims at exploring more PPIs of Glucosinolates biosynthetic pathways and removing PPIs falsely predicted. A support vector machine (SVM) predictor with the radial basis kernel function (RBF kernel) is trained based on the domain and domain-domain interaction (DDI) information of the amino acid sequences. In this paper, a symmetrical pair of feature vectors is used to represent the symmetrical relationship between two proteins, and 5-fold cross-validation is used to search the best SVM parameters. Then the best SVM parameters are used to train the SVM-based PPIs predictor. The proteins originate from gene AT4G14800 and ATSGS4810 (ID of Arabidopsis Genome Initiative (AGI)), ATSGOS730 and AT4G18040, ATlG04S10 and ATSGOS260 are affirmed interactive by this SVM-based PPIs predictor.

Protein-protein interactions PPIs are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. This paper aims at exploring more and removing PPIs falsely predicted PPIs involved in glucosinolate biosynthesis in Arabidopsis. A symmetric kernel function is proposed according to the approach of feature representation which combines the domain and domain-domain interaction DDI information in this paper. The performance of this kernel indicates SVM based PPIs predictor trained with this kernel is highly effective. According to the prediction result, proteins with Arabidopsis Genome Initiative AGI numbers AT4G14800 and AT5G54810, AT5G05730 and AT4G18040, AT1G04510 and AT5G05260 are affirmed as interactive among the 237 low level of confidence PPIs pairs. Furthermore, the SVM-based PPIs predictor is used to explore PPIs of AT1G74090 and AT5G07690 both of which are members of the four glucosinolate biosynthesis pathway proteins absent from AtPIN.