Androgen receptor blockade promotes response to BRAF/MEK-targeted therapy.

Treatment with therapy targeting BRAF and MEK (BRAF/MEK) has revolutionized care in melanoma and other cancers; however, therapeutic resistance is common and innovative treatment strategies are needed1,2. Here we studied a group of patients with melanoma who were treated with neoadjuvant BRAF/MEK-targeted therapy ( NCT02231775 , n = 51) and observed significantly higher rates of major pathological response (MPR; ≤10% viable tumour at resection) and improved recurrence-free survival (RFS) in female versus male patients (MPR, 66% versus 14%, P = 0.001; RFS, 64% versus 32% at 2 years, P = 0.021). The findings were validated in several additional cohorts2-4 of patients with unresectable metastatic melanoma who were treated with BRAF- and/or MEK-targeted therapy (n = 664 patients in total), demonstrating improved progression-free survival and overall survival in female versus male patients in several of these studies. Studies in preclinical models demonstrated significantly impaired anti-tumour activity in male versus female mice after BRAF/MEK-targeted therapy (P = 0.006), with significantly higher expression of the androgen receptor in tumours of male and female BRAF/MEK-treated mice versus the control (P = 0.0006 and P = 0.0025). Pharmacological inhibition of androgen receptor signalling improved responses to BRAF/MEK-targeted therapy in male and female mice (P = 0.018 and P = 0.003), whereas induction of androgen receptor signalling (through testosterone administration) was associated with a significantly impaired response to BRAF/MEK-targeted therapy in male and female patients (P = 0.021 and P < 0.0001). Together, these results have important implications for therapy.

Modeling and correct the GC bias of tumor and normal WGS data for SCNA based tumor subclonal population inferring.

BACKGROUND: 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. Existing SCNA based subclonal population inferring tools consider the GC bias of tumor and normal sample is of the same fature, and could be fully offset by read count ratio. However, we found that, the read count ratio on SCNA segments presents a Log linear biased pattern, which influence existing read count ratios based subclonal inferring tools performance. Currently no correction tools take into account the read ratio bias. RESULTS: We present Pre-SCNAClonal, a tool that improving tumor subclonal population inferring by correcting GC-bias at SCNAs level. Pre-SCNAClonal first corrects GC bias using Markov chain Monte Carlo probability model, then accurately locates baseline DNA segments (not containing any SCNAs) with a hierarchy clustering model. We show Pre-SCNAClonal's superiority to exsiting GC-bias correction methods at any level of subclonal population. CONCLUSIONS: Pre-SCNAClonal could be run independently as well as serving as pre-processing/gc-correction step in conjuntion with exsiting SCNA-based subclonal inferring tools.

Clonal dominance defines metastatic dissemination in pancreatic cancer.

Tumors represent ecosystems where subclones compete during tumor growth. While extensively investigated, a comprehensive picture of the interplay of clonal lineages during dissemination is still lacking. Using patient-derived pancreatic cancer cells, we created orthotopically implanted clonal replica tumors to trace clonal dynamics of unperturbed tumor expansion and dissemination. This model revealed the multifaceted nature of tumor growth, with rapid changes in clonal fitness leading to continuous reshuffling of tumor architecture and alternating clonal dominance as a distinct feature of cancer growth. Regarding dissemination, a large fraction of tumor lineages could be found at secondary sites each having distinctive organ growth patterns as well as numerous undescribed behaviors such as abortive colonization. Paired analysis of primary and secondary sites revealed fitness as major contributor to dissemination. From the analysis of pro- and nonmetastatic isogenic subclones, we identified a transcriptomic signature able to identify metastatic cells in human tumors and predict patients' survival.

Single cell clonotypic and transcriptional evolution of multiple myeloma precursor disease.

Multiple myeloma remains an incurable disease, and the cellular and molecular evolution from precursor conditions, including monoclonal gammopathy of undetermined significance and smoldering multiple myeloma, is incompletely understood. Here, we combine single-cell RNA and B cell receptor sequencing from fifty-two patients with myeloma precursors in comparison with myeloma and normal donors. Our comprehensive analysis reveals early genomic drivers of malignant transformation, distinct transcriptional features, and divergent clonal expansion in hyperdiploid versus non-hyperdiploid samples. Additionally, we observe intra-patient heterogeneity with potential therapeutic implications and identify distinct patterns of evolution from myeloma precursor disease to myeloma. We also demonstrate distinctive characteristics of the microenvironment associated with specific genomic changes in myeloma cells. These findings add to our knowledge about myeloma precursor disease progression, providing valuable insights into patient risk stratification, biomarker discovery, and possible clinical applications.

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.

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.

Distinct molecular and immune hallmarks of inflammatory arthritis induced by immune checkpoint inhibitors for cancer therapy.

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.

Single-cell dissection of intratumoral heterogeneity and lineage diversity in metastatic gastric adenocarcinoma.

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.

A Pipeline for Reconstructing Somatic Copy Number Alternation's Subclonal Population-Based Next-Generation Sequencing Data.

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.

WDNfinder: A method for minimum driver node set detection and analysis in directed and weighted biological network.

Structural controllability is the generalization of traditional controllability for dynamical systems. During the last decade, interesting biological discoveries have been inferred by applied structural controllability analysis to biological networks. However, false positive/negative information (i.e. nodes and edges) widely exists in biological networks that documented in public data sources, which can hinder accurate analysis of structural controllability. In this study, we propose WDNfinder, a comprehensive analysis package that provides structural controllability with consideration of node connection strength in biological networks. When applied to the human cancer signaling network and p53-mediate DNA damage response network, WDNfinder shows high accuracy on essential nodes prediction in these networks. Compared to existing methods, WDNfinder can significantly narrow down the set of minimum driver node set (MDS) under the restriction of domain knowledge. When using p53-mediate DNA damage response network as illustration, we find more meaningful MDSs by WDNfinder. The source code is implemented in python and publicly available together with relevant data on GitHub: https://github.com/dustincys/WDNfinder .

Using Semantic Association to Extend and Infer Literature-Oriented Relativity Between Terms.

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/.