Yongping Zhang, Shuting Jiang, Fuhong He, Yuanyuan Tian, Haiyang Hu, Li Gao, Lin Zhang, Aili Chen, Yixin Hu, Liyan Fan, Chun Yang, Bi Zhou, Dan Liu, Zihan Zhou, Yanxun Su, Lei Qin, Yi Wang, Hailong He, Jun Lu, Peifang Xiao, Shaoyan Hu, Qian-Fei Wang <<<

Abstract Background Cancer patients can achieve dramatic responses to chemotherapy yet retain resistant tumor cells, which ultimately results in relapse. Although xenograft model studies have identified several cellular and molecular features that are associated with chemoresistance in acute myeloid leukemia (AML), to what extent AML patients exhibit these properties remains largely unknown. Results We apply single-cell RNA sequencing to paired pre- and post-chemotherapy whole bone marrow samples obtained from 13 pediatric AML patients who had achieved disease remission, and distinguish AML clusters from normal cells based on their unique transcriptomic profiles. Approximately 50% of leukemic stem and progenitor populations actively express leukemia stem cell (LSC) and oxidative phosphorylation (OXPHOS) signatures, respectively. These clusters have a higher chance of tolerating therapy and exhibit an enhanced metabolic program in response to treatment. Interestingly, the transmembrane receptor CD69 is highly expressed in chemoresistant hematopoietic stem cell (HSC)-like populations (named the CD69+ HSC-like subpopulation). Furthermore, overexpression of CD69 results in suppression of the mTOR signaling pathway and promotion of cell quiescence and adhesion in vitro. Finally, the presence of CD69+ HSC-like cells is associated with unfavorable genetic mutations, the persistence of residual tumor cells in chemotherapy, and poor outcomes in independent pediatric and adult public AML cohorts. Conclusions Our analysis reveals leukemia stem cell and OXPHOS as two major chemoresistant features in human AML patients. CD69 may serve as a potential biomarker in defining a subpopulation of chemoresistant leukemia stem cells. These findings have important implications for targeting residual chemo-surviving AML cells. <<<

Abstract Background Telomeres form repeated DNA sequences at the ends of chromosomes, which shorten with each cell division. Yet, factors modulating telomere attrition and the health consequences thereof are not fully understood. To address this, we leveraged data from 326,363 unrelated UK Biobank participants of European ancestry. Results Using linear regression and bidirectional univariable and multivariable Mendelian randomization (MR), we elucidate the relationships between leukocyte telomere length (LTL) and 142 complex traits, including diseases, biomarkers, and lifestyle factors. We confirm that telomeres shorten with age and show a stronger decline in males than in females, with these factors contributing to the majority of the 5.4% of LTL variance explained by the phenome. MR reveals 23 traits modulating LTL. Smoking cessation and high educational attainment associate with longer LTL, while weekly alcohol intake, body mass index, urate levels, and female reproductive events, such as childbirth, associate with shorter LTL. We also identify 24 traits affected by LTL, with risk for cardiovascular, pulmonary, and some autoimmune diseases being increased by short LTL, while longer LTL increased risk for other autoimmune conditions and cancers. Through multivariable MR, we show that LTL may partially mediate the impact of educational attainment, body mass index, and female age at childbirth on proxied lifespan. Conclusions Our study sheds light on the modulators, consequences, and the mediatory role of telomeres, portraying an intricate relationship between LTL, diseases, lifestyle, and socio-economic factors. <<<

Keyur Talsania , Tsai-wei Shen , Xiongfong Chen , Erich Jaeger , Zhipan Li , Zhong Chen , Wanqiu Chen , Bao Tran , Rebecca Kusko , Limin Wang , Andy Wing Chun Pang , Zhaowei Yang , Sulbha Choudhari , Michael Colgan , Li Tai Fang , Andrew Carroll , Jyoti Shetty , Yuliya Kriga , Oksana German , Tatyana Smirnova , Tiantain Liu , Jing Li , Ben Kellman , Karl Hong , Alex R. Hastie , Aparna Natarajan , Ali Moshrefi , Anastasiya Granat , Tiffany Truong , Robin Bombardi , Veronnica Mankinen , Daoud Meerzaman , Christopher E. Mason , Jack Collins , Eric Stahlberg , Chunlin Xiao , Charles Wang , Wenming Xiao , Yongmei Zhao <<<

Abstract Background The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples. Results We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy. Conclusions A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods. <<<