Anthony P Y Liu, Kyle S Smith, Rahul Kumar, Leena Paul, Laure Bihannic, Tong Lin, Kendra K Maass, Kristian W Pajtler, Murali Chintagumpala, Jack M Su, Eric Bouffet, Michael J Fisher, Sridharan Gururangan, Richard Cohn, Tim Hassall, Jordan R Hansford, Paul Klimo, Frederick A Boop, Clinton F Stewart, Julie H Harreld, Thomas E Merchant, Ruth G Tatevossian, Geoffrey Neale, Matthew Lear, Jeffery M Klco, Brent A Orr, David W Ellison, Richard J Gilbertson, Arzu Onar-Thomas, Amar Gajjar, Giles W Robinson, Paul A Northcott <<<

Nearly one-third of children with medulloblastoma, a malignant embryonal tumor of the cerebellum, succumb to their disease. Conventional response monitoring by imaging and cerebrospinal fluid (CSF) cytology remains challenging, and a marker for measurable residual disease (MRD) is lacking. Here, we show the clinical utility of CSF-derived cell-free DNA (cfDNA) as a biomarker of MRD in serial samples collected from children with medulloblastoma (123 patients, 476 samples) enrolled on a prospective trial. Using low-coverage whole-genome sequencing, tumor-associated copy-number variations in CSF-derived cfDNA are investigated as an MRD surrogate. MRD is detected at baseline in 85% and 54% of patients with metastatic and localized disease, respectively. The number of MRD-positive patients declines with therapy, yet those with persistent MRD have significantly higher risk of progression. Importantly, MRD detection precedes radiographic progression in half who relapse. Our findings advocate for the prospective assessment of CSF-derived liquid biopsies in future trials for medulloblastoma. <<<

Guangxi Wang, Mantang Qiu, Xudong Xing, Juntuo Zhou, Hantao Yao, Mingru Li, Rong Yin, Yan Hou, Yang Li, Shuli Pan, Yuqing Huang, Fan Yang, Fan Bai, Honggang Nie, Shuangshuang Di, Limei Guo, Zhu Meng, Jun Wang, Yuxin Yin <<<

Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism-related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2, and 16:0-22:6; and triglycerides 16:0-18:1-18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography-mass spectrometry (MS)-based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention. <<<

Zhi-Min Zhang, Rui Lu, Pengcheng Wang, Yang Yu, Dongliang Chen, Linfeng Gao, Shuo Liu, Debin Ji, Scott B Rothbart, Yinsheng Wang, Gang Greg Wang, Jikui Song <<<

DNA methylation by de novo DNA methyltransferases 3A (DNMT3A) and 3B (DNMT3B) at cytosines is essential for genome regulation and development. Dysregulation of this process is implicated in various diseases, notably cancer. However, the mechanisms underlying DNMT3 substrate recognition and enzymatic specificity remain elusive. Here we report a 2.65-ångström crystal structure of the DNMT3A-DNMT3L-DNA complex in which two DNMT3A monomers simultaneously attack two cytosine-phosphate-guanine (CpG) dinucleotides, with the target sites separated by 14 base pairs within the same DNA duplex. The DNMT3A-DNA interaction involves a target recognition domain, a catalytic loop, and DNMT3A homodimeric interface. Arg836 of the target recognition domain makes crucial contacts with CpG, ensuring DNMT3A enzymatic preference towards CpG sites in cells. Haematological cancer-associated somatic mutations of the substrate-binding residues decrease DNMT3A activity, induce CpG hypomethylation, and promote transformation of haematopoietic cells. Together, our study reveals the mechanistic basis for DNMT3A-mediated DNA methylation and establishes its aetiological link to human disease. <<<

David Capper, David T W Jones, Martin Sill, Volker Hovestadt, Daniel Schrimpf, Dominik Sturm, Christian Koelsche, Felix Sahm, Lukas Chavez, David E Reuss, Annekathrin Kratz, Annika K Wefers, Kristin Huang, Kristian W Pajtler, Leonille Schweizer, Damian Stichel, Adriana Olar, Nils W Engel, Kerstin Lindenberg, Patrick N Harter, Anne K Braczynski, Karl H Plate, Hildegard Dohmen, Boyan K Garvalov, Roland Coras, Annett Hölsken, Ekkehard Hewer, Melanie Bewerunge-Hudler, Matthias Schick, Roger Fischer, Rudi Beschorner, Jens Schittenhelm, Ori Staszewski, Khalida Wani, Pascale Varlet, Melanie Pages, Petra Temming, Dietmar Lohmann, Florian Selt, Hendrik Witt, Till Milde, Olaf Witt, Eleonora Aronica, Felice Giangaspero, Elisabeth Rushing, Wolfram Scheurlen, Christoph Geisenberger, Fausto J Rodriguez, Albert Becker, Matthias Preusser, Christine Haberler, Rolf Bjerkvig, Jane Cryan, Michael Farrell, Martina Deckert, Jürgen Hench, Stephan Frank, Jonathan Serrano, Kasthuri Kannan, Aristotelis Tsirigos, Wolfgang Brück, Silvia Hofer, Stefanie Brehmer, Marcel Seiz-Rosenhagen, Daniel Hänggi, Volkmar Hans, Stephanie Rozsnoki, Jordan R Hansford, Patricia Kohlhof, Bjarne W Kristensen, Matt Lechner, Beatriz Lopes, Christian Mawrin, Ralf Ketter, Andreas Kulozik, Ziad Khatib, Frank Heppner, Arend Koch, Anne Jouvet, Catherine Keohane, Helmut Mühleisen, Wolf Mueller, Ute Pohl, Marco Prinz, Axel Benner, Marc Zapatka, Nicholas G Gottardo, Pablo Hernáiz Driever, Christof M Kramm, Hermann L Müller, Stefan Rutkowski, Katja von Hoff, Michael C Frühwald, Astrid Gnekow, Gudrun Fleischhack, Stephan Tippelt, Gabriele Calaminus, Camelia-Maria Monoranu, Arie Perry, Chris Jones, Thomas S Jacques, Bernhard Radlwimmer, Marco Gessi, Torsten Pietsch, Johannes Schramm, Gabriele Schackert, Manfred Westphal, Guido Reifenberger, Pieter Wesseling, Michael Weller, Vincent Peter Collins, Ingmar Blümcke, Martin Bendszus, Jürgen Debus, Annie Huang, Nada Jabado, Paul A Northcott, Werner Paulus, Amar Gajjar, Giles W Robinson, Michael D Taylor, Zane Jaunmuktane, Marina Ryzhova, Michael Platten, Andreas Unterberg, Wolfgang Wick, Matthias A Karajannis, Michel Mittelbronn, Till Acker, Christian Hartmann, Kenneth Aldape, Ulrich Schüller, Rolf Buslei, Peter Lichter, Marcel Kool, Christel Herold-Mende, David W Ellison, Martin Hasselblatt, Matija Snuderl, Sebastian Brandner, Andrey Korshunov, Andreas von Deimling, Stefan M Pfister <<<

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology. <<<

Cancer management, including supportive care, is complex and requires availability and synthesis of published and patient-specific data to make appropriate therapeutic decisions. Clinical decision support (CDS) may be an effective implementation strategy to support complex decision making although it is unclear whether it improves process outcomes, patient outcomes or both in cancer settings. We therefore conducted a systematic review to identify CDS that have been used to support therapeutic decision making in clinical cancer settings. Outcomes of interest included the effect of CDS on the process, such as clinician's decision making and effect on patient outcomes. Ten studies met inclusion criteria, with variability in the study design, setting, and intervention. Of the nine studies that measured process outcomes, five demonstrated significant improvement; and of the six that measured patient outcomes, four demonstrated significant improvement. All included studies utilized CDS that were informed by clinical practice guidelines. In conclusion, CDS to guide cancer therapeutic decision making is an understudied but promising area. Further research is needed. <<<

Zachariah H. Foda , Akshaya V. Annapragada , Kavya Boyapati , Daniel C. Bruhm , Nicholas A. Vulpescu , Jamie E. Medina , Dimitrios Mathios , Stephen Cristiano , Noushin Niknafs , Harry T. Luu , Michael G. Goggins , Robert A. Anders , Jing Sun , Shruti H. Mehta , David L. Thomas , Gregory D. Kirk , Vilmos Adleff , Jillian Phallen , Robert B. Scharpf , Amy K. Kim , Victor E. Velculescu <<<

Abstract Liver cancer is a major cause of cancer mortality worldwide. Screening individuals at high-risk, including those with cirrhosis and viral hepatitis, provides an avenue for improved survival, but current screening methods are inadequate. In this study, we used whole-genome cell-free DNA fragmentome analyses to evaluate 724 individuals from the US, EU, or Hong Kong with hepatocellular carcinoma (HCC) or who were at average or high-risk for HCC. Using a machine learning model that incorporated multi-feature fragmentome data, the sensitivity for detecting cancer was 88% in an average risk population at 98% specificity, and 85% among high-risk individuals at 80% specificity. We validated these results in an independent population. cfDNA fragmentation changes reflected genomic and chromatin changes in liver cancer, including from transcription factor binding sites. These findings provide a biological basis for changes in cfDNA fragmentation in patients with liver cancer and provide an accessible approach for non-invasive cancer detection. <<<

Keren Yizhak, François Aguet, Jaegil Kim, Julian M Hess, Kirsten Kübler, Jonna Grimsby, Ruslana Frazer, Hailei Zhang, Nicholas J Haradhvala, Daniel Rosebrock, Dimitri Livitz, Xiao Li, Eila Arich-Landkof, Noam Shoresh, Chip Stewart, Ayellet V Segrè, Philip A Branton, Paz Polak, Kristin G Ardlie, Gad Getz <<<

How somatic mutations accumulate in normal cells is poorly understood. A comprehensive analysis of RNA sequencing data from ~6700 samples across 29 normal tissues revealed multiple somatic variants, demonstrating that macroscopic clones can be found in many normal tissues. We found that sun-exposed skin, esophagus, and lung have a higher mutation burden than other tested tissues, which suggests that environmental factors can promote somatic mosaicism. Mutation burden was associated with both age and tissue-specific cell proliferation rate, highlighting that mutations accumulate over both time and number of cell divisions. Finally, normal tissues were found to harbor mutations in known cancer genes and hotspots. This study provides a broad view of macroscopic clonal expansion in human tissues, thus serving as a foundation for associating clonal expansion with environmental factors, aging, and risk of disease. <<<

Zongli Zheng, Matthew Liebers, Boryana Zhelyazkova, Yi Cao, Divya Panditi, Kerry D Lynch, Juxiang Chen, Hayley E Robinson, Hyo Sup Shim, Juliann Chmielecki, William Pao, Jeffrey A Engelman, A John Iafrate, Long Phi Le <<<

We describe a rapid target enrichment method for next-generation sequencing, termed anchored multiplex PCR (AMP), that is compatible with low nucleic acid input from formalin-fixed paraffin-embedded (FFPE) specimens. AMP is effective in detecting gene rearrangements (without prior knowledge of the fusion partners), single nucleotide variants, insertions, deletions and copy number changes. Validation of a gene rearrangement panel using 319 FFPE samples showed 100% sensitivity (95% confidence limit: 96.5-100%) and 100% specificity (95% confidence limit: 99.3-100%) compared with reference assays. On the basis of our experience with performing AMP on 986 clinical FFPE samples, we show its potential as both a robust clinical assay and a powerful discovery tool, which we used to identify new therapeutically important gene fusions: ARHGEF2-NTRK1 and CHTOP-NTRK1 in glioblastoma, MSN-ROS1, TRIM4-BRAF, VAMP2-NRG1, TPM3-NTRK1 and RUFY2-RET in lung cancer, FGFR2-CREB5 in cholangiocarcinoma and PPL-NTRK1 in thyroid carcinoma. AMP is a scalable and efficient next-generation sequencing target enrichment method for research and clinical applications. <<<

Cell-free DNA (cfDNA) in the circulating blood plasma of patients with cancer contains tumour-derived DNA sequences that can serve as biomarkers for guiding therapy, for the monitoring of drug resistance, and for the early detection of cancers. However, the analysis of cfDNA for clinical diagnostic applications remains challenging because of the low concentrations of cfDNA, and because cfDNA is fragmented into short lengths and is susceptible to chemical damage. Barcodes of unique molecular identifiers have been implemented to overcome the intrinsic errors of next-generation sequencing, which is the prevailing method for highly multiplexed cfDNA analysis. However, a number of methodological and pre-analytical factors limit the clinical sensitivity of the cfDNA-based detection of cancers from liquid biopsies. In this Review, we describe the state-of-the-art technologies for cfDNA analysis, with emphasis on multiplexing strategies, and discuss outstanding biological and technical challenges that, if addressed, would substantially improve cancer diagnostics and patient care. <<<

Lian Narunsky-Haziza , Gregory D. Sepich-Poore , Ilana Livyatan , Omer Asraf , Cameron Martino , Deborah Nejman , Nancy Gavert , Jason E. Stajich , Guy Amit , Antonio González , Stephen Wandro , Gili Perry , Ruthie Ariel , Arnon Meltser , Justin P. Shaffer , Qiyun Zhu , Nora Balint-Lahat , Iris Barshack , Maya Dadiani , Einav N. Gal-Yam , Sandip Pravin Patel , Amir Bashan , Austin D. Swafford , Yitzhak Pilpel , Rob Knight , Ravid Straussman <<<

Cancer-microbe associations have been explored for centuries, but cancer-associated fungi have rarely been examined. Here, we comprehensively characterize the cancer mycobiome within 17,401 patient tissue, blood, and plasma samples across 35 cancer types in four independent cohorts. We report fungal DNA and cells at low abundances across many major human cancers, with differences in community compositions that differ among cancer types, even when accounting for technical background. Fungal histological staining of tissue microarrays supported intratumoral presence and frequent spatial association with cancer cells and macrophages. Comparing intratumoral fungal communities with matched bacteriomes and immunomes revealed co-occurring bi-domain ecologies, often with permissive, rather than competitive, microenvironments and distinct immune responses. Clinically focused assessments suggested prognostic and diagnostic capacities of the tissue and plasma mycobiomes, even in stage I cancers, and synergistic predictive performance with bacteriomes. <<<

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation; analyses timings and patterns of tumour evolution; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity; and evaluates a range of more-specialized features of cancer genomes. <<<

Nazli Dizman, Luis Meza, Paulo Bergerot, Marice Alcantara, Tanya Dorff, Yung Lyou, Paul Frankel, Yujie Cui, Valerie Mira, Marian Llamas, Joann Hsu, Zeynep Zengin, Nicholas Salgia, Sabrina Salgia, Jasnoor Malhotra, Neal Chawla, Alex Chehrazi-Raffle, Ramya Muddasani, John Gillece, Lauren Reining, Jeff Trent, Motomichi Takahashi, Kentaro Oka, Seiya Higashi, Marcin Kortylewski, Sarah K Highlander, Sumanta K Pal <<<

Previous studies have suggested that the gut microbiome influences the response to checkpoint inhibitors (CPIs) in patients with cancer. CBM588 is a bifidogenic live bacterial product that we postulated could augment CPI response through modulation of the gut microbiome. In this open-label, single-center study (NCT03829111), 30 treatment-naive patients with metastatic renal cell carcinoma with clear cell and/or sarcomatoid histology and intermediate- or poor-risk disease were randomized 2:1 to receive nivolumab and ipilimumab with or without daily oral CBM588, respectively. Stool metagenomic sequencing was performed at multiple timepoints. The primary endpoint to compare the relative abundance of Bifidobacterium spp. at baseline and at 12 weeks was not met, and no significant differences in Bifidobacterium spp. or Shannon index associated with the addition of CBM588 to nivolumab-ipilimumab were detected. Secondary endpoints included response rate, progression-free survival (PFS) and toxicity. PFS was significantly longer in patients receiving nivolumab-ipilimumab with CBM588 than without (12.7 months versus 2.5 months, hazard ratio 0.15, 95% confidence interval 0.05-0.47, P = 0.001). Although not statistically significant, the response rate was also higher in patients receiving CBM588 (58% versus 20%, P = 0.06). No significant difference in toxicity was observed between the study arms. The data suggest that CBM588 appears to enhance the clinical outcome in patients with metastatic renal cell carcinoma treated with nivolumab-ipilimumab. Larger studies are warranted to confirm this clinical observation and elucidate the mechanism of action and the effects on microbiome and immune compartments. <<<

Yufeng Lin, Harry Cheuk-Hay Lau, Yali Liu, Xing Kang, Yiwei Wang, Nick Lung-Ngai Ting, Thomas Ngai-Yeung Kwong, Jing Han, Weixin Liu, Changan Liu, Junjun She, Sunny Hei Wong, Joseph Jao-Yiu Sung, Jun Yu <<<

BACKGROUND & AIMS: The enteric mycobiota is a major component of the human gut microbiota, but its role in colorectal cancer (CRC) remains largely elusive. We conducted a meta-analysis to uncover the contribution of the fungal mycobiota to CRC.METHODS: We retrieved fecal metagenomic data sets from 7 previous publications and established an additional in-house cohort, totaling 1329 metagenomes (454 with CRC, 350 with adenoma, and 525 healthy individuals). Mycobiota composition and microbial interactions were analyzed. Candidate CRC-enriched fungal species (Aspergillus rambellii) was functionally validated in vitro and in vivo.RESULTS: Multicohort analysis revealed that the enteric mycobiota was altered in CRC. We identified fungi that were associated with patients with CRC or adenoma from multiple cohorts. Signature CRC-associated fungi included 6 enriched (A rambellii, Cordyceps sp. RAO-2017, Erysiphe pulchra, Moniliophthora perniciosa, Sphaerulina musiva, and Phytophthora capsici) and 1 depleted species (A kawachii). Co-occurrent interactions among CRC-enriched fungi became stronger in CRC compared with adenoma and healthy individuals. Moreover, we reported the transkingdom interactions between enteric fungi and bacteria in CRC progression, of which A rambellii was closely associated with CRC-enriched bacteria Fusobacterium nucleatum. A rambellii promoted CRC cell growth in vitro and tumor growth in xenograft mice. We further identified that combined fungal and bacterial biomarkers were more accurate than panels with pure bacterial species to discriminate patients with CRC from healthy individuals (the area under the curve relative change increased by 1.44%-10.60%).CONCLUSIONS: This study reveals enteric mycobiota signatures and pathogenic fungi in stages of colorectal tumorigenesis. Fecal fungi can be used, in addition to bacteria, for noninvasive diagnosis of patients with CRC. <<<

Aspergillus fumigatus is an environmental saprobe and opportunistic human fungal pathogen. Despite an estimated annual occurrence of more than 300,000 cases of invasive disease worldwide, a comprehensive survey of the genomic diversity present in A. fumigatus-including the relationship between clinical and environmental isolates and how this genetic diversity contributes to virulence and antifungal drug resistance-has been lacking. In this study we define the pan-genome of A. fumigatus using a collection of 300 globally sampled genomes (83 clinical and 217 environmental isolates). We found that 7,563 of the 10,907 unique orthogroups (69%) are core and present in all isolates and the remaining 3,344 show presence/absence of variation, representing 16-22% of the genome of each isolate. Using this large genomic dataset of environmental and clinical samples, we found an enrichment for clinical isolates in a genetic cluster whose genomes also contain more accessory genes, including genes coding for transmembrane transporters and proteins with iron-binding activity, and genes involved in both carbohydrate and amino-acid metabolism. Finally, we leverage the power of genome-wide association studies to identify genomic variation associated with clinical isolates and triazole resistance as well as characterize genetic variation in known virulence factors. This characterization of the genomic diversity of A. fumigatus allows us to move away from a single reference genome that does not necessarily represent the species as a whole and better understand its pathogenic versatility, ultimately leading to better management of these infections. <<<

Ryan T Davis, Kerrigan Blake, Dennis Ma, Mari B Ishak Gabra, Grace A Hernandez, Anh T Phung, Ying Yang, Dustin Maurer, Austin E Y T Lefebvre, Hamad Alshetaiwi, Zhengtao Xiao, Juan Liu, Jason W Locasale, Michelle A Digman, Eric Mjolsness, Mei Kong, Zena Werb, Devon A Lawson <<<

Although metastasis remains the cause of most cancer-related mortality, mechanisms governing seeding in distal tissues are poorly understood. Here, we establish a robust method for the identification of global transcriptomic changes in rare metastatic cells during seeding using single-cell RNA sequencing and patient-derived-xenograft models of breast cancer. We find that both primary tumours and micrometastases display transcriptional heterogeneity but micrometastases harbour a distinct transcriptome program conserved across patient-derived-xenograft models that is highly predictive of poor survival of patients. Pathway analysis revealed mitochondrial oxidative phosphorylation as the top pathway upregulated in micrometastases, in contrast to higher levels of glycolytic enzymes in primary tumour cells, which we corroborated by flow cytometric and metabolomic analyses. Pharmacological inhibition of oxidative phosphorylation dramatically attenuated metastatic seeding in the lungs, which demonstrates the functional importance of oxidative phosphorylation in metastasis and highlights its potential as a therapeutic target to prevent metastatic spread in patients with breast cancer. <<<

The mechanisms and key factors involved in tumor environments for lung metastasis of CRC are still unclear. Here, using clinical samples from lung metastases of CRC patients, we found that intestinal immune network for IgA production was significantly dysregulated in lung metastases of CRC. Single-cell RNA sequencing discovered a subtype of B cells positive for Erbin, one member of the leucine-rich repeat and PDZ domain (LAP) family, was involved in the lung metastases. Erbin deletion in B cells suppressed lung metastasis of CRC in vivo. And, deletion of Erbin in B cells enhanced the killing effects of CD8 T cells on tumor cells. Mechanistically, Erbin knockout attenuated TGFβ-mediated suppression of migration of CXCR5 IgA cells and STAT6-mediated PD1 expression. Our study uncovered a key role of Erbin in regulating PD1 IgA B cells in lung metastasis of CRC. Targeting Erbin as well as combined use of neutralizing B cells and antibodies neutralizing PD1 suppresses lung metastasis of CRC in mice, suggesting the potential option for treatment of lung metastasis of CRC. <<<

The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed. <<<

Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer, Agata Laydon, Sameer Velankar, Gerard J Kleywegt, Alex Bateman, Richard Evans, Alexander Pritzel, Michael Figurnov, Olaf Ronneberger, Russ Bates, Simon A A Kohl, Anna Potapenko, Andrew J Ballard, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Ellen Clancy, David Reiman, Stig Petersen, Andrew W Senior, Koray Kavukcuoglu, Ewan Birney, Pushmeet Kohli, John Jumper, Demis Hassabis <<<

Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective. <<<

Over the past decade, RNA sequencing (RNA-seq) has become an indispensable tool for transcriptome-wide analysis of differential gene expression and differential splicing of mRNAs. However, as next-generation sequencing technologies have developed, so too has RNA-seq. Now, RNA-seq methods are available for studying many different aspects of RNA biology, including single-cell gene expression, translation (the translatome) and RNA structure (the structurome). Exciting new applications are being explored, such as spatial transcriptomics (spatialomics). Together with new long-read and direct RNA-seq technologies and better computational tools for data analysis, innovations in RNA-seq are contributing to a fuller understanding of RNA biology, from questions such as when and where transcription occurs to the folding and intermolecular interactions that govern RNA function. <<<