lab.bib

@Article{hsu2023correspondence,
  title = {Correspondence analysis for dimension reduction, batch integration, and visualization of single-cell RNA-seq data},
  author = {Lauren L Hsu and Aed{\a'\i}n C Culhane},
  year = {2023},
  month = {Jan},
  journal = {Scientific reports},
  volume = {13},
  number = {1},
  pages = {1197},
  eprint = {36681709},
  doi = {10.1038/s41598-022-26434-1},
  language = {eng},
  issn = {2045-2322},
  abstract = {Effective dimension reduction is essential for single cell RNA-seq (scRNAseq) analysis. Principal component analysis (PCA) is widely used, but requires continuous, normally-distributed data; therefore, it is often coupled with log-transformation in scRNAseq applications, which can distort the data and obscure meaningful variation. We describe correspondence analysis (CA), a count-based alternative to PCA. CA is based on decomposition of a chi-squared residual matrix, avoiding distortive log-transformation. To address overdispersion and high sparsity in scRNAseq data, we propose five adaptations of CA, which are fast, scalable, and outperform standard CA and glmPCA, to compute cell embeddings with more performant or comparable clustering accuracy in 8 out of 9 datasets. In particular, we find that CA with Freeman-Tukey residuals performs especially well across diverse datasets. Other advantages of the CA framework include visualization of associations between genes and cell populations in a "CA biplot," and extension to multi-table analysis; we introduce corralm for integrative multi-table dimension reduction of scRNAseq data. We implement CA for scRNAseq data in corral, an R/Bioconductor package which interfaces directly with single cell classes in Bioconductor. Switching from PCA to CA is achieved through a simple pipeline substitution and improves dimension reduction of scRNAseq datasets.},
  eprinttype = {pubmed},
}

@Article{wang2022anti,
  title = {Anti-CAIX BBζ CAR4/8 T cells exhibit superior efficacy in a ccRCC mouse model},
  author = {Yufei Wang and Alicia Buck and Marion Grimaud and Aedin C Culhane and Sreekumar Kodangattil and Cecile Razimbaud and Dennis M Bonal and Quang-De Nguyen and Zhu Zhu and Kevin Wei and Madison L O'Donnell and Ying Huang and Sabina Signoretti and Toni K Choueiri and Gordon J Freeman and Quan Zhu and Wayne A Marasco},
  year = {2022},
  month = {Mar},
  journal = {Molecular therapy oncolytics},
  volume = {24},
  pages = {385-399},
  eprint = {35118195},
  doi = {10.1016/j.omto.2021.12.019},
  language = {eng},
  issn = {2372-7705},
  abstract = {Improving CAR-T cell therapy for solid tumors requires a better understanding of CAR design and cellular composition. Here, we compared second-generation (BBζ and 28ζ) with third-generation (28BBζ) carbonic anhydrase IX (CAIX)-targeted CAR constructs and investigated the antitumor effect of CAR-T cells with different CD4/CD8 proportions in vitro and in vivo. The results demonstrated that BBζ exhibited superior efficacy compared with 28ζ and 28BBζ CAR-T cells in a clear-cell renal cell carcinoma (ccRCC) skrc-59 cell bearing NSG-SGM3 mouse model. The mice treated with a single dose of BBζ CD4/CD8 mixture (CAR4/8) showed complete tumor remission and remained tumor-free 72 days after CAR-T cells infusion. In the other CAR-T and control groups, tumor-infiltrating T cells were recovered and profiled. We found that BBζ CAR8 cells upregulated expression of major histocompatibility complex (MHC) class II and cytotoxicity-associated genes, while downregulating inhibitory immune checkpoint receptor genes and diminishing differentiation of regulatory T cells (Treg cells), leading to excellent therapeutic efficacy in vivo. Increased memory phenotype, elevated tumor infiltration, and decreased exhaustion genes were observed in the CD4/8 untransduced T (UNT) cells compared with CD8 alone, indicating that CD4/8 would be the favored cellular composition for CAR-T cell therapy with long-term persistence. In summary, these findings support that BBζ CAR4/8 cells are a highly potent, clinically translatable cell therapy for ccRCC.},
  eprinttype = {pubmed},
}

@Article{mirzayi2021reporting,
  title = {Reporting guidelines for human microbiome research: the STORMS checklist},
  author = {Chloe Mirzayi and Audrey Renson and Fatima Zohra and Shaimaa Elsafoury and Ludwig Geistlinger and Lora J Kasselman and Kelly Eckenrode and Janneke {van de Wijgert} and Amy Loughman and Francine Z Marques and David A MacIntyre and Manimozhiyan Arumugam and Rimsha Azhar and Francesco Beghini and Kirk Bergstrom and Ami Bhatt and Jordan E Bisanz and Jonathan Braun and Hector Corrada Bravo and Gregory A Buck and Frederic Bushman and David Casero and Gerard Clarke and Maria Carmen Collado and Paul D Cotter and John F Cryan and Ryan T Demmer and Suzanne Devkota and Eran Elinav and Juan S Escobar and Jennifer Fettweis and Robert D Finn and Anthony A Fodor and Sofia Forslund and Andre Franke and Cesare Furlanello and Jack Gilbert and Elizabeth Grice and Benjamin Haibe-Kains and Scott Handley and Pamela Herd and Susan Holmes and Jonathan P Jacobs and Lisa Karstens and Rob Knight and Dan Knights and Omry Koren and Douglas S Kwon and Morgan Langille and Brianna Lindsay and Dermot McGovern and Alice C McHardy and Shannon McWeeney and Noel T Mueller and Luigi Nezi and Matthew Olm and Noah Palm and Edoardo Pasolli and Jeroen Raes and Matthew R Redinbo and Malte R{\"u}hlemann and R Balfour Sartor and Patrick D Schloss and Lynn Schriml and Eran Segal and Michelle Shardell and Thomas Sharpton and Ekaterina Smirnova and Harry Sokol and Justin L Sonnenburg and Sujatha Srinivasan and Louise B Thingholm and Peter J Turnbaugh and Vaibhav Upadhyay and Ramona L Walls and Paul Wilmes and Takuji Yamada and Georg Zeller and Mingyu Zhang and Ni Zhao and Liping Zhao and Wenjun Bao and Aedin Culhane and Viswanath Devanarayan and Joaquin Dopazo and Xiaohui Fan and Matthias Fischer and Wendell Jones and Rebecca Kusko and Christopher E Mason and Tim R Mercer and Susanna-Assunta Sansone and Andreas Scherer and Leming Shi and Shraddha Thakkar and Weida Tong and Russ Wolfinger and Christopher Hunter and Nicola Segata and Curtis Huttenhower and Jennifer B Dowd and Heidi E Jones and Levi Waldron},
  year = {2021},
  month = {Nov},
  journal = {Nature medicine},
  volume = {27},
  number = {11},
  pages = {1885-1892},
  eprint = {34789871},
  doi = {10.1038/s41591-021-01552-x},
  language = {eng},
  issn = {1546-170X},
  abstract = {The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results.},
  eprinttype = {pubmed},
}

@Article{cao2021author,
  title = {Author Correction: Community-wide hackathons to identify central themes in single-cell multi-omics},
  author = {Kim-Anh L{\^e} Cao and Al J Abadi and Emily F Davis-Marcisak and Lauren Hsu and Arshi Arora and Alexis Coullomb and Atul Deshpande and Yuzhou Feng and Pratheepa Jeganathan and Melanie Loth and Chen Meng and Wancen Mu and Vera Pancaldi and Kris Sankaran and Dario Righelli and Amrit Singh and Joshua S Sodicoff and Genevieve L Stein-O'Brien and Ayshwarya Subramanian and Joshua D Welch and Yue You and Ricard Argelaguet and Vincent J Carey and Ruben Dries and Casey S Greene and Susan Holmes and Michael I Love and Matthew E Ritchie and Guo-Cheng Yuan and Aedin C Culhane and Elana Fertig},
  year = {2021},
  month = {Aug},
  journal = {Genome biology},
  volume = {22},
  number = {1},
  pages = {246},
  eprint = {34433496},
  doi = {10.1186/s13059-021-02468-y},
  language = {eng},
  issn = {1474-760X},
  eprinttype = {pubmed},
}

@Article{cao2021community,
  title = {Community-wide hackathons to identify central themes in single-cell multi-omics},
  author = {Kim-Anh L{\^e} Cao and Al J Abadi and Emily F Davis-Marcisak and Lauren Hsu and Arshi Arora and Alexis Coullomb and Atul Deshpande and Yuzhou Feng and Pratheepa Jeganathan and Melanie Loth and Chen Meng and Wancen Mu and Vera Pancaldi and Kris Sankaran and Dario Righelli and Amrit Singh and Joshua S Sodicoff and Genevieve L Stein-O'Brien and Ayshwarya Subramanian and Joshua D Welch and Yue You and Ricard Argelaguet and Vincent J Carey and Ruben Dries and Casey S Greene and Susan Holmes and Michael I Love and Matthew E Ritchie and Guo-Cheng Yuan and Aedin C Culhane and Elana Fertig},
  year = {2021},
  month = {Aug},
  journal = {Genome biology},
  volume = {22},
  number = {1},
  pages = {220},
  eprint = {34353350},
  doi = {10.1186/s13059-021-02433-9},
  language = {eng},
  issn = {1474-760X},
  eprinttype = {pubmed},
}

@Article{roel2021characteristics,
  title = {Characteristics and Outcomes of Over 300,000 Patients with COVID-19 and History of Cancer in the United States and Spain},
  author = {Elena Roel and Andrea Pistillo and Martina Recalde and Anthony G Sena and Sergio Fern{\a'a}ndez-Bertol{\a'\i}n and Maria Arag{\a'o}n and Diana Puente and Waheed-Ul-Rahman Ahmed and Heba Alghoul and Osaid Alser and Thamir M Alshammari and Carlos Areia and Clair Blacketer and William Carter and Paula Casajust and Aedin C Culhane and Dalia Dawoud and Frank DeFalco and Scott L DuVall and Thomas Falconer and Asieh Golozar and Mengchun Gong and Laura Hester and George Hripcsak and Eng Hooi Tan and Hokyun Jeon and Jitendra Jonnagaddala and Lana Y H Lai and Kristine E Lynch and Michael E Matheny and Daniel R Morales and Karthik Natarajan and Fredrik Nyberg and Anna Ostropolets and Jos{\a'e} D Posada and Albert Prats-Uribe and Christian G Reich and Donna R Rivera and Lisa M Schilling and Isabelle Soerjomataram and Karishma Shah and Nigam H Shah and Yang Shen and Matthew Spotniz and Vignesh Subbian and Marc A Suchard and Annalisa Trama and Lin Zhang and Ying Zhang and Patrick B Ryan and Daniel Prieto-Alhambra and Kristin Kostka and Talita Duarte-Salles},
  year = {2021},
  month = {Oct},
  journal = {Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology},
  volume = {30},
  number = {10},
  pages = {1884-1894},
  eprint = {34272262},
  doi = {10.1158/1055-9965.EPI-21-0266},
  language = {eng},
  issn = {1538-7755},
  abstract = {BACKGROUND: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza.
METHODS: We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes.
RESULTS: We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events.
CONCLUSIONS: Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent.
IMPACT: This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.},
  eprinttype = {pubmed},
}

@Article{sheffer2021genome,
  title = {Genome-scale screens identify factors regulating tumor cell responses to natural killer cells},
  author = {Michal Sheffer and Emily Lowry and Nicky Beelen and Minasri Borah and Suha Naffar-Abu Amara and Chris C Mader and Jennifer A Roth and Aviad Tsherniak and Samuel S Freeman and Olga Dashevsky and Sara Gandolfi and Samantha Bender and Jordan G Bryan and Cong Zhu and Li Wang and Ifrah Tariq and Govinda M Kamath and Ricardo De Matos Simoes and Eugen Dhimolea and Channing Yu and Yiguo Hu and Olli Dufva and Marios Giannakis and Vasilis Syrgkanis and Ernest Fraenkel and Todd Golub and Rizwan Romee and Satu Mustjoki and Aedin C Culhane and Lotte Wieten and Constantine S Mitsiades},
  year = {2021},
  month = {Aug},
  journal = {Nature genetics},
  volume = {53},
  number = {8},
  pages = {1196-1206},
  eprint = {34253920},
  doi = {10.1038/s41588-021-00889-w},
  language = {eng},
  issn = {1546-1718},
  abstract = {To systematically define molecular features in human tumor cells that determine their degree of sensitivity to human allogeneic natural killer (NK) cells, we quantified the NK cell responsiveness of hundreds of molecularly annotated 'DNA-barcoded' solid tumor cell lines in multiplexed format and applied genome-scale CRISPR-based gene-editing screens in several solid tumor cell lines, to functionally interrogate which genes in tumor cells regulate the response to NK cells. In these orthogonal studies, NK cell-sensitive tumor cells tend to exhibit 'mesenchymal-like' transcriptional programs; high transcriptional signature for chromatin remodeling complexes; high levels of B7-H6 (NCR3LG1); and low levels of HLA-E/antigen presentation genes. Importantly, transcriptional signatures of NK cell-sensitive tumor cells correlate with immune checkpoint inhibitor (ICI) resistance in clinical samples. This study provides a comprehensive map of mechanisms regulating tumor cell responses to NK cells, with implications for future biomarker-driven applications of NK cell immunotherapies.},
  eprinttype = {pubmed},
}

@Article{dhimolea2021embryonic,
  title = {An Embryonic Diapause-like Adaptation with Suppressed Myc Activity Enables Tumor Treatment Persistence},
  author = {Eugen Dhimolea and Ricardo de Matos Simoes and Dhvanir Kansara and Aziz Al'Khafaji and Juliette Bouyssou and Xiang Weng and Shruti Sharma and Joseline Raja and Pallavi Awate and Ryosuke Shirasaki and Huihui Tang and Brian J Glassner and Zhiyi Liu and Dong Gao and Jordan Bryan and Samantha Bender and Jennifer Roth and Michal Scheffer and Rinath Jeselsohn and Nathanael S Gray and Irene Georgakoudi and Francisca Vazquez and Aviad Tsherniak and Yu Chen and Alana Welm and Cihangir Duy and Ari Melnick and Boris Bartholdy and Myles Brown and Aedin C Culhane and Constantine S Mitsiades},
  year = {2021},
  month = {Feb},
  journal = {Cancer cell},
  volume = {39},
  number = {2},
  pages = {240-256.e11},
  eprint = {33417832},
  doi = {10.1016/j.ccell.2020.12.002},
  language = {eng},
  issn = {1878-3686},
  abstract = {Treatment-persistent residual tumors impede curative cancer therapy. To understand this cancer cell state we generated models of treatment persistence that simulate the residual tumors. We observe that treatment-persistent tumor cells in organoids, xenografts, and cancer patients adopt a distinct and reversible transcriptional program resembling that of embryonic diapause, a dormant stage of suspended development triggered by stress and associated with suppressed Myc activity and overall biosynthesis. In cancer cells, depleting Myc or inhibiting Brd4, a Myc transcriptional co-activator, attenuates drug cytotoxicity through a dormant diapause-like adaptation with reduced apoptotic priming. Conversely, inducible Myc upregulation enhances acute chemotherapeutic activity. Maintaining residual cells in dormancy after chemotherapy by inhibiting Myc activity or interfering with the diapause-like adaptation by inhibiting cyclin-dependent kinase 9 represent potential therapeutic strategies against chemotherapy-persistent tumor cells. Our study demonstrates that cancer co-opts a mechanism similar to diapause with adaptive inactivation of Myc to persist during treatment.},
  eprinttype = {pubmed},
}

@Article{shirasaki2021functional,
  title = {Functional Genomics Identify Distinct and Overlapping Genes Mediating Resistance to Different Classes of Heterobifunctional Degraders of Oncoproteins},
  author = {Ryosuke Shirasaki and Geoffrey M Matthews and Sara Gandolfi and Ricardo de Matos Simoes and Dennis L Buckley and Joseline Raja Vora and Quinlan L Sievers and Johanna B Br{\"u}ggenthies and Olga Dashevsky and Haley Poarch and Huihui Tang and Megan A Bariteau and Michal Sheffer and Yiguo Hu and Sondra L Downey-Kopyscinski and Paul J Hengeveld and Brian J Glassner and Eugen Dhimolea and Christopher J Ott and Tinghu Zhang and Nicholas P Kwiatkowski and Jacob P Laubach and Robert L Schlossman and Paul G Richardson and Aedin C Culhane and Richard W J Groen and Eric S Fischer and Francisca Vazquez and Aviad Tsherniak and William C Hahn and Joan Levy and Daniel Auclair and Jonathan D Licht and Jonathan J Keats and Lawrence H Boise and Benjamin L Ebert and James E Bradner and Nathanael S Gray and Constantine S Mitsiades},
  year = {2021},
  month = {Jan},
  journal = {Cell reports},
  volume = {34},
  number = {1},
  pages = {108532},
  eprint = {33406420},
  doi = {10.1016/j.celrep.2020.108532},
  language = {eng},
  issn = {2211-1247},
  abstract = {Heterobifunctional proteolysis-targeting chimeric compounds leverage the activity of E3 ligases to induce degradation of target oncoproteins and exhibit potent preclinical antitumor activity. To dissect the mechanisms regulating tumor cell sensitivity to different classes of pharmacological "degraders" of oncoproteins, we performed genome-scale CRISPR-Cas9-based gene editing studies. We observed that myeloma cell resistance to degraders of different targets (BET bromodomain proteins, CDK9) and operating through CRBN (degronimids) or VHL is primarily mediated by prevention of, rather than adaptation to, breakdown of the target oncoprotein; and this involves loss of function of the cognate E3 ligase or interactors/regulators of the respective cullin-RING ligase (CRL) complex. The substantial gene-level differences for resistance mechanisms to CRBN- versus VHL-based degraders explains mechanistically the lack of cross-resistance with sequential administration of these two degrader classes. Development of degraders leveraging more diverse E3 ligases/CRLs may facilitate sequential/alternating versus combined uses of these agents toward potentially delaying or preventing resistance.},
  eprinttype = {pubmed},
}

@Article{burn2020deep,
  title = {Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study},
  author = {Edward Burn and Seng Chan You and Anthony G Sena and Kristin Kostka and Hamed Abedtash and Maria Tereza F Abrah{\~a}o and Amanda Alberga and Heba Alghoul and Osaid Alser and Thamir M Alshammari and Maria Aragon and Carlos Areia and Juan M Banda and Jaehyeong Cho and Aedin C Culhane and Alexander Davydov and Frank J DeFalco and Talita Duarte-Salles and Scott DuVall and Thomas Falconer and Sergio Fernandez-Bertolin and Weihua Gao and Asieh Golozar and Jill Hardin and George Hripcsak and Vojtech Huser and Hokyun Jeon and Yonghua Jing and Chi Young Jung and Benjamin Skov Kaas-Hansen and Denys Kaduk and Seamus Kent and Yeesuk Kim and Spyros Kolovos and Jennifer C E Lane and Hyejin Lee and Kristine E Lynch and Rupa Makadia and Michael E Matheny and Paras P Mehta and Daniel R Morales and Karthik Natarajan and Fredrik Nyberg and Anna Ostropolets and Rae Woong Park and Jimyung Park and Jose D Posada and Albert Prats-Uribe and Gowtham Rao and Christian Reich and Yeunsook Rho and Peter Rijnbeek and Lisa M Schilling and Martijn Schuemie and Nigam H Shah and Azza Shoaibi and Seokyoung Song and Matthew Spotnitz and Marc A Suchard and Joel N Swerdel and David Vizcaya and Salvatore Volpe and Haini Wen and Andrew E Williams and Belay B Yimer and Lin Zhang and Oleg Zhuk and Daniel Prieto-Alhambra and Patrick Ryan},
  year = {2020},
  month = {Oct},
  journal = {Nature communications},
  volume = {11},
  number = {1},
  pages = {5009},
  eprint = {33024121},
  doi = {10.1038/s41467-020-18849-z},
  language = {eng},
  issn = {2041-1723},
  abstract = {Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.},
  eprinttype = {pubmed},
}

@Article{lane2020risk,
  title = {Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study},
  author = {Jennifer C E Lane and James Weaver and Kristin Kostka and Talita Duarte-Salles and Maria Tereza F Abrahao and Heba Alghoul and Osaid Alser and Thamir M Alshammari and Patricia Biedermann and Juan M Banda and Edward Burn and Paula Casajust and Mitchell M Conover and Aedin C Culhane and Alexander Davydov and Scott L DuVall and Dmitry Dymshyts and Sergio Fernandez-Bertolin and Kristina Fi{\v s}ter and Jill Hardin and Laura Hester and George Hripcsak and Benjamin Skov Kaas-Hansen and Seamus Kent and Sajan Khosla and Spyros Kolovos and Christophe G Lambert and Johan {van der Lei} and Kristine E Lynch and Rupa Makadia and Andrea V Margulis and Michael E Matheny and Paras Mehta and Daniel R Morales and Henry Morgan-Stewart and Mees Mosseveld and Danielle Newby and Fredrik Nyberg and Anna Ostropolets and Rae Woong Park and Albert Prats-Uribe and Gowtham A Rao and Christian Reich and Jenna Reps and Peter Rijnbeek and Selva Muthu Kumaran Sathappan and Martijn Schuemie and Sarah Seager and Anthony G Sena and Azza Shoaibi and Matthew Spotnitz and Marc A Suchard and Carmen O Torre and David Vizcaya and Haini Wen and Marcel {de Wilde} and Junqing Xie and Seng Chan You and Lin Zhang and Oleg Zhuk and Patrick Ryan and Daniel Prieto-Alhambra},
  year = {2020},
  month = {Nov},
  journal = {The Lancet. Rheumatology},
  volume = {2},
  number = {11},
  pages = {e698-e711},
  eprint = {32864627},
  doi = {10.1016/S2665-9913(20)30276-9},
  language = {eng},
  issn = {2665-9913},
  abstract = {BACKGROUND: Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis.
METHODS: In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the I 2 value was less than 0·4.
FINDINGS: The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Self-controlled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12-2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22-3·95]), chest pain or angina (1·15 [1·05-1·26]), and heart failure (1·22 [1·02-1·45]).
INTERPRETATION: Hydroxychloroquine treatment appears to have no increased risk in the short term among patients with rheumatoid arthritis, but in the long term it appears to be associated with excess cardiovascular mortality. The addition of azithromycin increases the risk of heart failure and cardiovascular mortality even in the short term. We call for careful consideration of the benefit-risk trade-off when counselling those on hydroxychloroquine treatment.
FUNDING: National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Senior Research Fellowship programme, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research and Development, IQVIA, Korea Health Industry Development Institute through the Ministry of Health and Welfare Republic of Korea, Versus Arthritis, UK Medical Research Council Doctoral Training Partnership, Foundation Alfonso Martin Escudero, Innovation Fund Denmark, Novo Nordisk Foundation, Singapore Ministry of Health's National Medical Research Council Open Fund Large Collaborative Grant, VINCI, Innovative Medicines Initiative 2 Joint Undertaking, EU's Horizon 2020 research and innovation programme, and European Federation of Pharmaceutical Industries and Associations.},
  eprinttype = {pubmed},
}

@Article{dhimolea2021pleiotropic,
  title = {Pleiotropic Mechanisms Drive Endocrine Resistance in the Three-Dimensional Bone Microenvironment},
  author = {Eugen Dhimolea and Ricardo de Matos Simoes and Dhvanir Kansara and Xiang Weng and Shruti Sharma and Pallavi Awate and Zhiyi Liu and Dong Gao and Nicholas Mitsiades and Joseph H Schwab and Yu Chen and Rinath Jeselsohn and Aed{\a'\i}n C Culhane and Myles Brown and Irene Georgakoudi and Constantine S Mitsiades},
  year = {2021},
  month = {Jan},
  journal = {Cancer research},
  volume = {81},
  number = {2},
  pages = {371-383},
  eprint = {32859606},
  doi = {10.1158/0008-5472.CAN-20-0571},
  language = {eng},
  issn = {1538-7445},
  abstract = {Although hormonal therapy (HT) inhibits the growth of hormone receptor-positive (HR+) breast and prostate cancers, HT resistance frequently develops within the complex metastatic microenvironment of the host organ (often the bone), a setting poorly recapitulated in 2D culture systems. To address this limitation, we cultured HR+ breast cancer and prostate cancer spheroids and patient-derived organoids in 3D extracellular matrices (ECM) alone or together with bone marrow stromal cells (BMSC). In 3D monocultures, antiestrogens and antiandrogens induced anoikis by abrogating anchorage-independent growth of HR+ cancer cells but exhibited only modest effects against tumor cells residing in the ECM niche. In contrast, BMSC induced hormone-independent growth of breast cancer and prostate cancer spheroids and restored lumen filling in the presence of HR-targeting agents. Molecular and functional characterization of BMSC-induced hormone independence and HT resistance in anchorage-independent cells revealed distinct context-dependent mechanisms. Cocultures of ZR75-1 and LNCaP with BMSCs exhibited paracrine IL6-induced HT resistance via attenuation of HR protein expression, which was reversed by inhibition of IL6 or JAK signaling. Paracrine IL6/JAK/STAT3-mediated HT resistance was confirmed in patient-derived organoids cocultured with BMSCs. Distinctly, MCF7 and T47D spheroids retained ER protein expression in cocultures but acquired redundant compensatory signals enabling anchorage independence via ERK and PI3K bypass cascades activated in a non-IL6-dependent manner. Collectively, these data characterize the pleiotropic hormone-independent mechanisms underlying acquisition and restoration of anchorage-independent growth in HR+ tumors. Combined analysis of tumor and microenvironmental biomarkers in metastatic biopsies of HT-resistant patients can help refine treatment approaches. SIGNIFICANCE: This study uncovers a previously underappreciated dependency of tumor cells on HR signaling for anchorage-independent growth and highlights how the metastatic microenvironment restores this malignant property of cancer cells during hormone therapy.},
  eprinttype = {pubmed},
}

@Article{hsu2020impact,
  title = {Impact of Data Preprocessing on Integrative Matrix Factorization of Single Cell Data},
  author = {Lauren L Hsu and Aedin C Culhane},
  year = {2020},
  month = {06},
  journal = {Frontiers in oncology},
  volume = {10},
  pages = {973},
  eprint = {32656082},
  doi = {10.3389/fonc.2020.00973},
  language = {eng},
  issn = {2234-943X},
  abstract = {Integrative, single-cell analyses may provide unprecedented insights into cellular and spatial diversity of the tumor microenvironment. The sparsity, noise, and high dimensionality of these data present unique challenges. Whilst approaches for integrating single-cell data are emerging and are far from being standardized, most data integration, cell clustering, cell trajectory, and analysis pipelines employ a dimension reduction step, frequently principal component analysis (PCA), a matrix factorization method that is relatively fast, and can easily scale to large datasets when used with sparse-matrix representations. In this review, we provide a guide to PCA and related methods. We describe the relationship between PCA and singular value decomposition, the difference between PCA of a correlation and covariance matrix, the impact of scaling, log-transforming, and standardization, and how to recognize a horseshoe or arch effect in a PCA. We describe canonical correlation analysis (CCA), a popular matrix factorization approach for the integration of single-cell data from different platforms or studies. We discuss alternatives to CCA and why additional preprocessing or weighting datasets within the joint decomposition should be considered.},
  eprinttype = {pubmed},
}

@Article{burn2020deep:1,
  title = {Deep phenotyping of 34,128 patients hospitalised with COVID-19 and a comparison with 81,596 influenza patients in America, Europe and Asia: an international network study},
  author = {Edward Burn and Seng Chan You and Anthony Sena and Kristin Kostka and Hamed Abedtash and Maria Tereza F Abrahao and Amanda Alberga and Heba Alghoul and Osaid Alser and Thamir M Alshammari and Maria Aragon and Carlos Areia and Juan M Banda and Jaehyeong Cho and Aedin C Culhane and Alexander Davydov and Frank J DeFalco and Talita Duarte-Salles and Scott L DuVall and Thomas Falconer and Sergio Fernandez-Bertolin and Weihua Gao and Asieh Golozar and Jill Hardin and George Hripcsak and Vojtech Huser and Hokyun Jeon and Yonghua Jing and Chi Young Jung and Benjamin Skov Kaas-Hansen and Denys Kaduk and Seamus Kent and Yeesuk Kim and Spyros Kolovos and Jennifer Lane and Hyejin Lee and Kristine E Lynch and Rupa Makadia and Michael E Matheny and Paras Mehta and Daniel R Morales and Karthik Natarajan and Fredrik Nyberg and Anna Ostropolets and Rae Woong Park and Jimyung Park and Jose D Posada and Albert Prats-Uribe and Gowtham A Rao and Christian Reich and Yeunsook Rho and Peter Rijnbeek and Lisa M Schilling and Martijn Schuemie and Nigam H Shah and Azza Shoaibi and Seokyoung Song and Matthew Spotnitz and Marc A Suchard and Joel Swerdel and David Vizcaya and Salvatore Volpe and Haini Wen and Andrew E Williams and Belay B Yimer and Lin Zhang and Oleg Zhuk and Daniel Prieto-Alhambra and Patrick Ryan},
  year = {2020},
  month = {Jun},
  journal = {medRxiv : the preprint server for health sciences},
  eprint = {32511443},
  doi = {10.1101/2020.04.22.20074336},
  language = {eng},
  abstract = {Background In this study we phenotyped individuals hospitalised with coronavirus disease 2019 (COVID-19) in depth, summarising entire medical histories, including medications, as captured in routinely collected data drawn from databases across three continents. We then compared individuals hospitalised with COVID-19 to those previously hospitalised with influenza. Methods We report demographics, previously recorded conditions and medication use of patients hospitalised with COVID-19 in the US (Columbia University Irving Medical Center [CUIMC], Premier Healthcare Database [PHD], UCHealth System Health Data Compass Database [UC HDC], and the Department of Veterans Affairs [VA OMOP]), in South Korea (Health Insurance Review & Assessment [HIRA]), and Spain (The Information System for Research in Primary Care [SIDIAP] and HM Hospitales [HM]). These patients were then compared with patients hospitalised with influenza in 2014-19. Results 34,128 (US: 8,362, South Korea: 7,341, Spain: 18,425) individuals hospitalised with COVID-19 were included. Between 4,811 (HM) and 11,643 (CUIMC) unique aggregate characteristics were extracted per patient, with all summarised in an accompanying interactive website (http://evidence.ohdsi.org/Covid19CharacterizationHospitalization/). Patients were majority male in the US (CUIMC: 52%, PHD: 52%, UC HDC: 54%, VA OMOP: 94%,) and Spain (SIDIAP: 54%, HM: 60%), but were predominantly female in South Korea (HIRA: 60%). Age profiles varied across data sources. Prevalence of asthma ranged from 4% to 15%, diabetes from 13% to 43%, and hypertensive disorder from 24% to 70% across data sources. Between 14% and 33% were taking drugs acting on the renin-angiotensin system in the 30 days prior to hospitalisation. Compared to 81,596 individuals hospitalised with influenza in 2014-19, patients admitted with COVID-19 were more typically male, younger, and healthier, with fewer comorbidities and lower medication use. Conclusions We provide a detailed characterisation of patients hospitalised with COVID-19. Protecting groups known to be vulnerable to influenza is a useful starting point to minimize the number of hospital admissions needed for COVID-19. However, such strategies will also likely need to be broadened so as to reflect the particular characteristics of individuals hospitalised with COVID-19.},
  eprinttype = {pubmed},
}

@Article{schwede2020impact,
  title = {The Impact of Stroma Admixture on Molecular Subtypes and Prognostic Gene Signatures in Serous Ovarian Cancer},
  author = {Matthew Schwede and Levi Waldron and Samuel C Mok and Wei Wei and Azfar Basunia and Melissa A Merritt and Constantine S Mitsiades and Giovanni Parmigiani and David P Harrington and John Quackenbush and Michael J Birrer and Aed{\a'\i}n C Culhane},
  year = {2020},
  month = {Feb},
  journal = {Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology},
  volume = {29},
  number = {2},
  pages = {509-519},
  eprint = {31871106},
  doi = {10.1158/1055-9965.EPI-18-1359},
  language = {eng},
  issn = {1538-7755},
  abstract = {BACKGROUND: Recent efforts to improve outcomes for high-grade serous ovarian cancer, a leading cause of cancer death in women, have focused on identifying molecular subtypes and prognostic gene signatures, but existing subtypes have poor cross-study robustness. We tested the contribution of cell admixture in published ovarian cancer molecular subtypes and prognostic gene signatures.
METHODS: Gene signatures of tumor and stroma were developed using paired microdissected tissue from two independent studies. Stromal genes were investigated in two molecular subtype classifications and 61 published gene signatures. Prognostic performance of gene signatures of stromal admixture was evaluated in 2,527 ovarian tumors (16 studies). Computational simulations of increasing stromal cell proportion were performed by mixing gene-expression profiles of paired microdissected ovarian tumor and stroma.
RESULTS: Recently described ovarian cancer molecular subtypes are strongly associated with the cell admixture. Tumors were classified as different molecular subtypes in simulations where the percentage of stromal cells increased. Stromal gene expression in bulk tumors was associated with overall survival (hazard ratio, 1.17; 95% confidence interval, 1.11-1.23), and in one data set, increased stroma was associated with anatomic sampling location. Five published prognostic gene signatures were no longer prognostic in a multivariate model that adjusted for stromal content.
CONCLUSIONS: Cell admixture affects the interpretation and reproduction of ovarian cancer molecular subtypes and gene signatures derived from bulk tissue. Elucidating the role of stroma in the tumor microenvironment and in prognosis is important.
IMPACT: Single-cell analyses may be required to refine the molecular subtypes of high-grade serous ovarian cancer.},
  eprinttype = {pubmed},
}

@Article{thorsson2019immune,
  title = {The Immune Landscape of Cancer},
  author = {V{\a'e}steinn Thorsson and David L Gibbs and Scott D Brown and Denise Wolf and Dante S Bortone and Tai-Hsien Ou Yang and Eduard Porta-Pardo and Galen F Gao and Christopher L Plaisier and James A Eddy and Elad Ziv and Aedin C Culhane and Evan O Paull and I K Ashok Sivakumar and Andrew J Gentles and Raunaq Malhotra and Farshad Farshidfar and Antonio Colaprico and Joel S Parker and Lisle E Mose and Nam Sy Vo and Jianfang Liu and Yuexin Liu and Janet Rader and Varsha Dhankani and Sheila M Reynolds and Reanne Bowlby and Andrea Califano and Andrew D Cherniack and Dimitris Anastassiou and Davide Bedognetti and Younes Mokrab and Aaron M Newman and Arvind Rao and Ken Chen and Alexander Krasnitz and Hai Hu and Tathiane M Malta and Houtan Noushmehr and Chandra Sekhar Pedamallu and Susan Bullman and Akinyemi I Ojesina and Andrew Lamb and Wanding Zhou and Hui Shen and Toni K Choueiri and John N Weinstein and Justin Guinney and Joel Saltz and Robert A Holt and Charles S Rabkin and Alexander J Lazar and Jonathan S Serody and Elizabeth G Demicco and Mary L Disis and Benjamin G Vincent and Ilya Shmulevich},
  year = {2019},
  month = {Aug},
  journal = {Immunity},
  volume = {51},
  number = {2},
  pages = {411-412},
  eprint = {31433971},
  doi = {10.1016/j.immuni.2019.08.004},
  language = {eng},
  issn = {1097-4180},
  eprinttype = {pubmed},
}

@Article{meng2019mogsa,
  title = {MOGSA: Integrative Single Sample Gene-set Analysis of Multiple Omics Data},
  author = {Chen Meng and Azfar Basunia and Bjoern Peters and Amin Moghaddas Gholami and Bernhard Kuster and Aed{\a'\i}n C Culhane},
  year = {2019},
  month = {Aug},
  journal = {Molecular & cellular proteomics : MCP},
  volume = {18},
  number = {8 suppl 1},
  pages = {S153-S168},
  eprint = {31243065},
  doi = {10.1074/mcp.TIR118.001251},
  language = {eng},
  issn = {1535-9484},
  abstract = {Gene-set analysis (GSA) summarizes individual molecular measurements to more interpretable pathways or gene-sets and has become an indispensable step in the interpretation of large-scale omics data. However, GSA methods are limited to the analysis of single omics data. Here, we introduce a new computation method termed multi-omics gene-set analysis (MOGSA), a multivariate single sample gene-set analysis method that integrates multiple experimental and molecular data types measured over the same set of samples. The method learns a low dimensional representation of most variant correlated features (genes, proteins, etc.) across multiple omics data sets, transforms the features onto the same scale and calculates an integrated gene-set score from the most informative features in each data type. MOGSA does not require filtering data to the intersection of features (gene IDs), therefore, all molecular features, including those that lack annotation may be included in the analysis. Using simulated data, we demonstrate that integrating multiple diverse sources of molecular data increases the power to discover subtle changes in gene-sets and may reduce the impact of unreliable information in any single data type. Using real experimental data, we demonstrate three use-cases of MOGSA. First, we show how to remove a source of noise (technical or biological) in integrative MOGSA of NCI60 transcriptome and proteome data. Second, we apply MOGSA to discover similarities and differences in mRNA, protein and phosphorylation profiles of a small study of stem cell lines and assess the influence of each data type or feature on the total gene-set score. Finally, we apply MOGSA to cluster analysis and show that three molecular subtypes are robustly discovered when copy number variation and mRNA data of 308 bladder cancers from The Cancer Genome Atlas are integrated using MOGSA. MOGSA is available in the Bioconductor R package "mogsa."},
  eprinttype = {pubmed},
}

@Article{li2018brca1,
  title = {BRCA1-IRIS promotes human tumor progression through PTEN blockade and HIF-1α activation},
  author = {Andrew G Li and Elizabeth C Murphy and Aedin C Culhane and Emily Powell and Hua Wang and Roderick T Bronson and Thanh Von and Anita Giobbie-Hurder and Rebecca S Gelman and Kimberly J Briggs and Helen Piwnica-Worms and Jean J Zhao and Andrew L Kung and William G Kaelin and David M Livingston},
  year = {2018},
  month = {Oct},
  journal = {Proceedings of the National Academy of Sciences of the United States of America},
  volume = {115},
  number = {41},
  pages = {E9600-E9609},
  eprint = {30254159},
  doi = {10.1073/pnas.1807112115},
  language = {eng},
  issn = {1091-6490},
  abstract = {BRCA1 is an established breast and ovarian tumor suppressor gene that encodes multiple protein products whose individual contributions to human cancer suppression are poorly understood. BRCA1-IRIS (also known as "IRIS"), an alternatively spliced BRCA1 product and a chromatin-bound replication and transcription regulator, is overexpressed in various primary human cancers, including breast cancer, lung cancer, acute myeloid leukemia, and certain other carcinomas. Its naturally occurring overexpression can promote the metastasis of patient-derived xenograft (PDX) cells and other human cancer cells in mouse models. The IRIS-driven metastatic mechanism results from IRIS-dependent suppression of phosphatase and tensin homolog (PTEN) transcription, which in turn perturbs the PI3K/AKT/GSK-3β pathway leading to prolyl hydroxylase-independent HIF-1α stabilization and activation in a normoxic environment. Thus, despite the tumor-suppressing genetic origin of IRIS, its properties more closely resemble those of an oncoprotein that, when spontaneously overexpressed, can, paradoxically, drive human tumor progression.},
  eprinttype = {pubmed},
}

@Article{steinobrien2018enter,
  title = {Enter the Matrix: Factorization Uncovers Knowledge from Omics},
  author = {Genevieve L Stein-O'Brien and Raman Arora and Aedin C Culhane and Alexander V Favorov and Lana X Garmire and Casey S Greene and Loyal A Goff and Yifeng Li and Aloune Ngom and Michael F Ochs and Yanxun Xu and Elana J Fertig},
  year = {2018},
  month = {Oct},
  journal = {Trends in genetics : TIG},
  volume = {34},
  number = {10},
  pages = {790-805},
  eprint = {30143323},
  doi = {10.1016/j.tig.2018.07.003},
  language = {eng},
  issn = {0168-9525},
  abstract = {Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.},
  eprinttype = {pubmed},
}

@Article{thorsson2018immune,
  title = {The Immune Landscape of Cancer},
  author = {V{\a'e}steinn Thorsson and David L Gibbs and Scott D Brown and Denise Wolf and Dante S Bortone and Tai-Hsien Ou Yang and Eduard Porta-Pardo and Galen F Gao and Christopher L Plaisier and James A Eddy and Elad Ziv and Aedin C Culhane and Evan O Paull and I K Ashok Sivakumar and Andrew J Gentles and Raunaq Malhotra and Farshad Farshidfar and Antonio Colaprico and Joel S Parker and Lisle E Mose and Nam Sy Vo and Jianfang Liu and Yuexin Liu and Janet Rader and Varsha Dhankani and Sheila M Reynolds and Reanne Bowlby and Andrea Califano and Andrew D Cherniack and Dimitris Anastassiou and Davide Bedognetti and Younes Mokrab and Aaron M Newman and Arvind Rao and Ken Chen and Alexander Krasnitz and Hai Hu and Tathiane M Malta and Houtan Noushmehr and Chandra Sekhar Pedamallu and Susan Bullman and Akinyemi I Ojesina and Andrew Lamb and Wanding Zhou and Hui Shen and Toni K Choueiri and John N Weinstein and Justin Guinney and Joel Saltz and Robert A Holt and Charles S Rabkin and Alexander J Lazar and Jonathan S Serody and Elizabeth G Demicco and Mary L Disis and Benjamin G Vincent and Ilya Shmulevich},
  year = {2018},
  month = {Apr},
  journal = {Immunity},
  volume = {48},
  number = {4},
  pages = {812-830.e14},
  eprint = {29628290},
  doi = {10.1016/j.immuni.2018.03.023},
  language = {eng},
  issn = {1097-4180},
  abstract = {We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.},
  eprinttype = {pubmed},
}