References
1. The Lancet Diabetes E. Spotlight on rare diseases. The lancet
Diabetes & endocrinology. 2019;7(2):75.
2. Boycott KM, Rath A, Chong JX, Hartley T, Alkuraya FS, Baynam G, et
al. International cooperation to enable the diagnosis of all rare
genetic diseases. Am J Hum Genet. 2017;100(5):695-705.
3. Wang Q, Dhindsa RS, Carss K, Harper AR, Nag A, Tachmazidou I, et al.
Rare variant contribution to human disease in 281,104 UK Biobank exomes.
Nature. 2021;597(7877):527-32.
4. Velleuer E, Carlberg C. Impact of epigenetics on complications of
Fanconi anemia: the role of vitamin D-modulated immunity. Nutrients.
2020;12(5).
5. Wu ZH. The concept and practice of Fanconi Anemia: from the clinical
bedside to the laboratory bench. Transl Pediatr. 2013;2(3):112-9.
6. Akobeng AK. Principles of evidence based medicine. Arch Dis Child.
2005;90(8):837-40.
7. Wheatley R, Diaz Caballero J, Kapel N, de Winter FHR, Jangir P, Quinn
A, et al. Rapid evolution and host immunity drive the rise and fall of
carbapenem resistance during an acute Pseudomonas aeruginosa infection.
Nature communications. 2021;12(1):2460.
8. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new
perspectives on genomes, pathways, diseases and drugs. Nucleic Acids
Res. 2017;45(D1):D353-D61.
9. Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, Evelo C.
WikiPathways: pathway editing for the people. PLoS Biol. 2008;6:e184.
10. Himmelstein DS, Baranzini SE. Heterogeneous network edge prediction:
a data integration approach to prioritize disease-associated genes. PLoS
computational biology. 2015;11(7):e1004259.
11. Niarakis A, Helikar T. A practical guide to mechanistic systems
modeling in biology using a logic-based approach. Briefings in
bioinformatics. 2021;22(4).
12. Harris LA, Beik S, Ozawa PMM, Jimenez L, Weaver AM. Modeling
heterogeneous tumor growth dynamics and cell-cell interactions at
single-cell and cell-population resolution. Curr Opin Syst Biol.
2019;17:24-34.
13. Lobitz S, Velleuer E. Guido Fanconi (1892-1979): a jack of all
trades. Nat Rev Cancer. 2006;6(11):893-8.
14. Fiesco-Roa MO, Giri N, McReynolds LJ, Best AF, Alter BP.
Genotype-phenotype associations in Fanconi anemia: a literature review.
Blood Rev. 2019;37:100589.
15. Alter BP. Inherited bone marrow failure syndromes: considerations
pre- and posttransplant. Blood. 2017;130(21):2257-64.
16. Dufour C. How I manage patients with Fanconi anaemia. Br J Haematol.
2017;178(1):32-47.
17. Alter BP, Giri N, Savage SA, Rosenberg PS. Cancer in the National
Cancer Institute inherited bone marrow failure syndrome cohort after
fifteen years of follow-up. Haematologica. 2018;103(1):30-9.
18. Kutler DI, Singh B, Satagopan J, Batish SD, Berwick M, Giampietro
PF, et al. A 20-year perspective on the International Fanconi Anemia
Registry (IFAR). Blood. 2003;101(4):1249-56.
19. Wang AT, Smogorzewska A. SnapShot: Fanconi anemia and associated
proteins. Cell. 2015;160(1-2):354- e1.
20. Ameziane N, May P, Haitjema A, van de Vrugt HJ, van Rossum-Fikkert
SE, Ristic D, et al. A novel Fanconi anaemia subtype associated with a
dominant-negative mutation in RAD51. Nature communications. 2015;6:8829.
21. Meetei AR, Levitus M, Xue Y, Medhurst AL, Zwaan M, Ling C, et al.
X-linked inheritance of Fanconi anemia complementation group B. Nat
Genet. 2004;36(11):1219-24.
22. Ceccaldi R, Sarangi P, D’Andrea AD. The Fanconi anaemia pathway: new
players and new functions. Nat Rev Mol Cell Biol. 2016;17(6):337-49.
23. Tischkowitz MD, Hodgson SV. Fanconi anaemia. J Med Genet.
2003;40(1):1-10.
24. Gluckman E. Improving survival for Fanconi anemia patients. Blood.
2015;125(24):3676.
25. Bonfim C, Ribeiro L, Nichele S, Bitencourt M, Loth G, Koliski A, et
al. Long-term survival, organ function, and malignancy after
hematopoietic stem cell tansplantation for Fanconi Anemia. Biol Blood
Marrow Transplant. 2016;22(7):1257-63.
26. Paustian L, Chao MM, Hanenberg H, Schindler D, Neitzel H, Kratz CP,
et al. Androgen therapy in Fanconi anemia: A retrospective analysis of
30 years in Germany. Pediatr Hematol Oncol. 2016;33(1):5-12.
27. Calado RT, Cle DV. Treatment of inherited bone marrow failure
syndromes beyond transplantation. Hematology Am Soc Hematol Educ
Program. 2017;2017(1):96-101.
28. Rose SR, Kim MO, Korbee L, Wilson KA, Ris MD, Eyal O, et al.
Oxandrolone for the treatment of bone marrow failure in Fanconi anemia.
Pediatr Blood Cancer. 2014;61(1):11-9.
29. Scheckenbach K, Morgan M, Filger-Brillinger J, Sandmann M, Strimling
B, Scheurlen W, et al. Treatment of the bone marrow failure in Fanconi
anemia patients with danazol. Blood Cells Mol Dis. 2012;48(2):128-31.
30. Kutler DI, Patel KR, Auerbach AD, Kennedy J, Lach FP, Sanborn E, et
al. Natural history and management of Fanconi anemia patients with head
and neck cancer: A 10-year follow-up. Laryngoscope. 2016;126(4):870-9.
31. Lin J, Kutler DI. Why otolaryngologists need to be aware of Fanconi
anemia. Otolaryngol Clin North Am. 2013;46(4):567-77.
32. Velleuer E, Dietrich R, Pomjanski N, de Santana Almeida Araujo IK,
Silva de Araujo BE, Sroka I, et al. Diagnostic accuracy of brush
biopsy-based cytology for the early detection of oral cancer and
precursors in Fanconi anemia. Cancer Cytopathol. 2020.
33. Howlett NG, Taniguchi T, Olson S, Cox B, Waisfisz Q, De Die-Smulders
C, et al. Biallelic inactivation of BRCA2 in Fanconi anemia. Science.
2002;297(5581):606-9.
34. Del Valle J, Rofes P, Moreno-Cabrera JM, Lopez-Doriga A, Belhadj S,
Vargas-Parra G, et al. Exploring the role of mutations in Fanconi anemia
genes in hereditary cancer patients. Cancers. 2020;12(4).
35. Pouliot GP, Degar J, Hinze L, Kochupurakkal B, Vo CD, Burns MA, et
al. Fanconi-BRCA pathway mutations in childhood T-cell acute
lymphoblastic leukemia. PLoS ONE. 2019;14(11):e0221288.
36. Carlberg C, Velleuer E. Cancer biology: how science worls. Springer
Textbook. 2021.
37. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell.
2000;100:57-70.
38. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Jr.,
Kinzler KW. Cancer genome landscapes. Science. 2013;339(6127):1546-58.
39. Webster ALH, Sanders MA, Patel K, Dietrich R, Noonan RJ, Lach FP, et
al. Genomic signature of Fanconi anaemia DNA repair pathway deficiency
in cancer. Nature. 2022;612(7940):495-502.
40. Hanahan D. Hallmarks of Cancer: New Dimensions. Cancer Discovery.
2022;12(1):31-46.
41. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et
al. The STRING database in 2021: customizable protein-protein networks,
and functional characterization of user-uploaded gene/measurement sets.
Nucleic Acids Res. 2021;49(D1):D605-D12.
42. Regev A, Teichmann SA, Lander ES, Amit I, Benoist C, Birney E, et
al. The Human Cell Atlas. Elife. 2017;6.
43. Sinha PP. Bioinformatics with R Cookbook. Packt Publishing. 2014.
44. Sinha GB. Fundamentals of Bioinformatics and Computational Biology.
Springer Berlin Heidelberg. 2015.
45. Keating SM, Waltemath D, Konig M, Zhang F, Drager A, Chaouiya C, et
al. SBML Level 3: an extensible format for the exchange and reuse of
biological models. Molecular systems biology. 2020;16(8):e9110.
46. de Jong H. Modeling and simulation of genetic regulatory systems: a
literature review. J Comput Biol. 2002;9(1):67-103.
47. Dominguez-Huttinger E, Christodoulides P, Miyauchi K, Irvine AD,
Okada-Hatakeyama M, Kubo M, et al. Mathematical modeling of atopic
dermatitis reveals ”double-switch” mechanisms underlying 4 common
disease phenotypes. The Journal of allergy and clinical immunology.
2017;139(6):1861-72 e7.
48. Balsa-Canto E, Banga JR, Egea JA, Fernandez-Villaverde A, de
Hijas-Liste GM. Global optimization in systems biology: stochastic
methods and their applications. Adv Exp Med Biol. 2012;736:409-24.
49. Tsiantis N, Balsa-Canto E, Banga JR. Optimality and identification
of dynamic models in systems biology: an inverse optimal control
framework. Bioinformatics. 2018;34(21):3780.
50. Shockley EM, Vrugt JA, Lopez CF. PyDREAM: high-dimensional parameter
inference for biological models in python. Bioinformatics.
2018;34(4):695-7.
51. Dominguez-Huttinger E, Boon NJ, Clarke TB, Tanaka RJ. Mathematical
Modeling of Streptococcus pneumoniae Colonization, Invasive Infection
and Treatment. Front Physiol. 2017;8:115.
52. Zi Z. Sensitivity analysis approaches applied to systems biology
models. IET systems biology. 2011;5(6):336-6.
53. Kuznetsov YA. Elements of applied bifurcation theory. Springer
International Publishing. 2004.
54. Bargaje R, Trachana K, Shelton MN, McGinnis CS, Zhou JX, Chadick C,
et al. Cell population structure prior to bifurcation predicts
efficiency of directed differentiation in human induced pluripotent
cells. Proc Natl Acad Sci U S A. 2017;114(9):2271-6.
55. Fey D, Halasz M, Dreidax D, Kennedy SP, Hastings JF, Rauch N, et al.
Signaling pathway models as biomarkers: Patient-specific simulations of
JNK activity predict the survival of neuroblastoma patients. Science
signaling. 2015;8(408):ra130.
56. Tanaka G, Dominguez-Huttinger E, Christodoulides P, Aihara K, Tanaka
RJ. Bifurcation analysis of a mathematical model of atopic dermatitis to
determine patient-specific effects of treatments on dynamic phenotypes.
Journal of theoretical biology. 2018;448:66-79.
57. Christodoulides P, Hirata Y, Dominguez-Huttinger E, Danby SG, Cork
MJ, Williams HC, et al. Computational design of treatment strategies for
proactive therapy on atopic dermatitis using optimal control theory.
Philos Trans A Math Phys Eng Sci. 2017;375(2096).
58. Niraj J, Farkkila A, D’Andrea AD. The Fanconi Anemia Pathway in
Cancer. Annu Rev Cancer Biol. 2019;3:457-78.
59. Semlow DR, Walter JC. Mechanisms of Vertebrate DNA Interstrand
Cross-Link Repair. Annu Rev Biochem. 2021;90:107-35.
60. Anderson NM, Simon MC. The tumor microenvironment. Curr Biol.
2020;30(16):R921-R5.
61. Zong Z, Zhou F, Zhang L. The fungal mycobiome: a new hallmark of
cancer revealed by pan-cancer analyses. Signal Transduct Target Ther.
2023;8(1):50.
62. Stark R, Grzelak M, Hadfield J. RNA sequencing: the teenage years.
Nat Rev Genet. 2019;20(11):631-56.
63. Yuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, et al. CancerSEA: a
cancer single-cell state atlas. Nucleic Acids Res. 2019;47(D1):D900-D8.
64. Akhoundova D, Rubin MA. Clinical application of advanced multi-omics
tumor profiling: Shaping precision oncology of the future. Cancer cell.
2022;40(9):920-38.
65. Karimi E, Yu MW, Maritan SM, Perus LJM, Rezanejad M, Sorin M, et al.
Single-cell spatial immune landscapes of primary and metastatic brain
tumours. Nature. 2023;614(7948):555-63.
66. Pelka K, Hofree M, Chen JH, Sarkizova S, Pirl JD, Jorgji V, et al.
Spatially organized multicellular immune hubs in human colorectal
cancer. Cell. 2021;184(18):4734-52 e20.
67. Rodriguez A, Zhang K, Farkkila A, Filiatrault J, Yang C, Velazquez
M, et al. MYC Promotes Bone Marrow Stem Cell Dysfunction in Fanconi
Anemia. Cell stem cell. 2021;28(1):33-47 e8.
68. Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, et al.
Dimensionality reduction for visualizing single-cell data using UMAP.
Nat Biotechnol. 2018.
69. Marcou Y, D’Andrea A, Jeggo PA, Plowman PN. Normal cellular
radiosensitivity in an adult Fanconi anaemia patient with marked
clinical radiosensitivity. Radiother Oncol. 2001;60(1):75-9.
70. Alter BP. Radiosensitivity in Fanconi’s anemia patients. Radiother
Oncol. 2002;62(3):345-7.
71. Laubenbacher R, Sluka JP, Glazier JA. Using digital twins in viral
infection. Science. 2021;371(6534):1105-6.
72. Masison J, Beezley J, Mei Y, Ribeiro H, Knapp AC, Sordo Vieira L, et
al. A modular computational framework for medical digital twins. Proc
Natl Acad Sci U S A. 2021;118(20).
73. Tao F, Qi Q. Make more digital twins. Nature. 2019;573(7775):490-1.
74. An G. Specialty Grand Challenge: What it Will Take to Cross the
Valley of Death: Translational Systems Biology, “True” Precision
Medicine, Medical Digital Twins, Artificial Intelligence and In Silico
Clinical Trials. Frontiers in Systems Biology. 2022;2.
75. Kovatchev B. A century of diabetes technology: signals, models, and
artificial pancreas control. Trends Endocrinol Metab. 2019;30(7):432-44.
76. Shang JK, Esmaily M, Verma A, Reinhartz O, Figliola RS, Hsia TY, et
al. Patient-specific multiscale modeling of the assisted bidirectional
glenn. Ann Thorac Surg. 2019;107(4):1232-9.
77. Swanson K, Wu E, Zhang A, Alizadeh AA, Zou J. From patterns to
patients: Advances in clinical machine learning for cancer diagnosis,
prognosis, and treatment. Cell. 2023;186(8):1772-91.
78. Marx V. Method of the Year: spatially resolved transcriptomics.
Nature methods. 2021;18(1):9-14.
79. Lewis SM, Asselin-Labat ML, Nguyen Q, Berthelet J, Tan X, Wimmer VC,
et al. Spatial omics and multiplexed imaging to explore cancer biology.
Nature methods. 2021;18(9):997-1012.
80. Maniatis S, Petrescu J, Phatnani H. Spatially resolved
transcriptomics and its applications in cancer. Curr Opin Genet Dev.
2021;66:70-7.
81. Stahl PL, Salmen F, Vickovic S, Lundmark A, Navarro JF, Magnusson J,
et al. Visualization and analysis of gene expression in tissue sections
by spatial transcriptomics. Science. 2016;353(6294):78-82.
82. Merritt CR, Ong GT, Church SE, Barker K, Danaher P, Geiss G, et al.
Multiplex digital spatial profiling of proteins and RNA in fixed tissue.
Nat Biotechnol. 2020;38(5):586-99.
83. Nirmal AJ, Maliga Z, Vallius T, Quattrochi B, Chen AA, Jacobson CA,
et al. The Spatial Landscape of Progression and Immunoediting in Primary
Melanoma at Single-Cell Resolution. Cancer Discov. 2022;12(6):1518-41.
84. Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B,
et al. Highly multiplexed imaging of tumor tissues with subcellular
resolution by mass cytometry. Nature methods. 2014;11(4):417-22.
85. Hernandez S, Lazcano R, Serrano A, Powell S, Kostousov L, Mehta J,
et al. Challenges and Opportunities for Immunoprofiling Using a Spatial
High-Plex Technology: The NanoString GeoMx(©) Digital Spatial Profiler.
Frontiers in oncology. 2022;12:890410.
86. Tang L. Spatially resolved DNA sequencing. Nature methods.
2022;19(2):139.
87. Zhao T, Chiang ZD, Morriss JW, LaFave LM, Murray EM, Del Priore I,
et al. Spatial genomics enables multi-modal study of clonal
heterogeneity in tissues. Nature. 2022;601(7891):85-91.