Antifungal heteroresistance causes prophylaxis failure and facilitates breakthrough candida parapsilosis infections

ABSTRACT Breakthrough fungal infections in patients on antimicrobial prophylaxis during allogeneic hematopoietic cell transplantation (allo-HCT) represent a major and often unexplained cause

of morbidity and mortality. _Candida parapsilosis_ is a common cause of invasive candidiasis and has been classified as a high-priority fungal pathogen by the World Health Organization. In

high-risk allo-HCT recipients on micafungin prophylaxis, we show that heteroresistance (the presence of a phenotypically unstable, low-frequency subpopulation of resistant cells (~1 in

10,000)) underlies breakthrough bloodstream infections by _C. parapsilosis_. By analyzing 219 clinical isolates from North America, Europe and Asia, we demonstrate widespread micafungin

heteroresistance in _C. parapsilosis_. Standard antimicrobial susceptibility tests, such as broth microdilution or gradient diffusion assays, which guide drug selection for invasive

infections, fail to detect micafungin heteroresistance in _C. parapsilosis_. To facilitate rapid detection of micafungin heteroresistance in _C. parapsilosis_, we constructed a predictive

machine learning framework that classifies isolates as heteroresistant or susceptible using a maximum of ten genomic features. These results connect heteroresistance to unexplained

antifungal prophylaxis failure in allo-HCT recipients and demonstrate a proof-of-principle diagnostic approach with the potential to guide clinical decisions and improve patient care. Access

through your institution Buy or subscribe This is a preview of subscription content, access via your institution ACCESS OPTIONS Access through your institution Access Nature and 54 other

Nature Portfolio journals Get Nature+, our best-value online-access subscription $29.99 / 30 days cancel any time Learn more Subscribe to this journal Receive 12 print issues and online

access $209.00 per year only $17.42 per issue Learn more Buy this article * Purchase on SpringerLink * Instant access to full article PDF Buy now Prices may be subject to local taxes which

are calculated during checkout ADDITIONAL ACCESS OPTIONS: * Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support SIMILAR CONTENT BEING VIEWED BY OTHERS

_CANDIDA PARAPSILOSIS_ COMPLEX IN THE CLINICAL SETTING Article 06 September 2023 PHENOTYPIC AND GENOTYPIC CHARACTERIZATION OF _CANDIDA PARAPSILOSIS_ COMPLEX ISOLATES FROM A LEBANESE

HOSPITAL Article Open access 10 February 2025 GENOTYPIC DIVERSITY AND UNRECOGNIZED ANTIFUNGAL RESISTANCE AMONG POPULATIONS OF _CANDIDA GLABRATA_ FROM POSITIVE BLOOD CULTURES Article Open

access 22 September 2023 DATA AVAILABILITY Clinical characteristics (age range, sex, underlying disease type, BSIs and antibiotic and antifungal medication exposure) of allo-HCT recipients

at the MSKCC with breakthrough fungal BSIs are listed in Supplementary Tables 1 and 2. The clinical data of patients from non-MSKCC institutes are not available. Genome sequencing data of

the _C. parapsilosis_ isolates in this study are available in the NCBI Sequence Read Archive under projects PRJNA1068185, PRJNA579121, PRJNA795920 and PRJNA748054. The accession number of

each isolate is listed in Supplementary Table 12. All genome sequencing data were analyzed with the _C. parapsilosis_ CDC317 reference genome data

(http://www.candidagenome.org/download/sequence/C_parapsilosis_CDC317/). Source data are provided with this paper. CODE AVAILABILITY Codes of this study are available at

https://github.com/liaochen1988/EchinocandinHeteroresistance.git. REFERENCES * Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a

systematic analysis. _Lancet_ 399, 629–655 (2022). Google Scholar  * Berman, J. & Krysan, D. J. Drug resistance and tolerance in fungi. _Nat. Rev. Microbiol._ 18, 319–331 (2020). CAS 

PubMed  PubMed Central  Google Scholar  * Andersson, D. I., Nicoloff, H. & Hjort, K. Mechanisms and clinical relevance of bacterial heteroresistance. _Nat. Rev. Microbiol._ 17, 479–496

(2019). CAS  PubMed  Google Scholar  * El-Halfawy, O. M. & Valvano, M. A. Antimicrobial heteroresistance: an emerging field in need of clarity. _Clin. Microbiol. Rev._ 28, 191–207

(2015). CAS  PubMed  PubMed Central  Google Scholar  * Band, V. I. & Weiss, D. S. Heteroresistance: a cause of unexplained antibiotic treatment failure? _PLoS Pathog._ 15, e1007726

(2019). CAS  PubMed  PubMed Central  Google Scholar  * Mondon, P. et al. Heteroresistance to fluconazole and voriconazole in _Cryptococcus neoformans_. _Antimicrob. Agents Chemother._ 43,

1856–1861 (1999). CAS  PubMed  PubMed Central  Google Scholar  * Sionov, E., Chang, Y. C., Garraffo, H. M. & Kwon-Chung, K. J. Heteroresistance to fluconazole in _Cryptococcus

neoformans_ is intrinsic and associated with virulence. _Antimicrob. Agents Chemother._ 53, 2804–2815 (2009). CAS  PubMed  PubMed Central  Google Scholar  * Ben-Ami, R. et al.

Heteroresistance to fluconazole is a continuously distributed phenotype among _Candida glabrata_ clinical strains associated with in vivo persistence. _mBio_ 7, e00655-16 (2016). * Band, V.

I. et al. Antibiotic failure mediated by a resistant subpopulation in _Enterobacter cloacae_. _Nat. Microbiol._ 1, 16053 (2016). CAS  PubMed  PubMed Central  Google Scholar  * Xiong, Y. et

al. Impact of carbapenem heteroresistance among multidrug-resistant ESBL/AmpC-producing _Klebsiella pneumoniae_ clinical isolates on antibiotic treatment in experimentally infected mice.

_Infect. Drug Resist._ 14, 5639–5650 (2021). CAS  PubMed  PubMed Central  Google Scholar  * Bert, F. et al. Prevalence, molecular epidemiology, and clinical significance of heterogeneous

glycopeptide-intermediate _Staphylococcus aureus_ in liver transplant recipients. _J. Clin. Microbiol._ 41, 5147–5152 (2003). CAS  PubMed  PubMed Central  Google Scholar  * Sieradzki, K.,

Roberts, R. B., Serur, D., Hargrave, J. & Tomasz, A. Heterogeneously vancomycin-resistant _Staphylococcus epidermidis_ strain causing recurrent peritonitis in a dialysis patient during

vancomycin therapy. _J. Clin. Microbiol._ 37, 39–44 (1999). CAS  PubMed  PubMed Central  Google Scholar  * Fisher, B. T. et al. Effect of caspofungin vs fluconazole prophylaxis on invasive

fungal disease among children and young adults with acute myeloid leukemia: a randomized clinical trial. _JAMA_ 322, 1673–1681 (2019). CAS  PubMed  PubMed Central  Google Scholar  * Walsh,

T. J. et al. Caspofungin versus liposomal amphotericin B for empirical antifungal therapy in patients with persistent fever and neutropenia. _N. Engl. J. Med._ 351, 1391–1402 (2004). CAS 

PubMed  Google Scholar  * Slavin, M. A. et al. Efficacy and safety of fluconazole prophylaxis for fungal infections after marrow transplantation—a prospective, randomized, double-blind

study. _J. Infect. Dis._ 171, 1545–1552 (1995). CAS  PubMed  Google Scholar  * Goodman, J. L. et al. A controlled trial of fluconazole to prevent fungal infections in patients undergoing

bone marrow transplantation. _N. Engl. J. Med._ 326, 845–851 (1992). CAS  PubMed  Google Scholar  * Epstein, D. J., Seo, S. K., Brown, J. M. & Papanicolaou, G. A. Echinocandin

prophylaxis in patients undergoing haematopoietic cell transplantation and other treatments for haematological malignancies. _J. Antimicrob. Chemother._ 73, i60–i72 (2018). CAS  PubMed 

PubMed Central  Google Scholar  * Douglas, C. M. et al. Identification of the _FKS1_ gene of _Candida albicans_ as the essential target of 1,3-β-d-glucan synthase inhibitors. _Antimicrob.

Agents Chemother._ 41, 2471–2479 (1997). CAS  PubMed  PubMed Central  Google Scholar  * Toth, R. et al. _Candida parapsilosis_: from genes to the bedside. _Clin. Microbiol. Rev._ 32,

e00111-18 (2019). * Daneshnia, F. et al. Worldwide emergence of fluconazole-resistant _Candida parapsilosis_: current framework and future research roadmap. _Lancet Microbe_ 4, e470–e480

(2023). CAS  PubMed  PubMed Central  Google Scholar  * Trevijano-Contador, N. et al. Global emergence of resistance to fluconazole and voriconazole in _Candida parapsilosis_ in tertiary

hospitals in Spain during the COVID-19 pandemic. _Open Forum Infect. Dis._ 9, ofac605 (2022). PubMed  PubMed Central  Google Scholar  * Thomaz, D. Y. et al. A Brazilian inter-hospital

candidemia outbreak caused by fluconazole-resistant _Candida parapsilosis_ in the COVID-19 era. _J. Fungi_ 8, 100 (2022). * Fekkar, A. et al. Hospital outbreak of fluconazole-resistant

_Candida parapsilosis_: arguments for clonal transmission and long-term persistence. _Antimicrob. Agents Chemother_. 95, e02036-20 (2023). * Zhai, B. et al. High-resolution mycobiota

analysis reveals dynamic intestinal translocation preceding invasive candidiasis. _Nat. Med._ 26, 59–64 (2020). CAS  PubMed  PubMed Central  Google Scholar  * Garcia-Effron, G., Katiyar, S.

K., Park, S., Edlind, T. D. & Perlin, D. S. A naturally occurring proline-to-alanine amino acid change in Fks1p in _Candida parapsilosis_, _Candida orthopsilosis_, and _Candida

metapsilosis_ accounts for reduced echinocandin susceptibility. _Antimicrob. Agents Chemother._ 52, 2305–2312 (2008). CAS  PubMed  PubMed Central  Google Scholar  * Pfaller, M. A., Moet, G.

J., Messer, S. A., Jones, R. N. & Castanheira, M. Geographic variations in species distribution and echinocandin and azole antifungal resistance rates among _Candida_ bloodstream

infection isolates: report from the SENTRY Antimicrobial Surveillance Program (2008 to 2009). _J. Clin. Microbiol._ 49, 396–399 (2011). PubMed  Google Scholar  * Ning, Y. et al. Decreased

echinocandin susceptibility in _Candida parapsilosis_ causing candidemia and emergence of a pan-echinocandin resistant case in China. _Emerg. Microbes Infect._ 12, 2153086 (2023). PubMed 

Google Scholar  * Wang, E. et al. The ever-evolving landscape of candidaemia in patients with acute leukaemia: non-susceptibility to caspofungin and multidrug resistance are associated with

increased mortality. _J. Antimicrob. Chemother._ 70, 2362–2368 (2015). CAS  PubMed  PubMed Central  Google Scholar  * Lortholary, O. et al. Recent exposure to caspofungin or fluconazole

influences the epidemiology of candidemia: a prospective multicenter study involving 2,441 patients. _Antimicrob. Agents Chemother._ 55, 532–538 (2011). CAS  PubMed  Google Scholar  *

Rolling, T. et al. Haematopoietic cell transplantation outcomes are linked to intestinal mycobiota dynamics and an expansion of _Candida parapsilosis_ complex species. _Nat. Microbiol_. 6,

1505–1515 (2021). CAS  PubMed  PubMed Central  Google Scholar  * Giske, C. G., Turnidge, J., Canton, R., Kahlmeter, G. & EUCAST Steering Committee. Update from the European Committee on

Antimicrobial Susceptibility Testing (EUCAST). _J. Clin. Microbiol._ 60, e0027621 (2022). PubMed  Google Scholar  * Lewis, K. Persister cells, dormancy and infectious disease. _Nat. Rev.

Microbiol._ 5, 48–56 (2007). CAS  PubMed  Google Scholar  * Olm, M. R. et al. inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial

strains. _Nat. Biotechnol._ 39, 727–736 (2021). CAS  PubMed  PubMed Central  Google Scholar  * Sionov, E., Chang, Y. C. & Kwon-Chung, K. J. Azole heteroresistance in _Cryptococcus

neoformans_: emergence of resistant clones with chromosomal disomy in the mouse brain during fluconazole treatment. _Antimicrob. Agents Chemother._ 57, 5127–5130 (2013). CAS  PubMed  PubMed

Central  Google Scholar  * Stone, N. R. et al. Dynamic ploidy changes drive fluconazole resistance in human cryptococcal meningitis. _J. Clin. Invest._ 129, 999–1014 (2019). PubMed  PubMed

Central  Google Scholar  * Yang, F. et al. Aneuploidy underlies tolerance and cross-tolerance to drugs in _Candida parapsilosis_. _Microbiol. Spectr._ 9, e0050821 (2021). PubMed  Google

Scholar  * Todd, R. T. & Selmecki, A. Expandable and reversible copy number amplification drives rapid adaptation to antifungal drugs. _eLife_ 9, e58349 (2020). CAS  PubMed  PubMed

Central  Google Scholar  * Lai, M. H., Silverman, S. J., Gaughran, J. P. & Kirsch, D. R. Multiple copies of _PBS2_, _MHP1_ or _LRE1_ produce glucanase resistance and other cell wall

effects in _Saccharomyces cerevisiae_. _Yeast_ 13, 199–213 (1997). CAS  PubMed  Google Scholar  * Oh, S.-H. et al. Agglutinin-like sequence (ALS) genes in the _Candida parapsilosis_ species

complex: blurring the boundaries between gene families that encode cell-wall proteins. _Front. Microbiol._ 10, 781 (2019). PubMed  PubMed Central  Google Scholar  * Yang, K. & Maiti, T.

ENNS: variable selection, regression, classification and deep neural network for high-dimensional data. Preprint at https://arxiv.org/abs/2107.03430 (2021). * Healey, K. R. et al. Prevalent

mutator genotype identified in fungal pathogen _Candida glabrata_ promotes multi-drug resistance. _Nat. Commun._ 7, 11128 (2016). CAS  PubMed  PubMed Central  Google Scholar  * Ostrowsky, B.

et al. _Candida auris_ isolates resistant to three classes of antifungal medications — New York, 2019. _MMWR Morb. Mortal._ _Wkly_ _Rep._ 69, 6–9 (2020). Google Scholar  * Park, J. Y.,

Bradley, N., Brooks, S., Burney, S. & Wassner, C. Management of patients with _Candida auris_ fungemia at Community Hospital, Brooklyn, New York, USA, 2016–20181. _Emerg. Infect. Dis._

25, 601–602 (2019). PubMed  PubMed Central  Google Scholar  * Kilburn, S. et al. Antifungal resistance trends of _Candida auris_ clinical isolates in New York and New Jersey from 2016 to

2020. _Antimicrob. Agents Chemother_. 66, aac0224221 (2022). * CDC. _Recommendations for Treatment of_ Candida auris _Infections_ www.cdc.gov/fungal/candida-auris/c-auris-treatment.html

(2021). * Taur, Y. et al. Reconstitution of the gut microbiota of antibiotic-treated patients by autologous fecal microbiota transplant. _Sci. Transl. Med_. 10, eaap9489 (2018). * Kim, S. G.

et al. Microbiota-derived lantibiotic restores resistance against vancomycin-resistant _Enterococcus_. _Nature_ 572, 665–669 (2019). CAS  PubMed  PubMed Central  Google Scholar  * Andes, D.

et al. In vivo comparison of the pharmacodynamic targets for echinocandin drugs against _Candida_ species. _Antimicrob. Agents Chemother._ 54, 2497–2506 (2010). CAS  PubMed  PubMed Central

  Google Scholar  * Butler, G. et al. Evolution of pathogenicity and sexual reproduction in eight _Candida_ genomes. _Nature_ 459, 657–662 (2009). CAS  PubMed  PubMed Central  Google Scholar

  * Benson, G. Tandem repeats finder: a program to analyze DNA sequences. _Nucleic Acids Res._ 27, 573–580 (1999). CAS  PubMed  PubMed Central  Google Scholar  * Cingolani, P. et al. A

program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of _Drosophila melanogaster_ strain _w_1118; _iso_-2; _iso_-3. _Fly_ 6, 80–92

(2012). CAS  PubMed  PubMed Central  Google Scholar  * Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. _Bioinformatics_ 26, 841–842

(2010). CAS  PubMed  PubMed Central  Google Scholar  * Lischer, H. E., Excoffier, L. & Heckel, G. Ignoring heterozygous sites biases phylogenomic estimates of divergence times:

implications for the evolutionary history of microtus voles. _Mol. Biol. Evol._ 31, 817–831 (2014). CAS  PubMed  Google Scholar  * Minh, B. Q. et al. IQ-TREE 2: new models and efficient

methods for phylogenetic inference in the genomic era. _Mol. Biol. Evol._ 37, 1530–1534 (2020). CAS  PubMed  PubMed Central  Google Scholar  * Hoang, D. T., Chernomor, O., von Haeseler, A.,

Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. _Mol. Biol. Evol._ 35, 518–522 (2018). CAS  PubMed  Google Scholar  * Yan, J. et al. Bow-tie signaling

in c-di-GMP: machine learning in a simple biochemical network. _PLoS Comput. Biol._ 13, e1005677 (2017). PubMed  PubMed Central  Google Scholar  * Paradis, E., Claude, J. & Strimmer, K.

APE: analyses of phylogenetics and evolution in R language. _Bioinformatics_ 20, 289–290 (2004). CAS  PubMed  Google Scholar  * Chawla, N. V., Bowyer, K. W., Hall, L. O. & Kegelmeyer,

W. P. SMOTE: synthetic minority over-sampling technique. _J. Artif. Intell. Res._ 16, 321–357 (2002). Google Scholar  * Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions.

_Nat. Biotechnol._ 38, 685–688 (2020). CAS  PubMed  PubMed Central  Google Scholar  * Kozlov, A. M., Darriba, D., Flouri, T., Morel, B. & Stamatakis, A. RAxML-NG: a fast, scalable and

user-friendly tool for maximum likelihood phylogenetic inference. _Bioinformatics_ 35, 4453–4455 (2019). CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We

thank E. Pamer and K. Wolfe for discussions and E. Zuniga for sequencing assistance. We thank D. Andes for providing clinical _C. parapsilosis_ isolates. This work was supported by the

National Key Research and Development Program of China (2021YFA0911300, B.Z.), National Institutes of Health grants R37 AI093808 (T.M.H.), R21 AI105617 (T.M.H.), R21 AI156157 (T.M.H.), P01

AI179406 (T.M.H. and J.B.X.), U19 AI158080 (D.S.W.), R01 AI141883 (D.S.W.), R01 AI148661 (D.S.W.), U01 AI124275 (J.B.X.), R01 AI137269 (J.B.X. and Y. Taur) and K99 AI175599 (C.L.), an

Investigator in the Pathogenesis of Infectious Diseases Award from the Burroughs Wellcome Fund (T.M.H. and D.S.W.), the Ludwig Center for Cancer Immunotherapy (T.M.H.), the Susan and Peter

Solomon Divisional Genomics Program (T.M.H.), Deutsche Forschungsgemeinschaft (German Research Foundation) grant RO-5328/1-2 (T.R.), the Chinese Academy of Medical Sciences Innovation Fund

for Medical Sciences (2021-I2M-1-044, L.Z.) and Science Foundation Ireland grants 19/FFP/6668 (G.B.) and 18/CRT/6214 (G.B.), and all authors from the MSKCC were supported by National

Institutes of Health P30 CA008748. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. AUTHOR INFORMATION Author

notes * These authors contributed equally: Bing Zhai, Chen Liao, Siddharth Jaggavarapu, Yuanyuan Tang. AUTHORS AND AFFILIATIONS * Key Laboratory of Quantitative Synthetic Biology, Shenzhen

Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China Bing Zhai & Yuanyuan Tang * Infectious Disease Service, Department

of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA Bing Zhai, Thierry Rolling, Mergim Gjonbalaj, N. Esther Babady, Ying Taur & Tobias M. Hohl * Immunology Program,

Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA Bing Zhai, Thierry Rolling & Mergim Gjonbalaj * Computational and Systems Biology Program, Sloan

Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA Chen Liao & Joao B. Xavier * Emory Antibiotic Resistance Center, Atlanta, GA, USA Siddharth Jaggavarapu 

& David S. Weiss * Department of Microbiology and Immunology, Emory University, Atlanta, GA, USA Siddharth Jaggavarapu & David S. Weiss * Emory Vaccine Center, Atlanta, GA, USA

Siddharth Jaggavarapu & David S. Weiss * Division of Infectious Diseases, Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA Siddharth Jaggavarapu & David

S. Weiss * Department of Laboratory Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing,

China Yating Ning & Li Zhang * Beijing Key Laboratory for Mechanisms Research and Precision Diagnosis of Invasive Fungal Diseases, Beijing, China Yating Ning, Tianshu Sun & Li Zhang

* Clinical Biobank, Medical Research Center, National Science and Technology Key Infrastructure on Translational Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical

Sciences and Peking Union Medical College, Beijing, China Tianshu Sun * School of Biomolecular and Biomedical Science, Conway Institute, University College Dublin, Belfield, Dublin, Ireland

Sean A. Bergin & Geraldine Butler * Clinical Microbiology Service, Department of Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA Edwin Miranda & N.

Esther Babady * Institute for Medical Microbiology and Virology, University Medical Center Göttingen, Göttingen, Germany Oliver Bader * Department of Medicine, Weill Cornell Medical College,

New York, NY, USA Ying Taur & Tobias M. Hohl * Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA Tobias M. Hohl Authors * Bing Zhai View

author publications You can also search for this author inPubMed Google Scholar * Chen Liao View author publications You can also search for this author inPubMed Google Scholar * Siddharth

Jaggavarapu View author publications You can also search for this author inPubMed Google Scholar * Yuanyuan Tang View author publications You can also search for this author inPubMed Google

Scholar * Thierry Rolling View author publications You can also search for this author inPubMed Google Scholar * Yating Ning View author publications You can also search for this author

inPubMed Google Scholar * Tianshu Sun View author publications You can also search for this author inPubMed Google Scholar * Sean A. Bergin View author publications You can also search for

this author inPubMed Google Scholar * Mergim Gjonbalaj View author publications You can also search for this author inPubMed Google Scholar * Edwin Miranda View author publications You can

also search for this author inPubMed Google Scholar * N. Esther Babady View author publications You can also search for this author inPubMed Google Scholar * Oliver Bader View author

publications You can also search for this author inPubMed Google Scholar * Ying Taur View author publications You can also search for this author inPubMed Google Scholar * Geraldine Butler

View author publications You can also search for this author inPubMed Google Scholar * Li Zhang View author publications You can also search for this author inPubMed Google Scholar * Joao B.

Xavier View author publications You can also search for this author inPubMed Google Scholar * David S. Weiss View author publications You can also search for this author inPubMed Google

Scholar * Tobias M. Hohl View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS The study was conceived by B.Z., C.L., S.J., D.S.W. and T.M.H.

MSKCC clinical data were collected and analyzed by Y. Taur and B.Z. _C. parapsilosis_ isolates from the MSKCC were collected and processed by B.Z., T.R., M.G., E.M. and N.E.B. Isolates from

China were maintained and processed by Y.N., T.S. and L.Z. Isolates from Europe were collected and processed by S.A.B., G.B. and O.B. Antifungal phenotypic assays were performed by S.J. and

Y.N. Whole-genome sequencing analyses were carried out by C.L. and Y. Tang. Association analyses and machine learning modeling were performed by C.L., Y. Tang and J.B.X. The manuscript was

written by B.Z., C.L., S.J., Y. Tang, D.S.W. and T.M.H. All coauthors reviewed and edited the manuscript. CORRESPONDING AUTHORS Correspondence to Bing Zhai, David S. Weiss or Tobias M. Hohl.

ETHICS DECLARATIONS COMPETING INTERESTS T.R. is a current employee of BioNTech. All other authors have no competing interests. PEER REVIEW PEER REVIEW INFORMATION _Nature Medicine_ thanks

Hendrik Poeck and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Alison Farrell, in collaboration with the _Nature

Medicine_ team. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA

EXTENDED DATA FIG. 1 MICAFUNGIN BREAKTHROUGH INFECTIONS IN ALLO-HCT RECIPIENTS IN MSKCC FROM JAN. 2016 TO DEC. 2020. The dashed-line boxes indicate the patients or fungal isolates analyzed

in respective figure panels or tables. EXTENDED DATA FIG. 2 ANTIFUNGAL SUSCEPTIBILITY OF _C. PARAPSILOSIS_ BLOOD ISOLATES TO ECHINOCANDIN DRUGS. (A, B) Reproducibility of the PAP method in

determining micafungin heteroresistance: A, three repeats of PAP assays of the 15 _C. parapsilosis_ blood isolates in Table 1. B, micafungin susceptibility phenotypes (susceptible or

heteroresistant) classified by the PAP curves in panel A. (C, D) Antifungal susceptibility of _C. parapsilosis_ isolates to (C) caspofungin and (D) anidulafungin. Isolates MSK65, MSK794,

MSK795, and MSK2384 are heteroresistant to micafungin (colored in orange), while the isolates MSK804, MSK811, and MSK2386 are susceptible to micafungin (colored in blue). Among these

isolates, the phenotypes of caspofungin and anidulafungin heteroresistance were generally consistent with their micafungin heteroresistance phenotype, with exceptions of MSK795

(heteroresistant to micafungin and caspofungin, susceptible to anidulafungin) and MSK804 (susceptible to micafungin and anidulafungin, heteroresistant to caspofungin). (E) Etest results of

four additional _C. parapsilosis_ blood isolates. MSK67 and MSK795 are micafungin-heteroresistant (orange font), MSK811 and MSK1191 are micafungin susceptible (blue font). Notably, both

heteroresistant isolate (MSK795) and susceptible isolate (MSK811) could have clean inhibition zone around the Etest strip. Again, these observations showed the challenge of using Etest to

differentiate micafungin-heteroresistant and micafungin-susceptible isolates. EXTENDED DATA FIG. 3 MICAFUNGIN HETERORESISTANCE IN _CANDIDA AURIS_ CLINICAL ISOLATES. _Candida auris_ isolate

(A – D) 386 and (E – H) 381 were obtained from CDC and FDA AR Isolate Bank. (A, E) Micafungin E-test results for _C. auris_ isolates (A) 386 and (E) 381. (B, F) Micafungin population

analysis profiles for _C. auris_ isolates (B) 386 and (F) 381. Percent survival was calculated compared to growth on drug-free agar. (C, G) CFU dynamics of total and resistant subpopulations

of _C. auris_ isolates (C) 386 and (G) 381 under micafungin pressure (0.5 µg/mL). Cultures were plated at indicated time points for enumeration of total (circles) and resistant (squares)

cells. (D, H), Frequency of micafungin-resistant subpopulation of _C. auris_ isolates (D) 386 and (H) 381 during continuous passage. The frequency was calculated after a 24-hour growth

period in YPD broth (Pre-treatment), followed by a 48-hour passage in YPD broth with 0.5 µg/mL micafungin (In micafungin), and subsequently, a passage in YPD broth without micafungin for 24 

hours (Subculture). Bar height: mean; error bars: standard deviation of technical replicates, n = 3. EXTENDED DATA FIG. 4 TRACKING THE STABILITY OF MICAFUNGIN SUSCEPTIBILITY PHENOTYPES OF

IDENTICAL _C. PARAPSILOSIS_ FECAL ISOLATES WITHIN PATIENTS. Each filled circle represents a fecal isolate and all isolates shown in each row are identical. To simplify presentation,

identical isolates collected on the same day have been consolidated into a single, filled circle. The susceptible and heteroresistant isolates obtained from Patient 2 were displayed at two

separate rows. EXTENDED DATA FIG. 5 TWELVE _C. PARAPSILOSIS_ ISOLATES WITH ANEUPLOIDY. The mean copy number of consecutive fixed-size 5 kb windows over the entire genome were plotted for the

isolates with aneuploidy. The mean copy number was computed as the mean read depth across each 5 kb window divided by that averaged over normal diploid chromosomes. Chr: chromosome, S:

susceptible, HR: heteroresistant. EXTENDED DATA FIG. 6 DIFFERENT TYPES OF COPY NUMBER VARIATIONS (CNVS). To illustrate our approach for characterizing CNVs, we have provided five examples of

open reading frames (ORFs) with distinct CNV types. In each panel, the light gray curve represents distribution of read depth of each position within an ORF. The solid red line fits a

Gaussian kernel to this distribution. The vertical dashed red line indicates the peak center of the fitted distribution. The percentage contribution of each peak to the entire distribution

is provided in the panel. The feature values of these ORFs used in following analyses (for example, statistical association, machine learning) are shown in the table at the bottom right.

Each ORF is described by a categorical feature (CNV_cat) and a quantitative feature (CNV_quant). CNV_cat specifies the CNV types (normal diploid ORF, partial amplification, full

amplification, full deletion, partial deletion), while CNV_quant specifies the copy number of the amplified or deleted regions (the value of CNV_quant is 2 in the absence of amplification

and deletion). Source data EXTENDED DATA FIG. 7 DISTRIBUTIONS OF FEATURE 100 (A) AND 4470 (B) ACROSS THE PHYLOGENETIC TREE. Both features are single nucleotide variants. The biological

functions of the open reading frames to which they belong are shown. _Saccharomyces cerevisiae_, S.c. GSC2 is the paralog of FKS1 in S. cerevisiae genome. EXTENDED DATA FIG. 8 HIERARCHICAL

CLUSTERING OF PAIRWISE POPANI (POPULATION AVERAGE NUCLEOTIDE IDENTITY) VALUES ACROSS ALL 219 _C. PARAPSILOSIS_ ISOLATES. Each row or column represents an individual isolate. A gray box

indicates a popANI value of at least 99.999% between the respective row and column isolates, while a white box indicates a value below this cutoff. Using the cutoff, we identified 91

clusters of varying sizes (_inset_, red circle; see Methods). For each cluster that contains at least two isolates, any pair of isolates within this cluster has a popANI value of at least

99.999%, indicating that these isolates are all identical to each other. The _inset_ curve also shows the impact of the popANI threshold on the number of identified clusters. The exact

pairwise popANI values are available in Source Data. Source data EXTENDED DATA FIG. 9 SCHEMATIC DIAGRAM OF MACHINE LEARNING FRAMEWORK IN PREDICTING MICAFUNGIN SUSCEPTIBILITY PHENOTYPE. The

framework implemented a nested cross-validation procedure that involves an outer cross-validation loop for evaluating model performance and an inner cross-validation loop for feature

selection and hyperparameter tuning. LASSO: Least Absolute Shrinkage and Selection Operator; ENNS: Ensemble Neural Network Selection; XGBoost: Extreme Gradient Boosting. EXTENDED DATA FIG.

10 PERFORMANCE OF MACHINE LEARNING MODELS. (A) Precision, recall, and accuracy scores of machine learning models constructed with different combinations of feature selection algorithms

(LOGIT, LASSO, ENNS) and classifiers (RF, XGBoost). In all boxplots, each dot represents a single train-test split, which was randomly repeated 50 times. The models were ranked based on

their median scores in the ascending order. LOGIT+XGBoost always achieved the highest median score. LOGIT, Logistic regression; LASSO, Least Absolute Shrinkage and Selection Operator; ENNS,

Ensemble Neural Network Selection; RF, Random Forest; XGBoost, Extreme Gradient Boosting. (B) Impact of training data resampling on machine learning model performance. The distributions of

precision, recall, accuracy, and F1 score were generated across 50 train-test splits. For this analysis, LOGIT was used as the feature selector and XGBoost was used as the classifier.

Under-sampling randomly selected existing samples from the susceptible class, while over-sampling used SMOTE (Synthetic Minority Over-sampling TEchnique) to generate synthetic samples of the

heteroresistant class. No RS: No resampling; US: Under-sampling; OS: Over-sampling. (C) Distribution of feature importance scores across 50 train-test splits. Features were selected by the

combination of LOGIT and XGBoost. The number in the parenthesis of each feature indicates its rank in statistical association analysis of micafungin heteroresistance using the entire

dataset. (D) Misclassification (both false-positive and negative predictions) percentage for all isolates misclassified at least once among 50 random train-test splits. The number within

parenthesis following each isolate identifier indicates how many times the isolate was included in the test set. SUPPLEMENTARY INFORMATION REPORTING SUMMARY SUPPLEMENTARY TABLES 1–12

Supplementary Table 1. Clinical characteristics (age range, sex, underlying disease type, BSIs) of allo-HCT recipients in the MSKCC with breakthrough fungal BSIs between days 0 and 30 after

transplantation. Supplementary Table 2. Antibiotic and antifungal medication exposure of allo-HCT recipients in the MSKCC with breakthrough fungal BSIs between days 0 and 30 after

transplantation. Supplementary Table 3. Information of _C. parapsilosis_ isolates from the MSKCC. Supplementary Table 4. PAP data of all 219 _C. parapsilosis_ isolates in this study.

Supplementary Table 5. Micafungin-exposure information of non-allo-HCT patients in the MSKCC. Supplementary Table 6. Information of _C. parapsilosis_ isolates from non-MSKCC USA

institutions. Supplementary Table 7. Information of _C. parapsilosis_ isolates from European institutions. Supplementary Table 8. Information of _C. parapsilosis_ isolates from Chinese

institutions. Supplementary Table 9. ORFs with amplification that overlap with inverted repeats of at least 100 bp. Supplementary Table 10. Logistic regression analysis of the correlation

between genomic features and micafungin heteroresistance. The logistic regression coefficients, along with their lower and upper confidence intervals and _P_adj values, are provided. Here,

the genomic features refer to the merged features in Source Data Fig. 4. Only features with _P_adj values < 0.05 are included in the table. Supplementary Table 11. LASSO and phylogenetic

LASSO analyses of the correlation between genomic features and micafungin heteroresistance. Features with nonzero coefficients in at least one analysis were included in the table.

Supplementary Table 12. SRA project IDs of whole-genome sequencing data of 219 _C. parapsilosis_ isolates in this study. SOURCE DATA SOURCE DATA FIG. 3 SNVs and indels identified from the

219 _C. parapsilosis_ isolates. SOURCE DATA FIG. 4 Mapping between raw and merged features. Raw features of SNVs, indels and CNVs were merged if they held identical values across all 219

isolates. The identifiers of raw SNV and indel features follow the pattern ‘Chromosome ID:Position:SNP (or indel):Reference allele:Alternative allele=X’, where _X_ = 1, 2 denotes

heterozygous and homozygous alternative alleles, respectively. For categorical CNVs, the raw feature identifier follows the pattern ‘CNV_cat__[open reading frame identifier]=X’, where _X_ = 

1, 2, 3, 4 represent full amplification, partial amplification, full deletion and partial deletion, respectively. The identifiers of raw quantitative CNV features have the format

‘CNV_quant__[open reading frame identifier]’. SOURCE DATA EXTENDED DATA FIG. 6 CNVs of ORFs identified from 219 _C. parapsilosis_ isolates. SOURCE DATA EXTENDED DATA FIG. 8 Clustering of _C.

parapsilosis_ isolates based on popANI. In each cluster, all isolates are identical to each other (that is, all pairwise popANI values ≥ 99.999%). The column ‘Used’ indicates which isolates

have been included in the development of machine learning models. RIGHTS AND PERMISSIONS Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this

article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of

such publishing agreement and applicable law. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Zhai, B., Liao, C., Jaggavarapu, S. _et al._ Antifungal heteroresistance causes

prophylaxis failure and facilitates breakthrough _Candida parapsilosis_ infections. _Nat Med_ 30, 3163–3172 (2024). https://doi.org/10.1038/s41591-024-03183-4 Download citation * Received:

29 May 2022 * Accepted: 08 July 2024 * Published: 02 August 2024 * Issue Date: November 2024 * DOI: https://doi.org/10.1038/s41591-024-03183-4 SHARE THIS ARTICLE Anyone you share the

following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to clipboard Provided by the Springer

Nature SharedIt content-sharing initiative