Fecal microbiota diversity disruption and clinical outcomes after auto-HCT: a multicenter observational study

Abstract

We previously described clinically relevant reductions in fecal microbiota diversity in patients undergoing allogeneic hematopoietic cell transplantation (allo-HCT). Recipients of high-dose chemotherapy and autologous HCT (auto-HCT) incur similar antibiotic exposures and nutritional alterations. To characterize the fecal microbiota in the auto-HCT population, we analyzed 1161 fecal samples collected from 534 adult recipients of auto-HCT for lymphoma, myeloma, and amyloidosis in an observational study conducted at 2 transplantation centers in the United States. By using 16S ribosomal gene sequencing, we assessed fecal microbiota composition and diversity, as measured by the inverse Simpson index. At both centers, the diversity of early pretransplant fecal microbiota was lower in patients than in healthy controls and decreased further during the course of transplantation. Loss of diversity and domination by specific bacterial taxa occurred during auto-HCT in patterns similar to those with allo-HCT. Above-median fecal intestinal diversity in the periengraftment period was associated with decreased risk of death or progression (progression-free survival hazard ratio, 0.46; 95% confidence interval, 0.26-0.82; P = .008), adjusting for disease and disease status. This suggests that further investigation into the health of the intestinal microbiota in auto-HCT patients and posttransplant outcomes should be undertaken.

Conflict of interest statement

Conflict-of-interest disclosure: N.K. received research funding from Gilead Sciences and an honorarium from Back Bay Life Sciences. S.L. received travel support from Celgene, Sanofi, and Neovii. A.L.C.G. received support from Seres Therapeutics. G.L.S. received research funding from Janssen and Amgen. A.D.S. received support from Seres Therapeutics. C.S.S. served as a consultant on advisory boards for Juno Therapeutics, Sanofi-Genzyme, Spectrum Pharmaceuticals, Novartis, Genmab, Precision Biosciences, Kite (a Gilead Company), Celgene, Gamida Cell, and GlaxoSmithKline and received research funding for clinical trials from Juno Therapeutics, Celgene, Precision Biosciences, and Sanofi-Genzyme. H.J.L. served on advisory boards for Takeda, Janssen, and Celgene, served as a consultant for Caelum Biosciences and Karyopharm, and received research support from Takeda. M.-A.P. received honoraria from AbbVie, Bellicum, Celgene, Bristol Myers Squibb, Incyte, Merck, Novartis, Nektar Therapeutics, Omeros, and Takeda, served on data safety monitoring boards for Cidara Therapeutics, Servier, and Medigene and scientific advisory boards for MolMed and NexImmune, has received research support for clinical trials from Incyte, Kite/Gilead, and Miltenyi Biotec, has served as a volunteer for and as a member of the Board of Directors of American Society for Transplantation and Cellular Therapy and Be The Match (National Marrow Donor Program), and on the Center for International Blood and Marrow Transplant Research Cellular Immunotherapy Data Resource Committee. D.J.C. received research funding from Genentech. A.E.S. received support from Seres Therapeutics. R.R.J. has consulted for Karius, Merck, Microbiome DX, and Prolacta, has served on the scientific advisory boards of Kaleido, Maat Pharma, and Seres, and has received patent royalties licensed to Seres. S.G. received research funding from Amgen, Actinuum, Celgene, Johnson & Johnson, Miltenyi, Takeda, and Omeros, and has served on advisory boards for Amgen, Actinuum, Celgene, Johnson & Johnson, Janssen, Jazz Pharmaceutical, Takeda, Novartis, Kite, and Spectrum Pharma. K.A.M received travel support and honoraria or consulting fees from Karius. E.G.P. has received speaker honoraria from Bristol Myers Squibb, Celgene, Seres Therapeutics, MedImmune, Novartis, and Ferring Pharmaceuticals and is an inventor on patent application #WPO2015179437A1 (entitled “Methods and compositions for reducing Clostridium difficile infection”) and #WO2017091753A1 (entitled “Methods and compositions for reducing vancomycin-resistant enterococci infection or colonization”) and holds patents that receive royalties from Seres Therapeutics. M.R.M.v.d.B. has received research support and stock options from Seres; has received stock options from Notch Therapeutics; has received royalties from Wolters Kluwer; consulted and received honorarium from, or participated in, advisory boards for Seres Therapeutics, Jazz Pharmaceuticals, Rheos, Therakos, WindMIL Therapeutics, Amgen, Merck & Co, Inc, Magenta Therapeutics, Frazier Healthcare Partners, Nektar Therapeutics, Notch Therapeutics, Forty Seven Inc, Priothera, Ceramedix, DKMS (Deutsche KnochenMarkSpenderdatei), Pharmacyclics (spouse), Kite Pharmaceuticals (spouse); has IP Licensing with Seres Therapeutics and Juno Therapeutics; and holds a fiduciary role on the Foundation Board of DKMS (a nonprofit organization). J.U.P. has received research funding, intellectual property fees, and travel reimbursement from Seres Therapeutics and consulting fees from DaVolterra. The remaining authors declare no competing financial interests.

© 2021 by The American Society of Hematology.

Figures

Graphical abstract

Figure 1.

OS and PFS in auto-HCT patients is correlated with perineutrophil engraftment fecal diversity. (A) Histogram of sample collection times relative to transplantation for 1161 samples from 534 unique patients. (B) Fecal diversity, as measured by 16S sequencing and the inverse Simpson index, declined comparably at both institutions during the course of transplantation in 841 samples from 384 unique patients at MSKCC and 207 samples from 121 unique patients at Duke. Each point represents a stool sample color coded by institution. Curves are loess-smoothed averages. The y-axis was square-root scaled using the scale_y_sqrt function of the R package ggplot2. The same data are plotted together with samples through day 100 in supplemental Figure 1. (C) Fecal diversity in early pretransplant samples (defined as the earliest sample available per patient during days −15 to −3) was lower than the median diversity of 313 samples from 212 healthy participants of the HMP and 34 samples from 34 healthy MSKCC volunteers. The data from HMP and healthy MSKCC volunteers have been previously published and are reused here as controls. MSKCC includes 70 samples from 70 unique patients and Duke includes 38 samples from 38 unique patients. MSKCC healthy volunteer median was 15.87 (standard error [SE], 1.39), HMP median was 12.05 (SE, 0.50), MSKCC auto-HCT median was 9.39 (SE, 0.67), and Duke median was 9.68 (SE, 1.04). Another pretransplant window is used in supplemental Figure 6. The ends of the whisker lines represent the minimum and maximum values within 1.5× the interquartile range. (D) Patients with higher diversity in available perineutrophil engraftment (days +9 to +16) samples are at a reduced risk of progression or death. Patients were split into above-median (high-diversity) and below-median (low-diversity) groups in each of these day +17 landmark analyses by using the median inverse Simpson index (4.03) as the cutoff value. In all, 181 MSKCC and 59 Duke patients’ samples were analyzed. Analyses of diversity as a continuous variable and multivariable models are provided in supplemental Table 4 and analyses of pretransplant therapies are provided in supplemental Figure 6.

Figure 2.

Characterization of patterns of injury to microbiota during auto-HCT. (A-C) Microbiota composition of 1161 samples from 534 patients visualized via the t-SNE algorithm. Each point represents a stool sample; those with more similar microbiota composition are clustered together. Dotted lines were manually annotated to highlight features of interest. (A) Higher diversity samples (red and yellow) are clustered away from lower diversity (blue and gray) samples. (B) The same t-SNE projection as in panel A is color coded by day of sample collection. Pretransplant samples (dark purple) cluster in the region of high-diversity samples, whereas samples from days −1 to 20 (light pink and light green) cluster in the low-diversity regions. Of note, some late samples (dark green) again cluster in the early pretransplant or high-diversity region. (C) The same t-SNE projection as in panels A and B is color coded by the most abundant taxon. This shows a cluster of samples with a preponderance of Enterococcus (dark green) in an area of low diversity. Bray-Curtis distances were calculated on operational taxonomic unit (OTU) abundances, and points were color-coded by higher taxonomic ranks as indicated in the color key. f, family; g, genus; o, order; p, phylum. (D) Domination was assessed at the OTU level; bars are color coded at the genus level according to the color key. (E) The fraction of samples with intestinal domination (defined as >30% relative abundance by any single OTU) was >50% by day 7 and was >75% by day 14. (F) Diversity was lower in patients with lymphoma than in those with myeloma or amyloidosis. (G) The same t-SNE projection as in panels A-C is color coded by underlying disease (lymphoma, myeloma, or amyloidosis). Samples did not cluster together significantly by underlying disease.