• Selection of GVHD prophylaxis affects posttransplant immune reconstitution.

  • In adults, early CD4 recovery is associated with improved OS, PFS, and TRM, but not relapse, incidence of infections, or chronic GVHD.

Abstract

Allogeneic hematopoietic cell transplantation (allo-HCT) can provide curative treatment for hematologic malignancies but is associated with prolonged lymphopenia that may contribute to an increased risk of infection and relapse, resulting in decreased survival. We hypothesized that patients with rapid and robust CD4 T- and B-cell recovery have improved survival and decreased treatment-related mortality (TRM). A total of 2089 patients were included who underwent first allo-HCT for acute myeloid leukemia/acute lymphoblastic leukemia/myelodysplastic syndrome from 2008 to 2019 reported to the Center for International Blood and Marrow Transplant Research with available CD4 counts at days 100 and 180. Patients (median age, 51 years [range, 2-75]) were categorized into 4 groups based on graft-versus-host disease (GVHD) prophylaxis: ex vivo T-cell depletion (TCD/CD34), posttransplant cyclophosphamide, calcineurin inhibitor alone (CNI), or CNI with antithymocyte globulin. Based upon survival, we could identify optimal cutoff points for CD4+ T cells in pediatric (age of <20 years) patients: 248 × 106/L and 420 × 106/L at days 100 and 180, respectively; and in adult (age of >20 years) patients: 104 × 106/L and 115 × 106/L at days 100 and 180, respectively. In adults, day-100 CD4 count was associated with overall survival (OS), progression-free survival (PFS), and TRM but not relapse, incidence of infections, or chronic GVHD. Similarly, CD4 counts above the cutoff point at day 180 in adults were associated with improved OS, PFS, and TRM but no other outcomes. No clinical associations for CD4 counts were identifiable in pediatric patients. These findings underscore the importance of tailoring transplant strategies for adults to optimize immune recovery and improve patient outcomes.

Allogeneic hematopoietic cell transplantation (allo-HCT) is an established treatment for hematologic malignancies and marrow failure syndromes. However, it is associated with significant adverse outcomes, including infection, relapse, and graft-versus-host disease (GVHD). Previous mostly single-center studies have shown that posttransplant T-cell reconstitution correlates with risk of infections, overall survival (OS), relapse, and treatment-related mortality (TRM).1-15 However, limited data exist on the effect of the quantitative and functional recovery of T cells on relapse and survival.16-18 Furthermore, delayed recovery of immunoglobulin A (IgA) secretion (a surrogate marker of functional B-cell recovery demonstrating the capacity of B lymphocytes for isotype switching) compared with IgG has been long recognized as a distinct pathological feature after allo-HCT.19-24 In addition, acute GVHD (aGVHD) or chronic GVHD may further delay the recovery of mucosal immunity and serum IgA levels.25,26 

We conducted a comprehensive analysis within the Center for International Blood and Marrow Transplant Research (CIBMTR) Database to substantiate these findings from multiple US institutions. This study analyzed transplantation outcomes in adult and pediatric patients who underwent allo-HCT for acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and acute lymphoblastic leukemia (ALL), with a focus on how recovery of CD4 counts and IgA levels correlated with transplant outcomes.

Data sources

The CIBMTR is a working group of >500 transplant centers worldwide that provide detailed patient, disease, and transplant characteristics along with outcomes of consecutive transplantations. All patients included in this study provided consent to participate in the CIBMTR Database. The institutional review boards of the Medical College of Wisconsin and the National Marrow Donor Program approved this study.

Data collection and criteria for selection

All patients aged ≥2 years who received a first allo-HCT for AML, ALL, or MDS from related or unrelated donors between 2008 and 2019 were included in this cohort analysis. All included patients required comprehensive report-form data and were required to have CD4 counts reported at day 100 and day 180 after transplant. The most recent lymphocyte counts measured since the date of the last report were used in the analysis. The 1-year completeness index was 98%, and the data set was finalized as of 1 September 2019.

Outcomes

OS was the primary end point. Death from any cause was an event, and surviving patients were censored at last follow-up. Progression-free survival (PFS) was defined as survival without relapse or progression. Progression or relapse was defined as progressive disease or recurrence after a complete remission; death without relapse or progression was the competing risk. TRM was defined as death from any cause without relapse or progression; relapse or progression was the competing risk. Acute and chronic GVHD were graded using standard criteria.27,28 The incidences of bacterial, viral, and fungal infections by day 100 post-HCT were described, with death as the competing risk.

Statistical analysis

Patient-, disease-, and transplant-related factors were compared between groups using the χ2 test for categorical variables and the Kruskal-Wallis test for continuous variables. All outcomes were examined from day 100 and day 180 after transplant separately in landmark analyses to account for the defining event creating the cohorts. A P value threshold of .01 was used to determine statistical significance for all comparisons.

Our primary hypothesis was that patients with rapid and robust recovery of adaptive immunity have improved survival and decreased TRM. Absolute values of circulating CD4 T cells, CD8 T cells, natural killer cells, B cells, and IgA levels were available tools toward this objective. Cutoff points were used to dichotomize CD4 and IgA into low and high groups based on whether a patient’s value was either less than or more than the cutoff point. Optimal cutoff points were obtained by finding the cutoff points that maximized the partial likelihoods of univariable Cox proportional hazards models of OS. We also assessed whether cutoff points for both CD4 and IgA are different for patients aged <20 and ≥20 years. Outcomes were compared between patients with values less than and more than each cutoff point. OS and PFS were estimated for the low/high CD4 and IgA groups determined by these cutoff points using the Kaplan-Meier estimator and compared between groups with the log-rank test. Cumulative incidences of relapse/progression, TRM, infections, and aGVHD and chronic GVHD were described for these groups by the Aalen-Johansen estimator and compared between groups using the Gray test. Cox proportional hazards regression was used separately for each outcome to compare low and high CD4 and IgA groups at day 100 and day 180 landmarks (further details can be found in the supplemental Methods).

Patient and transplant characteristics

The study included 2089 patients aged >2 years, from 105 centers, who received a first allo-HCT using a matched related donor, mismatched related donor, matched unrelated donor, or mismatched unrelated donor, including umbilical cord blood (Table 1). AML was the most common indication for allo-HCT (47%). The median time from transplant to diagnosis was 7 months (range, 1-549).

Table 1.

Patient characteristics

CharacteristicWhole cohortTCDPTCyCNICNI+ATGP value
No. of patients 2089 207 304 1203 375  
No. of centers 105 30 46 94 63  
Patient related       
Recipient age, n (%)      <.01  
Median age (range), y 51 (2-75) 52 (3-74) 56 (2-74) 47 (2-75) 53 (2-74)  
      <.01  
<20 years 459 (22.0) 43 (20.8) 23 (7.6) 307 (25.5) 86 (22.9)  
≥20 years 1630 (78.0) 164 (79.2) 281 (92.4) 896 (74.5) 289 (77.1)  
Recipient sex, n (%)      .55  
Male 1190 (57) 126 (61) 177 (58) 672 (56) 215 (57)  
Female 899 (43) 81 (39) 127 (42) 531 (44) 160 (43)  
Recipient race, n (%)      .01  
White 1650 (79) 157 (76) 221 (73) 965 (80) 307 (82)  
African American 235 (11) 33 (16) 52 (17) 109 (9) 41 (11)  
Asian 100 (5) 10 (5) 15 (5) 60 (5) 15 (4)  
Native Hawaiian or other Pacific Islander 9 (0) 1 (0) 1 (0) 7 (1) 0 (0)  
American Indian or Alaska Native 9 (0) 0 (0) 0 (0) 6 (0) 3 (1)  
≥1 race 22 (1) 0 (0) 4 (1) 14 (1) 4 (1)  
Missing 64 (3) 6 (3) 11 (4) 42 (3) 5 (1)  
Ethnicity, n (%)      .02  
Hispanic or Latino 223 (11) 16 (8) 33 (11) 146 (12) 28 (7)  
Not Hispanic or Latino 1816 (87) 189 (91) 267 (88) 1020 (85) 340 (91)  
Nonresident of the United States 9 (0) 2 (1) 1 (0) 6 (0) 0 (0)  
Missing 41 (2) 0 (0) 3 (1) 31 (3) 7 (2)  
HCT-CI score, n (%)      <.01  
582 (28) 50 (24) 54 (18) 358 (30) 120 (32)  
298 (14) 27 (13) 45 (15) 175 (15) 51 (14)  
295 (14) 30 (14) 46 (15) 170 (14) 49 (13)  
≥3 912 (44) 100 (48) 159 (52) 498 (41) 155 (41)  
Missing 2 (0) 0 (0) 0 (0) 2 (0) 0 (0)  
Previous autologous transplant, n (%)      .17  
No 2045 (98) 199 (96) 301 (99) 1178 (98) 367 (98)  
Yes 44 (2) 8 (4) 3 (1) 25 (2) 8 (2)  
Karnofsky/Lansky performance score, n (%)      .02  
<90 729 (35) 67 (32) 125 (41) 402 (33) 135 (36)  
≥90 1348 (65) 139 (67) 175 (58) 798 (66) 236 (63)  
Missing 12 (1) 1 (0) 4 (1) 3 (0) 4 (1)  
ALC at HCT, median (range), ×109/L 2.3 (0.0-77.3) 2.0 (0.0-77.3) 2.4 (0.0-23.0) 2.3 (0.0-74.5) 2.2 (0.0-42.4) .50  
Missing 101 (5) 6 (3) 7 (2) 67 (6) 21 (6) .05  
Donor related       
Donor age, y      <.01  
Median (range) 36 (18-76) 40 (19-73) 36 (18-74) 36 (18-76) 31 (18-70)  
Donor sex, n (%)      .01  
Male 1238 (59) 117 (57) 199 (65) 686 (57) 236 (63)  
Female 805 (39) 87 (42) 104 (34) 483 (40) 131 (35)  
Missing 46 (2) 3 (1) 1 (0) 34 (3) 8 (2)  
Donor/recipient CMV serostatus, n (%)      <.01  
+/+ 511 (24) 62 (30) 121 (40) 236 (20) 92 (25)  
+/− 173 (8) 16 (8) 32 (11) 83 (7) 42 (11)  
−/+ 424 (20) 34 (16) 82 (27) 226 (19) 82 (22)  
−/− 424 (20) 51 (25) 67 (22) 207 (17) 99 (26)  
CB −/recipient + 333 (16) 30 (14) 0 (0) 267 (22) 36 (10)  
CB −/recipient − 198 (9) 11 (5) 0 (0) 167 (14) 20 (5)  
CB −/recipient CMV unknown 7 (0) 0 (0) 0 (0) 7 (1) 0 (0)  
Missing 19 (1) 3 (1) 2 (1) 10 (1) 4 (1)  
Disease related       
Disease, n (%)      <.01  
AML 974 (47) 101 (49) 157 (52) 562 (47) 154 (41)  
ALL 460 (22) 43 (21) 64 (21) 296 (25) 57 (15)  
MDS 655 (31) 63 (30) 83 (27) 345 (29) 164 (44)  
Time from diagnosis to transplant, median (range), d 7 (1-549) 7 (1-370) 7 (2-549) 7 (1-364) 8 (1-497) .01  
AML/ALL disease status, n (%)      .10  
CR1 887 (62) 92 (64) 140 (63) 523 (61) 132 (63)  
CR2 348 (24) 31 (22) 48 (22) 219 (26) 50 (24)  
CR≥3 56 (4) 4 (3) 4 (2) 40 (5) 8 (4)  
PIF/relapse 142 (10) 16 (11) 29 (13) 76 (9) 21 (10)  
Missing 1 (0) 1 (1) 0 (0) 0 (0) 0 (0)  
AML/ALL cytogenetic score, n (%)      .09  
Normal 89 (6) 8 (6) 9 (4) 65 (8) 7 (3)  
Favorable 102 (7) 9 (6) 11 (5) 61 (7) 21 (10)  
Intermediate 613 (43) 65 (45) 100 (45) 348 (41) 100 (47)  
Poor 597 (42) 56 (39) 99 (45) 364 (42) 78 (37)  
Not tested 10 (1) 3 (2) 0 (0) 5 (1) 2 (1)  
Missing 23 (1) 3 (2) 2 (1) 15 (2) 3 (1)  
MDS IPSS-R cytogenetic score, n (%)      <.01  
Very good 6 (1) 0 (0) 0 (0) 4 (1) 2 (1)  
Good 287 (44) 23 (37) 38 (46) 154 (45) 72 (44)  
Intermediate 157 (24) 15 (24) 18 (22) 78 (23) 46 (28)  
Poor 99 (15) 6 (10) 9 (11) 57 (17) 27 (16)  
Very poor 90 (14) 12 (19) 17 (20) 46 (13) 15 (9)  
Not tested 8 (1) 4 (6) 1 (1) 2 (1) 1 (1)  
Missing 8 (1) 3 (5) 0 (0) 4 (1) 1 (1)  
Conditioning intensity, n (%)      <.01  
MAC 1370 (66) 165 (80) 133 (44) 846 (70) 226 (60)  
RIC 480 (23) 35 (17) 49 (16) 271 (23) 125 (33)  
NMA 237 (11) 6 (3) 121 (40) 86 (7) 24 (6)  
Missing 2 (0) 1 (0) 1 (0) 0 (0) 0 (0)  
Graft type, n (%)      <.01  
BM 336 (16) 3 (1) 95 (31) 178 (15) 60 (16)  
PB 1215 (58) 163 (79) 209 (69) 584 (49) 259 (69)  
CB 538 (26) 41 (20) 0 (0) 441 (37) 56 (15)  
Donor group, n (%)      <.01  
HLA-identical sibling 412 (20) 28 (14) 20 (7) 322 (27) 42 (11)  
Other related: matched 27 (1) 3 (1) 7 (2) 17 (1) 0 (0)  
Other related: mismatched 1 antigen/allele 20 (1) 2 (1) 11 (4) 4 (0) 3 (1)  
Other related: mismatched ≥2 antigen/allele 269 (13) 63 (30) 196 (64) 9 (1) 1 (0)  
Other related: matching missing 22 (1) 3 (1) 15 (5) 3 (0) 1 (0)  
Well matched unrelated (8/8) 670 (32) 49 (24) 40 (13) 369 (31) 212 (57)  
Partially matched unrelated (7/8) 107 (5) 15 (7) 11 (4) 30 (2) 51 (14)  
Mismatched unrelated (≤6/8) 11 (1) 2 (1) 3 (1) 1 (0) 5 (1)  
Unrelated (matching unknown) 13 (1) 1 (0) 1 (0) 7 (1) 4 (1)  
Single cord 224 (11) 36 (17) 0 (0) 148 (12) 40 (11)  
Double cord 313 (15) 5 (2) 0 (0) 292 (24) 16 (4)  
CB, single or double unknown 1 (0) 0 (0) 0 (0) 1 (0) 0 (0)  
GVHD prophylaxis, n (%)      <.01  
Ex vivo TCD 62 (3) 62 (30) 0 (0) 0 (0) 0 (0)  
CD34 selection 145 (7) 145 (70) 0 (0) 0 (0) 0 (0)  
PTCy + other(s) 286 (14) 0 (0) 286 (94) 0 (0) 0 (0)  
PTCy alone 18 (1) 0 (0) 18 (6) 0 (0) 0 (0)  
TAC/CSA + MMF ± other(s) (except PTCy) 662 (32) 0 (0) 0 (0) 537 (45) 125 (33)  
TAC/CSA + MTX ± other(s) (except MMF, PTCy) 788 (38) 0 (0) 0 (0) 573 (48) 215 (57)  
TAC/CSA + other(s) (except MMF, MTX, and PTCy) 128 (6) 0 (0) 0 (0) 93 (8) 35 (9)  
ATG/Campath, n (%)      <.01  
ATG alone 501 (24) 126 (61) 0 (0) 0 (0) 375 (100)  
No ATG or alemtuzumab 1588 (76) 81 (39) 304 (100) 1203 (100) 0 (0)  
Steroids, days 0-100, n (%)      <.01  
No 1202 (58) 152 (73) 200 (66) 620 (52) 230 (61)  
Yes 887 (42) 55 (27) 104 (34) 583 (48) 145 (39)  
TBI usage, n (%)      <.01  
TBI (single dose of >500 cGy or fractionated of >800 cGy) 648 (31) 79 (38) 62 (20) 448 (37) 59 (16)  
TBI (single dose of ≤500 cGy or fractionated ≤ 800 cGy), other agents delivered at MA doses 59 (3) 17 (8) 8 (3) 33 (3) 1 (0)  
TBI (single dose >200 and ≤500 cGy, or fractionated >200 and ≤800 cGy) 57 (3) 12 (6) 7 (2) 21 (2) 17 (5)  
TBI of 200 cGy 267 (13) 7 (3) 117 (38) 126 (10) 17 (5)  
Non-TBI regimen 1056 (51) 91 (44) 109 (36) 575 (48) 281 (75)  
Missing 2 (0) 1 (0) 1 (0) 0 (0) 0 (0)  
IV immunoglobulin day 0-100, n (%)      <.01  
No 1348 (65) 87 (42) 226 (74) 796 (66) 239 (64)  
Yes 740 (35) 120 (58) 78 (26) 406 (34) 136 (36)  
Missing 1 (0) 0 (0) 0 (0) 1 (0) 0 (0)  
Follow-up, median (range), mo 55 (5-148) 60 (6-145) 33 (5-73) 60 (5-148) 60 (6-144)  
CharacteristicWhole cohortTCDPTCyCNICNI+ATGP value
No. of patients 2089 207 304 1203 375  
No. of centers 105 30 46 94 63  
Patient related       
Recipient age, n (%)      <.01  
Median age (range), y 51 (2-75) 52 (3-74) 56 (2-74) 47 (2-75) 53 (2-74)  
      <.01  
<20 years 459 (22.0) 43 (20.8) 23 (7.6) 307 (25.5) 86 (22.9)  
≥20 years 1630 (78.0) 164 (79.2) 281 (92.4) 896 (74.5) 289 (77.1)  
Recipient sex, n (%)      .55  
Male 1190 (57) 126 (61) 177 (58) 672 (56) 215 (57)  
Female 899 (43) 81 (39) 127 (42) 531 (44) 160 (43)  
Recipient race, n (%)      .01  
White 1650 (79) 157 (76) 221 (73) 965 (80) 307 (82)  
African American 235 (11) 33 (16) 52 (17) 109 (9) 41 (11)  
Asian 100 (5) 10 (5) 15 (5) 60 (5) 15 (4)  
Native Hawaiian or other Pacific Islander 9 (0) 1 (0) 1 (0) 7 (1) 0 (0)  
American Indian or Alaska Native 9 (0) 0 (0) 0 (0) 6 (0) 3 (1)  
≥1 race 22 (1) 0 (0) 4 (1) 14 (1) 4 (1)  
Missing 64 (3) 6 (3) 11 (4) 42 (3) 5 (1)  
Ethnicity, n (%)      .02  
Hispanic or Latino 223 (11) 16 (8) 33 (11) 146 (12) 28 (7)  
Not Hispanic or Latino 1816 (87) 189 (91) 267 (88) 1020 (85) 340 (91)  
Nonresident of the United States 9 (0) 2 (1) 1 (0) 6 (0) 0 (0)  
Missing 41 (2) 0 (0) 3 (1) 31 (3) 7 (2)  
HCT-CI score, n (%)      <.01  
582 (28) 50 (24) 54 (18) 358 (30) 120 (32)  
298 (14) 27 (13) 45 (15) 175 (15) 51 (14)  
295 (14) 30 (14) 46 (15) 170 (14) 49 (13)  
≥3 912 (44) 100 (48) 159 (52) 498 (41) 155 (41)  
Missing 2 (0) 0 (0) 0 (0) 2 (0) 0 (0)  
Previous autologous transplant, n (%)      .17  
No 2045 (98) 199 (96) 301 (99) 1178 (98) 367 (98)  
Yes 44 (2) 8 (4) 3 (1) 25 (2) 8 (2)  
Karnofsky/Lansky performance score, n (%)      .02  
<90 729 (35) 67 (32) 125 (41) 402 (33) 135 (36)  
≥90 1348 (65) 139 (67) 175 (58) 798 (66) 236 (63)  
Missing 12 (1) 1 (0) 4 (1) 3 (0) 4 (1)  
ALC at HCT, median (range), ×109/L 2.3 (0.0-77.3) 2.0 (0.0-77.3) 2.4 (0.0-23.0) 2.3 (0.0-74.5) 2.2 (0.0-42.4) .50  
Missing 101 (5) 6 (3) 7 (2) 67 (6) 21 (6) .05  
Donor related       
Donor age, y      <.01  
Median (range) 36 (18-76) 40 (19-73) 36 (18-74) 36 (18-76) 31 (18-70)  
Donor sex, n (%)      .01  
Male 1238 (59) 117 (57) 199 (65) 686 (57) 236 (63)  
Female 805 (39) 87 (42) 104 (34) 483 (40) 131 (35)  
Missing 46 (2) 3 (1) 1 (0) 34 (3) 8 (2)  
Donor/recipient CMV serostatus, n (%)      <.01  
+/+ 511 (24) 62 (30) 121 (40) 236 (20) 92 (25)  
+/− 173 (8) 16 (8) 32 (11) 83 (7) 42 (11)  
−/+ 424 (20) 34 (16) 82 (27) 226 (19) 82 (22)  
−/− 424 (20) 51 (25) 67 (22) 207 (17) 99 (26)  
CB −/recipient + 333 (16) 30 (14) 0 (0) 267 (22) 36 (10)  
CB −/recipient − 198 (9) 11 (5) 0 (0) 167 (14) 20 (5)  
CB −/recipient CMV unknown 7 (0) 0 (0) 0 (0) 7 (1) 0 (0)  
Missing 19 (1) 3 (1) 2 (1) 10 (1) 4 (1)  
Disease related       
Disease, n (%)      <.01  
AML 974 (47) 101 (49) 157 (52) 562 (47) 154 (41)  
ALL 460 (22) 43 (21) 64 (21) 296 (25) 57 (15)  
MDS 655 (31) 63 (30) 83 (27) 345 (29) 164 (44)  
Time from diagnosis to transplant, median (range), d 7 (1-549) 7 (1-370) 7 (2-549) 7 (1-364) 8 (1-497) .01  
AML/ALL disease status, n (%)      .10  
CR1 887 (62) 92 (64) 140 (63) 523 (61) 132 (63)  
CR2 348 (24) 31 (22) 48 (22) 219 (26) 50 (24)  
CR≥3 56 (4) 4 (3) 4 (2) 40 (5) 8 (4)  
PIF/relapse 142 (10) 16 (11) 29 (13) 76 (9) 21 (10)  
Missing 1 (0) 1 (1) 0 (0) 0 (0) 0 (0)  
AML/ALL cytogenetic score, n (%)      .09  
Normal 89 (6) 8 (6) 9 (4) 65 (8) 7 (3)  
Favorable 102 (7) 9 (6) 11 (5) 61 (7) 21 (10)  
Intermediate 613 (43) 65 (45) 100 (45) 348 (41) 100 (47)  
Poor 597 (42) 56 (39) 99 (45) 364 (42) 78 (37)  
Not tested 10 (1) 3 (2) 0 (0) 5 (1) 2 (1)  
Missing 23 (1) 3 (2) 2 (1) 15 (2) 3 (1)  
MDS IPSS-R cytogenetic score, n (%)      <.01  
Very good 6 (1) 0 (0) 0 (0) 4 (1) 2 (1)  
Good 287 (44) 23 (37) 38 (46) 154 (45) 72 (44)  
Intermediate 157 (24) 15 (24) 18 (22) 78 (23) 46 (28)  
Poor 99 (15) 6 (10) 9 (11) 57 (17) 27 (16)  
Very poor 90 (14) 12 (19) 17 (20) 46 (13) 15 (9)  
Not tested 8 (1) 4 (6) 1 (1) 2 (1) 1 (1)  
Missing 8 (1) 3 (5) 0 (0) 4 (1) 1 (1)  
Conditioning intensity, n (%)      <.01  
MAC 1370 (66) 165 (80) 133 (44) 846 (70) 226 (60)  
RIC 480 (23) 35 (17) 49 (16) 271 (23) 125 (33)  
NMA 237 (11) 6 (3) 121 (40) 86 (7) 24 (6)  
Missing 2 (0) 1 (0) 1 (0) 0 (0) 0 (0)  
Graft type, n (%)      <.01  
BM 336 (16) 3 (1) 95 (31) 178 (15) 60 (16)  
PB 1215 (58) 163 (79) 209 (69) 584 (49) 259 (69)  
CB 538 (26) 41 (20) 0 (0) 441 (37) 56 (15)  
Donor group, n (%)      <.01  
HLA-identical sibling 412 (20) 28 (14) 20 (7) 322 (27) 42 (11)  
Other related: matched 27 (1) 3 (1) 7 (2) 17 (1) 0 (0)  
Other related: mismatched 1 antigen/allele 20 (1) 2 (1) 11 (4) 4 (0) 3 (1)  
Other related: mismatched ≥2 antigen/allele 269 (13) 63 (30) 196 (64) 9 (1) 1 (0)  
Other related: matching missing 22 (1) 3 (1) 15 (5) 3 (0) 1 (0)  
Well matched unrelated (8/8) 670 (32) 49 (24) 40 (13) 369 (31) 212 (57)  
Partially matched unrelated (7/8) 107 (5) 15 (7) 11 (4) 30 (2) 51 (14)  
Mismatched unrelated (≤6/8) 11 (1) 2 (1) 3 (1) 1 (0) 5 (1)  
Unrelated (matching unknown) 13 (1) 1 (0) 1 (0) 7 (1) 4 (1)  
Single cord 224 (11) 36 (17) 0 (0) 148 (12) 40 (11)  
Double cord 313 (15) 5 (2) 0 (0) 292 (24) 16 (4)  
CB, single or double unknown 1 (0) 0 (0) 0 (0) 1 (0) 0 (0)  
GVHD prophylaxis, n (%)      <.01  
Ex vivo TCD 62 (3) 62 (30) 0 (0) 0 (0) 0 (0)  
CD34 selection 145 (7) 145 (70) 0 (0) 0 (0) 0 (0)  
PTCy + other(s) 286 (14) 0 (0) 286 (94) 0 (0) 0 (0)  
PTCy alone 18 (1) 0 (0) 18 (6) 0 (0) 0 (0)  
TAC/CSA + MMF ± other(s) (except PTCy) 662 (32) 0 (0) 0 (0) 537 (45) 125 (33)  
TAC/CSA + MTX ± other(s) (except MMF, PTCy) 788 (38) 0 (0) 0 (0) 573 (48) 215 (57)  
TAC/CSA + other(s) (except MMF, MTX, and PTCy) 128 (6) 0 (0) 0 (0) 93 (8) 35 (9)  
ATG/Campath, n (%)      <.01  
ATG alone 501 (24) 126 (61) 0 (0) 0 (0) 375 (100)  
No ATG or alemtuzumab 1588 (76) 81 (39) 304 (100) 1203 (100) 0 (0)  
Steroids, days 0-100, n (%)      <.01  
No 1202 (58) 152 (73) 200 (66) 620 (52) 230 (61)  
Yes 887 (42) 55 (27) 104 (34) 583 (48) 145 (39)  
TBI usage, n (%)      <.01  
TBI (single dose of >500 cGy or fractionated of >800 cGy) 648 (31) 79 (38) 62 (20) 448 (37) 59 (16)  
TBI (single dose of ≤500 cGy or fractionated ≤ 800 cGy), other agents delivered at MA doses 59 (3) 17 (8) 8 (3) 33 (3) 1 (0)  
TBI (single dose >200 and ≤500 cGy, or fractionated >200 and ≤800 cGy) 57 (3) 12 (6) 7 (2) 21 (2) 17 (5)  
TBI of 200 cGy 267 (13) 7 (3) 117 (38) 126 (10) 17 (5)  
Non-TBI regimen 1056 (51) 91 (44) 109 (36) 575 (48) 281 (75)  
Missing 2 (0) 1 (0) 1 (0) 0 (0) 0 (0)  
IV immunoglobulin day 0-100, n (%)      <.01  
No 1348 (65) 87 (42) 226 (74) 796 (66) 239 (64)  
Yes 740 (35) 120 (58) 78 (26) 406 (34) 136 (36)  
Missing 1 (0) 0 (0) 0 (0) 1 (0) 0 (0)  
Follow-up, median (range), mo 55 (5-148) 60 (6-145) 33 (5-73) 60 (5-148) 60 (6-144)  

ALC, absolute lymphocyte count; CB, cord blood; CMV, cytomegalovirus; CR1/2/3, complete remission; CSA, cyclosporin; HCT-CI, HCT-specific comorbidity index; IPSS-R, revised international prognostic scoring system; MA, myeloablative; MAC, myeloablative conditioning; MMF, mycophenolate mofetil; MTX, methotrexate; NMA, non-myeloablative; PIF, primary induction failure; RIC, reduced intensity conditioning; TAC, tacrolimus.

Pearson χ2 test.

Kruskal-Wallis test.

Because GVHD prophylaxis is often linked to donor type and can have a significant impact on immune recovery, we divided patients into 4 predefined cohorts: (1) ex vivo T-cell depletion (TCD)/CD34-selection with or without antithymocyte globulin (ATG; n = 207); (2) unmodified grafts with posttransplant cyclophosphamide (PTCy; n = 304); (3) unmodified grafts with calcineurin inhibitor–based GVHD prophylaxis without ATG (CNI; n = 1203); and (4) unmodified grafts with CNI-based GVHD prophylaxis with ATG (CNI+ATG; n = 375).

Median age for the whole study cohort was 51 years (range, 2-75). Patients in the CNI cohort were younger (median age, 47 years [range, 2-75]) than those in the other groups (TCD median age, 52 years [range, 3-74]; PTCy, 56 [range, 2-74]; and CNI+ATG, 53 [range, 2-74]; P < .01). Patients in the PTCy cohort were less likely to be White (73%) vs the TCD (76%), CNI (80%), and CNI+ATG (82%) cohorts (P < .01), more likely to have HCT-specific comorbidity index score of >3 (52%) vs the TCD (48%), CNI (41%), and CNI+ATG (41%) cohorts (P < .01), and a Karnofsky performance status score of <90 (41%) vs the TCD (32%), CNI (33%), and CNI+ATG (36%) cohorts (P = .02). Differences were also noted in graft source and donor type, with mismatched donors (69%) and bone marrow (BM) grafts (31%) being more common in the PTCy group, and cord blood more common in the CNI group (36%; P < .01). Finally, myeloablative conditioning was most common in the TCD group (80%; P < .01), whereas high-dose total body irradiation (TBI) was more common in the TCD (38%) and CNI (37%; P < .01) groups. No differences were observed between cohorts for patient sex, disease status, or cytogenetic score. Additional details are provided in supplemental Table 1.

Clinical outcomes: engraftment, GVHD, infections, and cause of death

Time to neutrophil engraftment was shortest in the TCD cohort (median, 11 days [range, 1-65]) compared with CNI+ATG (median, 13 days [range, 0-52]), PTCy (median, 17 days [range, 1-100]), and CNI (median, 17 days [range, 1-86]; P < .01; Table 2). A similar pattern was observed for platelet engraftment. Time to aGVHD onset did not differ significantly between groups, although the use of steroids in the first 100 days was highest in the CNI cohort (48%) compared with the CNI+ATG (39%), PTCy (34%), and TCD (27%) cohorts (P < .01; Table 1). The cumulative incidence of grade 2 to 4 aGVHD by day 100 for this selected population with CD4 available at day 100 was highest in the CNI cohort (44.6%; 95% confidence interval [CI], 41-48) compared with the TCD (27.8%; 95% CI, 20-36), CNI+ATG (28.8%; 95% CI, 23-35), and PTCy (28.2%; 95% CI, 22-35) cohorts (P < .0001). Similarly, grade 3 to 4 aGVHD at 100 days was elevated in the CNI cohort at 12.6% in contrast to the other cohorts (6.3%-8.9%).

Table 2.

Engraftment, GVHD, and infections

Whole cohortTCDPTCyCNICNI+ATGP value
Characteristic       
Time to neutrophil recovery, median (range), d 16 (0-86) 11 (1-65) 17 (1-100) 17 (1-86) 13 (0-52) <.01  
Time to platelet recovery, median (range), d 23 (0-98) 18 (1-88) 27 (1-95) 24 (0-98) 20 (1-98) <.01  
Time to aGVHD, median (range), d 34 (2-100) 36 (5-180) 35 (13-187) 33 (4-210) 35 (10-208) .11  
Infections before day 100       
Bacterial infections, n (%)      .05  
No 1140 (55) 120 (58) 183 (60) 643 (53) 194 (52)  
Yes 948 (45) 87 (42) 120 (39) 560 (47) 181 (48)  
Missing 1 (0) 0 (0) 1 (0) 0 (0) 0 (0)  
Time to bacterial infection median (range), d 11 (1-100) 8 (1-90) 11 (1-99) 11 (1-100) 12 (1-100) .08  
Viral infections, n (%)      .74  
No 1078 (52) 103 (50) 157 (52) 634 (53) 184 (49)  
Yes 1008 (48) 104 (50) 146 (48) 568 (47) 190 (51)  
Missing 3 (0) 0 (0) 1 (0) 1 (0) 1 (0)  
Time to viral infection, median (range), d 28 (1-100) 27 (1-95) 31 (1-98) 27 (1-99) 28 (1-100) .35  
Fungal infections, n (%)      .25  
No 1994 (95) 199 (96) 296 (97) 1145 (95) 354 (94)  
Yes 91 (4) 8 (4) 7 (2) 57 (5) 19 (5)  
Missing 4 (0) 0 (0) 1 (0) 1 (0) 2 (1)  
Time to fungal infection, median (range), d 28 (1-98) 33 (5-80) 20 (8-54) 22 (1-98) 32 (2-78) .63  
Any infection, n (%)      .27  
No 653 (31) 63 (30) 111 (37) 377 (31) 102 (27)  
Yes 1435 (69) 144 (70) 193 (63) 825 (69) 273 (73)  
Missing 1 (0) 0 (0) 0 (0) 1 (0) 0 (0)  
Time to any infection, median (range), d 15 (1-99) 14 (1-85) 14 (1-99) 15 (1-98) 17 (1-96) .74  
Outcomes       
Cumulative incidence of grade 2 to 4 aGVHD at 100 days, % (95% CI) 37.7 (35-40.5) 27.8 (20.1-36.2) 28.2 (21.8-35.2) 44.6 (41-48.4) 28.8 (22.9-35) <.001  
Cumulative incidence of grade 3 to 4 aGVHD at 100 days, % (95% CI) 10.6 (8.9-12.4) 8.4 (4.1-14.1) 6.3 (3.2-10.4) 12.6 (10.2-15.1) 8.9 (5.4-13) .002  
Whole cohortTCDPTCyCNICNI+ATGP value
Characteristic       
Time to neutrophil recovery, median (range), d 16 (0-86) 11 (1-65) 17 (1-100) 17 (1-86) 13 (0-52) <.01  
Time to platelet recovery, median (range), d 23 (0-98) 18 (1-88) 27 (1-95) 24 (0-98) 20 (1-98) <.01  
Time to aGVHD, median (range), d 34 (2-100) 36 (5-180) 35 (13-187) 33 (4-210) 35 (10-208) .11  
Infections before day 100       
Bacterial infections, n (%)      .05  
No 1140 (55) 120 (58) 183 (60) 643 (53) 194 (52)  
Yes 948 (45) 87 (42) 120 (39) 560 (47) 181 (48)  
Missing 1 (0) 0 (0) 1 (0) 0 (0) 0 (0)  
Time to bacterial infection median (range), d 11 (1-100) 8 (1-90) 11 (1-99) 11 (1-100) 12 (1-100) .08  
Viral infections, n (%)      .74  
No 1078 (52) 103 (50) 157 (52) 634 (53) 184 (49)  
Yes 1008 (48) 104 (50) 146 (48) 568 (47) 190 (51)  
Missing 3 (0) 0 (0) 1 (0) 1 (0) 1 (0)  
Time to viral infection, median (range), d 28 (1-100) 27 (1-95) 31 (1-98) 27 (1-99) 28 (1-100) .35  
Fungal infections, n (%)      .25  
No 1994 (95) 199 (96) 296 (97) 1145 (95) 354 (94)  
Yes 91 (4) 8 (4) 7 (2) 57 (5) 19 (5)  
Missing 4 (0) 0 (0) 1 (0) 1 (0) 2 (1)  
Time to fungal infection, median (range), d 28 (1-98) 33 (5-80) 20 (8-54) 22 (1-98) 32 (2-78) .63  
Any infection, n (%)      .27  
No 653 (31) 63 (30) 111 (37) 377 (31) 102 (27)  
Yes 1435 (69) 144 (70) 193 (63) 825 (69) 273 (73)  
Missing 1 (0) 0 (0) 0 (0) 1 (0) 0 (0)  
Time to any infection, median (range), d 15 (1-99) 14 (1-85) 14 (1-99) 15 (1-98) 17 (1-96) .74  
Outcomes       
Cumulative incidence of grade 2 to 4 aGVHD at 100 days, % (95% CI) 37.7 (35-40.5) 27.8 (20.1-36.2) 28.2 (21.8-35.2) 44.6 (41-48.4) 28.8 (22.9-35) <.001  
Cumulative incidence of grade 3 to 4 aGVHD at 100 days, % (95% CI) 10.6 (8.9-12.4) 8.4 (4.1-14.1) 6.3 (3.2-10.4) 12.6 (10.2-15.1) 8.9 (5.4-13) .002  

Kruskal-Wallis test.

Pearson χ2 test.

Gray test.

No statistically significant differences were observed between the groups in terms of occurrence of any infection (range, 63%-73%), and bacterial (range, 39%-48%), viral (range, 47%-51%), or fungal (range, 2%-5%) infection, or the time to the various types of infection. Further details on infections are in supplemental Table 2. Overall, 689 patients (33%) died. Causes of death included relapse (52%), GVHD (15%), infection (13%), and organ failure (8%).

Immune reconstitution

We first examined immune reconstitution from the day 100 and day 180 data sets for the whole cohort. Absolute lymphocyte count increased from day 100 (median, 0.8 × 109/L [range, 0.0 × 109 to 7.7 × 109/L]) to day 180 (median, 1.0 × 109/L [range, 0.0 × 109 to 9.9 × 109]) after HCT (Table 3). Increases were observed across lymphocyte subsets, including CD4 and CD8 T cells, and CD19/CD20 B cells. The increase in CD8 was more pronounced, resulting in a decrease in CD4:CD8 ratio from day 100 to day 180. When assessing functional responses of B cells by measuring immunoglobulin levels, we did not detect significant differences in IgG levels between day 100 and day 180, although 35% of patients received IV immunoglobulin supplementation after transplant. Although there were no notable increases in IgA levels, there was a rise in IgM levels.

Table 3.

Immune recovery at days 100 and 180

Whole cohortTCDPTCyCNICNI+ATGP value
Immunerecovery laboratory data at day 100       
ALC, median (range), ×109/L 0.8 (0.0-7.7) 0.8 (0.0-4.6) 0.7 (0.0-3.3) 0.8 (0.0-7.7) 0.8 (0.0-7.4) <.01  
Missing 72 (3) 1 (0) 10 (3) 49 (4) 12 (3)  
CD4, ×106/L, median (range) 158.0 (1.0-1000.0) 82.0 (1.0-979.0) 134.5 (4.0-858.0) 201.0 (1.0-1000.0) 106.0 (1.0-792.0) <.01  
CD8, median (range), ×106/L 146.0 (1.0-5708.0) 84.0 (1.0-2402.0) 127.5 (5.0-2613.0) 150.0 (1.0-5708.0) 176.0 (1.0-4270.0) <.01  
Missing 98 (5) 6 (3) 20 (7) 66 (5) 6 (2)  
CD4:CD8 ratio, median (range) 1.0 (0.0-1000.0) 0.9 (0.1-35.5) 0.9 (0.0-26.0) 1.3 (0.0-93.5) 0.5 (0.0-21.0) <.01  
Missing 98 (5) 6 (3) 20 (7) 66 (5) 6 (2)  
CD19/CD20, median (range), ×106/L 64.0 (1.0-2000.0) 143.0 (1.0-1000.0) 84.0 (1.0-2000.0) 62.5 (1.0-2000.0) 25.0 (1.0-1287.0) .05  
Missing 1466 (70) 152 (73) 191 (63) 849 (71) 274 (73)  
CD56, median (range), ×106/L 185.0 (3.0-3000.0) 249.5 (17.0-2912.0) 165.0 (3.0-2000.0) 181.0 (5.0-3000.0) 183.5 (7.0-2000.0) <.01  
Missing 910 (44) 71 (34) 122 (40) 566 (47) 151 (40)  
IgG, median (range), mg/dL 640.0 (52.0-2800.0) 668.0 (168.0-1990.0) 593.0 (74.0-2800.0) 636.0 (52.0-2343.0) 684.0 (75.0-1985.0) <.01  
Missing 276 (13) 18 (9) 31 (10) 185 (15) 42 (11)  
IgA, median (range), mg/dL 59.0 (2.0-630.0) 49.0 (4.0-630.0) 58.5 (2.0-357.0) 59.0 (2.0-457.0) 73.5 (5.0-561.0) <.01  
Missing 913 (44) 81 (39) 118 (39) 485 (40) 229 (61)  
IgM, median (range), mg/dL 38.0 (3.0-399.0) 29.0 (4.0-294.0) 28.0 (5.0-215.0) 41.5 (3.0-399.0) 48.0 (4.0-362.0) <.01  
Missing 909 (44) 81 (39) 117 (38) 481 (40) 230 (61)  
Immune recovery laboratory data at day 180       
ALC, median (range), ×109/L 1.0 (0.0-9.9) 1.0 (0.0-9.4) 1.0 (0.0-7.3) 1.1 (0.0-9.9) 0.9 (0.0-6.3) .04  
Missing 82 (4) 8 (4) 9 (3) 49 (4) 16 (4)  
CD4, median (range), ×106/L 209.0 (1.0-1000.0) 140.0 (1.0-994.0) 183.5 (5.0-700.0) 246.0 (4.0-1000.0) 143.0 (2.0-7000.0) <.01  
CD8, median (range), ×106/L 230.0 (1.0-8982.0) 158.0 (3.0-5289.0) 383.0 (5.0-4548.0) 216.0 (1.0-8982.0) 253.0 (1.1-5294.0) <.01  
Missing 84 (4) 4 (2) 20 (7) 54 (4) 6 (2)  
CD4:CD8 ratio, median (range) 0.8 (0.0-6000.0) 0.8 (0.0-50.6) 0.5 (0.0-49.2) 1.1 (0.0-45.0) 0.5 (0.0-12.5) <.01  
Missing 84 (4) 4 (2) 20 (7) 54 (4) 6 (2)  
CD19/CD20, median (range), ×10/L 136.0 (1.0-2000.0) 242.5 (1.0-1604.0) 134.0 (2.0-2000.0) 133.0 (1.0-2000.0) 98.0 (1.0-2000.0) <.01  
Missing 1384 (66) 147 (71) 164 (54) 812 (67) 261 (70)  
CD56, median (range), ×106/L 175.0 (2.0-3000.0) 195.0 (15.0-2356.0) 158.0 (2.0-2000.0) 175.0 (3.0-2052.0) 172.0 (2.0-3000.0) .11  
Missing 896 (43) 72 (35) 126 (41) 550 (46) 148 (39)  
IgG, median (range), mg/dL 654.0 (49.0-2930.0) 748.0 (121.0-2339.0) 649.0 (107.0-2610.0) 625.0 (49.0-2930.0) 699.0 (51.0-2432.0) <.01  
Missing 404 (19) 31 (15) 73 (24) 236 (20) 64 (17)  
IgA, median (range), mg/dL 64.0 (3.0-470.0) 64.0 (5.0-470.0) 57.5 (5.0-391.0) 64.0 (4.0-468.0) 77.0 (3.0-244.0) .30  
Missing 994 (48) 86 (42) 160 (53) 508 (42) 240 (64)  
IgM, median (range), mg/dL 59.0 (3.0-397.0) 81.0 (4.0-383.0) 45.0 (4.0-311.0) 59.0 (3.0-386.0) 61.0 (4.0-397.0) <.01  
Missing 1005 (48) 88 (43) 162 (53) 515 (43) 240 (64)  
Whole cohortTCDPTCyCNICNI+ATGP value
Immunerecovery laboratory data at day 100       
ALC, median (range), ×109/L 0.8 (0.0-7.7) 0.8 (0.0-4.6) 0.7 (0.0-3.3) 0.8 (0.0-7.7) 0.8 (0.0-7.4) <.01  
Missing 72 (3) 1 (0) 10 (3) 49 (4) 12 (3)  
CD4, ×106/L, median (range) 158.0 (1.0-1000.0) 82.0 (1.0-979.0) 134.5 (4.0-858.0) 201.0 (1.0-1000.0) 106.0 (1.0-792.0) <.01  
CD8, median (range), ×106/L 146.0 (1.0-5708.0) 84.0 (1.0-2402.0) 127.5 (5.0-2613.0) 150.0 (1.0-5708.0) 176.0 (1.0-4270.0) <.01  
Missing 98 (5) 6 (3) 20 (7) 66 (5) 6 (2)  
CD4:CD8 ratio, median (range) 1.0 (0.0-1000.0) 0.9 (0.1-35.5) 0.9 (0.0-26.0) 1.3 (0.0-93.5) 0.5 (0.0-21.0) <.01  
Missing 98 (5) 6 (3) 20 (7) 66 (5) 6 (2)  
CD19/CD20, median (range), ×106/L 64.0 (1.0-2000.0) 143.0 (1.0-1000.0) 84.0 (1.0-2000.0) 62.5 (1.0-2000.0) 25.0 (1.0-1287.0) .05  
Missing 1466 (70) 152 (73) 191 (63) 849 (71) 274 (73)  
CD56, median (range), ×106/L 185.0 (3.0-3000.0) 249.5 (17.0-2912.0) 165.0 (3.0-2000.0) 181.0 (5.0-3000.0) 183.5 (7.0-2000.0) <.01  
Missing 910 (44) 71 (34) 122 (40) 566 (47) 151 (40)  
IgG, median (range), mg/dL 640.0 (52.0-2800.0) 668.0 (168.0-1990.0) 593.0 (74.0-2800.0) 636.0 (52.0-2343.0) 684.0 (75.0-1985.0) <.01  
Missing 276 (13) 18 (9) 31 (10) 185 (15) 42 (11)  
IgA, median (range), mg/dL 59.0 (2.0-630.0) 49.0 (4.0-630.0) 58.5 (2.0-357.0) 59.0 (2.0-457.0) 73.5 (5.0-561.0) <.01  
Missing 913 (44) 81 (39) 118 (39) 485 (40) 229 (61)  
IgM, median (range), mg/dL 38.0 (3.0-399.0) 29.0 (4.0-294.0) 28.0 (5.0-215.0) 41.5 (3.0-399.0) 48.0 (4.0-362.0) <.01  
Missing 909 (44) 81 (39) 117 (38) 481 (40) 230 (61)  
Immune recovery laboratory data at day 180       
ALC, median (range), ×109/L 1.0 (0.0-9.9) 1.0 (0.0-9.4) 1.0 (0.0-7.3) 1.1 (0.0-9.9) 0.9 (0.0-6.3) .04  
Missing 82 (4) 8 (4) 9 (3) 49 (4) 16 (4)  
CD4, median (range), ×106/L 209.0 (1.0-1000.0) 140.0 (1.0-994.0) 183.5 (5.0-700.0) 246.0 (4.0-1000.0) 143.0 (2.0-7000.0) <.01  
CD8, median (range), ×106/L 230.0 (1.0-8982.0) 158.0 (3.0-5289.0) 383.0 (5.0-4548.0) 216.0 (1.0-8982.0) 253.0 (1.1-5294.0) <.01  
Missing 84 (4) 4 (2) 20 (7) 54 (4) 6 (2)  
CD4:CD8 ratio, median (range) 0.8 (0.0-6000.0) 0.8 (0.0-50.6) 0.5 (0.0-49.2) 1.1 (0.0-45.0) 0.5 (0.0-12.5) <.01  
Missing 84 (4) 4 (2) 20 (7) 54 (4) 6 (2)  
CD19/CD20, median (range), ×10/L 136.0 (1.0-2000.0) 242.5 (1.0-1604.0) 134.0 (2.0-2000.0) 133.0 (1.0-2000.0) 98.0 (1.0-2000.0) <.01  
Missing 1384 (66) 147 (71) 164 (54) 812 (67) 261 (70)  
CD56, median (range), ×106/L 175.0 (2.0-3000.0) 195.0 (15.0-2356.0) 158.0 (2.0-2000.0) 175.0 (3.0-2052.0) 172.0 (2.0-3000.0) .11  
Missing 896 (43) 72 (35) 126 (41) 550 (46) 148 (39)  
IgG, median (range), mg/dL 654.0 (49.0-2930.0) 748.0 (121.0-2339.0) 649.0 (107.0-2610.0) 625.0 (49.0-2930.0) 699.0 (51.0-2432.0) <.01  
Missing 404 (19) 31 (15) 73 (24) 236 (20) 64 (17)  
IgA, median (range), mg/dL 64.0 (3.0-470.0) 64.0 (5.0-470.0) 57.5 (5.0-391.0) 64.0 (4.0-468.0) 77.0 (3.0-244.0) .30  
Missing 994 (48) 86 (42) 160 (53) 508 (42) 240 (64)  
IgM, median (range), mg/dL 59.0 (3.0-397.0) 81.0 (4.0-383.0) 45.0 (4.0-311.0) 59.0 (3.0-386.0) 61.0 (4.0-397.0) <.01  
Missing 1005 (48) 88 (43) 162 (53) 515 (43) 240 (64)  

Kruskal-Wallis test.

Next, we examined 4 predefined cohorts (Table 3; Figure 1A). Patients in the CNI group had the highest CD4 at both time points, reaching a median of 201.0 × 106/L (range, 1.0 × 106 to 1000.0 × 106/L) and 246.0 × 106/L (range, 4.0 × 106 to 1000.0 × 106/L) on days 100 and 180, respectively (P < .01). In contrast, none of the other groups reached a median CD4 of >200 × 106/L, even at day 180. CD4 lymphopenia was most pronounced in the TCD cohort, followed by the CNI+ATG and PTCy cohorts, particularly at day 100. By day 180, there was no difference between the TCD and CNI+ATG groups. We found slightly different patterns in CD8 recovery (Table 3; Figure 1B). Although CD8 T cells were lowest in the TCD cohort at both time points, less marked differences were observed between the other 3 cohorts, with counts being highest in the CNI+ATG cohort at day 100 and in the PTCy cohort at day 180. In contrast to T cells, the TCD group had the highest B and natural killer cells at both time points (Table 3; Figure 1C-D). Although there were significant differences between the 4 groups in IgG and IgM levels at both time points and in IgA levels early, no specific pattern was identified.

Figure 1.

Recovery of immune subsets at days 100 and 180 in different cohorts based on GVHD prophylaxis (ppx). (A) CD4 T cells; (B) CD8 T cells; (C) natural killer cells; and (D) B cells.

Figure 1.

Recovery of immune subsets at days 100 and 180 in different cohorts based on GVHD prophylaxis (ppx). (A) CD4 T cells; (B) CD8 T cells; (C) natural killer cells; and (D) B cells.

Close modal

CD4 T-cell and IgA cutoff points

To assess the impact of immune recovery on HCT outcomes, we determined optimal cutoff points for CD4 T cells and IgA levels by using maximum likelihood estimation in univariable Cox proportional hazards models of OS. For CD4, we identified the following cutoff points in pediatric (age < 20 years) patients: 248 × 106/L and 420 × 106/L at day 100 and day 180, respectively; and in adults (age > 20 years): 104 × 106/L and 115 × 106/L at day 100 and day 180, respectively (Table 4). For pediatric patients, a variable selection procedure was used to build a logistic model for the likelihood of having high IgA (≥29 mg/dL). No covariates were found to be associated with high IgA by these procedures. Therefore, no logistic model is provided. In adults, a cutoff level of 114 mg/dL was defined for IgA at day 180.

Table 4.

Logistic model of likelihood of having a high CD4 count or IgA level greater than the cutoff point for adult patients older than 20 years

VariableCategorynOR99% CIP value
Landmark model predicted by CD4 and IgA       
Likelihood of having high day-100 CD4 count (>104 × 106/L)       
 Graft source BM 205 1.000 — .002 (2 df) 
  PB 1145 1.661 1.068-2.583 .003 
  CB 280 0.749 0.433-1.296 .174 
 GVHD prophylaxis CNI 896 1.000 — <.001 (3 df) 
  CNI+ATG 289 0.189 0.127-0.281 <.001 
  TCD 164 0.159 0.098-0.258 <.001 
  PTCy 281 0.327 0.216-0.496 <.001 
 Steroid use No 943 1.000 —  
  Yes 687 0.615 0.455-0.829 <.001 
Likelihood of having high day-180 CD4 count (>115 × 106/L)       
 GVHD prophylaxis CNI 469 1.000 — <.001 (3 df) 
  CNI+ATG 94 0.170 0.089-0.324 <.001 
  TCD 91 0.155 0.080-0.300 <.001 
  PTCy 130 0.460 0.246-0.858 .001 
 Steroid use No 436 1.000 —  
  Yes 348 0.440 0.274-0.706 <.001 
Likelihood of having high day-180 IgA (>114 mg/dL)       
 Steroid use No 436 1.000 —  
  Yes 348 0.489 0.318-0.751 <.001 
Landmark model predicted by CD4 only       
Likelihood of having high day-180 CD4 count (>115 × 106/L)       
 Graft source BM 200 1.000 — .001 (2 df) 
  PB 1119 1.724 1.080-2.752 .003 
  CB 276 1.068 0.598-1.906 .770 
 GVHD prophylaxis CNI 885 1.000 — <.001 (3 df) 
  CNI+ATG 284 0.239 0.159-0.360 <.001 
  TCD 157 0.186 0.114-0.306 <.001 
  PTCy 269 0.582 0.365-0.927 .003 
 Steroid use No 925 1.000 —  
  Yes 670 0.549 0.399-0.756 <.001 
VariableCategorynOR99% CIP value
Landmark model predicted by CD4 and IgA       
Likelihood of having high day-100 CD4 count (>104 × 106/L)       
 Graft source BM 205 1.000 — .002 (2 df) 
  PB 1145 1.661 1.068-2.583 .003 
  CB 280 0.749 0.433-1.296 .174 
 GVHD prophylaxis CNI 896 1.000 — <.001 (3 df) 
  CNI+ATG 289 0.189 0.127-0.281 <.001 
  TCD 164 0.159 0.098-0.258 <.001 
  PTCy 281 0.327 0.216-0.496 <.001 
 Steroid use No 943 1.000 —  
  Yes 687 0.615 0.455-0.829 <.001 
Likelihood of having high day-180 CD4 count (>115 × 106/L)       
 GVHD prophylaxis CNI 469 1.000 — <.001 (3 df) 
  CNI+ATG 94 0.170 0.089-0.324 <.001 
  TCD 91 0.155 0.080-0.300 <.001 
  PTCy 130 0.460 0.246-0.858 .001 
 Steroid use No 436 1.000 —  
  Yes 348 0.440 0.274-0.706 <.001 
Likelihood of having high day-180 IgA (>114 mg/dL)       
 Steroid use No 436 1.000 —  
  Yes 348 0.489 0.318-0.751 <.001 
Landmark model predicted by CD4 only       
Likelihood of having high day-180 CD4 count (>115 × 106/L)       
 Graft source BM 200 1.000 — .001 (2 df) 
  PB 1119 1.724 1.080-2.752 .003 
  CB 276 1.068 0.598-1.906 .770 
 GVHD prophylaxis CNI 885 1.000 — <.001 (3 df) 
  CNI+ATG 284 0.239 0.159-0.360 <.001 
  TCD 157 0.186 0.114-0.306 <.001 
  PTCy 269 0.582 0.365-0.927 .003 
 Steroid use No 925 1.000 —  
  Yes 670 0.549 0.399-0.756 <.001 

df, degrees of freedom.

Factors that affected the chance of achieving the CD4 cutoff point in adults (n = 1630) at day 100 were graft source (peripheral blood [PB] vs BM: odds ratio [OR], 1.66; P = .003), GVHD prophylaxis (vs CNI: CNI+ATG: OR, 0.19; TCD: OR, 0.16; PTCy: OR, 0.33; all P < .001) and steroid use (OR, 0.62; P < .001; Table 4). At day 180, we performed landmark analyses that included CD4 and IgA as well as CD4 only. In the combined CD4/IgA landmark, factors that predicted likelihood of high CD4 were GVHD prophylaxis (vs CNI: CNI+ATG: OR, 0.17; TCD: OR, 0.16; PTCy: OR, 0.46; all P < .001) and steroid use in the first 100 days (OR, 0.44; P < .001). Only steroid use impacted the ability to achieve high IgA (OR, 0.49; P < .001). In the CD4 only day-180 landmark, graft source (PB vs BM: OR, 1.72; P = .003) was also predictive of higher CD4 in addition to GVHD prophylaxis and steroid use.

In pediatric patients (n = 459), factors that impacted the chance of achieving the CD4 cutoff point at day 100 were graft source (PB vs BM: OR, 3.52; P = .002) and GVHD prophylaxis (vs CNI: CNI+ATG: OR, 0.38; P = .002 and TCD: OR, 0.12; P < .001; Table 5). In the CD4/IgA day-180 landmark analysis, factors that predicted the likelihood of a high CD4 were patient age, graft source, and no steroid use. In the CD4-only day-180 landmark, lower age, receipt of umbilical cord blood, and absence of steroid use were predictive of higher CD4 (Table 5).

Table 5.

Logistic model of likelihood of having high CD4 count above the cutoff point for pediatric patients younger than 20 years

VariableCategorynOR99% CIP value
Landmark model predicted by CD4 and IgA       
Likelihood of having high day-100 CD4 count (>248 × 106/L)       
 Graft source BM 131 1.000 — .008 (2 df) 
  PB 70 3.524 1.235-10.054 .002 
  CB 258 1.469 0.761-2.835 .132 
 GVHD prophylaxis CNI 307 1.000 — <.001 (3 df) 
  CNI+ATG 86 0.380 0.171-0.848 .002 
  TCD 43 0.124 0.029-0.529 <.001 
  PTCy 23 0.716 0.189-2.708 .518 
Likelihood of having high day-180 CD4 count (>420 × 106/L)       
 Patient age, y  293 0.905 0.837-0.980 .001 
 Graft Source BM 59 1.000 — .007 (2 df) 
  PB 38 0.928 0.164-5.265 .912 
  CB 196 3.182 1.028-9.851 .008 
 Steroid use No 142 1.000 —  
  Yes 151 0.290 0.126-0.668 <.001 
Landmark model predicted by CD4 only       
Likelihood of having high day-180 CD4 count (>420 × 106/L)       
 Patient age, y  447 0.928 0.873-0.987 .002 
 Graft source BM 127 1.000 — .004 (2 df) 
  PB 67 0.609 0.182-2.041 .291 
  CB 253 2.031 0.975-4.232 .013 
 Steroid use No 251 1.000 —  
  Yes 196 0.350 0.178-0.691 <.001 
VariableCategorynOR99% CIP value
Landmark model predicted by CD4 and IgA       
Likelihood of having high day-100 CD4 count (>248 × 106/L)       
 Graft source BM 131 1.000 — .008 (2 df) 
  PB 70 3.524 1.235-10.054 .002 
  CB 258 1.469 0.761-2.835 .132 
 GVHD prophylaxis CNI 307 1.000 — <.001 (3 df) 
  CNI+ATG 86 0.380 0.171-0.848 .002 
  TCD 43 0.124 0.029-0.529 <.001 
  PTCy 23 0.716 0.189-2.708 .518 
Likelihood of having high day-180 CD4 count (>420 × 106/L)       
 Patient age, y  293 0.905 0.837-0.980 .001 
 Graft Source BM 59 1.000 — .007 (2 df) 
  PB 38 0.928 0.164-5.265 .912 
  CB 196 3.182 1.028-9.851 .008 
 Steroid use No 142 1.000 —  
  Yes 151 0.290 0.126-0.668 <.001 
Landmark model predicted by CD4 only       
Likelihood of having high day-180 CD4 count (>420 × 106/L)       
 Patient age, y  447 0.928 0.873-0.987 .002 
 Graft source BM 127 1.000 — .004 (2 df) 
  PB 67 0.609 0.182-2.041 .291 
  CB 253 2.031 0.975-4.232 .013 
 Steroid use No 251 1.000 —  
  Yes 196 0.350 0.178-0.691 <.001 

For adults grouped into low and high CD4 cohorts (<104 × 106/L), respective cumulative incidences of grade 2 to 4 aGVHD at 100 days were similar at 39.9% and 36.0% (P = .13). However, grade 3 to 4 aGVHD was higher in the low-CD4 compared with high-CD4 cohort (12.5% vs 7.8%, respectively; P = .005). In the pediatric cohort, grade 2 to 4 aGVHD (41.4% vs 35.2%, respectively; P = .21) and grade 3 to 4 aGVHD (17.3% and 10.6%, respectively; P = .046) did not differ between low- and high-CD4 cohorts.

Transplant outcomes

Cox proportional hazards regression models were used for each outcome from day-100 and day-180 landmarks separately to examine transplant outcomes. Analyses examined pediatric and adult patients separately to account for the identified cutoff points of CD4 count, CD8 count, and IgA levels.

OS

Using a day-100 landmark, higher survival was seen during the first year after transplant for adults who achieved CD4 ≥104 × 106/L (overall mortality [OM] hazard ratio [HR], 0.60; 99% CI, 0.42-0.85; P < .001; Table 6); however, because of nonproportional hazards, the benefit of early CD4 recovery was lost for patients surviving beyond 1 year after transplant (OM HR, 1.03; 99% CI, 0.77-1.38; P = .79). Other variables associated with higher OS when adjusted for CD4 include early- or intermediate-stage acute leukemia, younger age, and absence of steroid use in the first 100 days. For pediatric patients, CD4 above and below the cutoff point had no significant impact on OS when adjusted for graft source (OM HR, 1.38; 99% CI, 0.80-2.36; P = .13; Table 7). Achieving CD8 above the cutoff point (≥500 × 106/L) had no impact on survival for either pediatric or adult patients.

Table 6.

Cox regression model of OS for adult patients aged 20 years or older

VariableCategorynEventsOM HR99% CIP value
Landmark model predicted by CD4 and IgA        
Day 100        
 CD4  <104 × 106/L 529 205 1.000 — .001 (2 df) 
  >104 × 106/L, within 12 months after HCT 1101 386 0.595 0.416-0.853 <.001 
  >104 × 106/L, after 12 months post-HCT   1.031 0.772-1.376 .788 
 Patient age, y  1630  1.016 1.008-1.025 <.001 
 Primary disease Acute leukemia early or intermediate stage 820 269 1.000 — <.001 (3 df) 
  Acute leukemia advanced stage 170 79 1.697 1.220-2.362 <.001 
  Acute leukemia unknown stage 26 14 1.552 0.765-3.151 .110 
  MDS any stage 614 229 1.105 0.865-1.411 .295 
 Steroid use No 943 302 1.000 —  
  Yes 687 289 1.379 1.114-1.708 <.001 
Day 180        
 CD4 and IgA levels CD4, <115 × 106/L; IgA, <114 mg/dL 156 68 1.000 — <.001 (3 df) 
  CD4, <115 × 106/L; IgA, ≥114 mg/dL 39 20 1.331 0.685-2.587 .267 
  CD4, ≥115 × 106/L; IgA, <114 mg/dL 407 125 0.595 0.401-0.883 .001 
  CD4, ≥115 × 106/L; IgA, ≥114 mg/dL 182 65 0.801 0.507-1.265 .211 
 Patient age, y  784  1.021 1.008-1.033 <.001 
 Primary disease Acute leukemia early or intermediate stage 419 137 1.000 — .006 (3 df) 
  Acute leukemia advanced stage 76 36 1.938 1.186-3.166 .001 
  Acute leukemia unknown stage 14 0.847 0.260-2.757 .717 
  MDS any stage 275 100 1.072 0.753-1.526 .614 
 Steroid use No 436 136 1.000 —  
  Yes 348 142 1.419 1.033-1.950 .005 
Landmark model predicted by CD4 only at day 180        
 CD4 count  <115 × 106/L 411 187 1.000 — <.001 (2 df) 
  ≥115 × 106/L, within 10 months after HCT 1184 380 0.318 0.205-0.493 <.001 
  ≥115 × 106/L, after 10 months post-HCT   0.812 0.613-1.075 .056 
 Patient age, y  1595  1.016 1.008-1.026 <.001 
 Primary disease Acute leukemia early or intermediate stage 804 260 1.000 — <.001 (3 df) 
  Acute leukemia advanced stage 167 76 1.746 1.246-2.446 <.001 
  Acute leukemia unknown stage 24 12 1.376 0.642-2.949 .281 
  MDS any stage 600 219 1.085 0.846-1.391 .400 
 Steroid use No 925 291 1.000 —  
  Yes 670 276 1.345 1.082-1.674 <.001 
VariableCategorynEventsOM HR99% CIP value
Landmark model predicted by CD4 and IgA        
Day 100        
 CD4  <104 × 106/L 529 205 1.000 — .001 (2 df) 
  >104 × 106/L, within 12 months after HCT 1101 386 0.595 0.416-0.853 <.001 
  >104 × 106/L, after 12 months post-HCT   1.031 0.772-1.376 .788 
 Patient age, y  1630  1.016 1.008-1.025 <.001 
 Primary disease Acute leukemia early or intermediate stage 820 269 1.000 — <.001 (3 df) 
  Acute leukemia advanced stage 170 79 1.697 1.220-2.362 <.001 
  Acute leukemia unknown stage 26 14 1.552 0.765-3.151 .110 
  MDS any stage 614 229 1.105 0.865-1.411 .295 
 Steroid use No 943 302 1.000 —  
  Yes 687 289 1.379 1.114-1.708 <.001 
Day 180        
 CD4 and IgA levels CD4, <115 × 106/L; IgA, <114 mg/dL 156 68 1.000 — <.001 (3 df) 
  CD4, <115 × 106/L; IgA, ≥114 mg/dL 39 20 1.331 0.685-2.587 .267 
  CD4, ≥115 × 106/L; IgA, <114 mg/dL 407 125 0.595 0.401-0.883 .001 
  CD4, ≥115 × 106/L; IgA, ≥114 mg/dL 182 65 0.801 0.507-1.265 .211 
 Patient age, y  784  1.021 1.008-1.033 <.001 
 Primary disease Acute leukemia early or intermediate stage 419 137 1.000 — .006 (3 df) 
  Acute leukemia advanced stage 76 36 1.938 1.186-3.166 .001 
  Acute leukemia unknown stage 14 0.847 0.260-2.757 .717 
  MDS any stage 275 100 1.072 0.753-1.526 .614 
 Steroid use No 436 136 1.000 —  
  Yes 348 142 1.419 1.033-1.950 .005 
Landmark model predicted by CD4 only at day 180        
 CD4 count  <115 × 106/L 411 187 1.000 — <.001 (2 df) 
  ≥115 × 106/L, within 10 months after HCT 1184 380 0.318 0.205-0.493 <.001 
  ≥115 × 106/L, after 10 months post-HCT   0.812 0.613-1.075 .056 
 Patient age, y  1595  1.016 1.008-1.026 <.001 
 Primary disease Acute leukemia early or intermediate stage 804 260 1.000 — <.001 (3 df) 
  Acute leukemia advanced stage 167 76 1.746 1.246-2.446 <.001 
  Acute leukemia unknown stage 24 12 1.376 0.642-2.949 .281 
  MDS any stage 600 219 1.085 0.846-1.391 .400 
 Steroid use No 925 291 1.000 —  
  Yes 670 276 1.345 1.082-1.674 <.001 

Evidence of nonproportional hazards was found by testing a time-dependent covariate for significance (P < .01). A piecewise proportional hazards model was constructed such that proportionality is not violated for the variable within each separate interval.

Table 7.

Cox regression model of OS of pediatric patients aged less than 20 years

VariableCategorynEventsOM HR99% CIP value
Landmark model predicted by CD4 and IgA        
Day 100        
 CD4 count <248 × 106/L 316 61 1.000 —  
  ≥248 × 106/L 143 37 1.375 0.802-2.358 .128 
 Graft source BM 131 25 1.000 — <.001 (2 df) 
  PB 70 27 2.400 1.171-4.919 .002 
  CB 258 46 0.879 0.462-1.675 .608 
Day 180        
 CD4 count <420 × 106/L 231 50 1.000 —  
  ≥420 × 106/L 62 0.468 0.153-1.427 .079 
 IgA level <29 mg/dL 68 12 1.000 —  
  ≥29 mg/dL 225 44 1.337 0.574-3.116 .377 
 Conditioning intensity MAC 284 51 1.000 —  
  RIC or NMA 4.037 1.199-13.596 .003 
Landmark model predicted by CD4 only at day 180        
 CD4 count <420 × 106/L 347 79 1.000 —  
  ≥420 × 106/L 100 14 0.690 0.323-1.472 .207 
 Graft source BM 127 24 1.000 — .001 (2 df) 
  PB 67 25 2.335 1.116-4.887 .003 
  CB 253 44 0.933 0.484-1.801 .787 
VariableCategorynEventsOM HR99% CIP value
Landmark model predicted by CD4 and IgA        
Day 100        
 CD4 count <248 × 106/L 316 61 1.000 —  
  ≥248 × 106/L 143 37 1.375 0.802-2.358 .128 
 Graft source BM 131 25 1.000 — <.001 (2 df) 
  PB 70 27 2.400 1.171-4.919 .002 
  CB 258 46 0.879 0.462-1.675 .608 
Day 180        
 CD4 count <420 × 106/L 231 50 1.000 —  
  ≥420 × 106/L 62 0.468 0.153-1.427 .079 
 IgA level <29 mg/dL 68 12 1.000 —  
  ≥29 mg/dL 225 44 1.337 0.574-3.116 .377 
 Conditioning intensity MAC 284 51 1.000 —  
  RIC or NMA 4.037 1.199-13.596 .003 
Landmark model predicted by CD4 only at day 180        
 CD4 count <420 × 106/L 347 79 1.000 —  
  ≥420 × 106/L 100 14 0.690 0.323-1.472 .207 
 Graft source BM 127 24 1.000 — .001 (2 df) 
  PB 67 25 2.335 1.116-4.887 .003 
  CB 253 44 0.933 0.484-1.801 .787 

At day 180, models were created using a composite variable of CD4 and IgA levels for adults, as well as examining only the cutoff points of CD4 (Table 6). For the composite variable, there was no additional discernment when forcing IgA levels at day 180 into the model with similar improvement in OS for patients with a CD4 value at or above the cutoff point (115 × 106/L) irrespective of an IgA above or below the cutoff point. Additionally, high IgA did not mitigate the negative impact of low CD4 at day 180. Similar to the day-100 landmark, other variables associated with higher OS when adjusted for CD4 and IgA include early- or intermediate-stage acute leukemia, younger age, and absence of steroid use before day 100. For pediatric patients, there was no impact on survival of CD4 (cutoff point, 420 × 106/L) or IgA (cutoff point, 29 mg/dL) at day 180 (Table 7).

Other transplant outcomes

Using the same day-100 and day-180 landmarks with CD4 and composite CD4/IgA variable, we looked at additional transplant outcomes in adult (supplemental Table 3) and pediatric patients (supplemental Table 4).

Using a day-100 landmark, higher PFS was seen for adults who achieved CD4 of ≥104 × 106/L (progression/mortality HR, 0.79; 99% CI, 0.65-0.97; P = .004; supplemental Table 3A), as well as in patients with early- or intermediate-stage acute leukemia and whose conditioning did not include TBI. Similar to what was observed for OS, there was no additional discernment when forcing the IgA at day 180 into the model with similar improvement in PFS for patients with CD4 at or above the cutoff point (115 × 106/L) irrespective of IgA. Early- or intermediate-stage acute leukemia retained association with improved PFS when adjusting for CD4 and IgA. When we examined CD4 only at the day-180 landmark, we observed similar findings for PFS to those at the day-100 landmark, including the dichotomous impact of TBI use, with >500 cGy improving PFS compared with no TBI regimens, whereas doses of <500 cGy having an unfavorable association with PFS.

CD4 counts were not associated with relapse in adults at either landmark, irrespective of the inclusion or exclusion of IgA (supplemental Table 3B). Variables reducing relapse risk included early- or intermediate-stage acute leukemia, GVHD prophylaxis, and TBI of >500 cGy.

In contrast, TRM followed the same pattern as OS (supplemental Table 3C). Improved TRM was observed in the first year after HCT in adults who achieved CD4 of ≥104 × 106/L (HR, 0.45; 99% CI, 0.26-0.80; P < .001) using a day-100 landmark, and in the first 10 months after HCT in those achieving CD4 of ≥115 × 106/L (HR, 0.22; 99% CI, 0.11-0.45; P < .001) using a day-180 landmark. IgA at day 180 did not add information.

For adults, risk of bacterial, fungal, and viral infections occurring after day 100 were similar irrespective of the CD4 cutoff point at day 100 (supplemental Table 3D). However, steroid use increased the risks for bacterial (HR, 1.62; 99% CI, 1.20-2.18; P < .001) and fungal (HR, 1.98; 99% CI, 1.32-2.97; P < .001) but not viral infections. These findings were similar when examining for the main effect of the composite CD4/IgA variable or just CD4 at day 180.

Finally, we found that CD4 above and below the median had no impact on chronic GVHD (supplemental Table 3D). Factors that affected chronic GVHD included GVHD prophylaxis, graft source, and steroid use. Those with serotherapy, PTCy (P = .001) or TCD experienced less chronic GVHD (P < .001). These findings were consistent using the day-100 landmark and day-180 landmark using the composite CD4/IgA or CD4-alone cutoff point.

Next, we performed similar analyses in pediatric patients (supplemental Table 4). CD4 above and below the day-100 cutoff point of 248 × 106/L and the day-180 cutoff point of 420 × 106/L had no impact on PFS when adjusted for graft source, relapse, TRM, infections, or chronic GVHD. Similar findings were observed for the day-180 IgA cutoff points of 29 mg/dL. However, steroid use before day 100 was associated with increased risk of TRM (HR, 3.48; 99% CI, 1.15-10.51; P = .004) and chronic GVHD (HR, 1.98; 99% CI, 1.37-2.86; P < .001) in the day-100 landmark models.

Analyses with CD4 cutoff point of 50 × 106/L

Given that some prior studies in children have shown that achieving a CD4 of 50 × 106/L by day 100 after allo-HCT is associated with improved outcomes,29-32 we also examined this cutoff point in this larger CIBMTR study. In adults, factors that affected the chance of achieving a CD4 count of 50 × 106/L at day 100 were type of GVHD prophylaxis (vs CNI: CNI+ATG:, OR, 0.16; TCD: OR, 0.09; and PTCy: OR, 0.33; all P < .001) and steroid use (OR, 0.54; P < .001). Similar to the aforementioned observations with the identified cutoff point, an improved survival was seen during the first year after transplant for adults who achieved CD4 of ≥50 × 106/L (HR, 0.52; 99% CI, 0.35-0.79; P < .001); however, the benefit of this early CD4 recovery was lost for patients surviving beyond 1 year after transplant (HR, 1.13; 99% CI, 0.76-1.68; P = .43). Other variables improving OS when adjusted for CD4 include early- or intermediate-stage acute leukemia and absence of steroid use in the first 100 days. At day 180, only the type of GVHD prophylaxis (vs CNI: CNI+ATG: OR, 0.33; TCD: OR, 0.23; both P < .001) predicted likelihood of achieving the CD4 cutoff point of 50 × 106/L, and achieving a CD4 of 50 × 106/L predicted OS (HR, 0.51; 99% CI, 0.32-0.82; P < .001). For pediatric patients, CD4 above and below 50 × 106/L had no impact on OS when adjusted for graft source at day 100 and day 180.

The study sheds light on the importance of immune reconstitution after allo-HCT and its potential impact on patient outcomes. Our findings align with previous research indicating that early recovery of immune cells, particularly CD4 T cells, is associated with improved OS, and lower TRM rates.1-3,9,29-36 However, this study expands on previous single-center experiences by validating these findings for adults although surprisingly not for children in this large multicenter population, enhancing the generalizability of the results. We also did not find a relationship with relapse noted in earlier smaller studies.

The observed differences in immune reconstitution between GVHD prophylaxis groups highlight the potential influence of treatment strategies on immune recovery. Patients receiving a T-replete graft with CNI-based GVHD prophylaxis exhibited better CD4 T-cell recovery, which may be associated with improved OS. However, prospective randomized clinical trials have examined different approaches for GVHD prophylaxis, including ATG, PTCy, and CD34 selection.37-40 Although some trials have shown differences in incidences of acute or chronic GVHD and in composite end points such as GVHD-free, relapse-free survival, most have not shown differences in OS. One recent exception was the BMT CTN 1301 trial, in which patients aged ≤65 years with AML, ALL, and MDS received a myeloablative allo-HCT from an 8/8 HLA-matched donor using 1 of 3 different strategies to prevent GVHD.39 Recipients of a CD34-selected graft had decreased OS related to increased TRM in the absence of any difference in relapse. This increased TRM was related to increased infections attributed to delayed immune recovery. It should be noted that a recent analysis of the long-term follow-up of the trial no longer showed statistically significant differences in OS between the 3 arms.41 Furthermore, studies have demonstrated that dosing and timing of ATG can be critical factors that determine posttransplant immune recovery and transplant outcomes.42-44 Taken globally, these findings underscore the importance of tailoring treatment approaches to optimize immune recovery in adult patients and thus reduce complications.

Among the main findings of this study is the association in adult patients of CD4 recovery at day 100 and day 180 with critical transplant outcomes, including OS, PFS, and TRM. Importantly, we showed that immune reconstitution did not affect relapse, chronic GVHD, or infections, including viral infections. The findings on infection are of interest, given recent CIBMTR reports showing that PTCy is associated with an increased risk of viral infections.45,46 This study also identified specific CD4 cutoff points at day 100 and day 180 that differed between adult and pediatric patients. Although these cutoff points were at least double those of the previously identified pediatric cutoff point of 50 × 106/L by day 100 after allo-HCT,29-32 we were able to independently confirm the validity of this end point in adults but failed to do so in children.

The study’s evaluation of immune markers beyond CD4 T cells, such as IgA levels and CD8 T cells, provides a more comprehensive view of immune reconstitution. Delayed recovery of IgA secretion, particularly in the presence of acute or chronic GVHD, may have implications for mucosal immunity and overall infection risk. Although the study did not identify specific patterns in IgG and IgM levels, these assessments contribute to a more comprehensive understanding of posttransplant immune function.

Our study does have some limitations because of its retrospective nature, being cross-sectional at only 2 time points with day 100 the earliest available, the exclusion of patients who died before day 100 and the fact that not all centers collect immune reconstitution data. This might lead to biased reporting of specific GVHD prophylaxis strategies, as well as reported GVHD incidence. Nevertheless, all graft manipulation cohorts were represented by >2000 patients. CD4 and IgA measurements were collected during posttransplant assessments at days 100 and 180, but not at baseline, and only the most recent data for each time point was considered. Consequently, we do not have the data to analyze these variables as time-dependent factors in a Cox model of transplant outcomes starting at the day of transplant. A landmark analysis approach was necessary because of this limitation of the CIBMTR’s data collection schedule. We also did not have data on mucosal IgA levels, and did not correct for potential patient or donor congenital IgA deficiency, but the incidence is rare enough not to have affected our results. Another limitation is that data on antimicrobial prophylaxis, timing, and dosing of IV immunoglobulin and steroids were not captured in this registry. Finally, the laboratory parameters included do not represent what most would consider state-of-art immunological assessment including but not limited to T-cell subsets, T-cell repertoire using next-generation sequencing, and T-cell receptor excision circles.34,47,48 Nevertheless, our study has several important strengths including the inclusion of >2000 patients from 105 centers, inclusion of adult and pediatric patients, and most of the common stem cell sources and transplant types, as well as using assays that are readily performed in routine clinical settings. We also adjusted for common patient and graft specific factors that affect transplant outcomes, and many cases demonstrated the importance of CD4 recovery even when adjusting for these factors. This is, to our knowledge, the largest study to date to address these important questions and the only study that has included such a large number of centers.

In conclusion, this article emphasizes the significance of immune reconstitution in adult allo-HCT recipients and its impact on clinical outcomes. This CIBMTR study’s large, multicenter approach strengthens the evidence supporting the association between early immune recovery and improved survival in the adult allo-HCT setting, confirming our hypothesis for CD4 T-cell and IgA levels as suitable biomarkers. The choice of GVHD prophylaxis significantly affected immune reconstitution patterns. These findings underscore the importance of tailoring transplant strategies for adults to optimize immune recovery and improve patient outcomes. De novo thymopoiesis can resume significantly earlier in children than in adults; therefore, more nuanced variables and leukocyte subsets may need to be considered to attain clinical relevance with GVHD, graft source, graft manipulation, and GVHD prophylaxis all having a greater impact than absolute CD4 T-cell recovery.

This research was supported, in part, by National Institutes of Health/National Cancer Institute (NCI) Cancer Center Support grant P30 CA008748 (M.-A.P.). The Center for International Blood and Marrow Transplant Research is supported primarily by the Public Health Service grant U24CA076518 from the NCI, the National Heart, Lung, and Blood Institute, and the National Institute of Allergy and Infectious Diseases; grant 75R60222C00011 from the Health Resources and Services Administration; and grants N00014-23-1-2057 and N00014-24-1-2057 from the Office of Naval Research. Support is also provided by the Medical College of Wisconsin, National Marrow Donor Program, Gateway for Cancer Research, and the Pediatric Transplantation and Cellular Therapy Consortium, and from the following commercial entities: AbbVie; Actinium Pharmaceuticals, Inc; Adaptive Biotechnologies Corporation; ADC Therapeutics; Adienne SA; Alexion; AlloVir, Inc; Amgen, Inc; Astellas Pharma US; AstraZeneca; Atara Biotherapeutics; BeiGene; BioLineRx; Blue Spark Technologies; bluebird bio, Inc; Blueprint Medicines; Bristol Myers Squibb Co; CareDx Inc; CSL Behring; CytoSen Therapeutics, Inc; DKMS; Elevance Health; Eurofins Viracor, DBA Eurofins Transplant Diagnostics; Gamida Cell, Ltd; Gift of Life Biologics; Gift of Life Marrow Registry; GlaxoSmithKline; HistoGenetics; Incyte Corporation; Iovance; Janssen Research and Development, LLC; Janssen/Johnson & Johnson; Jasper Therapeutics; Jazz Pharmaceuticals, Inc; Karius; Kashi Clinical Laboratories; Kiadis Pharma; Kite, a Gilead company; Kyowa Kirin; Labcorp; Legend Biotech; Mallinckrodt Pharmaceuticals; Med Learning Group; Medac GmbH; Merck and Co; Mesoblast; Millennium, the Takeda Oncology Co; Miller Pharmacal Group, Inc; Miltenyi Biotec, Inc; MorphoSys; MSA-EDITLife; Neovii Pharmaceuticals AG; Novartis Pharmaceuticals Corporation; Omeros Corporation; OptumHealth; Orca Bio, Inc; OriGen Biomedical; Ossium Health, Inc; Pfizer, Inc; Pharmacyclics, LLC, an AbbVie company; PPD Development, LP; REGiMMUNE; Registry Partners; Rigel Pharmaceuticals; Sanofi; Sarah Cannon; Seagen Inc; Sobi, Inc; STEMCELL Technologies; Stemline Technologies; STEMSOFT; Takeda Pharmaceuticals; Talaris Therapeutics; Vertex Pharmaceuticals; Vor Biopharma Inc; and Xenikos BV.

Contribution: M.-A.P., P.S., and M.R. conceived and designed the study; M.R. and N.H. collected and assembled data; N.H. and M.J.M. provided data analysis; M.-A.P. prepared the first draft of the manuscript; and all authors contributed to data interpretation and revision of the manuscript.

Conflict-of-interest disclosure: M.-A.P. reports honoraria from Adicet, Allogene, AlloVir, Caribou Biosciences, Celgene, Bristol Myers Squibb, Equilium, Exevir, ImmPACT Bio, Incyte, Karyopharm, Kite/Gilead, Merck, Miltenyi Biotec, MorphoSys, Nektar Therapeutics, Novartis, Omeros, Orca Bio, Sanofi, Syncopation, VectivBio AG, and Vor Biopharma; serves on data safety monitoring boards for Cidara Therapeutics and SELLAS Life Sciences; serves on the scientific advisory board of NexImmune; has ownership interests in NexImmune, Omeros, and Orca Bio; and has received institutional research support for clinical trials from Allogene, Incyte, Kite/Gilead, Miltenyi Biotec, Nektar Therapeutics, and Novartis. P.S. serves as an ad hoc consultant to Forge Bio. M.R. reports compensation as an employee (executive director, clinical development) of Kura Oncology and reports equity stock options as an employee of Kura Oncology. R.F.C. reports compensation from ADMA Biologics, Janssen, MSD/Merck, Roche, Takeda Pharmaceuticals, Shionogi, Genentech, Astellas Pharma, AiCuris, Adagio Therapeutics, Oxford Immunotec, Karius, Moderna, and Ansun Pharmaceuticals, and significant payments from Merck, Karius, Aicuris, Ansun Pharmaceuticals, Takeda Pharmaceuticals, Genentech, Oxford Immunotec, and Eurofins Viracor. J.A.H. reports compensation for consulting roles from AlloVir, EVERSANA, GeoVax, Grifols, Karius, Modulus, Senti Bio, Takeda Pharmaceuticals, and UpToDate; serves on a data safety monitoring board for Moderna; reports compensation for speaking from Gilead Australia; reports research support from AlloVir, Deverra, GeoVax, Merck, Oxford Immunotec, and Takeda Pharmaceuticals; and received significant payments for participation on advisory board meeting from Takeda Pharmaceuticals. The remaining authors declare no comping financial interests.

Correspondence: Miguel-Angel Perales, Adult Bone Marrow Transplantation Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, 530 E 74th St, Box 59, New York, NY 10021; email: peralesm@mskcc.org.

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Author notes

The Center for International Blood and Marrow Transplant Research (CIBMTR) supports accessibility of research in accord with the National Institutes of Health Data Sharing Policy and the National Cancer Institute Cancer Moonshot Public Access and Data Sharing Policy. The CIBMTR only releases deidentified data sets that comply with all relevant global regulations regarding privacy and confidentiality. Active and retired CIBMTR forms are available online at https://cibmtr.org/CIBMTR/Data-Operations/Data-Collection-Forms.

The full-text version of this article contains a data supplement.

Supplemental data