key: cord-0988891-xd7x393g
authors: Huang, Alexander; Bange, Erin; Han, Nicholas; Wileyto, E. Paul; Kim, Justin; Gouma, Sigrid; Robinson, James; Greenplate, Allison; Porterfield, Florence; Owoyemi, Olutosin; Naik, Karan; Zheng, Cathy; Galantino, Michael; Weisman, Ariel; Ittner, Carolin; Kugler, Emily; Baxter, Amy; Weirick, Madison; McAllister, Christopher; Babady, Ngolela Esther; Kumar, Anita; Widman, Adam; Dewolf, Susan; Boutemine, Sawsan; Roberts, Charlotte; Budzik, Krista; Tollett, Susan; Wright, Carla; Perloff, Tara; Sun, Lova; Mathew, Divij; Giles, Josephine; Oldridge, Derek; Wu, Jennifer; Alanio, Cecile; Adamski, Sharon; Vella, Laura; Kerr, Samuel; Cohen, Justine; Oyer, Randall; Massa, Ryan; Maillard, Ivan; Maxwell, Kara; Maslak, Peter; Vonderheide, Robert; Wolchok, Jedd D.; Hensley, Scott; Wherry, E.; Meyer, Nuala; DeMichele, Angela; Vardhana, Santosha; Mamtani, Ronac; Oniyide, Oluwatosin; Agyekum, Roseline; Dunn, Thomas; Jones, Tiffanie; Giannini, Heather; Garfall, Alfred; Reilly, John
title: CD8 T cells compensate for impaired humoral immunity in COVID-19 patients with hematologic cancer
date: 2021-02-02
journal: Res Sq
DOI: 10.21203/rs.3.rs-162289/v1
sha: efd04c4e2388b36df41975af30c0fad9c3accb1a
doc_id: 988891
cord_uid: xd7x393g

Cancer patients have increased morbidity and mortality from Coronavirus Disease 2019 (COVID-19), but the underlying immune mechanisms are unknown. In a cohort of 100 cancer patients hospitalized for COVID-19 at the University of Pennsylvania Health System, we found that patients with hematologic cancers had a significantly higher mortality relative to patients with solid cancers after accounting for confounders including ECOG performance status and active cancer status. We performed flow cytometric and serologic analyses of 106 cancer patients and 113 non-cancer controls from two additional cohorts at Penn and Memorial Sloan Kettering Cancer Center. Patients with solid cancers exhibited an immune phenotype similar to non-cancer patients during acute COVID-19 whereas patients with hematologic cancers had significant impairment of B cells and SARS-CoV-2-specific antibody responses. High dimensional analysis of flow cytometric data revealed 5 distinct immune phenotypes. An immune phenotype characterized by CD8 T cell depletion was associated with a high viral load and the highest mortality of 71%, among all cancer patients. In contrast, despite impaired B cell responses, patients with hematologic cancers and preserved CD8 T cells had a lower viral load and mortality. These data highlight the importance of CD8 T cells in acute COVID-19, particularly in the setting of impaired humoral immunity. Further, depletion of B cells with anti-CD20 therapy resulted in almost complete abrogation of SARS-CoV-2-specific IgG and IgM antibodies, but was not associated with increased mortality compared to other hematologic cancers, when adequate CD8 T cells were present. Finally, higher CD8 T cell counts were associated with improved overall survival in patients with hematologic cancers. Thus, CD8 T cells likely compensate for deficient humoral immunity and influence clinical recovery of COVID-19. These observations have important implications for cancer and COVID-19-directed treatments, immunosuppressive therapies, and for understanding the role of B and T cells in acute COVID-19.

49% had a recorded ECOG performance status of 2 or higher ( Table 1) . During follow up, 48% 115 of subjects required ICU level care, and 38% of patients died within 30 days of admission 116 (Table 2) , consistent with previously reported rates for severe COVID-19 in this population 30, 34, 117 37 . 118

To understand key determinants of COVID-19 disease severity, we performed univariate 119 analysis to identify factors associated with all-cause mortality within 30 days of discharge. We 120 included relevant covariates, including patient factors such as age, race, gender, and smoking history (ever versus never) 2, 38-40 ; cancer-specific factors including ECOG performance status 35 , 122 status of cancer (e.g., active versus remission) 36, 36 ; cancer type (e.g., heme versus solid 123 cancer) 29, 34, 36, 41, 42 ; and cancer treatment 26, 37 . Increased mortality was significantly associated 124 with prior or current smoking (p = 0.028), poor ECOG performance (ECOG 3-4, p=0.001), and 125 active cancer status (p=0.024) (Fig. 1) . In addition, patients with hematologic cancers (mostly 126 lymphoma and leukemia), appeared to have an increased risk of mortality relative to solid 127 cancers (54% versus 33% respectively, p=0.075) ( Table 3) . This is consistent with recent data 128

showing increased disease severity and mortality in patients with hematologic malignancies 23, 29, 129 [34] [35] [36] 41 . Notably, cancer treatment, including cytotoxic chemotherapy, was not significantly 130 associated with COVID-19 mortality, also consistent with published literature in patients with 131 cancer 29, 30, 34, 36, 41 . 132

To determine whether the increased mortality observed in patients with hematologic 133 malignancy was independent of potential confounding effects from smoking history, poor ECOG 134 performance, and active cancer, which were not corrected for in the prior studies, we performed 135 multivariable logistic regression. Patients with hematologic cancers tended to be younger, male, 136 less likely to have coexisting comorbidities, and more likely to have received recent cytotoxic 137 chemotherapy (Supplemental Table 1 ). In this fully adjusted analysis, hematologic malignancy 138 was strongly associated with mortality, in comparison to solid cancer (OR 3.3, 95% CI 1.01-139 10.8, p=0.048) ( Table 3) . Similar results were observed in time-to-event analyses using Kaplan 140

Meyer methods (Fig. 2a , median overall survival (mOS) not reached for patients with solid 141 cancers vs 47 days for patients with heme cancers, p-value=0.030) and Cox regression models 142 (Table 3 , HR 2.56, 95% CI 1. 19-5.54, p=0.017) . Moreover, patients with hematologic cancers 143 had higher levels of many inflammatory markers on admission laboratory testing, including 144 ferritin, IL-6, and LDH (Fig. 2b) . There were no significant differences in CRP, fibrinogen, D-145 dimer, lymphocyte counts, and neutrophil counts, while ESR was higher in patients with solid cancer (Extended Data Fig. 1 a,b) . Thus, hematologic malignancy was an independent risk 147 factor of death, with signs of a dysregulated inflammatory response. 148

To understand the immune landscape in cancer patients, as compared to patients without 150 cancer, we leveraged an observational study of hospitalized COVID-19 patients at the 151 University of Pennsylvania Health System where blood was collected (MESSI-COVID) 15 . This 152 analysis included 130 subjects with flow cytometric and/or serologic analysis. In particular, we 153 focused on 22 subjects with active cancer (Supplemental Tables 2, 3), including patients 154 undergoing cancer-directed therapies such as chemotherapy, immunotherapy, or B cell directed 155 therapies (Supplemental Table 4 ). Age, gender, and race were similarly distributed in 19 patients with active cancer and those without, and both groups had a similar timeframe of 157 symptom onset and disease severity (Fig. 3a, Supplemental Table 2 ). However, cancer 158 patients had a higher all-cause mortality (36.4% versus 11.1%, Fig. 3a) , consistent with our 159 COPE clinical cohort, and what has been reported in other cohorts of COVID-19 patients 23, 26, 29, 160 30 . 161

As humoral immunity is critical for protective immunity against SARS-CoV-2, we 162 hypothesized that a defect in SARS-CoV-2-specific antibodies may be associated with the 163 increase in mortality seen in patients with active cancer. We assessed the levels of IgM and IgG 164 antibodies that recognized the SARS-CoV-2 receptor binding domain (RBD), using an enzyme-165 linked immunosorbent assay (ELISA) based approach 43, 44 . Cancer patients had significantly 166 decreased SARS-CoV-2-specific IgG and IgM responses compared to non-cancer patients 167 (Extended Data Fig. 2a) . These differences were not due to the timing of SARS-CoV-2 168 infection as time from symptom onset was similar (Supplemental Table 2 ). As hematologic 169 malignancies directly involve the lymphoid and myeloid immune compartments, we suspected 170 that hematologic cancers may have an impaired humoral immunity against SARS-CoV-2.

information on CD4 T cells, CD8 T cells, and B cells. EMD and clustering of 20 solid cancer, 31 273 hematologic cancer, and 6 remission patients identified 4 immune phenotypes (Extended Data 274 Fig. 6a,b and Fig. 4b,c ) that corresponded to the immune phenotypes 1,2,4, and 5 identified in 275 the Penn-MESSI cohort (Fig. 3c,d) . The Penn phenotype 3, the only cluster that did not have 276 cancer patients, was not identified in the MSKCC cancer cohort. Consistent with the Penn data, 277 MSKCC EMD cluster 5, with depleted of CD4 and CD8 T cells and preserved B cells, had the 278 highest mortality of 71%, and was associated with a high disease severity and viral load (Fig  279   4d) . 280

Intriguingly, the clinical outcomes of patients with immune phenotype 4 was the greatest 281 contributor to the overall mortality difference between patients with solid and liquid cancers; 282 hematologic cancer patients with phenotype 4 had a mortality of 61% versus 9% in patients with 283 solid cancers (Extended Data Fig. 7a) , with a corresponding higher viral load as assessed by 284 RT-PCR threshold cycle (Extended Data Fig. 7b ). Immune phenotype 4 was characterized by 285 robust CD4 responses and decreased, but still intact, CD8 responses (Extended Data Fig 6b) . 286

Within immune phenotype 4, patients with solid and hematologic cancers had similar CD4 and 287 CD8 T cell counts (Extended Data Figure 7c ). However, patients with hematologic cancers had 288 near-complete abrogation of B cells (phenotype 4A), that corresponded with a mortality rate of 289 Fig 7a and d) . In contrast, patients with solid cancers had intact B cells counts 290 (phenotype 4B, Extended Data Fig 7a and d) , with a mortality of 9%. Thus, in a setting with 291 similar CD4 and CD8 T cell numbers, B cell depletion was associated with higher mortality; B 292 cells, therefore, likely play an important role in acute Anti-CD20 therapy (αCD20) with rituximab or obinutuzumab-containing regimens 294 depleted B cells with near-complete abrogation of SARS-CoV-2-specific IgG and IgM responses 295 (Fig. 4e) . Notably, hematologic cancer patients on chemotherapy and solid cancer patients on 296 immune checkpoint blockade also had significant depletion of B cells (Extended Data Fig. 8a ). αCD20 therapy was not associated with quantitative changes in CD4 and CD8 T cells. However, 298 patients treated with anti-CD20 therapy displayed dramatic reduction in CD4 and CD8 naïve 299 and memory T cells, instead skewing towards effector differentiation and an activated HLA-300 DR+CD38+ phenotype (Extended Data Fig. 8b,c) . Importantly, despite the loss of B cells and 301 humoral immunity, αCD20 therapy was not associated with increased mortality, disease 302 severity, or viral load when compared to chemotherapy or observation (Fig. 4f) . 303

We sought to understand why αCD20 therapy was not associated with greater mortality 304 in these patients. Patients treated with αCD20 therapy were restricted to immune phenotypes 1 305 and 4, characterized by depleted B cells (Fig 4g) . However, phenotype 1, characterized by 306 preserved CD8 T cells, was associated with a lower mortality (Fig 4h) . Indeed, αCD20 treated 307 patients who survived their COVID-19 hospitalization had higher CD8 T cell counts (Fig 4i) , and 308 lower viral load (Extended Data Fig. 9a) . We extended these analyses to other patients with 309 hematologic cancers, including those on chemotherapy who also had quantitative (Extended 310 Data Fig. 8a) , and possibly qualitative B cell defects. Hematologic cancer patients who survived 311 had higher CD8 T cell count (Fig. 4j) , which was not seen in solid cancer patients (Extended 312 Data Fig. 9b) . Conversely, CD4 T cell counts were not associated with mortality, and higher B 313 cell counts were associated with increased mortality (Extended Data Fig. 9b, Fig 4j) . Thus, 314 patients with hematologic cancers, in the setting of defective humoral immunity, were more 315 highly dependent on adequate CD8 T cell counts than patients with solid cancers. Finally, 316

Classification and Regression Tree Analysis (CART) identified a CD8 T cell level that was 317 predictive of survival after COVID-19 in patients with hematologic cancers (Fig. 4k) . Taken 318 together, these findings suggest that CD8 T cells are critical for anti-viral immunity in 319 hematologic malignancy patients and may at least partially mitigate the negative impact of B-cell 320 depletion on COVID outcomes. 321

A notable feature of the COVID-19 pandemic has been the dramatic heterogeneity in clinical 324 presentations and outcomes, yet mechanistic explanations for the wide variance in disease 325 severity have remained elusive. Early on, acute phase reactants and systemic cytokines were 326 implicated in patient outcomes 46 and hospital stay and mortality were decreased by 327 dexamethasone 47 , suggesting that an excessive host immune response might contribute to 328 COVID-19 mortality. However, there were also indications that inadequate host immunity might 329 contribute to adverse COVID-19 outcomes, including the association of lymphopenia with 330 mortality as well as the potentially inferior outcomes of patients on chronic immunosuppression, 331 such as patients with autoimmune diseases or organ transplant recipients [48] [49] [50] [51] . Recent studies 332 defined immune signatures associated with severe COVID-19, including activated CD4, CD8 T 333 cells, plasmablasts, and robust antibody responses 15, 16, 20, 52 . Nevertheless, the individual roles 334 of these cell types in acute COVID-19 remained unclear. We speculated that investigating both 335 the clinical outcomes and immunologic profile of cancer patients might shed valuable insight into 336 how arms of the immune system contribute to viral control and mortality during Immune investigation in hematologic malignancies is especially relevant because the disease 338 directly impacts the lymphoid and myeloid immune cells, and is commonly treated with 339 myelosuppressive and B cell-depleting therapies including CD20 targeting antibodies. 340

Our investigation reveals several novel findings. First, we establish in a prospective 341 clinical cohort that hematologic malignancy is an independent predictor of COVID-19 mortality 342 after adjusting for ECOG performance and disease status. We observed a higher mortality rate 343 in patients with hematologic (53%) versus solid cancers (34%), which were substantially higher 344 than in the general population (2.7%) 28 . The high mortality rates for hematologic cancer in this 345 study were consistent with a recent meta-analysis of 2,361 hospitalized patients with 346 hematologic cancer (40%) 53 . This finding highlights the importance of transmission mitigation efforts for this vulnerable population 54 . Furthermore, we demonstrate that excess mortality 348 observed with hematologic cancers persisted (HR 2.5) after adjustment for independent 349 predictors of cancer mortality, including age, smoking history, poor performance status, and 350 active or advanced disease. Adjustment for these factors was necessary to determine that the 351 increased mortality difference seen in hematologic cancer was in fact, driven by cancer subtype, 352 rather than differences in patient characteristics. These data can better inform hospitalized 353 patients with hematologic cancers of their expected outcomes, irrespective of performance 354 status or active cancer status, thereby improving decision-making between best supportive care 355 or aggressive interventions. The disease-specific increased risk of COVID-19 associated 356 mortality in hematologic cancer patients may also influence the prioritization and distribution of 357 vaccinations to this very high-risk population. 358

Second, using high dimensional analyses, we define immune phenotypes associated 359 with mortality during COVID-19. In particular, we identify the immune phenotype that drives the 360 mortality difference between solid and liquid malignancy. A balanced immunity that included 361 CD4, CD8, and B cells responses (phenotypes 2 and 4b) was associated with low mortality. In 362 contrast, an immune signature with robust B cell and humoral responses, but absent T cell 363 responses (phenotype 5), was associated with the highest mortality (>60%). A high mortality for 364 patients with immune phenotype 5 was consistent in both the Penn and MSKCC cohorts, and in 365 patients with solid cancer, hematologic cancer, and infected patients without cancer. Thus, 366 humoral immunity alone is often not sufficient in acute COVID-19. In fact, greater B cell 367 responses was associated with higher mortality in both solid and liquid cancer. B cell responses 368 may be a marker of disease severity, as seen with plasmablasts 15, 20 and neutrophils 20, 55, 56 in 369 severe COVID-19. Alternatively, some components of the B cell and humoral responses may be 370 aberrant and pathogenic, as may be the case with autoantibodies targeting type I interferons in 371 severe COVID-19 57 . 372

Consistent with recent data 58 , patients with solid cancers had a similar cellular immune landscape and SARS-CoV-2-specific IgG responses as compared to patients without cancer. 374

Patients with hematologic cancers, however, had substantial defects in B cells and humoral 375 immunity. These defects were associated with a high mortality or 45%, as compared to 25% in 376 solid cancers. This difference in survival was driven by immune phenotype 4, which was 377 characterized by robust CD4 T cell responses in conjunction with a diminished, but not absent 378 CD8 T response. This phenotype (phenotype 4B), in the setting of preserved B cells seen in 379 solid cancer patients, was associated with a low mortality of 9%. However, this phenotype in the 380 setting of depleted B cells (phenotype 4A) seen in liquid cancer patients, was associated with a 381 mortality of 61%. This highlights the fact that CD8 T cell responses that are normally sufficient 382 may no longer be adequate in the setting of compromised humoral immunity. Thus, CD4 or B 383 cells responses, in the absence of an intact CD8 T cell response, may not be sufficient to 384

control an acute SARS-CoV-2 infection. This is reminiscent of published data demonstrating 385 that uncoordinated immune responses in the elderly was associated with severe disease and 386 poor outcomes 59 . 387

Finally, by leveraging a population of COVID-19 patients in the setting of B cell depletion 388 (anti-CD20), we uncovered a critical protective role for CD8 T-cell responses. CD8 T cells are 389 known to be critical for viral clearance, particularly in response to higher viral inocula 60 . Recent 390 data from transgenic mouse models show that both CD4 and CD8 T cells are necessary for 391 optimal viral clearance of SARS-CoV-2 61 . In patients treated with anti-CD20, absolute CD8 T 392 cell count, but not CD4 counts, was associated with survival from COVID-19 and lower viral 393 load. Although conclusions are limited by sample size, these data suggest that CD8 T cells play 394 a key role in limiting SARS-CoV-2, even in the absence of humoral immunity. Indeed, SARS-395

CoV-2-specific CD8 T cell responses have been identified in acute and convalescent 396 individuals 59, 62-65 . Further, in our cohort, absolute CD8 counts were predictive of outcomes in 397 the broader cohort of patients with hematologic malignancy. The compensatory role of CD8 T 398 cells was restricted to patients with hematologic, but not solid, malignancies. Thus, CD8 T cells likely play an important role in the setting of quantitative and qualitative B cell dysfunction in 400 patients with lymphoma, multiple myeloma, and leukemia, undergoing anti-CD20, 401 chemotherapy, or Bruton tyrosine kinase (BTK) inhibition. CD8 T cell counts may inform on the 402 need for closer monitoring and a lower threshold for hospitalization in COVID-19 patients with 403 hematologic malignancies. Furthermore, the clinical benefit of dexamethasone, which 404 demonstrated an overall mortality benefit in hospitalized COVID-19+ patients but is known to 405 suppress CD8 T cell responses 66 , should be investigated further in patients who recently 406 received anti-CD20 therapy. 407

Recent analysis demonstrated that patients treated with B-cell depleting agents had the 408 highest mortality rate, although this analysis did not account for whether the risk was modulated 409 by CD8 count. Our findings do not exclude the possibility that B-cell depleting therapies may be 410 associated with adverse outcomes in this population but rather extend these findings to suggest 411 that an adequate CD8-dependent T cell response is essential for patients in whom humoral 412 immunity is compromised. We did, however, observe a profound depletion of both naive CD4 413 and CD8 T cells in patients receiving B-cell depleting agents. Naive T-cells, and particularly 414 naive CD4 T cells, are known to require tonic TCR signaling driven by APC-presented self-415 antigens for persistence 67, 68 . We speculate that depletion of functional B cells, particularly in the 416 context of B cell depleting therapy, might lead to concomitant naive T cell depletion and a 417 corresponding increase in the effector and activated CD8 T cells. Although the clinical relevance 418 of naive T cell depletion in the setting of anti-CD20 is still unclear -it is notable that depletion of 419 naive T cells in the elderly was associated with increased disease severity and poor 420 outcomes 59 . 421

Importantly, both B-cell depleting therapies and cytotoxic chemotherapy agents which 422

can compromise the T-cell compartment are mainstays of lymphoma therapy. Both are 423 administered, often in combination, with curative intent for patients with aggressive lymphomas, but also for debulking or palliation in patients with indolent lymphomas. Based on our data, we 425 would suggest that oncologists and patients considering treatment regimens that combine B cell 426 depletion with cytotoxic agents carefully weigh the associated increased risk of immune 427 dysregulation against the benefit of disease control when making an educated decision on 428

whether to initiate such treatments, particularly in non-curative settings. 429

Finally, our finding that CD8 T cell immunity is critical for survival in hematologic 

We conducted a prospective cohort study of patients with cancer hospitalized with 453 COVID-19 (UPCC 06920). Informed consent was obtained from all patients. Adult patients with 454 a current or prior diagnosis of cancer and hospitalized with a probable or confirmed diagnosis of 455 COVID-19, as defined by the WHO criteria 72 , within the University of Pennsylvania Health 456 System (UPHS) between April 28, 2020 and September 15, 2020 were approached for consent. 457

Hospital, Pennsylvania Hospital, and Lancaster General Hospital. The index date was defined 459 as the first date of hospitalization within the health system for probable or confirmed Repeat hospitalizations within 7 days of discharge were considered within the index admission. 461

Patients who died prior to being approached for consent were retrospectively enrolled. Patients 462 were followed from the index date to 30-days following their discharge or until death by any 463 cause. This study was approved by the institutional review boards of all participating sites. 464

Baseline characteristics including patient (age, gender, race/ethnicity, comorbidities, 466 smoking history, body mass index) and cancer (tumor type, most recent treatment, ECOG 467 performance status, active cancer status) factors as well as COVID-19 related clinical factors 468 including change in levels of care, complications, treatments such as need for mechanical 469 ventilation, laboratory values (complete blood counts with differentials and inflammatory 470 markers including LDH, CRP, ferritin, and IL-6), and final disposition were extracted by trained 471 research personnel using standardized abstraction protocols. Active cancer status was defined 472 by diagnosis or treatment within 6 months of admission date. Cancer treatment status was 473 determined by the most recent treatment within 3 months prior to admission date. 474

The primary study endpoint was all-cause mortality within 30-days of hospital discharge. 475

Disease severity was categorized using the NIH ordinal scale including all post-hospitalization 476 categories: 1,hospitalized, not requiring supplemental oxygen but requiring ongoing medical 477 care; 2, hospitalized requiring any supplemental oxygen; 3, hospitalized requiring noninvasive 478 mechanical ventilation or use of high-flow oxygen devices; 4, hospitalized receiving invasive 479 mechanical ventilation or extracorporeal membrane oxygenation (ECMO); 5, death 73 , and was 480 assessed every 7 days throughout a patients admission. 481

Cohort characteristics were compared using standard descriptive statistics. to determine the association between cancer type and mortality and identically adjusted 501 for age, sex, smoking status, active cancer status, and ECOG performance 502 status. Overall survival (OS) was measured from date of hospitalization to last follow up 503 or death and the median OS was estimated using Kaplan-Meier method and differences 504 by cancer subtype compared using log-rank test.

Information on clinical cohort, sample processing, and flow cytometry is described in Mathew et 508 al, Science 2020. Briefly, Patients admitted to the Hospital of the University of Pennsylvania with 509 a positive SARS-CoV-2 PCR test were screened and approached for informed consent within 3 510 days of hospitalization. Peripheral blood was collected from all subjects and clinical data were 511 abstracted from the electronic medical record into standardized case report forms. All 512 participants or their surrogates provided informed consent in accordance with protocols 513 approved by the regional ethical research boards and the Declaration of Helsinki. Methods for 514 PBMC processing, flow cytometry, and antibodies used were previously described 15 . 515 516 Serologic enzyme-linked immunosorbent assay (ELISA) 517

ELISAs were completed using plates coated with the receptor binding domain (RBD) of 518 the SARS-CoV-2 spike protein as previously described 44 . Briefly, Prior to testing, plasma and 519 serum samples were heat-inactivated at 56°C for 1 hour. Plates were read at an optical density 520 To group individuals based on lymphocyte landscape, pairwise Earth Mover's Distance 567 (EMD) value was calculated on the lymphocyte UMAP axes using the emdist package in R. 568

Resulting scores were hierarchically clustered using the hclust package in R. Tumor types with less than 2 subjects: CNS-2, Thyroid-2, Thymus-1, Neuroendocrine-1 # Diagnosis or treatment within 6 months *Single agent immunotherapy, targeted therapy, monoclonal antibodies (n=22) Absolute counts of CD8+, CD4+, and CD19+ cells in solid cancer patients (alive n=16; dead n=7). (All) Significance determined by Mann Whitney test: *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001. Median and 95% CI shown. Bladder 0 (0%) 1 (6.7%) 1 (4.5%)

Breast 0 (0%) 6 (40.0%) 6 (27.3%) GBM 0 (0%) 1 (6.7%) 1 (4.5%)

Head and Neck 0 (0%) 1 (6.7%) 1 (4.5%) NSCLC 0 (0%) 1 (6.7%) 1 (4.5%)

Ovarian 0 (0%) 1 (6.7%) 1 (4.5%)

Pancreas 0 (0%) 1 (6.7%) 1 (4.5%)

Prostate 0 (0%) 2 (13.3%) 2 (9.1%) SCLC 0 (0%) 1 (6.7%) 1 (4.5%)

Cancer Treatment αCD20 + Chemotherapy 1 (14.3%) 0 (0%) 1 (4.5%)

Chemo 5 (71.4%) 4 (26.7%) 9 (40.9%) None 1 (14.3%) 1 (6.7%) 2 (9.1%) Chemotherapy + Radiation 0 (0%) 1 (6.7%) 1 (4.5%)

Hormonal 0 (0%) 4 (26.7%) 4 (18.2%)

Hormonal + CDK Inhibitor 0 (0%) 1 (6.7%) 1 (4.5%)

Hormonal + Radiation 0 (0%) 1 (6.7%) 1 (4.5%)

ICB 0 (0%) 3 (20.0%) 3 (13.6%) 

Solid (N=78)

Heme (N=22)

Race White 24 (30.8%) 9 (40.9%)

Unknown 5 (6.41%)

Smoking History, Current/Prior 44

Comorbidities Cardiac 63 (80.8%) 15 (68.2%) Pulmonary

Use of immunosuppressive drugs + 23

Female 49 (45.4%) 14 (63.6%) 63 (48.5%)

Disease Severity (median)

Convalescent Plasma