An atypical promyelocytic sarcoma and pleural effusion in a patient with Gorham's disease: Efficiency of ATRA/ATO-based treatment

Key Clinical Message

This is the first case of a promyelocytic sarcoma diagnosed on pleural effusion and exposed the difficulty of demonstrating a leukemic phase in patients with bone diseases, such as Gorham's disease. It also showed that promyelocytic sarcoma can be treated by ATRA/ATO-based therapy with an efficient and tolerated response.

Abstract

Myeloid sarcoma (MS) is a rare extramedullary tumoral infiltration of immature myeloid cells and can occur in different sites of the body, without leukemic infiltration. A 38-year-old woman patient presented at emergency with a pleural effusion, bicytopenias, and Gorham's disease, a very rare bone disorder. In the following days, she worsened with a chylothorax and pancytopenias. Pleural puncture cytologically revealed promyelocytes with Auer rods. Cytogenetic and molecular analyses subsequently confirmed the presence of the t(15:17) translocation. However, no circulating phase of these atypical promyelocytes was found. Similarly, no other origin was identified. We conclude that the patient had a MS of unknown etiology in the form of a pleural effusion with pathological promyelocytes. The patient was treated with a combination of oral all-trans retinoic acid (ATRA) and arsenic trioxide (ATO) with a cytological and molecular remission persisting 3 months after diagnosis. We report here the first case of a promyelocytic MS of pleural origin without concomitant evidence of acute promyelocytic leukemia. We also show the efficacy of ATRA/ATO treatment in this etiology.

Abstract 2177: Roles of lactate dehydrogenase (LDH) in melanoma: an underestimated prognostic biomarker

Introduction: High LDH activity is associated with a poor prognosis in many cancer types, especially in melanoma where it is the strongest indicator of poor outcome even with the most potent anticancer immunotherapies or targeted therapies. Although high LDH blood activity is often associated with high tumor burden, this is not always the case and there is no definitive demonstration that blood LDH activity is directly derived from tumors cells. Our objectives were 1) to explore the biological impact of LDHA and LDHB independently of their metabolic function 2) to explore the respective prognostic values of the 5 LDH isoforms as well as the relationship between tumor and blood LDH isoforms repartition in melanoma patients.

Material, Patients, Methods: A375 melanoma cells cultured in glucose or galactose (to evaluate glycolytic independent effects) were silenced for LDHA or LDHB expression using si-RNAs. RNAseq analysis of total RNAs and polysomal RNAs was performed to explore the transcriptional and translational impacts of LDHA and LDHB expression. In melanoma patients treated with immunotherapy, the enzymatic activities of each of the 5 LDH isoforms (encoded by LDHA and LDHB) were measured in tumors and blood samples. The prognostic values of the distinct isoforms as well as the correlation between the isoforms distribution in blood and tumors were evaluated.

Results: In vitro, independently of their metabolic effect, both LDHA and LDHB had distinct transcriptional and translational impacts on the control of several key oncogenic pathways (adhesion, survival, proliferation, immunity) with a more important effect of LDHA vs LDHB silencing. In vivo, among 64 melanoma patients including 25% with high LDH, high LDH1 (B4) activity was significantly associated with response and overall survival (OS) whereas high LDH4 (A3B1) was inversely associated with response and OS. LDH1 and LDH4 were associated with survival even among patients with normal LDH levels. For patients with paired (tumor and blood) isoforms analysis, there was no correlation between isoforms repartition suggesting that blood LDH activity is not directly related to tumor LDH. Conclusion: In addition to their metabolic activity, LDH enzymes, particularly LDHA, control several key oncogenic pathways at the transcriptional level but also at the translational level for a set of genes involved in cancer biology. LDH1 and LDH4 isoforms blood levels are differentially correlated with response to immunotherapy and survival. They are more robust prognostic biomarkers than total blood LDH level used in our daily practice. Thus, LDH is much more than just a marker of tumor burden in melanoma and the origins, the significance and the roles of blood LDH isoforms need to be revisited. These results highlight the need to develop anticancer drugs targeting LDH, and especially LDHA.

Citation Format: Laura Soumoy, Virginie Quidville, Lisa Fredeau, Giuseppina Claps, Caroline Pradon, Ludovic Lacroix, Emilie Routier, Djaouida Belkadi, Séverine Roy, Feras Chehade, Stephan Vagner, Caroline Robert. Roles of lactate dehydrogenase (LDH) in melanoma: an underestimated prognostic biomarker [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2177.

Succinate: A Serum Biomarker of SDHB-Mutated Paragangliomas and Pheochromocytomas

CONTEXT

Pheochromocytomas and paragangliomas (PPGL) are rare neuroendocrine tumors that are frequently associated with succinate dehydrogenase (SDH) germline mutations. When mutated, SDH losses its function, thus leading to succinate accumulation.

OBJECTIVE

In this study, we evaluated serum succinate levels as a new metabolic biomarker in SDHx-related carriers.

METHODS

Retrospective monocentric study of 88 PPGL patients (43 sporadic, 35 SDHB, 10 SDHA/C/D), 17 tumor-free familial asymptomatic carriers (13 SDHB, 4 SDHC/D), and 60 healthy controls. Clinical, biological, and imaging data were reviewed. Serum succinate levels (n = 280) were quantified by an ultra-performance liquid chromatography coupled to a tandem mass spectrometry method and correlated to SDHx mutational status, disease extension, and other biological biomarkers.

RESULTS

Serum succinate levels > 7 μM allowed identification of tumor-free asymptomatic SDHB-mutated cases compared to a healthy control group (100% specificity; 85% sensitivity). At PPGL diagnosis, SDHB-mutated patients had a significantly increased median succinate level (14 μM) compared to sporadic patients (8 μM) (P < 0.01). Metastatic disease extension was correlated to serum succinate levels (r = 0.81). In the SDHB group, patients displaying highest tumor burdens showed significant increased succinate levels compared to the sporadic group (P < 0.0001).

CONCLUSIONS

In this pilot study, we showed that serum succinate level is an oncometabolic biomarker that should be useful to identify SDHB-related carriers. Succinate levels are also a marker of metabolic tumor burden in patients with a metastatic PPGL and a potential marker of treatment response and follow-up.

The Polarity and Specificity of Antiviral T Lymphocyte Responses Determine Susceptibility to SARS-CoV-2 Infection in Patients with Cancer and Healthy Individuals

Vaccination against coronavirus disease 2019 (COVID-19) relies on the in-depth understanding of protective immune responses to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). We characterized the polarity and specificity of memory T cells directed against SARS-CoV-2 viral lysates and peptides to determine correlates with spontaneous, virus-elicited, or vaccine-induced protection against COVID-19 in disease-free and cancer-bearing individuals. A disbalance between type 1 and 2 cytokine release was associated with high susceptibility to COVID-19. Individuals susceptible to infection exhibited a specific deficit in the T helper 1/T cytotoxic 1 (Th1/Tc1) peptide repertoire affecting the receptor binding domain of the spike protein (S1-RBD), a hotspot of viral mutations. Current vaccines triggered Th1/Tc1 responses in only a fraction of all subject categories, more effectively against the original sequence of S1-RBD than that from viral variants. We speculate that the next generation of vaccines should elicit Th1/Tc1 T-cell responses against the S1-RBD domain of emerging viral variants.

SIGNIFICANCE

This study prospectively analyzed virus-specific T-cell correlates of protection against COVID-19 in healthy and cancer-bearing individuals. A disbalance between Th1/Th2 recall responses conferred susceptibility to COVID-19 in both populations, coinciding with selective defects in Th1 recognition of the receptor binding domain of spike. See related commentary by McGary and Vardhana, p. 892. This article is highlighted in the In This Issue feature, p. 873.

Abstract P4-11-01: Development of a clinico-bio-behavioral model for cancer-related fatigue (CRF) incorporating inflammatory biomarkers and proteomic data

Background: We previously developed a clinico-behavioral model of CRF and reported an increased risk of severe CRF among survivors of breast cancer (BC) receiving adjuvant hormonal therapy (HT) (Di Meglio A, ASCO 2021). We now aim to comprehensively explore the contribution of relevant serum proteins in explaining CRF. We adopted a multimodal approach, both (1) hypothesis-driven, based on the rationale that deregulation of systemic inflammatory processes and mediators of immunologic or neuroendocrine activation are associated with vulnerability to CRF, and (2) discovery-driven, based on proteomic analyses. Methods: Women with stage I-III HR+/HER2- tumors receiving HT (N=1153) were included from the multicenter, prospective CANTO cohort (NCT01993498). The primary outcome of interest was severe post-treatment global CRF at year-2 (Y2) after diagnosis (score ≥ 40/100, EORTC QLQ-C30). Secondary outcomes included CRF dimensions (physical, emotional, cognitive; EORTC QLQ-FA12). For the hypothesis-driven analyses, pre-treatment blood samples were profiled (Randox Laboratories Limited, UK) at diagnosis of BC, using a multi-biomarker panel assessing IL6, TNFα, IL1RA, CRP, IL2, IL1β, IFNγ, IL10, IL1A, IL4, and IL8. Pre-specified pre-treatment clinico-behavioral covariates (age, BMI, smoking status, psychological, and pre-treatment symptom burden, based on previously developed models) were forced into a multivariable logistic regression. Biomarkers were retained by Augmented Backwards Elimination (p&lt;0.05) only if significantly associated with CRF. For the discovery approach, we used hyper-reaction monitoring mass spectrometry for the unbiased quantification of all detectable peptides and proteins in human plasma samples at diagnosis (Biognosys, CH), among a discovery subset (N=462). We then aimed to identify a proteomic signature associated with severe CRF at Y2. Log-transformed protein intensities were analyzed in terms of differential expression. The proteins that were identified to be significantly different among the patients reporting and not reporting severe CRF were then used to train a logistic regression model. Results: Prevalence of severe global CRF increased from 21.6% at diagnosis to 34.8% at Y2. In the final model, higher pre-treatment levels of IL6 and lower levels of IFNγ and IL10 were significant predictors of severe global CRF at Y2 (Table). The AUC of this clinico-bio-behavioral model was 0.78 (95%CI 0.75 - 0.82) and was suggestive of an improved performance as compared to clinico-behavioral models. Among CRF dimensions, a significant association emerged only between CRP and severe cognitive CRF (outcome prevalence at Y2 14.2%; adjusted OR per CRP log-unit increase 1.40 [95%CI 1.01-1.93]).

In the discovery subset, several proteins were identified as differentially regulated (p&lt;0.05) among patients reporting and not reporting severe CRF at Y2. Most of these were related to coagulation pathways (including C4BPA, C4BPB, HABP2, PLF4, PROS). However, models incorporating proteomic data did not seem to augment the predictive ability compared to clinico-behavioral models. Conclusions: Using clinical and biological pre-treatment measurements, it may be possible to identify a subset of BC patients at high risk for increased post-treatment CRF while on HT. This provides the possibility of testing dedicated preventive interventions.

Table. Clinico-bio-behavioral model of pre-treatment predictors of severe global CRF at Y2, incorporating circulating inflammatory biomarkers.Adjusted OR§ (95% CI)Age, per additional 1 year0.98 (0.96-0.99)BMI, per additional unit1.02 (0.99-1.06)Current smoker, vs never2.27 (1.47-3.51)Former smoker, vs never0.97 (0.64-1.46)Anxiety case*, vs normal1.13 (0.75-1.70)Doubtful anxiety*, vs normal1.11 (0.73-1.68)Pre-treatment Insomnia**, per additional 10 points1.09 (1.04-1.15)Pre-treatment Pain**, per additional 10 points1.10 (1.01-1.18)Severe pre-treatment CRF**, vs no4.70 (3.13-7.05)IL6***1.72 (1.25-2.36)IL1RA***1.24 (0.85-1.81)IL2***1.43 (0.99-2.08)IFNγ***0.54 (0.30-0.95)IL10***0.40 (0.18-0.87)IL4***1.47 (0.67-3.20)IL8***1.15 (0.83-1.60)OR= Odds Ratio; CI= Confidence Interval; §by all factors in Table; *HADS; **QLQ-C30; ***per log-unit increase

Citation Format: Antonio Di Meglio, Stergios Christodoulidis, Davide Soldato, Antonin Della Noce, Daniele Presti, Julie Havas, Florine Dubuisson, Barbara Pistilli, Valerie Camara-Clayette, Cecile Charles, Patricia A Ganz, Julienne Bower, Ann H Partridge, Alexandra Jacquet, Sibille Everhard, Sandrine Boyault, Fabrice André, Paul-Henry Cournede, Stefan Michiels, Caroline Pradon, Ines Vaz-Luis. Development of a clinico-bio-behavioral model for cancer-related fatigue (CRF) incorporating inflammatory biomarkers and proteomic data [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-11-01.

Abstract P4-11-34: An integrated clinical, behavioral and biological model to predict the risk of weight gain among breast cancer survivors (BCS)

Background: Weight management is an integral part of survivorship care. Excess weight in BCS is associated with worse clinical outcomes and quality of life. Early identification of BCS at risk of gaining substantial weight could lead to prompt and tailored interventions. We aimed at developing a predictive model of weight gain that integrates clinical, behavioral and biological data. Methods: We included patients with stage I-III BC from the CANTO cohort (NCT01993498). CANTO collects longitudinal data, including objective anthropometric measures, at diagnosis (dx), 1 (T1), 2 (T2) and 4 (T3) years after dx. In addition, profiling of blood samples obtained at dx was performed for two sub-cohorts with HR+/HER2- BC for quantification of: (1) inflammatory and metabolic biomarkers (IL6, TNFα, IL1RA, CRP, IL2, IL1β, IFNγ, IL10, IL1A, IL4, IL8, ADPN, LEPT, INS, RETN) and (2) detectable proteins using hyper reaction mass spectrometry (Biognosys). Our outcome of interest was weight gain (increase ≥ 5%) compared to dx. First, multivariable logistic regression with bootstrapped Augmented Backwards Elimination (ABE) retained associations between weight gain and clinico-behavioral covariates. To assess contribution of biologic data, ABE retained associations between weight gain and biomarkers, correcting for significant covariates. Models were validated using internal cross-validation and overoptimism-correction. For proteomics, proteins relative intensity was calculated, and a bootstrapped differential protein expression analysis identified proteins associated with weight gain that were then included in logistic regression. Models performance was assessed in terms of Area Under the Curve (AUC). Results: In the overall cohort (N=9541) mean age was 56.8 (SD 11.4), mean BMI was 25.9 Kg/m2 (SD 5.4), 48.9% of pts were overweight or obese, and 52.9% received chemotherapy (CT). Overall, 16.9% (T1), 23.4% (T2), and 27.2% (T3) BCS gained weight (absolute mean change (95% CI): 6.1 kg (5.9-6.2), 6.7 kg (6.5-6.9) and 7.2 kg (6.9-7.3) at T1, T2, T3, respectively). In clinico-behavioral models, younger age, current smoking, lower income and education, receipt of CT and radiotherapy were associated with increased risk of weight gain (Table). Among 1261 BCS with biomarkers data, higher levels of IL1α (OR for 1-unit log increase [95%CI] 0.11 [0.02 - 0.65]) and of ADPN (1.36 [1.01 - 1.85]) were associated with lower and higher risk of weight gain at T2 and T3, respectively. Performance of models integrating these biomarkers was similar to clinico-behavioral models. Among 462 BCS with proteomic profiling, preliminary data showed that higher relative abundance of IgG Fc Binding Protein (OR 0.44, p&lt;.05) and Tubulin-1 (OR 0.73, p&lt;.05) was associated with lower risk of weight gain at T1. AUC of model integrating clinical and proteomics data was 0.74 (0.58-0.90). Conclusions: Over one-in-four BCS in the CANTO cohort experienced meaningful weight gain 4 years after dx. This large, multidimensional study confirms the role of clinico-behavioral risk factors for weight gain. However, the predictive ability of clinico-behavioral models seems suboptimal. The exploitation of additional data dimensions, including serum proteins and proteomic data, may help improve predictive ability and inform underlying biological processes implicated in weight gain after BC. Further studies will aim at improving model stability, particularly for proteomics analyses.

Table. Models of weight gain in the overall cohort.T1 (N= 8397)T2 (N= 7663)T3 (N= 5802)Clinical predictors OR* (95% CI)OR* (95% CI)OR* (95% CI)Age, 1-year increase0.96 (0.94 - 0.97)0.96 (0.95 - 0.97)0.96 (0.95 - 0.97)BMI, 1-unit increaseNRNS0.97 (0.94 - 0.99)Education, primary vs collegeNS1.57 (1.04 - 2.39)NREducation, high school vs college1.38 (1.04 - 1.83)1.54 (1.21 - 1.98)NRIncome, ≥ 1500 and &lt;3000 vs &gt;3000NRNR1.29 (1.00 - 1.66)Smoke, current vs never1.70 (1.24 - 2.33)NR1.53 (1.12 - 2.08)Chemotherapy, yes vs no1.40 (1.07 - 1.82)1.31 (1.01 - 1.69)NRRadiotherapy, yes vs no2.10 (1.10 - 3.99)NR1.83 (1.08 - 3.12)AUC (95% CI) - clinical models0.65 (0.63 - 0.68)0.64 (0.61 - 0.67)0.65 (0.63 - 0.68)AUC (95% CI) - clinical + inflammatory and metabolic biomarkers models, [N]0.65 (0.60 - 0.70), [1179]0.66 (0.62 - 0.70), [948]0.67 (0.63 - 0.71), [1017]AUC (95% CI) - clinical and proteomics models, [N]0.74 (0.58 - 0.90), [462]0.65 (0.50 - 0.81), [462]NEOR= Odds Ratio, CI= Confidence Interval, NR= Not Retained, NS= Not significant, NE= Not evaluated *Adjusted by age, menopause, smoke, socioeconomic, psychological, tumor and treatments **Significant covariates from previous models were forced and ABE selected significant variables among all circulating biomarkers.

Citation Format: Davide Soldato, Antonio Di Meglio, Caroline Pradon, Antonin Della Noce, Daniele Presti, Julie Havas, Florine Dubuisson, Barbara Pistilli, Valerie Camara-Clayette, Fabrice André, Alexandra Jacquet, Sibille Everhard, Sandrine Boyault, Paul-Henry Cournede, Stefan Michiels, Ines Vaz-Luis, Stergios Christodoulidis. An integrated clinical, behavioral and biological model to predict the risk of weight gain among breast cancer survivors (BCS) [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-11-34.

Abstract P4-11-09: Cancer-related cognitive impairment (CRCI) in early breast cancer (BC) survivors

Background: Up to 35% BC survivors who receive adjuvant treatment (tx) experience severe CRCI, which has a significant impact on quality of life, disrupting daily functioning as well as self-esteem, self-confidence, and work ability. However, limited tools exist to predict the risk of CRCI. We aimed to develop a comprehensive model of severe CRCI, including clinical and serum inflammatory protein data. Methods: We included 8875 patients (pts) with stage I-III BC from the multicenter, prospective CANTO cohort (NCT01993498). Longitudinal data were collected at diagnosis (dx), 1 (T1), 2 (T2) and 4 (T3) years post-dx. Our outcome of interest was severe cognitive impairment at T1, T2, and T3 (score &lt; 75/100, EORTC QLQ-C30, Giesinger JM 2020). Multivariable logistic regression models retained associations between baseline clinical variables (sociodemographic, psychological, tumor, and tx-related) with severe CRCI by bootstrapped Augmented Backwards Elimination (ABE). Among a subset of patients with HR+/HER2- BC (N= 1151), blood samples were profiled at dx using a multi-biomarker inflammatory panel assessing IL6, TNFα, IL1RA, CRP, IL2, IL1β, IFNγ, IL10, IL1A, IL4, IL8, and monocyte chemoattractant protein-1 (MCP-1). All biomarkers were incorporated simultaneously into a model of severe CRCI and retained only if significantly associated with CRCI by ABE (p&lt;0.05). Previously retained clinical associations were forced into the model. Results: In the overall cohort, mean age at dx was 56.7 years (SD 11.3), and 52.7% and 81.3% of pts received chemotherapy and hormonal therapy, respectively. Prevalence rates of severe CRCI were 31.2% (dx), 31.4% (T1), 30.9% (T2), and 29.9% (T3). Severe post-tx CRCI was consistently associated with severe pre-tx pain and severe pre-tx CRCI. Severe pre-tx fatigue, younger age, anxiety symptoms and hot flashes at dx were also associated with increased odds of severe CRCI at some post-dx time-points (Table 1). Models Area Under the Curve (AUC) were 0.73 (95% confidence intervals [CI] 0.70-0.76) at T1, 0.69 (CI 0.65-0.72) at T2, and 0.68 (CI 0.63-0.72) at T3. Among pts with available serum biomarkers, no significant associations were observed between inflammatory proteins and CRCI at any time point. Performance of models incorporating inflammatory biomarkers was similar to clinical-only models (Table 2). Conclusions: Almost 1/3 of BC survivors in this cohort reported severe CRCI. This rate was stable throughout the survivorship period and did not seem to be affected by cancer-specific or tx-related factors, or inflammatory biomarkers. Pts age and concomitant symptom burden at dx emerged as consistent associations with severe CRCI. A description of the average population risk of CRCI using a self-reported, global evaluation scale may not fully describe the granularity of this phenomenon. Further studies building on dedicated, objective measurements, may help identify latent classes of pts experiencing a major decline in cognitive function following BC tx, and for whom a contribution of biology may help explain inter-individual variability and underlying biological processes.

Table 1.Models of severe CRCI in the overall cohort: clinical predictors.T1 (N=7724)T2 (N=6825)T3 (N=4706)OR* (95% CI)OR* (95% CI)OR* (95% CI)Severe Pain**, vs no1.50 (1.09-2.07)1.93 (1.39-2.69)1.55 (1.03-2.34)Severe pre-tx CRCI**, vs no3.69 (2.70-5.05)2.53 (1.85-3.46)2.21 (1.47-3.32)Severe Fatigue**, vs no1.50 (1.06-2.11)1.61 (1.13-2.28)1.08 (0.69-1.70)Age (continous)0.98 (0.97-0.99)NR0.98 (0.96-0.99)Menopause, post- vs pre-NR0.73 (0.54-0.98)NRAnxiety, case vs normalNRNR1.82 (1.13-2.92)Anxiety, borderline vs normalNRNR1.84 (1.17-2.91)Hot flashes, vs no1.25 (0.92-1.69)1.20 (0.87-1.65)1.64 (1.10-2.43)Corrected AUC0.73 (0.70-0.76)0.69 (0.65-0.72)0.68 (0.63-0.72)OR= Odds Ratio, CI= Confidence Interval, NR= Not Retained; *Adjusted by BMI, alcohol, smoke, socioeconomic, psychological, tumor and tx; **QLQ-C30

Table 2.Models of severe CRCI in the overall cohort**: biological biomarkers.T1 (N=1094)T2 (N=1091)T3 (N=870)OR* (95% CI)OR* (95% CI)OR* (95% CI)IL6NR0.80 (0.46-1.40)1.01 (0.64-1.60)IL1RA0.66 (0.37-1.17)0.88 (0.50-1.55)NRCRP0.94 (0.60-1.48)1.44 (0.92-2.27)NRIL20.93 (0.55-1.57)1.10 (0.61-1.97)NRIL1βNR1.55 (0.71-3.40)NRIFNγ1.86 (0.69-5.01)0.75 (0.25-2.22)NRIL101.05 (0.34-3.27)1.27 (0.58-2.78)NRIL1A0.71 (0.15-3.33)0.80 (0.17-3.66)NRIL80.96 (0.58-1.58)NRNRTNFαNR1.35 (0.67-2.73)NRMCP-11.07 (0.64-1.78)0.83 (0.51-1.35)0.80 (0.48-1.31)Corrected AUC0.72 (0.67-0.77)0.70 (0.65-0.75)0.67 (0.62-0.72)OR= Odds Ratio, CI= Confidence Interval, NR= Not Retained; *Adjusted by BMI, alcohol, smoke, socioeconomic, psychological, tumor and tx; ** Clinical predictors from previous models were forced in the models at each time-point

Citation Format: Daniele Presti, Florence Joly, Davide Soldato, Stergios Christodoulidis, Antonin Della Noce, Julie Havas, Florine Dubuisson, Barbara Pistilli, Valerie Camara-Clayette, Fabrice André, Anne-Laure Martin, Alexandra Jacquet, Sandrine Boyault, Ivan Bièche, Charles Coutant, Paul-Henry Cournede, Stefan Michiels, Caroline Pradon, Ines Vaz-Luis, Antonio Di Meglio. Cancer-related cognitive impairment (CRCI) in early breast cancer (BC) survivors [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-11-09.

Sarilumab in adults hospitalised with moderate-to-severe COVID-19 pneumonia (CORIMUNO-SARI-1): An open-label randomised controlled trial

Background

Patients with COVID-19 pneumonia can have increased inflammation and elevated cytokines, including interleukin (IL)-6, which might be deleterious. Thus, sarilumab, a high-affinity anti-IL-6 receptor antibody, might improve the outcome of patients with moderate-to-severe COVID-19 pneumonia.

Methods

We did a multicentric, open-label, Bayesian randomised, adaptive, phase 2/3 clinical trial, nested within the CORIMUNO-19 cohort, to test a superiority hypothesis. Patients 18 years or older hospitalised with COVID-19 in six French centres, requiring at least 3L/min of oxygen but without ventilation assistance and a WHO Clinical Progression Scale [CPS] score of 5 were enrolled. Patients were randomly assigned (1:1) via a web-based system, according to a randomisation list stratified on centre and with blocks randomly selected among 2 and 4, to receive usual care plus 400 mg of sarilumab intravenously on day 1 and on day 3 if clinically indicated (sarilumab group) or usual care alone (usual care group). Primary outcomes were the proportion of patients with WHO-CPS scores greater than 5 on the 10-point scale on day 4 and survival without invasive or non-invasive ventilation at day 14. This completed trial is closed to new participants and is registered with ClinicalTrials.gov, NCT04324073.

Findings

165 patients were recruited from March 27 to April 6, 2020, and 148 patients were randomised (68 patients to the sarilumab group and 80 to the usual care group) and followed up for 90 days. Median age was 61·7 years [IQR 53·0–71·1] in the sarilumab group and 62·8 years [56·0–71·7] in the usual care group. In the sarilumab group 49 (72%) of 68 were men and in the usual care group 59 (78%) of 76 were men. Four patients in the usual care group withdrew consent and were not analysed. 18 (26%) of 68 patients in the sarilumab group had a WHO-CPS score greater than 5 at day 4 versus 20 (26%) of 76 in the usual care group (median posterior absolute risk difference 0·2%; 90% credible interval [CrI] −11·7 to 12·2), with a posterior probability of absolute risk difference greater than 0 of 48·9%. At day 14, 25 (37%) patients in the sarilumab and 26 (34%) patients in the usual care group needed ventilation or died, (median posterior hazard ratio [HR] 1·10; 90% CrI 0·69–1·74) with a posterior probability HR greater than 1 of 37·4%. Serious adverse events occurred in 27 (40%) patients in the sarilumab group and 28 (37%) patients in the usual care group (p=0·73).

Interpretation

Sarilumab treatment did not improve early outcomes in patients with moderate-to-severe COVID-19 pneumonia. Further studies are warranted to evaluate the effect of sarilumab on long-term survival.

Funding

Assistance publique—Hôpitaux de Paris

Prolonged SARS-CoV-2 RNA virus shedding and lymphopenia are hallmarks of COVID-19 in cancer patients with poor prognosis

Patients with cancer are at higher risk of severe coronavirus infectious disease 2019 (COVID-19), but the mechanisms underlying virus-host interactions during cancer therapies remain elusive. When comparing nasopharyngeal swabs from cancer and noncancer patients for RT-qPCR cycle thresholds measuring acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in 1063 patients (58% with cancer), we found that malignant disease favors the magnitude and duration of viral RNA shedding concomitant with prolonged serum elevations of type 1 IFN that anticorrelated with anti-RBD IgG antibodies. Cancer patients with a prolonged SARS-CoV-2 RNA detection exhibited the typical immunopathology of severe COVID-19 at the early phase of infection including circulation of immature neutrophils, depletion of nonconventional monocytes, and a general lymphopenia that, however, was accompanied by a rise in plasmablasts, activated follicular T-helper cells, and non-naive Granzyme B+FasL+, EomeshighTCF-1high, PD-1+CD8+ Tc1 cells. Virus-induced lymphopenia worsened cancer-associated lymphocyte loss, and low lymphocyte counts correlated with chronic SARS-CoV-2 RNA shedding, COVID-19 severity, and a higher risk of cancer-related death in the first and second surge of the pandemic. Lymphocyte loss correlated with significant changes in metabolites from the polyamine and biliary salt pathways as well as increased blood DNA from Enterobacteriaceae and Micrococcaceae gut family members in long-term viral carriers. We surmise that cancer therapies may exacerbate the paradoxical association between lymphopenia and COVID-19-related immunopathology, and that the prevention of COVID-19-induced lymphocyte loss may reduce cancer-associated death.

Metabolomic analyses of COVID-19 patients unravel stage-dependent and prognostic biomarkers

The circulating metabolome provides a snapshot of the physiological state of the organism responding to pathogenic challenges. Here we report alterations in the plasma metabolome reflecting the clinical presentation of COVID-19 patients with mild (ambulatory) diseases, moderate disease (radiologically confirmed pneumonitis, hospitalization and oxygen therapy), and critical disease (in intensive care). This analysis revealed major disease- and stage-associated shifts in the metabolome, meaning that at least 77 metabolites including amino acids, lipids, polyamines and sugars, as well as their derivatives, were altered in critical COVID-19 patient's plasma as compared to mild COVID-19 patients. Among a uniformly moderate cohort of patients who received tocilizumab, only 10 metabolites were different among individuals with a favorable evolution as compared to those who required transfer into the intensive care unit. The elevation of one single metabolite, anthranilic acid, had a poor prognostic value, correlating with the maintenance of high interleukin-10 and -18 levels. Given that products of the kynurenine pathway including anthranilic acid have immunosuppressive properties, we speculate on the therapeutic utility to inhibit the rate-limiting enzymes of this pathway including indoleamine 2,3-dioxygenase and tryptophan 2,3-dioxygenase.