Pharmacogenomic Prediction of Bleomycin-Induced Pneumonitis in South East Asian Hodgkin Lymphoma Patients

• Published in: Blood Vol. 132; no. Supplement 1; p. 4111
• Main Authors: Lee, Joanne Shu Xian, Poon, Li Mei, Liu, Xin, Yuen, Yi Ching, Somasundaram, Nagavalli, Chin, Suk Teng, Teoh, Chia Meng, Yan, Benedict, Chan, Hian Li Esther Hian Li, Chee, Yen-Lin, Tang, Tiffany, Farzana, Noorul, Aw, Caroline Hui Ting, Khor, Chiea Chuen, Ngeow, Joanne Yuen Yie, Ong, Choon Kiat, Tai, E Shyong, Chng, Wee Joo, Goh, Boon Cher, Lee, Soo Chin, Aminkeng, Folefac
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 29-11-2018
• ISSN: 0006-4971; 1528-0020
• DOI: 10.1182/blood-2018-99-113952

Background
Hodgkin lymphoma (HL) accounts for 15% of all cancer diagnosis in young adults. Standard of care is ABVD (Adriamycin, Bleomycin, Vinblastine, Dacarbazine), which has resulted in overall cures of 70-90%. In the setting of HL, bleomycin is still one of the cornerstones in its treatment with the omission of bleomycin in the randomized trial of GHSG HD13 showing poorer outcomes. A major complication of HL treatment is Bleomycin induced pneumonitis (BIP) occurring in 0.4-28% of patients, with up to 10% mortality. Currently, we are unable to predict which patients are more prone. Given that human genomic variations underlie both disease susceptibility and drug response, uncovering genetic biomarkers predisposing to BIP is imperative to predicting and preventing BIP.
Methods
In this first pharmacogenomics study of BIP in South-East Asian patients, we perform both candidate gene and GWAS analyses. 96 HL patients were recruited from the National University Cancer Institute, Singapore (NCIS). BIP was clinically characterized and graded by CTCAEv4. Genetic association analysis was performed by regression (additive model) accounting for non-genetic predictor variables.
Results
Age at the start of bleomycin treatment, sex, genetic ancestry, concomitant chemotherapy treatment, treatment with G-CSF, stage of HL disease, pretreatment creatinine clearance, weight, chest irradiation and smoking history did not differ significantly between patients with and without BIP. The total number of treatment cycles (P=0.006) , cumulative exposure (P=0.01) and total exposure (P=0.028) were significantly lower in the BIP group as Bleomycin was omitted from further chemotherapy regimens once BIP was diagnosed.
We independently replicated the association of HFE rs1799945 (H63D) with BIP (Cases: 15.38% vs Controls: 1.72%, P=0.0069, OR(95%CI)=2.89 (1.88-88.59)), while BLMH rs1050565 was not associated with BIP (P=0.92, OR(95%CI)=1.07 (0.29-3.93)). H63D carriers have 12.44 (1.98-78.17) times the odds of developing BIP compared to non-carriers (P=0.0088). We uncover a significant association of H63D with the severity of BIP (P=0.00087). HIST1H1T rs198846, a downstream variant in LD with H63D was also associated with BIP and its severity. H63D was predicted to be highly pathogenic (CADD Score=24.40) and at the top 1% of the most deleterious mutations in the human genome.
The GWAS uncovered 4 novel genetic biomarkers with suggestive evidence of association with BIP (Unknown locus on Chr 17, MRC2 rs8072984, TMEM260 rs1124062 and KIF26B rs12747330). MRC2 rs8072984 is involved with remodeling of extracellular matrix, and defects in this could cause poor lung remodeling after bleomycin insult. TMEM260 rs1124062 and KIF26B rs12747330 are both involved with renal anomalies and development suggesting that defects in these could affect bleomycin clearance.
Conclusion
This is the first pharmacogenomics study of BIP in South East Asian patients, looking at both candidate gene and GWAS analyses. Our results suggest that iron regulation could be the driver in the etiopathogenesis of BIP, supporting the utility of H63D testing in the risk stratification of patients receiving Bleomycin. Moreover, GWAS analyses have discovered 4 novel markers that suggest an association with BIP, which will be further evaluated and should be independently replicated in a larger cohort.
Chng:Celgene: Consultancy, Honoraria, Other: Travel, accommodation, expenses, Research Funding; Takeda: Consultancy, Honoraria, Other: Travel, accommodation, expenses; Janssen: Consultancy, Honoraria, Other: Travel, accommodation, expenses, Research Funding; Merck: Research Funding; Amgen: Consultancy, Honoraria, Other: Travel, accommodation, expenses; Aslan: Research Funding.