scholarly journals Recipients of Myelotoxic Chemotherapy Have Increased Prevalence of Clonal Hematopoiesis of Indeterminate Potential (CHIP) with a Typical Distribution of Chip-Associated Mutations

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3841-3841
Author(s):  
Adam J Olszewski ◽  
Anna Dorota Chorzalska ◽  
Annette S. Kim ◽  
Peter J. Quesenberry ◽  
Mary L Lopresti ◽  
...  

Abstract Background: Recent studies (Coombs et al., Cell Stem Cell 2017) have identified presence of clonal hematopoiesis of indeterminate potential (CHIP) in samples of solid tumors. CHIP is more prevalent among cancer survivors who subsequently develop therapy-related myeloid neoplasm (Gillis et al., Lancet Oncol 2017; Takahashi et al., Lancet Oncol 2017; Gibson et al., J Clin Oncol 2017). However, the relationship between CHIP and exposure to myelotoxic chemotherapy delivered as part of treatment for solid tumor is uncertain. We hypothesized that CHIP is more prevalent among recipients of myelotoxic chemotherapy compared with age-matched population. Methods: In this prospective, cross-sectional study, we collected peripheral blood samples from survivors of breast cancer or lymphoma who had received anthracycline- and/or alkylator-containing chemotherapy as part of their curative cancer therapy. All subjects had to be clinically free of cancer, and not have any hematologic disorders or unexplained cytopenias. We recruited patients age 50 to 70, because according to published population datasets (Jaiswal et al. and Genovese et al., NEJM 2014) in a cohort with mean age of 60 the expected CHIP prevalence would be about 5%. To minimize any potential contamination by circulating tumor cells, we isolated genomic DNA from purified CD45+ cells. We determined presence of CHIP by next-generation sequencing using an Illumina TruSeq Custom Amplicon kit (MiSeq V2.2). The assay targeted 757 coding exons of 96 genes commonly mutated in hematologic malignancies, including 20 CHIP-defining genes. To establish presence of CHIP, we required a known pathogenic variant with variant allele fraction (VAF) ≥ 2%. According to a pre-specified statistical plan, assuming one-sided alpha of 0.05, the study had 80% power to reject the null hypothesis of baseline CHIP prevalence of 5% in a cohort with sample size of 80. Results: Among 80 enrolled subjects, median age was 62 years (interquartile range, 56-67). There were 78% women, and 88% of subjects were white non-Hispanic. Patients had received doxorubicin- and/or cyclophosphamide-containing adjuvant or curative chemotherapy for breast cancer (56%) or lymphoma (44%). Median time from completion of chemotherapy to enrollment was 27 months (interquartile range, 11-59). We have completed sequencing of 72 samples (updated analysis will be provided at the meeting). Mean coverage depth was 1418x (±224), and ≥200x coverage was achieved in a mean 91.4% (±1.8%) of target amplicons. We detected CHIP in 12 subjects (17%; binomial 95% confidence interval: 10-27%; P=.0002 for the null hypothesis test of 5% prevalence). Mean VAF for the CHIP mutations was 5.3% (range, 1.4% to 29.9%), and patients had up to 4 CHIP-associated mutations (Fig. A). The CHIP-associated mutations had a typical distribution with most common mutations in DNMT3A, ASXL1, SRSF2, and TET2 (Fig. B). There was only 1 TP53 mutation, previously suggested to associate with exposure to chemotherapy (Coombs et al., 2017). Potentially germline variants of unknown significance (VUS) were found in 78% of patients, at mean VAF 49% (Fig. C), most commonly in ATM (12%), NOTCH2 (7%), BCORL1, and DNMT3B (6% each). Additionally, 2 patients had low-VAF variants suspicious for CHIP: ATM c.6059G>A (3.0%); PRPF8 c.790T>C (VAF 2.3%). Presence of CHIP was not significantly associated with age (within the narrow age range in the study cohort), sex, race, type of cancer (breast or lymphoma), count of white cells, red cells, platelets, or time elapsed from completion of chemotherapy. Conclusions: We have detected a significantly increased, more than 3 times the expected value, prevalence of CHIP among cancer survivors who had received myelotoxic chemotherapy. However, the distribution of mutations was typical for CHIP, without previously suggested over-representation of TP53. Further research is ongoing to determine whether presence of CHIP is related to a direct mutagenic effect of chemotherapy or competitive advantage of pre-existing CHIP clones after the hematopoietic stress of chemotherapy. Our data indicate that an affordable next-generation sequencing screen may be useful for detection of CHIP in cancer patients who are planning adjuvant chemotherapy, or as a surveillance tool after such therapy, to predict the risk of a therapy-related myeloid neoplasm and optimize personalized treatment strategies. Disclosures Olszewski: TG Therapeutics: Research Funding; Genentech: Research Funding; Spectrum Pharmaceuticals: Consultancy, Research Funding. Kim:Aushon Biosciences: Consultancy; LabCorp, Inc.: Consultancy; Papgene, Inc: Consultancy. Fenton:Astellas Pharma US: Other: Spouse employment. Reagan:Alexion: Honoraria; Takeda Oncology: Research Funding; Pfizer: Research Funding.

2020 ◽  
Vol 111 (4) ◽  
pp. 1375-1384 ◽  
Author(s):  
Po‐Han Lin ◽  
Ming Chen ◽  
Li‐Wei Tsai ◽  
Chiao Lo ◽  
Tzu‐Chun Yen ◽  
...  

2019 ◽  
Vol 21 (2) ◽  
pp. 307-317 ◽  
Author(s):  
Sounak Gupta ◽  
Chad M. Vanderbilt ◽  
Paolo Cotzia ◽  
Javier A. Arias-Stella ◽  
Jason C. Chang ◽  
...  

Author(s):  
Kar-Yan Su ◽  
Wai-Leng Lee ◽  
Vinod Balasubramaniam

One in eight women will be diagnosed with breast cancer (BC) in their lifetime, resulting in over 2 million cases annually. BC is the most common cancer among women. Unfortunately, the etiology of majority of cases remains unknown. Recently, evidence has shown that the human microbiota plays an important role in health and disease. Intriguingly, studies have revealed the presence of microorganisms in human breast tissue, which was previously presumed to be sterile. Next-generation sequencing technologies have paved way for the investigation of breast microbiota, uncovering bacterial signatures that are associated with BC. Some of the bacterial species were found to possess pro-carcinogenic and/or anti-carcinogenic properties, suggesting that the breast microbiota has potentially crucial roles in maintenance of breast health. In this review, we summarize the recent findings on breast tissue microbiota and its interplay with BC. Bacterial signatures identified via next-generation sequencing as well as their impact on breast carcinogenesis and cancer therapies are reviewed. Correlation of breast tissue microbiota and other factors, such as geographical and racial differences, in BC is discussed. Additionally, we discuss the future directions of research on breast microbiota as well as its potential role in prevention, diagnosis and treatment of BC.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4359-4359
Author(s):  
Koji Sasaki ◽  
Rashmi Kanagal-Shamanna ◽  
Guillermo Montalban-Bravo ◽  
Rita Assi ◽  
Kiran Naqvi ◽  
...  

Abstract Introduction: Clearance of detected somatic mutations at complete response by next-generation sequencing is a prognostic marker for survival in patients with acute myeloid leukemia (AML). However, the impact of allelic burden and persistence of clonal hematopoiesis of indeterminate potential (CHIP)-associated mutations on survival remains unclear. The aim of this study is to evaluate the prognostic impact of allelic burden of CHIP mutations at diagnosis, and their persistence within 6 months of therapy. Methods: From February 1, 2012 to May 26, 2016, we reviewed 562 patients with newly diagnosed AML. Next-generation sequencing was performed on the bone marrow samples to detect the presence of CHIP-associated mutations defined as DNMT3A, TET2, ASXL1, JAK2 and TP53. Overall survival (OS) was defined as time period from the diagnosis of AML to the date of last follow-up or death. Univariate (UVA) and multivariate Cox proportional hazard regression (MVA) were performed to identify prognostic factors for OS with p value cutoff of 0.020 for the selection of variables for MVA. Landmark analysis at 6 months was performed for the evaluation of the impact of clearance of CHIP, FLT3-ITD, FLT3D835, and NPM1 mutations. Results: We identified 378 patients (74%) with AML with CHIP mutations; 134 patients (26%) with AML without CHIP mutations. The overall median follow-up of 23 months (range, 0.1-49.0). The median age at diagnosis was 70 years (range, 17-92) and 66 years (range, 20-87) in CHIP AML and non-CHIP AML, respectively (p =0.001). Of 371 patients and 127 patients evaluable for cytogenetic in CHIP AML and non-CHIP AML, 124 (33%) and 25 patients (20%) had complex karyotype, respectively (p= 0.004). Of 378 patients with CHIP AML, 183 patients (48%) had TET2 mutations; 113 (30%), TP53; 110 (29%), ASXL1; 109 (29%), DNMT3A; JAK2, 46 (12%). Of 378 patients, single CHIP mutations was observed in 225 patients (60%); double, 33 (9%); triple, 28 (7%); quadruple, 1 (0%). Concurrent FLT3-ITD mutations was detected in 47 patients (13%) and 12 patients (9%) in CHIP AML and non-CHIP AML, respectively (p= 0.287); FLT3-D835, 22 (6%) and 8 (6%), respectively (p= 0.932); NPM1 mutations, 62 (17%) and 13 (10%), respectively (p= 0.057). Of 183 patients with TET2-mutated AML, the median TET2 variant allele frequency (VAF) was 42.9% (range, 2.26-95.32); of 113 with TP53-mutated AML, the median TP53 VAF, 45.9% (range, 1.15-93.74); of 109 with ASXL1-mutated AML, the median ASXL1 VAF was 34.5% (range, 1.17-58.62); of 109 with DNMT3A-mutated AML, the median DNMT3A VAF was 41.8% (range, 1.02-91.66); of 46 with JAK2-mutated AML, the median JAK2 VAF was 54.4% (range, 1.49-98.52). Overall, the median OS was 12 months and 11 months in CHIP AML and non-CHIP AML, respectively (p= 0.564); 16 months and 5 months in TET2-mutated AML and non-TET2-mutated AML, respectively (p <0.001); 4 months and 13 months in TP53-mutated and non-TP53-mutated AML, respectively (p< 0.001); 17 months and 11 months in DNMT3A-mutated and non-DNMT3A-mutated AML, respectively (p= 0.072); 16 months and 11 months in ASXL1-mutated AML and non-ASXL1-mutated AML, respectively (p= 0.067); 11 months and 12 months in JAK2-murated and non-JAK2-mutated AML, respectively (p= 0.123). The presence and number of CHIP mutations were not a prognostic factor for OS by univariate analysis (p=0.565; hazard ratio [HR], 0.929; 95% confidence interval [CI], 0.722-1.194: p= 0.408; hazard ratio, 1.058; 95% confidence interval, 0.926-1.208, respectively). MVA Cox regression identified age (p< 0.001; HR, 1.036; 95% CI, 1.024-1.048), TP53 VAF (p= 0.007; HR, 1.009; 95% CI, 1.002-1.016), NPM1 VAF (p=0.006; HR, 0.980; 95% CI, 0.967-0.994), and complex karyotype (p<0.001; HR, 1.869; 95% CI, 1.332-2.622) as independent prognostic factors for OS. Of 33 patients with CHIP AML who were evaluated for the clearance of VAF by next generation sequencing , landmark analysis at 6 months showed median OS of not reached and 20.3 months in patients with and without CHIP-mutation clearance, respectively (p=0.310). Conclusion: The VAF of TP53 and NPM1 mutations by next generation sequencing can further stratify patients with newly diagnosed AML. Approximately, each increment of TP53 and NPM1 VAF by 1% is independently associated with 1% higher risk of death, and 2% lower risk of death, respectively. The presence of CHIP mutations except TP53 does not affect outcome. Disclosures Sasaki: Otsuka Pharmaceutical: Honoraria. Short:Takeda Oncology: Consultancy. Ravandi:Macrogenix: Honoraria, Research Funding; Seattle Genetics: Research Funding; Sunesis: Honoraria; Xencor: Research Funding; Jazz: Honoraria; Seattle Genetics: Research Funding; Abbvie: Research Funding; Macrogenix: Honoraria, Research Funding; Bristol-Myers Squibb: Research Funding; Orsenix: Honoraria; Abbvie: Research Funding; Jazz: Honoraria; Xencor: Research Funding; Orsenix: Honoraria; Sunesis: Honoraria; Amgen: Honoraria, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Research Funding; Astellas Pharmaceuticals: Consultancy, Honoraria; Amgen: Honoraria, Research Funding, Speakers Bureau; Astellas Pharmaceuticals: Consultancy, Honoraria. Kadia:BMS: Research Funding; Abbvie: Consultancy; Takeda: Consultancy; Jazz: Consultancy, Research Funding; Takeda: Consultancy; Amgen: Consultancy, Research Funding; Celgene: Research Funding; Novartis: Consultancy; Amgen: Consultancy, Research Funding; BMS: Research Funding; Jazz: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Novartis: Consultancy; Abbvie: Consultancy; Celgene: Research Funding. DiNardo:Karyopharm: Honoraria; Agios: Consultancy; Celgene: Honoraria; Medimmune: Honoraria; Bayer: Honoraria; Abbvie: Honoraria. Cortes:Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Astellas Pharma: Consultancy, Research Funding; Arog: Research Funding.


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