- Email: [email protected]
Predicting Thrombocytopenia in Patients With Breast Cancer Treated With Ado-trastuzumab Emtansine
Journal Pre-proof Predicting thrombocytopenia in breast cancer patients treated with ado-trastuzumab emtansine Natansh D. Modi, Michael J. Sorich, Andrew Rowland, Ross A. McKinnon, Bogda Koczwara, Michael D. Wiese, Ashley M. Hopkins PII:
S1526-8209(19)30715-3
DOI:
https://doi.org/10.1016/j.clbc.2019.10.001
Reference:
CLBC 1050
To appear in:
Clinical Breast Cancer
Received Date: 27 June 2019 Accepted Date: 6 October 2019
Please cite this article as: Modi ND, Sorich MJ, Rowland A, McKinnon RA, Koczwara B, Wiese MD, Hopkins AM, Predicting thrombocytopenia in breast cancer patients treated with ado-trastuzumab emtansine, Clinical Breast Cancer (2020), doi: https://doi.org/10.1016/j.clbc.2019.10.001. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Crown Copyright © 2019 Published by Elsevier Inc. All rights reserved.
Predicting thrombocytopenia in breast cancer patients treated with ado-trastuzumab emtansine Authors Natansh D Modi1, Michael J Sorich2#, Andrew Rowland2, Ross A McKinnon2, Bogda Koczwara2,3, Michael D Wiese1 and Ashley M Hopkins2# #-Ashley M Hopkins and Michael J Sorich contributed equally to this study
Affiliations 1) School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia. 2) College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia. 3) Department of Medical Oncology, Flinders Medical Centre, Adelaide, South Australia, Australia.
Corresponding author Natansh D Modi Email: [email protected] Address: 5D317, Flinders Medical Centre, Bedford Park, SA, 5042 Phone: +61 8 8201 5647
Predicting thrombocytopenia in breast cancer patients treated with ado-trastuzumab emtansine Authors Natansh D Modi1, Michael J Sorich2#, Andrew Rowland2, Ross A McKinnon2, Bogda Koczwara2,3, Michael D Wiese1 and Ashley M Hopkins2# #-Ashley M Hopkins and Michael J Sorich contributed equally to this study
Affiliations 1) School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia. 2) College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia. 3) Department of Medical Oncology, Flinders Medical Centre, Adelaide, South Australia, Australia.
Corresponding author Natansh D Modi Email: [email protected] Address: 5D317, Flinders Medical Centre, Bedford Park, SA, 5042 Phone: +61 8 8201 5647
Page | 1
Conflict of Interest Associate Professor Rowland, Professor Sorich and Professor McKinnon report grants from Pfizer, outside the submitted work. The authors have no other conflicts of interest to disclose.
Referees (Suggested Reviewers) 1) Alessandra
Fabi,
Istituto
Nazionale
Tumori
“Regina
Elena”,
[email protected] 2) Ghideon Ezaz, Beth Israel Deaconess Medical Center, [email protected] 3) George Dranitsaris, Augmentium Pharma Consulting Inc., [email protected]
Page | 2
MicroAbstract Currently there are no clinical prediction models of thrombocytopenia risk in breast cancer patients treated with ado-trastuzumab emtansine (T-DM1); a potentially serious toxicity associated with its use. This study presents a prediction model for grade ≥ 3 thrombocytopenia optimally defined by race and pre-treatment platelet count. The model can be used to interpret personalized risk-benefit ratios of T-DM1 initiation.
Keywords Thrombocytopenia; ado-trastuzumab emtansine; clinical prediction model; advanced breast cancer.
Page | 3
Abstract Introduction Thrombocytopenia is a common and potentially serious adverse event of ado-trastuzumab emtansine (T-DM1) use in advanced breast cancer (ABC), and risk factors have been minimally explored. The aim was to develop a clinical prediction model from clinicopathological data that allows quantification of personalized risks of thrombocytopenia from T-DM1.
Materials and Methods Data from the clinical trials EMILIA, TH3RESA and MARIANNE was pooled. Cox proportional hazard analysis was used to assess the association between pre-treatment clinicopathological data and grade ≥ 3 thrombocytopenia occurring within the first 365 days of T-DM1 use. A multivariable clinical prediction model was developed using a backwards elimination process.
Results Of 1620 participants, 141 (9%) experienced grade ≥ 3 thrombocytopenia. On univariable analysis, BMI, race, brain metastasis, platelet count, WBC count and concomitant corticosteroid use were significantly associated with grade ≥ 3 thrombocytopenia (P<0.05). The multivariable prediction model was optimally defined by race (Asian vs non-Asian) and platelet count (100-220 vs 220-300 vs >300 x109/L). Large discrimination between prognostic subgroups was observed; the highest risk subgroup (Asians & platelet count 100220 x109/L) had a 40% probability of grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy, compared to 2% for the lowest risk subgroup (non-Asians & platelet count >300 x109/L).
Page | 4
Conclusion A clinical prediction model, defined by race and pre-treatment platelet count, was able to discriminate subgroups with significantly different risks of grade ≥ 3 thrombocytopenia following T-DM1 initiation. The model allows improved interpretation of personalized risks and the risk-benefit ratio of T-DM1.
Page | 5
Introduction Overexpression of human epidermal growth factor receptor 2 (HER2) occurs in up to 30% of breast cancer (BC) patients and without anti-HER2 therapy is associated with aggressive tumour growth and poor prognosis.
1
Ado-trastuzumab emtansine (T-DM1) is an antibody-
drug conjugate (ADC) targeting HER2 to deliver both the anti-HER2 actions of trastuzumab and the cytotoxic actions of DM1 (emtansine).
2
T-DM1 is a recommended second-line and
later treatment option for HER2 positive advanced breast cancer (ABC),
1-3
and improves
overall survival (OS) and progression-free survival (PFS) compared to previously recommended therapies (e.g lapatinib plus capecitabine). 2, 3 T-DM1 is also being explored as a curative treatment (neo-adjuvant setting) for HER2 positive breast cancer. 4 Thrombocytopenia, characterized by low platelet count is a potentially serious adverse event that may be induced by T-DM1 therapy. thrombocytopenia is approximately 19%.
5-7
2
Following T-DM1 initiation incidence of
In comparison, occurrence of thrombocytopenia
is infrequent with trastuzumab therapy, even when used in combination with docetaxel/paclitaxel.
7
It is hypothesized that thrombocytopenia is induced by the emtansine
component of the ADC,
8
however, the mechanism by which T-DM1 induces
thrombocytopenia has not been fully elucidated. The KADCYLA (brand name of T-DM1) drug label indicates that Asians have a higher incidence of thrombocytopenia.
9
The drug
label also advises caution of T-DM1 use in patients concomitantly using anticoagulants and with decreased platelet count (<100x109/L)
9
; but overall, the risk factors and the
personalized risks of developing thrombocytopenia following T-DM1 initiation have been minimally explored. If a clinical prediction model of thrombocytopenia risk from T-DM1 therapy can be developed using routinely collected clinicopathological data, it will enable clinicians and patients to better interpret personalized risk and risk-benefit ratio of T-DM1 therapy. Page | 6
The aim of this study was to develop a clinical prediction model from clinicopathological data that allows the quantification of personalized risks of thrombocytopenia following TDM1 initiation.
Methods Patient Population Individual participant data (IPD) from the phase III clinical trials EMILIA [NCT00829166], 5, 10
TH3RESA [NCT01419197],
this
retrospective
pooled
6, 11
and MARIANNE [NCT01120184]
secondary
analysis
study.
IPD
7, 12
was
was utilized in accessed
via
clinicalstudydatarequest.com. Secondary analysis of anonymized IPD was deemed negligible risk research by the Southern Adelaide Local Health Network, Office for Research and Ethics and was exempt from review. EMILIA included patients with HER2 positive, unresectable, locally advanced or metastatic BC with documented disease progression to trastuzumab and a taxane. Patients were randomly assigned 1:1 to either lapatinib plus capecitabine, or T-DM1. 5 TH3RESA included patients with HER2-positive, unresectable, locally advanced, recurrent or metastatic BC with documented disease progression to trastuzumab and lapatinib in the advanced setting and had received a taxane in any setting. Patients were randomly assigned 1:2 to a treatment of physician’s choice, or T-DM1. 11 MARIANNE included patients with HER2 positive, unresectable, progressive or recurrent locally advanced, or previously untreated metastatic BC – treatment naïve in the advanced setting. Patients were randomly assigned 1:1:1 to trastuzumab plus a taxane, T-DM1 plus placebo, or T-DM1 plus pertuzumab. 7 All studies dosed T-DM1 at 3.6 mg/kg via intravenous infusion every 21 days.
Page | 7
Inclusion criteria for each of EMILIA, TH3RESA and MARIANNE included a pre-treatment platelet count ≥ 100x109/L. IPD utilized in this retrospective pooled analysis consisted of HER2 positive advanced breast cancer patients treated with T-DM1 in any setting (including in combination with pertuzumab).
Predictors and Outcomes Adverse events were reported in EMILIA using NCI CTCAE (Common Terminology Criteria for Adverse Events) version 3.0, 5 whereas, TH3RESA and MARIANNE used NCI CTCAE version 4.0.
7, 11
The primary assessed outcome was grade ≥ 3 thrombocytopenia
occurring within 365 days of T-DM1 initiation. Assessed covariates were pre-selected based on prior evidence and biological plausibility. Pre-treatment characteristics considered were age (years), race (Asian or Non-Asian), body mass index (BMI; Normal, Overweight or Underweight), ECOG performance status (ECOG PS), presence of brain metastasis, platelet count, white blood cell count (WBC) above or below the lower limit of normal (LLN), concurrent diabetes mellitus, any prior taxane use, and concomitant usage of aspirin, a beta-blocker, furosemide or a corticosteroid.
Statistical Analysis Univariable Cox proportional hazard analysis was used to assess the association between potential predictors and grade ≥ 3 thrombocytopenia occurring within the first 365 days of TDM1 therapy. Associations were reported as hazard ratios (HR) with 95% confidence intervals (95%CI). Statistical significance was set at a threshold of P<0.05 and was determined via the likelihood ratio test. Visual checks were used to assess potential nonlinear effects of continuous variables and cut-point appropriateness. All analyses were stratified by study. Page | 8
A clinical prediction model was developed using a backwards elimination process. Initially all statistically significant univariates were included in the model. To facilitate the clinical use of the model, the backwards elimination process was conducted with a focus on selecting the minimal number of predictors that maintained prediction performance. Prediction performance was assessed via the concordance statistic (c-statistic). Variables were excluded from the model via a stepwise backwards elimination of non-significant variables (P>0.05) and variables that did not increase the c-statistic by 0.02. Kaplan-Meier analysis was used for plotting and estimating probabilities. All data analysis was conducted using R version 3.4.3. 13
Results Patient Population Data was available from 1620 patients (Supplementary Table 1), of which 309 (19%) experienced any grade thrombocytopenia within the first 365 days of T-DM1 therapy, including 141 events (9% of all patients) of grade ≥ 3 (Supplementary Table 2). Amongst all episodes of thrombocytopenia, 43 (15%) required a dose interruption, 67 (22%) required a dose reduction, and 47 (15%) required T-DM1 withdrawal (Supplementary Table 2). Median time to grade ≥ 3 thrombocytopenia was 12 days and 70% of grade ≥ 3 events occurred within the first 42 days of T-DM1 therapy.
Univariate Analysis The relationship between pre-treatment platelet count and grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy was best described by cut-offs of 100-220 vs 220-300 vs >300 x109/L. Univariable Cox proportional hazard analysis identified Asian race, presence of brain metastasis, pre-treatment platelet count 100-220 & 220-300 x109/L, WBC below the LLN and concomitant use of corticosteroids as significantly associated with an increased risk Page | 9
of grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy (P <0.05; Table 1). Overweight BMI was associated with a decreased risk (P <0.05; Table 1).
Clinical prediction model The backwards elimination process resulted in a clinical prediction model for grade ≥ 3 thrombocytopenia following T-DM1 initiation that was optimally defined by race (Asian vs non-Asian) and pre-treatment platelet count (100-220 vs 220-300 vs >300 x109/L) (Table 2). The discriminative performance (c-statistic) of the model was 0.765 (Table 2 and Figure 1). As presented in Figure 1, of the six defined subgroups, the highest risk of grade ≥ 3 thrombocytopenia was in Asians with a platelet count 100-220 x109/L and the lowest risk subgroup was non-Asians with a platelet count >300 x109/L. The probability of having grade ≥ 3 thrombocytopenia within the first 42 days of T-DM1 therapy in the highest risk subgroup was 29% [95% CI; 20%-36%], compared to 1% [0%-2%] for the lowest risk subgroup (Figure 1). The probability of having grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy in the highest risk subgroup was 40% [30%-49%], compared to 2% [0%-4%] for the lowest risk subgroup (Figure 1). Supplementary Figure 1 presents the Kaplan-Meier plots for risk of grade ≥ 3 thrombocytopenia following T-DM1 initiation by defined pre-treatment subgroups.
Discussion This study used large pooled data to develop and present the first clinical prediction model of grade ≥ 3 thrombocytopenia following T-DM1 initiation in HER2 positive ABC. The model defined the risk of grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy, which ranged from 2 to 40% according to patient race (Asian vs non-Asian) and pretreatment platelet count (100-220 vs 220-300 vs >300 x109/L).
Page | 10
This study had a focus on facilitating the clinical use of the developed model, including using routinely available clinicopathologic data (recognizing parsimony can inhibit the clinical use of models) and avoiding the need to use complex mathematical calculations within the clinic.14 For example a least absolute shrinkage and selection operator (LASSO) derived model may result in greater prediction performance, however simplicity of model use was prioritized. 15 The derived clinical prediction model for grade ≥ 3 thrombocytopenia following T-DM1 initiation was optimally defined by race (Asian vs non-Asian) and pre-treatment platelet count (100-220 vs 220-300 vs >300 x109/L). The developed model aligns with the preliminary data presented in the KADCYLA drug label,
9
and other literature which
indicates Asian’s are more susceptible to the adverse effects of some chemotherapies.
16, 17
The current study additionally provides patients and clinicians the ability to interpret personalized risks (Figure 1). Furthermore, presence of brain metastasis, WBC count and concomitant corticosteroids use were also identified as pre-treatment characteristics significantly associated with grade ≥ 3 thrombocytopenia – outlining the need for caution within these patient groups. Prior research indicates no statistical difference in T-DM1 pharmacokinetics according to race, 18 and such evidence suggests that the higher risk of developing thrombocytopenia from T-DM1 in Asians is likely pharmacodynamically driven. Uppal et al.
8
outlined a FcγRIIa
dependent mechanism of T-DM1 internalization which results in thrombocytopenia via impairment of megakaryocyte differentiation. Ethnic variation between Asians and nonAsians in the FcγRIIa allotype has been described, which may explain the higher risk of thrombocytopenia with T-DM1 in Asians. 19 However, further testing and pharmacodynamic analysis is needed to fully ascertain as to why Asians as a group have a higher risk of thrombocytopenia with T-DM1. Page | 11
Randomized controlled trials are the gold standard for evaluating the benefits of therapeutic interventions, however strict inclusion can limit generalizability of results.
20
Opposing this,
adverse event data is rigorously collected within trials facilitating the development of welldefined prediction models,
21
which is demonstrated by the development of a strong
performing prediction model in this study (c = 0.765).
22
Further, this study pooled large
(n=1620) and high quality data from three trials (EMILIA, TH3RESA and MARIANNE) to increase both study power and generalizability. Ultimately this allowed the development of a well performing and highly discriminatory clinical prediction model which could be used by patients and clinicians to better interpret the risk-benefit ratio of T-DM1 than is currently possible in ABC patients. Nonetheless, the emerging advent of large electronic health record platforms will present future opportunities to externally validate the presented model within observational datasets of patients using T-DM1 in routine clinical care – which in the future may include T-DM1 use as a neo-adjuvant treatment. While the presented model was well powered, there were few participants (n=26) concomitantly treated with anticoagulants at baseline, and therefore this cautionary factor in the drug label was not evaluated – although the rationale as to why anticoagulant therapy will lower platelets in unclear. The available data predefined all Asian ancestries to one subgroup, and it is acknowledged that substantial variability may occur between Asian’s ancestries (e.g. weight and metabolic differences) which ultimately may impact a specific individual’s risk of T-DM1 induced thrombocytopenia. The study clearly identified race and pre-treatment platelet count to be distinct predictors that allow the interpretation of the risk of grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy. The presented probabilities are applicable to patients with HER2 positive ABC who align with the inclusion criteria of EMILIA, TH3RESA and MARIANNE. This study demonstrated a difference of 23% (29 versus 6) between the highest and lowest risk Page | 12
subgroup for the probability of grade ≥ 3 thrombocytopenia within the first 42 days of TDM1 therapy (Figure 1). Furthermore, a difference of 27% (40 versus 13) was demonstrated at 365 days (Figure 1). Such a large and substantial discrimination between the subgroups exemplifies the ability of the developed prediction model to inform on clinically significant difference in thrombocytopenia risk to clinicians and patients considering the T-DM1 use.
Conclusion In conclusion, a clinical prediction model based upon race and pre-treatment platelet count was well performing and highly discriminatory for the prediction of grade ≥ 3 thrombocytopenia following treatment with T-DM1. The model can be used to provide personalized risks of thrombocytopenia and enable patients and clinicians to interpret the individual risk-benefit ratio of T-DM1 initiation.
Clinical Practice Points •
Thrombocytopenia is a common and potentially serious adverse event associated with ado-trastuzumab emtansine (T-DM1) use.
•
Risk factors for T-DM1 induced thrombocytopenia have been minimally explored, and there are no existing clinical prediction models to provide personalized risks.
•
This study is the first to present a prediction model for grade ≥ 3 thrombocytopenia following T-DM1 initiation.
•
A clinical prediction model optimally defined by race and pre-treatment platelet count was able to discriminate patient subgroups with significantly different risks of grade ≥ 3 thrombocytopenia following T-DM1 initiation.
•
The model can be used to provide personalized risks of thrombocytopenia and enable patients and clinicians to better interpret the risk-benefit ratio of T-DM1 initiation.
Page | 13
Page | 14
Funding Research undertaken with the financial support of Cancer Council South Australia’s Beat Cancer Project on behalf of its donors and the State Government through the Department of Health (Grant ID: 1159924 and 1127220). R.A.M receives financial support from the Cancer Council’s Beat Cancer Project with support from their donors and the South Australian Department of Health. A.R. is supported by a Beat Cancer Mid-Career Research Fellowship from Cancer Council SA. B.K is supported by the National Breast Cancer Foundation Practitioner grant. A.M.H is a researcher funded by a Postdoctoral Fellowship from the National Breast Cancer Foundation, Australia (PF-17-007).
Page | 15
References 1. 2. 3.
4.
5. 6.
7.
8.
9. 10.
11.
12.
13. 14.
15. 16.
17.
18.
Iqbal N, Iqbal N. Human Epidermal Growth Factor Receptor 2 (HER2) in Cancers: Overexpression and Therapeutic Implications. Molecular Biology International. 2014;2014:9. Diéras V, Bachelot T. The success story of trastuzumab emtansine, a targeted therapy in HER2-positive breast cancer. Targeted Oncology. 2014;9:111-122. Yan H, Yu K, Zhang K, Liu L, Li Y. Efficacy and safety of trastuzumab emtansine (T-DM1) in the treatment of HER2-positive metastatic breast cancer (MBC): a meta-analysis of randomized controlled trial. Oncotarget. 2017;8:102458-102467. Hurvitz SA, Martin M, Symmans WF, et al. Neoadjuvant trastuzumab, pertuzumab, and chemotherapy versus trastuzumab emtansine plus pertuzumab in patients with HER2-positive breast cancer (KRISTINE): a randomised, open-label, multicentre, phase 3 trial. The Lancet Oncology. 2018;19:115-126. Verma S, Miles D, Gianni L, et al. Trastuzumab Emtansine for HER2-Positive Advanced Breast Cancer. New England Journal of Medicine. 2012;367:1783-1791. Krop IE, Kim S-B, González-Martín A, et al. Trastuzumab emtansine versus treatment of physician's choice for pretreated HER2-positive advanced breast cancer (TH3RESA): a randomised, open-label, phase 3 trial. The Lancet Oncology. 2014;15:689-699. Perez EA, Barrios C, Eiermann W, et al. Trastuzumab Emtansine With or Without Pertuzumab Versus Trastuzumab Plus Taxane for Human Epidermal Growth Factor Receptor 2–Positive, Advanced Breast Cancer: Primary Results From the Phase III MARIANNE Study. Journal of Clinical Oncology. 2016;35:141-148. Uppal H, Doudement E, Mahapatra K, et al. Potential Mechanisms for Thrombocytopenia Development with Trastuzumab Emtansine (T-DM1). Clinical Cancer Research. 2015;21:123. KADCYLA (ado-trastuzumab emtansine) injection [package insert]: South San Francisco, CA: Genentech Inc; 2013. Diéras V, Miles D, Verma S, et al. Trastuzumab emtansine versus capecitabine plus lapatinib in patients with previously treated HER2-positive advanced breast cancer (EMILIA): a descriptive analysis of final overall survival results from a randomised, open-label, phase 3 trial. Lancet Oncol. 2017;18:732-742. Krop IE, Kim S-B, Martin AG, et al. Trastuzumab emtansine versus treatment of physician's choice in patients with previously treated HER2-positive metastatic breast cancer (TH3RESA): final overall survival results from a randomised open-label phase 3 trial. The Lancet Oncology. 2017;18:743-754. Perez EA, Barrios CH, Eiermann W, et al. Phase III, randomized study of first-line trastuzumab emtansine (T-DM1) ± pertuzumab (P) vs. trastuzumab + taxane (HT) treatment of HER2-positive MBC: Final overall survival (OS) and safety from MARIANNE. Journal of Clinical Oncology. 2017;35:1003-1003. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing - R version 3.4.32017. Steyerberg EW, Eijkemans MJC, Harrell Jr FE, Habbema JDF. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Statistics in Medicine. 2000;19:1059-1079. Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal. 2014;35:1925-1931. O'Donnell PH, Dolan ME. Cancer pharmacoethnicity: ethnic differences in susceptibility to the effects of chemotherapy. Clinical cancer research : an official journal of the American Association for Cancer Research. 2009;15:4806-4814. Phan VH, Moore MM, McLachlan AJ, Piquette-Miller M, Xu H, Clarke SJ. Ethnic differences in drug metabolism and toxicity from chemotherapy. Expert Opinion on Drug Metabolism & Toxicology. 2009;5:243-257. Lu D, Girish S, Gao Y, et al. Population pharmacokinetics of trastuzumab emtansine (TDM1), a HER2-targeted antibody-drug conjugate, in patients with HER2-positive metastatic
Page | 16
19.
20.
21. 22.
breast cancer: clinical implications of the effect of covariates. Cancer Chemother Pharmacol. 2014;74:399-410. Osborne JM, Chacko GW, Brandt JT, Anderson CL. Ethnic variation in frequency of an allelic polymorphism of human Fcγ RIIA determined with allele specific oligonucleotide probes. Journal of Immunological Methods. 1994;173:207-217. Kabisch M, Ruckes C, Seibert-Grafe M, Blettner M. Randomized controlled trials: part 17 of a series on evaluation of scientific publications. Deutsches Arzteblatt international. 2011;108:663-668. Phillips R, Hazell L, Sauzet O, Cornelius V. Analysis and reporting of adverse events in randomised controlled trials: a review. BMJ Open. 2019;9:e024537. Révész D, Engelhardt EG, Tamminga JJ, et al. Decision support systems for incurable nonsmall cell lung cancer: a systematic review. BMC medical informatics and decision making. 2017;17:144-144.
Page | 17
Tables and Figures Table 1: Univariable associations between pre-treatment characteristics and risk of T-DM1 induced grade ≥ 3 thrombocytopenia in ABC patients
Events/Subj (%) HR 95% CI P-value Age (years) 1 0.99 to 1.02 0.934 Race <0.001 Non-Asian 70/1306 (5%) 1 Asian 71/314 (23%) 5.11 3.67 to 7.14 Body Mass Index 0.002 Normal 77/707 (11%) 1 Overweight 56/846 (7%) 0.56 0.40 to 0.79 Underweight 5/37 (14%) 1.29 0.52 to 3.18 ECOG PS 1.28 0.93 to 1.77 0.127 Brain Metastasis No 125/1524 (8%) Yes 16/94 (17%) 2.08 1.22 to 3.54 0.007 Platelet Count (x109/L) <0.001 > 300 10/448 (2%) 1 220-300 45/697 (6%) 2.86 1.44 to 5.67 100-220 86/475 (18%) 8.44 4.39 to 16.3 White Blood Cells 0.002 ≥ LLN 117/1469 (8%) 1 < LLN 24/150 (16%) 1.98 1.27 to 3.07 Prior taxane (all settings) No 32/492 (7%) Yes 109/1128 (10%) 1.06 0.59 to 1.90 0.854 Furosemide No 135/1582 (9%) Yes 6/38 (16%) 1.78 0.78 to 4.05 0.167 Aspirin No 133/1548 (9%) Yes 8/72 (11%) 1.33 0.65 to 2.71 0.436 Corticosteroids No 125/1499 (8%) Yes 16/121 (13%) 1.71 1.01 to 2.88 0.044 Beta-blocker No 129/1433 (9%) Yes 12/187 (6%) 0.71 0.39 to 1.27 0.247 Diabetes mellitus No 132/1507 (9%) Yes 9/113 (8%) 1.15 0.58 to 2.27 0.694 CI= Confidence interval, HR= Hazard ratio, Subj= Number of subjects, ECOG PS= Eastern cooperative oncology group performance status, LLN= Lower limit of normal
Page | 18
Table 2: Multivariable clinical prediction model of grade ≥ 3 thrombocytopenia following T-DM1 initiation in ABC patients
HR 95% CI P-value Asian race 4.31 3.08 to 6.03 <0.001 Platelet Count (x109/L) <0.001 220-300 2.65 1.34 to 5.26 100-220 6.9 3.57 to 13.3 CI=confidence interval, HR=hazard ratio Notes: 1620 subjects and 141 (8.7%) events. C-statistic = 0.765, AIC = 1594.7
Page | 19
Figure 1: Percentage risks of developing grade ≥ 3 thrombocytopenia within the first 42 and 365 days of T-DM1 therapy according to race and pre-treatment platelet count
Page | 20
Supplementary Tables and Figures Supplementary Table 1: Summary of pre-treatment characteristic for patients who received T-DM1 by study
The actual treatment given Pertuzumab + Trastuzumab emtansine Placebo + Trastuzumab emtansine Trastuzumab emtansine Study EMILIA MARIANNE TH3RESA Age (years) Race Asian Non-Asian Body mass index Normal Overweight Underweight Missing ECOG PS 0 1 2 Missing Brain Metastasis No Yes Missing 9
Platelets (x10 /L) White Blood Cells ≥ LLN < LLN Missing Prior taxane (all settings) Furosemide No Yes Aspirin No Yes Corticosteroids No Yes Beta-blocker No Yes Diabetes Mellitus No Yes
Total No. 1,620
EMILIA No. 490
MARIANNE No. 727
TH3RESA No. 403
366 (23%)
0 (0%)
366 (50%)
0 (0%)
361 (22%)
0 (0%)
361 (50%)
0 (0%)
893 (55%)
490 (100%)
0 (0%)
403 (100%)
490 (30%) 727 (45%) 403 (25%) 52 (44 - 60)
490 (100%) 0 (0%) 0 (0%) 53 (45 - 60)
0 (0%) 727 (100%) 0 (0%) 52 (43 - 61)
0 (0%) 0 (0%) 403 (100%) 53 (46 - 60)
314 (19%) 1306 (81%)
92 (19%) 398 (81%)
165 (23%) 562 (77%)
57 (14%) 346 (86%)
707 (44%) 846 (52%) 37 (2%) 30 (2%)
208 (42%) 266 (54%) 8 (2%) 8 (2%)
309 (43%) 390 (54%) 22 (3%) 6 (1%)
190 (47%) 190 (47%) 7 (2%) 16 (4%)
950 (59%) 644 (40%) 23 (1%) 3 (0%)
298 (61%) 191 (39%) 0 (0%) 1 (0%)
473 (65%) 253 (35%) 1 (0%) 0 (0%)
179 (44%) 200 (50%) 22 (5%) 2 (0%)
1,524 (94%) 94 (6%) 2 (0%) 255 (212 307)
450 (92%) 40 (8%) 0 (0%) 250 (208 303)
716 (98%) 10 (1%) 1 (0%) 257 (216 306)
358 (89%) 44 (11%) 1 (0%) 260 (209 313)
1,469 (91%) 150 (9%) 1 (0%) 492 (30%)
434 (89%) 56 (11%) 0 (0%) 2 (0%)
666 (92%) 60 (8%) 1 (0%) 489 (67%)
369 (92%) 34 (8%) 0 (0%) 1 (0%)
1,582 (98%) 38 (2%)
477 (97%) 13 (3%)
718 (99%) 9 (1%)
387 (96%) 16 (4%)
1,548 (96%) 72 (4%)
468 (96%) 22 (4%)
692 (95%) 35 (5%)
388 (96%) 15 (4%)
1,499 (93%) 121 (7%)
454 (93%) 36 (7%)
685 (94%) 42 (6%)
360 (89%) 43 (11%)
1,433 (88%) 187 (12%)
436 (89%) 54 (11%)
650 (89%) 77 (11%)
347 (86%) 56 (14%)
1,507 (93%) 113 (7%)
478 (98%) 12 (2%)
660 (91%) 67 (9%)
369 (92%) 34 (8%)
Data are median (IQR) or number of patients (%). ECOG PS = Eastern cooperative oncology group performance status, LLN = lower limit of normal
Page | 21
Supplementary Table 2: Incidence of thrombocytopenia by study
Total
EMILIA
MARIANNE
n=1,620 n=490 n=727 83 (11%) Any Grade 309 (19%) 149 (30%) Grade ≥ 2 241 (15%) 113 (23%) 75 (10%) Grade ≥ 3 141 (9%) 70 (14%) 48 (7%) Grade ≥ 4 30 (2%) 13 (3%) 12 (2%) Data are median (IQR) or number of patients (%).
Page | 22
TH3RESA n=403 77 (19%) 53 (13%) 23 (6%) 5 (1%)
Supplementary Figure 1: Kaplan Meier plots of cumulative risk of thrombocytopenia
Page | 23
S1526-8209(19)30715-3
DOI:
https://doi.org/10.1016/j.clbc.2019.10.001
Reference:
CLBC 1050
To appear in:
Clinical Breast Cancer
Received Date: 27 June 2019 Accepted Date: 6 October 2019
Please cite this article as: Modi ND, Sorich MJ, Rowland A, McKinnon RA, Koczwara B, Wiese MD, Hopkins AM, Predicting thrombocytopenia in breast cancer patients treated with ado-trastuzumab emtansine, Clinical Breast Cancer (2020), doi: https://doi.org/10.1016/j.clbc.2019.10.001. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Crown Copyright © 2019 Published by Elsevier Inc. All rights reserved.
Predicting thrombocytopenia in breast cancer patients treated with ado-trastuzumab emtansine Authors Natansh D Modi1, Michael J Sorich2#, Andrew Rowland2, Ross A McKinnon2, Bogda Koczwara2,3, Michael D Wiese1 and Ashley M Hopkins2# #-Ashley M Hopkins and Michael J Sorich contributed equally to this study
Affiliations 1) School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia. 2) College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia. 3) Department of Medical Oncology, Flinders Medical Centre, Adelaide, South Australia, Australia.
Corresponding author Natansh D Modi Email: [email protected] Address: 5D317, Flinders Medical Centre, Bedford Park, SA, 5042 Phone: +61 8 8201 5647
Predicting thrombocytopenia in breast cancer patients treated with ado-trastuzumab emtansine Authors Natansh D Modi1, Michael J Sorich2#, Andrew Rowland2, Ross A McKinnon2, Bogda Koczwara2,3, Michael D Wiese1 and Ashley M Hopkins2# #-Ashley M Hopkins and Michael J Sorich contributed equally to this study
Affiliations 1) School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia. 2) College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia. 3) Department of Medical Oncology, Flinders Medical Centre, Adelaide, South Australia, Australia.
Corresponding author Natansh D Modi Email: [email protected] Address: 5D317, Flinders Medical Centre, Bedford Park, SA, 5042 Phone: +61 8 8201 5647
Page | 1
Conflict of Interest Associate Professor Rowland, Professor Sorich and Professor McKinnon report grants from Pfizer, outside the submitted work. The authors have no other conflicts of interest to disclose.
Referees (Suggested Reviewers) 1) Alessandra
Fabi,
Istituto
Nazionale
Tumori
“Regina
Elena”,
[email protected] 2) Ghideon Ezaz, Beth Israel Deaconess Medical Center, [email protected] 3) George Dranitsaris, Augmentium Pharma Consulting Inc., [email protected]
Page | 2
MicroAbstract Currently there are no clinical prediction models of thrombocytopenia risk in breast cancer patients treated with ado-trastuzumab emtansine (T-DM1); a potentially serious toxicity associated with its use. This study presents a prediction model for grade ≥ 3 thrombocytopenia optimally defined by race and pre-treatment platelet count. The model can be used to interpret personalized risk-benefit ratios of T-DM1 initiation.
Keywords Thrombocytopenia; ado-trastuzumab emtansine; clinical prediction model; advanced breast cancer.
Page | 3
Abstract Introduction Thrombocytopenia is a common and potentially serious adverse event of ado-trastuzumab emtansine (T-DM1) use in advanced breast cancer (ABC), and risk factors have been minimally explored. The aim was to develop a clinical prediction model from clinicopathological data that allows quantification of personalized risks of thrombocytopenia from T-DM1.
Materials and Methods Data from the clinical trials EMILIA, TH3RESA and MARIANNE was pooled. Cox proportional hazard analysis was used to assess the association between pre-treatment clinicopathological data and grade ≥ 3 thrombocytopenia occurring within the first 365 days of T-DM1 use. A multivariable clinical prediction model was developed using a backwards elimination process.
Results Of 1620 participants, 141 (9%) experienced grade ≥ 3 thrombocytopenia. On univariable analysis, BMI, race, brain metastasis, platelet count, WBC count and concomitant corticosteroid use were significantly associated with grade ≥ 3 thrombocytopenia (P<0.05). The multivariable prediction model was optimally defined by race (Asian vs non-Asian) and platelet count (100-220 vs 220-300 vs >300 x109/L). Large discrimination between prognostic subgroups was observed; the highest risk subgroup (Asians & platelet count 100220 x109/L) had a 40% probability of grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy, compared to 2% for the lowest risk subgroup (non-Asians & platelet count >300 x109/L).
Page | 4
Conclusion A clinical prediction model, defined by race and pre-treatment platelet count, was able to discriminate subgroups with significantly different risks of grade ≥ 3 thrombocytopenia following T-DM1 initiation. The model allows improved interpretation of personalized risks and the risk-benefit ratio of T-DM1.
Page | 5
Introduction Overexpression of human epidermal growth factor receptor 2 (HER2) occurs in up to 30% of breast cancer (BC) patients and without anti-HER2 therapy is associated with aggressive tumour growth and poor prognosis.
1
Ado-trastuzumab emtansine (T-DM1) is an antibody-
drug conjugate (ADC) targeting HER2 to deliver both the anti-HER2 actions of trastuzumab and the cytotoxic actions of DM1 (emtansine).
2
T-DM1 is a recommended second-line and
later treatment option for HER2 positive advanced breast cancer (ABC),
1-3
and improves
overall survival (OS) and progression-free survival (PFS) compared to previously recommended therapies (e.g lapatinib plus capecitabine). 2, 3 T-DM1 is also being explored as a curative treatment (neo-adjuvant setting) for HER2 positive breast cancer. 4 Thrombocytopenia, characterized by low platelet count is a potentially serious adverse event that may be induced by T-DM1 therapy. thrombocytopenia is approximately 19%.
5-7
2
Following T-DM1 initiation incidence of
In comparison, occurrence of thrombocytopenia
is infrequent with trastuzumab therapy, even when used in combination with docetaxel/paclitaxel.
7
It is hypothesized that thrombocytopenia is induced by the emtansine
component of the ADC,
8
however, the mechanism by which T-DM1 induces
thrombocytopenia has not been fully elucidated. The KADCYLA (brand name of T-DM1) drug label indicates that Asians have a higher incidence of thrombocytopenia.
9
The drug
label also advises caution of T-DM1 use in patients concomitantly using anticoagulants and with decreased platelet count (<100x109/L)
9
; but overall, the risk factors and the
personalized risks of developing thrombocytopenia following T-DM1 initiation have been minimally explored. If a clinical prediction model of thrombocytopenia risk from T-DM1 therapy can be developed using routinely collected clinicopathological data, it will enable clinicians and patients to better interpret personalized risk and risk-benefit ratio of T-DM1 therapy. Page | 6
The aim of this study was to develop a clinical prediction model from clinicopathological data that allows the quantification of personalized risks of thrombocytopenia following TDM1 initiation.
Methods Patient Population Individual participant data (IPD) from the phase III clinical trials EMILIA [NCT00829166], 5, 10
TH3RESA [NCT01419197],
this
retrospective
pooled
6, 11
and MARIANNE [NCT01120184]
secondary
analysis
study.
IPD
7, 12
was
was utilized in accessed
via
clinicalstudydatarequest.com. Secondary analysis of anonymized IPD was deemed negligible risk research by the Southern Adelaide Local Health Network, Office for Research and Ethics and was exempt from review. EMILIA included patients with HER2 positive, unresectable, locally advanced or metastatic BC with documented disease progression to trastuzumab and a taxane. Patients were randomly assigned 1:1 to either lapatinib plus capecitabine, or T-DM1. 5 TH3RESA included patients with HER2-positive, unresectable, locally advanced, recurrent or metastatic BC with documented disease progression to trastuzumab and lapatinib in the advanced setting and had received a taxane in any setting. Patients were randomly assigned 1:2 to a treatment of physician’s choice, or T-DM1. 11 MARIANNE included patients with HER2 positive, unresectable, progressive or recurrent locally advanced, or previously untreated metastatic BC – treatment naïve in the advanced setting. Patients were randomly assigned 1:1:1 to trastuzumab plus a taxane, T-DM1 plus placebo, or T-DM1 plus pertuzumab. 7 All studies dosed T-DM1 at 3.6 mg/kg via intravenous infusion every 21 days.
Page | 7
Inclusion criteria for each of EMILIA, TH3RESA and MARIANNE included a pre-treatment platelet count ≥ 100x109/L. IPD utilized in this retrospective pooled analysis consisted of HER2 positive advanced breast cancer patients treated with T-DM1 in any setting (including in combination with pertuzumab).
Predictors and Outcomes Adverse events were reported in EMILIA using NCI CTCAE (Common Terminology Criteria for Adverse Events) version 3.0, 5 whereas, TH3RESA and MARIANNE used NCI CTCAE version 4.0.
7, 11
The primary assessed outcome was grade ≥ 3 thrombocytopenia
occurring within 365 days of T-DM1 initiation. Assessed covariates were pre-selected based on prior evidence and biological plausibility. Pre-treatment characteristics considered were age (years), race (Asian or Non-Asian), body mass index (BMI; Normal, Overweight or Underweight), ECOG performance status (ECOG PS), presence of brain metastasis, platelet count, white blood cell count (WBC) above or below the lower limit of normal (LLN), concurrent diabetes mellitus, any prior taxane use, and concomitant usage of aspirin, a beta-blocker, furosemide or a corticosteroid.
Statistical Analysis Univariable Cox proportional hazard analysis was used to assess the association between potential predictors and grade ≥ 3 thrombocytopenia occurring within the first 365 days of TDM1 therapy. Associations were reported as hazard ratios (HR) with 95% confidence intervals (95%CI). Statistical significance was set at a threshold of P<0.05 and was determined via the likelihood ratio test. Visual checks were used to assess potential nonlinear effects of continuous variables and cut-point appropriateness. All analyses were stratified by study. Page | 8
A clinical prediction model was developed using a backwards elimination process. Initially all statistically significant univariates were included in the model. To facilitate the clinical use of the model, the backwards elimination process was conducted with a focus on selecting the minimal number of predictors that maintained prediction performance. Prediction performance was assessed via the concordance statistic (c-statistic). Variables were excluded from the model via a stepwise backwards elimination of non-significant variables (P>0.05) and variables that did not increase the c-statistic by 0.02. Kaplan-Meier analysis was used for plotting and estimating probabilities. All data analysis was conducted using R version 3.4.3. 13
Results Patient Population Data was available from 1620 patients (Supplementary Table 1), of which 309 (19%) experienced any grade thrombocytopenia within the first 365 days of T-DM1 therapy, including 141 events (9% of all patients) of grade ≥ 3 (Supplementary Table 2). Amongst all episodes of thrombocytopenia, 43 (15%) required a dose interruption, 67 (22%) required a dose reduction, and 47 (15%) required T-DM1 withdrawal (Supplementary Table 2). Median time to grade ≥ 3 thrombocytopenia was 12 days and 70% of grade ≥ 3 events occurred within the first 42 days of T-DM1 therapy.
Univariate Analysis The relationship between pre-treatment platelet count and grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy was best described by cut-offs of 100-220 vs 220-300 vs >300 x109/L. Univariable Cox proportional hazard analysis identified Asian race, presence of brain metastasis, pre-treatment platelet count 100-220 & 220-300 x109/L, WBC below the LLN and concomitant use of corticosteroids as significantly associated with an increased risk Page | 9
of grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy (P <0.05; Table 1). Overweight BMI was associated with a decreased risk (P <0.05; Table 1).
Clinical prediction model The backwards elimination process resulted in a clinical prediction model for grade ≥ 3 thrombocytopenia following T-DM1 initiation that was optimally defined by race (Asian vs non-Asian) and pre-treatment platelet count (100-220 vs 220-300 vs >300 x109/L) (Table 2). The discriminative performance (c-statistic) of the model was 0.765 (Table 2 and Figure 1). As presented in Figure 1, of the six defined subgroups, the highest risk of grade ≥ 3 thrombocytopenia was in Asians with a platelet count 100-220 x109/L and the lowest risk subgroup was non-Asians with a platelet count >300 x109/L. The probability of having grade ≥ 3 thrombocytopenia within the first 42 days of T-DM1 therapy in the highest risk subgroup was 29% [95% CI; 20%-36%], compared to 1% [0%-2%] for the lowest risk subgroup (Figure 1). The probability of having grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy in the highest risk subgroup was 40% [30%-49%], compared to 2% [0%-4%] for the lowest risk subgroup (Figure 1). Supplementary Figure 1 presents the Kaplan-Meier plots for risk of grade ≥ 3 thrombocytopenia following T-DM1 initiation by defined pre-treatment subgroups.
Discussion This study used large pooled data to develop and present the first clinical prediction model of grade ≥ 3 thrombocytopenia following T-DM1 initiation in HER2 positive ABC. The model defined the risk of grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy, which ranged from 2 to 40% according to patient race (Asian vs non-Asian) and pretreatment platelet count (100-220 vs 220-300 vs >300 x109/L).
Page | 10
This study had a focus on facilitating the clinical use of the developed model, including using routinely available clinicopathologic data (recognizing parsimony can inhibit the clinical use of models) and avoiding the need to use complex mathematical calculations within the clinic.14 For example a least absolute shrinkage and selection operator (LASSO) derived model may result in greater prediction performance, however simplicity of model use was prioritized. 15 The derived clinical prediction model for grade ≥ 3 thrombocytopenia following T-DM1 initiation was optimally defined by race (Asian vs non-Asian) and pre-treatment platelet count (100-220 vs 220-300 vs >300 x109/L). The developed model aligns with the preliminary data presented in the KADCYLA drug label,
9
and other literature which
indicates Asian’s are more susceptible to the adverse effects of some chemotherapies.
16, 17
The current study additionally provides patients and clinicians the ability to interpret personalized risks (Figure 1). Furthermore, presence of brain metastasis, WBC count and concomitant corticosteroids use were also identified as pre-treatment characteristics significantly associated with grade ≥ 3 thrombocytopenia – outlining the need for caution within these patient groups. Prior research indicates no statistical difference in T-DM1 pharmacokinetics according to race, 18 and such evidence suggests that the higher risk of developing thrombocytopenia from T-DM1 in Asians is likely pharmacodynamically driven. Uppal et al.
8
outlined a FcγRIIa
dependent mechanism of T-DM1 internalization which results in thrombocytopenia via impairment of megakaryocyte differentiation. Ethnic variation between Asians and nonAsians in the FcγRIIa allotype has been described, which may explain the higher risk of thrombocytopenia with T-DM1 in Asians. 19 However, further testing and pharmacodynamic analysis is needed to fully ascertain as to why Asians as a group have a higher risk of thrombocytopenia with T-DM1. Page | 11
Randomized controlled trials are the gold standard for evaluating the benefits of therapeutic interventions, however strict inclusion can limit generalizability of results.
20
Opposing this,
adverse event data is rigorously collected within trials facilitating the development of welldefined prediction models,
21
which is demonstrated by the development of a strong
performing prediction model in this study (c = 0.765).
22
Further, this study pooled large
(n=1620) and high quality data from three trials (EMILIA, TH3RESA and MARIANNE) to increase both study power and generalizability. Ultimately this allowed the development of a well performing and highly discriminatory clinical prediction model which could be used by patients and clinicians to better interpret the risk-benefit ratio of T-DM1 than is currently possible in ABC patients. Nonetheless, the emerging advent of large electronic health record platforms will present future opportunities to externally validate the presented model within observational datasets of patients using T-DM1 in routine clinical care – which in the future may include T-DM1 use as a neo-adjuvant treatment. While the presented model was well powered, there were few participants (n=26) concomitantly treated with anticoagulants at baseline, and therefore this cautionary factor in the drug label was not evaluated – although the rationale as to why anticoagulant therapy will lower platelets in unclear. The available data predefined all Asian ancestries to one subgroup, and it is acknowledged that substantial variability may occur between Asian’s ancestries (e.g. weight and metabolic differences) which ultimately may impact a specific individual’s risk of T-DM1 induced thrombocytopenia. The study clearly identified race and pre-treatment platelet count to be distinct predictors that allow the interpretation of the risk of grade ≥ 3 thrombocytopenia within the first 365 days of T-DM1 therapy. The presented probabilities are applicable to patients with HER2 positive ABC who align with the inclusion criteria of EMILIA, TH3RESA and MARIANNE. This study demonstrated a difference of 23% (29 versus 6) between the highest and lowest risk Page | 12
subgroup for the probability of grade ≥ 3 thrombocytopenia within the first 42 days of TDM1 therapy (Figure 1). Furthermore, a difference of 27% (40 versus 13) was demonstrated at 365 days (Figure 1). Such a large and substantial discrimination between the subgroups exemplifies the ability of the developed prediction model to inform on clinically significant difference in thrombocytopenia risk to clinicians and patients considering the T-DM1 use.
Conclusion In conclusion, a clinical prediction model based upon race and pre-treatment platelet count was well performing and highly discriminatory for the prediction of grade ≥ 3 thrombocytopenia following treatment with T-DM1. The model can be used to provide personalized risks of thrombocytopenia and enable patients and clinicians to interpret the individual risk-benefit ratio of T-DM1 initiation.
Clinical Practice Points •
Thrombocytopenia is a common and potentially serious adverse event associated with ado-trastuzumab emtansine (T-DM1) use.
•
Risk factors for T-DM1 induced thrombocytopenia have been minimally explored, and there are no existing clinical prediction models to provide personalized risks.
•
This study is the first to present a prediction model for grade ≥ 3 thrombocytopenia following T-DM1 initiation.
•
A clinical prediction model optimally defined by race and pre-treatment platelet count was able to discriminate patient subgroups with significantly different risks of grade ≥ 3 thrombocytopenia following T-DM1 initiation.
•
The model can be used to provide personalized risks of thrombocytopenia and enable patients and clinicians to better interpret the risk-benefit ratio of T-DM1 initiation.
Page | 13
Page | 14
Funding Research undertaken with the financial support of Cancer Council South Australia’s Beat Cancer Project on behalf of its donors and the State Government through the Department of Health (Grant ID: 1159924 and 1127220). R.A.M receives financial support from the Cancer Council’s Beat Cancer Project with support from their donors and the South Australian Department of Health. A.R. is supported by a Beat Cancer Mid-Career Research Fellowship from Cancer Council SA. B.K is supported by the National Breast Cancer Foundation Practitioner grant. A.M.H is a researcher funded by a Postdoctoral Fellowship from the National Breast Cancer Foundation, Australia (PF-17-007).
Page | 15
References 1. 2. 3.
4.
5. 6.
7.
8.
9. 10.
11.
12.
13. 14.
15. 16.
17.
18.
Iqbal N, Iqbal N. Human Epidermal Growth Factor Receptor 2 (HER2) in Cancers: Overexpression and Therapeutic Implications. Molecular Biology International. 2014;2014:9. Diéras V, Bachelot T. The success story of trastuzumab emtansine, a targeted therapy in HER2-positive breast cancer. Targeted Oncology. 2014;9:111-122. Yan H, Yu K, Zhang K, Liu L, Li Y. Efficacy and safety of trastuzumab emtansine (T-DM1) in the treatment of HER2-positive metastatic breast cancer (MBC): a meta-analysis of randomized controlled trial. Oncotarget. 2017;8:102458-102467. Hurvitz SA, Martin M, Symmans WF, et al. Neoadjuvant trastuzumab, pertuzumab, and chemotherapy versus trastuzumab emtansine plus pertuzumab in patients with HER2-positive breast cancer (KRISTINE): a randomised, open-label, multicentre, phase 3 trial. The Lancet Oncology. 2018;19:115-126. Verma S, Miles D, Gianni L, et al. Trastuzumab Emtansine for HER2-Positive Advanced Breast Cancer. New England Journal of Medicine. 2012;367:1783-1791. Krop IE, Kim S-B, González-Martín A, et al. Trastuzumab emtansine versus treatment of physician's choice for pretreated HER2-positive advanced breast cancer (TH3RESA): a randomised, open-label, phase 3 trial. The Lancet Oncology. 2014;15:689-699. Perez EA, Barrios C, Eiermann W, et al. Trastuzumab Emtansine With or Without Pertuzumab Versus Trastuzumab Plus Taxane for Human Epidermal Growth Factor Receptor 2–Positive, Advanced Breast Cancer: Primary Results From the Phase III MARIANNE Study. Journal of Clinical Oncology. 2016;35:141-148. Uppal H, Doudement E, Mahapatra K, et al. Potential Mechanisms for Thrombocytopenia Development with Trastuzumab Emtansine (T-DM1). Clinical Cancer Research. 2015;21:123. KADCYLA (ado-trastuzumab emtansine) injection [package insert]: South San Francisco, CA: Genentech Inc; 2013. Diéras V, Miles D, Verma S, et al. Trastuzumab emtansine versus capecitabine plus lapatinib in patients with previously treated HER2-positive advanced breast cancer (EMILIA): a descriptive analysis of final overall survival results from a randomised, open-label, phase 3 trial. Lancet Oncol. 2017;18:732-742. Krop IE, Kim S-B, Martin AG, et al. Trastuzumab emtansine versus treatment of physician's choice in patients with previously treated HER2-positive metastatic breast cancer (TH3RESA): final overall survival results from a randomised open-label phase 3 trial. The Lancet Oncology. 2017;18:743-754. Perez EA, Barrios CH, Eiermann W, et al. Phase III, randomized study of first-line trastuzumab emtansine (T-DM1) ± pertuzumab (P) vs. trastuzumab + taxane (HT) treatment of HER2-positive MBC: Final overall survival (OS) and safety from MARIANNE. Journal of Clinical Oncology. 2017;35:1003-1003. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing - R version 3.4.32017. Steyerberg EW, Eijkemans MJC, Harrell Jr FE, Habbema JDF. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Statistics in Medicine. 2000;19:1059-1079. Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal. 2014;35:1925-1931. O'Donnell PH, Dolan ME. Cancer pharmacoethnicity: ethnic differences in susceptibility to the effects of chemotherapy. Clinical cancer research : an official journal of the American Association for Cancer Research. 2009;15:4806-4814. Phan VH, Moore MM, McLachlan AJ, Piquette-Miller M, Xu H, Clarke SJ. Ethnic differences in drug metabolism and toxicity from chemotherapy. Expert Opinion on Drug Metabolism & Toxicology. 2009;5:243-257. Lu D, Girish S, Gao Y, et al. Population pharmacokinetics of trastuzumab emtansine (TDM1), a HER2-targeted antibody-drug conjugate, in patients with HER2-positive metastatic
Page | 16
19.
20.
21. 22.
breast cancer: clinical implications of the effect of covariates. Cancer Chemother Pharmacol. 2014;74:399-410. Osborne JM, Chacko GW, Brandt JT, Anderson CL. Ethnic variation in frequency of an allelic polymorphism of human Fcγ RIIA determined with allele specific oligonucleotide probes. Journal of Immunological Methods. 1994;173:207-217. Kabisch M, Ruckes C, Seibert-Grafe M, Blettner M. Randomized controlled trials: part 17 of a series on evaluation of scientific publications. Deutsches Arzteblatt international. 2011;108:663-668. Phillips R, Hazell L, Sauzet O, Cornelius V. Analysis and reporting of adverse events in randomised controlled trials: a review. BMJ Open. 2019;9:e024537. Révész D, Engelhardt EG, Tamminga JJ, et al. Decision support systems for incurable nonsmall cell lung cancer: a systematic review. BMC medical informatics and decision making. 2017;17:144-144.
Page | 17
Tables and Figures Table 1: Univariable associations between pre-treatment characteristics and risk of T-DM1 induced grade ≥ 3 thrombocytopenia in ABC patients
Events/Subj (%) HR 95% CI P-value Age (years) 1 0.99 to 1.02 0.934 Race <0.001 Non-Asian 70/1306 (5%) 1 Asian 71/314 (23%) 5.11 3.67 to 7.14 Body Mass Index 0.002 Normal 77/707 (11%) 1 Overweight 56/846 (7%) 0.56 0.40 to 0.79 Underweight 5/37 (14%) 1.29 0.52 to 3.18 ECOG PS 1.28 0.93 to 1.77 0.127 Brain Metastasis No 125/1524 (8%) Yes 16/94 (17%) 2.08 1.22 to 3.54 0.007 Platelet Count (x109/L) <0.001 > 300 10/448 (2%) 1 220-300 45/697 (6%) 2.86 1.44 to 5.67 100-220 86/475 (18%) 8.44 4.39 to 16.3 White Blood Cells 0.002 ≥ LLN 117/1469 (8%) 1 < LLN 24/150 (16%) 1.98 1.27 to 3.07 Prior taxane (all settings) No 32/492 (7%) Yes 109/1128 (10%) 1.06 0.59 to 1.90 0.854 Furosemide No 135/1582 (9%) Yes 6/38 (16%) 1.78 0.78 to 4.05 0.167 Aspirin No 133/1548 (9%) Yes 8/72 (11%) 1.33 0.65 to 2.71 0.436 Corticosteroids No 125/1499 (8%) Yes 16/121 (13%) 1.71 1.01 to 2.88 0.044 Beta-blocker No 129/1433 (9%) Yes 12/187 (6%) 0.71 0.39 to 1.27 0.247 Diabetes mellitus No 132/1507 (9%) Yes 9/113 (8%) 1.15 0.58 to 2.27 0.694 CI= Confidence interval, HR= Hazard ratio, Subj= Number of subjects, ECOG PS= Eastern cooperative oncology group performance status, LLN= Lower limit of normal
Page | 18
Table 2: Multivariable clinical prediction model of grade ≥ 3 thrombocytopenia following T-DM1 initiation in ABC patients
HR 95% CI P-value Asian race 4.31 3.08 to 6.03 <0.001 Platelet Count (x109/L) <0.001 220-300 2.65 1.34 to 5.26 100-220 6.9 3.57 to 13.3 CI=confidence interval, HR=hazard ratio Notes: 1620 subjects and 141 (8.7%) events. C-statistic = 0.765, AIC = 1594.7
Page | 19
Figure 1: Percentage risks of developing grade ≥ 3 thrombocytopenia within the first 42 and 365 days of T-DM1 therapy according to race and pre-treatment platelet count
Page | 20
Supplementary Tables and Figures Supplementary Table 1: Summary of pre-treatment characteristic for patients who received T-DM1 by study
The actual treatment given Pertuzumab + Trastuzumab emtansine Placebo + Trastuzumab emtansine Trastuzumab emtansine Study EMILIA MARIANNE TH3RESA Age (years) Race Asian Non-Asian Body mass index Normal Overweight Underweight Missing ECOG PS 0 1 2 Missing Brain Metastasis No Yes Missing 9
Platelets (x10 /L) White Blood Cells ≥ LLN < LLN Missing Prior taxane (all settings) Furosemide No Yes Aspirin No Yes Corticosteroids No Yes Beta-blocker No Yes Diabetes Mellitus No Yes
Total No. 1,620
EMILIA No. 490
MARIANNE No. 727
TH3RESA No. 403
366 (23%)
0 (0%)
366 (50%)
0 (0%)
361 (22%)
0 (0%)
361 (50%)
0 (0%)
893 (55%)
490 (100%)
0 (0%)
403 (100%)
490 (30%) 727 (45%) 403 (25%) 52 (44 - 60)
490 (100%) 0 (0%) 0 (0%) 53 (45 - 60)
0 (0%) 727 (100%) 0 (0%) 52 (43 - 61)
0 (0%) 0 (0%) 403 (100%) 53 (46 - 60)
314 (19%) 1306 (81%)
92 (19%) 398 (81%)
165 (23%) 562 (77%)
57 (14%) 346 (86%)
707 (44%) 846 (52%) 37 (2%) 30 (2%)
208 (42%) 266 (54%) 8 (2%) 8 (2%)
309 (43%) 390 (54%) 22 (3%) 6 (1%)
190 (47%) 190 (47%) 7 (2%) 16 (4%)
950 (59%) 644 (40%) 23 (1%) 3 (0%)
298 (61%) 191 (39%) 0 (0%) 1 (0%)
473 (65%) 253 (35%) 1 (0%) 0 (0%)
179 (44%) 200 (50%) 22 (5%) 2 (0%)
1,524 (94%) 94 (6%) 2 (0%) 255 (212 307)
450 (92%) 40 (8%) 0 (0%) 250 (208 303)
716 (98%) 10 (1%) 1 (0%) 257 (216 306)
358 (89%) 44 (11%) 1 (0%) 260 (209 313)
1,469 (91%) 150 (9%) 1 (0%) 492 (30%)
434 (89%) 56 (11%) 0 (0%) 2 (0%)
666 (92%) 60 (8%) 1 (0%) 489 (67%)
369 (92%) 34 (8%) 0 (0%) 1 (0%)
1,582 (98%) 38 (2%)
477 (97%) 13 (3%)
718 (99%) 9 (1%)
387 (96%) 16 (4%)
1,548 (96%) 72 (4%)
468 (96%) 22 (4%)
692 (95%) 35 (5%)
388 (96%) 15 (4%)
1,499 (93%) 121 (7%)
454 (93%) 36 (7%)
685 (94%) 42 (6%)
360 (89%) 43 (11%)
1,433 (88%) 187 (12%)
436 (89%) 54 (11%)
650 (89%) 77 (11%)
347 (86%) 56 (14%)
1,507 (93%) 113 (7%)
478 (98%) 12 (2%)
660 (91%) 67 (9%)
369 (92%) 34 (8%)
Data are median (IQR) or number of patients (%). ECOG PS = Eastern cooperative oncology group performance status, LLN = lower limit of normal
Page | 21
Supplementary Table 2: Incidence of thrombocytopenia by study
Total
EMILIA
MARIANNE
n=1,620 n=490 n=727 83 (11%) Any Grade 309 (19%) 149 (30%) Grade ≥ 2 241 (15%) 113 (23%) 75 (10%) Grade ≥ 3 141 (9%) 70 (14%) 48 (7%) Grade ≥ 4 30 (2%) 13 (3%) 12 (2%) Data are median (IQR) or number of patients (%).
Page | 22
TH3RESA n=403 77 (19%) 53 (13%) 23 (6%) 5 (1%)
Supplementary Figure 1: Kaplan Meier plots of cumulative risk of thrombocytopenia
Page | 23