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Research articles · Apr 30, 2021

Modelling the Economic Impact of Next Generation Sequencing and Precision Medicine on Childhood Cancer Management—a Microsimulation Approach

Owen Tan, Deborah J Schofield, Tracey O’Brien, Toby Trahair, Rupendra N Shrestha

GeIMPACT: Centre for Economic Impacts of Genomic Medicine, Macquarie Business School, North Ryde NSW, Australia; School of Women’s and Children’s Health, Randwick, NSW, Australia; Kids Cancer Centre, Randwick, NSW, Australia; Children’s Cancer Institute, Randwick, NSW, Australia

Volume 14(1) Spring 2021

Cite as: O. Tan, D. Schofield, T. O’Brien, T. Trahair, R. Shrestha; 2021; Modelling the Economic Impact of Next Generation Sequencing and Precision Medicine on Childhood Cancer Management—a Microsimulation Approach; International Journal of Microsimulation; 14(1); 73–91. doi: 10.34196/ijm.00230

Abstract

Precision medicine is a new approach to identify the best treatment available to patients based on their genomic information. However, no economic evaluation of genome sequencing has been reported for the treatment of childhood cancers, which is critical to evaluate the feasibility of implementing patient’s genome sequencing as part of a publicly funded treatment strategy. We have developed a microsimulation model, PeCanMOD, to evaluate the cost and benefit of applying the Next Generation Sequencing (NGS) in the management of childhood cancer. This paper describes the construction of PeCanMOD. We used linked datasets of children under 18 year of age, living in New South Wales (NSW), Australia, who have had cancer, as a base population. Their records were extracted from the NSW Central Cancer Registry and were linked to mortality and hospital datasets. In addition, we simulated the genomic landscape of the cancer registry population, through information obtained from 1,200 molecularly profiled paediatric cancer from the Foundation Medicine. The model simulated the number of individuals eligible for precision medicine, and the incremental cost of treatment per life year gained if precision medicine was introduced for late stage cancer patients as a final treatment option. Cost of drugs, and hospital admission were included in the model. Data on response rate and probability of survival was imputed based on the latest available evidence. Each unit record in the model was weighted using input from the Australian Institute of Health and Welfare (AIHW) to reflect total paediatric cancer population in Australia. The model demonstrates the application of microsimulation modelling to simulate the impacts of NGS and precision medicine on costs and health outcomes for childhood cancer.

This article contains 3 figures and 8 tables.

Figures

Figure 1. Structure of PeCanMOD
Figure 2. Model schematic of decision tree for testing and initial treatment.
Figure 3. Heat map shows frequency of mutations (normalised and represented as Z-scores) in each gene in (A) reference population, i.e. Foundation Medicine dataset, and (B) NSW Central Cancer Registry population simulation output.

Tables

Table 1. Characteristics of childhood cancer population in NSW Central Cancer Registry and selected individuals used in the model.
Total childhood cancer population from NSW Central Cancer Registry (2001-2012) Selected individuals for simulation model ‡
N % N %
Sex     Sex    
Male 1639 55.26 Male 303 56.01
Female 1327 44.74 Female 238 43.99
Age at diagnosis     Age at diagnosis    
0-4 1117 37.66 0-4 193 35.67
5-9 528 17.8 5-9 99 18.3
10-14 632 21.31 10-14 119 22
15-17 689 23.23 15-17 130 24.03
Cancer types     Cancer types    
-Acute lymphoblastic leukaemia 712 23.78 Brain 159 29.39
-Brain 342 11.42 Acute lymphoblastic leukaemia 92 17.01
-Hodgkin’s disease 210 7.01 Bone 54 9.98
-Non-Hodgkin’s lymphoma 186 6.21 Acute myeloid leukaemia 42 7.76
-Bone 177 5.91 Connective tissue, peripheral nerves 38 7.02
-Acute myeloid leukaemia 168 5.61 Other endocrine glands 33 6.1
-Connective tissue, peripheral nerves 148 4.94 Non-Hodgkin’s Lymphoma 22 4.07
-Kidney 134 4.48 All other cancer types† 101 18.6
-Other endocrine glands 113 3.77    
-Melanoma of skin 106 3.54    
-Colon 70 2.34    
-Thyroid 68 2.27    
-Testis 67 2.24    
-Central nervous system 67 2.24    
-Ill-defined and unspecified site 60 2    
-Eye 59 1.97    
-Other lymphatic, hematopoietic 49 1.64    
-Liver 41 1.37    
-Ovary 41 1.37    
-Other myeloid leukaemia 27 0.9    
-Other thoracic organs 20 0.67    
-All other cancer types† 129 4.31    
Year of diagnosis     Year of diagnosis    
2001* 144 4.86 2001* 34 6.28
2002 261 8.80 2002 60 11.09
2003 249 8.40 2003 58 10.72
2004 259 8.73 2004 45 8.32
2005 241 8.13 2005 52 9.61
2006 261 8.80 2006 41 7.58
2007 229 7.72 2007 45 8.32
2008 226 7.62 2008 40 7.39
2009 256 8.63 2009 46 8.5
2010 270 9.10 2010 40 7.39
2011 277 9.34 2011 32 5.91
2012 293 9.88 2012 48 8.87

2001 data started from 1st of July.

Including cancer types with equal or less than 20 records.

Selected individuals were those who were eventually deceased due to their illness.

Table 2. Comparison of reference datasets for genomic variants distribution imputation.
Datasets % of base file matched with reference dataset (by cancer types)
Foundation Medicine Pediatric Portal 73
Grobner et al. 57
Rusch et al. 24
Ma et al. 10
Table 3. Genomic variants eligible for precision medicine and the corresponding drugs.
Drugs Genomic variants eligible for precision medicine
Larotrectinib NTRK1,NTRK2,NTRK3
Erdafitinib FGFR1,FGFR2,FGFR3,FGFR4
Tazemetostat EZH2 gain of function,EZH2,BRG1,INI1,SMARCA4 inactivation,SMARCB1 inactivation
Samotolisib TSC1,TSC2
Selumetinib Sulfate BRAF,GNA11,GNAQ,HRAS,KRAS,NF1,NRAS
Ensartinib ALK fusion protein, ALK gene mutation, ALK gene translocation,ROS1 fusion positive,ROS1 gene mutation,ROS1 gene translocation
Vemurafenib BRAF v600x
Olaparib Deleterious ATM, Deleterious BRCA1, Deleterious BRCA2, Deleterious RAD51C, Deleterious RAD51D
Palbociclib RB1
Ulixertinib ARAF,BRAF,GNA11,GNAQ,HRAS,KRAS,MAP2K1,MAPK1,NF1,NRAS
Table 4. Model inputs-- response rate to drugs.
Drugs Mean response rate Distribution § Source/note
Larotrectinib 0.73 Binomial (55,0.73) Food and Drug Administration, 2018
Erdafitinib 0.322 Binomial (87,0.322) Food and Drug Administration, 2019
Tazemetostat* 0.38 or 0.05 Binomial(21,0.38) or Binomial(43,0.05) Italiano et al., 2018
Samotolisib 0.34 Binomial (47,0.34) Bendell et al., 2018
Selumetinib Sulfate 0.17 Binomial (36,0.17) Jain et al., 2014
Ensartinib 0.69 Binomial (13,0.69) Horn et al., 2017
Vemurafenib† range (0.17-0.769) Hyman et al., 2015
Olaparib 0.53 Binomial (92,0.53) Golan et al., 2019
Palbociclib‡ 0.5 Triangular (0.25, 0.5, 0.75) n.a.
Ulixertinib 0.14 Binomial (101,0.14) Sullivan et al., 2018

Depending on cancer types (for blood cancers, response rate was assumed to be 0.38, and 0.05 for solid cancers).

response rate varies by cancer types.

There is no data available for Palbociclib, so we assumed 0.5 response rate with a triangular distribution of ±0.25.

Binomial (N,p), triangular (a, c, b).

Table 5. Model inputs-- duration of response.
Drugs Mean duration of response Weibull (shape, scale) Source/note
Larotrectinib 6 months (2.45,10.45) Food and Drug Administration, 2018
Erdafitinib 5.4 months (1.86,6.58) Food and Drug Administration, 2019
Tazemetostat 12.4 months (1.8,19.7) Italiano et al., 2018
Samotolisib 6 months (1.55,7.6) Bendell et al., 2018
Selumetinib Sulfate 2 months (1.3,2.65) Jain et al., 2014
Ensartinib 5.8 months (1.57,7.3) Horn et al., 2017
Vemurafenib* range (3-13 months) (1.81,8.57) Hyman et al., 2015
Olaparib 6 months (1.95,21.73) Golan et al., 2019
Palbociclib 9.5 months (1.53,12.06) McShane et al., 2018
Ulixertinib 6.6 months (1.73,8.16) Sullivan et al., 2018

Duration of response varies by cancer types.

Table 6. Model inputs, including costs of medicine, drug admission at hospital, toxicity management, and sequencing.
Drugs Mean monthly cost (US$) Mean monthly cost (AU$) Source/note
Larotrectinib 11,000 (range: 8,250-13,750) 15,629 (range:11,722-19,536) Herper, 2018
Erdafitinib 16,380 (range: 12,285-20,475) 23,273 (range: 17,455-29,091) Pagliarulo, 2019
Tazemetostat N/A 11,658 (range: 8,744-14,573) Average of drug prices of Larotrectinib, Erdafitinib, Vemurafenib, Olaparib, and Palbociclib
Samotolisib N/A 11,658 (range: 8,744-14,573) Average of drug prices of Larotrectinib, Erdafitinib, Vemurafenib, Olaparib, and Palbociclib
Selumetinib Sulfate N/A 11,658 (range: 8,744-14,573) Average of drug prices of Larotrectinib, Erdafitinib, Vemurafenib, Olaparib, and Palbociclib
Ensartinib N/A 11,658 (range: 8,744-14,573) Average of drug prices of Larotrectinib, Erdafitinib, Vemurafenib, Olaparib, and Palbociclib
Vemurafenib N/A 8,189 (range: 6,142-10,236) Pharmaceutical BenefitsScheme, 2019c
Olaparib N/A 6,961 (range: 5,221-8,701) Pharmaceutical BenefitsScheme, 2019a
Palbociclib N/A 4,239 (range: 3,179-5,299) Pharmaceutical BenefitsScheme, 2019b
Ulixertinib N/A 11,658 (range: 8,744-14,573) Average of drug prices of Larotrectinib, Erdafitinib, Vemurafenib, Olaparib, and Palbociclib
Hospital care Mean monthly cost per person (US$) Mean monthly cost per person (AU$)  
Admission for drug treatment N/A 901 (range: 676-1,126) AR-DRGs
Managing toxicity/adverse drug events N/A 5,230 (range: 3,923-6,538)  
Sequencing Mean cost per service (USD) Mean cost per service (AUD)  
Whole-genome sequencing 3,347 (range: 2,032-30,805) 4,926 (range: 2,991-45,333) Gordon et al., 2020; Schwarze et al., 2020; Schwarze et al., 2018; Weymann et al., 2017
Targeted multi-gene panel sequencing 1,236 (range: 525-6916) 1,433 (range: 773-10,178) Gordon et al., 2020; Hamblin et al., 2017an van Amerongen et al., 2016; Yu et al., 2018
ABL1 ACVR1B AKT1 AKT2 AKT3 ALK APC APCDD1 AR ARAF ARHGAP26 ARID1A ARID1B ARID2 ASXL1 ATM ATR ATRX AURKA
AURKB AXIN1 BAP1 BARD1 BCL10 BCL11B BCL2 BCL2A1 BCL2L2 BCL6 BCL7A BCOR BCORL1 BRAF BRCA1 BRCA2 BRD4 BRIP1 C17orf39
CARD11 CASP8 CBFB CBL CCND1 CCND2 CCND3 CCNE1 CCT6B CD22 CD274 CD36 CDC73 CDH1 CDH2 CDH20 CDK12 CDK4 CDK6
CDKN1B CDKN2A CDKN2B CDKN2C CEBPA CHD2 CHEK2 CHUK CIC CIITA CKS1B CPS1 CRBN CREBBP CRKL CRLF2 CSF3R CTCF CTNNA1
CTNNB1 CUX1 CYLD DAXX DDR2 DDX3X DICER1 DIS3 DNMT3A DOT1L EED EGFR ELP2 EP300 EPHA3 EPHA5 EPHA7 EPHB1 EPHB4
EPHB6 ERBB2 ERBB3 ERBB4 ERG ESR1 ETS1 ETV1 ETV6 EWSR1 EZH2 FAM123B FAM46C FANCA FANCI FANCM FAS FAT1 FAT3
FBXO11 FBXW7 FGF10 FGF14 FGF19 FGF23 FGF3 FGF4 FGF6 FGFR1 FGFR2 FGFR3 FGFR4 FLCN FLT1 FLT3 FLT4 FOXO1 FOXP1
FUBP1 FUS GABRA6 GATA1 GATA2 GATA3 GATA4 GLI1 GNAS GRIN2A GRM3 H3F3A HDAC4 HEY1 HGF HIST1H1D HIST1H2AM HLA HLA.A
HNF1A HOXA9 HRAS ICK IDH1 IDH2 IGF1 IGF1R IGF2R IGH IKBKE IKZF1 IKZF2 IKZF3 IL7R IRF2 IRF8 IRS2 JAK1
JAK2 JAK3 JUN KDM4C KDM5A KDM5C KDM6A KDR KEAP1 KEL KIT KRAS LRP1B LRRK2 LZTR1 MAGI2 MALT1 MAP2K1 MAP2K4
MAP3K1 MAP3K13 MAP3K14 MAP3K6 MAP3K7 MAPK1 MCL1 MDM2 MDM4 MED12 MEF2C MEN1 MET MKI67 MLH1 MLL MLL2 MLL3 MLLT10
MPL MRE11A MSH2 MSH3 MSH6 MTOR MUTYH MYC MYCL1 MYCN MYH11 MYST3 NCOR1 NCOR2 NF1 NF2 NFE2L2 NFKBIA NKX2.1
NOD1 NOTCH1 NOTCH2 NOTCH3 NOTCH4 NPM1 NRAS NSD1 NTRK1 NTRK3 NUP214 NUP98 PALB2 PARK2 PASK PAX3 PAX5 PBRM1 PBX1
PDCD11 PDCD1LG2 PDGFB PDGFRA PDGFRB PHF6 PIK3C2B PIK3C2G PIK3CA PIK3CG PIK3R1 PIK3R2 PLAG1 PLCG2 PMS2 POLE POT1 PPP2R1A PRDM1
PREX2 PRKCI PRKDC PTCH1 PTEN PTPN11 PTPN6 PTPRD PTPRO RAD50 RAD51 RAD51C RAD54L RAF1 RANBP2 RARA RB1 REL RET
RICTOR RNF213 RNF43 ROS1 RPTOR RUNX1 RUNX1T1 SDHA SDHD SETBP1 SETD2 SF3B1 SH2B3 SLIT2 SMAD4 SMARCA4 SMARCB1 SMARCD1 SOX10
SOX2 SPEN SPTA1 SRC SRSF2 SS18 STAG2 STAT4 STAT6 STK11 SUFU SUZ12 TAF1 TAL1 TBL1XR1 TBX3 TCF3 TERT TET2
TLL2 TMEM30A TNFRSF11A TNKS TNKS2 TP53 TRRAP TSC1 TSC2 TSHR TYK2 U2AF1 VHL WHSC1 WT1 XPO1 XRCC2 ZMYM3 ZNF217
ZNF703 ZRSR2
Datasets % records linked
NSW Central Cancer Registry (base population) 100
NSW Admitted Patient Data Collection 98.8
NSW Emergency Department Data Collection 87.8
Registry of Births, Deaths and Marriages Deaths registry 18.3
Cause of Death Unit Record File 17.7