Presentation on tailoring adjuvant radiotherapy for breast cancers based on Genomic risk prediction methods - a review. Presented in MVR CANCON 2018

1. Adjuvant Radiation based on Genomic
Risk Factors - Emerging Scenarios
Dr Santam Chakraborty
Associate Consultant, Radiation Oncology

2. Disclosures
No conflicts of interest to declare
Do not have personal experience with any of the described genomic risk tools.
This is NOT a systematic review
Slides will be available at : https://www.slideshare.net/santam (mostly !!)

3. When are these useful ?
Allows
prediction of
benefit from
Radiotherapy
Allows
prediction of
increased
toxicity from
Radiotherapy

4. Scope of Talk
How well do established
genomic risk predictors help
tailor radiotherapy
2
How well do molecular
subtypes of breast cancer
allow us to tailor
radiotherapy
1
What is on the horizon ?4
How well do new Radiation
specific genomic risk
predictors help tailor
radiotherapy
3

5. Emerging Scenario
Gene expression OR gene
mutation OR miRNA
expression OR proteomic OR
genomic OR single nucleotide
polymorphism
AND
Breast Cancer

6. Breast Cancer Molecular Subtypes

7. Breast Cancer Subtypes
Subtypes of breast cancer based on:
● Proliferation
● Hormone receptor signalling
● HER2 signalling
● Expression of basal epithelial
markers
Have differing prognosis within the TNM
stages.

8. LRR as a function of Subtype
● Luminal A : 0.8 - 8%
● Luminal B : 1.5% - 8.7%
● HER-2 enriched:
○ Without anti-HER2 : 4 - 15%
○ With anti-HER2 : 1.7%
● Triple negative : 3 - 17%
Data from: McGuire A, Lowery AJ, Kell MR, Kerin MJ,
Sweeney KJ. Locoregional Recurrence Following Breast
Cancer Surgery in the Trastuzumab Era: A Systematic
Review by Subtype. Ann Surg Oncol. 2017
Oct;24(11):3124–32.

9. Are Subtypes Predictive : PMRT
Kyndi M, Sørensen FB, Knudsen H, Overgaard M, Nielsen HM, Overgaard J, et al. Estrogen receptor, progesterone receptor, HER-2, and response to
postmastectomy radiotherapy in high-risk breast cancer: the Danish Breast Cancer Cooperative Group. J Clin Oncol. 2008 Mar 20;26(9):1419–26.
ER+/HER2- ER+/HER+ TNBC ER-/HER2+
44% vs 33% 38 % vs 15% 39% vs 32% 17% vs 28%

10. Are Subtypes Predictive : BCS
Sjöström M, Lundstedt D, Hartman L, Holmberg E, Killander F, Kovács A, et al. Response to Radiotherapy After Breast-Conserving Surgery in Different
Breast Cancer Subtypes in the Swedish Breast Cancer Group 91 Radiotherapy Randomized Clinical Trial. J Clin Oncol. 2017 Oct 1;35(28):3222–9.
HR 0.45
HR 0.33
HR 1.29 HR 0.25

11. What about low risk Luminal A ?
● CALGB 9343: (TAM + RT vs TAM)
○ 70 + / T1 (< 2 cm) / ER +ve
○ IBTR HR : 0.18 (2% vs 10% at 10 years)
○ IBTRs associated with DM in control arm.
○ No difference in OS
● PRIME II (RT vs No RT + choice of ET)
○ 65+ / T1 (< 3 cm) / ER +ve (87% had T size of 2 cm or less)
○ IBTR HR : 0.19 (1.3% vs 4% at 5 years)
○ IBTRs assoc with DM in control arm
○ No difference in OS

12. Genomic Risk Prediction Tools

13. Genomic Risk Prediction Tools
● Several commercial systems available based on gene expression:
○ Oncotype Dx : RT PCR, 21 genes
○ Mammaprint : Microarray, 70 genes
○ PAM50 : Ion-torrent, 50 genes
○ Endopredict : RT PCR, 12 genes
● All predict the risk of distant metastases - benefit from adj chemotherapy
● Predictive of benefit of chemotherapy
○ TAILORx
○ MINDACT

14. Do they prognosticate for Local recurrence ?
Author Method N Dataset LR Risk (High vs Low) 10 years
Mamounas
(2010)
Oncotype Dx 1674 NSABP B14/20 16% vs 4%
Drukker
(2014)
MammaPrint 1053 Netherland Cancer Institute 2% vs 6%
Fitzal (2015) Endopredict 3714 ABCSG 8 9% vs 2.5%
Mamounas
(2017)
Oncotype Dx 1065 NSABP 28 12% vs 3%

15. Do they predict benefit from RT
Drukker CA, Elias SG, Nijenhuis MV, Wesseling J, Bartelink H, Elkhuizen P, et al. Gene expression profiling to predict the risk of locoregional recurrence in
breast cancer: a pooled analysis. Breast Cancer Res Treat. 2014 Dec;148(3):599–613.

16. Do they predict benefit from RT
● Observational cohort study (NCD) - 5224 patients
● T1-2, N1 ER +ve , Post mastectomy
● 52% has a low risk score on Oncotype Dx
● HR of death:
○ Low risk (52%) : 2.82 (1.7 - 4.8)
○ Intermediate risk (25%) : 0.98 (0.6 - 1.62)
○ High risk (22%) : 0.96 (0.6 - 1.53)
Goodman CR, Seagle BLL, Shahabi S, Strauss JB. Oncotype Score and Benefit of Post-Mastectomy Radiotherapy in T1-2 N1 Breast
Cancer. Int J Radiat Oncol Biol Phys. 2017 Oct 1;99(2):S53.

17. Why they fail to predict ?
Speers C, Zhao SG, Liu M, Bartelink H, Pierce
LJ, Feng FY. Development and validation of a
novel radiosensitivity signature in human breast
cancer. Clin Cancer Res [Internet]. 2015 Apr 22;
Available from:
http://dx.doi.org/10.1158/1078-0432.CCR-14-28
98

18. What about Oncotype Dx: DCIS ?
● Developed in a low risk cohort which DID NOT receive RT.
● 12 genes instead of 21.
○ PR and GSTM1 predict distant recurrence and breast cancer mortality
○ ER predictive of benefit from endocrine therapy

19. How does it help ?
● Classify patients into risk categories:
○ 10.6% in low risk vs 26% in high/intermediate risk
○ 3.7% invasive recurrence in low risk vs 19% in high risk.
● Significant independent predictor of IBE in addition to tumor size and
menopausal status.

20. How does it Help ?
● Age ≥ 50 years
● Tumor size ≤ 2.5 cm
● Grade 1 - 2
● Clear margins
Rakovitch E, Nofech-Mozes S, Hanna W, Sutradhar R,
Baehner FL, Miller DP, et al. Multigene Expression Assay and
Benefit of Radiotherapy After Breast Conservation in Ductal
Carcinoma in Situ. J Natl Cancer Inst [Internet]. 2017 Apr
1;109(4). Available from: http://dx.doi.org/10.1093/jnci/djw256

21. Alternative Genomic Prediction Tools
● Existing tools with exception of Oncotype Dx DCIS tailored for predicting
distant relapse and benefit from endocrine therapy.
● New tools therefore required that predict the benefit from RT as well as the
risks from the same.
● Some selected tools:
○ Radiosensitivity Index and GARD (Torres Rocca & Scott et al)
○ DBCG82bc derived signature (Tramm et al)
○ Radiotype Dx(Speers et al)

22. Radiosensitivity Index (RSI, Torres-Roca, 2005)
What is it? A 10 gene signature predicting intrinsic radio-resistance
How developed Modelling of post radiation clonogenic survival with gene expression along with tissue
of origin, ras and p53 mutation (in 60 NCI cancer cell lines)
Modelled On A mix of Head neck / Rectal and Esophageal cancers
Method Correlate radiation response against the target gene expression
Stats Significance analysis of microarray followed by linear regression
Validation External Validation in different cancers (head neck, rectum and esophageal)
Accuracy AUC 0.84, Sensitivity 80% and PPV 86% for response. Also predictive of prognosis in
HNC
Utility Predict radio-resistance across cancer cell lines

23. Radiosensitivity Index (RSI, Torres-Roca, 2015)
● Tested in 343 breast cancer patients treated with BCS + RT (high local
recurrence in cohort, underutilized endocrine therapy)
● Endpoint : LRFS
● RSI-S lowest 25th percentile of RSI score
● Patients with high mitotic index, high proliferation and TN patients were RSI-S
● RSI alone failed to predict LRR
● Combined with intrinsic subtype and age and predicted local recurrence
better.
● RT dose escalation decreased local recurrence only in patients with Luminal
A with RSI-R subgroup

24. GARD (Scott, 2016)
● Extrapolation of the RSI to derive a genome adjusted biologically effective
dose
● Essentially the traditional biological effective dose is modified using the
radiation sensitivity index (RSI)
● Highly radiosensitive tumours will thus have higher GARD values with the
same physical dose
● Predicts clinical outcomes in multiple cancers
● In the ERSAMUS breast cancer cohort 5 year DMFS is superior in patients
receiving higher GARD.

25. DBCG 82c/g Signature (Tramm, 2014)
What is it? A 7 gene signature predicting benefit of PMRT
How developed Microarray based gene expression profile from FFT samples
Modelled On 273 DBCG 82b/c breast cancer patients
Method Gene expression profile predicting benefit from PMRT. Validation done on formalin
fixed tissue with qRT-PCR
Stats Two step cox proportional hazards with lasso penalty and weighted cross validated
score index based on gene expression levels
Validation External validation in 1001 patients with > 7 nodes removed.
Accuracy NA (used hazard ratios for validation of predictive accuracy)
Utility Predict patients with very low risk of recurrence and who do not benefit from PMRT

26. DBCG 82c/g Signature (Tramm, 2014)

27. Radiotype Dx (Speers, 2015)
What is it? 51 gene index developed in University of Michigan
How developed Integration of post radiation clonogenic survival with breast cancer specific radiation
sensitivity signature (from 16 cell lines)
Developed In European EBC treated with BCS and RT. (Netherlands / France)
Method Correlate the basal gene expression () with the radiation sensitivity (SF2Gy)
Stats Unsupervised hierarchical clustering
Validation External Validation in similar cohort, Currently being validated in two UK trials
Accuracy AUC 0.72, Sensitivity 84% and NPV 89% for Local Recurrence
Utility Predict accurately who will develop recurrence after adjuvant RT in BCS

28. Radiotype Dx (Speers, 2015)
Pathways
BRCA1 modulation of DNA
damage response
Hereditary breast cancer signalling
ATM signalling
DNA double strand break by
Homologous Recombination
Cell Cycle Control
Cellular Functions
DNA replication, recombination
and Repair
Cell Cycle
Cellular Development

29. Toxicity Prediction

30. Toxicity Prediction

31. Something to ponder !!

32. Multiple Ways to Radiosensitivity
Variation of survival across and within
cell lineages
SCNA predicting sensitivity to RT
Top 19 gene mutations predicting sensitivity to RT

33. Conclusions & Summary
Molecular Subtypes
Gene Expression Tools
● Prognosticate but DO NOT predict
● May be combined with RSI to derive a predictive
model
● May be useful to predict a “low enough” risk that
justifies omission of RT
● Mostly prognosticate , some predict
● Can be combined with other factors
● Radiation specific signatures needed -
RadiotypeDx undergoing evaluation
● Can predict toxicity with reasonable accuracy

34. The way forward

35. Thank You