1. Limitations of GEP Tests
Technical challenges
- Quantity and quality of material (10-30% cases have insufficient tissue/RNA)
“Off-label” CUP tumours
- Potential for false positives if tested tumour not in classifier
Accuracy is tumour type dependent
- Classification of gastroenteropancreatic, SCC and mullerian tumours

2. Uneven classification performance
Kerr et al 2012 Clin Cancer Res

3. Uneven classification performance
Pillai et al 2013 Mol Diagn

4. Limitations of GEP Tests
Technical challenges
- Quantity and quality of material (10-30% cases have insufficient tissue/RNA)
“Off-label” CUP tumours
- Potential for false positives if tested tumour not in classifier differential
Accuracy is tumour type dependent
- Classification of gastroenteropancreatic, SCC and mullerian tumours
Misclassifications or no similarity match
- Misclassified latent primary (~25%) or no similarity match (~5% CUP)

5. MEREDITH: A multi-platform integration approach
Integration mRNA, Methylation, miR, CNV
TCGA integrated analysis
cancers, 19 types, 4 data types
PCA Dimensionality reduction and (tSNE) 2D clustering
18 clusters
8 show exclusivity of one cancer type
5 subtype exclusive
3 complex mix (SCC, CRC, Kidney)
Supervised ToO Classification (AUC)
mRNA gene-expression = 0.93
DNA methylation = 0.93
miR expression =0.93
CNV= 0.72
Combined data = 0.94
Taskesen et al Scientific Reports v6, a24949 (2016)

6. MEREDITH: A multi-platform integration approach
Zoom from all sample tSNE
COPs clustered alone
COP-II
COP -I (KIRP)
Cluster outside primary – “COPs”
COP I – Cluster with other cancer types n=50 (enriched LUAD, LUSC, KIRC)
COP II- Cluster together and away from other cancers n= 14 (LI, ACC, LUSC,BRCA)
cell cycle, RB-P107, immune, RNA splicing

7. Talk outline
Brief history of gene-expression profiling for cancer type classification
Current commercially available tests - development and performance
Clinical application
Problems and limitations
How DNA sequencing and mutation profiling can potentially help

8. SUPER case with no similarity match
Prior History: March 2017
T2N2M0 NSCLC (Stage 3A). ‘Low grade adenocarcinoma’
Treatment: Concurrent ChemoRT (Cisplatin/Etoposide),
Outcome: Partial response on repeat CT after treatment
Patient: 63 yo female, smoker (at least 40 pack yr hx).
NSCLC ??
CUP Presentation March 2018
PET: new right thigh soft tissue metastasis and small L adrenal met. Histopathology (03/2018): Thigh biopsy
Moderately differentiated adenocarcinoma of uncertain origin. AE1/AE3 ++, CK19+, TTF1-, CDX2+++, CK7-, CK20 -, CD56+, synaptophysin +, chromogranin +, GATA3+, ER+, HER2+ (ISH neg), GCDFP-, SOX10 -. PAX8 -. PDL1 <1%
Linda Mileshkin (SUPER Study Lead)

9. SUPERDx Nanostring ToO Assay (CUPGuidev2)
Low confidence prediction of tissue of origin

10. Whole genome sequencing
Patient samples:
Tissue sources: Tumour (organoid) and blood sample
Assay:
WGS: 38xN/60xT.
Driver mutations: KRAS, STK11, CDKN2A, CTNNB1, STAG2, MGA, TMEM127
Tumour mutation Burden:
7.39 mutations/Mb (TMB - Intermediate ( mutations/Mb)) 52 coding mutations

11. Smoking signature 4 supports metastatic lung cancer
COSMIC Mutation signatures
Test case 1105
C>A
C>G
C>T
T>A
T>C
T>G
Trinucleotide e.g. CCT
Alexandrov et al Nature Aug 22;500(7463):415-21
6 × 4 × 4 = 96 possible events

12. ToO classification based on whole genome sequencing
ICGC WGS
machine learning
2267 samples
18 cancer types
Input features
Regional mutation density (RMD)
(passenger mutation in heterochromatin state regions in cell of origin precursor cell)
Oncogenic driver mutations (OGM)
COSMIC Mutation signatures (MS96)
Overall Accuracy= 92%
Contribution of data type
Salvadores et al PLoS Comput Biol

13. Summary
Gene-expression profiling can accurately classify primary & metastatic tumours
(~85-90% accuracy known primary)
Useful in resolving CUP cases with concordance of ~75% in latent primaries and IHC valid.
Evidence of clinical impact
- Change in patient management
- Improved survival in cases receiving a site directed therapy
Limitations: - Tissue amount and quality (10-30%)
- Uneven classification performance across tumour types
- Misclassification “off-label tumours”
Mutation profiling can help for “molecularly” undifferentiated CUP cases

14. Acknowledgements CUP Consumer Representatives Patients and Family
SUPER Team Linda Mileshkin Penelope Schofield David Bowtell Krista Fisher Colin Wood Dariush Etemadamoghadam Alex Murray Lisa Guccione Ellen Schaef Tharani Sivakumaran SUPER Study Sites CUPGuide Keith Byron Adam Kowalcyk Fan Shi Justin Bedo Tothill RADIO Lab Atara Posner Shiva Balanchander Andrew Pattison UMCCR (WGS Platform) Sean Grimmond Oliver Hofmnn UMCCR Genomics AOCS/Bowtell Lab Nadia Traficante Sian Fereday Joy Hendley Bioinformatics Jason Li Kaushalya Amarasinghe Ken Doig Joshy George Pathology (Peter Mac) Stephen Fox Owen Prall Andrew Fellowes Huiling Xu Anna Tanska Ain Roesley David Choong
CUP Consumer Representatives Patients and Family
Kym Sheehan
John Symons (CUP Foundation Jo’s Friends)