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ePoster Display

1133P - Whole genome sequencing can classify diagnostically challenging tumors

Date

16 Sep 2021

Session

ePoster Display

Presenters

Luuk Schipper

Citation

Annals of Oncology (2021) 32 (suppl_5): S921-S930. 10.1016/annonc/annonc707

Authors

L.J. Schipper1, P. Snaebjornsson2, K.G. Samsom3, L.J.W. Bosch2, F. Lalezari4, P. Priestley5, C. Shale5, N. Jacobs6, A.J. van den Broek6, P. Roepman6, J.J.M. van der Hoeven6, N. Steeghs7, E. Cuppen6, G. Meijer3, E.E. Voest7, K. Monkhorst3

Author affiliations

  • 1 Molecular Oncology, Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital (NKI-AVL), 1066 CX - Amsterdam/NL
  • 2 Department Of Pathology, Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital (NKI-AVL), 1066CX - Amsterdam/NL
  • 3 Department Of Pathology, Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital (NKI-AVL), 1066 CX - Amsterdam/NL
  • 4 Department Of Radiology, Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital (NKI-AVL), 1066CX - Amsterdam/NL
  • 5 Medical Department, Hartwig Medical Foundation Australia, Sidney/AU
  • 6 Medical Department, Hartwig Medical Foundation, 1098XH - Amsterdam/NL
  • 7 Department Of Medical Oncology, Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital (NKI-AVL), 1066 CX - Amsterdam/NL
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Abstract 1133P

Background

Cancer of Unknown Primary (CUP) accounts for approximately 3% of all metastatic cancers. Despite modern imaging techniques and extensive pathology work-up in CUPs, tumor origin remains unknown in the majority of these patients. Large-scale genome sequencing studies have revealed that tumor types can be classified based on distinct patterns of somatic variants and other genomic characteristics. Here, we investigated the clinical value of genome-driven tumor type prediction using whole genome sequencing (WGS) in the diagnostic workup of patients with CUPs and other tumors that are challenging to diagnose.

Methods

A WGS-based tumor type ‘cancer of unknown primary prediction algorithm’ (CUPPA) was developed on a large pan-cancer WGS database of metastatic cancer patients (>4000 samples) and validated on 451 of those samples. We assessed the clinical utility of this prediction algorithm in a cohort of 60 independent patients with diagnostically complex tumors who underwent a prospective WGS analysis in the WIDE study [1].

Results

CUPPA correctly predicted the primary tumor type in 84% of samples in the total validation cohort (380/451 patients), and reached a 95% accuracy in high-confidence predictions (n=340). Using high-confidence predictions only, a probable diagnosis could be reached for 11 out of 22 CUP patients (50%). Diagnoses included non-small cell lung (n=3), gastroesophageal (n=3), prostate, kidney, cholangiocarcinoma, melanoma and sarcoma. Furthermore, in 38 tumors with an inconclusive final diagnosis, a high-confidence prediction alleviated diagnostic uncertainty in 24 patients (63%).

Conclusions

Genome-based tumor type prediction has diagnostic value to classify tumor types with high accuracy. The CUPPA algorithm is a method to assist clinical decision making in diagnostically complex tumors. [1] 1189O - Monkhorst K, Samsom K, Schipper L, et al. Validation of whole genome sequencing in routine clinical practice. ESMO Virtual Congress 2020.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

P. Snaebjornsson: Financial Interests, Institutional, Expert Testimony: MEDtalks; Non-Financial Interests, Institutional, Advisory Role: MSD; Non-Financial Interests, Institutional, Advisory Board: Bayer. N. Steeghs: Financial Interests, Institutional, Advisory Board: AIMM Therapeutics; Financial Interests, Institutional, Advisory Board: Boehringer Ingelheim; Financial Interests, Institutional, Advisory Board: Ellipses Pharma; Financial Interests, Institutional, Research Grant: AbbVie; Financial Interests, Institutional, Research Grant: Acutate Therapeutics; Financial Interests, Institutional, Research Grant: Amgen; Financial Interests, Institutional, Research Grant: Array; Financial Interests, Institutional, Research Grant: AstraZeneca/MedImmune; Financial Interests, Institutional, Research Grant: Bayer; Financial Interests, Institutional, Research Grant: Bleuprint Medicines; Financial Interests, Institutional, Research Grant: Boehringer Ingelheim; Financial Interests, Institutional, Research Grant: Bristol-Myers Squibb; Financial Interests, Institutional, Research Grant: Cantargia; Financial Interests, Institutional, Research Grant: CellCentric; Financial Interests, Institutional, Research Grant: Cytovation; Financial Interests, Institutional, Research Grant: Deciphera; Financial Interests, Institutional, Research Grant: Genentech/Roche; Financial Interests, Institutional, Research Grant: GlaxoSmithKline; Financial Interests, Institutional, Research Grant: Incyte; Financial Interests, Institutional, Research Grant: Lilly; Financial Interests, Institutional, Research Grant: Merck; Financial Interests, Institutional, Research Grant: Sharp & Dohme; Financial Interests, Institutional, Research Grant: Merus; Financial Interests, Institutional, Research Grant: Molecular Partners; Financial Interests, Institutional, Research Grant: Novartis; Financial Interests, Institutional, Research Grant: Pfizer; Financial Interests, Institutional, Research Grant: Pierre Fabre; Financial Interests, Institutional, Research Grant: Roche; Financial Interests, Institutional, Research Grant: Sanofi; Financial Interests, Institutional, Research Grant: Taiho; Financial Interests, Institutional, Research Grant: Takeda. G. Meijer: Financial Interests, Institutional, Research Grant: Exact Sciences; Financial Interests, Institutional, Research Grant: Sysmex; Financial Interests, Institutional, Research Grant: Sentinel Diagnostics; Financial Interests, Institutional, Research Grant: Personal Genome Diagnostics; Financial Interests, Institutional, Research Grant: Hartwig Medical Foundation; Financial Interests, Personal, Ownership Interest: CRCbioscreen. K. Monkhorst: Financial Interests, Institutional, Research Grant: AstraZeneca; Financial Interests, Institutional, Invited Speaker: MSD; Financial Interests, Institutional, Invited Speaker: Roche; Financial Interests, Institutional, Invited Speaker: Benecke; Financial Interests, Institutional, Advisory Board: Pfizer; Financial Interests, Institutional, Advisory Board: BMS; Financial Interests, Institutional, Advisory Board: AbbVie; Financial Interests, Institutional, Advisory Board: Diaceutics; Financial Interests, Institutional, Advisory Board: Lilly; Financial Interests, Institutional, Advisory Board: Bayer; Financial Interests, Institutional, Advisory Board: Boehringer Ingelheim; Non-Financial Interests, Institutional, Other: Takeda; Non-Financial Interests, Institutional, Other: PGDx; Non-Financial Interests, Institutional, Other: Delfi; Financial Interests, Institutional, Invited Speaker: AstraZeneca; Financial Interests, Institutional, Advisory Board: Roche. All other authors have declared no conflicts of interest.

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