Genomic Profiling

In the field of oncology, understanding specific genomic alterations within tumors has become a cornerstone for diagnosis, prognosis, and therapeutic decisions. Numerous cancer types have showcased the clear clinical advantages of pinpointing and targeting these key genomic changes, toward the shift in precision oncology through a systematic evaluation of "actionable" cancer genomic mutations.

Traditionally, molecular diagnostic labs have offered individual assays to identify candidates suitable for FDA-approved targeted therapies or specific molecularly driven clinical trials. However, testing each gene separately can quickly deplete invaluable tumor samples. Moreover, this approach complicates the process, increases costs, and prolongs the duration to results. Next-Generation Sequencing (NGS) overcomes these challenges by offering a more detailed and broader genomic profile without relying solely on predefined knowledge of alterations common to a particular tumor type.

Illustratively, the IMPRESS-Norway study is a nationwide, prospective clinical trial. It aims to gauge the effectiveness of commercially accessible anti-cancer medications prescribed to patients with "advanced/metastatic" cancer, where potential actionable mutations are identified through molecular diagnostics. Parallelly, our research has started into the genomic profiles of 500 "early-stage" lung cancer patients participating in the TNM-I trial. We utilized comprehensive targeted sequencing panels that explore 523 genes (Illumina TSO500). In collaboration with Dana Farber/Harvard Cancer Center (DF/HCC), our ongoing investigations are centered around understanding the influence of variant alterations and mutational loads on patients' immune responses, supplement with transcriptomics and pathomics data.

The project is ongoing.

Publications

• Rakaee M, Andersen S, Giannikou K, Paulsen EE, Kilvaer TK, Busund LTR, Berg T, Richardsen E, Lombardi AP, Adib E, Pedersen MI, Tafavvoghi M, Wahl SGF, Petersen RH, Bondgaard AL, Yde CW, Baudet C, Licht P, Lund-Iversen M, Grønberg BH, Fjellbirkeland L, Helland Å, Pøhl M, Kwiatkowski DJ, Donnem T. Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial. Annals of Oncology 2023 DOI

Norwegian Cancer Society