“It used to be that the cost of whole-genome sequencing was prohibitive, but the steep decline in sequencing costs today means that this is now something of a myth,” said Serena Nik-Zainal, PhD, who is a coauthor on the TNBC letter, a Cancer Research UK (CRUK) clinician scientist, and a researcher at the University of Cambridge. “A more significant concern is the expertise of analysis and interpretation of whole-genome sequencing. WGS remains demanding in terms of expertise.”
Dr Nik-Zainal noted that a big help in bridging that expertise gap, and enabling more researchers to lean on WGS for their studies, is the use of technological tools that help researchers not only wade through data but also derive meaning from it.
“The steep decline in cost today means that sequencing a tumor and matched normal pair is likely cheaper than a standard CT [computed tomography] scan of the chest, abdomen, and pelvis,” added Johan Staaf, PhD, who is also a coauthor on the TNBC letter and a researcher in oncology and pathology at Lund University in Sweden. His point was that a once prohibitively expensive tool like WGS is now on par, in terms of cost, with other tests that are regularly ordered as part of a cancer patient’s diagnosis and/or treatment. It puts WGS into context as an increasingly accessible tool that can aid in the personalized care of cancer patients.
Dr Staaf said the current standard of care for breast cancer is chemotherapy, and that when some patients respond well and others don’t, it has historically been difficult to identify why this occurs — and it is also a challenge to suggest better treatment for the patients who don’t see success with chemotherapy. WGS is changing that by allowing researchers to find tumor features that are targetable, such as activating mutations and mismatched repair deficiency. Clinicians can act on those features, “given that you identify them,” he said, something that WGS makes more possible than ever.
Angus said that in her study they used WGS to identify patients with DNA profiles well matched to the intended outcomes of existing cancer treatments — such as prescribing immunotherapy for patients with a high mutational tumor burden — based on similar studies in lung cancer showing this to be an effective treatment model. She was also able to identify patients with a specific mutation who might benefit from a drug already approved by the US Food and Drug Administration (FDA) but that is indicated to treat another tumor type (ie, off-label use).