Can the data analysis model accelerate the adoption of innovation for biomarker testing?

Following our article on the changes in the NGS field , there are some interesting updates that may shed light on some of the changes in the way testing is being performed today.

It is undeniable that NGS can unlock multiple opportunities for patients. It is impressive how in few years, we have seen a major leap from small panel to larger panels and now more and more we hear from WGS/WES pilot projects in multiple countries.?

Yet, with great power of detecting multiple biomarkers and using large panels or broad sequencing comes 3 challenges:

1)??????? The limited amount of sample to an ever-increasing number of biomarkers and methods that will be performed.

2)??????? The low availability of trained professionals for molecular data interpretation and bioinformaticians.

3)??????? The heavy burden for many health agencies around the world to keep up with the reimbursement for all innovation.

Interestingly, there is a (partial) solution that addresses all these points: Software as a Service (SaaS). It is not a new concept, and companies in the likes of SOPHiA GENETICS and Molecular Health have enjoyed some degree of success, but recently SaaS became even more interesting solution for labs as they have the lowest impact in the workflow and especially in countries with decentralized testing and with lower presence of commercial players. Here is how SaaS can deliver to some of those challenges:

1)??????? The larger the panel a lab does, the less it is prone to change providers and to adopt different ancillary methods for routine, as samples are scarce. The internal validation and adoption of new solutions in NGS will take 6 to 9 months and require a large effort from the already overburden lab personnel. The idea for pathologists to work with larger panels or WGS/WES is to get the maximum information from a sample. Therefore, providing a solution that can get more from the sequencing that has already been generated will be more attractive to a lab than performing a new panel.

2)??????? There is a clear shortage of skilled personnel in pathology labs, which does impact the turnaround time and the amount of work that can be performed. 赛默飞世尔科技 has addressed some of this issue with Genexus. Yet, bioinformaticians are one of the biggest bottlenecks here and in some countries it is not uncommon that multiple institutions would share the same professional to run their analysis. Illumina is currently improving the data analysis and reporting towards a medical device standard. But it is with the partnership between Illumina and Myriad Genetics that the importance and compatibility of the SaaS model becomes clear. Especially with HRD, both Sophia and SeqOne have put the model to the test and have collected multiple success stories, both for quality and compatibility.

3)??????? Introduction of new codes in a health system can be very cumbersome and the more cost intensive, the longer the time and requirements. Considering that different countries are looking into a broad panel or WGS/WES testing upfront, adding a new test might be regarded as “testing the same thing twice”. During the adoption of HRD, there were innumerous discussions on reimbursement, especially on how to reimburse BRCA, as BRCA is already part of the HRD panel. Adding different analysis to an already existing sequencing (i.e., TMB, HRD, gene signatures, etc) can reduce the overall burden to the health system, while also enabling universal broad testing.

?As the service from SaaS would be as a medical device, this can enable labs to adopt solution with minimal disruption as well as reduce the current burden on personnel as new hires are difficult to be approved in the public system.

?Additionally, this model would provide the possibility for testing new biomarkers just by data re-analysis, which in principle can take place at different time points, depending on the needs, progression and possibly availability of treatment or clinical trials. With the advance of Artificial Intelligence, data reanalysis will be part of the routine empowered by an increasing data interoperability and it will lead to a better tailored solution for patients.

Finally, this model will affect how pharma will select their CDx partners and how these alliances will play out in the different geographies.

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