Rational design of cancer gene panels with OncoPaD

OncoPaD designs highly cost-effective panels

We compared the cost-effectiveness of OncoPaD designed panels to that of several available panels in three research scenarios. To carry out the comparisons, we first defined (and computed in silico) the cost-effectiveness of a gene panel as the balance between the fraction of samples of a cohort with mutations in genes contained in it (coverage), and the total DNA amount (Kbps). We used this in silico representation as a proxy of the real-life cost-effectiveness of a gene panel.

We first compared the cost-effectiveness of OncoPaD panels and 13 widely-employed panels, including the TruSight Amplicon Cancer Panel provided by Illumina, the Gene Read DNAseq Targeted Panels v2 from QIAGEN and the xGen® Pan-Cancer Panel of Integrated DNA Technologies, the only one including in its design a list of cancer driver genes [17] on a ~7000 tumors pan-cancer cohort (Fig. 2a, Additional file 4: Table S3A). In the coverage versus DNA amount space presented in Fig. 2a, the closer a panel (individual circles) is to the top right corner, the higher its coverage of mutated tumors in the cohort and the lower its content of DNA and, therefore, the higher its cost-effectiveness. For example, the MSK-IMPACT panel would achieve the highest coverage (90 %), but at the cost of sequencing 1030 Kbps of DNA from each sample. The Comprehensive Cancer Panel (Ion AmpliSeq™) and the Pan-cancer (FoundationOne®) panels would attain 84 % and 80 % coverage by sequencing 1130 and 634 Kbps of DNA, respectively. On the other hand, an OncoPaD designed panel for all cancer types including Tier 1 genes and hotspots would achieve 79 % coverage, but sequencing only 355 Kbps of DNA, roughly half of that sequenced by the latter and less than one-third of the former, thus with higher cost-effectiveness (blue circles). If the task at hand was the design of a panel to screen the same pan-cancer cohort for known targetable mutations (within our in-house database of biomarkers; see “Methods” for details), the highest cost-effectiveness would correspond to an OncoPaD designed panel including hotspots for drug profiling (Tiers 1 and 2), where the starting list of genes is specifically selected for mutations that influence the effect of a drug. Such a panel would cover 68 % of the pan-cancer samples sequencing only 83 Kbps of DNA (red circles).

Cost-effectiveness of OncoPaD and widely employed panels. a Cost-effectiveness of pan-cancer panels. The bubble plot presents in the x-axis the cohort coverage of each panel—i.e. proportion of samples of the pan-cancer cohort mutated in genes and/or hotspots of the panel—versus the amount of DNA (Kbps) included in each panel (y-axis). The size of the bubbles represents the proportion of genes in the panel that are cancer driver genes according the four lists integrated in OncoPaD (see “Methods”). Red bubbles correspond to OncoPaD panels focused on drug profiling, i.e. considering as input driver genes drug biomarkers; blue bubbles are OncoPaD panels based on driver genes; gray bubbles represent other widely employed panels. b Cost-effectiveness of panels in the evaluation of solid tumors. c Cost-effectiveness of cancer type-specific panels. OncoPaD panels fine-tuned for glioblastoma (pale green area), breast cancer (pale red area), and colorectal cancer (pale yellow area) were built and assessed in comparison to four pan-cancer and one solid tumor-specific widely employed panels. All data on coverage and DNA amount used to build these graphs is available in Additional file 4: Table S3

We speculated that the cost-effectiveness of OncoPaD panels should increase the more homogeneous the cohort under screening is in terms of cancer types represented because their design relies on tumor type specific drivers. Therefore, we next compared the cost-effectiveness of OncoPaD and commercially available panels screening only the subset of solid tumors within the pan-cancer cohort (Fig. 2b, Additional file 4: Table S3B). Here, the advantage of OncoPaD panels among all those evaluated is more apparent. Specifically, an OncoPaD hotspots (Tier 1) designed panel would cover the highest fraction of solid tumors in the cohort (83 %), sequencing only 291 Kbps of DNA. To stratify solid tumors potentially responsive to anti-cancer agents, three OncoPaD designs would provide information about all tumors in the cohort, followed by the OncoVantage Solid Tumor Mutation Analysis (Quests diagnostics) (97 %). Finally, we compared the cost-effectiveness of panels in screening tumor type-specific cohorts (Fig. 2c, Additional file 4: Table S3C). While all assayed panels would detect between three-quarters and four-fifths of breast carcinomas, between three-quarters and nine-tenths of glioblastomas and virtually all colorectal adenocarcinomas, OncoPaD designed panels would do that by sequencing a dramatically smaller amount of DNA. For instance, the Comprehensive Cancer Panel (Ion AmpliSeq™) panel would cover 99 % of the tumors in the colorectal cohort, sequencing 862.21 Kbps of DNA, compared to 97 % with 21.61 Kbps of DNA (40 times less) of an OncoPAD whole genes Tier 1 panel, consequently increasing the number of samples that can be analyzed in parallel and/or increasing sequencing coverage. It is also important to bear in mind that while the genes in all OncoPaD panels are drivers of each tumor types, other panels include genes which are not implied in tumorigenesis in the tumor type(s) of the panel cohort (or any tumor type) and may lead to the detection of false positives. This would increase their likelihood of detecting false-positive mutations (either germline or somatic unrelated to tumorigenesis) [3], a feature which may turn key when the material sequenced comes from a paraffin-fixed sample with no normal DNA to filter the variants in the patient’s genome.

Additionally, we assessed the cost-effectiveness of available solid tumor panels (see above) and OncoPaD solid tumors panels on a cohort of cervical and endocervical cancer which is not currently included in the OncoPaD pan-cancer cohort (Additional file 2: Figure S2), to assess the capacity of extrapolation of the catalog of driver genes included in the tool to novel not covered cancer types. An OncoPaD panel of Tier 1 genes exhibited the highest cost-effectiveness, with the Centrogene panel producing a greater coverage of the tumors of the cohort, but at the expense of sequencing four times more DNA. Note that OncoPaD will be continuously updated as new sequenced tumor cohorts and lists of novel cancer driver genes and drug biomarkers become available.

In summary, OncoPaD designed panels present better cost-effectiveness than their currently available counterparts. Furthermore, the availability of several lists of genes relevant to tumorigenesis in different cancer types or specifically informative of the response to anti-cancer drugs provides them a unique versatility with respect to available one-size-fits-many solutions.